Journal of Water and Climate Change Sample Issue by IWA Publishing

05.1

2014

Spatial and short-term temporal distribution of fugitive methane and nitrous oxide emission from a decentralised sewage mining plant: a pilot study Peter W. Schouten, Ashok Sharma, Stewart Burn and Nigel Goodman

ABSTRACT The decentralisation of wastewater treatment operations exposes several environmental consequences. This includes the fugitive emission of two greenhouse gases, nitrous oxide (N2O) and methane (CH4). The magnitude of these emissions is presently unclear. Therefore, it is necessary to measure the extent of the release of N2O and CH4 gas from decentralised wastewater treatment plants (WWTPs) in order to quantify the impact these emissions will have on the environment and to

Peter W. Schouten (corresponding author) Ashok Sharma Stewart Burn Nigel Goodman CSIRO Land and Water, Highett, Victoria, Australia E-mail: peter.schouten@csiro.au

determine strategies to reduce them. Specifically, this pilot study employed an online non-dispersive infrared (NDIR) gas analyser and flux hood to evaluate the spatial and short-term temporal distribution of N2O and CH4 flux over half a day, from an aeration tank system within a decentralised sewage mining plant. The aeration tank system was able to emit N2O fluxes of up to 11.6 g N2O m 2 day 1 and CH4 fluxes of up to 1.1 g CH4 m 2 day 1. The N2O and CH4 fluxes varied rapidly over short time intervals in the same position (as high as 45% for N2O and 36% for CH4) and could almost triple in magnitude between two different positions across the surface of the aeration tank (within a distance no greater than 1.5 to 2 m). Key words

| decentralised, fugitive, gas, spatial, temporal, wastewater

INTRODUCTION Atmospheric nitrous oxide (N2O) and methane (CH4) are

CO2 (Intergovernmental Panel on Climate Change ).

both known to directly enhance the greenhouse effect, and

Additionally, N2O can breakdown and eliminate ozone

as such they are regarded as greenhouse gases (GHGs).

(O3) in the stratosphere. This has a negative impact upon

Recently obtained data show that current tropospheric con-

the Earth’s ecosystem, as the reduction of O3 results in

centrations are between 322 and 323 parts per billion (109)

greater levels of biologically damaging downwelling solar

(ppb) for N2O and are between 1750 and 1871 ppb for CH4

ultra violet (UV) radiation being able to penetrate the atmos-

(Blasing ). Despite existing in lower concentrations in

phere and reach the surface of the Earth (McKenzie et al.

the troposphere in comparison to carbon dioxide (CO2)

). Both UVB (short wavelength) and UVA (long wave-

(that has a current tropospheric concentration of 390.5

length) radiation can harm both humans and animals by

parts per million (ppm)), N2O and CH4 are much more

initiating topical, cellular and even DNA damage to

effective at trapping infrared radiation (heat) in comparison

unprotected living tissue (Parisi et al. ).

to CO2, and as a result can have a greater inﬂuence upon

During the sewage treatment process, N2O is produced

positive radiative forcing. More precisely, N2O has 298

in and released from wastewater containing a concentration

times greater atmospheric heating potential over 100 years

of nitrogen-based organic material, typically originating

in comparison to CO2, and CH4 has 25 times greater atmos-

from waste matter produced by humans. N2O can be gener-

pheric heating potential over 100 years in comparison to

ated and emitted via two pathways: the oxidation of

doi: 10.2166/wcc.2013.070

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

ammonia to nitrate under aerobic conditions (nitriﬁcation)

systems, such as aeration tanks. Numerous investigations

and the reduction of nitrate to dinitrogen gas under anaero-

have been performed measuring the emission of CH4 from

bic conditions (denitriﬁcation). Generally, 0.5 and 1% of all

a variety of wastewater treatment systems treating sewage

nitrogen (N) entering a wastewater treatment plant (WWTP)

derived from human activity, such as the following: Czepiel

is converted and emitted as N2O gas (Townsend-Small et al.

et al. (); Foley et al. (); Diaz-Valbuena et al. ();

). This N to N2O gas conversion percentage can vary

Wang et al. (); Global Water Research Commission

greatly from WWTP to WWTP and can be correlated to a

(); and Daelman et al. ().

number of biochemical and physical parameters such as:

The vast majority of the preceding gas ﬂux studies have

acidity (pH), air stripping, dissolved oxygen (DO) level,

been made at large-scale WWTPs treating sewage for exten-

rapid changes in treatment system conditions, chemical

sive catchment areas and sizeable populations (at times

oxygen demand (COD), inﬂuent N concentration and

greater than one million people). One study has estimated

spikes in ammonium and nitrite (Kampschreur et al.

the carbon footprint of two small-scale decentralised

a; Law et al. ). Across a WWTP, fugitive N2O

WWTPs using mass balance modelling and a software pack-

gases are most likely to be measured from anaerobic/

age (Chong et al. ). However, there has been no attempt

anoxic tanks, efﬂuent holding lagoons and activated sludge

made to measure the emission of fugitive N2O and CH4

aeration tanks (from which the highest N2O emissions are

using any online gas analysis techniques from small-scale

expected to occur) (Global Water Research Commission

decentralised WWTPs servicing small catchments (Sharma

). The emissions of N2O from a vast cross-section of

et al. ). As a result, there is a signiﬁcant gap in the litera-

wastewater treatment facilities has been extensively detailed

ture that needs to be addressed. The installation of

in the literature, with some examples being: Czepiel et al.

decentralised WWTPs is rapidly increasing worldwide,

(); Ho Ahn et al. (); Global Water Research Com-

particularly

mission (); Townsend-Small et al. (); and Winter

developments and in small-scale self-sufﬁcient communities,

et al. ().

such as retirement villages and sustainable townships

new

residential

greenﬁeld

and

inﬁll

The production of CH4 at a WWTP occurs in anaerobic

(Sharma et al. ). As such, there is a requirement to inves-

wastewater treatment processes and is a resultant by-

tigate and quantify the environmental impact and GHG

product

footprint associated with the ongoing development and

methanogenic bacteria. Once produced, CH4 gas can be

operation of these particular sites. By doing this it can be

ejected into the atmosphere by either mechanical aeration

determined if decentralised WWTPs have a measurable

or surface diffusion. Several physical and biochemical par-

inﬂuence upon positive atmospheric radiative forcing, and

ameters can be used to indicate the potential for CH4

ultimately climate change. By identifying the biochemical

production: pH, sewage temperature, levels of sulphate

and physical processes occurring at these decentralised

reducing bacteria and toxicants, competition between

WWTPs that correlate to these fugitive emissions, various

methanogenic bacteria, wastewater treatment degree and

mitigation strategies to limit or eliminate these emissions

retention time (El-Fadel & Massoud ; Wang et al.

may be developed and applied by site engineers and water

). Fugitive CH4 emissions most commonly take place

management authorities in the future. These outcomes

in the inﬂuent works, primary clariﬁers/sedimentation, aera-

may also assist in the development of innovative fugitive

anaerobic

decomposition

performed

tion tanks, sludge processing/digestion and in efﬂuent

gas capture and recycling technologies. Additionally, gas

holding lagoons (Global Water Research Commission

ﬂux data sets obtained from the campaigns performed at

). In addition, large amounts of CH4 have been found

decentralised WWTPs could be used to validate and cali-

to be produced and released within sewer systems (Guisa-

brate synthetic GHG emissions data calculated from

sola et al. ). After being generated in sewers, dissolved

current models (with one such example being the National

CH4 can continue to travel into WWTPs and may be

Greenhouse and Energy Reporting System (NGERS)

stripped out and emitted into the atmosphere. This CH4

implemented by the Australian Government). Doing this

stripping can take place extensively in artiﬁcially aerated

will help to greatly improve the accuracy of GHG and

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

carbon footprint reporting conducted by water management

After this, the clariﬁed water is passed through a buffer

authorities.

tank and a sand ﬁlter tank. Final disinfection is performed

The objective of this pilot study is to improve upon the

by a UV unit. A percentage of the disinfected water is sent

currently minimal amount of information available on the

to a backwash tank, where it is treated with a chemical

extent and magnitude of GHG emissions released from

mix (sodium hypochlorite and caustic soda) and is returned

decentralised WWTPs. The formal hypothesis that this

back to the sand ﬁlter in order to clean the sand and to pre-

study speciﬁcally aimed to evaluate was as follows: is there

vent bacterial growth. Finally, the treated water is stored in a

a measurable variation in the spatial distribution of the

32.5 ML holding pond from which it can be readily accessed

N2O and CH4 gas ﬂuxes across the length of an aeration

by the greenkeepers. Figure 1 displays a simpliﬁed sewage

tank running an activated sludge treatment regime, and do

treatment ﬂowchart from entry to exit for the sewer

N2O and CH4 gas ﬂuxes vary by any amount over a short-

mining plant, and Table 1 shows a summary of its

term temporal interval? In order to test this hypothesis,

operational metadata and annual ﬂow characteristics.

N2O and CH4 gas ﬂuxes were measured in real-time (using a portable online gas analysis system) from an aeration

Field fugitive gas analysis procedure

tank system operating at a decentralised sewer mining plant (servicing a small population catchment of no more

The ﬁeld gas analysis detailed in this project followed a

than 3,000 people), over the time span of a single day

methodology similar to that employed by Tremblay et al.

during its window of peak operation. As no previous

() based on the original measurement system detailed

investigation has attempted to directly determine if the dis-

by Carignan (). Gas capture was made on the surface

tribution of GHG ﬂux is isotropic or anisotropic across the

of the sewage with a buoyant airtight ﬂux hood (Ac’Scent

surface of a treatment tank/reactor, there is a need to

Flux Hood, St Croix Sensory Inc., USA) connected to a pri-

resolve this.

mary standard calibrated (traceable to the National Institute of Standards and Technology) non-dispersive infrared (NDIR) gas analysis unit (VA-3000 Series, Horiba Ltd,

MATERIALS AND METHODS

Japan). Surface off-gases were trapped inside the ﬂux hood

Treatment plant description and speciﬁcations The WWTP selected for analysis is a modern (commissioned in 2007) sewer mining plant that has been built on site at a suburban 18 hole public golf course located in the western suburbs of Melbourne, Australia (37 470 39″ S, 144 460 W

49″ E, Altitude: 44 m). The function of the sewer mining plant is to extract a relatively small amount of domestic wastewater from a main sewer line and to treat it to safe standard so it can be used to continuously irrigate the golf course throughout an entire year by using biological nutrient removal (BNR) methods. In general, the WWTP operates by mechanically pumping in raw wastewater through a bar screen. Following this, the wastewater is sent to a balance tank and then to two aeration tanks working in parallel. Return activated sludge is cycled between the two aeration tanks and a settling tank. Any excess solids that are produced during this stage are sent back into the sewer line.

Figure 1

Treatment ﬂowchart for the wastewater mining plant.

P. W. Schouten et al.

Table 1

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

Sewer mining plant operational metadata and parameters

Sewer mining WWTP

Function

Sewer mining for golf course irrigation

Predominant inﬂuent type

Domestic

Catchment population (approximate)

3,000

Yearly COD mass load (metric tonnes)

Yearly BOD mass load (metric tonnes)

46.4

Yearly total N mass load (metric tonnes)

Yearly inﬂow (ML) (daily inﬂow (ML))

100 (0.274) Figure 2

The seven separate sections from which gas ﬂuxes were measured across the aeration tank. The sections are in numerical order from the inﬂuent point of entry.

and passed through to a gas conditioning system (VS-3000 Series, Horiba Ltd, Japan) via an internal pump working

measurement series. Speciﬁcally, in this study N2O and

at a constant ﬂow rate. The gas conditioning system

CH4 gases were measured from the aeration tank only, as

removed any water vapour and other particulates from the

it has the most substantial physical footprint (48 m2 surface

gas stream before it entered the NDIR gas analysis unit.

area for one aeration tank; 96 m2 for both aeration tanks)

After the N2O and CH4 constituent gas concentrations

and was the most atmospherically exposed (i.e. it has the lar-

had been evaluated by the NDIR gas analysis unit, the

gest uncovered surface area) of all the treatment systems/

sampled gases were sent back into the ﬂux hood so that

tanks on site, and as a result it had the highest potential to

they were mixed continuously, enabling a more accurate

deliver the most substantial cumulative emissions. Sup-

estimation of gas concentration to be obtained over time

plementary N2O and CH4 emissions measurements were

(Lambert & Frechette ; Tremblay et al. ).

made on the settling, buffer and ﬁlter tanks over a time

Gas concentrations were recorded over 10 (±5) minutes at evenly spaced intervals to reduce the inﬂuence of the

span of 1 to 2 hours. Both N2O and CH4 emissions were found to be negligible on each of these tanks.

‘chamber effect’ (Venterea et al. ). In order to determine

A tightened rope was used to hold the ﬂux hood in the

the spatial distribution of the N2O and CH4 gas ﬂuxes, the

same position and to reduce the effect of wave action on

gas concentration measurements were made successively

the sewage surface. After each measurement, the ﬂux

in seven separate sections across one of the aeration tanks

hood was removed from the sewage surface so that gas

(in this study it was assumed that the N2O and CH4 gas

concentrations could return to ambient levels before the

ﬂuxes emitted from both of the aeration tanks were sym-

next measurement. The gas concentration data were

metrical as both tanks have the same inﬂuent intake and

recorded continuously to a laptop computer starting at

operate under the same conditions and speciﬁcations simul-

11:07 AM until 3:15 PM (27 March 2012). From this

taneously). These seven sections are displayed and labelled

sampling regime, temporal gas ﬂux patterns could be

in Figure 2. The sections were each separated by a distance

readily determined over the time of day at which the inﬂu-

of approximately 1.5 to 2 m, and each section had its own

ent organic loading was most likely to be at its peak. A full

characteristic DO content due to the uneven oxygen distri-

24 hour interval of gas ﬂux measurements could not be

bution supplied by the submerged aeration system in

made at the sewer mining plant due to strict workplace

operation at the bottom of the aeration tank. This aeration

health and safety restrictions that are enforced by the

system was in constant operation throughout the entire

sewer mining plant operations management authority.

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

One of these restrictions is a complete ban on any onsite

tank (measured by the NDIR gas analysis unit) are shown

work performed by external contractors after daylight

in Table 2.

hours. As such, it was not possible for the measurement series to be expanded to run over a complete 24 hour

Data analysis procedure

time period in order to obtain a full diurnal gas emission analysis. Figure 3(a)) displays the deployment of the ﬂux

In order to calculate N2O and CH4 gas ﬂuxes, linear

hood on top of the aeration tank and Figure 3(b)) shows

regression was applied to the gas concentration data over

the NDIR gas analyser, the gas conditioner and the

the sampling time. Only data sets with a coefﬁcient of deter-

laptop computer used as a data logger throughout the

mination (R 2 value) greater than 0.5 were employed for ﬂux

measurement campaign.

calculations. The average R 2 calculated over the CH4 gas con-

Continuous measurement of various water quality parameters were made simultaneously to the gas ﬂux

centration data sets was 0.78 ± 0.14 (±1 σ) and the average R 2 calculated across the N2O gas concentration data sets

measurements using a novel online water quality measure-

was 0.79 ± 0.11 (±1 σ). Speciﬁcally, over the entire measure-

ment system (Sewer Sentinel, Commonwealth Scientiﬁc

ment campaign, 96% of all 24 CH4 emission concentration

and Industrial Research Organisation, Australia). Sewage

data sets had an R 2 of over 0.5, and 100% of all 24 N2O emis-

was extracted from the aeration tank to the online water

sion concentration data sets had an R 2 of 0.5 or greater.

quality measurement system via a masticating pump

Following the application of linear regression, gas ﬂux was

(Piranha 08 Grinding Pump, ABS-Sulzer, Switzerland).

calculated directly using the following equation (Tremblay

The pump was positioned in close proximity to the ﬂux

et al. ):

hood at all times during each measurement and was lowered approximately 0.5 to 1 m under the sewage surface. The water quality parameters that were measured included:

Flux ¼

m×V ×α×β A×γ

temperature, pH and DO. More water quality data and operational data such as influent flow rate, influent total N mass

where m is the slope from the line of best ﬁt set to the gas con-

load, influent total COD, DO delivery profiles and inflow

centration data over the sampling time (ppm second 1); V is

pump timings were obtained from the WWTP site engineers

the volume under the ﬂux hood (m3); α is a gas concentration

as required. Mean values for the water quality parameters as

conversion factor (for CH4: 655.47 μg m 3 ppm 1; for N2O:

measured in each particular section of the aeration tank (by

1798.56 μg m 3 ppm 1); β is a temporal conversion factor

the online water quality measurement system) and mean

(86400 seconds day 1); A is the area under the ﬂux hood

raw fugitive emissions from each section of the aeration

(m2) and γ is a magnitude conversion factor (1 × 106 μg g 1).

Figure 3

The gas capture and analysis system deployed on site at the sewer mining site. (a) The ﬂux chamber on the surface of the aeration tank. (b) The gas analysis system, gas conditioner and laptop (used to control the gas analysis system and to record gas concentrations).

P. W. Schouten et al.

Table 2

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

Peripheral water quality metadata and average raw fugitive emissions for the sewer mining site

Dissolved oxygen (mg L 1) (Mean (μ),

pH (Mean (μ),

Sewage temperature ( C) (Mean (μ), ±Std. W

±Std. Dev (σ))

Dev (σ))

Aeration tank section 1.

0.315 (±0.1)

7.48 (±0.04)

Aeration tank section 2.

0.28 (±0.04)

Aeration tank section 3.

CH4 emission (g CH4 m 2 d 1) (Mean (μ),

N2O emission (g N2O m 2 d 1) (Mean (μ),

Number of

±Std. Dev (σ))

measurements (N)

22.03 (±0.05)

0.53 (±0.06)

1.86 (±0.52)

7.6 (±0.03)

22.23(±0.05)

0.39 (±0.14)

3.78 (±1.7)

0.345 (±0.037)

7.6 (±0.03)

22.35 (±0.06)

0.99 (±0.09)

6.31 (±0.36)

Aeration tank section 4.

0.66 (±0.057)

7.7 (±0.03)

22.4 (±0)

0.58 (±0)

8.89 (±0)

Aeration tank section 5.

0.31 (±0.014)

7.7 (±0.03)

22.5 (±0)

0.51 (±0.045)

10.7 (±0.014)

Aeration tank section 6.

0.235 (±0.021)

22.5 (±0)

7.9 (±0.04)

0.27 (±0.088)

7.97 (±2.79)

Aeration tank section 7.

0.5 (±0.007)

7.9 (±0.001)

22.5 (±0)

0.4 (±0.035)

11.01 (±0.86)

Flux is given in g m 2 d 1. To evaluate the total daily

where N2OEF and CH4EF are the annual emission factors

carbon footprint for the sewer mining plant, the calculated

for N2O and CH4 respectively; (N2O)MEAN is the mean of

CH4 and N2O ﬂuxes were converted to their equivalent

the N2O ﬂux measurements (metric tonnes) integrated

CO2 (CO2e) values by multiplying them by their respective

over the tank surface area made over the sampling period

100 year global warming potential conversion factors

(extrapolated over 365 days); (NINFLUENT)ANNUALTOTAL is

(25 for CH4 and 298 for N2O) currently purported by the

the annual total N (metric tonnes) arriving in the inﬂuent at

Intergovernmental Panel on Climate Change ().

the sewer mining site; (CH4)MEAN is the mean of the CH4

To directly compare the N2O and CH4 emissions from

ﬂux measurements (metric tonnes) integrated over the tank

the aeration tank to those made in other studies, the

surface area made over the sampling period (extrapolated

fluxes must first be normalised to a specific water quality

over 365 days) and (CODINFLUENT)ANNUALTOTAL (metric

parameter and converted to an emission factor. As such,

tonnes) is the annual total COD arriving in the inﬂuent at

to make a rudimentary emission factor estimation, the

the sewer mining site. These emissions factor calculations

mean N2O emission measured over the sampling period

assume that the emission of N2O and CH4 gas from the aera-

across each section in the aeration tank was extrapolated

tion tanks remains constant throughout the year.

over 365 days and normalised to the total annual NINFLUENT, and the mean CH4 emission recorded over the sampling period in each section of the aeration tank was

RESULTS AND DISCUSSION

extrapolated over 365 days and normalised to the total annual CODINFLUENT using the following two respective

Greenhouse gas measurements – short-term temporal

equations:

and spatial distribution

ðN2 OÞMEAN N2 OEF ¼ × 100% ; ðNINFLUENT ÞANNUALTOTAL ðCH4 ÞMEAN × 100% CH4 EF ¼ ðCODINFLUENT ÞANNUALTOTAL

Figure 4 shows the CH4 and N2O ﬂux measured throughout the sampling period across each section of the aeration tank system. In this ﬁgure the measurements made in the seven separate sections across the aeration tank took place at the following times: Section 1–11:07 AM to 12:10 PM;

P. W. Schouten et al.

Figure 4

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

CH4 and N2O ﬂux measured throughout the day (11:00 AM to 3:20 PM) across each section of the aeration tank system.

Section 2–12:28 PM to 1:22 PM; Section 3–1:32 PM to 1:56

From Figure 4, it can be seen that N2O ﬂux signiﬁcantly

PM; Section 4–2:09 PM to 2:17 PM; Section 5–2:28 PM to

increased in magnitude over the trial period across Section 1

2:37 PM; Section 6–2:50 PM to 3:00 PM; Section 7–3:08

to Section 7, reaching a maximum value of 11.6 g N2O m 2

PM to 3:15 PM. Throughout the trial, the sewage temperature

day 1 in Section 7, and a minimum value of 1.01 g N2O m 2

remained stable between 22 and 22.5 C. Due to the low

day 1 in Section 1. From the sectional values, the average

temperature differential across the aeration tank (and

calculated N2O ﬂux was estimated to be 5.6 ± 3.45 g N2O

during the trial period) no discernible link between tempera-

m 2 day 1 (±1 σ), with an associated percentage variation

ture and N2O and CH4 ﬂux could be ascertained. In Figure 4,

of 61%, a value much higher than the percentage variation

it is clear that CH4 ﬂux varied extensively throughout the trial

obtained for the CH4 measurements. In each separate sec-

period from Section 1 to Section 7, reaching a peak value of

tion the temporal variations in N2O ﬂux were close to

1.1 g CH4 m 2 day 1 in Section 3, and a minimum value of

being as large as the variation measured across the aeration

0.21 g CH4 m 2 day 1 in Section 6. Speciﬁcally, the average

tank during the entire trial period. As was the case for the

CH4 fluxes, N2O fluxes also fluctuated by the greatest

(±1 σ). The percentage variation (μ/σ × 100%) across all of

amounts in Section 2 (45%) and Section 6 (35%) again

the CH4 ﬂux measurements was a sizeable 44%. In each sep-

over respective measurement intervals of 54 min and

arate section the temporal variations in CH4 ﬂux were at times

10 min. The average N2O ﬂux after being converted to its

almost as large as the variation measured across the aeration

equivalent CO2 value was 1,761 ± 1,070 g CO2e m 2 day 1

tank over the entire trial. In particular, CH4 ﬂuxes varied by as

(±1 σ), with a daily emission per unit of inﬂuent volume of

much as 36 and 32% in Section 2 and Section 6, throughout

46,320 g CO2e ML 1. From this, by making the assumption

relatively short measurement times of 54 minutes and 10 min-

that these emissions remain relatively constant throughout

calculated CH4 ﬂux was 0.55 ± 0.24 g CH4 m

day

utes respectively. The average CH4 ﬂux after conversion to its

the year, the total annual CO2e footprint due to N2O emis-

equivalent CO2 value was 11.5 ± 5.1 g CO2e m 2 day 1

sions for the site was found to be 61.7 × 103 kg (with a

(±1 σ), with a daily emission per unit of inﬂuent volume of

related emission factor (N2OEF) of 1.8), a footprint much

302 g CO2e ML 1. Assuming that this average output remains

greater than the value estimated for the CH4 emissions. As

the same each day over the space of a year, the total annual

a result, the total annual CO2e footprint of 62.1 × 103 kg

CO2e footprint due to CH4 emissions for the sewer mining

(21 kg CO2e person 1) for the site is largely dominated

site is equal to 403 kg (0.13 kg CO2e person 1), with an associ-

by N2O related emissions by a contribution factor of 154

ated emission factor (CH4EF) of 0.022.

to 1.

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

The calculated emission factors for both CH4 (0.022) and N2O (1.8) for the aeration tank at the sewer mining plant fell within the expected range of emission factors that have been previously estimated for large-scale centralised

WWTPs

techniques

and

using

comparable

calculation

ﬂux

methods

measurement

(Global

Water

Research Commission ). In particular, the N2O emission factor calculated for the sewer mining site ﬁts well within the generally expected interval of 0 to 4% conversion of inﬂuent total N to direct N2O emissions (Kampschreur et al. b; Chong et al. ), and the CH4 emission factor is in the same order of magnitude as CH4 emission factors estimated for large-scale WWTPs recently analysed in both France and the Netherlands (0.005 to 0.04)

Figure 5

The sectional distribution of CH4 and N2O fluxes across the aeration tank measured from the sewage entry point (Section 1) to the sewage exit point pffiffiffiffi (Section 7). The error bars represent the standard error (σ= N) as calculated for the measurements made in each particular section across the aeration tank.

(Global Water Research Commission ). Consequently, this outcome implies that at the sewer mining plant, the conversion of inﬂuent COD to CH4 gas and inﬂuent N to N2O

to 3.78 g N2O m 2 day 1), and again between Section 2

gas occurs at approximately the same efﬁciency rate as what

and Section 3 (from 3.78 g N2O m 2 day 1 up to 6.31 g

takes place at large-scale centralised WWTPs. However, the

N2O m 2 day 1). As such, the distribution of the N2O and

total environmental impact of the resultant N2O and CH4

CH4 ﬂuxes across the aeration tank can be regarded as ani-

emissions released from the sewer mining plant will most

sotropic (i.e. the ﬂuxes change with direction lengthways

probably be reduced in comparison to large-scale centra-

across the tank). In particular, the spatial distribution of

lised WWTPs, as the total daily volume of treated

the CH4 ﬂuxes did not follow any recognisable pattern

wastewater moving through the sewer mining plant is

from entry to exit, while the N2O ﬂuxes displayed a deﬁnite

much lower, meaning that there is far less total organic

gradual increase. This suggests that the emissions of CH4

material available that can be readily converted into N2O

generally occur at random across the surface area of the

and CH4 gas.

aeration tank, while emissions of N2O do not take place sto-

The distribution of averaged N2O and CH4 ﬂuxes

chastically, and may be loosely correlated to the positioning

across each section of the aeration tank as measured is

of the bubblers/oxygen supply in the aeration system and to

shown in Figure 5. It is clear from Figure 5 that both the

the distribution of the sludge/nutrient loading within the

averaged N2O and CH4 ﬂuxes did not display a deﬁnite

water column. However, it is unclear whether these N2O

homogenous (even) distribution across the length of the

and CH4 emission patterns take place consistently like this

aeration tank. Instead all of the ﬂuxes vary by a measur-

throughout each day of the year, or are entirely an artefact

able amount from the inlet (Section 1) to the outlet

of the particular organic load entering the sewer mining

(Section 7), with large discrepancies measured across

plant at that time of day. Consequently, further measure-

small distances (no greater than 1.5 to 2 m). For example,

ments of the spatial distribution of the N2O and CH4

sizeable differences in CH4 ﬂux were seen to occur most

emissions are required throughout different seasons in

considerably from Section 2 to Section 3, where a near

order to conclusively determine their overall variability.

threefold increase in ﬂux was measured (from 0.39 g CH4

The objective of the research performed in this investi-

m 2 day 1 up to 1 g CH4 m 2 day 1), and from Section 5

gation was only to measure the N2O and CH4 emissions

to Section 6, across which a 48% decrease in ﬂux occurred

released from the aeration tank system in order to determine

(from 0.51 g CH4 m 2 day 1 down to 0.27 g CH4 m 2 day 1).

if the spatial and temporal distribution of gas ﬂux in an acti-

Also, a doubling of N2O ﬂux was measured between

vated sludge aeration system was variable over a short time

Section 1 and Section 2 (from 1.86 g N2O m 2 day 1

period. However, at the sewer mining site it is predicted that

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

not only do N2O and CH4 emissions emerge from the aera-

detailing the total site GHG footprint, these factors should

tion tanks, but they may also be released from the ﬁnal

be taken into account and quantiﬁed appropriately.

efﬂuent holding pond, which may be signiﬁcant due to its substantial volume and surface area. Further emissions of

Greenhouse gas measurements – correlation to

N2O gas may be prevalent in this ﬁnal treatment stage, as

wastewater physical parameters

the solubility of N2O in water is high, and as such N2O generated upstream in other treatment processes may remain in

Figure 6(a) and Figure 6(b) show respectively the

the efﬂuent for an indeﬁnite period of time. In addition,

measured CH4 and N2O ﬂuxes versus the measured dis-

reactive nitrogen that is present in ﬁnal efﬂuent may be

solved oxygen concentration for each section across the

applied across the golf course (via watering performed by

aeration tank and the measured CH4 and N2O ﬂuxes

the greenkeepers), and as such will be available for nitriﬁca-

versus the measured pH value for each section across

tion and denitriﬁcation resulting in further possible N2O

the aeration tank. In Figure 6(a) there appears to be

emissions, adding further to the GHG footprint related to

only a very weak positive linear correlation between

the sewer mining plant. Consequently, in further studies

both DO level and the N2O and CH4 ﬂuxes, with the R 2

Figure 6

(a) Regression lines for CH4 (dashed line) and N2O (solid line) ﬂuxes versus the measured dissolved oxygen concentration for each corresponding section across the aeration tank. (b) Regression lines for CH4 (dashed line) and N2O (solid line) ﬂuxes versus the measured pH value for each corresponding section across the aeration tank.

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

value being slightly greater for the relationship between

high or low N2O emissions occurring at that particular

DO level and N2O ﬂux (0.18) in comparison to the

point in time.

relationship between DO level and CH4 ﬂux (0.07). In the aeration tank it is assumed that the majority of the emitted CH4 is not being generated inside the tank, but

CONCLUSIONS

is arriving into the tank via the sewer and is being agitated and stripped out via aeration. As such, the weak link

This pilot study is the ﬁrst of its kind to present both fugitive

between the increasing CH4 ﬂux and increasing DO may

N2O and CH4 emissions data as measured by an online

moderately imply that the sections of the aeration tank

infrared gas analysis system at a decentralised sewer

where greater levels of aeration/oxygenation are occurring

mining plant. The continuation of monitoring and analysis

is also where the highest amounts of CH4 gas stripping is

of fugitive GHG emissions made at various types of decen-

taking place. The link between oxygen concentration and

tralised WWTPs is important, as there are very few data

N2O generation and emission has been tested and detailed

currently available on the total GHG footprint related to

previously in a number of studies performed in aeration

these sites. Measurements similar to what has been pre-

tanks at real-world WWTPs. These investigations have

sented in this study can be used to calibrate and optimise

shown highly conﬂicting results, with some illustrating

existing GHG modelling frameworks (such as the current

that DO levels are positively correlated with N2O emis-

Australian NGERS) in order to improve the accuracy of

sions (Schulthess et al. ; Ho Ahn et al. ), while

carbon footprint reporting performed by water management

others reported that DO levels are negatively correlated

authorities and operators, which has been shown to be too

to N2O emissions (Kampschreur et al. a; Winter et al.

general in application and at times highly inaccurate

). Due to the relatively low DO levels (<1 mg L 1) exist-

(Global Water Research Commission ). In addition, the

ent across the entire aeration tank, it is thought that the

detailed measurement of fugitive emissions at decentralised

high levels of N2O were primarily generated during the

WWTPs not only helps to better quantify the magnitude and

nitrification stage, in the nitrifier denitrification process,

extent of fugitive N2O and CH4 emissions over time but can

which takes place following poor and inadequate aeration

also assist in determining how N2O and CH4 gases are pro-

or during a large peak in ammonium (Global Water

duced and released from wastewater. As a result, they can

Research Commission ). As such the site operators

also aid in the development of innovative strategies on

may wish to increase the output of the aeration system (if

how N2O and CH4 fugitive emissions can be reduced, cap-

possible) in order to reduce the occurrence of this scenario,

tured

and possibly reduce the generation of N2O.

required to determine if decentralised WWTPs, such as the

In Figure 6(b) it can be seen that a weak negative linear 2

and recycled.

However,

further evaluation

sewer mining site evaluated in this study, are capable of

relationship exists between pH and CH4 ﬂux (R ¼ 0.14).

being continuously self powered by the conversion of their

Conversely, a relatively strong positive linear relationship

emissions into a sustainable energy supply. An economic

developed between pH and N2O ﬂux (R ¼ 0.68). Previous

assessment is also needed to determine if such an energy

investigations have shown that variations in the pH of

recycling scheme would be ﬁnancially viable.

sampled activated sludge can be directly linked to emissions

The formal hypothesis originally detailed in the Introduc-

of N2O gas (Thorn & Sorensson ), with lower pH read-

tion section appears to have been proven, as the spatial and

ings correlating to higher N2O production. The results

temporal distributions of N2O and CH4 ﬂux directly

obtained in the aeration tank at the sewer mining plant dis-

measured in real-time across the aeration tank were found

play the opposite of this, with higher pH readings generally

to vary signiﬁcantly over both short distances and time inter-

correlating to higher N2O ﬂuxes. From this, it may be poss-

vals. The anisotropic spatial distribution of both N2O and

ible that in the aeration tank at the sewer mining plant,

CH4 ﬂuxes suggests that GHG ﬂux measurements made

online pH measurements could be employed as a simple

with a ﬂux hood in a single position over an extended time

indicator by site engineers to determine the probability of

period on an aeration tank surface may not be adequate for

P. W. Schouten et al.

Fugitive methane and nitrous oxide emission from a sewage mining plant

Journal of Water and Climate Change

05.1

2014

GHG measurements on tanks with a large surface area, such

amount and location of the N2O and CH4 generated in

as the aeration tank at the sewer mining site analysed in this

the connecting sewers, in order to ascertain if these gases

study. Failure to acknowledge that the magnitude of both

can be captured, reduced or eliminated.

N2O and CH4 ﬂuxes can change by sizeable amounts over short distances across an aeration tank may lead to a considerable misestimation in the averaged N2O and CH4

ACKNOWLEDGEMENTS

emissions data, and consequently in the overall GHG footprint calculated for the WWTP under investigation. As

The lead author would like to thank CSIRO Land and Water

such, it is recommended that ﬂux measurements are made

for providing postdoctoral funding to assist in the

in quick staggered time intervals at multiple evenly spaced

completion of this manuscript. The lead author would also

positions across the entire measurable surface of an aeration

like to thank Jeremy Guneratne from City West Water for

tank, in order to better ascertain both temporal and spatial

his assistance during the measurement campaign.

flux variations. Another way to overcome this problem is to deploy an array of separate flux hoods and gas analysis (or gas storage) systems across an aeration tank surface at even separations to take flux measurements simultaneously. However, this solution may be financially prohibitive, logistically difficult and may violate safety regulations at some WWTPs. Another possible solution is to position an open-path eddy covariance gas flux measurement unit near to the surface area of an aeration tank to provide high-accuracy continuous measurements of total gas transfer for an extended time period. However, eddy covariance may not be entirely useful due to the fact that emissions from treatment tanks generally do not effect ambient air GHG concentrations in air more than several metres away from the source (Townsend-Small et al. ). Also, it must be ensured that the surface under investigation resides within the measurement footprint area of the eddy covariance unit. This can be very difficult to maintain, as the shape of the measurement footprint can vary many times over the space of a day, primarily due to fluctuations in wind direction. From the results provided by this pilot study it is clear that further work is now required to better determine the seasonal and annual N2O and CH4 emissions produced on site, and to determine the sensitivity of the sewer mining plant to short- and long-term variations in influent properties and local environmental conditions. From this, a broad extrapolation could be made to evaluate the overall influence that the ongoing increase in the uptake of decentralised

WWTPs

and

wastewater

treatment

systems

(similar to the sewer mining site) will have on both the Australian

and

the

worldwide

carbon

budget.

Further

measurements are also needed to evaluate the total

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Schulthess, R., Wild, D. & Gujer, W.  Nitric and nitrous oxides from denitrifying activated sludge at low oxygen concentration. Water Sci. Technol. 30 (6), 123–132. Sharma, A., Cook, S., Tjandraatmadja, G. & Gregory, A.  Impediments and constraints in the uptake of water sensitive urban design measures in greenfield and infill developments. Water Sci. Technol. 65 (2), 340–352. Sharma, A. K., Grant, A. L., Grant, T., Pamminger, F. & Opray, L.  Environmental and economic assessment of urban water services for a Greenfield development. Environ. Eng. Sci. 25 (5), 921–934. Thorn, M. & Sorensson, F.  Variation of nitrous oxide formation in the denitrification basin in a wastewater treatment plant with nitrogen removal. Water Res. 30 (6), 1543–1547. Townsend-Small, A., Pataki, D. E., Tseng, L. Y., Tsai, C-Y. & Rosso, D.  Nitrous oxide emissions from wastewater treatment and water reclamation plants in southern California. J. Environ. Qual. 40, 1542–1550. Tremblay, A., Bastien, J., Demarty, M. & Demers, C.  Eastmain-1 net GHG emissions project – The use of automated systems to measure greenhouse gas emissions from reservoirs. In: Proceedings of Waterpower XVI, 27 to 30 July, Spokane, Washington. Tremblay, A., Lambert, M. & Gagnon, L.  Do hydroelectric reservoirs emit greenhouse gases? Environ. Manage. 33 (1), S509–S517. Venterea, R. T., Spokas, K. A. & Baker, J. M.  Accuracy and precision analysis of chamber-based nitrous oxide gas flux estimates. Soil Sci. Soc. Am. J. 73, 1087–1093. Wang, J., Zhang, J., Xie, H., Qi, P., Ren, Y. & Hu, Z.  Methane emissions from a full-scale A/A/O wastewater treatment plant. Bioresour. Technol. 102, 5479–5485. Winter, P., Pearce, P. & Colquhoun, K.  Contribution of nitrous oxide emissions from wastewater treatment to carbon accounting. J. Water Clim. Change. 3 (2), 95–109.

First received 14 May 2013; accepted in revised form 24 July 2013. Available online 22 October 2013

05.1

2014

A critical comparison of using a probabilistic weather generator versus a change factor approach; irrigation reservoir planning under climate change Michael Green and Edward Keith Weatherhead

ABSTRACT In the UK, there is a growing interest in constructing on-farm irrigation reservoirs, however deciding the optimum reservoir capacity is not simple. There are two distinct approaches to generating the future daily weather datasets needed to calculate future irrigation need. The change factor approach perturbs the observed record using monthly change factors derived from downscaled climate models. This assumes that whilst the climate changes, the day-to-day climate variability itself is stationary. Problems may arise where the instrumental record is insufﬁcient or particularly suspect.

Michael Green Edward Keith Weatherhead (corresponding author) School of Applied Sciences, Building 52, Cranfield University, Cranfield, Bedfordshire, UK E-mail: k.weatherhead@cranfield.ac.uk

Alternatively, probabilistic weather generators can be used to identify options which are considered more robust to climate change uncertainty because they consider non-stationary climate variability. This paper explores the difference between using the change factor approach and a probabilistic weather generator for informing farm reservoir design at three sites in the UK. Decision outcomes obtained using the current normal practice of 80% probability of non-exceedance rule and simple economic optimisations are also compared. Decision outcomes obtained using the change factor approach and probabilistic weather generators are signiﬁcantly different; whether these differences translate to real-world differences is discussed. This study also found that using the 80% probability of non-exceedance rule could potentially result in maladaptation. Key words

| adaptation, change factor, irrigation demand, UKCP09, WaSim, weather generator

BACKGROUND Water is integral to the UK’s ability to grow high quality hor-

resource strategy to avoid the restrictions and environmental

ticultural produce. In the UK, approximately 150,000 ha are

impact of abstraction during summer months (Weatherhead

irrigated during a dry year (Knox et al. ). The sustainabil-

et al. ). Climate change is expected to simultaneously

ity of irrigated production is however under threat from

increase water demand and reduce water availability (Kang

competition for water from other sectors, new legislation

et al. ).

designed to enhance environmental protection, and climate change (Weatherhead et al. ).

The unpredictability of the future climate is perhaps the greatest challenge facing the water industry (Harris et al.

Water resources in many catchments are already strained.

). In the UK at least, much of the current infrastructure,

During summer, many existing water sources become increas-

including irrigation reservoirs, was built on the assumption

ingly unreliable and new licences for summer abstractions are

that the climate in which it was built would endure for its

now widely unobtainable or are issued with tight minimum

entire lifetime – this is no longer the case (Harris et al. ).

ﬂow or minimum level constraints. Increasingly farmers, agri-

Two responses have emerged in reaction to the risks

business and water resource managers are being encouraged

posed by future climate change, namely mitigation and

to build on-farm irrigation reservoirs as part of their water

adaptation

doi: 10.2166/wcc.2013.073

(Füssel

).

Mitigation

refers

‘an

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

anthropogenic intervention to reduce the sources or

climate. A sufﬁciently long daily weather record is essential

enhance the sinks of greenhouse gases’ (Intergovernmental

to adequately gauge the amount of water required. For the

Panel on Climate Change (IPCC )). In contrast, adap-

baseline period (1961–1990), irrigation demand calculations

tation, studied in this paper, refers to ‘the adjustment in

are often based on the observed record, though this may be

natural or human systems in response to actual or expected

substituted with a synthetic series from a weather generator

climatic stimuli or their effects which moderates harm or

provided it has been suitably calibrated (Green & Weather-

exploits beneﬁcial opportunities’ (Parry et al. , p. 6).

head ). Similarly, a sufﬁciently long record of future

In the UK, adaptation planning emerged as a policy issue

daily weather data is required to model irrigation demand

in 1997 in response to the formulation of the UK Climate

under the effects of climate change. Future weather data

Impacts Programme (UKCIP) (Hedger et al. ), receiving

are typically generated from downscaled global climate

renewed interest with the passing of the Climate Change Act

models (GCMs). GCM outputs are often only available as

2008 (Tang & Dessai ). The apparent ‘failure’ of high

monthly values (Holman et al. ), which are generally

proﬁle climate change protocols (e.g. the Kyoto protocol)

insufﬁcient for modelling dry year supplemental irrigation

has undermined conﬁdence in the success of mitigation

demand and many hydrological processes. They can how-

efforts, making adaptation a more attractive surrogate

ever be used to perturb an observed or synthetic daily

(Anderson & Bows ; Fung et al. ; Sanderson et al.

series using the ‘change factor’ approach (Loaiciga et al.

; Harris et al. ).

), elsewhere referred to as perturbation or the ‘delta-

A number of approaches to adaptation have been ident-

change’ method (Prudhomme et al. ). A change factor

iﬁed. Vulnerability-led adaptation includes methods aimed at

is obtained for each month in the future series, these ﬁgures

identifying and reducing present community/system vulner-

are then used to perturb an observed baseline daily series to

ability; thereby reducing future exposure to potentially

produce a future series, i.e. applying a January monthly

damaging impacts. Scenario-led adaptation, studied here,

change factor of 10% to an observed series would make

uses future climate change projections to assess future climate

all of the daily values in the future series for the month of

change impacts. Downscaled regional-scale climate scenario

January þ10% larger (Holman et al. ). A criticism of

data can be fed into impact models; the outputs are then

the change factor approach is that it assumes that the cli-

used to inform adaptation, to maximise potential beneﬁts

mate variability is stationary, e.g. the same patterns of wet

and/or minimise potential risks (Wilby & Dessai ). A

and dry days will occur in the future dataset as in the orig-

hybrid approach, combining elements of vulnerability-led

inal baseline (Harris et al. ). Despite this, it remains a

and scenario-led approaches has recently emerged, though is

popular approach, given its relative simplicity and low com-

not the focus of this paper (Brown & Wilby ).

putation demands (e.g. Daccache et al. ). Alternatively, a

Scenario-led adaptation is limited by the ﬁnancial and

probabilistic weather generator can be used to generate mul-

technical capacity of the individuals undertaking the adap-

tiple future time series using perturbed synthetic baselines.

tation; their risk appetite, the availability of high quality

Unlike the conventional change factor approach, weather

downscaled climate change information and the type of

generators are not dependent on the individual having

adaptation options being considered (Adger et al. ;

access to a suitably long observed record (Green &

Dessai et al. ). Despite greater awareness of its beneﬁts

Weatherhead ) nor do they assume that the future cli-

(Füssel ; Ranger et al. ), few real-world cases of

mate variability is stationary, making them an attractive

scenario-led adaptation decisions have been realised (Tomp-

tool for supporting robust decision making (Groves &

kins et al. ), with large sector and regional differences in

Lempert ; Dessai et al. ; Lempert & Groves ;

the type of adaptation considered. This limited uptake has

Harris et al. ). The change factor approach and

been attributed to a variety of factors; see Moser and

UKCP09 weather generator (Semenov ; Wilks & Wilby

Ekstrom () for an extensive discussion.

) are both examples of statistical downscaling (Wilby

Scenario-led adaptation is used here to model irrigation

et al. ), while they are not utilised here, alternative

demand and inform farm reservoir design in a semi-humid

methods collectively referred to as dynamical downscaling

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

techniques also exist (Mearns et al. ). An extensive dis-

increases as the potato develops). Guidance values covering

cussion of the merits and weaknesses of these and other

crop development and root depths are provided for selected

downscaling techniques can be found elsewhere and in

crops within WaSim, and up to three crops can be combined

greater detail (Prudhomme et al. ; Fowler et al. ).

in a cropping pattern (Hess & Counsell ).

The primary source of future climate projections in the

In the ﬁeld of irrigated agriculture, decision makers have

UK is the UKCP09 dataset (Murphy et al. ). UKCP09

typically relied on the design dry year rule for estimating the

provides 10,000 probabilistic climate projections at a

volume of irrigation required. A design dry year is deﬁned in

25 km scale resolution generated from a perturbed ensemble

the UK as a year with an 80% probability of non-exceedance

experiment using the HadCM3 GCM. These are provided in

(roughly equivalent to the older ‘fourth driest year of ﬁve’

the format of monthly change factors. Alternatively, daily

rule of thumb). This rule of thumb is generally considered

(and even hourly) projections, and at a ﬁner spatial resol-

the ‘best practice approach’ and forms the basis of most

ution of 5 km , are readily available as outputs from

water allocation for UK irrigated agriculture (Weatherhead &

UKCP09’s weather generator (Jones et al. ). The

Knox ).

weather generator provides baseline (‘control’) and future

This study explores the difference between using the

scenario sequences for three different greenhouse gases

change factor approach and the UKCP09 weather generator

emission scenarios (low, medium and high) and for selected

for modelling future irrigation demand and informing reser-

30 year time-slices (centred around the 2020s, 2030s, 2040s,

voir design at three sites in the UK. Decision outcomes are

2050s, 2060s, 2070s and 2080s respectively).

obtained using the 80% probability of non-exceedance rule

These daily weather datasets can be imported into soil

and an economic optimisation and compared.

water balance models such as WaSim, freely available via the Cranﬁeld University website, to model the irrigation demand of various crops (Hess & Counsell ). WaSim

METHOD

simulates inflow (infiltration) and outflow (evapotranspiration and drainage) and storage of soil water in response to

A previous study by Green and Weatherhead () found

climate, irrigation and drainage (Depeweg & Fabiola

that the weather generator was reasonably calibrated at a

Otero ). WaSim has proven invaluable across a range

number of UK sites. Three sites representing different agro-cli-

of previous studies including determining irrigation require-

matic conditions distributed around the UK were selected as

ments,

the

case studies. These particular sites were chosen because they

performance of sub-surface drainage systems and studying

optimising

water

management,

assessing

had the most complete record for the baseline period.

the effects of climate change on water resources (Depeweg

Brooms Barn is located in the county of Suffolk, near Bury

& Fabiola Otero ; Hirekhan et al. ; Warren &

St Edmunds, approximately 30 km east of Cambridge and is

Holman ). WaSim divides the soil proﬁle into ﬁve

the driest of the investigated sites. Slaidburn is located in

layers, water moves from upper layers to lower layers

the district of Lancashire, approximately 60 km north-west

when the water content of the respective layer exceeds

of Leeds and is the wettest site with an average annual rainfall

ﬁeld capacity. The ﬁrst three layers are comprised of the sur-

of 1,515 mm for the baseline period. Lastly, Woburn is situ-

face layer (0–0.15 m), the active root zone layer (0.15-root

ated in the county of Bedfordshire, 50 km north-west of

depth) and the unsaturated layer below the root zone (root

London and is marginally wetter than Brooms Barn but

depth-water table). The remaining two layers are comprised

with slightly lower annual evapotranspiration. Observed cli-

of the saturated layer above drain depth (water table – drain

mate data were extracted for the baseline period from the

depth) and the saturated layer below drain depth (depth

weather station at each site. Additional hydroclimatology

drain – impermeable layer). The boundary between the

data for the baseline period are shown in Table 1.

second and third layers changes in response to root

All 10,000 monthly change factor climate projections

growth (e.g. in the case of potatoes, layer 2 has zero thick-

were extracted from the UKCP09 sample ensemble for the

ness when root depth is less than 0.15 m, and then

single 25 km2 grid square overlying each weather station,

M. Green & E. K. Weatherhead

Table 1

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

Weather station sites and records used

Station

Lat.

Long.

Elevation (m AOD)

Average annual (1961–1990)

Data

Rain (mm)

From

Evapotranspiration (mm)

Brooms Barn

52.260

0.567

588

585

1964

1990

Slaidburn

53.987

2.433

192

1,515

487

1961

1990

Woburn

52.014

0.595

632

564

1961

1990

for each emission scenario (i.e. low, medium and high) for

As the weather generator offers a much greater spatial

the 2050s time slice (i.e. 2040–2069). Baseline evapotran-

resolution of 5 km2, data were generated for a grouping of

spiration and monthly evapotranspiration change factors

25 individual grid squares (i.e. a combined area of 25 km2)

were estimated using Penman-Monteith (Monteith );

overlying each weather station, to be directly comparable

wind speed was assumed to be the same as the observed

with the 10,000 member ensemble 25 km2 grid square. It

baseline (1969–1990) due to the lack of earlier baseline

should be noted that the weather generator and 10,000

data and future projections of wind speed.

member sample ensemble spatial grids differ slightly in

Ten thousand climate projections were simultaneously

their orientation which may create subtle differences in

generated using the UKCP09 weather generator, using the

the projected climate, though because of the large areas

same ID codes to allow direct comparison, again for each

used, the impact is considered somewhat negligible. Despite

weather station and each emissions scenario. The UKCP09

this, the potential impacts on the outcomes of this study are

weather generator was previously found to be reasonably

an acknowledged limitation.

calibrated at these sites with the exception of some extreme

Next, WaSim was used to model irrigation demand at

events (which are beyond the scope of our analysis and do

each site. In its basic format, WaSim is not capable of pro-

not impact the reservoir design) (Green & Weatherhead

cessing multiple climate ﬁles succinctly, so a modiﬁed

).

version was developed and employed for this study to

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

read-in multiple climate ﬁles and output a single results ﬁle

A further £20 k was added, representing the assumed con-

containing the daily irrigation demand for each of the

nection costs of 3-phase electricity. Annual OPEX was

10,000 climate ﬁles. A potato crop was simulated with a

assumed to be 1% of CAPEX, representing the low main-

planting depth of 0.15 m, max root depth of 0.7 m and

tenance cost of clay reservoirs (Weatherhead et al. ).

planting date of 1st April. A rule based irrigation schedule

Each of the 10,000 sequences was then used to calculate

was modelled based on best practice guidelines including

the net present value (NPV) of a range of reservoir sizes,

scab control (Defra ). This schedule consisted of 4

with usable storage capacities equivalent to 0–1,000 mm

periods (1 non-irrigation followed by 2 irrigation and 1

depth over the area irrigated (i.e. 0 to 10,000 m3·ha 1).

non-irrigation), applying 15 mm of water early in the grow-

NPV provides a measure of the present value of the differ-

ing season whenever the root zone deﬁcit exceeded 18 mm

ence between the assumed costs and beneﬁts of a

during period 2 (15th May–30th June) and applying 25 mm

decision. NPV was calculated by discounting the annual

of water whenever the root zone deﬁcit exceeded 30 mm

net beneﬁt of the reservoir loss OPEX costs with a

during period 3 (30th June–31st Aug). Irrigation early in

lumped (non-discounted) CAPEX in year 0, assuming cur-

the growing season is essential for some varieties for mini-

rent government discount rate guidelines of 3.5% on

mising the chance of potato scab, a common bacterial

investments of up to 30 years (HM Treasury ). Each

blight which can severely reduce the market value of pro-

reservoir was assumed to last 30 years, representing

duce (Liu et al. ). Irrigation is also important for

their typical life cycle. The optimum reservoir capacity,

promoting

crop

deﬁned as the size providing the highest NPV was calcu-

canopy growth, reducing the chance of uneven growth

lated for each of the 10,000 sequences. The median,

and thumbnail cracking and reducing crop damage

mean, quartile and extreme values for each site, emission

during harvesting (Defra ). The soil type was set as

scenario and dataset were identiﬁed as before.

higher

tuber

numbers,

accelerating

sandy loam, which is the dominant soil type for potato

The Mann–Whitney U-test (Mann & Whitney ) was

crops in England, with an assumed saturation of 43.3%

used to establish whether there were signiﬁcant differences

and ﬁeld capacity of 24.5%.

between the change factor and weather generator datasets

The irrigation demand was calculated for each year in

in terms of both the 80% dry year irrigation demands and

the 10,000 × 30 year sequences for each site and emission

the optimum reservoir capacities. The Mann–Whitney

scenario, using both the change factor and weather genera-

U-test was chosen due to the non-parametric nature of

tor datasets. The values within each sequence were then

the data even after applying transformations. The Mann–

ranked from smallest to largest. The irrigation demand

Whitney U-test is used to test the equality of two population

during the design dry year (referred to hereafter as 80%

medians. It is considered the non-parametric alternative to

dry year irrigation demand) was calculated for each of the

the 2-sample t-test, it assumes that the populations are

10,000 sequences, using the 80% probability of non-excee-

independent and have a similar distribution shape. Unlike

dance rule. The median, mean, quartile and extreme

the 2-sample t-test it does not require the two populations

values for each site, emission scenario and dataset were

to be normally distributed.

identiﬁed.

In addition, a sensitivity analysis was undertaken to

For the economic evaluation, typical costs and

establish how sensitive the decision outcome was to the

beneﬁts for clay agricultural reservoirs were obtained

choice of discount rate, beneﬁt of the water and earthwork

from a concurrent study (Weatherhead et al. ). The

costs. Each parameter was varied in turn, keeping the

economic beneﬁt of the water contained within each

other parameters ﬁxed, and the median optimum reservoir

reservoir was calculated on the basis of average water

capacity identiﬁed, calculating the percentage difference

use, assuming an average net beneﬁt (for potatoes) of

before and after varying each parameter. The discount rate

£1.56/m3 of water used (Morris et al. ). Earthwork

was initially ﬁxed at 3.5%, water beneﬁt at £1.56 m 3 and

costs were assumed to be £1.125/m

of earth moved,

plus an additional 15% reﬂecting site investigation costs.

earthworks at £1.125 m 3, and subsequently scaled up and down using a linear coefﬁcient.

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

RESULTS AND DISCUSSION

Journal of Water and Climate Change

05.1

2014

increases with reservoir capacity. The NPV range is larger for the weather generator dataset than for the change factor

The 80% dry year irrigation demands were compared

dataset for all the reservoir capacities considered. For the

between the change factor and weather generator sequences

change factor dataset, the median optimum reservoir capacity

for each site and emission scenario (Figure 1). The median

was 340 mm. In contrast, the weather generator estimated the

80% dry year irrigation demand was similar across both

median optimum reservoir capacity to be marginally smaller

datasets. Both also had a similar interquartile and extreme

at 320 mm, but with a 20% larger NPV. Similar results were

range. These results support the assumption that the

recorded for all three emission scenarios for all three sites.

weather generator was reasonably calibrated with the

Statistical analysis was undertaken to establish whether

observed record (Green & Weatherhead ) and suggest

there was signiﬁcant difference between using the weather

that using the UKCP09 weather generator instead of the

generator and change factor datasets in terms of (1) the

conventional change factor approach may not necessarily

80% dry year irrigation demand and (2) the optimum reser-

lead to more robust decision making.

voir capacity. The 80% dry year irrigation demand values

Next, the economic performance of various reservoir

obtained using the weather generator dataset were signiﬁ-

capacities generated using the full 10,000 change factor and

cantly greater than those from using the change factor

weather generator sequences were compared against each

dataset. In contrast, the optimum reservoir capacities from

other for each site and emission scenario. Figure 2 shows

the weather generator dataset were signiﬁcantly lower

the results obtained for the site of Woburn using the

than from the change factor dataset. However, while the

medium emission scenario. Despite subtle differences in the

differences were statistically signiﬁcant at the 95 conﬁdence

projected NPV, both datasets showed a similar trend in

interval (95CI) (Table 2), the difference in the 80% dry year

NPV against reservoir capacity. The weather generator pro-

irrigation demand was generally less than 25 mm, which

jected a higher NPV for most reservoir capacities, based on

is only the depth of a typical single application of water.

the median projection, with the exception of small reservoirs

The difference in the optimum reservoir capacities was

with a capacity of less than 100 mm.yr . The NPV range (i.e.

similarly small (though generally >25 mm), with the

the difference between the maximum payoff and minimum

exception of the Brooms Barn site. These results again

payoff for each reservoir size) is initially quite narrow and

suggest that using the weather generator in place of the

Figure 1

Median (–), mean (×), quartile and extreme values of the 80% dry year irrigation demand for the change factor (CF) and weather generator (WG) sequences for each site and emission scenario.

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

Finally, the optimum reservoir capacity was directly compared with the dry year irrigation demand calculated using a range on probability of non-exceedance values (80, 85, 90, 95 and 100%). Based on these initial ﬁndings, the 80% probability of exceedance rule appears to underestimate the optimum reservoir capacity at Brooms Barn and Woburn and overestimate the optimum reservoir capacity at Slaidburn (the wettest site), with a difference of between 120 to þ100 mm (Figure 3). The 95% probability of non-exceedance rule had a smaller difference of between 0 to þ170 mm. Visual comparison would suggest that the Figure 2

95% probability of non-exceedance rule is much closer to Median, quartile and extreme values of NPV against reservoir capacity for the change factor (CF) and UKCP09 weather generator (WG) sequences for the

the optimum reservoir capacity at the sites of Brooms

Woburn site and medium emission scenario.

Barn and Woburn. However at the site of Slaidburn, all ﬁve probability of non-exceedance rules tested appear to

conventional change factor, while theoretically leading to

considerably overestimate the optimum reservoir capacity

more robust decision making, in reality is unlikely to greatly

(see Figure 3). This result should serve as a warning to

affect the decision outcome.

those stakeholders who do not consider the underlying

Table 2

Results of Mann–Whitney U-test statistical analysis comparing 80% dry year irrigation demand and optimum reservoir capacity obtained using economic optimisation with change factor (CF) and weather generator (WG) datasets, showing median reservoir capacity, whether they are signiﬁcantly different and using 95 conﬁdence interval (95CI)

Site

Brooms Barn

Criteria

80% Dry year irrigation demand

Emission scen.

Low

Data source

Res. capacity

270

Optimum reservoir capacity

Med WG

280

High WG

290

270

Low WG

300

360

Med WG

310

370

High WG

320

370

330

Sig. difference?

Yes

P-value (95CI)

0.00

000

0.00

Site

Slaidburn

Criteria

80% Dry year irrigation demand

Emission scen.

Data source

Res. capacity

100

Optimum reservoir capacity

M WG

130

110

H WG

110

L WG

120

H WG

Sig. difference?

Yes

P-value (95CI)

0.00

Site

Woburn

Criteria

80% Dry year irrigation demand

Emission scen.

Low

Data source

Res. capacity

240

270

260

270

260

290

320

300

340

320

340

320

Optimum reservoir capacity

Med

High

Low

Med

High

Sig. difference?

Yes

P-value (95CI)

0.00

M. Green & E. K. Weatherhead

Figure 3

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

Differences between the median dry year irrigation demands using 80 to 100% exceedance rules and the median optimum reservoir capacity, for the change factor (CF) and weather generator (WG) sequences for each site and emission scenario.

economics of their decision; blind use of probability of non-

this site. Increasing the value of water above £1.56.m 3

exceedance rules can lead to maladaptation with stake-

had surprisingly little effect on the optimum reservoir

holders either over-designing or under-designing their assets.

capacity, increasing it by only 9.7% even up to a value of

The results of this study are dependent on several

£4.68.m 3; this reﬂects the point that useful capacity is lim-

assumptions including (1) discount rate, (2) earth work

ited by demand, with decreasing returns on additional

costs and (3) monetary beneﬁt of the water. Each of these

capacity.

variables is a potential source of uncertainty and may poten-

These variations in median optimum reservoir capacity

tially affect the optimum reservoir capacity. As a result, a

were subsequently compared to the capacities given by the

sensitivity analysis was undertaken to establish whether

simpler % exceedance rules, in this case the 80 and 95%

altering these parameters changed the perceived optimum

dry year irrigation demand. For the Woburn site and the

reservoir capacity.

base variable values, the 95% probability of non-excee-

The sensitivity analysis is presented here for the site of

dance rule out performs the 80% probability of non-

Woburn, for the medium emission scenario and the weather

exceedance rule (Figure 4). At larger discount rates

generator dataset. Similar results were obtained for the other

(>7%) the 80% rule works better, and for lower earthwork

sites and emission scenarios and for the change factor data-

costs (less than £1.80.m 3) the two rules are equally close.

set. The optimum reservoir capacity was largely insensitive

For all water values, the 95% probability of non-exceedance

to the discount rate, evident from the near horizontal line,

rule was nearer the optimum value, but both rules failed to

with larger discount rates slightly favouring smaller reser-

show that the reservoir was no longer economically viable

voirs (Figure 4). The reservoir capacity was more sensitive

when the water value was less than £0.78.m 3. More case

to earthworks costs, with larger earthworks costs favouring

studies would be needed to conﬁrm these are general

smaller reservoirs, again as expected. The value of the water

results, but they suggest that the 80% rule may be

in the reservoir had the largest effect on the optimum reser-

misleading.

the reservoir produced a

It should be noted that these ﬁndings are conditional

negative NPV and was no longer economically viable at

on the view that the median optimum reservoir capacity

voir capacity; below £0.78.m

M. Green & E. K. Weatherhead

Figure 4

Probabilistic weather generator versus a change factor approach

Journal of Water and Climate Change

05.1

2014

Sensitivity analysis comparing optimum reservoir capacity against discount rate, water beneﬁt and earthworks cost, showing changes relative to base parameter values, for the Woburn site and medium emission scenario. The 80 and 95% dry year irrigation demands are also shown for comparison.

of the 10,000 sequences represents the most appropriate

with UKCP09 was unable to reproduce observations of

course of action (akin to the ‘Laplacian’ view of invest-

key climate variables including sunshine duration and

ment appraisal) (French ). Decision makers who are

solar irradiation.

particularly risk averse or risk seeking may disagree with

In later versions of the UKCP09 weather generator,

this assumption and may instead use the quartile or

modiﬁcations were made to the weather generator to

even best/worst case projections, though for the vast

improve its predictive capabilities, which were later veriﬁed

majority of stakeholders our stated assumptions should

by Eames et al. (). They found that the weather genera-

sufﬁce.

tor was capable of producing weather data that were

GCMs providing ‘high’ resolution daily projections

consistent with historical monthly observations of wind,

are few in number and those which do are considered

speed,

less accurate (Palutikof et al. ; Huth et al. ). As

irradiation, maximum temperature, minimum temperature

direct

irradiation,

diffuse

irradiation,

global

a result, GCM climate change projections often need to

and mean temperature. This result is consistent with pre-

be downscaled both spatially and temporally before they

vious ﬁndings by Green & Weatherhead () which

can be of any use for decision makers. Numerous down-

showed that the UKCP09 was capable of reproducing

scaling approaches are available, including but not

observed precipitation and evapotranspiration and annual

limited to the change factor approach and UKCP09

irrigation demand reasonably well. Eames et al. ()

weather generator considered here. Different downscaling

also noted that subsequent iterations of the UKCP09

techniques come with their own advantages and disad-

weather generator had issues reproducing a realistic distri-

vantages; see Wilby et al. () and Fowler et al. ()

bution of sunshine hours and direct and diffuse irradiation

for extensive reviews. The UKCP09 weather generator is

which can lead to absurd conclusions. We expect that the

theoretically better than the conventional change factor

UKCP09 weather generator will be gradually improved

approach, given that it allows for non-stationary variabil-

over time to reduce or remove these concerns; while they

ity to be simulated and thus incorporated into climate

did not affect the ﬁndings of this study they may have

change

implications for other applications where hourly data are

risk

assessments

and

adaptation

planning

(Harris et al. ). The UKCP09 weather is however

of high importance.

not without its ﬂaws, a previous study by Tham et al.

A criticism of the change factor method, as pre-

() found that the weather generator initially released

viously noted, is that it assumes that the temporal and

M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

spatial structure of future precipitation and evapotranspiration remains unchanged (Diaz-Nieto & Wilby ; Fowler et al. ; Minville et al. ; Harris et al. ). In some situations, it is necessary to evaluate changes in climate variability and not just changes in means (Semenov et al. ). Despite this, the change factor approach remains popular because of its simplicity and is useful for converting monthly change factors into daily projections needed to model most hydrological processes without incurring excessive expense (Minville et al. ).

CONCLUSIONS This study found that use of a weather generator did not greatly alter the decision outcome compared to using the conventional and relatively crude change factor approach, suggesting that the changes in day-to-day climate variability that are simulated by the weather generator are not significant enough to warrant action when informing irrigation reservoir design. This result is contrary to the expectation that the UKCP09 weather generator lends itself to more robust decision making; in reality the difference between the two approaches is negligible. The core benefits of the weather generator may continue to make it an attractive tool to use, those being that it provides hourly climate data and readily available evapotranspiration data. Whether these benefits outweigh its fundamental limitations including the poor simulation of extreme meteorological events, is subject to the sensitivity of each application and the user’s requirements. The study also found that the ‘best-practice’ approach of using the 80% probability of non-exceedance rule is inadequate and designers should instead investigate the fundamental economics (e.g. NPV) that underpin the decision making process.

ACKNOWLEDGEMENTS We would like to thank the Engineering and Physical Sciences Research Council (EPSRC) and HR Wallingford for funding this research.

Journal of Water and Climate Change

05.1

2014

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M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

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M. Green & E. K. Weatherhead

Probabilistic weather generator versus a change factor approach

Tang, S. & Dessai, S.  Usable science? The UK Climate Projections 2009 and decision support for adaptation planning. Weather, Climate, and Society 4, 300–313. Tham, Y., Muneer, T., Levermore, G. J. & Chow, D.  An examination of UKCIP02 and UKCP09 solar radiation data sets for the UK climate related to their use in building design. Building Services Engineering Research and Technology 32, 207–228. Tompkins, E. L., Adger, N., Boyd, E. G., Nicholson-Cole, S., Weatherhead, E. K. & Arnell, N. W.  Observed adaptation to climate change: UK evidence of transition to a well-adapting society. Global Environmental Change 20, 627–635. Warren, A. J. & Holman, I. P.  Evaluating the effects of climate change on the water resources for the city of Birmingham, UK. Water and Environment Journal 26, 361–370.

Journal of Water and Climate Change

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Weatherhead, E. K. & Knox, J. W.  Predicting and mapping the future demand for irrigation water in England and Wales. Agricultural Water Management 43, 203–218. Weatherhead, E. K., Nox, J. W. & Kay, M. G.  Thinking about an irrigation reservoir? Technical Report. Centre for Water Science, Cranﬁeld University. Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P. & Mearns, L. O.  Guidelines for use of climate scenarios developed from statistical downscaling methods. Supporting material of the Intergovernmental Panel on Climate Change, available from the DDC of IPCC TGCIA, 27. Wilby, R. L. & Dessai, S.  Robust adaptation to climate change. Weather 65, 180–185. Wilks, D. S. & Wilby, R. L.  The weather generator game: a review of stochastic weather models. Progress in Physical Geography 23, 329–357.

First received 29 May 2013; accepted in revised form 26 July 2013. Available online 22 October 2013

05.1

2014

Proposing a new semi-parametric weather generator algorithm and comparison of six algorithms for non-precipitation climatic variables Behnam Ababaei, Teymour Sohrabi and Farhad Mirzaei

ABSTRACT Six different weather generator models were compared. The ﬁrst two models (M1 and M2) use a ﬁrstorder autoregressive daily model and the third model (M3) uses a newly proposed semi-parametric method to reproduce the correlation and autocorrelation of the variables. Three other models (M1-2, M2-2 and M3-2) are the combinations of these models with an adjustment algorithm for the lowfrequency variances (SL). The comparison revealed that M1-2 model (daily weather generator with the SL adjustment algorithm) and the M2-2 model (daily weather generator in combination with a monthly weather generator and the SL adjustment algorithm) are the best models in the study area. All the studied models have acceptable performance in relation to the shape of the probability

Behnam Ababaei (corresponding author) Teymour Sohrabi Farhad Mirzaei Department of Irrigation and Reclamation Engineering, Faculty of Agricultural Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, P.O. Box: 4111, Karaj, 31587-77871, Iran E-mail: Behnam.ab@gmail.com; B.Ababaei@ut.ac.ir

distribution functions. Three ﬁrst models have deﬁciencies in relation to the inter-annual standard deviations. The M2 and M3 models, in which the high-frequency standard deviation (SH) is used instead of the total standard deviation values (ST), slightly underestimated these inter-annual variations. But, the performance of the M1 model is considerably poorer than the other two models. The results revealed that the adjustment of the inter-annual standard deviations improves model performance. Moreover, the newly proposed algorithm has the potential for multi-station simulations. Key words

| low-frequency variances, semi-parametric algorithm, weather generator

INTRODUCTION Weather generators (WGs) are widely used in different ﬁelds

Semenov & Porter ; Semenov et al. ; Ababaei et al.

of science. These models are able to reproduce important stat-

b). Moreover, application of WG models usually results

istical properties of observed time series. The simulation of

in variance underestimation and/or biased estimation of aver-

daily climatic time series is the most important and

age values of agricultural or hydrological model outputs

common application of these models. Besides precipitation,

(Richardson ; Semenov & Porter ; Mearns et al.

as the most important variable affecting environmental pro-

; Jones & Thornton ; Hansen & Mavromatis ).

cesses (Hutchinson ), supplementary algorithms are

For a series including a years and each year consisting of

also proposed to simulate other variables. Sensitivity analysis

ni daily data (i ¼ 1 … a) of weather variable y, total standard

of crop simulation models revealed that using the regional

deviation (STD) of y for each month can be estimated by

daily average temperature values results in the overestimation

Equation (1)

of crop production. Hence, the accurate simulation of crop

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uPa Pni 2 u j¼1 (yij Y) t i¼1 Pa ST ¼ i¼1 (ni 1)

production requires synthetic data which can mimic the daily variations of climatic variables (Nonhebel ;

doi: 10.2166/wcc.2013.168

(1)

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

This variance can be divided into two elements (Hansen

Journal of Water and Climate Change

05.1

2014

METHODS AND MATERIALS

& Mavromatis ): (1) high-frequency STD (SH) which is related to days within a month (Equation (2)) and (2) low-

Methodology

frequency standard deviation (SL) which is related to the inter-annual STD of each month (STD of monthly means,

Six different WG algorithms were compared as combi-

Equation (3))

nations of two daily algorithms for climatic variables including minimum (Tmin) and maximum (Tmax) tempera-

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uPa Pni u 2 j¼1 (yij yi ) t i¼1 Pa SH ¼ i¼1 (ni a)

tures, relative humidity (RH) and wind speed (Wind) with (2)

a monthly WG and an adjustment algorithm of interannual variances. The properties of the studied stations

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! ! " #ffi u a a u 1 X X SL ¼ t ni 1 ST 2 ni a SH2 a 1 i¼1 i¼1

located in Qazvin Province of Iran are shown in Table 1. (3)

Monthly weather generator To handle the problem of underestimation of low-frequency

Many WG models simulate ST using short-term stochas-

variations, a four-variable ﬁrst-order monthly autoregressive

tic processes and are unable to reproduce low-frequency

model is used to reproduce the series of monthly mean

variances (Hansen & Mavromatis ). Some methods

values meanwhile to keep the correlation of these values

have been proposed for changing inter-annual properties of climatic variables for reproducing these low-frequency

Yi (t) ¼ C × Yi (t Δt) þ D × e(t)

(4)

variations and a review can be found in Dubrovsky et al. ().

where Δt: time intervals (1 month), C and D: 4*4 coefﬁcient

The main objective of this study is to assess a newly pro-

matrices (Matalas ), e(t): a white-noise variable (4-D)

posed semi-parametric WG algorithm which is capable of

and Y*: matrix of the ﬁnal STDs. Matrices C and D are cal-

reproducing monthly and inter-annual statistics of observed

culated

climatic times series. This algorithm is compared with a

(unconditional). But, after simulating the monthly standard

well-known and relatively simple algorithm which is used

variables, they are scaled using the mean and standard devi-

in a few well-tested WG models like LARS-WG (Semenov

ation values of wet and dry days separately. This monthly

& Barrow ; Ababaei et al. a) and WGEN (Richard-

model is used in M2 and M3 models.

for

dry

and

wet

days

simultaneously

son , ; Richardson & Wright ). Both algorithms are combined with a monthly WG and an adjustment algor-

Daily weather generators

ithm to improve the performance of the WG models in relation to the inter-annual variances (low-frequency

Three daily WG algorithms were compared. M1 model uses

variances).

a method similar to the one implemented in LARS-WG.

Table 1

Properties of the studied stations

Missing percentage

Missing percentage (at least

Station

Coordinates

Altitude (m)

Period

(precipitation)

one of the other variables)

Qazvin (QA)

N36-15, E50-03

1279

1959–2008

–

0.3%

Takestan (TK)

N36-03, E49-42

1283

1961–2008

–

Nirougah (NR)

N36-11, E50-15

1285

1984–2007

36%

Magsal (MG)

N36-08, E50-07

1265

1976–1999

32%

85%

–

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

05.1

2014

This model uses semi-empirical distributions (SED) instead

variables (i.e. 4). This matrix is constructed for each

of normal distribution and has more control on the range

calendar month and for dry and wet days separately.

of selected standard variables and does not use a Fourier

3. Simulation of the standard variables is carried out using

function to simulate seasonal cycles. Standard variables

multivariate normal distributions (MND). For this purpose,

selected from SED are transformed into correlated variables

mean and covariance of each column of matrix As is esti-

using a four-variable ﬁrst-order auto regression model

mated for each month. Then, standard variables are

(Equation (5))

generated using a MND. For each day t, 500 random vectors are generated each consisting of eight variables. The (5)

ﬁrst four variables are related to day t and the second

where z(t): standard variables for day t, A and B: coefﬁ-

4. The Euclidean distance of the second four variables of all

cient matrices (Matalas ) and ε(t): a white-noise

these 500 generated vectors with the vector of the stan-

variable. Afterwards, these standard variables are scaled

dard variables of day t–1 (simulated in the previous

z(t) ¼ [A]z(t 1) þ [B]ε(t)

four variables are related to day t–1 (Equation (7)).

to the nonstandard variables using long-term monthly mean and STD of each variable (Equation (6)) Tk (t) ¼

time step) is calculated. 5. The generated vectors are sorted in an ascendant order and the ﬁrst four variables of the vector with the lowest

Dry day: Mk,0 (t) þ STDk,0 (t)zk (t) Wet day: Mk,1 (t) þ STDk,1 (t)zk (t)

(6)

distance are selected as the standard variables of day t. This step is accomplished because we are trying to ﬁnd a generated vector for which its second four standard

where Tk(t): the climatic variable k on day t, M and STD:

variables are very similar to the standard vector of day t

the mean and STD value of climatic time series and the

and then choose its ﬁrst four standard variables for day

indices 0 and 1 indicate dry and wet days, respectively.

t. This leads to preserving auto- and cross-correlation

This step was carried out for wet and dry days separately.

between variables on day t–1 and day t.

M2 model is very similar to M1 model. The only difference

6. Upon generating the standard variables for the whole

is that a monthly weather generator (MWG; see section

period, these variables are transformed into the nonstan-

above) is used in M2 model. Since a part of the total var-

dard variables using the long-term SH values and the

iances (ST) is simulated by MWG, ST values are replaced

mean values resulted from MWG.

with high-frequency variances (SH) in Equation (6). After simulating the standard variables for each day, these variables were scaled using the monthly mean values resulted

Adjusting low-frequency variances

from MWG and the SH values of each variable. M3 model is proposed here and uses a semi-parametric algorithm to simulate the daily standard variables. The steps

to the inter-annual variances (low-frequency variances), the series of monthly mean and STD values of climatic vari-

of this model are as follows: 1. Each weather variable is standardized (scaled) using the long-term mean and STD values of each month (for dry and wet days separately). 2. Matrices As (standard variables) are constructed for each month Asm,t ¼ [X1,t , X2,t , :::, Xn,t , X1,t 1 , X2,t 1 , :::, Xn,t 1 ]

To improve the performance of the WG models in relation

ables are constructed for all the months in the whole period (for wet and dry days separately). These series are then standardized (Equation (8)) in a way that their mean and STD values become equal to the observed values × Y ¼ (X X)

σY þY σX

(8)

(7)

where m: month index, t: time index (day number), Xi,t:

the mean where X: the simulated mean or STD value, X: value of the simulated monthly mean or STD series, Y:

climatic variable i on day t, and n: total number of

the mean value of the observed monthly mean or STD

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

05.1

2014

series, and σ: the STD of these series. Afterwards, daily time

The SRMSE values (Table 2) were calculated as the

series are transformed by an equation similar to Equation

average values of all studied climatic variables. For the QA

(8). This process is carried out for dry and wet days separ-

station, the highest coefﬁcient of variation (CV) between

ately. Combination of this adjustment algorithm with the

the models is related to the monthly mean and STD

WG models results in M1-2, M2-2 and M3-2 models.

values. While for the TK station, the maximum CV value is related to the monthly and inter-annual STD and for the NR station, it is related to the monthly STD values. The

Model assessment

best performance among the models is related to the Historic precipitation occurrence time series at all the

monthly mean and STD values while the highest SRMSE

stations were repeated several times until they span at

values are related to the daily and monthly auto-correlation

least 500 years. This was done to prevent the defects of

values. For the MG station, the performances of both daily

the precipitation simulation models from inﬂuencing the

algorithms are comparable. The reason for the lower per-

assessment. In order to assess the models, standardized

formance of the models at this station is the multiplicity of

root mean square errors (SRMSE) was used

the missing data. Especially, the low-frequency adjustment algorithm is very sensitive to missing values.

"P n SRMSE ¼

i¼1

(Pi Oi )2 n

#0:5 ×

100 O

Table 3 shows the matrices of the standardized SRMSE (9)

indices. The lowest values of each index are in gray. At three stations, M1-2 and M2-2 are the best models. The average SRMSE values of M2-2 model for the QA, TK and NR

The SRMSE values of the models were calculated for

stations are 8.9, 10.2 and 11.9%, respectively. According to

monthly mean and STD, inter-annual STD, different per-

these results, M2-2 (or M1-2) model at the QA, TK, and NR

centiles of the probability distribution functions (1 to 100

stations and M1-2 model at the MG station outperformed

with the intervals of 10) and daily and monthly lag-1 auto-

the other models and were selected as the best models.

correlation of all the variables. The ﬁnal matrices (for each station) consisted of six columns (six models) and six rows

Assessing the best models

(six indices). All values were inverted and the minimum values were estimated for each row. The inverted values

The hypothesis of the equality of the mean values (t-test) was

of each row were divided by this minimum value and

not rejected for any of the studied variables at any station

then were scaled into the range [0–1]. Finally, the average

(p > 0.01). But, the hypothesis of the equality of the variances

value of each column was estimated and the model with

(F-test) was rejected for wind speed in QA (in January) and

the lower value (i.e. 0) was selected as the best. This

NR (in January, February and October). At the MG station,

method assigns similar weights to all the indices.

this hypothesis was rejected for relative humidity (in February) and for wind speed (in May). The only reason for these rejections is the existence of the missing data.

RESULTS AND DISCUSSION

The observed and simulated (from the best models) monthly mean values of all the variables are shown in

Choosing the best models

Figure 1. It is clear that the M1-2 and M2-2 models are capable of simulating monthly mean values at all the

At the MG station, C and D matrices could not be estimated

stations. The average SRMSE values for simulating the

because of the high number of missing data in some months.

monthly mean values are 0.16, 1.05 and 1.72% for the QA,

Also the covariance of the matrix of standardized variables

TK and NR stations, respectively and 1.74% for the MG

was asymmetrical and generating the random standard variables

station (M1-2).

by the multivariate normal distribution was impossible. Therefore, at this station, only M1 and M1-2 models were assessed.

The observed and simulated monthly STD values of all the variables are shown in Figure 2. It can be seen that

Table 2

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

05.1

2014

The SRMSE values of the studied models

Qazvin (QA) Models

M1-2

M2-2

M3-2

Min

Max

Mean

Monthly mean

0.06

0.20

1.20

0.16

1.61

0.15

0.06

1.61

0.56

108%

Monthly STD

0.09

0.91

5.39

1.14

5.72

1.26

0.09

5.72

2.42

93%

Inter-annual STD

28.14

6.88

8.62

7.72

8.36

7.65

6.88

28.14

11.23

68%

Percentiles

13.28

12.92

15.27

16.79

22.57

25.50

12.92

25.50

17.72

27%

Daily Lag-1 autocorrelation

15.33

13.67

20.24

20.72

24.95

25.78

13.67

25.78

20.11

22%

Monthly Lag-1 autocorrelation

17.50

19.55

6.38

7.12

6.19

7.16

6.19

19.55

10.65

53%

Average

12.40

9.02

9.52

8.94

11.57

11.25

8.94

12.40

10.45

13%

M3-2

Max

Mean

Takestan (TK) Models

Monthly mean Monthly STD

M1-2

0.34

1.05

2.54

M2-2

1.04

2.61

1.01

Min

0.34

2.61

1.43

59%

0.44

1.58

5.28

2.00

6.79

1.97

0.44

6.79

3.01

74%

Inter-annual STD

32.72

7.40

7.71

5.14

9.54

5.14

32.72

11.27

86%

Percentiles

13.24

15.63

16.61

18.96

21.19

24.65

13.24

24.65

18.38

20%

9.47

11.88

19.42

19.40

22.42

22.62

9.47

22.62

17.54

29%

Daily Lag-1 autocorrelation Monthly Lag-1 autocorrelation

26.11

23.50

15.91

15.39

16.99

16.62

15.39

26.11

19.09

22%

Average

13.72

10.17

11.24

10.32

13.26

12.00

10.17

13.72

11.79

11%

M2-2

M3-2

Min

Max

Mean

Nirougah (NR) Models

Monthly mean Monthly STD

M1-2

0.20

1.71

3.83

1.72

4.48

1.67

0.20

4.48

2.27

64%

0.24

2.83

6.57

2.95

8.39

2.88

0.24

8.39

3.98

68%

Inter-annual STD

31.04

7.17

9.02

5.38

9.21

5.67

5.38

31.04

11.25

80%

Percentiles

14.29

17.02

19.04

22.38

26.54

31.96

14.29

31.96

21.87

27%

Daily Lag-1 autocorrelation

10.03

16.04

30.73

27.93

34.74

31.39

10.03

34.74

25.14

36%

Monthly Lag-1 autocorrelation

40.26

35.15

10.57

10.99

12.19

12.29

10.57

40.26

20.24

62%

Average

16.01

13.32

13.29

11.89

15.92

14.31

11.89

16.01

14.13

10%

M2-2

M3-2

Min

Max

Mean

Magsal (MG) Models

M1-2

Monthly mean

0.30

1.74

–

0.30

1.74

1.02

71%

Monthly STD

0.45

10.49

–

0.45

10.49

5.47

92%

Inter-annual STD

36.77

7.37

–

7.37

36.77

22.07

67%

Percentiles

13.51

18.92

–

13.51

18.92

16.21

17%

Daily Lag-1 autocorrelation

11.97

16.76

–

11.97

16.76

14.36

17%

Monthly Lag-1 autocorrelation

39.45

41.39

–

39.45

41.39

40.42

Average

17.07

16.11

–

16.11

17.07

16.59

B. Ababaei et al.

Table 3

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

05.1

2014

The standardized SRMSE values of the studied models

Qazvin (QA) Models

M1-2

M2-2

M3-2

Monthly mean

0.00

0.09

0.74

0.07

1.00

0.06

Monthly STD

0.00

0.15

0.94

0.19

1.00

0.21

Inter-annual STD

1.00

0.00

0.08

0.04

0.07

0.04

Percentiles

0.03

0.00

0.19

0.31

0.77

1.00

Daily Lag-1 autocorrelation

0.14

0.00

0.54

0.58

0.93

1.00

Monthly Lag-1 autocorrelation

0.85

1.00

0.01

0.07

0.00

0.07

Average

0.34

0.21

0.42

0.21

0.63

0.40

Takestan (TK) Models

M1-2

M2-2

M3-2

Monthly mean

0.00

0.31

0.97

0.31

1.00

0.29

Monthly STD

0.00

0.18

0.76

0.25

1.00

0.24

Inter-annual STD

1.00

0.08

0.09

0.00

0.16

0.00

Percentiles

0.00

0.21

0.30

0.50

0.70

1.00

Daily Lag-1 autocorrelation

0.00

0.18

0.76

0.98

1.00

Monthly Lag-1 autocorrelation

1.00

0.76

0.05

0.00

0.15

0.11

Average

0.33

0.29

0.49

0.30

0.67

0.44

Nirougah (NR) Models

M1-2

M2-2

M3-2

Monthly mean

0.00

0.35

0.85

0.35

1.00

0.34

Monthly STD

0.00

0.32

0.78

0.33

1.00

0.32

Inter-annual STD

1.00

0.07

0.14

0.00

0.15

0.01

Percentiles

0.00

0.15

0.27

0.46

0.69

1.00

Daily Lag-1 autocorrelation

0.00

0.24

0.84

0.72

1.00

0.86

Monthly Lag-1 autocorrelation

1.00

0.83

0.00

0.01

0.05

0.06

Average

0.33

0.48

0.31

0.65

0.43

M2-2 model is very accurate at simulating these values at all

average SRMSE values for simulating the inter-annual

the stations. The average SRMSE values for simulating the

STD values are 7.72, 5.14 and 5.38% for the QA, TK

monthly STD values are 1.14, 2.0 and 2.95% for the QA,

and NR stations, respectively and 7.37% for the MG

TK and NR stations, respectively and 10.5% for the MG

station (M1-2).

station (M1-2). The models are more capable in dry days.

M1 model performed relatively well for the inter-

Since generated inter-annual variances are adjusted

annual STDs of minimum and maximum temperatures

for wet and dry days by M1-2 and M2-2 models, there

(Figure 4) at the QA station, but its performance was

cannot be any difference between observed and simulated

not as robust in relation to relative humidity and wind

values. From Figure 3, it is observed that the M2-2 model

speed and resulted in the underestimation of these

is considerably capable of simulating unconditional values

inter-annual variations. At the TK station (Figure 5),

(wet and dry days together) at all the stations. The

the M2 model also underestimated the inter-annual

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

Figure 1

The monthly mean values of all variables at all the stations and for all calendar months (observed vs. simulated).

Figure 2

The monthly standard deviation values of all variables at all the stations and for all calendar months (observed vs. simulated).

variations but its performance is better than M1 because of using MWG.

05.1

2014

The q-q plots of daily minimum temperature and relative humidity are depicted in Figure 7 for the QA station. It can

The daily lag-1 auto-correlation values of all the vari-

be seen that the quantiles of climatic variables are accurately

ables at all the stations and for all calendar months are

regenerated by both models. From Table 2, it can be con-

shown in Figure 6. It is obvious that the best models are

cluded that the performance of the ďŹ rst daily algorithm is

acceptable at simulating the short-term dependencies

better than the proposed algorithm. This is mostly because

between variables. The lowest SRMSE values are 20.7,

of the exceptional characteristics of wind speed. Without

11.9 and 27.9% for the QA, TK and NR stations, respect-

considering this climatic variable, the performances of all

ively, and 16.8% for the MG station.

studied models are comparable.

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

Figure 3

The inter-annual standard deviation values of all variables at all the stations and for all calendar months (observed vs. simulated).

Figure 4

Inter-annual standard deviation resulting from M1 model at the QA station: Tmin (upper left), Tmax (upper right), RH (lower left) and wind speed (lower right).

05.1

2014

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

Figure 5

Inter-annual standard deviation resulting from M2 model at the TK station: Tmin (upper left), Tmax (upper right), RH (lower left) and wind speed (lower right).

Figure 6

The daily lag-1 auto-correlation values of all variables at all the stations and for all calendar months (observed vs. simulated).

05.1

2014

B. Ababaei et al.

Figure 7

Proposing a new semi-parametric weather generator algorithm

Journal of Water and Climate Change

05.1

2014

The q-q plots of minimum temperature (upper) and relative humidity (lower) at the QA station: M2-2 (left), M3-2 (right) (observed vs. simulated).

CONCLUSIONS

inter-annual variations. But, the performance of M1 model is considerably poorer than the other two models.

In this research, six different weather generator models were

The results revealed that the adjustment of the inter-

analyzed and compared. The ﬁrst two models (M1 and M2)

annual STDs of wet and dry days after simulating the daily

use a ﬁrst-order autoregressive algorithm for generating the

weather time series substantially improves model perform-

daily standard variables and the third model (M3) uses a

ance. Moreover, the newly proposed algorithm (M3) has

newly proposed semi-parametric method to reproduce the

the potential to simulate climatic variables for one indepen-

correlation and autocorrelation of climatic variables. Three

dent station or for neighboring (i.e. correlated) stations. A

other models (M1-2, M2-2 and M3-2) are the combinations

similar approach was used by Ababaei () to develop a

of these models with a monthly weather generator and an

multi-station weather generator model. This issue could be

adjustment algorithm for the low-frequency variances (SL).

the subject of future research.

The comparison revealed that the M1-2 and M2-2 models are the best models in the study area. All the studied models are very capable of reproducing the monthly mean and STD values. Daily models have deﬁciencies in relation to the inter-annual STD values. The M2 and M3 models, in which the high-frequency STD (SH) is used instead of the total STD values (ST), slightly underestimated these

REFERENCES Ababaei, B.  Development and Application of a Planning Support System to Assess Strategies Related to Land and Water Resources for Adaptation to Climate Change. PhD Thesis, University of Tehran, 327 pp. (in Farsi).

B. Ababaei et al.

Proposing a new semi-parametric weather generator algorithm

Ababaei, B., Sohrabi, T. M., Mirzaei, F. & Karimi, B. a Evaluation of a stochastic weather generator in different climates. Computer and Information Science 3, 217–229. Ababaei, B., Sohrabi, T. M., Mirzaei, F., Rezaverdinejad, V. & Karimi, B. b Climate change impact on wheat yield and analysis of the related risks: (Case Study: Esfahan Ruddasht Region) (in Farsi). Journal of Water and Soil Science 3, 135–150. Dubrovsky, M., Buchtele, J. & Zalud, Z.  High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modeling. Climatic Change 63, 145–179. Hansen, J. W. & Mavromatis, T.  Correcting low-frequency variability bias in stochastic weather generators. Agriculture and Forest Meteorology 109, 297–310. Hutchinson, M. F.  Stochastic space-time models from ground-based data. Agricultural and Forest Meteorology 73, 237–264. Jones, P. G. & Thornton, P. K.  A rainfall generator for agricultural applications in the tropics. Agricultural and Forest Meteorology 63, 1–19. Matalas, N. C.  Mathematical assessment of synthetic hydrology. Water Resources Research 3 (4), 937–945. Mearns, L. O., Rosenzweig, C. & Goldberg, R.  The effect of changes in daily and inter-annual climatic variability

Journal of Water and Climate Change

05.1

2014

on CERES-wheat: a sensitivity study. Climate Change 32, 257–292. Nonhebel, S.  The effects of use of average instead of daily weather data in crop growth simulation models. Agricultural Systems 44, 377–396. Richardson, C. W.  Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resources Research 17, 182–190. Richardson, C. W.  Weather simulation for crop management models. Transactions of the American Society of Agricultural Engineers 28, 1602–1606. Richardson, C. W. & Wright, D. A.  WGEN: A Model for Generating Daily Weather Variables. US Department of Agriculture, Agricultural Research Service ARS-8, USDA, USA, p. 83. Semenov, M. A. & Porter, J. R.  Climatic variability and the modeling of crop yields. Agricultural and Forest Meteorology 73, 265–283. Semenov, M. A. & Barrow, E. M.  LARS-WG 3.0 User’s Manual: A Stochastic Weather Generator for Use in Climate Impact Studies. Rothamsted Research, Harpenden, UK. Semenov, M. A., Brooks, R. J., Barrow, E. M. & Richardson, C. W.  Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Climate Research 10, 95–107.

First received 20 May 2013; accepted in revised form 29 September 2013. Available online 2 December 2013

05.1

2014

Development of HydroClimatic Conceptual Streamﬂow (HCCS) model for tropical river basin Parag P. Bhagwat and Rajib Maity

ABSTRACT Combined processes of land-surface hydrology and hydroclimatology influence the response of a watershed to different hydroclimatic variables. In this paper, streamflow response of a watershed to hydrometeorological variables is investigated over a part of two tropical Indian rivers – Narmada and Mahanadi. The proposed HydroClimatic Conceptual Streamflow (HCCS) model is able to consider the time-varying basin characteristics and major hydrologic processes to model basin-scale streamflow

Parag P. Bhagwat Rajib Maity (corresponding author) Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India E-mail: rajib@civil.iitkgp.ernet.in; rajibmaity@gmail.com

using climate inputs at a daily scale. In addition, the proposed model is able to provide additional overall estimates of ground water recharge component and evapotranspiration component from the entire basin. Moreover, ability to consider the time-varying watershed characteristics and hydroclimatic inputs renders the proposed model usable for assessment of future streamflow variation. This application is also investigated for both the study basins. In general, the methodological approach of the proposed model can be applied to other tropical basins for daily streamflow modelling as well as future streamflow assessment. Key words

| climate, conceptual model, hydroclimatology, prediction, streamﬂow

INTRODUCTION In the context of a changing climate, identiﬁcation of stream-

better or at least comparable performance to existing

ﬂow response to other hydroclimatological variables is a

approaches.

research challenge (Kumar & Maity ; Maity & Kashid ). Most of the existing approaches attempt to consider

Background and literature review

and conceptualize different hydrological processes. However, time varying watershed characteristics are not

A brief review (with respect to the huge amount of literature

considered. As a consequence, consistency (decadal to cli-

available) of existing approaches reveals that the hydrologi-

matic scale) of the model performance is affected under a

cal models for streamﬂow estimation can be grouped into

changing climate and changing watershed characteristics

three broad categories viz: (a) physically based models, (b)

that are not accounted for. Here lies the importance of this

conceptual models, and (c) artiﬁcial intelligence (AI)-based

study since recent observation of climate change has added

models. In physically based models, existing knowledge of

a new dimension towards this overall research direction in

all possible hydrological processes is represented through

hydrologic modelling. The question examined was, is it poss-

a set of mathematical equations. Examples of some popular

ible to model watershed response as streamﬂow with the

physically based models include System Hydrologique Euro-

simultaneous consideration of changing climate and time

pean (Abbott et al. a,b), Better Assessment Science

varying watershed characteristics? As such, it was found

Integrating point and Non-point Sources (EPA ), Soil

that there is a need to develop a streamﬂow model having

and Water Assessment Tool (SWAT) (Eckhardt & Arnold

few parameters, which will be able to consider time varying

; Grizzetti et al. ), and Community Land Model

watershed characteristics and climatic inputs, and provide

(Lawrence et al. ). However, an inability for accurate

doi: 10.2166/wcc.2013.015

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

mathematical representation of hydrological processes con-

properties of the watershed. As a consequence, the outcome

sidering the huge spatial variability, computational demands

of such models sometimes becomes crude. Sometimes, even

and overparameterization effects, and non-availability of

the major components, such as evapotranspiration, ground

required comprehensive data sets make the results of phys-

water recharge component and streamﬂow, are not separ-

ically based models poor in many cases (Piotrowski &

ated from each other. Moreover, typical absence of short-

Napiorkowski ; Zeng et al. ).

term (i.e., fortnight to seasonal) and/or long-term (i.e., deca-

Conceptual models are also derived using (relatively

dal to centennial) dynamic characteristics raise questions

simpler) mathematical equations, which mainly consider

against its applicability to capture the dynamic, time-varying

the major hydrological process of the complex hydrological

response of watershed to long-term climate change impact

cycle, which is difﬁcult to understand fully. However, in the

analysis studies, which are particularly essential in a chan-

absence of complete knowledge, they may be represented in

ging climate.

a simpliﬁed way by means of the system concept. A system is

AI-based models are much simpler with respect to the

a set of connected parts that form the whole. By using the

understanding of underlying physical processes. These

system concept, effort is directed to the construction of a

models are developed based on interrelationships between

model relating inputs and outputs rather than to the extre-

input and output values of the concerned variables. In the

mely difﬁcult task of exact representation of the system

last two decades, use of AI-based models has increased due

details, which may not be signiﬁcant from a practical point

to their lower dependence on physical understanding of the

of view or may not be known. Nevertheless, knowledge of

underlying process(es) by the users, and provide quick as

the physical system helps in developing a conceptually

well as reasonably impressive results compared with the

clear model. The major hydrological processes considered

other two types of models discussed before. Some of the

are mostly rainfall, evapotranspiration, inﬁltration, surface

most popular AI-based models, which are applied to hydrolo-

runoff, etc., which have signiﬁcant importance towards the

gical modelling, are based on artiﬁcial neural network (Kumar

watershed output. As mentioned before, the physically

et al. ), genetic programming (Maity & Kashid ), and

based models require a large number of data and still pro-

support vector machines (SVMs). However, apart from the cri-

duce poor results due to a variety of reasons. Hence,

ticism of the ‘black-box’ nature, the performance of such

conceptual models are used as alternative models for water-

models is often found to be excellent and difﬁcult to replicate

sheds. Some of the generally used conceptual models are the

using other conceptual/physically based approaches. This

Stanford watershed model, which is an early ﬁnding in con-

said, the same criticism which was mentioned in the case of

ceptual hydrological modelling by Crawford & Linsley

conceptual models earlier, exists for such modelling

(), the Institute Royal Meteorology Belgium (IRMB)

approaches as well. That is, due to their static nature, studies

model (Bultot & Dupriez ), HYDROLOG model

related to long-term climate change impact analysis may not

which was later modiﬁed by Chiew & McMahon () to

be always possible with this modelling approach.

the MODifed HYDROLOG (MODHYDROLOG) model, Hydrologiska Byråns Vattenbalansavdelning (HBV) model

Motivation and objectives

which was developed at the Swedish Meteorological and Hydrological Institute (SMHI) in 1972 (Bergström ),

Speciﬁcally, this study attempts to develop a basin-scale

and the Hydrologic Simulation Program-FORTRAN model

daily hydroclimatic streamﬂow model (named as HydroCli-

which is a modiﬁed version of Stanford model ( Johnson

matic Conceptual Streamﬂow (HCCS) model), considering

et al. ), and so on. These models are designed to

the time-varying watershed characteristics and major hydro-

approximate the general hydrological processes, which dic-

logic processes to model basin-scale streamﬂow using

tate the hydrological cycle. However, most of the

climate inputs. The developed modelling approach is con-

conceptual models consider all hydrological processes to

ceptual in nature. Its performance is investigated for two

be lumped together, greatly simplify the hydrological pro-

Indian tropical river basins. Performances of AI-based

cesses

machine learning approaches are found to overshadow the

involved,

and

not

consider

time-varying

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

performance of other modelling approaches. Thus, the per-

dependent. The proposed methodology is motivated by

formance of the proposed model is compared with the

SACramento Soil Moisture Accounting model (Burnash

performance of a highly popular AI-based machine-learning

et al. ) and leaky bucket model (Huang et al. ) as

approach known as least square-support vector regression

far as an initial water balance equation is concerned. How-

(LS-SVR) (Suykens et al. ). Performance of the proposed

ever, subsequent conceptualizations differ as the major

model is also compared with other popular conceptual mod-

focus of these models is on soil moisture simulation whereas

elling approaches that are effective at daily scale in addition

the major focus of the proposed HCCS model is on stream-

to AI.

ﬂow prediction.

The ability to consider the hydroclimatic inputs is

The HCCS model is conceptual in nature and able to

expected to make the proposed HCCS model usable also

predict the daily variation of streamﬂow while estimating

for future projected climate. Time-varying watershed charac-

the spatially averaged evapotranspiration loss and ground

teristics are considered that renders the approach dynamic,

water recharge from the entire catchment. The model is

which is found to be very useful for a changing climate con-

also suitable for simulating the future streamﬂow variation

dition and analysis of streamﬂow variation under future

using such future projected climate data over the basin.

climate. The parameter that controls the time-varying water-

The model is described below.

shed characteristics is conceptualized. This parameter may

The ‘System Wetness Condition’ (SWC) is a represen-

be projected to the future with different assumptions on

tation of the amount of water that is stored in the near-

change in watershed characteristics and may be used for

surface strata of the watershed as depression storage, soil

future. Thus, the speciﬁc objectives of this paper are: (1)

water retention, reservoir storage, etc. SWC is time dependent

the development of the HCCS model considering the time

and is denoted as V(t), where t is the time subscript. V(t)

varying watershed characteristics and using climatic

ranges between zero and maximum capacity of the system

inputs; (2) application of the developed HCCS model to

wetness, Vmax, which is the maximum water holding capacity

two Indian river basins (Mahanadi and Narmada) and inves-

of the watershed in the form of soil moisture at the top strata

tigation of their performance; (3) comparison of the

of the soil, depression storage, reservoirs, etc. Whereas V(t)

developed HCCS model with other popular conceptual

changes at a much faster rate depending on the rainfall and

modelling approaches as well as with AI-based the LS-

other weather conditions, Vmax also varies over time but at

SVR model; and (4) study of future streamﬂow variation

a much slower pace. Vmax depends on the combination of

under projected climate during 2026–2035, 2076–2085

different types of land use land cover (LULC), such as, urban-

using the proposed HCCS model.

ization, forest cover, existence of reservoirs, etc. Temporal change of this characteristic (Vmax) is slower than other inﬂuencing parameters, such as, rainfall, wetness

METHODOLOGY

condition (V(t)) and other weather conditions. In this modelling approach, Vmax is also considered to vary over time,

HCCS model

which makes it suitable for application over a longer time frame and more importantly in the changing climate. In

In the proposed HCCS model, the watershed is treated as a

the application of the proposed model, it is shown that

‘system’ that receives rainfall, processes it, and generates

this property changes over a multi-year scale. This is a

streamﬂow as its ‘response’. The ‘response’ depends on var-

very important aspect to be considered in hydroclimatic

ious factors, depending on the time-dependent as well as

streamﬂow modelling to study the variation of streamﬂow

time-invariant characteristics of the watershed. For instance,

under projected future climate owing to changed character-

topology, shape of the catchment are treated as time-

istics of watershed status.

invariant whereas wetness condition of watershed, rainfall

SWC at time t, V(t) is calculated based on the water bal-

over catchment, continuous loss due to evaporation, rate

ance in the watershed. The components of the water balance

of ground water recharge, etc., are treated as time

in the model are precipitation, evaporation, streamﬂow, and

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

loss due to ground water recharge. For a watershed, the

in this study later. Equation (2) can be rearranged as follows

change in SWC over time can be expressed as

(for time t):

dV(t) ¼ PðtÞ EðtÞ SðtÞ GðtÞ dt

(1)

V ðtÞ ¼ B½SðtÞ b

(3)

where B ¼ Vmax =ða Smax Þb and b ¼ 1/b0 . where V(t) is the SWC at time t expressed in terms of depth

Another major component is the loss due to evapotran-

of water per unit area of watershed, P(t) is the depth of pre-

spiration, E(t), which is estimated using the following relation:

cipitation

over

the

watershed,

E(t)

the

total

evapotranspiration loss from the catchment, which is the sum of evapotranspiration from land surface and direct

EðtÞ ¼

Ep ðtÞ V ðtÞ Vmax

(4)

evaporation loss from depression storages, reservoirs, etc., S(t) represents the streamﬂow divided by the catchment

where Ep(t) is the potential evapotranspiration and V(t) is the

area of watershed, and G(t) is the loss due to deep percola-

SWC at time t. Substituting V(t) in Equation (4) from Equation

tion that joins ground water as ground water recharge

(3)

component from the watershed. As mentioned earlier, Vmax is the maximum capacity of the

EðtÞ ¼ Ep ðtÞ

system wetness. Hence, V(t)/Vmax denotes dynamic condition of the system or the proportion of SWC, a value of 0 indicates a completely dry system and a value of 1 indicates a fully wet system. In this proposed model, it is assumed that this proportion determines system response and other components in the water balance equation are linked with this. Physically, this factor indicates the overall status of the watershed at time t that controls the loss of water from the system through various components. These are conceptualized as follows. First, the streamflow is expected to be generated by the system depending on its wetness condition. The generated streamflow (per unit area of the watershed) is conceptualized to have a non-linear relationship with the SWC V(t). If non-dimensionalized with respect to their respective maximum possible values which are denoted as Vmax (defined earlier) and Smax, the assumption is expressed as

B½SðtÞ b Vmax

(5)

Potential evapotranspiration from climatic data can be estimated using any standard method, such as the Hargreaves method (Hargreaves et al. ; Hargreaves ) or the Penman–Monteith method (Monteith ). The Hargreaves method is used in this study and presented in Appendix A (available online at http://www.iwaponline.com/jwc/005/ 015.pdf). The ground water recharge component G(t) can be assumed to be non-linearly associated with SWC, V(t). This is expressed as follows: GðtÞ ¼ α½V ðtÞ β

(6)

For simplicity, β is assumed to be 1, i.e., ground water recharge component is assumed to vary linearly with

0 SðtÞ V ðtÞ b ¼a Smax Vmax

SWC. This assumption of linearity was also made by earlier (2)

researchers in the similar context of ground water recharge component in the leaky bucket model (Huang et al. ).

whereas the conceptualization of Vmax is explained earlier,

However, it may be noted here that the development of

the concept of Smax is not completely new. Rather it is ana-

the subsequent equations is still possible without this

logous to estimated limiting value (ELV), which is deﬁned as

assumption. The aforementioned assumption reduces the

the largest magnitude possible for a hydrologic event at a

burden of one extra parameter only. Using Equation (3) in

given location, based on the best available hydrologic infor-

Equation (6) with β ¼ 1, G(t) can be expressed as

mation (Chow et al. ). This assumption, expressed in Equation (2), will be checked for two watersheds considered

GðtÞ ¼ k½SðtÞ b

(7)

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

where k ¼ α, B. From Equation (1), expressing the left hand

basin, a basin with many dams and reservoirs is expected

side with ﬁnite difference scheme, gives

to have more water holding capacity. On the other hand, highly urbanized, deforested basins with increased paved

V ðt þ 1Þ V ðtÞ ¼ PðtÞ EðtÞ SðtÞ GðtÞ Δt

(8)

surfaces may exhibit low water holding capacity. Thus, for a particular river basin this property is expected to change depending on the status of watershed development, such

Substituting the expressions of V(t), E(t) and G(t) in

estation, etc. It is expected to perceive such changes over a

term of S(t) gives h

B fSðt þ 1Þg fSðtÞg Δt

multi-year scale. Thus, a proper estimation of the projected

i ¼ PðtÞ Ep ðtÞ

B½SðtÞ b SðtÞ k½SðtÞ b Vmax

¼ >Sðt þ 1Þ ¼ "

(

fSðtÞgb þ

as, construction of dams and reservoirs, urbanization, defor-

Δt B½SðtÞ b PðtÞ Ep ðtÞ SðtÞ k½SðtÞ B Vmax

)#1 b b

status of this parameter should precede the estimation of the future streamﬂow. This interpretation of time varying characteristics differs, in general, from other standard conceptual models. A historical analysis is carried out for two study river basins in the next section to investigate the

(9)

where B, b, k and Vmax are the model parameters that characterize the basin considered in the study. All these parameters may change depending on the change in watershed condition, e.g., urbanization, deforestation, construction of dams and reservoirs, etc.

change of this parameter over the last couple of decades. This parameter has a unit of length (say m or mm). Parameters b and B The inverse of parameter b is the measure of degree of nonlinearity between S(t)/Smax and V(t)/Vmax as expressed in Equation (2). Parameter B is a function of Vmax and Smax [ ¼ Vmax =(a Smax )b ]. While making the analogy of Smax

Discussion on different model parameters

with ELV it should not be confused with the fact that the

The physical analogy of different parameters used in the

to be modiﬁed depending on the change in the characteristics

model is discussed in this section.

of the upstream catchment. This fact is not unrealistic. For

value of Smax does not change over time. Rather it is expected

instance the chance and magnitude of ﬂash ﬂood increase Parameter k

with increased urbanization. Thus Smax may also be subjected to change depending on the characteristics of the upstream

The parameter k indicates the net contribution of catchment

catchment which is associated with the change in Vmax. How-

to ground water recharge. Theoretically, this value may be

ever, these changes are expected in long-term temporal scale

positive or negative. Spatially, surface water may contribute

(multi-year or decadal) depending on the rate of change in the

to ground water at some location whereas ground water may

watershed characteristics.

contribute to surface water at some other location within the catchment. Thus, the net contribution to ground water from

Parameter estimation, validation, and future projection

entire catchment may be positive or negative. If the net contribution to ground water from entire catchment is positive,

Model calibration is performed using the daily streamﬂow,

k will be positive and vice versa. This parameter is unitless.

rainfall, maximum temperature, minimum temperature, and average temperature data. Four parameters of conceptual

Parameter Vmax

rainfall-runoff model, i.e., Vmax, b, B and k, are estimated during the model calibration period. These parameters

The parameter Vmax indicates the overall water holding

depend on the catchment characteristics, which inﬂuence

capacity of the watershed. Compared with a virgin river

the system response and may also be interrelated. Thus,

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

these parameters are to be simultaneously estimated during

and projected to future). However, some of these par-

model calibration by minimizing the mean square error

ameters may not show a liner/non-linear trend over the

(MSE). The values of the parameters that yield minimum

historical time. If no plausible trend information is

mean square error (MMSE) are used as estimated par-

obtained for some parameters, it is recommended to use

ameters. During both model development and testing

either average value of that parameter over the historical

periods, the model performance can be investigated through

period or the latest information of that parameter. This

different statistics that measure the association between the

aspect is illustrated later with respect to the study basins

observed and predicted daily streamﬂow values.

considered for demonstration.

To investigate the slow change in any parameter, parameter estimation is carried out during a couple of years in a decade and the analysis is repeated for successive dec-

STUDY AREAS AND DATA USED

ades in the past. The estimated parameter values for different periods need to be investigated for possible

Tropical rainfed river basins in India are either east ﬂowing

change over time. This change might be modelled and if

or west ﬂowing. The performance of the proposed model is

a trend is found that might be projected in future assuming

investigated over the last couple of decades for two Indian

different cases of change in watershed characteristics: (1)

River basins, Mahanadi (east ﬂowing) and Narmada (west

no further change (latest value is used for future); (2)

ﬂowing). Upstream parts of both these basins are con-

same trend of change projected to future; and (3) change

sidered. In Figure 1 (Mahanadi) and Figure 2 (Narmada),

is slower approaching a constant (a non-linear equation

study areas with locations of climate stations and streamﬂow

may be ﬁtted to observed parameter values in the past

gauging stations are shown. Study area considered for

Figure 1

Mahanadi River basin with the study area (up to Basantpur gauging station).

P. P. Bhagwat & R. Maity

Figure 2

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Narmada River basin with the study area (up to Sandia gauging station).

Mahanadi River basin is 61,152 km2 up to Bastantpur gau-

1998 (6 months). Since streamﬂow data are missing for

ging station and that for Narmada River basin is

these periods, other data corresponding to these periods are

25,912 km2 up to Sandia gauging station.

ignored from the analysis, i.e., these periods are excluded

Daily streamﬂow at Basantpur (Mahanadi River basin) is

from analysis. For Mahanadi basin, daily gridded (1 lat × 1 long) rainW

obtained from the ofﬁce of Executive Engineer, Mahanadi

Division, Central Water Commission (CWC), India. Daily

fall

streamﬂow data (Jan 1, 1973 to Dec 31, 2003) are used for

Department (IMD). There are no missing data for rainfall.

this study. There is no major structure (apart from few

Daily temperature records (maximum and minimum) from

medium and minor reservoirs) in the catchment of Mahanadi

two stations (Raipur and Pendra Road) located in the catch-

data

are

obtained

from

India

Meteorological

up to Basantpur and there are no missing streamﬂow data for

ment (see Figure 1) are obtained from the National Climatic

this basin. Streamﬂow data at Sandia (Narmada River basin),

Data Centre (NCDC) Climate Data Online (http://www.

operated by the Water Resources Agency, are also obtained

ncdc.noaa.gov/cdo-web/). As per the website information,

from CWC for the period June 1, 1978 to May 31, 2000. How-

these data are supplied by IMD to NCDC. Temperature

ever, out of this entire duration of 23 years, streamﬂow data

data are missing for some (non-contiguous) days (approxi-

are missing for some non-contiguous periods (total 3 years

mately 250 days out of 31 years). These values are

8 months). The missing data are from Dec 1, 1987 to May

replaced by the long-term average of that date.

31, 1988 (6 months); Jun 1, 1993 to May 31, 1994 (1 year);

Daily observed rainfall and temperature data for the

May 1–31, 1996 (1 month); Oct 1, 1996 to Nov 30, 1996 (2

Narmada basin (also obtained from IMD) are recorded at

months); Apr 1, 1997 to Aug 31, 1997 (5 months); Jun 1,

ﬁve different stations within or around the basin. These

1998 to May 31, 1999 (1 year) and Dec 1, 1997 to May 31,

stations are Jabalpur, Malanjkhand, Mandla, Narshinghpur

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

and Umaria. Locations of these stations are shown in

development period. Developed models are used for testing

Figure 2. For some (non-contiguous) days (approximately

during June 1987 to May 1990 and June 1997 to May 2000.

150 days out of 23 years), rainfall and temperature data

Estimated parameters for both the basins are shown in

are missing. These values are replaced by the average of

Table 1.

other surrounding stations for that date.

For both the river basins, it is noticed that the parameters vary over different periods of analysis. This issue is investigated with respect to LULC change in the context

RESULTS AND DISCUSSION

of future streamﬂow variation later. Before that, the model performances during development and testing periods are

Model calibration and testing

investigated to assess the potential of the developed model.

Parameter estimation

Model performances

Model calibration is performed using the daily streamﬂow,

Model performances are investigated for all the develop-

rainfall, maximum temperature, minimum temperature, and

ment periods and the corresponding testing periods for

average temperature data. Four parameters of HCCS

both the basins. Model performances are presented through

model, i.e., Vmax, b, B and k are estimated during model cali-

(i) time series plot of observed and predicted streamﬂow to

bration (model development period). As mentioned above,

compare their correspondence, (ii) a scatter plot between

an MMSE criterion is followed to estimate these parameters.

observed and predicted streamﬂow to compare their associ-

An initial guess of the range of different parameters is necess-

ation and (iii) statistical measures to quantify and assess the

ary since the parameter may vary from watershed to

potential of prediction. The statistical measures include

watershed and also from one period to another period. How-

mean square error (MSE), correlation coefﬁcient (CC),

ever, the initial guess of range should be made in such a way

and Nash-Sutcliffe efﬁciency (NSE).

that the value of estimated parameter should not lie on the

Model performance during the three-fold development

border of the selected range. It is recommended to keep

and testing periods for the Mahanadi River is shown in

the initial guess as wide as possible at the cost of compu-

Figure 3. A comparison plot between observed and predicted

tation time. Due to the advent of fast computational

streamﬂow for the ﬁrst model development period (Jan 01,

facilities, computational time is not a major issue. Thus, the

1973 to Dec 31, 1980) is shown in top panel of Figure 3(a).

ranges of Vmax is considered to be 50–1,000 with an interval

MSE, CC and NSE values for this period are found to be

of computation as 0.1. Similarly, ranges of b, B and k are con-

1.133, 0.93 and 0.86, respectively (Table 2). Model par-

sidered to be 0.1–1, 10–200 and 0.0–0.5 with their interval of

ameters are estimated from this the period (Jan 01, 1973 to

computation as 0.01, 1 and 0.001, respectively.

Dec 31, 1980) and the developed model is tested during Jan

Three-fold model development and testing is adopted for

01, 1981 to Dec 31, 1983 (First model testing period). The

the Mahanadi River basin. On the other hand, two-fold

bottom panel of Figure 3(a) shows the comparison plots

model development and testing is adopted for the Narmada River basin. This is based on the availability of data. For

Table 1

Estimated Model Parameters during Different Development Periods

Mahanadi, model parameters are estimated during (a) Jan 1, 1973 to Dec 31, 1980, (b) Jan 1, 1984 to Dec 31, 1990 and (c) Jan 1, 1994 to Dec 31, 2000. Models developed over these periods are tested during (a) Jan 1, 1981 to Dec 31, 1983, (b) Jan 1, 1991 to Dec 31, 1993 and (c) Jan 1, 2001 to Dec 31, 2003, respectively. For the Narmada River basin, June 1978 to May 1987 is used as ﬁrst development period and June 1990 to May 1997 is used as second

Model Parameters Basin Name

Calibration Period

Mahanadi Jan 01, 1973 to Dec 31, 1980 Jan 01, 1984 to Dec 31, 1990 Jan 01, 1994 to Dec 31, 2000 Narmada

Vmax

22 0.63 0.113 214.0 98 0.28 0.043 244.4 48 0.54 0.080 270.3

Jun 01, 1978 to May 31, 1987 26 0.57 0.093 315.0 Jun 01, 1990 to May 31,1997 52 0.44 0.015 316.3

P. P. Bhagwat & R. Maity

Figure 3

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

(a) Observed and predicted streamflow for first development period (Jan 02, 1973 to Dec 31, 1980) and corresponding testing period (Jan 02, 1981 to Dec 31, 1983) for Mahanadi River Basin. (b) Observed and predicted streamflow for second development period (Jan 02, 1984 to Dec 31, 1990) and corresponding testing period (Jan 02, 1991 to Dec 31, 1993) for Mahanadi River Basin. (c) Observed and predicted streamflow for third development period (Jan 02, 1994 to Dec 31, 2000) and corresponding testing period (Jan 02, 2001 to Dec 31, 2003) for Mahanadi River Basin.

Table 2

Model performance statistics for development and testing periods. Testing period values are shown in parentheses

Performance Statistics Basin Name

Development Period (Testing Period)

MSE

NSE

Mahanadi

Jan 01, 1973 to Dec 31, 1980 (Jan 01, 1981 to Dec 31, 1983) Jan 01, 1984 to Dec 31, 1990 (Jan 01, 1991 to Dec 31, 1993) Jan 01, 1994 to Dec 31, 2000 (Jan 01, 2001 to Dec 31, 2003)

1.133 (0.379) 0.546 (0.601) 0.551 (1.513)

0.93 (0.94) 0.93 (0.95) 0.94 (0.88)

0.86 (0.88) 0.87 (0.90) 0.88 (0.77)

Narmada

Jun 01, 1978 to May 31, 1987 (Jun 01, 1987 to May 31, 1990) Jun 01, 1990 to May 31, 1997 (Jun 01, 1997 to May 31, 2000)

4.43 (2.77) 4.66 (7.20)

0.83 (0.86) 0.90 (0.96)

0.69 (0.72) 0.81 (0.86)

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

between observed and predicted streamﬂows during this

development and testing periods. These are shown in the

period. MSE, CC and NSE values for this period are found

supplementary

to be 0.379, 0.94 and 0.88, respectively (Table 2). For the

online at http://www.iwaponline.com/jwc/005/015.pdf).

document

(Figures

S-1–S-10,

available

second and third periods of analyses, plots between observed

Visual inspection of these plots reveals that the perform-

and predicted streamﬂows are shown in Figure 3(b) and 3(c),

ance of the proposed HCCS model is reasonably good for

respectively, with the development period (upper) and the

both Mahanadi and Narmada River basins. Performance

testing period (lower) shown in each.

in terms of statistical measures for all pairs of development

Comparison plots between observed and predicted

and testing periods for both the study basins are shown pre-

streamﬂow for Narmada River basin during Jul 01, 1978 to

viously in Table 2. The high value of NSE (range ∼ 0.69–0.9)

May 31, 1987 (First model development period) and corre-

and CC (range ∼ 0.83–0.96) between observed and predicted

sponding testing period (Jun 02, 1987 to May 31, 1990) are

streamﬂow indicate the efﬁcacy of the proposed model. In

shown in Figure 4(a), top and bottom panel, respectively.

some cases, slightly better values of these statistics during

Similarly, the plots between observed and predicted stream-

the testing period as compared with the development

ﬂows during Jun 01, 1990 to May 31, 1997 (Second model

period may be contradictory to the usual experience. How-

development period) and corresponding testing period

ever, this is not impossible. A close inspection of observed

(Jun 01, 1997 to May 31, 2000) are shown in the top and

and predicted values for such cases during development

bottom panel, respectively, of Figure 4(b). In addition to

and testing periods reveals the existence of some very high

these plots, readers may refer to the scatter plots between

values in the development period. Such high values are

observed and predicted streamﬂow values during all the

not present during the testing period. Thus, the model is

Figure 4

(a) Observed and predicted streamflow for the first development period (Jul 02, 1978 to May 31, 1987) and corresponding testing period (Jun 02, 1987 to May 31, 1990) for Narmada River Basin. (b) Observed and predicted streamflow for the second development period (Jun 02, 1990 to May 31, 1997) and corresponding testing period (Jun 02, 1997 to May 31, 2000) for Narmada River basin.

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

able to predict with slightly better accuracy during the test-

Figures S-11–S-13 (available online at http://www.

ing period.

iwaponline.com/jwc/005/015.pdf). Though these estimates

While comparing the performance between two basins,

are not compared with the observed values (due to non-

it is noticed that the performance of Mahanadi River basin

availability), seasonality in those estimated is visible and

even better than that of the Narmada River. This is due to

matches with the nature of Indian hydroclimatology.

the fact that the available data length is more and disconti-

However, some observations may appear contrary to the

nuity/missing data are less for Mahanadi basin. Thus,

general experience. For instance, in 1987, the actual eva-

availability of longer data length leads to better performance

potranspiration value in October (14.15 mm) is more

of the proposed HCCS model.

than that in June (4.06 mm) and July (6.31 mm) for Nar-

Other outputs from HCCS model

these months. As mentioned in the Methodology section,

mada. It is due to the notably different SWC (V(t)) for actual evapotranspiration E(t), is conceptualized as a As mentioned before, the ground water component and

function of potential evapotranspiration (Ep(t)) and V(t).

evaporation components can also be estimated as other

For instance, V(t) was found to be very low in July due

outputs of the model. To note again, these estimates are

to the sustained low rainfall. In June (just previous

spatially averaged magnitudes over the entire catchment.

month), total observed rainfall (56.7 mm) was much

The daily series of these components and monthly esti-

lower (55%) than that is observed normally (mean ¼

mates (derived from the daily estimates) are shown for

122.3 mm). Thus, even if the total observed rainfall in

Narmada River Basin for the period Jun 1987 to May

July (299.0 mm) was near normal (mean ¼ 278.3 mm),

1990 (Figure 5). These estimates for Mahanadi River

E(t) was found to be low.

Basin for the period Jan 1981 to Dec 1983 are shown in Figure 6. Figures for the rest of periods are shown in

Figure 5

It may be further noted that the values of ground water component

and

evaporation

components

seems

Daily (top) and monthly (bottom) evapotranspiration (left) and ground water (right) component for Narmada River basin for the period Jun 1987 to May 1990.

P. P. Bhagwat & R. Maity

Figure 6

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Daily (left) and monthly (right) evapotranspiration and ground water (right) component for Mahanadi River basin for the period Jan 1981 to Dec 1983.

correspond well with each other. As reported in Equation

Relationship between S(t) and V(t)

(4), actual evapotranspiration is conceptualized as a function of potential evapotranspiration and SWC. Whereas ground

As explained in the Methodology, that the streamﬂow (per

water recharge is conceptualized as a non-linear function

unit area of the watershed), S(t) is conceptualized to have

of SWC (Equation (6)). Thus, the correspondence between

a non-linear relationship with the SWC, V(t). In this section,

these two outputs is indeed due to the model structure. It

model generated V(t) and observed streamﬂow values (S(t))

was also mentioned earlier that the parameter β in Equation

are analyzed to investigate this issue. Different plots are pre-

(6) is assumed to be 1, i.e., ground water recharge component

pared for Narmada and Mahanadi River basin. Percentage

is assumed to vary linearly with SWC. Thus, as can be

explained through a non-linear curve of form, y ¼ a xb is

noticed from Equation (6), non-linearity can be invoked at

computed, and displayed in those ﬁgures. Figure 7 shows

the cost one additional parameter. This may result in the

such plots for Narmada basin during model development

non-linear association between these model outputs.

and testing periods. Similar plots for the Mahanadi River

While comparing these estimates between two basins, it

basin are shown in Figure 8. It can be observed that for

is observed that, in general, the evapotranspiration is less

most of the cases, the best ﬁt non-linear curves can explain,

in Narmada than Mahanadi. This is due to a combined

on an average 93% (range 74–98%) of the association

effect of low SWC (Vt) and meteorological conditions

between observed S(t) and model generated V(t). For Maha-

responsible for potential evapotranspiration. On the other

nadi River basin, the percentage explained by the non-linear

hand, ground water recharge component varies over differ-

curve is even better than that observed for Narmada River.

ent time periods both for Narmada and for Mahanadi.

This is perhaps due to the existence of a reservoir (Bargi)

SWC (Vt) is responsible for this as conceptualized in the

that started operating in 1990 inducing the effect of

methodology, resulting in time-varying recharge amount.

human operation. However, a sort of generalization on the

P. P. Bhagwat & R. Maity

Figure 7

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Relationship between observed S(t) and modeled V(t) for Narmada River basin during different periods.

nature of this curve for both the basins can be made.

Comparison of performances of proposed HCCS model

Towards this, a linear equation between observed S(t) and

with other models

model generated V(t) was also derived (not shown in ﬁgures). It is observed that the percentage of association

HCCS model performance is compared with other con-

captured by the linear equation drops to as low as 58%

ceptual models that are effective at daily scale. These

(range 58–77%). This conﬁrms the assumption of a non-

are Australian Water Balance Model (AWBM) (Boughton

linear relationship between streamﬂow and SWC made in

; Boughton & Carroll ), Sacramento model (Bur-

the Methodology.

nash et al. ), SIMpliﬁed HYDrolog (SIMHYD)

The equations for the best-ﬁt non-linear curves are

(Porter ; Porter & McMahon , ), Soil Moist-

shown in the respective plots. The coefﬁcients are expected

ure Accounting and Routing (SMAR) model (O’Connell

to be similar to the estimated parameters. By comparing the

et al. ) and Tank Model (Sugawara ; Sugawara

equations of the best-ﬁt curves with the Equation (3) in the

et al. ). Details of these models and its software pack-

Methodology, the multiplying constants and the powers

age one can be found from Rainfall Runoff Library (RRL)

are the estimates of B and b, respectively. By comparing

(available at http://www.toolkit.net.au/Tools/RRL). The

them with the values shown in Table 1, it is noticed that

performances of these models are shown in Table 3a

the estimated parameters and these values are fairly close

along with their parameter values for the study period

to each other.

(presented in the supplementary document in Tables

Figure 8

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Relationship between streamﬂow S(t) and modeled V(t) for Mahanadi River basin.

S1–S5, available online at http://www.iwaponline.com/

It is worthwhile to mention here (based on the literature

jwc/005/015.pdf). It is noticed that the performances of

review) that the performance of AI-based machine learning

all these models are inferior (if not remarkably inferior

approaches are found to overshadow the performance of

in some cases) to the proposed HCCS model.

other modelling approaches. Thus, the performance of the

12.90 (40.6) 0.73 (0.82) 0.49 (0.23) 11.91 (39.4) 0.77 (0.87) 0.53 (0.25) 13.53 (40.2) 0.70 (0.72) 0.47 (0.24) 11.90 (33.9) 0.74 (0.86) 0.53 (0.37) 12.52 (33.87) 0.71 (0.70) 0.51 (0.35)

0.77 (0.69) 0.59 (0.46) 5.87 (6.79) 8.02 (8.08) 0.66 (0.61) 0.44 (0.35)

Jun 01, 1978 to May 31, 1987 (Jun 01, 1987 to May 31,1990) Jun 01, 1990 to May 31, 1997 (Jun 01, 1997 to May 31,2000)

6.04 (7.41) 0.76 (0.67) 0.57 (0.49)

6.85 (7.18) 0.73 (0.67) 0.52 (0.43)

6.04 (6.98) 0.76 (0.69) 0.58 (0.44)

0.83 (0.74) 0.66 (0.52) 1.63 (3.30) 2.34 (4.44) 0.75 (0.65) 0.52 (0.36) 1.44 (2.60) 0.85 (0.79) 0.70 (0.63)

1.47 (3.64) 0.85 (0.75) 0.70 (0.47)

1.38 (2.90) 0.85 (0.77) 0.71 (0.58)

0.81 (0.79) 0.53 (0.50) 1.91 (2.58) 1.38 (1.98) 0.82 (0.80) 0.66 (0.62)

Jan 01, 1973 to Dec 31, 1980 (Jan 01, 1981 to Dec 31, 1983) Jan 01, 1984 to Dec 31, 1990 (Jan 01, 1991 to Dec 31, 1993) Jan 01, 1994 to Dec 31, 2000 (Jan 01, 2001 to Dec 31, 2003) Mahanadi

1.08 (1.47) 0.86 (0.85) 0.73 (0.72)

1.11 (1.72) 0.85 (0.82) 0.73 (0.67)

1.20 (1.68) 0.84 (0.82) 0.71 (0.68)

0.82 (0.79) 0.64 (0.53) 2.87 (1.44) 2.76 (1.77) 0.82 (0.71) 0.65 (0.66) 2.69 (0.99) 0.82 (0.83) 0.66 (0.68) 2.56 (1.13) 0.83 (0.85) 0.68 (0.63)

Development Period (Testing Period)

2.10 (0.88) 0.86 (0.86) 0.74 (0.71)

MSE MSE CC MSE

HydroClimatic Conceptual Streamﬂow model

Basin Name

MSE

NSE

MSE

NSE

SimHyd Sacramento AWBM

Journal of Water and Climate Change

05.1

2014

proposed model is also compared only with a highly popular AI-based machine learning approach known as LS-SVR (Suykens et al. ). The streamflow is expressed in the same way (divided by the catchment area) as in the HCCS model to facilitate the performance comparison. Rainfall, maximum temperature, minimum temperature, and lagged streamflow values are used as input data. Streamflow of the current day is considered as the output. It is established by Bray & Han () that SVM-based approaches with normalized input data outperform those with non-normalized input data. Therefore, the data are normalized and finally the model outputs are back-transformed to their original range by denormalization. The normalization (also backtransformation) is done using (Samsudin et al. ), yi ¼ 0:1 þ

Si 1:2 maxðSi Þ

(10)

where yi are the normalized data for ith day, Si is the observed value for ith day and max(Si) is the maximum of all Si (during the model development period). The LS-SVR model has two parameters (regularization parameter, γ, kernel parameter, σ) to be determined. These parameters are interdependent, and their (near) optimal values are often obtained by a trial and error method. The grid search method is used to find the optimum parameters of LS-SVR. The CC is used for selecting the best performing model parameters. Different statistical measures are used to evaluate the performance of the model during development and testing period. Both the study areas are explained in detail above. Training and testing data for Mahanadi River basin are from period Jan 1, 1973 to Dec 31, 1995 and Jan 1, 1996 to Dec 31, 2003, respectively, and for Narmada River basin, they are Jun 1, 1978 to May 31, 1995 and Jun 1, 1995 to May 31, 2000, respectively. The performance of both study areas is assessed with the remaining testing data points. Model performance statistics are obtained between observed and modeled river flow values during training and testing period. The combination that yields comparable performance during training and testing period is selected, which ensures the optimum parameter values without the fear of overfitting. The values of

Narmada

Performances of other conceptual models during different model development and testing periods (within parentheses)

NSE

SMAR

NSE

Tank

NSE

P. P. Bhagwat & R. Maity

Table 3a

γ and σ 2 for which the difference between the training and testing period performances is minimum, are identiﬁed. It

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

is found from that the best combination of γ and σ 2 is 1 and

Thus, in brief, while comparing the performance of the pro-

0.36, respectively, for the Mahanadi River basin. Similarly,

posed HCCS model (Table 2) with other conceptual models

for the Narmada River basin the best combination of γ

(Table 3a) and LS-SVR (Table 3b), overall it is found that the

and σ 2 is 25 and 0.36, respectively.

performance of the proposed HCCS model is better.

Model performance during the training and testing period is assessed in terms of aforementioned statistical measures and shown in Table 3b. Comparison ﬁgures between observed

FUTURE STREAMFLOW VARIATION

and predicted streamﬂows are shown in the supplementary document (Figures S-14–S-17, available online at http://

It was mentioned earlier that in the proposed approach,

www.iwaponline.com/jwc/005/015.pdf) for both the river

time-varying watershed characteristics are considered that

basins. Fair correspondence between observed and predicted

render the approach dynamic. Moreover, ability to consider

river ﬂow values is found. In low and medium range of ﬂow

the hydroclimatic inputs (rainfall, maximum temperature,

values, the correspondence is found to be better. Different

minimum temperature, and average temperature) is useful

statistical measures like CC, MSE, and NSE are used to deter-

for studying the change in streamﬂow under projected

mine the performance of the developed LS-SVR model.

future climate. Thus, the proposed HCCS model is used

Values of MSE, CC and NSE for Mahanadi River basin are

for future climate study. The same watersheds are con-

1.333, 0.89 and 0.79, respectively, during the training period,

sidered for this purpose to compare the current streamﬂow

and are 1.011, 0.85 and 0.73, respectively, during the testing

variation with the future streamﬂow variations over different

period (Table 3b). Similarly, for the Narmada River basin,

months in the year.

these values are found to be 4.545, 0.86 and 0.75 (training), and 9.901, 0.86 and 0.70 (testing), respectively. These performance measures are compared with the

Temporal change in maximum system wetness capacity (Vmax) and other parameters

performance of the proposed HCCS model. Average NSE for the proposed HCCS model for Mahanadi river is found

Temporal change of Vmax is a very important aspect to be

to be 0.87 whereas the same for LS-SVR (even using a

considered in hydroclimatic modelling to study the variation

longer data length) is 0.79. Similarly, the CC and MSE are

of streamﬂow under a changing climate and also the chan-

also found to be better in case of HCCS model than that

ging

of LS-SVR for both Mahanadi and Narmada rivers. Only

wetness capacity (Vmax) and its variation over time are the

in the case of ﬁrst development period (Jun 01, 1978 to

unique characteristics of a particular watershed. As men-

May 31, 1987) and testing (Jun 01, 1987 to May 31, 1990)

tioned before, this concept is based on the physical

for Narmada river, performance of HCCS model was

properties and their change due to a combination of

found to be less than that of LS-SVR while considering CC

human activities (urbanization, deforestation, construction

and NSE values. Again, MSE is better for HCCS model

of reservoirs, etc.) and the effect of climatic change over

than LS-SVR for this set of development and testing periods.

the basin. To investigate this aspect with respect to the

characteristics

watershed.

Maximum

system

observed changes in the basin for a physical explanation, Table 3b

Performances of LS-SVR during training period and testing period (within parentheses)

LANDSAT data are obtained from Earth Science Data Interface (ESDI) at the Global Land Cover Facility (avail-

MSE Training

CC Training

NSE Training

able at http://glcfapp.glcf.umd.edu:8080/esdi/). Though

River Basin

(Testing)*

good quality images are not available before 2000 at a con-

Mahanadi

1.333 (1.011)

0.89 (0.85)

0.79 (0.73)

tinuous interval, analysis is carried out with the images

Narmada

4.545 (9.901)

0.86 (0.86)

0.75 (0.70)

that are available during some of the years over different

* For Mahanadi: Training period – Jan 1, 1973 to Dec 31, 1995; Testing period – Jan 1, 1996

study periods. Having this limitation on data availability,

to Dec 31, 2003. * For Narmada: Training period – Jun 1, 1978 to May 31, 1995; Testing period – Jun 1, 1995

changes in LULC over the study basins are investigated.

to May 31, 2000.

Three

major

categories,

namely

vegetation

(forest,

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

agriculture, grassland, etc.), water body and settlement, are

Journal of Water and Climate Change

Table 5b

05.1

2014

Land use and land cover change matrix in Mahanadi basin (up to Basantpur) between 1990 and 2000. Cell description as per Table 5a

considered to be able to make a physical analogy with change in Vmax. Results are shown in Table 4 for both the

Area (km2) in

Area (km2) in the year of 2000

basins. A little perturbation of total area in different years

the year of 1990

Vegetation

is due to some unclassiﬁed pixels, which can be ignored.

Vegetation

41,277 (78.38%)

1,938 (3.68%)

9,450 (17.94%)

Further, the conversion of a particular class to another

Water Body

518 (35.41%)

673 (46.00%)

272 (18.59%)

class is also shown in Tables 5a and 5b (for Mahanadi),

Settlement

4,163 (59.28%)

146 (2.08%)

2,714 (38.64%)

Water Body

Settlement

and Tables 5c and 5d (for Narmada). Each cell of Tables 5a–d indicates the magnitude change from LULC type shown in row name to the type shown in

Table 5c

column heading for that cell. Changes in different LULC classes are clearly noticed and a possible link with the

Land use and land cover change matrix in Narmada basin (up to Sandia) between 1972 and 1990. Cell description as per Table 5a

Area (km2) in

Area (km2) in the year of 1990

the year of 1972

Vegetation

Water Body

Settlement

to increase (decrease) with the increase (decrease) in area

Vegetation

21,219 (88.31%)

557 (2.32%)

2,251 (9.37%)

under the water body. On the other hand, Vmax is supposed

Water Body

437 (83.24%)

73 (13.90%)

15 (2.86%)

to decrease due to conversion of land from vegetation or

Settlement

1,312 (72.45%)

79 (4.36%)

420 (23.19%)

change in the parameter Vmax is indicated. Vmax is supposed

water body to settlement. However, if the land under vegetation converted into a water body (due to inundation of Table 4

Table 5d Change in LULC over for the study basins

Land use and land cover change matrix in Narmada basin (up to Sandia) between 1990 and 2000. Cell description as per Table 5a

Area (km2) in the year of

Area (km2) in

Area (km2) in the year of 2000

Basin name

Description

1972*

1990*

2000*

the year of 1990

Vegetation

Water Body

Settlement

Mahanadi

Vegetation Water Body Settlement

52,505 1,473 7,026

52,665 1,463 7,023

45,957 2,758 12,435

Vegetation

19,161 (83.41%)

485 (2.11%)

3,327 (14.48%)

Water Body

331 (46.69%)

279 (39.35%)

99 (13.96%)

Vegetation Water Body Settlement

24,038 525 1,812

22,978 709 2,689

21,172 774 4,430

Settlement

1,675 (62.31%)

10 (0.37%)

1,003 (37.31%)

Narmada

vegetation behind the newly constructed reservoir), Vmax is

*Image dates for the study area in Mahanadi basin. 1972: Image dates – Dec 15, 1972 (1 block); Dec 16, 1972 (3 blocks); Dec 17, 1972 (1 block);

expected to increase. For Mahanadi, signiﬁcant increase in

Nov 21, 1975; Jan 08, 1977; Feb 27, 1973; . 1990: Image dates – Nov 10, 1990 (3 blocks); Nov 17, 1990 (3 blocks).

the water body is observed from 1990 to 2000 (almost

2000: Image dates – Nov 20, 2000 (1 block); Dec 15, 2000 (2 blocks); Dec 09, 2001 (2

89%). There is a decrease in vegetation by 6,700 km2 out

blocks); Nov 11, 1999 (1 block);.

of which almost 2,000 km2 is converted to water body.

*Image dates for the study area in Narmada basin. 1972: Image dates – Dec 16, 1972; Dec 17, 1972; Nov 30, 1972; Feb 02, 1973 (1 block each). 1990: Image dates – Nov 21, 1989 (1 block); Nov 17, 1990 (2 blocks) . 2000: Image dates –Dec 29, 2000; Nov 20, 2000; Dec 09, 2001 (1 block each).

These changes cause increase in Vmax. A gradual increase (77% over a 30-year period) in settlement was also found that might cause a decrease in Vmax. A combination of all these effects results in an overall increase in Vmax.

Table 5a

LULC change matrix in Mahanadi basin (up to Basantpur) between 1972 and 1990. Each cell indicates the magnitude (percentage) change from LULC type shown in row name to the type shown in column heading for that cell

On the other hand, change in water body at Narmada basin from 1972 to 1990/2000 is due to construction of the Bargi Dam in 1989–90. As a result, Vmax is supposed

Area (km2) in

Area (km2) in the year of 1990

the year of 1972

Vegetation

Water Body

Settlement

Vegetation

47,902 (91.26%)

814 (1.55%)

3,774 (7.19%)

Water Body

460 (31.23%)

449 (30.48%)

564 (38.29%)

Settlement

4,153 (59.11%)

200 (2.85%)

2,673 (38.04%)

to increase. However, at the same time, signiﬁcant increase in settlement (144% over 30 years) and decrease in vegetation cause Vmax to decrease. As a combined outcome, Vmax remains almost same (Table 1). Thus, Vmax reﬂects a combined response of all types of LULC changes.

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

For other parameters (B, k and b), we have not found

can be tested for statistical signiﬁcance. The trend being

any plausible trend, though ﬂuctuations are observed. A

non-linear, is tested at logarithmic scale and null hypothesis

slight decreasing trend in k can be attributed to the decrease

of not having any trend (zero slope) is comfortably rejected

in vegetation which contributes maximum percentage of the

with very low p-values (∼10 6–10 7). Construction of the

total basin area. Further, increase in settlement also sup-

Bargi dam was completed in 1989–90 that leads to 13/14

ports the decreasing trend of k. However, ﬂuctuations are

year data period and beyond in Figure 9. This is shown in

also observed in k as in other parameters (B and b) without

this ﬁgure, which reﬂects a very small jump in Vmax value

any noticeable trend. Thus, the latest values of these par-

from 15 to 17 years of data length. It is necessary to note

ameters are used for the future. Use of latest value for

that the gross storage capacity (GSC) of Bargi 392 McM.

some parameters may have an effect on the uncertainty in

Let us take a typical value of Vmax ¼ 200 mm. In volumetric

the streamﬂow estimate for the future time. However, maxi-

unit (multiplied with catchment area), this leads to 5,182

mum uncertainty may occur at a daily scale, that is, the day-

McM. This means the construction of Bargi dam adds only

to-day variation of streamﬂow, which is not the goal of this

a small part to Vmax (7.6%). As indicated in the discussion

part of the analysis. For future time, we have shown a

on model parameters above, the concept of Vmax reﬂects the

month-wise variation of streamﬂow, averaged over a

combined effect of the entire catchment, in which reservoirs

decade (2026–2035 and 2076–2085). This may also not be

are only a part of it. For instance, urbanization and deforesta-

free from the uncertainty; however, the effect might be less

tion lead to a decrease in Vmax. Thus, Vmax reﬂects a combined

crucial than that at a daily scale.

response of all such changes, which was also explained above

As stated above, we were not able to provide a theoretical

with respect to the LULC change.

argument better than this due to much less information on

It can be seen from Figure 9 that the estimate of Vmax gets

LULC during the analysis period and lack of time synchroni-

more or less stable as the data length increases for both the

zation between analysis periods and date of the satellite

river basins. For Narmada, the estimate remains more or

image. Secondly (not an argument though), even if it was

less the same for data length greater than approximately 7

possible to develop a theoretical relationship with improved

years whereas the same for Mahanadi River basin is observed

LULC information, that may not be useful in the context of

for a data length approximately 5 years. To investigate the

future assessment for which future information on LULC is

temporal change in Vmax, considered data length should not

required, which might be very tricky, if not impossible since

be too short to obtain a wrong (noisy) estimate and also,

LULC map is not available for future time. Thus, a trend

should not be too lengthy to miss the meaningful temporal

line approach is adopted to project Vmax into the future,

trend/pattern, if any. Considering this, a data length of succes-

which is discussed in the subsequent sections.

sive 5 to 10-year periods can be considered to assess the change of Vmax over time.

Optimum data length to estimate Vmax Temporal change in Vmax First, the optimum length of data needed to get a more or less stable estimate of Vmax is investigated. This is carried out by

Estimates of Vmax for successive 5-year periods (with one or

estimating the value of the parameter over a variable length

two periods of different lengths) are obtained for both the

of data (3–20 years). Estimated values of Vmax are plotted

basins. Results for Narmada River basin is shown in

against the data length. Figure 9 shows the variation of Vmax

Figure 10. Similar analysis is performed for Mahanadi

values with respect to the data length for Narmada (top

River basin and shown in Figure 11. In general, it is

panel) and Mahanadi (bottom panel) basin, respectively.

observed from these ﬁgures that these estimates show an

Vmax is estimated deterministically, not probabilistically.

overall gradual increase in Vmax. A smooth variation of

Since Vmax is not estimated probabilistically, it is not possible

Vmax over time is observed in case of Narmada River basin

to show whether the values are statistically different or not.

whereas high ﬂuctuations are noticed for the Mahanadi

Rather the trend of the change in Vmax, shown in Figure 9,

River basin.

P. P. Bhagwat & R. Maity

Figure 9

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Variation of Vmax values with respect to the data length for Narmada River (top) and Mahanadi River (bottom).

Linear as well as logarithmic trend lines for the historical change in the estimates of Vmax are also shown in Figures 10 and 11. For Narmada, linear trend line is

and the equation for best-ﬁt logarithmic trend line is found to be Vmax ¼ 59:5 lnð XÞ þ 254:1

(12)

described by the following equation: where X is expressed as Vmax ¼ 26:3 X þ 233:7

(11)

X¼

ðMid year 1st Mid yearÞ þ1 5

(13)

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Figure 10

Change of Vmax over time for Narmada River ﬁtted with linear and logarithmic trend lines. Here X ¼ ðMid year 1st Mid yearÞ=5 þ 1 and 1st Mid year for this case is 1979.5. Refer text for the explanation for three projected lines.

Figure 11

Change of Vmax over time for Mahanadi River ﬁtted with linear and logarithmic trend lines. Here X ¼ ðMid year 1st Mid yearÞ=5 þ 1 and 1st Mid year for this case is 1976.5. Refer text for the explanation for three projected lines.

‘Mid year’ in Equation (13) is the middle of the period for which an estimate of Vmax is required. For instance,

Similarly, for Mahanadi River, linear trend line is described by

1978 is the ‘Mid year’ for the period 1976–1980 and 2030.5 is the ‘Mid year’ for the period 2026–2035. ‘1st Mid year’ for Narmada (as seen from Figure 10) is 1979.5.

Vmax ¼ 32:01 X þ 140:2

(14)

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

and the equation for best-ﬁt logarithmic trend line is found

(RCM), known as PRECIS (Providing REgional Climates

to be

for Impact Studies) (Jones et al. ). PRECIS simulations corresponding to the IPCC-SRES A1B emission scenario are

Vmax ¼ 78 lnð XÞ þ 168:7

(15)

carried out for a continuous period of 1961–2098. The base line period considered is from 1961 to 1990. As is well

where X is expressed similarly as shown in Equation (13)

known, RCMs dynamically downscale global model simu-

and ‘1st Mid year’ for Mahanadi (as seen from Figure 11)

lations to superimpose the regional details of speciﬁc

is 1976.5.

regions of interest. Analysis is carried out at the Indian Insti-

For the linear trend line, the changes in the future may

tute of Tropical Meteorology (IITM), Pune, to develop the

follow three possible paths. These are: (i) remain constant

high-resolution climate change scenarios for impact assess-

(line 1 – untouched henceforth); (ii) increase with the

ment studies (Krishna Kumar et al. ). While carrying

same linear trend observed in the past (line 2 – business-

out a rigorous analysis, they found that the model shows

as-usual scenario); and (iii) increase at a faster rate than

reasonable skill in simulating the monsoon climate over

that observed in the past (line 3 – intense water manage-

India.

ment/utility activity in terms of construction of reservoirs).

Speciﬁc to the study basins, the correspondence is

These are also shown in Figures 10 and 11. It might be

checked between observed and modelled (PRECIS) rainfall,

very difﬁcult to quantify the growth rate of the trend line

maximum and minimum temperature at monthly scale over

without linking it to the policy makers’ decision for the

the study basins during the historical period of analysis

basin. For instance, higher demand of water may lead to

(Narmada basin: July 1978 to May 2000; and Mahanadi

construction of new reservoir, which will increase the

basin: January 1973 to December 2003). Correlation coefﬁ-

water storage capacity for the basin. Thus, it is very difﬁcult

cient (r) and index of agreement (d1) (Willmott et al. )

to quantify this rate. However, if an overall trend of change

are computed to assess the correspondence. These statistics

is found for the entire catchment that can be projected in

are expressed as

future with speciﬁc assumptions. Here we demonstrate the future projection by adopting a logarithmic trend line that makes an assumption of a continuous increase over time but at a slower rate as time passes by. Please note that a logarithmic growth rate (and no change also) is used in the analysis; continuous rate of increase in not adopted. The log-

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n P Oi O Pi P i¼1

r ¼ sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffisffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n n P 2 2 P Oi O Pi P i¼1

i¼1 n P

arithmic growth rate is estimated from the historical trend as explained before. On the other hand, a logarithmic trend line indicates a continuous increase over time but at a slower rate in the future.

d1 ¼ 1

(16)

jP i O i j

i¼1

n P þ Oi O Pi O

(17)

i¼1

r varies from 1 to þ1; 1 indicates negative association, 0 Future climate data – PRECIS data

indicates no association and þ1 indicates positive association. d1 varies from 0 to 1; 0 indicates complete

Daily rainfall and temperature data (maximum and mini-

disagreement and 1 indicates perfect agreement. Results

mum) for future climate of both river basins are obtained

are shown in Table 6. It is noticed that the observed and

from the Indian Institute of Tropical Meteorology (IITM),

PRECIS data for all the variables correspond very well to

Pune. For this study, averaged data over two future periods

each other. Among the three variables, minimum tempera-

(2026–35 and 2076–85) are used to analyze the future

ture is found to be the best in terms of both CC and

streamﬂow scenario for both study basins.

degree of agreement. Rainfall is found to have a higher

These data were generated using the widely popular Hadley Centre’s high resolution Regional Climate Model

degree of agreement as compared with maximum temperature in both the basins.

P. P. Bhagwat & R. Maity

Table 6

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Correspondence between observed and modeled (PRECIS) data over the study basins during the historical period of analysis (Narmada basin –July 1978 to May 2000 and Mahanadi basin – January 1973 to December 2003) Statistics

Basin Name

Variable

Mahanadi

Rainfall Max. temperature Min. temperature

0.74 0.79 0.93

0.69 0.63 0.83

Narmada

Rainfall Max. temperature Min. temperature

0.77 0.82 0.94

0.72 0.64 0.78

Streamflow scenario under future climate Possible future streamflow variation using two possible cases, namely ‘no further change’ and ‘logarithmic growth’, for both the basins are investigated and analyses are carried out at daily scale. However, daily results are converted to monthly as it is more meaningful than daily variation for future periods (after 20 or 70 years) and then change in Vmax for Narmada and Mahanadi River basin is analysed and, based on the ‘best fit line’, the Vmax values are projected for both basins. The projected Vmax values are used to model future streamflow generation. Other par-

Figure 12

Possible future monthly streamﬂow variation using ‘no further change’ (top) and ‘logarithmic growth’ (bottom) condition for Narmada River.

ameters like b, B and k are kept constant, as observed in

both the possible conditions, i.e., for ‘no further change’

the last decade training period. Results are reported separ-

and for ‘logarithmic growth’ conditions, it is found that

ately in the following sections.

streamﬂow magnitudes are going to increase in comparison to the past observations, particularly for monsoon periods.

Narmada River basin

Also for the post-monsoon period, the streamﬂow is going to increase marginally.

In ‘no further change’ condition, the same Vmax values are considered, i.e., Vmax for Narmada River basin is 316.3 mm

Mahanadi River basin

for both future periods (2026–35 and 2076–85). Another possible condition is ‘logarithmic growth’ where Vmax varies in a

For the Mahanadi River basin similar analysis is per-

logarithmic scale. In this case, the Vmax values for the Nar-

formed. Two possible future conditions (‘no further

mada River basin are 397.39 mm for 2026–35 and

change’ and ‘logarithmic growth’) are analyzed. The results

435.62 mm for 2076–85. These are computed using Equation

are shown in Figure 13 for ‘no further change’ and ‘logar-

(12). Monthly variations of streamﬂow for the Narmada River

ithmic growth’ conditions. In ‘no further change’ condition,

basin are shown in Figure 12 for ‘no further change’ and ‘log-

the same Vmax values (270.3 mm) are considered for both

arithmic growth’ conditions, respectively.

future periods (2026–35 and 2076–85). For ‘logarithmic

Average month-wise variations of streamﬂow values are

growth’ conditions, Vmax values are computed using

shown for the past two decades (1978–1989 and 1990–2000)

Equation (15). Vmax values are obtained as 360.53 mm for

as well as future two decades (2026–2035 and 2076–2085)

2026–35 and 408.71 mm for 2076–85. Figure 13 shows

for comparison. Though there is not much difference for

the comparison between four periods – past two decades

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

Journal of Water and Climate Change

05.1

2014

Generalization for other tropical river basins The methodology, being general and having some potential aspects, can be applied to any other tropical watersheds. It is recommended to have a good representative rainfall, temperature and streamflow time series for the study basin. Model parameters are to be obtained from this historical information to capture the watershed characteristics. Watershed characteristics can be more accurately represented through the parameters if the streamflow series consists of all possible flow ranges depicting all possible conditions that are expected to be available in a reasonably long period of data set. A long period of information will also ensure the representation of the gradual change in parameters for the study basin over the successive period of 5–10 years. Thus, a minimum of 30 years of data is recommended. Once the historical trends of different parameters are obtained, these are to be projected to the future time of interest. Since, different watershed might have gone through different process of LULC change, each watershed will have a unique signature in the trend of parameter values over the historical period. Figure 13

Possible future monthly streamﬂow variation using ‘no further change’ (top) and ‘logarithmic growth’ (bottom) condition for Mahanadi River.

Thus, the trend has to be uniquely determined for individual watersheds. These are some of the important aspects in generalizing the approach, presented in this paper, for other

(1978–1989 and 1990–2000) as well as future two decades

tropical basin.

(2026–2035 and 2076–2085). For Mahanadi River basin also, streamﬂow magnitudes are found to increase for future time periods. It is further observed that these

SUMMARY AND CONCLUSIONS

increased magnitudes are high particularly for the months of June to August.

Recent observations on the impacts of climate change

These observations indicate that in the near future, mon-

motivate to develop a basin-scale streamﬂow model having

soon is going to be more concentrated over fewer months. In

few parameters, which will be able to consider time varying

general, it is found that streamﬂow is going to increase for

watershed characteristics and climatic inputs, and provide

the monsoon period for both the study areas. Thus, speciﬁc

better or at least comparable performance to that of AI-

measures may be required in order to control streamﬂow in

based approaches. In this paper, HCCS is developed consid-

future. Implication of the temporal redistribution of stream-

ering the time varying watershed characteristics and using

ﬂow magnitude lies in modiﬁcation of water management,

climatic inputs. Performance of the proposed HCCS model

hydraulic design practices, etc. Since the monsoon stream-

is investigated over last couple of decades for two Indian

ﬂow increases and non-monsoon streamﬂow decreases,

river basins, Mahanadi and Narmada. Ability to consider

higher volume of storage requirement might be necessary

the time-varying watershed characteristics and hydrocli-

in future in order to meet the required demand. Design prac-

matic inputs renders the proposed model usable for

tices for different hydraulic structures should be suitably

assessment of future streamﬂow variation. The HCCS

revised in order to avoid natural hazards due to extreme

model is also applied to study future streamﬂow variation

streamﬂow during monsoon months.

for both the basins.

P. P. Bhagwat & R. Maity

HydroClimatic Conceptual Streamﬂow model

The speciﬁc conclusions from this study are as follows.

Journal of Water and Climate Change

05.1

2014

REFERENCES

The proposed HCCS model is able to consider the timevarying watershed characteristics and major hydrologic processes to model basin-scale streamflow using climate inputs. Performance of the proposed HCCS model is found to be impressive for both the study basins considered in the study. While comparing the performance of the proposed HCCS model with the performance of other popular conceptual models and with LS-SVR (one of the popular AIbased machine learning approaches), it is found that the overall performance of HCCS model is better in general and remarkably better in some cases. In addition, the proposed model is also able to provide additional overall (spatially averaged) estimates of ground water recharge component and evapotranspiration component from the entire catchment. Though these estimates are not compared with the observed values (due to nonavailability), seasonality in these estimated is visible which matches with the reality for Indian hydroclimatology. The proposed HCCS model is also suitable for future streamflow modelling with projected climate data. The proposed HCCS model is used to model future streamflow variation utilizing the projected climate data (PRECIS data) during two future periods from 2026–2035 and 2076–2085 using two possible cases related to watershed characteristic changes, namely, ‘no further change’ and ‘logarithmic growth’. Compared with historical observation, it is observed that the streamflow magnitudes are going to increase during early monsoon months and marginally increase or remain almost same during the late monsoon and non-monsoon months. The methodology, being general and having some potential aspects, can be applied to any other tropical watersheds. However, the methodology needs a good representative rainfall, temperature and streamflow time series, consisting of all possible flow ranges, for estimation of parameters during calibration.

ACKNOWLEDGMENT The study is partially supported by a project sponsored by Space Application Centre (SAC), Indian Space Research Organisation (ISRO), Ahmedabad.

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Maity, R. & Kashid, S. S.  Importance analysis of local and global climate inputs for basin-scale streamflow prediction. Water Resources Research 47, W11504. Monteith, J. L.  Evaporation and the environment. In: The state and movement of water in living organisms. XIXth Symposium Society for Experimental Biology, Cambridge University Press, Swansea, Wales, pp. 205–234. O’Connell, P. E., Nash, J. E. & Farrell, J. P.  River flow forecasting through conceptual models. Part 2. The Brosna catchment at Ferbane. Journal of Hydrology 10, 317–329. Piotrowski, A. P. & Napiorkowski, J. J.  Product-Units neural networks for catchment runoff forecasting. Advances in Water Resources 49, 97–113. Porter, J. W.  The Synthesis of Continuous Streamflow from Climatic Data by Modelling With a Digital Computer. PhD Thesis, Monash University, Australia. Porter, J. W. & McMahon, T. A.  Application of a catchment model in south eastern Australia. Journal of Hydrology 24, 121–134. Porter, J. W. & McMahon, T. A.  The Monash model: User manual for daily program HYDROLOG. Research Report no. 2/76, Department of Civil Engineering, Monash University, Australia, p. 41. Samsudin, R., Saad, P. & Shabri, A.  River flow time series using least squares support vector machines. Hydrology and Earth System Sciences 15, 1835–1852. Sugawara, M.  The flood forecasting by a series storage type model. International Symposium on floods and their computation, Leningrad, USSR, pp. 1–6. Sugawara, M., Ozaki, E., Watanabe, I. & Katsuyama, Y.  Tank model and its application to Bird Creek, Wollombi Brook, Bikin River, Kitsu River, Sanga River and Nam Mune. Research Note, National Research Center for Disaster Prevention, Kyoto, Japan, 11, pp. 1–64. Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B. & Vandewalle, J.  Least Squares Support Vector Machines. World Scientific, Singapore. Willmott, C. J., Robeson, S. M. & Matsuura, K.  Short Communication, A refined index of model performance. International Journal of Climatology 32, 2088–2094. Zeng, X., Kiviat, K. L., Sakaguchi, K. & Mahmoud, A. M. A.  A toy model for monthly river flow forecasting. Journal of Hydrology 452–453, 226–231.

First received 30 January 2013; accepted in revised form 20 October 2013. Available online 2 December 2013

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Coping with climate change in Amsterdam – a watercycle perspective Jan Peter van der Hoek, Paulien Hartog and Eilard Jacobs

ABSTRACT Amsterdam has the ambition to develop as a competitive and sustainable European metropolis. Water and Amsterdam are closely related, and water and climate change are closely related. Therefore, to be sustainable and economically strong, it is necessary for Amsterdam to anticipate the changes in climate that will take place in the Netherlands during the coming decades. Waternet, the watercycle company of Amsterdam and surroundings, has built a response strategy focusing on water management to contribute to the aim of making Amsterdam ‘waterproof’ for the next decades. This response strategy has two building blocks: adaptation and mitigation. With respect to adaptation the focus is on safety against ﬂooding, discharge of rainwater without nuisance for the public, ecological healthy water in compliance with the European Water Framework Directive, a

Jan Peter van der Hoek (corresponding author) Paulien Hartog Eilard Jacobs Waternet, Korte Ouderkerkerdijk 7, 1096 AC Amsterdam, The Netherlands E-mail: jan.peter.van.der.hoek@waternet.nl Jan Peter van der Hoek Department of Water Management, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands

reliable drinking water supply in compliance with the Dutch Drinking Water Regulations and the European Drinking Water Directive, and an efﬁcient and effective wastewater treatment in compliance with the European Urban Wastewater Treatment Directive. With respect to mitigation the focus is on energy recovery from the watercycle and nutrient recovery from wastewater. The strategy is carried out in close cooperation with partners on a regional level and a national level. Key words

| adaptation, energy, mitigation, water, water management, watercycle

INTRODUCTION Amsterdam is the capital of the Netherlands. It has an

The winters will become wetter with more extreme precipi-

important economic function, and is characterized by inten-

tation while the number of extreme rain events in the

sive spatial dynamics. Urban densiﬁcation is an important

summer will increase, accompanied by a decrease in rain

development. According to the vision of the authorities of

days. The sea level will rise with an absolute rise in 2050

the city of Amsterdam (City of Amsterdam a), Amster-

between 15 and 35 cm, an effect that is enhanced by land

dam has to be a strong and sustainable city in 2040.

subsidence in the western part of the Netherlands. Climate

Amsterdam continues to develop further as the core city of

change will also affect the city of Amsterdam. Water is pre-

an internationally competitive and sustainable European

sent everywhere in and around Amsterdam. Amsterdam is

metropolis. To be a sustainable city, it is important to antici-

surrounded by water and is partly located below sea level.

pate climate change.

Amsterdam has organized its water management very well

In the Netherlands, the KNMI (Royal Dutch Meteorological Institute) has developed four climate change scenarios based on IPCC reports (KNMI ). Variables in these

but, looking to climate change and the effects, is Amsterdam ‘waterproof’ for the future? Waternet, the ﬁrst watercycle company in the Nether-

scenarios are the extent of temperature rise, 1 C or 2 C in

lands (van der Hoek et al. ), is responsible for the

2050, and the change in wind pattern in Western Europe.

water management in Amsterdam. The activities of Water-

In all four scenarios, it is expected that the temperature

net concern drinking water supply, sewerage, wastewater

will rise, resulting in milder winters and warmer summers.

treatment,

doi: 10.2166/wcc.2013.060

surface

water

management,

groundwater

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

05.1

2014

management, control of the canals in Amsterdam and ﬂood

building block covers mitigation. The mitigation strategy

protection. Waternet has developed a response strategy to

focuses on the reduction of greenhouse gas emissions and

cope with climate change.

the recovery of energy from the watercycle as a compen-

In management concepts, a strategy is related to the mis-

sation measure. Greenhouse gas emissions have been

sion and vision of an organization (Rampersad ). While

calculated based on the international Greenhouse Gas Pro-

a mission describes the reason of being and a vision the

tocol (WRI & WBCDS ). This protocol was applied to

long-term purposes of an organization, a strategy is a sort

calculate the emissions by Waternet (Janse & Wiers ,

of action plan of how to realize these purposes. The mission

; Wiers et al. ).

of Waternet is to control and manage the water cycle in an integrated and societal responsible way. Integrated means that attention is given to all activities of Waternet and that coherence is strived after. Societal responsible means that a long-term perspective is taken into account focused on sustainability, and that all relevant partners are involved. These starting points are reﬂected in the response strategy of Waternet of how to cope with climate change. First, in the strategy all activities of Waternet are considered and

RESULTS AND DISCUSSION Adaptation strategy The adaptation strategy to cope with climate change in Amsterdam focuses on ﬁve aspects:

balanced. Attention is given to coherence between actions

1. safety

and synergy between actions. Second, Waternet cooperates

2. discharge of rain water

with other partners in the development and implementation

3. ecological healthy water

of this strategy. As an example, the Physical Planning

4. drinking water

Department of the city of Amsterdam is an important part-

5. wastewater treatment.

ner as spatial planning is closely related to water management, especially in an urban environment. Also

Safety

with neighbouring municipalities, there is close cooperation, and the strategy is also attuned to national policies. To incor-

This concerns protection against ﬂooding. The Netherlands

porate scientiﬁc knowledge in the strategy cooperation is

is located in a river delta and the western part of the Nether-

also established with universities and knowledge institutes.

lands is below sea level. Due to climate change the

Third, long-term decisions on this strategy are made at the

discharges of the rivers Rhine and Meuse will reach more

Waternet management level and at the City Council level

extreme values (de Bruijn & Mazijk ; Doomen et al.

to guarantee a coherent strategy for the city. As climate

) and rainfall will become more intensive (KNMI ).

change is characterized by a high uncertainty the strategy

At the moment, Amsterdam is relatively safe against

is periodically on the management agenda to anticipate

ﬂooding (City of Amsterdam b). However, ﬂooding

new developments and insights.

will have a huge economic impact and as it is the policy to further develop Amsterdam as a competitive sustainable European metropolis (City of Amsterdam a), effects

METHODS

will become more dramatic.

For a strategy to be effective, it is important that it can be

in the protection against ﬂooding (Koeze & Van Drimmelen

implemented. Therefore, Waternet has operationalized the

). In the concept of multi-layered safety (van den Brink

Amsterdam already uses a multi-layered safety approach

response strategy by identifying two speciﬁc building

et al. ), shown in Figure 1, the ﬁrst layer is ﬂood protec-

blocks. The ﬁrst building block is adaptation to climate

tion and the implementation of (new) safety standards by

change. As Waternet is a watercycle company, this building

taking technical and spatial measures. The second layer

block covers all elements of water management. The second

involves sustainable and ‘climate proof’ spatial planning to

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

05.1

2014

intensity of extreme precipitation events. This may lead to problems with stormwater handling in cities (Nilsen et al. ). In Amsterdam, the effects of urban densification include reduced infiltration, increased volumes of runoff and higher and earlier peak flow rates. These result from the increased percentage of impervious surfaces. The city of Amsterdam intends to take measures based on the principle of retaining– storing–discharging to cope with more intense rainfall (City of Amsterdam b). With respect to discharge of rainwater through the sewers, innovative sewer networks will be introduced based on real-time control to optimize and balance storage capacity of the sewers and pumping capacity of the boosters, resulting in an equalized flow to the wastewater treatment plants (de Korte et al. ). In urban areas the purpose is to delay the discharge by introducing green roofs and by introducing infiltration ponds in the green suburbs, where space is available. Also, the discharge of rainwater directly to surface waters, abundantly present in Amsterdam, is considered. In the public space, new gutters and storm drainage collection systems will be introduced in the streets that can temporarily Figure 1

The concept of multi-layered safety.

store rainwater without being a public nuisance. Ecological healthy water

reduce the economic damage and the number of casualties in case of ﬂooding.

Due to an increase in temperature, the surface water temp-

The third and ﬁnal safety layer involves disaster manage-

erature will rise with negative consequences for algal

ment aimed at bringing people and animals to safe locations

growth and botulism. To retain ecological healthy water in

and to restore the damage. At the moment, Amsterdam

the canals of Amsterdam, the water in the canals is refreshed

updates the multi-layered approach because of the intended

with water from the IJ-lake and Markerlake several times a

growth of the economic value of Amsterdam, because of

week. In this case, ecological healthy water is deﬁned as sur-

spatial developments and urban densiﬁcation close to

face water which complies with the European Water

dykes with a too low safety standard, and because of some

Framework Directive (European Union ). This fresh

weak spots in the protection system. Waternet together

water is also used to ﬂush the regional water system north

with the Physical Planning Department of the City of

of Amsterdam to prevent this system from becoming brack-

Amsterdam are the initiators and have the lead in the

ish. As a result of droughts, fresh water will be less available.

update of the multi-layered safety approach. In this context

Thus, there is competition between these two applications of

two pilots are carried out in Amsterdam. On a national

this fresh water. For that reason, an experiment is being car-

level, there is close cooperation with the Ministry of Infra-

ried out in which the canals are only ﬂushed based on

structure and the Environment.

oxygen depletion in the canals, saving water from the IJlake and Markerlake (Korving et al. ). The water quality

Discharge of rainwater

in the canals has improved substantially in the past years by reducing emissions to the canals from sewers and house

With respect to discharge of rainwater, it is important to realize

boats, improving wastewater treatment and moving the

that climate change will result in an increased frequency and

emission of wastewater treatment plant efﬂuent downstream

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

05.1

2014

of the canal system. First results show that flushing from a two to four times a week routine can be changed to an approach in which the actual oxygen concentration in the canal system determines the need for flushing. In 2010 this resulted in flushing only during the summer period and, in 2011, no flushing was needed. In addition, the European Water Framework Directive (European Union ) is used as a tool to improve the ecological water quality with structural measures. These measures are described in more detail in the Dutch River Basin Management Plan Rhine, western part (National Authority ; Regional Policy Council Rijn-West ) and in the Water Management Plan of the Water Authority Amstel, Gooi and Vecht (Water Authority Amstel, Gooi and Vecht ). These measures mostly include steps to improve the ecological infrastructure. Considering the effect of heavy showers due to climate change and the anticipated stormwater overflows, it is important to know that 75% of Amsterdam is equipped with a separated sewer system, so stormwater overflows are not a problem in relation to ecological healthy water. A further reduction of emissions from the wastewater systems (sewer and sewage treatment) is not necessary yet.

Figure 2

Drinking water infrastructure, Amsterdam.

Drinking water and treatment plants a reliable drinking water supply is guarWith respect to drinking water, Waternet operates two pro-

anteed, i.e., the drinking water demand can be fulﬁlled and

duction plants to supply drinking water to Amsterdam.

the drinking water quality complies with the Dutch drinking

Figure 2 shows the infrastructure.

water regulations (State Journal ) and with the European

The Dune plant uses water from the river Rhine. The water

Drinking Water Directive (European Union ). Recently,

is abstracted from the river Rhine in the centre of the Nether-

the National Institute for Public Health and the Environ-

lands, pretreated in Nieuwegein (WRK station near Utrecht),

ment RIVM concluded that due to climate change the

transported to the dune area west of Amsterdam for artiﬁcal

quality of surface water in the future may deteriorate to

recharge and ﬁnally post-treatment is carried out. The Lake

such an extent that by 2050 it will be unsuitable for drinking

plant uses seepage water from the Bethunepolder north of

water production (Wuijts et al. ). The results apply to

Utrecht. After coagulation, sedimentation, reservoir storage

almost all locations in the Netherlands where surface

in Lake Loenderveen and rapid sand ﬁltration the water is

water is abstracted for drinking water (abstraction points).

transported to a post-treatment plant south-east of Amsterdam. 3

To anticipate climate change, new sources are con-

The total production is about 86 million m /year, of which

sidered. The focus is on sources free of anthropogenic

70% is produced at the Dune plant and 30% at the Lake plant.

substances and sources with a constant quality to facilitate

Both sources have been evaluated for quantity and qual-

relatively simple treatment. For this reason, water reuse

ity (de Bruijn & Mazijk ; Bernhardi & Van den Berg

and water recycling is not seen as a future drinking water

; Doomen et al. ). To date, at the raw water intakes

source. In a case study for the city of Amsterdam it was con-

of Waternet these sources are not in danger, either by quan-

cluded that under normal conditions drinking water that

tity or by quality. This implies that with these two sources

meets the Dutch drinking water quality standards could be

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

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2014

produced from treated wastewater efﬂuent, but additional redundancy should be built in to meet the standards under extreme operating conditions (Rietveld et al. ). In addition, extensive risk assessment and risk management are required to safeguard a reliable drinking water supply at any time, using this source. Also, the application of a dual water supply system is not a viable option. In such a system household water with a lower quality for other purposes than human consumption, and drinking water for human consumption are supplied simultaneously. Waternet experimented with such a system in the new housing estate ‘IJburg’ in 1998–1999, but the results were not successful related to the economics and sustainability of the system (van der Hoek et al. ). In addition, in an evaluation of several pilots with a dual water supply system in the Netherlands, it was concluded that the health risks of these systems are very difﬁcult to manage (Oesterholt ) and in the Dutch drinking water regulations (State Journal ) these systems are not allowed. The use of brackish groundwater as a new drinking water source appears much more attractive. Brackish groundwater is free of anthropogenic substances and has a constant quality. Therefore, the use of brackish groundwater

Figure 3

PURO, a new concept for drinking water production from brackish groundwater.

has gained much attention in the Netherlands (Oosterhof et al. ). A concept of brackish groundwater treatment is PURO (Figure 3), which is studied by Waternet and a col-

separated sewer system for 75%. However, by using waste-

league drinking water company, Oasen (Timmer et al. ).

water treatment techniques able to comply with very

In this concept the brackish groundwater is desalinated by

stringent nitrogen and phosphorus standards for surface

reverse osmosis at a low recovery, without the addition of

water, negative effects of an increasing water temperature

chemicals. The membrane elements are placed in the

on surface water quality (e.g., algal blooms) are avoided.

groundwater wells. The concentrate is returned at great

Waternet operates two wastewater treatment plants in

depth in an aquifer where the natural salt concentration is

Amsterdam. Both plants comply with the regulations as

equal to that of the concentrate, so no salination takes

described in the Urban Wastewater Treatment Directive

place. The pressure required to force water through the

(European Union ). In the period 2009–2011, the total

membranes, about 8 bar, is naturally present at the depth

nitrogen and phosphorus removal efﬁciencies of the plant

where the membranes are placed in the well. As a conse-

‘West’ were 85–88% and 91%, respectively, and the total

quence, the environmental impact of this process is very

nitrogen and phosphorus removal efﬁciencies of the plant

low.

‘Westpoort’ were 88–92% and 93–94%, respectively. Both wastewater treatment plants are located in the ‘Western har-

Wastewater treatment

bour area’, which is vulnerable to ﬂooding. Because of the importance of this speciﬁc infrastructure for public health

With respect to wastewater treatment, the wastewater treat-

and ecological healthy water, local measures are planned

ment process itself is not really affected by climate change

to improve the water safety and to minimize the effects of

(e.g., heavy rainfall) as Amsterdam is supplied with a

ﬂooding. These measures are raising the area, constructing

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

05.1

2014

small dikes around the area and implementation of crisis

and use of green electricity, a compensation is required of

management (Koeze & Van Drimmelen ).

69,000 ton CO2-eq. For part of this, compensation can be

In addition, the wastewater treatment process is also

reached through additional energy efﬁciency measures:

used in the mitigation strategy, which will be discussed in

15,900 ton CO2-eq based on an energy efﬁciency improve-

the next section.

ment of 2% per year. The remaining compensation, 53,000 ton CO2-eq, may be realized through the production of

Mitigation strategy

renewable energy by Waternet. Of course, wind energy and solar energy are possibilities, but for Waternet, as a

In 2007 the International Panel on Climate Change rec-

watercycle company, it is much more attractive and challen-

ommended to strive for an ambitious reduction of carbon

ging to use the energy content of water. Waternet has

dioxide-equivalent (CO2) emission levels in order to stabil-

developed initiatives to recover energy from surface water,

ize global warming (IPCC ). Activities in the watercycle in Amsterdam (drinking water treatment and distribution, wastewater collection

wastewater, groundwater and drinking water to compensate for greenhouse gas emissions (Mol et al. ; van der Hoek ).

and treatment, water management) contribute about 2% to

In a ﬁrst inventory, performed in 2010–2011, it was esti-

the greenhouse gas emissions in Amsterdam (Janse &

mated that sustainable energy may be recovered from the

Wiers , ). This is relatively low compared with

watercycle (thermal energy from surface water, wastewater

other countries. For example, water-related greenhouse gas

and ground water, and chemical energy from wastewater)

emissions in Australian cities represent 8% of the national

up to an amount of 148,000 ton CO2-eq, which exceeds

greenhouse gas emissions with a range of 4–16% being poss-

the required compensation of 53,000 ton CO2-eq (van der

ible for individual cities (Kenway et al. ).

Hoek ). However, this study is concerned with just a

Figure 4 shows the greenhouse gas emissions from

ﬁrst estimate without taking into account the technical,

activities in the watercycle in Amsterdam, and the required

economical, managerial and political feasibility of the

compensation to operate the watercycle climate neutral.

speciﬁc projects. In a more detailed study, all projects

The year 1990 is the reference year. At that time the emission was 89,000 ton CO2-eq. In 2009, the emission has been reduced to 62,800 ton CO2-eq. This reduction

have been evaluated on their technical and economical feasibility (van Odijk et al. ). In addition, from a managerial and political point of view, not for all projects can the

has been realized through the purchase and use of renew-

avoided greenhouse gas emission be accounted entirely to

able energy to an amount of 45,000 ton CO2-eq (‘green

Waternet, as several parties are involved who also take

electricity’), through energy efﬁciency measures, and

their share in the targeted greenhouse gas emission

through process optimizations, especially with respect to

reduction. This evaluation and speciﬁcation resulted in a

the use of raw materials. The year 2020 is the target year

more detailed estimation. Figure 5 shows the results

to operate climate neutral. Assuming a steady purchase

expressed as minimum and maximum scenarios. In both scenarios the speciﬁc projects are taken into account which are under consideration at the moment. Six categories are distinguished, either based on chemical energy recovery or thermal energy recovery from the watercycle. The energy recovery is recalculated in avoided greenhouse gas emissions. In the minimum scenario, it is assumed that only those projects will be realized which are very cost-effective (very low costs or even cost savings to abate a set amount of CO2-eq. emissions), and that for several projects only 50%

Figure 4

Greenhouse gas emissions from the Amsterdam watercycle.

of the avoided greenhouse gas emission can be accounted

J. P. van der Hoek et al.

Coping with climate change in Amsterdam

Journal of Water and Climate Change

05.1

2014

treatment (Klaversma et al. ). Three measures have been considered: phosphate removal and recovery in wastewater treatment; pH correction with carbon dioxide instead of HCl in drinking water treatment; and change of materials for drinking water distribution pipes (ductile iron instead of PVC). Only the ﬁrst measure, in which less FeCl3 is dosed and struvite is recovered from wastewater with addition of MgCl2, has a signiﬁcant effect on the climate footprint. Introduction of this measure will reduce the greenhouse gas emission by 1,120 ton CO2-eq per year. Hence, the mitigation strategy of Waternet is now based on measures that cover scopes 1, 2 and 3. Figure 5

Energy recovery from the watercycle expressed as avoided greenhouse gas emissions.

CONCLUSIONS to Waternet. In the maximum scenario, it is assumed that all projects will be realized and that 100% of the avoided green-

Amsterdam aims to develop as a competitive and sustain-

house gas emissions can be accounted to Waternet.

able European metropolis. Sustainability implies that

In the minimum scenario, the energy recovery equals

Amsterdam responds to climate change. From a watercycle

27,300 ton CO2-eq/year, in the maximum scenario the

perspective, it is possible to contribute to this target. Water-

energy recovery equals 82,800 ton CO2-eq/year. Realization

net, the watercycle company of Amsterdam, is in charge of

of the speciﬁc projects may result in the required compen-

all water-related tasks in and around Amsterdam. By adap-

sation of 53,000 ton CO2-eq/year.

tation measures focusing on safety against ﬂooding,

The city of Amsterdam has the ambition to reach a

discharge of rainwater without nuisance to the public, eco-

reduction of greenhouse gas emissions of 40% by 2025

logical healthy water, reliable drinking water and effective

(City of Amsterdam ), which implies a reduction of

and efﬁcient wastewater treatment it is possible to make

3,100,000 ton CO2-eq/year. This means that the watercycle

Amsterdam ‘waterproof’ in the future. By mitigation

may contribute 0.9–2.7% to this ambition.

measures focusing on energy recovery from the watercycle

The required greenhouse gas reduction of 53,000 ton

it is possible to operate the watercycle climate neutral and

CO2-eq can be divided into three categories as deﬁned by

to contribute 0.9–2.7% to the target of reducing the green-

the Greenhouse Gas Protocol (WRI & WWBCDS ):

house gas emission in Amsterdam by 40% by 2025.

direct greenhouse gas emissions (scope 1), indirect greenhouse gas emissions from electricity consumption (scope 2) and other indirect greenhouse gas emissions, e.g., green-

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05.1

2014

Estimation of the water footprint of sugarcane in Mexico: is ethanol production an environmentally feasible fuel option? María Eugenia Haro, Ines Navarro, Ralph Thompson and Blanca Jimenez

ABSTRACT Energy policies are taken throughout the world to reduce fossil fuel emissions from transportation sources. Agriculturally based biofuels are currently the only alternatives to liquid fossil fuels. However, as biofuel production spreads, so too do its cascading impacts on environment and food security. This paper analyzes the impact of Mexican ethanol-sugarcane policy on water resources. The water footprint of sugarcane (WFsc) was quantiﬁed for an agricultural region in Jalisco, Mexico, and used to estimate anthropologic water demand and stress index. This analysis was performed using historical

María Eugenia Haro Ines Navarro (correponding author) Ralph Thompson Blanca Jimenez Institute of Engineering, Universidad Nacional Autónoma de México, Apdo. Postal 70472, 04510 Coyoacán, México D.F. E-mail: INavarroG@iingen.unam.mx

climate data, and for projected changes under scenarios A2 and B1, using ECHAM and GFDL models. The average historical water footprint of sugarcane was estimated as 104.9 m3/ton, total average water demand as 152.3 Mm3/year and a historical water scarcity index as 59%. Under climate change, the footprint might increase 2% by 2020 and 3–4% by 2050. The available water is predicted to fall 4–7% by 2020, and 6–8% by 2050, with negative effects on water stress. Due to the strong inﬂuence of local factors on water footprint and stress, additional research is needed for all Mexican sugarcane regions, in order to evaluate the feasibility of the policy regarding the use of ethanol for transportation. Key words

| biofuel, climate change, ethanol, sugarcane, water footprint

INTRODUCTION Increasing energy use, concerns about air quality, climate

Agriculturally based biofuels from food crops, such

change resulting from greenhouse gas (GHG) emissions,

as corn and sugarcane, used to produce ethanol, and

and falling global oil reserves make switching to sustainable,

soybeans, jatropha and palms, used for biodiesel pro-

low-emissions fuels a high priority. Some proposals to

duction, are currently the only alternatives to liquid fossil

reduce fossil fuel dependence are the use of hydrogen fuel

fuels being produced proﬁtably and in large volumes

cells, hybrid electric vehicle technologies, and the use of

(Himmel et al. ), despite the potential of second-

alternative biofuels such as ethanol and biodiesel. Relative

generation biofuels such as cellulosic-ethanol and algae-

to the fossil fuels they displace, Hill et al. () suggest

biodiesel.

that GHG emissions may be reduced 12% by the production and combustion of ethanol, and 41% by biodiesel.

Biofuel is potentially part of the solution to the world’s energy and climate change problems. But as biofuel pro-

Aggressive renewable energy policies have helped the

duction spreads around the world and the market for

biofuel industry grow at a rate few could have predicted.

biofuels expands, so too do its cascading indirect impacts

Global production of ethanol and biodiesel increased from

in the social, economic, and environmental spheres.

around 18.2 billion liters in 2000 to 79.5 billion liters in

The growing demand for cleaner burning and more sus-

2008. In the United States alone, the production of corn

tainable fuels, such as ethanol, is likely to generate changes

ethanol was estimated at 34 billion liters in 2008 (Earley

in agricultural cropping patterns and land management prac-

& McKeown ).

tices. Many feedstocks require a large area, and their efﬁcient

doi: 10.2166/wcc.2013.056

M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

large-scale production generally involves monocultures

the water footprint varies within crop categories as well as

(Sawyer ).

by production region, and their estimation of the water foot-

Depending on the location, previous land use and technology, the direct ecological effects of the expansion of

print of sugarcane under irrigated agriculture (104 m3/ton) was almost half the global average (200 m3/ton).

cane monoculture may include depletion or loss of biodiver-

Recent studies address local-regional blue, grey and/or

sity, and soil erosion or loss of fertility, with the latter due to

water footprint of bioethanol production based on historical

contamination, compaction, and loss of organic matter (Honty

data analysis (Dalla et al. ), or sugarcane-ethanol

& Gudynas ). There is also an impact on water resources

production targets that implies competition for water

– for example, cane production and processing consumes

resources (Kongboon & Sampattagul ). Another example

huge quantities of water, as much as four liters per liter of

is the Ghana study where sugarcane cultivation is planned

ethanol. Furthermore, clear ﬁelds accelerate run-off, redu-

and Black et al. () found that it is possible to generate

cing inﬁltration of rainwater into the soil and aquifers, and

approximately 75% of the yield achieved in the Sao Paulo

potentially affecting water supplies in downstream reservoirs

region, provided there is sufﬁcient irrigation, and only

during the dry season (Lima & Silva ). Water may also

explored an idealized temperature increase climate change

become polluted with pesticides, and nitrogen and phos-

scenario. Jeswani & Azapagic () particularly, analyzed

phorus from fertilizers (Hill et al. ). Impacts on the

the freshwater consumption from corn-derived ethanol asses-

atmosphere include massive CO2 emissions due to woodland

sing the impacts of water use on a life cycle basis; they

clearing; greenhouse gas emissions (N2O) from fertilizer use;

concluded that further methodological developments are

and smoke and ash emissions from the widespread practice

needed and additional research should also examine if Life

of burning sugarcane ﬁelds before manual cutting.

Cycle Assessment (LCA) is an appropriate tool to address

Water consumption for the production of biofuel feed-

these issues or other tools should be used instead.

stocks is a relevant impact of concern, recognizing that

In this context, and in keeping with international trends,

the global freshwater withdrawal has increased nearly

Mexican energy policy has established mitigation goals to

seven-fold in the past century (Gleick ). With a growing

reduce GHG emissions. One of its strategies is the use of bio-

population, coupled with changing dietary preferences,

fuels for transportation (SEMARNAT-INE ), with the

water withdrawals are expected to continue to increase in

short-term objective of producing ethanol from the 40 million

the coming decades (Rosengrant & Ringler ).

tons of sugarcane that are on average produced nationally

The recent major studies on global water consumption

each year. However, there is no published research regarding

by agriculture were performed by Rost et al. (), Siebert &

the viability of producing sugarcane for ethanol production

Döll (, ), Liu et al. (), Hanasaki et al. (), Liu

from the point of view of water availability.

& Yang (), Mekonnen & Hoekstra (), and Knox

Considering that Mexico is an arid and semi-arid

et al. (). These studies estimate the ‘blue’ virtual water

country which consumes large volumes of water per capita

content for several crops, based on coarse spatial resolutions

(roughly 4,306 m3/capita/year) and taking into consider-

that treat countries, continents, or even the entire world as a

ation that the agricultural sector accounts for around 77%

whole. The blue virtual water consumption for the pro-

of the total blue water resources (SEMARNAT ), the

duction of a crop, also known as the crop’s water

topic of water is relevant to the proposed mitigation plans

footprint, is deﬁned as the total volume of freshwater that

for biofuel production.

is used to produce the product (Hoekstra et al. ).

For that reason, the aim of this research is to assess the

Mekonnen & Hoekstra () found that the global aver-

water footprint of sugarcane by focusing on accounting the

age water footprint varies according to the type of crop,

blue water footprint only as an indicator of consumptive use

increasing from sugar crops (roughly 200 m /ton), vege-

of water, fresh surface or groundwater, due to national

tables (300 m3/ton), roots and tubers (400 m3/ton), fruits

water scarcity. In addition, three irrigation systems (ﬂood,

(1,000 m /ton), cereals (1,600 m /ton), and oil crops

sprinkler, drip) are evaluated to analyze their impact on

(2,400 m3/ton), to pulses (4,000 m3/ton). They found that

water saving with the purpose of contributing to the

M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

exploration water strategies. It is done within a geographically

3,902 m3/person/yr (2005) has been estimated with a high

delineated area on a daily-monthly temporal analysis with

water stress index of about 44% (CONAGUA ).

available data. And in order to get an idea of what the foot-

Tamazula is considered as an agricultural region; the

print size means, a water balance model is applied as

main crops are sugarcane, beans and chickpeas. There is a

Hoekstra et al. () suggest, to assess the available fresh-

sugar mill for sugar production, which during the 75 days

water and the water scarcity index, with locally speciﬁc

of sugar harvesting in 2010 processed almost two million

input data.

liters of ethylic alcohol as a secondary product (Unión Nacio-

This study quantiﬁes the blue water footprint of sugar-

nal de Cañeros (UNC) ). The major productive activities

cane (WFsc) production in the agricultural area of

are focused in those of the sugar mill and the alcohol pro-

Tamazula, Jalisco State, Mexico, for a baseline scenario. A

duction; other minor activities are livestock, forestry to

water balance model taking local climatic conditions into

supply paper mill, and mining extraction (iron, barite and

account was used to calculate, with a daily time step, crop

gypsum).

water requirements over time, current crop water use, and

In the Tamazula region, sugarcane is grown in more than

ﬁnally the crop blue water footprint. This analysis was per-

15,500 ha, and has an average annual yield of 110 ton/ha –

formed using historical climate data (baseline scenario),

the second highest in Mexico (UNC ), where the average

and also for projected changes under scenarios A2 and

national yields are 92.4 ton/ha in irrigated land and

B1, using the models ECHAM and GFDL. The water avail-

64.1 ton/ha in non-irrigated land (SAGARPA ). More-

ability and the demand on water services were also

over, the land used to grow sugarcane has complex

estimated for the region in order to evaluate the current

irrigation systems, which have been progressively updated

impact of the WFsc on the local water resources.

to improve efﬁciency. In around half of the land, water is supplied by traditional ﬂood irrigation systems, with the remaining half evenly split between sprinklers and drip irri-

METHODOLOGY

gation systems. These irrigation systems are not common practice spread over the ﬁve sugarcane regions of the

Site description

country, where only 40% of the national sugarcane surface uses irrigation systems, mainly through ﬂood irrigation chan-

The Tamazula region is located at the western part of Mexico,

nels, and supply 49% of the national sugar production.

in Jalisco state, in the municipality of Tamazula de Gordiano, and has a population of 126,246 (Comisión Estatal del Agua

Estimation of the water footprint

(CEA) ). The study region is located in a plain area with a mean altitude of 1,100 meters above sea level (MASL). The region has a tropical climate tempered by altitude, with W

The approach proposed by Hoekstra et al. () for the water footprint of crops was used for the speciﬁc conditions

mean annual temperature and precipitation of 20.9 C and

of sugarcane grown in the Tamazula region. This technique

897 mm, respectively (CEA ). The historical (1980–2007)

provides a framework to analyze the link between human

temperature (minimum, maximum and average values) and

consumption and appropriation of local freshwater. There-

precipitation daily recorded in the meteorological station

fore, the water footprint of sugarcane (WFsc, m3/ton) was

14141 (19 41 12.93″N, 103 13 53.17″W, 1152 MASL) and

calculated by dividing the total volume of crop water

the relative humidity and wind speed data daily recorded in

requirement (CWU, m3/ha), by the crop yield (Y, ton/ha)

the agro-climatological station ITS-Tamazula (19 40 N,

(Equation (1)). The crop water requirement is the water

103 150 W, 1140 MASL) during 2010, were the available

needed for ideal growing conditions, measured from plant-

data for this study.

ing to harvest, and allowing for evapotranspiration. ‘Ideal

The main surface water resource in the region is the Coa3

conditions’ means that adequate soil water is maintained

huayana River with an average annual ﬂow rate of 10.6 m /s.

by rainfall and/or irrigation so that it does not limit plant

In the river basin a low per capita water availability level of

growth and crop yield. CWU is calculated using the sum

M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

of the daily crop evapotranspiration (ETsc, mm/day) over

blue biofuel demand) to the total available blue water

the length of the sugarcane growing period, from the day

resources (WAblue), as follows:

of planting (d ¼ 1) to the day of harvest (d ¼ N). A constant factor of 10 is used in Equation (2) to convert from water 3

depths (mm) to water volumes per unit area (m /ha). WFsc ¼

CWU Y

CWU ¼ 10

N X

(1)

WSblue ¼

WDblue WAblue

(4)

A blue water scarcity index of one hundred percent means that all the available blue water is being consumed. Water stress conditions are considered to have occurred

ETsc,d

(2)

for values over 20%, and any value above this indicates excess demand and probable environmental stress.

d¼1

Total blue water availability (WAblue, m3/yr) was estiThe daily evapotranspiration for sugarcane, ETsc, was

mated as natural renewable water, which is the amount of

calculated from the reference crop evapotranspiration

water that is replaced by precipitation each season minus

(ETo), and the crop factor for sugarcane (Ksc) at the different

the amount lost due to evapotranspiration, as described by

stages in its growing cycle, as described by Brouwer &

Equation (5):

Heibloem (). ETo expresses the evaporating power of the atmosphere at a speciﬁc location and time of the year, and does not consider the crop characteristics or soil fac-

WAblue ¼ [P E] × A × f

(5)

tors. For this research, ETo was estimated with the

where P is the cumulative precipitation (mm/yr); A is the

Penman-Monteith method suggested by the Food and

area of the zone under consideration, in this case the Tama-

Agriculture Organization (FAO)-56 (Allen et al. )

zula municipality (km2); f is a unit conversion constant; and

(Equation (3)) for local radiation (sunshine), air temperature,

E is the cumulative evapotranspiration (mm/yr) estimated

relative humidity, and wind speed conditions, based on

using the cumulative precipitation and the average tempera-

measurements recorded by a climatic station in Tamazula.

ture (T C) in Equations (6) and (7), following Turc ():

ET0 ¼

1 ΔðRn GÞ þ ρa cp ððes ea Þ=ra Þ λ Δ þ γ ð1 þ ðrs =ra ÞÞ

(3)

E¼h

P 0:9 þ (P=L)2

i0:5

(6)

where λ is latent heat of vaporization; Δ represents slope of saturation vapor pressure temperature relationship, Rn is net radiation, G is soil heat flux, (es–ea) represents vapor pressure deficit of the air, ρa is mean air density at constant pressure, cp is specific heat of the air, γ is psychometric constant, and rs and

L ¼ 300 þ 25T þ 0:05T 3

(7)

Total anthropogenic blue water demand (WDblue) was estimated using Equation (8):

ra are (bulk) surface and aerodynamic resistances. WDblue ¼ Dmuni þ Dagri Estimation of water scarcity The blue water scarcity index (WSblue), also known as water intensity use index, was calculated over the municipal area of Tamazula, using the approach proposed by Hoekstra

where Dmuni is the municipal demand, estimated by multiplying the total population by the usage per person (given as 150 L/person/day by Aparicio et al. ); and Dagri is the agricultural demand, estimated using Equation (9):

et al. (). The blue water scarcity in a deﬁned area (WSblue) is deﬁned as the ratio of the areas total anthropogenic blue water demand (WDblue) (which includes the

(8)

Dagri ¼ ðWFsc Y Þ

3 X i¼1

Ai fi

(9)

M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

where: WFsc and Y are the calculated water footprint of

In addition, the WFsc calculated refers to the evapotran-

sugarcane (m3/ton) and the local sugarcane yield (ton/ha)

spiration of irrigation water from the crop ﬁeld only. It

respectively, and the areas (Ai) and inefﬁciencies ( fi) take

excludes the evaporation of water from artiﬁcial surface

their values from the types of irrigation used in the region,

water reservoirs built for storing irrigation water and also

as shown in Table 1. Note that this calculation only con-

the evaporation of water from transport canals that bring

siders demand for the irrigation of sugarcane, and due to a

the irrigation water from the place of abstraction to the ﬁeld.

lack of available data, demand for other crops and from industry is not included.

Climate change scenarios

Baseline scenario

Precipitation and temperature projections were used in this research to develop a future sugarcane production scenario.

The WFsc baseline scenario was calculated with precipitation

For this study two global climate models (GCMs) –

and temperature historical data (station 14141, 1980–2007)

ECHAM5 and GFDL – were selected to obtain the projec-

based on daily evapotranspiration estimation, considering

tions for the A2 and B1 scenarios, which are among those

the daily mean for the climatic parameters. It was assumed

which are recommended for climate impact studies for

that is possible to transfer data, without losing characteristic

Mexico (Conde et al. ). Conde et al. () analyzed the

in climate patterns description, between meteorological

results of bias estimation for Mexican projections generated

station 14141 and ITS-Tamazula data; since both are located

from 20 general circulation models. They showed the root

at a similar elevation (±12 m) and within the same area of

mean square error and the bias and root mean square

inﬂuence, according to results of the Thiessen method analy-

error corrected by bias. Their main conclusion was that

sis applied to the region. Therefore, the daily local climatic

three models were chosen to reasonably represent the uncer-

data of relative humidity and wind speed recorded in ITS-

tainty range (ECHAM, HADGEM, and GFDL models

Tamazula station were used; these values for the year 2010

under A2 and B1 scenarios). These models provide a

were the only data available, and were taken as a constant

broad range of possible temperature increases and, more

value for the 27 years estimation. Another assumption, due

importantly, they provide increases as well as reductions

to the absence of historical data, was the use of data from

in precipitation for the impact studies in Mexico. In fact,

2010 sugarcane production as constant values, for yield,

those results of the bias analysis, revealed that the

total irrigation area and the percentage area irrigated with

ECHAM model had a better performance at a global level

different irrigation systems. This assumption agrees with the

and for the region of Mexico than all other models (Conde

Hoekstra et al. () approach proposed when different

et al. ); meanwhile, the GFDL model showed the worst

periods are combined in one analysis: for example, taking

performance.

production and yield data for a recent period but data on cli-

The projected monthly precipitation and temperature

mate (temperature and precipitation) as an average for the

data for 2020 and 2050 were obtained from the Paciﬁc Cli-

past 30 years.

mate Impacts Consortium (PCIC) Regional Analysis Tool

It should be emphasized that the assumptions mentioned were also applied for the climate change scenarios.

(PCIC ), as recommended for Mexican studies (Conde et al. ), which provides downscaling of future climate projections to regions and local sites.

Table 1

PCIC is an interface to derive estimates of future climate

Values used in the calculation of agricultural water demand

projections from individual GCMs and ensembles from a Type of irrigation

Area (ha) Ai

Inefﬁciency factora fi

Flood

7,750

1.00

erature and (accumulated) precipitation projections are

Sprinkler

3,875

0.70

available as anomalies data in C and %, respectively. For

Drip

3,875

0.56

this study, the monthly mean temperature and accumulated

Value from Narayanamoorthy (2004, 2006).

0.5 grid resolution. The (minimum, mean, maximum) tempW

precipitation anomalies were downloaded (Table 2) from

M. E. Haro et al.

Table 2

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

Precipitation and temperature data, 2020 and 2050 W

Mean temperature anomalies [ C] 2020

Precipitation anomalies [%] 2050

GFDL

ECHAM 5

GFDL

2020

2050

ECHAM 5

GFDL

ECHAM 5 A2

GFDL

ECHAM 5

Jan

0.7

1.2

0.9

1.1

1.4

1.8

Feb

1.1

0.9

0.8

1.3

1.2

1.4

2.3

Mar

1.6

1.3

1.1

2.5

2.4

2.8

2.4

Apr

1.6

1.4

1.3

0.7

2.5

May

1.4

1.9

1.3

0.9

2.9

1.8

2.4

2.1

Jun

1.3

1.5

1.1

0.7

2.4

1.4

2.1

Jul

1.3

1.1

1.2

0.9

1.9

1.6

2.3

Aug

1.1

1.2

0.9

2.2

1.4

2.4

2.1

Sept

1.1

0.7

2.3

1.3

2.1

1.7

Oct

0.9

0.6

0.5

1.5

1.6

2.2

1.5

Nov

0.7

1.1

0.6

1.7

1.5

2.1

1.9

Dec

0.7

1.1

2.1

the grid corresponding to the Tamazula region; the data col-

Thus, the WFsc estimated for historical data represent

umns recorded in Table 2 correspond to the mean

the baseline for comparison purposes with climate change

temperature and precipitation anomalies projected over

scenarios. In addition, changed values for yield and the agri-

2011–2040 time period for 2020 data columns, and 2041–

cultural land area devoted to sugarcane production were

2070 time period for 2050 data columns. The development

considered, and a regional balance was also analyzed with

done for the 2020 and 2050 data horizon, agrees with the

the

time periods recognized for the projections downloaded

methodology previously described.

future

water

availability

calculation,

using

the

from the Paciﬁc Climate Impacts Consortium’s Regional Analysis Tool used for this research (Murdock &

Data used

Spittlehouse ). For estimating the future monthly sugarcane water foot-

For crop evapotranspiration estimation, relative humidity

print and water availability in the region as well, the

and wind speed daily data recorded in the ITS-Tamazula

historical daily precipitation and temperature data (1980–

station during 2010 were used, as well as local data such

2007) from the meteorological station 14141, were corrected

as the altitude in Tamazula. The values used for the other

with the projected anomalies from the PCIC; this procedure

parameters were the average quantities founded in the

is in agreement with the Murdock & Spittlehouse ()

FAO database (); Tables 3 and 4 summarize the

study that recommends to application of the anomalies to

values used in this study.

the baseline climate of the location of interest. Figure 1 illustrates the mean monthly precipitation behavior between the historical data and the 1961–1990 baseline data, which was

RESULTS AND DISCUSSION

used in the PCIC process to generate the future estimations; it shows that the highest difference between the mean values

The analysis was performed with historical data from

occurred for August and April months, but it is less than one

meteorological station 14141 for the period 1980–2007

standard deviation (0.6 and 0.8 respectively).

between February and September, during which irrigation

M. E. Haro et al.

Figure 1

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

Mean precipitation behavior between baselines of observed data 1980–2007 and PCIC 1961–1990 data.

is practiced on the 15,500 ha of Tamazula sugarcane land.

2020 and 2050. In contrast, the GFDL climate model is pre-

The historical precipitation behavior observed in the

dicting markedly higher available water (11–38%) than the

region varied from 0 to 95 mm/d. Daily temperature

historical average availability. However, both models in gen-

recorded by the meteorological station varied from 3 to

eral are consistent with predicting less water by 2050 than

2020.

45 C, with an average of 22 C. With this information and for the parameters data listed in Tables 3 and 4, the daily

Using the historical weather data to calculate the crop

reference evapotranspiration ET0 was estimated for histori-

water demand, and the current value of the yield

cal data. It showed a variation from 1.9 to 7.1 mm/day,

(110 tons/ha), the historical sugarcane water footprint for

with the highest values observed between April and

the irrigation period (Figure 2) was calculated to vary

September.

between 100 to 107 m3/ton with annual average of

Applying the collected data as described in the method-

104.9 m3/ton; a value which is comparable to the estimated

ology, the total historical average available (renewable)

range between 120 and 410 m3/ton (Gerbens-Leenes &

water resource in the Tamazula region was calculated to

Hoekstra ) and which leads to an estimate of historical

be 255 Mm3/year. According to the climate models con-

average of 152.3 Mm3/year for the total anthropologic

sidered, this value is predicted to fall in the future

water demand (of which, 145.7 Mm3/year is related to the

(Table 5) under the ECHAM model (4–8%) with the

average irrigation

ECHAM B1 being the worst scenario for both horizon, Table 3

sugarcane,

and the

remaining

6.6 Mm /year is the estimated average municipal demand).

Parameters used to calculate the sugarcane water footprint in the Tamazula region

Parameter

Symbol

Units

Latent heat of vaporization

MJ/kg

Rate of change of saturation vapor pressure with temperature

Kpa/ C

Min

Mean

Max

0.009

0.03

0.05

Net radiation

MJ/m d

Soil heat ﬂux

MJ/m2d

0.0014

Mean air density

ρa

Kg/m3

Speciﬁc heat capacity of air

MJ/kg C

0.0009

0.001

Aerodynamic resistance

KPa/ C

210

1228

Wind speed

m/s

Temperature

C/d

M. E. Haro et al.

Table 4

Estimation of the water footprint of sugarcane in Mexico

Constants used to calculate the sugarcane water footprint in the Tamazula region

Journal of Water and Climate Change

05.1

2014

climate model taking these changes into account. The results are shown in Table 6, and under inspection it can be seen that there is no great difference predicted between scenarios or

Constant

Symbol

Units

Value

Yield

ton/ha

110

models, with all models predicting an increase of approxi-

Psychometric constant

KPa/ C

0.058

mately 2% in water footprint by 2020, and a 3 to 4%

Bulk surface resistance

s/m

increase by 2050, when compared to the value for historical

Altitude

1152

annual average water footprint (104.9 m3/ton). The table also shows the predicted total water demand, based on the average predicted water footprint of sugarcane, which

Table 5

Total available water in Tamazula as projected under the four climate change models

pared to the historical average of 152.3 Mm3/year for the 3

Year

shows a similarly modest increment over time when com-

A2 [Mm /year]

total anthropologic water demand.

B1 [Mm /year]

GFDL

ECHAM

GFDL

ECHAM

2020

296.5

245.8

352.1

236.5

demand had to be estimated using a crude multiplication

2050

306.2

240.8

283.6

234.3

of the population and typical personal usage, in the case of

It should be noted that due to limited available data, the

municipal demand; and of the irrigated area with the crop The 145.7 Mm3/yr estimation of agricultural water

water demand per hectare, using an ‘inefﬁciency factor’ to

demand considers three irrigation systems in Tamazula

allow for different irrigation infrastructure, in the case of agri-

(see Table 1); so the local water footprint varies between

cultural demand; ignoring other crops and industrial users.

58.7 m3/ton for drip irrigation, 73.4 m3/ton for sprinkler irri-

Due to this, the estimate of demand presented here is only

gation and 104.9 m /ton under a ﬂood irrigation system.

a rough approximation of the true demand. Even if more

Thus, there is net water saving of 18.5% which represents

detailed data were available, an annual variability in the

around 33 million m due to installed water irrigation sys-

total water demand is expected in the region, due to the con-

tems, and it is equivalent to providing drinking water for

tinuous expansion of agricultural land in the Tamazula region

more than 604,000 people for a year.

for sugarcane production. This development is not seen in

Under climate change, the evapotranspiration, and hence

any other sugarcane region in the country.

crop water demand, is expected to be affected by changes in

Other factors that may inﬂuence the irrigated area, and

temperature and precipitation, among other factors, so the

therefore total water demand, are national variations in the

crop water demand was recalculated for each scenario and

price of sugar and its indirect sub-products, as well as

Figure 2

Water footprint baseline estimated for annual irrigation period.

M. E. Haro et al.

Table 6

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

Water footprint and total blue water demand under A2 and B1 climate change scenarios Predicted water footprint [m3/ton]

Year

Total water demand [Mm3/year]

GFDL

ECHAM

GFDL

ECHAM

GFDL

ECHAM

GFDL

ECHAM

2020

107.2

107.0

107.2

106.4

155.6

155.2

155.6

154.5

2050

109.0

107.9

108.7

158.1

156.5

157.7

ﬂuctuations in the price of other crops. For example, some

The predicted values for the water stress index were cal-

years ago, an increase in the price of avocado encouraged

culated assuming that the irrigation area and sugarcane

its production, and reduced the land area devoted to sugar-

yield per hectare remain constant at their 2010 values. How-

cane within a year. Thus, it is recognized that an important

ever, this may not be the case – changing climatic conditions

obstacle in the support of sugarcane production for biofuel

may reduce the yield, which may lead to a corresponding

use is the lack of a governmental policy that guarantees a

increase in irrigated area in order to compensate. In an

competitive price of ethanol vs. sugar price, in order to

attempt to analyze some of the potential effects of this, the

encourage investments in technology to improve agricul-

calculations were redone, ﬁrstly just assuming a reduced

tural production, and upgrade reﬁnery plants for the

yield of 72 tons/ha (the national average), and secondly

production of ethanol. Finally, it should be highlighted

assuming a reduced yield and a compensated 10% increase

that the Tamazula region is atypical nationally in terms of

in the irrigated area. The ﬁrst scenario leads to a 53%

its high yield, and may represent a best case scenario for

increase in the sugarcane water footprint (Table 8). The

Mexico in terms of its water footprint. Furthermore, despite

second scenario gives the same increase in water footprint,

the high yield, the production is insufﬁcient to meet the

and also increases the total water demand by 29%. This

goals established by Mexican government.

has the effect of increasing the predicted water stress

Using the estimates for the total historical average avail-

(Table 9).

able water, 255 Mm /year, and the total water demand, the

The results show that when using current values of yield

water stress could be calculated for the different time

and irrigated area, the water stress index is predicted to

periods considered. For the historical scenario, the calcu-

increase to between 51 and 77% by 2020, and between 60

lated average value was 59.7%, which is higher than 20% and hence indicates a condition of water stress, although it

Table 8

is less than the 2009 estimation (see site description). The

yield drops to the national average (72 ton/ha)

results of the analysis under the different climate change scenarios are shown in Table 7. The models vary in the

Predicted water footprint of sugarcane under climate change, assuming the

A2 [m3/ton]

B1 [m3/ton]

Year

severity of their predictions, but all predict an increase in

GFDL

ECHAM

GFDL

ECHAM

water stress in the future under climate change, with

2020

163.8

163.4

163.8

162.6

ECHAM predicting higher values as a result of its lower

2050

166.6

166.5

164.8

166.1

predictions for available water. Table 7

Water stress index in Tamazula, as projected under A2 and B1 climate change

Table 9

scenarios

Water stress index in Tamazula under climate change, assuming the yield drops to the national average (72 ton/ha) and the irrigated area increases by 10%

A2 [%]

B1 [%]

A2 [%]

Year

B1 [%]

Year GFDL

ECHAM

GFDL

ECHAM

2020

61.6

74.2

51.9

76.7

2050

60.7

77.2

64.8

79.1

GFDL

ECHAM

GFDL

ECHAM

2020

67.8

81.6

57.1

84.4

2050

66.8

84.9

71.3

87.0

M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

Journal of Water and Climate Change

05.1

2014

and 80% by 2050. If we assume that climate change or other

research second generation biofuels, and use them to

factors lead to a drop in the local yield to the present

replace the ﬁrst generation technologies.

national average, and that the irrigated area increases by 10% as a result, the predicted water stress is between 57 and 84% by 2020, and between 66 and 87% by 2050. There-

REFERENCES

fore, as water stress index is sensitive to local conditions, it needs to be calculated on a case by case basis for all sugarcane producing regions if a serious analysis of the feasibility of producing ethanol for transportation is to be undertaken.

CONCLUSIONS This preliminary approach to water demand in one of the most productive sugarcane regions of the country, reveals that historical sugarcane water footprint for the irrigation period varies between 100 to 107 m3/ton with annual average of 104.9 m3/ton; but is has been improved to 58 and 73 m3/ton in agricultural Tamazula land where efﬁcient irrigation systems are applied, showing that important amounts of water savings may be achieved as well. However, under future climate change scenarios and unpredictable agricultural conditions the water footprint is predicted to increase to between 106 and 167 m3/ton. In addition, the water model balance applied indicates that future predicted water stress index might be greater than 51%. Based on these results, local studies are suggested mainly in the low and medium water availability regions of the country, where 90% of the sugarcane surface is located. Any local study should compare the water footprint to an appropriate local water balance model at a monthly level in order to identify temporal or long-term water scarcity; it will contribute to formulation of strategies to reduce water footprints and associated local impacts, and will ensure national energy and water security when considering any biofuels mitigation measure. For decision making purposes, this information should be combined with impact analyses for other types of alternative energy, such as wind or geothermal in the case of Mexico, although this depends on the ﬁnal use of the energy, given biofuels can be easily used in existing transport systems while the others are limited to use in electric vehicles. In light of what has been found, Mexico should

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M. E. Haro et al.

Estimation of the water footprint of sugarcane in Mexico

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Narayanamoorthy, A.  Impact assessment of drip irrigation in India: The case of sugarcane. Development Policy Review 22 (4), 443–462. Narayanamoorthy, A.  Potential for Drip and Sprinkler Irrigation in India. Draft, IWMI-CPWF Project on Strategic Analyses of India’s National River-Linking Project, International Water Management Institute, Colombo, Sri Lanka. PCIC  Pacific Climate Impacts Consortium Regional Analysis Tool (BETA). Available online at: http://pacificclimate.org/ tools-and-data [Accessed 13 June 2012]. Rosengrant, M. W. & Ringler, C.  Impact on food security and rural development of transferring water out of agriculture. Water Policy 1 (6), 567–586. Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J. & Schaphoff, S.  Agricultural green and blue water consumption and its influence on the global water system. Water Resources Research 44, W09405. SAGARPA  Estudio de gran visión para la identificación de necesidades de riego y drenaje en las zonas de abasto cañeras y propuestas de tecnificación en zonas potenciales como base para el desarrollo de proyectos de inversión. Etapa I. SAGARPA-PRONAC-SIAP, 83 pp. Sawyer, D.  Climate change, biofuels and eco-social impacts in the Brazilian Amazon and Cerrado. Philosophical Transactions of the Royal Society B 363, 1747–1752. SEMARNAT-INE  Mexico’s Fourth National Communication to the United Nations Framework. Convention on Climate Change. Comité Intersecretarial de Cambio Climático México. SEMARNAT  Estadísticas del Agua en México (2011). SEMARNAT – CONAGUA. Siebert, S. & Döll, P.  The global crop water model (GCWM): Documentation and first results for irrigated crops. Frankfurt Hydrology Paper 07, Institute of Physical Geography, University of Frankfurt, Frankfurt am Main, Germany. Available online at: www.geo.unifrankfurt.de/ipg/ag/dl/ f_publikationen/2008/FHP_07_Siebert_and_Doell_2008.pdf [Accessed 15 March 2011]. Siebert, S. & Döll, P.  Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology 384, 198–207. Turc, L.  Le bilan d’eau sols. Relation entre le precipitation, l’évaporation et l’écoulement. Ann Agron. 5, 491–596; 6, 5–131. UNC Unión Nacional de Cañeros  Estadísticas de la Agroindustria de la caña de azúcar, 2002-2011 (Ingenio: Tamazula). Unión Nacional de Cañeros, A.C. – CNPR. Available online at: http://www.caneros.org.mx/ site_caneros/estadisticas/ingenios/tamazula.pdf [Accessed 7 July 2012].

First received 29 August 2012; accepted in revised form 26 July 2013. Available online 22 October 2013

81 © Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada 2014 Journal of Water and Climate Change

05.1

2014

Potential ecological impact of climate change on the water quality of an intensively managed agricultural watershed in Quebec, Canada Beáta Novotná, Eric van Bochove and Georges Thériault

ABSTRACT This study investigated the effects of climate change (CC) on water redistribution in a microwatershed of a lowland agricultural area of the Bras d’Henri River in the temperate cold climate of Quebec, Canada. A Water Flow and Balance Simulation Model (WaSiM-ETH) was used to simulate the hydrology using current climatic conditions and land use characteristics, applying Richards’ equation. A one-day temporal resolution was used with a spatial resolution of 2 × 2 m. The CC scenarios Coupled Global Climate Model, version 1 (CGCM1) and Hadley Centre Coupled Model, version 3.1 (HadCM3.1) were downscaled to the regional level and integrated into WaSiM-ETH and evaluated for both current and modified climatic conditions. Mean annual precipitation values (P) increased from 15–33%, evapotranspiration from 7–26%, and discharge (Q) from 16–45%. The identification of the significant water quality problem represented by average value of total suspended solids (TSS) 265.29 (kg ha 1), nutrients: nitrogen (NO3-N) 16.83 (kg ha 1) and total phosphorus (TP) 0.59 (kg ha 1) for the whole evaluated period; and (TSS) 148.09 (kg ha 1), (NO3-N) 5.65 (kg ha 1) and (TP) 0.31 (kg ha 1) during the days with surface runoff, in relation to water quantity and CC creates the basis for erosion risk assessment. Key words

Beáta Novotná* Georges Thériault Soils and Crops Research and Development Centre, Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Quebec, QC, G1V 2J3, Canada *Present address: Department of Landscape Engineering, Faculty of Horticulture and Landscape Engineering, Slovak Agricultural University in Nitra, Hospodarska 7, 949 76 Nitra, Slovak Republic Eric van Bochove (corresponding author) Ofﬁce of Knowledge and Technology Transfer, Science and Technology Branch, Agriculture and Agri-Food Canada, Complexe Jules-Dallaire, 2828 Laurier Blvd, Room 640, Québec, QC, G1V 0B9, Canada E-mail: eric.vanbochove@agr.gc.ca

| agriculture, climate change, ecological impact, hydrologic model, watershed

ABBREVIATIONS Atmosphere-Ocean General Circu-

discharge

lation Model

SWE

snow water equivalent

BMP

best management practices

time horizon

climate change

total phosphorus

Coupled Global Climate Model,

TSS

total suspended solids

version 1

WaSiM-ETH

Water Flow and Balance Simulation

AOGCM

CGCM1 and CX1 CM

Model

regional climate change scenario models

evapotranspiration

GCM

General Circulation Models

GHG

greenhouse gas

INTRODUCTION

HadCM3.1 and HAD Hadley Centre Coupled Model,

Agriculture is essentially an adjunct to natural ecosystems

version 3.1

and depends on weather and climate (Wreford et al. ).

Intergovernmental Panel on Climate

As such, it is extremely vulnerable to climate change (CC)

Change

(Intergovernmental Panel on Climate Change (IPCC)

precipitation

). Long-term changes in daily temperature extremes

IPCC P doi: 10.2166/wcc.2013.121

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

have been recently estimated for many regions of the world.

still poorly documented (Menzel et al. ). Regardless of

Especially since the 1950s, these records reveal a decrease

CC scenario, a two-fold increase in CO2 concentration

in the number of very cold days and nights and an increase

leads to increased runoff mainly in winter and spring, with

in the number of extremely hot days and warm nights (IPCC

spring runoff occurring up to one month earlier (Arora

a). For the Northern Hemisphere, the IPCC predicts a W

; Cunderlik & Burn ; Pike et al. ). A ﬁfty-year

mean temperature increase of between 1.1 and 6.4 C

trend analysis reports that annual mean discharge will

during the 21st century. Over the last century, mean

decline across Canadian watersheds (Zhang et al. ),

annual global surface temperature has already increased

with monthly increases in March and April, and decreases

by approximately 0.4 to 0.8 C (Climate Change ),

in summer and fall. Quilbé et al. (a) report a slight but

while mean temperature over southern Canada rose an aver-

statistically signiﬁcant decrease in annual discharge with

age of 0.9 C between 1900 and 1998 (Zhang et al. ;

increased winter discharge and decreased spring peak ﬂow

Pohl et al. ). In Quebec, seasonal temperature trends

in the Chaudière watershed, Quebec. There was no apparent

between 1948 and 2005 differed by season, with an increase

effect of CC on summer low ﬂows. The magnitude and direc-

of about 0.9 C for winter, 1.0 C for spring, 0.5 C for

tion of shifts in hydrological regimes varies across Canada

summer, and 0.2 C for autumn, i.e., 0.6 C for annual temp-

and among studies (Rodenhuis et al. ). In order to

erature (Osborn et al. ). Rising temperature will

predict regional hydrological consequences of CC, it is

diminish the snowpack and increase evaporation, thereby

necessary

affecting the seasonal availability of water (IPCC b).

changes. This approach will provide more precise estimates

Globally

averaged

annual

precipitation

has

also

document

local

and

watershed-speciﬁc

than those from General Circulation Models (GCM).

increased over the last century, especially in upper-latitude

The outcomes resulting from precipitation regime

regions of Canada (IPCC a). According to the Soil

changes are useful, applying much more complex methods

and Water Conservation Society (), Canada showed

because of interactions between precipitation and land

statistically signiﬁcant positive trends in the average

management.

number of days with heavy rain. Global trends for 1-day

Extrapolated relationships from changes in precipi-

and multi-day heavy precipitation show a tendency toward

tation over the past century suggest increases in soil

more days with heavy precipitation (Easterling et al. ;

erosion from 4 to 95% with increases in runoff from 6 to

Christensen et al. ; IPCC a). A thousand-year cli-

100%, which may already be evident on some croplands

matic simulation using the Atmosphere-Ocean General

(Wreford et al. ). Assessing the current and future

Circulation Model (AOGCM) shows an increase in mean

environmental risks and opportunities in Canada, CC will

winter precipitation of 20 to 30%. Since the 1970s, the fre-

increase the potential for soil erosion due to surface

quency of heavy snowfall has decreased in the south and

runoff. The potential for large increases in magnitude and

increased in the north of Canada (Zhang et al. ).

extent of soil erosion and runoff is considerable under simu-

Several reports refer to a broad reduction in insolation on the Earth’s surface since 1990 (Wild et al. ). This

lated

conditions

for

future

precipitation.

Different

landscapes vary greatly in their vulnerability to soil erosion

decrease of available solar radiation for actual and potential

and runoff. Several studies, i.e., Bouraoui et al. () and

evapotranspiration (ET) may be related to increasing cloud

Ekstrand & Wallenberg (), report that CC is responsible

cover and precipitation, but also to soil moisture which

for decreased snow cover, increased winter runoff, and also

increases actual ET closer to the potential ET (IPCC

for increased diffuse nutrient losses. Intensive agricultural

a). These considerable changes, including changes in

practices release signiﬁcant amounts of nutrients, especially

average air temperature and total precipitation courses,

nitrogen and phosphorus, faecal bacteria, and sediment into

will have a signiﬁcant impact on watershed hydrology,

receiving water bodies (Monaghan et al. ). To under-

including snowmelt (Pike et al. ).

stand the adaptation process in a wider context, an

While potential CC has been studied to some extent in recent times, hydrological responses to these changes are

analysis of the vulnerability of speciﬁc regions to CC is required.

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

This study provides a thorough analysis of how CC

were taken from August 2005. Annual discharge varied

affects a small agricultural sub-watershed of the Chaudière

between 500 and 1,000 mm from 1931 to 1960 (Environ-

River basin, Quebec, according to temporal trends for the

ment

long-term annual discharge and its seasonal variations. A

precipitation (P), discharge (Q), and evapotranspiration

Canada

).

Monthly

air

temperature

(T),

micro-watershed, part of the Bras d’Henri Watershed

(ET) values were calculated from monitored data (2006 to

Evaluation of Beneﬁcial Management Practices project

2009) in the Bras d’Henri micro-watershed, Quebec, and

(van Bochove ), was selected for this study. The

are summarised in Table 1.

inclusion of upper and lower limits of a hydrologic

The Bras d’Henri micro-watershed is located in an area

response to CC could be applied to other Canadian water-

dominated by rolling terrain, with Precambrian outcrops

sheds with similar climatic conditions. The results can

interspersed with mantles of glacial moraine (Wiken ).

help to develop strategies for water resource decision-

According to the Canadian System of Soil Classiﬁcation,

makers and for farmers to adapt their best management

podzolic soils (Beaurivage, Valère, and Neubois series) rep-

practices (BMP) to unpredictable and extreme weather

resent 45% of the area while gleysolic soils cover 44%

events, longer and more frequent droughts, and pest infes-

(LeBras series). The dominant soil textural class is sandy

tations in order to improve water quality, reduce

loam. The land is mainly used for agriculture: 53% annual

greenhouse gas (GHG) emissions and production costs,

crops (corn, soybean, oat); 30% pasture; 13% forest; and

and improve rural economic development. This study pro-

4% other surfaces (settlements, roads, and ditches). The

vides bases for policy, operational, and management

mixed forest is comprised of maple, spruce, and spruce

changes for speciﬁc regions affected by changing climatic

mew trees. The Bras d’Henri micro-watershed was selected for the

conditions.

current study because it has existing rich sources of water Study area

quality, soil, agricultural management practices, and has hydrometric data available too. This watershed also

The study site (Figures 1(a) and 1(b)) is an intensively-man2

has one of the highest concentrations of animal pro-

aged agricultural second order catchment (2.36 km )

duction in the province of Quebec. It ranks as the

located in the Bras d’Henri sub-watershed (158 km2),

second poorest in Quebec in terms of its phosphorus

south of the St. Lawrence River and Quebec City, Quebec,

load (Agriculture and Agri-Food Canada (AAFC) ).

Canada. This region has a cold temperate climate (Environ-

In addition, it is a part of the Watershed Evaluation of

ment Canada ). The Bras d’Henri belongs to the

BMP project (WEBs) because of its intensive agricultural

Beaurivage basin (709 km ) which is one of the main tribu-

production and resultant substantial decrease in surface

taries of the Chaudière River (6,682 km2). The Bras d’Henri

water quality.

micro-watershed is circular, with a valley length of 2.55 km, and is drained by two stream branches. The altitude above

Model description

sea level ranges from 152 to 178 m with an average slope The ability of the WaSiM-ETH (Water Flow and Balance

of 1.75%. This watershed is located in the agroclimatic zone 3

Simulation Model) (Schulla & Jasper ) to describe the

(Environment Canada ) of the Appalachian region.

hydrological processes of lowland catchments with possible

According to this classiﬁcation, the sum of the daily temp-

temporal climatic changes was evaluated. WaSiM-ETH is a

erature with an average above 5 C is 1,530 C, for

spatially distributed and grid-based hydrological catchment

duration of 126 days. The average precipitation is 519 mm

model which was developed primarily to simulate the

from May to September which is well-suited for oat,

water balance of mountainous regions (Bormann & Elfert

wheat, and barley crops (Dubé ). Meteorological and

). The model is fully distributed, and uses physically-

water quality data were monitored at the micro-watershed

based algorithms to describe most of the hydrological pro-

outlet from April 2004, and hydrometric measurements

cesses. It facilitates modelling of the watershed hydrology,

Figure 1

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Localisation of the studied micro-watershed in the Bras d’Henri, Beaurivage, and Chaudière in the map of the Provence of Quebec, Canada (a), in the watershed network (b) and (c) localisation of the 13 regional meteorological stations providing data used for model calibration.

including subsurface processes, within different levels of

CC scenarios

complexity. WaSiM-ETH has been successfully applied to a wide range of scales, from lysimeter sites to macro-scale

Regional CC scenario models (CM) were used in this study

river basins ( Jasper et al. ). The main model com-

because they provide more realistic scenarios of CC on a

ponents are speciﬁed in Jasper et al. ().

regional scale than direct applications of GCM. Regional

Table 1

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Monthly air temperature (T), precipitation (P), discharge (Q) and evapotranspiration (ET) values calculated from monitoring data (2006–2009) in the Bras d’Henri micro-watershed, Quebec, Canada

Total Jan

Feb

Mar

Apr

May

June

July

Aug

Sept

2.9

5.6

1.5

9.8

14.9

20.4

22.6

22.2

18.3

18.4

12.6

0.2

6.1

12.8

16.1

13.1

100

133

Pmax total (mm)

110

128

102

154

149

104

Pmin total (mm)

119

Q total (mm)

117

ET real (mm)

Tmax average ( C) W

Tmin average ( C)

Oct

Nov

Dec

average

11.7

1.2

5.8

13.8

0.9

116

1004

119

170

118

1415

688

440

366

18 8.7

9.4 sums

Ptotal (mm)

climate models provide highly resolved information (spatial

eco-district climatic database was developed from climate

and temporal) derived from physically-based models that

station data using a spatial interpolation procedure; missing

apply many variables, which represents some weather

data were ﬁlled-in by using estimates from nearby stations.

extremes better than GCM. These CM were: CGCM1

The spatial interpolation procedure combined inverse dis-

(Coupled Global Climate Model, version 1) (Flato et al.

tance-squared

), here named CX1 and HadCM3.1 (Hadley Centre

approach. Cross-validation was performed to evaluate the

weighting

with

the

nearest-neighbour

Coupled Model, version 3.1) (Gordon et al. ), here

accuracy of the interpolation procedure (Xu et al. ).

named HAD and used to predict the potential CC effect at a daily time step. These models included the anthropogenic involved CC as well as its natural variability. Both models

MATERIALS AND METHODS

used transmission studies for the continual increase of GHG with the AOGCM. The emission scenario IS92a

For potential ecological impacts of CC on water quality in

GHG þ A was adapted for the model CX1, and the scenario

the watershed, the projections were constructed in three

SRES-A2 was adapted for the model HAD. The emission

steps (Figure 2). In the ﬁrst step, the model WaSiM-ETH

scenario IS92a GHG þ A included a 1% equivalent CO2

was calibrated and veriﬁed for the present time period and

increase per year and sulphate aerosols (Flato & Boer

climatic conditions, represented by the years 2006–2009.

). The SRES-A2 scenario dataset included GHG (CO2,

In the second step, the calibrated model was applied for

CH4, N2O) and sulphate aerosol direct effects, which

the past time horizon (TH), named baseline in this study,

increased slightly less than 1% per year, based on IPCC

and represented by years 1951–2004. Finally, climatic scen-

SRES-A2 for 1990–2100. Generally, the IS92 scenario was

arios were selected and applied with the same model

characterised by more warming and more substantial

parameters for the future. The future is expressed by TH

changes in precipitation than the SRES scenario (Jasper

2040, 2070, and 2100 integrated respectively by the periods

et al. ). Archived Agriculture and Agri-Food Canada cli-

2010–2039, 2040–2069, and 2070–2099.

matic data were used to interpolate data for past time horizons in order to develop the climatic scenarios for the

Model calibration and veriﬁcation

centroids of Ecodistrict 540. The meteorological input data for these CMs were: average daily temperature, total daily

Calibration years (2006 and 2008) and validation years

precipitation, and average incoming daily solar radiation.

(2007 and 2009) were randomly selected. One-day time

The daily data for these variables were extrapolated from

step and grid cells of 2 m were used. The following climatic

the

data were selected from 13 available regional stations

historical

meteorological

series.

agricultural

B. Novotná et al.

Figure 2

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Scheme of the model WaSiM-ETH application for CC impact prediction in the Bras d’Henri micro-watershed, Quebec, Canada: 1st step – present state (2006–2009), 2nd step – baseline (1951–2004) and 3rd step represented by TH 2040, TH 2070 and TH 2100 according to CC scenario CX1 (a) and CC scenario HAD (b). Legend: WaSiM-ETH (Water Flow and Balance Simulation Model), CC (climate change), TH (time horizon), CX1 (Coupled Global Climate Model, version 1), HAD (Hadley Centre Coupled Model, version 3.1), SWE (snow water equivalent).

(Figure 1(b)) to run the model: average daily temperature

model were: the control soil percolation, direct runoff,

from 10 stations (Chabot, Giroux, Scott, Beauceville,

direct runoff from snowmelt, baseﬂow, and interﬂow gener-

Quebec, Saint Flavien, Beaurivage, Charny, Valle Jonction,

ation. The most sensitive parameters within the snow model

Kinnear’s Mills), the sum of daily precipitation from 13

were: threshold temperature for snowmelt, the temperature

stations (Chabot, Giroux, Scott, Beauceville, Quebec, Saint

at which 50% of the precipitation falls as snow and 50%

Flavien, Saint Pierre, Beaurivage, Saint Severin, Beausejour,

as rain, and the temperature range for the transition from

Charny, Valle Jonction, Kinnear’s Mills), wind speed from

snow to rain, and degree-days. Parameters were considered

three stations (Chabot, Giroux, Charny), relative air humid-

to be uniformly distributed within the feasible parameter

ity from three stations (Chabot, Giroux, Charny), and

range according to Wriedt & Rode ().

vapour pressure from two stations (Chabot, Giroux). The

Model calibration was initially carried out for the principal

stations were selected according to the nearest distance

parameters in the watershed. These parameters, such as reces-

and the data availability. Less than 5% of the meteorological

sion of surface runoff and interﬂow, scaling of interﬂow

data were missing, the periods were varying depending on

calculation, and leakage factors, were used as conceptual

measurement errors and the instrument defectiveness.

model components. Subsequently the groundwater parameters

Values not available on a daily basis were interpolated

(drainable porosity, hydraulic conductivity), river channel par-

to obtain the same temporal resolution as for those from

ameters (width, depth) and drainage parameters (depth,

automatic stations.

horizontal spacing) were calibrated as well, because those

The spatially distributed data used were: digital

parameters cannot be derived directly from the available data

elevation model (acquired from a GPS-RTK (Global Posi-

(Bormann & Elfert ). On the basis of a successful calibration

tioning System – Real Time Kinematic) survey over the

in our study watershed, a regionalisation of the parameters was

watershed in 2005–2006), zone grids, land use (obtained

applied to an unobserved basin using the same parameter set

annually from farmer’s survey), texture class (derived from

(Schulla ) for validation. During the analysis of CC scenario

an intensive soil survey from 2004–2005), slope angle, and

effects on the water balance, soil and water management para-

exposition (Figure 3). Data were weighted according to the

meterisation remained constant while the following land

inverse distance interpolation method. The most sensitive

cover vegetation parameters were changed: fraction of veg-

parameters for manual calibration within the WaSiM-ETH

etation cover per cell, albedo, leaf area index, canopy height,

Figure 3

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Representation of the land use (a), soil texture (b) and hill shade – slope (c) in the Bras d’Henri river micro-watershed, Quebec, Canada.

canopy resistance, interception capacity, rooting depth, and

period included 229 and 84 days in 2007 and 2009, respect-

hydraulic head for beginning dryness stress.

ively. Only continuous discharge durations (750 days) were included in the modelling simulations because winter dis-

Precipitation correction

charge measurements were missing for the frozen stream durations (497 days). Veriﬁcation was achieved by comparing

The use of hydrological models in climate impact studies

the modelled discharge values with measured discharge data.

requires the correction of precipitation data to reduce and

The accuracy of the model simulations varied from moderate

correct systematic errors. According to Goodison et al.

to strong (Table 2) to compare simulated and measured daily

(), the wind-induced average loss of 2 to 10% of rain

discharges: Pearson correlation r, Nash-Sutcliffe efﬁciency

measured values accounted for up to 60% of snow measured

coefﬁcient E (Nash & Sutcliffe ), and coefﬁcient of

values from unshielded gauges that had wind speeds greater

Nash-Sutcliffe efﬁciency with logarithmic values ln E.

than 4 m s . The correcting precipitation measurements

Figures 4(a) and 4(b) show the CC in average monthly

methodology according to Sevruk () is applied for the

temperature and precipitation for TH comparing the present

WaSiM-ETH sub-model. The precipitation correction was

state based on CX1 and HAD scenarios. Model simulation

carried out separately for rain and snow using the wind

results according to CC scenarios (CX1 and HAD) and

speed factor within the model. The differentiation between

three TH in the Bras d’Henri micro-watershed are presented

rain and snow was identiﬁed by using the threshold temp-

in Table 3. These simulations show that annual P and ET

erature for snow changing to rain. The precipitation

will generally and steadily increase by 7 to 33% between

correction was used for the daily values measured in the

baseline and 2100, while Q will irregularly intensify by 16

Bras d’Henri watershed, and for future climatic scenarios.

to 45%. Average monthly temperature and precipitation also show increasing trends (Figures 4(a) and (b)).

RESULTS

Temperature trends

Two durations covering 223 days in 2006 and 214 days in

The CC scenarios indicate varying seasonal deviations from

2008 were used for model calibration, while the veriﬁcation

baseline data for all TH. Monthly temperatures generally

Table 2

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Evaluation of the WaSiM-ETH model simulation accuracy using Pearson correlation coefficient (r), Nash-Sutcliffe coefficient of efficiency (E), and Nash-Sutcliffe efficiency with logarithmic value (ln E) between the simulated and measured daily discharges in Bras d’Henri watershed (2006–2009) Nash-Sutcliffe

Coefﬁcient E

Nash-Sutcliffe efﬁciency

coefﬁcient r

Coefﬁcient r interpretation

coefﬁcient E

interpretation

with log values 1n E

Apr–Nov 2006

0.87

Strong, high correlation

0.85

Good efﬁciency

0.86

Apr–Nov 2008

0.78

Strong, high correlation

0.60

Satisfactory efﬁciency

0.49

Apr–Nov 2007

0.60

Moderate correlation

0.52

Satisfactory efﬁciency

0.48

Mar–May 2009

0.76

Strong, high correlation

0.74

Satisfactory efﬁciency

0.55

Pearson correlation Period of the year

Calibration

Validation

Figure 4

Changes in monthly mean temperature (T, C) and total monthly precipitation (P, mm) for time horizons of 2040, 2070, and 2100 compared with the baseline years (1951–2004) in the Bras d’Henri micro-watershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

Table 3

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Projected total annual precipitation (P), evapotranspiration (ET) and discharge (Q); surface runoff (Qsurf), inﬁltration (Qinf), snow runoff (Qsnow), their changes (Δ) and runoff coefﬁcient (RC) in the Bras d’Henri micro-watershed according to two CC scenarios (CX1 and HAD) and three THs (2040, 2070 and 2100). The changes were calculated using the baseline period (1951–2004) average values

Baseline

ΔP

ΔET

ΔQ

Qsurf

ΔQsurf

Qinf

ΔQinf

Qsnow

ΔQsnow

(mm)

(%)

(mm)

(%)

(mm)

(%)

(mm)

(%)

(mm)

(%)

(mm)

(%)

882

366

440

355

0.50

CX1 2040

1018

393

539

107

433

0.53

CX1 2070

1031

406

538

105

434

0.52

CX1 2100

1032

432

512

436

0.50

HAD 2040

1045

402

553

109

444

0.53

HAD 2070

1172

440

636

130

506

0.54

HAD 2100

1146

463

512

100

490

0.45

P (precipitation), ΔP (precipitation increase), ET (evapotranspiration), ΔET (evapotranspiration increase), Q (total discharge), ΔQ (total discharge increase), Qsurf (surface runoff), ΔQsurf

(surface runoff increase), Qinf (infiltration), ΔQinf (infiltration increase), Qsnow (discharge from snowmelt), ΔQsnow (discharge from snowmelt increase), and RC (runoff coefficient) as total annual values.

remain negative from December to March and positive from

precipitation according to CC scenarios was highly variable

April to November for both CC scenarios and for all evalu-

relative to the baseline.

ated TH. According to the CC scenario CX1, average W

monthly temperatures increased over time; 1.22 C in W

According to the scenario CX1, mean annual precipitation increased from 15% (136 mm) in 2040, 16.9%

2040, 2.37 C in 2070, and up to 4.06 C in 2100 as indicated

(149 mm) in 2070, and up to 17% (150 mm) in 2100. Total

by signiﬁcant deviations from the baseline (Figure 4(a)). All

monthly precipitation was predicted to increase in January,

monthly temperatures increased for the three evaluated hor-

March, April, May, August, September, November, and

izons, except November for TH 2040 and December for THs

December for all three TH from about 3 mm in September

2040 and 2070 which remained stable. Similar trends were

2100 up to 40 mm in May 2070. For July it is considered

predicted by the HAD for the Bras d’Henri river basin

to decrease about 1 mm in 2040 and 16 mm in 2070. How-

(Figure 4(b)). In fact, average annual temperatures deviated

ever, results differed for the TH from February, June, and

from the baseline in the range of 1.21 C in 2040, 2.61 C in W

October. In fact, precipitation decreased in February and

2070, and 4.79 C in 2100. Following the latest CC scenario,

October (1 mm), but increased in June (6 mm) for TH

no temperature change was predicted for February 2040 but

2040. For TH 2070, precipitation increased in February

all monthly temperatures increased for the modelled TH.

(8 mm) and in June (1 mm), but decreased in October

The range spanned by CX1 was greatest in February,

(4 mm), and increased for TH 2100 in February (6 mm)

April, and October; by HAD in April, August, and Novem-

and October (30 mm), but decreased in June (4 mm).

ber. Similarly, the range spanned by CX1 and HAD was least for December.

The CC scenario HAD showed monthly precipitation increases for all evaluated TH: 18% (163 mm) in 2040, 32.9% (290 mm) in 2070, and 29.9% (264 mm) in 2100.

Precipitation trends

For TH 2040, the least increase is expected in April (2 mm) and the greatest in May (30 mm). Finally, it

Similar to changes in temperature, total monthly precipi-

showed increases of 2 mm for April, up to 48 mm in Septem-

tation values differed from the baseline for both CC

ber for 2070, and of 7 mm in April and up to 41 mm in May

scenarios and all evaluated TH (Figures 4(a) and (b));

for 2100. According to this scenario, there were only two

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

exceptions for TH 2100, where precipitation would decrease

Journal of Water and Climate Change

05.1

2014

Discharge trends

by 4 mm in July and by 7 mm in September (Figure 4(b)). Annual and seasonal changes in precipitation are key for Evapotranspiration trends

predicting changes in annual runoff. Figures 6(a) and 6(b) represent changes in average monthly discharge as pre-

Changes in average daily temperature trends and total

dicted by the two CC scenarios in comparison with the

annual precipitation amounts increase ET signiﬁcantly.

baseline years of 1951–2004. Discharge values were pro-

The highest ET increase is expected for the May to August

jected daily for evaluated THs. Results from both CC

duration for both CC scenarios and all TH. Figure 5 shows

scenarios show signiﬁcant increases in discharge. The esti-

a signiﬁcant total monthly ET increasing trend for both

mated increase in precipitation will also enhance runoff

CC scenarios. The annual ET will increase by 7% (27 mm)

peaks occurring earlier in the season and with greater inten-

in 2040, 11% (40 mm) in 2070, and up to 18% (66 mm) in

sity. According to the CC scenario CX1, total annual

2100 according to CX1 scenario results. For HAD, annual

discharge will increase by 23% (100 mm) for TH 2040, by

ET will increase by 10% (36 mm) in 2040, 20% (74 mm) in

22% (98 mm) for TH 2070, and by 16% (72 mm) for TH

2070, and 26% (96 mm) in 2100. The greatest ET increases

2100. For the CC scenario HAD, total annual discharge

are expected in 2100 by 49% (February) for CX1 and 36%

will increase by 26% (113 mm) for TH 2040, by 45%

(May) for HAD, respectively.

(196 mm) for TH 2070, and by 34% (150 mm) for TH 2100.

Figure 5

Changes in mean monthly discharge (Q, mm) for time horizons of 2040, 2070, and 2100 compared with the baseline years (1951–2004) in the Bras d’Henri micro-watershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

B. Novotná et al.

Figure 6

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Changes in mean monthly evapotranspiration (ET, mm) for time horizons of 2040, 2070, and 2100 compared with the baseline years (1951–2004) in the Bras d’Henri microwatershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

The runoff coefﬁcient (RC), expressed by the ratio of modelled discharge to precipitation, can indicate the

precipitation decrease or with a slight precipitation increase during these periods (Figures 4(a) and 4(b)).

environmental humidity of a region (Yan & Liu ): RC < 0.1 indicates a drought zone, 0.1 < RC < 0.3 indicates

Surface runoff and inﬁltration trends

a transitional belt, 0.3 < RC < 0.5 a humid area, and RC > 0.5 represents a wet area. The modelling results (Table 3)

The combined effects of temperature and precipitation affect

suggest that the RC will increase from 0.50 (1951–2004,

surface runoff and inﬁltration differently. According to

baseline value) to 0.53 in 2040 for both CC scenarios and

model results, an increase in surface runoff would be most

up to 0.52 and 0.54 in 2070 for CC scenarios CX1 and

apparent in the spring (February to March) for all evaluated

HAD, respectively. However, the modelling results for TH

TH. It is assumed that liquid winter precipitation will

2100 revealed lower RC of 0.50 and 0.45 for CC scenarios

directly contribute to the total discharge instead of being

CX1 and HAD, respectively, which means decreasing

deposited to the snow cover directly. Because average pre-

water storage in the micro-watershed.

cipitation is assumed to increase for most TH, and

Discharge decreased in July and October for TH 2070

temperatures are expected to be negative in January, surface

and in June, July, and September for TH 2100 by CX1

runoff tends to decrease for CX1 and all TH, as well as for

(Figures 6(a) and 6(b) for July and September for TH 2100

HAD in TH 2040 with an increasing trend in TH 2070

by HAD). This effect is closely related to the monthly sum

and TH 2100.

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Surprisingly, decreased surface runoff is expected in

(56 mm) in TH 2040 and 19% (62 mm) in TH 2070, but

April despite snowmelt due to a higher inﬁltration capacity

will decrease by 43% (32 mm) in TH 2100 (Figure 10(a)).

as a result of increasing average air temperatures. This effect

An SWE increase of 23% (69 mm) is also predicted by

is predicted for both CC scenarios (Figures 7(a) and 7(b)). A

HAD for the TH 2040, but SWE will decrease by 8%

higher inﬁltration capacity is mainly predicted during the

(52 mm) in TH 2070 and 32% (38 mm) in TH 2100

summer and autumn for all TH, with the exception of

(Figure 10(b)). CCs will also reduce the number of days

summer months and winter months for HAD (Figures 8(a)

with snow cover compared with the baseline period; by 3

and 8(b)). Increasing inﬁltration capacity is closely related

days in TH 2040, 7 days in TH 2070, and 20 days in TH

to increasing temperature trends which decrease soil

2100 according to the CX1 scenario, and 9 days in TH

moisture (Figures 9(a) and 9(b)).

2040, 10 days in TH 2070, and 28 days in TH 2100 for the HAD scenario.

Snow water equivalent and snowmelt trends

The snowmelt hydrology will continue to dominate in this watershed. Changes in precipitation during the winter

Results indicate that CC will have a broad inﬂuence on snow

will increase SWE, whereas rising temperature effects with

water equivalent (SWE) and snowmelt timing in this water-

prevailing precipitation trends will result in increased snow-

shed due to differences in the type and amount of

melt runoff in the spring. However, results also show a

precipitation. In the Bras d’Henri micro-watershed, the CC

decline in snowmelt amounts for the whole inter-winter

scenario CX1 indicates that SWE will increase by 11%

period. Therefore, reduced SWE in further time periods

Figure 7

Changes in average monthly snow water equivalent (SWE, mm) for time horizons of 2040, 2070, and 2100 compared with the baseline years (1951–2004) in the Bras d’Henri micro-watershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

Figure 8

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Changes in average monthly inﬁltration (Qinﬁlt, mm) for time horizons of 2040, 2070 and 2100 in comparison with the baseline years (1951–2004) in the Bras d’Henri microwatershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

will produce an earlier snowmelt and a higher runoff from

442.22 kg ha 1 in 2009 during the monitoring period with

snow in March and May for both CC scenarios (Figures 10(a)

surface runoff. Sediment yield is a key indicator of sediment

and 10(b)).

transport and erosion in catchments. Elevated TSS values during surface runoff indicate a higher risk of erosion.

Water quality

Values of (NO3-N) varied from 13.60 kg ha 1 in 2008 to 21.30 kg ha 1 in 2009 during the monitored period, and

Total suspended solids (TSS), nutrients: nitrogen (NO3-N),

from 1.90 kg ha 1 in 2008 to 10.99 kg ha 1 in 2009 during

and total phosphorus (TP), were summarised according to

the monitoring period with surface runoff.

daily loads and are presented as total loads (Table 4).

According to these results, TP annual ﬂux shows vari-

These values were computed using surface and total dis-

ation, but increases with the total discharge (Table 4),

charge daily values determined by the WaSiM-ETH model

and ﬂuctuates during the vegetative period (data not

for the evaluation period from 2006 to 2009. For the

shown). In general, TP quantity increased with increasing

winter and snowmelt period (November–April), water qual-

TSS content. Because the soils are heavily fertilized,

ity attributes were not determined because the watershed

excess TP varied from 0.36 kg ha 1 in 2008 to 0.88 kg

was mostly frozen. During times of surface runoff, TSS

ha 1 in 2009 during the monitoring period, and from

export tended to be higher. Annual TSS varied from

0.04 kg ha 1 in 2008 to 0.69 kg ha 1 in 2009 during the

146.28 kg ha 1 in 2006 to 493.64 kg ha 1 in 2009 during

monitoring period with surface runoff. Consumption of

the monitoring period, and from 28.98 kg ha 1 in 2008 to

TP depends on the amount and type of available P and

B. Novotná et al.

Figure 9

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Changes in daily relative soil moisture from 0 to 50 mm (Θ, mm) for time horizons of 2040, 2070 and 2100 in comparison with the baseline years (1951–2004) in the Bras d’Henri micro-watershed, Quebec, Canada, as estimated by the model WaSiM-ETH according to CC scenario CX1 (a) and CC scenario HAD (b).

intake is limited by excess nitrates. However, vegetative

Projected changes in average annual temperature vary

dynamics are driven primarily by soil nitrogen rather

from 1.22 to 4.79 C. According to IPCC (a), changes

than soil phosphorus.

are from 1.5 to 4.5 C, while Roy et al. () predicts an

On the basis of these results and according to Govern-

increase of 1 to 5 C for the southern Quebec region.

ment of Quebec criteria (), overall water quality of the

Precipitation changes vary depending on CC scenario

watershed has the worst properties (category E) in the pro-

and TH, i.e., annual precipitation amounts vary from 15 to

ﬁle St. Gilles (second order watershed). For TSS, it varies

33% for this investigated watershed; about 10% according

from very good to good (categories A and B). For the

to Roy et al. ().

range of values for nitrates and nitrites, water quality attri-

Annual increase in ET varies from 7 to 18% according to

butes are worst (category E), but for TP characteristics,

CX1 and from 10 to 26% using HAD, which agrees with

water quality attributes are good (category B).

Quilbé et al. (a, b). A substantial increase in ET was also reported in Michaud et al. () and Fluet et al. (). The current study indicates that over the basin, pre-

DISCUSSION

cipitation and ET will increase for most of the seasons but their variation depends on CC scenario and TH. Hydrologic

Changes in the annual water balance of the Bras d’Henri

changes result mainly from a combination of these variables.

watershed are considerably modiﬁed according to both CC

Total discharge increased during the winter due to

scenarios for all evaluated TH. The most signiﬁcant

higher temperatures but decreased during the summer and

change is revealed for TH 2100 for both CC scenarios.

fall for TH 2040. Increased annual discharge was 16 to

B. Novotná et al.

Figure 10

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

Table 4

TSS, nutrients: nitrogen (NO3-N) and total phosphorus (TP) as daily and total

23% for CX1 and 16 to 45% for HAD. Similar results are

loads calculated using surface and total discharge values estimated with the WaSiM-ETH model for the evaluated period of 2006 (20 April to 30 November),

reported in Minville et al. (), i.e., an increase in peak dis-

2007 (2 May to 20 November), 2008 (6 May to 25 December), and 2009 (10 April

charge for all projections and TH of CGCM3 and

to 30 November) in the Bras d’Henri micro-watershed (2.36 km2)

Evaluation period

HadCM3.1. Singh et al. () reports an increase from

TSS

NO3-N

(kg ha 1)

Total per year

6.7 to 20.2% for GCM: GISS84 and GFDL80, and up to 250% of the maximum water discharge was reported by Roy et al. (). Likewise, as in Roy et al. (), the maxi-

2006

146.28

17.53

0.60

mum discharge values under Quebec conditions are

2007

204.06

14.90

0.53

usually associated with spring snowmelt (Su et al. ).

2008

217.19

13.60

0.36

Under a future climate, the basin will react differently

2009

493.64

21.30

0.88

during spring snowmelt according to temperature increases

Average

265.29

16.83

0.59

and storm runoff events. This result indicates that maximum

Total during surface runoff

water discharge in the spring will occur sooner and the

2006

72.02

4.33

0.29

amount will exceed the maximum ﬂow discharge currently

2007

49.12

5.36

0.21

observed in the autumn period.

2008

28.98

1.90

0.04

Snowmelt events dominate surface and subsurface

2009

442.22

10.99

0.69

events in this watershed (Jamieson et al. ) and will be

Average

148.09

5.65

0.31

affected by CC. The present paper conﬁrms this too. The

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

climate scenarios predict that precipitation increases during

spatial distributions. Other problems with the considered

the winter months will increase SWE, mainly for TH 2040.

CC scenarios were: (1) the reference period of observational

However, due to higher annual average temperatures, SWE

data, which enabled a comparison between modelled (pre-

will be lower for further TH. This prediction would indicate

dicted) and baseline (observed) data; (2) possible shifts in

a decreasing number of days with snow cover and lower

the occurrence and intensity of extreme events that CC scen-

SWE values. As emphasised by Jasper et al. (), more

arios did not take into account; and (3) changes in the

deﬁnitive conclusions can be drawn for parameters closely

distribution of temperature as well as more frequently occur-

correlated with temperature (ET and SWE), whereas a sig-

ring heat waves as emphasised by Jasper et al. () and in

niﬁcantly

parameters

accordance with Schär et al. (). Use of only two CC

dominated largely by changes in precipitation, especially

scenarios is another limitation of this study, according to

discharge.

Minville et al. (); more CC scenarios would be very

greater

spread

found

for

The present state of water quality in the watershed is in

time-consuming.

category E (from A–E classiﬁcation), which is the worst

The great uncertainty about all projected future hydrolo-

classiﬁcation. Moreover, as CC affects water balance redis-

gic variables depends on both climate data and simulated

tribution, subsequent erosion will threaten agricultural

hydrologic regimes (Prudhomme et al. ; Minville et al.

productivity and sustainability, thereby adversely affecting

). Since uncertainty in climate studies does exist, the

water quality. Because agricultural soils are susceptible to

presented outputs could be of use to policymakers and

extreme environmental events, i.e., drought and ﬂooding,

state data- and water resource-managers in adapting affected

which are commonly predicted to increase due to CC,

agricultural systems to CC. The results of the current study,

land use change could exacerbate these effects. The pre-

therefore, demonstrate the important role that soil and water

sented paper conﬁrms results of Bullock et al. (), those

management in agricultural ecosystems need to play to

report that warmer winters may decrease the protective

mitigate and adapt to CC impacts.

snow cover, thereby increasing soil exposure to water and wind erosion. Increases in the frequency of freeze-thaw cycles would hasten the breakdown of soil particles and

CONCLUSIONS

increase the risk of soil erosion, especially if producers respond to drought conditions by increasing their use of

In the current study, the WaSiM-ETH model was used to

tillage summer fallow.

evaluate the effect of CC on river discharge under different

The advantage of this small river basin scale study is that

CC scenarios. Input data were taken from a high resolution

it provides greater efﬁciency and effectiveness, applicable as

regional climate model driven by different datasets. Model-

a tool for integrated catchment management. The possible

ling the probable CC consequences on the water balance in

runoff change predictions are caused by changing snow

this intensively managed agricultural micro-watershed ident-

cover and frozen ground over different THs. The impact of

iﬁed signiﬁcant changes in the individual water balance

CC on snow and frozen ground as sensitive storage variables

variables (IPCC a). The WaSiM-ETH model, initially

in the water cycle is highly non-linear and more physically-

established to simulate the watershed water balance of moun-

and process-oriented modelling is required in accordance

tainous regions mainly covered by forests, was applied to

with United Nations Environment Programme (UNEP)

lowland areas. It was also applied to agricultural land to

(). The models of less complexity react more sensitively

simulate hydrological processes on a small watershed scale

to the changing climate conditions (Ludwig et al. ).

under cold climatic conditions. The model reproduced

Jasper et al. () outline a potential limitation of a

water discharge fairly well with good correlation during

scenario-based approach to impacts assessment. The plausi-

both the calibration and veriﬁcation periods. This result indi-

bility of CC scenarios considered was the main concern.

cates the model’s suitability to be extended to lowland

This issue is most important for total monthly precipitation,

agricultural land and the greater watershed scale. Applying

where they noted signiﬁcant variation in annual courses and

a high resolution regional climatic model in conjunction

B. Novotná et al.

Impact of climate change on the water quality of a watershed in Quebec, Canada

Journal of Water and Climate Change

05.1

2014

with a conceptual hydrological model enables the capture of

Bras d’Henri-Fourchette WEBs (Watershed evaluation of

local variability of river discharge for present-day climate,

BMP) Project. The authors thank Jean-Thomas Denault and

using boundary values that are also assimilated with

Catherine Bossé for their excellent help on data preparation,

predicted future climatic conditions. Model simulations

Sam Gameda for information on CC scenarios and Christina

detected signiﬁcant changes in the water balance of the

McRae for her valuable help with English revision.

Bras d’Henri micro-watershed for both CM. Regardless, the results of the current study improve our understanding of future climatic effects on a local scale.

REFERENCES

These results corroborate early reports (i.e., Mokhov et al. ), and the outputs can be used to predict future climatic changes concerning watershed hydrology for watersheds with similar climatologic variables. Moreover, this study highlights the importance of using different methods and data sources to assess potential CC effects on watershed hydrology. Finally, the predicted CC impact on micro-watershed hydrological parameters, such as increased precipitation and discharge and decreased snow cover, will increase surface water quality problems. The CC effects on stream water quality will increase agricultural soil erosion on frozen soils at snowmelt and during the snow-free season on bare soils. Current agricultural BMP must be better adapted to CC in order to reduce, or decrease, sediment and nutrient losses to streams during intense runoff events on soils at risk of erosion. Cover crops could be used to protect land from water erosion and new plant species could provide a buffer to protect dormant plants that are covered by snow and ice during the winter and spring seasons. As proposed by Oleson & Porter (), particular thought should be given to reduce soil erosion, prevent nitrogen and phosphorus leaching, conserve soil moisture, increase crop rotation diversity, modify microclimates to reduce temperature extremes and provide shelter, and consider carefully any land-use changes, especially those that involve the abandonment or intensiﬁcation of existing agricultural land or the cultivation of new land. In conclusion, the methodology presented here can be used to ascertain climatic and hydrologic characteristics linked to geographical location and climatic zone.

ACKNOWLEDGEMENTS This project received ﬁnancial support from Agriculture and Agri-Food Canada under the A-Base Project #190 and the

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Process design and management for integrated ﬂood risk management: exploring the multi-layer safety approach for Dordrecht, The Netherlands Sebastiaan van Herk, Chris Zevenbergen, Berry Gersonius, Hans Waals and Ellen Kelder

ABSTRACT New flood risk management policies account for climate and socio-economic change by embracing a more integrated approach. Their implementation processes require: collaboration between a group of stakeholders; combining objectives and funding from various policy domains; consideration of a range of possible options at all spatial scale levels and for various time horizons. Literature provides limited guidance on how to organise a collaborative planning process to devise integrated flood risk management (IFRM) plans. This paper presents a case study where a recently developed framework for process design and management has been used and evaluates whether or not the collaborative planning process led to an IFRM plan. The case study is Dordrecht (NL) where the new multi-layer-safety (MLS) approach has been applied in the context of the Delta Programme. The Delta Programme investigates how the Netherlands can adapt to the effects of climate change. MLS comprises three flood safety layers to reduce flood risk: flood protection, spatial planning, and emergency response. The framework has been shown to be effective in the delivery of an IFRM plan, it has been enriched by defining the interfaces between and phasing of planning activities, and can be further improved to better guide implementation and governance activities. Key words

| collaborative planning, ﬂood management, integration, multi-layer safety

Sebastiaan van Herk (corresponding author) Chris Zevenbergen Delft University of Technology, Stevinweg 1, Delft 2628 CN, The Netherlands E-mail: sebasherk@gmail.com Sebastiaan van Herk Bax & Willems Consulting Venturing, Roger de Lluria 120, Barcelona 08037, Spain Sebastiaan van Herk Chris Zevenbergen Berry Gersonius UNESCO-IHE Institute for Water Education, P.O. Box 3015 DA Delft, The Netherlands Hans Waals Waterschap Hollandse Delta, P.O. Box 4103, 2988 DC Ridderkerk, The Netherlands Ellen Kelder Gemeente Dordrecht, P.O. Box 8, 3300 AA Dordrecht, The Netherlands

INTRODUCTION Traditionally, ﬂood management practices in Europe have

the Environment Agency in England and Wales (EA

focused on predominantly defensive approaches to reduce

), and the multi-layer safety (MLS) approach in the

the probability of ﬂooding (Newman et al. ). However,

Netherlands (V&W ) that has been studied for this

in the past two decades major ﬂood disasters have created

research. The EU Flood Directive endorses an integrated

the need to shift from ﬂood protection to a more integrated

approach to ﬂood risk management (IFRM). It requires

approach in which ﬂood risk is actively managed to also

the member states to prepare IFRM plans for areas where

reduce ﬂood impacts (White ; Dawson et al. ). This

risk is deemed signiﬁcant. These should include appropriate

rethinking of and change in the traditional approach is

objectives and measures that focus on the reduction of the

included in policies such as the EU Flood Directive (EC

likelihood of ﬂooding and/or on the reduction of the poten-

), the source-pathway-receptor framework as used by

tial adverse consequences. IFRM plans are meant to address

doi: 10.2166/wcc.2013.171

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Process design and management for integrated ﬂood risk management

Journal of Water and Climate Change

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all phases of the ﬂood risk management cycle, but focus par-

White () lists a range of activities and possible measures

ticularly on prevention, protection and preparedness

for IFRM, but does not provide ideas on how to organise

(adapted from EC ).

these activities into a coherent process. van de Ven et al.

1. Protection: taking measures to reduce the likelihood of floods in specific areas, such as by building flood defences or preventing high river discharges. 2. Prevention: using spatial planning and adaptation of buildings to prevent or reduce damage if a flood occurs. 3. Preparedness: improving organisational preparation for floods, such as emergency plans, risk maps, risk communication, early-warning systems, and flood insurance. 4. Emergency relief: providing emergency relief in the case of a flood, such as evacuating communities, erecting temporary flood defences and providing medical help. 5. Recovery and lessons learned: mitigating the social and economic impacts on the affected communities, and undertaking surveys to identify the conditions that led to effective mitigation and adaptation measures, or an effective emergency response to the flood event.

() propose a step-wise process comprising vulnerability analysis, selection of a strategy and selection of measures, that is an idealised-type of process that so far lacks empirical evidence. van Herk et al. (a) used empirical evidence to develop a framework to design and coordinate a collaborative planning process that clusters the activities described by White () and van de Ven et al. () into three groups: system analysis; planning, design and engineering; and governance. This framework requires validation to provide practical guidance for prescriptive, rather than descriptive use. If an IFRM process is organised using this framework, does it support the production of an integrated plan? This paper describes the process of the development of an IFRM plan that was organised from the outset following the collaborative planning framework (van Herk et al. a) and aims to evaluate the process and the framework in

The Netherlands is opting for a MLS approach, which is

terms of supporting the delivery of an integrated plan. An

laid out in its National Water Plan (V&W ). This is a

IFRM plan comprises integration of objectives including

three-layered approach that goes beyond ﬂood protection,

across spatial and temporal scales (see also Dovers ;

which is its ﬁrst safety layer. The two other layers are

Zevenbergen et al. ; Rijke et al. ). The research

aimed at reducing the consequences of ﬂooding by adapting

described here is based on a case study in the city of Dor-

the spatial layout (second safety layer) and enhancing emer-

drecht in the Netherlands, where an IFRM plan has been

gency response (third safety layer), respectively. Hence,

developed based on the MLS approach. MLS inherently

MLS explicitly calls for the consideration of measures to

embraces IFRM as it stimulates the combination, coordi-

reduce adverse consequences and to operationalise these

nation and planning of investments in measures from the

measures.

three ﬂood safety layers. IFRM based on MLS aims to

The implementation of these policies and subsequently

develop an integrated strategy to reduce present and future

the development of IFRM plans result in the need to com-

ﬂood risk that comprises a portfolio of options. Options at

bine objectives and funding from various policy domains;

multiple scale levels in the ﬂooding system that can be

to consider a wider range of possible measures at all spatial

implemented at various moments in time, and can be com-

scale levels and for various time horizons; to involve mul-

bined with objectives from other related policy domains

tiple disciplines and, as a result to collaborate between a

such as transport, housing, and environment. Hence, the

group of stakeholders with various interests and means

MLS approach and the planning framework used in Dor-

(e.g. Potter et al. ). Few authors (White ; van de

drecht embody generic IFRM concepts that may be used

Ven et al. ; van Herk et al. a) offer guidance on

for international comparison. In practice, the case study in

how to organise a collaborative planning process to devise

Dordrecht has replication potential because it is a pilot pro-

IFRM plans. Few examples of such processes have been

ject of the Dutch Delta Programme (Gersonius et al. )

identiﬁed, in practice, documented and evaluated (Pahl-

that will coordinate the implementation of Dutch water

Wostl et al. ) as the implementation of truly integrated

Policy for the coming decades (Deltacommissie ;

FRM plans is still in its infancy (Huntjens et al. ).

Zevenbergen et al. a, b).

102

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Process design and management for integrated ﬂood risk management

Evaluation framework and methods

Journal of Water and Climate Change

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2014

composition, ﬂow paths, nature and vulnerability of urban fabric, and critical infrastructure (White ). Planning,

A mixed-method case study approach has been used to ana-

design and engineering aims to develop strategies and

lyse the collaborative planning process and evaluate it in

measures through workshops with multiple stakeholders

terms of supporting the delivery of an integrated plan.

and typically results in such products as land use plans,

First, the IFRM plan has been evaluated in terms of inte-

urban masterplan designs, or designs of speciﬁc elements

gration of objectives across spatial and temporal scales.

such as levees or buildings. The governance activity supports

Integrated outputs may align and balance multiple objec-

decision making by involving participants in discussing and

tives. Flood risk inherently comprises multiple and

deﬁning their ambitions and roles regarding a project or

sometimes competing objectives, such as reducing: individ-

policy, or the problems and solutions as deﬁned in the activi-

ual risk (probability of death of one person); group risk

ties of system analysis and planning, design and engineering.

(function of probability of ﬂood event and related number

Complex decision making, such as for IFRM, involves stake-

of fatalities); and economic risk (function of probability of

holders with different views, stakes and values in interpreting

ﬂood and related direct and indirect material damage).

and valuing information appropriately, mobilizing speciﬁc

Moreover, physical interventions to reduce ﬂood risk need

knowledge to underline viewpoints (Koppenjan & Klijn

to be incorporated into spatial planning and thus need to

). Semi-structured individual and group interviews

be aligned with objectives related to, for example, housing,

have been conducted with participants (n ¼ 17) to analyse

nature, economics, water quality, transport, etc., to increase

the planning process and its contribution to an integrated

the political and ﬁnancial feasibility of the implementation

plan. These interviews focused speciﬁcally on the collabora-

of the interventions (White ; Veerbeek et al. ).

tive

IFRM outputs can comprise single or multiple options

generated; and how these supported dialogue for decision

from all relevant spatial scales in the ﬂooding system (see

making throughout the development of the IFRM plan. The

process:

activities

conducted;

knowledge

they

also Adger et al. ; Zevenbergen et al. ). A MLS

interviewees were representatives from the ministry, water

plan or strategy can comprise measures to protect, prevent

board, province, municipality, safety region, and research

and prepare, as an example, individuals or communities,

institutes involved. The researchers were able to gather

individual buildings or an entire city or catchment. The

much data and gain a clear understanding of the activities

plans are developed considering various time scales in

and their contribution to the integrated outputs, as the

order to balance short- and long-term costs and beneﬁts

researchers were themselves also participants in the colla-

and anticipate (potential) future change, such as climate

borative planning process and thus engaged in delivery as

(ibid). Various documents have been analysed including:

well as observation. This is action research (Checkland &

the ﬁnal plan (DPRD ); intermediate products from

Holwell ; Flood ). The researchers designed and

the different planning activities (MARE ); external

facilitated the collaborative planning process themselves

research reports from directly involved or external research-

and followed the process framework to evaluate its potential

ers (e.g. Gersonius et al. , ; van Herk et al. b);

to deliver integrated outputs.

minutes of meetings; policy documents; and media.

Validation interviews were conducted with participants

The collaborative planning process has been analysed

in the process, that were not interviewed previously, to vali-

following the same framework that has been used to design

date the inﬂuence of the collaborative process on the

and coordinate the process (van Herk et al. a). The plan-

integrated outputs (n ¼ 6). The interviewees represented

ning activities have been classiﬁed into three groups: system

four government agencies and two private stakeholders.

analysis; planning, design and engineering; and governance,

Additionally a survey has been conducted among the partici-

that generate knowledge to contribute to decision making

pants (n ¼ 42) of a workshop on multi-layered safety for the

(Van Buuren ). System analysis generates information

case study. Respondents were asked about the opportunities

to analyse and address problems in, for example, the areas

and barriers for an integrated process and to develop inte-

at risk from ﬂooding, green infrastructure mapping, ground

grated outputs based on the MLS approach to get an

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understanding of the potential for wider uptake. The qualitat-

vision for ﬂood safety and insight into possible measures to

ive data obtained from the open questions have been

improve ﬂood safety, potentially combined with other goals

analysed for recurrent similar statements. The respondents

to be converted into a regional IFRM plan. This research

comprised representatives of police, ﬁre brigade, red cross,

focuses on the pilot’s second goal by analysing how the colla-

national, regional and local government, utility companies,

borative process supported the delivery of integrated outputs

construction companies, engineering ﬁrms, research insti-

in the IFRM plan from 2010 to 2013. The pilot project took

tutes and universities.

place under the direction of the regional authorities (municipality, province, water board and safety region), where the national government actively participated, but did not

APPLICATION IN A CASE STUDY: PILOT DORDRECHT

direct. A collaborative planning process was required for the pilot project, because multiple stakeholders needed to be involved to develop and implement measures in the

This section describes the case study, the collaborative plan-

three safety layers. The distribution of responsibilities

ning process, and evaluates qualitatively the resulting IFRM

among government agencies is regulated and clearly deﬁned

plan of MLS in Dordrecht in terms of integrated outputs.

for ﬂood protection in the area. The water authority Hollandse Delta is responsible for operation and maintenance

Case description

of the levee system around Dordrecht, whilst the National Government sets related protection standards and funds

The application of the process framework is here illustrated

the investment costs for these levees. MLS, however, requires

using the example of IFRM for the Island of Dordrecht, the

collaboration between a wider group of stakeholders with

Netherlands. Surrounded by a series of rivers and canals –

various interests and means that they are not (yet) coherently

the Oude Maas, Beneden Merwede, Wantij, Nieuwe

managed, funded and/or regulated. The second safety layer of

Merwede, Dordtse Kil – the city of Dordrecht is located

spatial planning is primarily the responsibility of the Munici-

on an island. The population of Dordrecht consists of

pality of Dordrecht at a local level and of the Province of

around 120,000 inhabitants. Most residential areas are

South Holland at a regional level. Additionally, many other

located in a single polder area of about 7 ha, which is pro-

stakeholders affect or are affected by spatial developments

tected by a 37 km long dyke-ring. The unembanked areas

such as private developers, landowners, and inhabitants.

along the riversides have a higher ground level and com-

The Safety Region South-Holland-South is responsible for

prise: a part of the historical inner city; industrial areas;

emergency planning in the third safety layer. The Safety

residential areas; recreational and nature areas. The Island of

Region comprises emergency services such as the ﬁre bri-

Dordrecht lies in the transition zone between the tidal reach

gade, police and ambulance. Also, increased preparedness

and the river regime reach where the extreme water stages

of operators of critical infrastructures such as water and

are inﬂuenced by both the high river runoff and storm surges

energy utilities and communication companies can reduce

from the sea. Together with the city of Rotterdam, a number

ﬂood consequences.

of smaller cities and the surrounding agricultural and nature

For the pilot in Dordrecht stakeholders came together in

areas, the Island of Dordrecht is located in the Rijnmond-

a Learning and Action Alliance, a ‘shadow network’ that

Drechtsteden area. One of the six regional sub-programmes

comprised voluntary participation of representatives from

of the Dutch Delta programme that was set up for this area.

all of the organisations involved. A shadow network was

In the context of the Dutch Delta Programme, in 2010 the

considered necessary by the participants, as there is no blue-

Dutch government commissioned a MLS pilot project for the

print to organise the IFRM process for the application of the

Island of Dordrecht (among others) to improve ﬂood safety in

new MLS approach. A shadow network is an independent

an area-oriented development process. The goal of this pilot

platform for collaboration to work with alternative

project was two-fold: (1) to deliver enhanced knowledge

approaches for governing socio-ecological systems (here:

and expertise to national policy; and (2) to develop a regional

ﬂooding system) to experiment and generate alternative

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solutions to emerging problems (Olsson et al. ). The

selected as a central concept that aimed to strengthen and

shadow network consists of representatives of relevant sta-

align the capacity of organizations, communities and individ-

keholders with no formal authority. Consequently, it does

uals each operating at different spatial scales, to cope with

not adhere to existing governance structures and provides

and recover from an extreme ﬂood event (i.e. above the cur-

a learning environment where stakeholders can leave

rent protection standard). Measures that contribute to

entrenched positions to work and learn together uncon-

increased self-reliance comprise the realization of a ‘delta

strained by formal political positions (van Herk et al.

dyke’ and compartmentalizing the dyke ring area into three

a). Such informal participatory platforms can occur

subdivisions by using the existing regional dykes (see

even in a rigid and strongly structured administrative

Figure 1). The northeast part of the dyke ring is to be made

environment (Moellenkamp et al. ). Existing insti-

virtually unbreachable (100 times lower probability of failure

tutional arrangements are often not sufﬁcient for IFRM

than for the current design standard) by heightening and/or

(Wong & Brown ; Huntjens et al. ) and the same

widening the dyke sections. This allows for overtopping of

holds true for the Netherlands and MLS. The existing pro-

the levees with ﬂood water, rather than breaching, and thus

cedures and instruments in the Netherlands adhere to

reduces the potential consequences of a ﬂood event. The

persistent government structures and policies that were

three compartments allow for a differentiation of ﬂood risk

designed based on a sectoral approach for ﬂood protection,

and speciﬁc strategies for each compartment. The south

rather than integrated approach to ﬂood risk management

‘compartment 3’ consists mainly of agricultural land where

(van der Brugge et al. ). Hence, the ﬁrst goal of the

the consequences of a ﬂood would be lower than in the two

pilot was to support national policy development. Related

north compartments. Compartment 3 will have a lower

to the second goal, to make an IFRM plan, the IFRM process comprised an initial stage of strategy development, while actual realisation of the plan, including the delivery of urban (re)development and infrastructure development projects, if at all, is foreseen as happening over the coming decades. These aspirations required flexible network structures and governance that allowed for learning. Integrated outputs The question addressed here is if the IFRM plan for Dordrecht comprises integrated outputs: in objectives, and across spatial and temporal scales. Integration across spatial scales is central to the MLS strategy and IFRM plan. The flood hazard can only be reduced at a national level, as it can be caused inter alia by a storm surge at the North Sea (downstream) and peak discharge in the rivers (upstream). The flood hazards were taken as a starting point for the IFRM plan for Dordrecht. On the regional scale, in this case the Island of Dordrecht that includes the dyke ring area and the unembanked areas, the flooding system has been designed to be ‘self-reliant’ in case of a flood. Evacuation of the entire population from

Figure 1

Illustration of the IFRM plan for the Island of Dordrecht. The fine black line indicates the dyke ring, with a virtually unbreachable delta dyke in the North East that merely allows for overtopping of floodwater, and the compartmentalizing of the dyke ring area into three subdivisions by secondary dykes (grey lines) allowing for differentiation of flood risk and specific strategies. No

the island was not considered feasible, or effective (see:

further development is allowed in compartment 3. 65% of inhabitants of

sub-section on system analysis). Self-reliance was, therefore,

and shelters are to be created in compartment 1.

compartment 2 can be evacuated to compartment 1 prior to a ﬂood event,

105

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Journal of Water and Climate Change

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protection standard and further urban development there is

sectoral policies, planning frameworks, and ‘normal’ invest-

to be avoided. The northwest ‘compartment 2’, that has a

ment and renovation cycles. The construction of the delta

higher individual ﬂood risk, could be evacuated to the north-

dyke is to be combined with already envisaged dyke reinforce-

east ‘compartment 1’ in the case of being threatened by a

ment projects, such as for the dyke section at Kop van het

levee breach. These measures also provide opportunities for

Land in the East. Several schools have been selected to

effective measures at the local, neighbourhood scale that

serve as shelters to receive evacuees in times of emergency.

are proposed to further reduce the consequences of ﬂooding

When regular renovations to these schools are scheduled,

and enable the functioning of the socio-economic system

they will be adapted to additional requirements for a shelter

during and after a ﬂood event, such as: individual protection

where opportune. The (Wieldrechtste) Zeedijk, a regional

of critical infrastructure networks (e.g., energy supply) and

dyke separating the urbanised areas in the north from agricul-

nodes (e.g., hospitals); creation of safe havens or shelters;

tural and natural areas in the south, is to be strengthened to

elevated access and egress routes within and between com-

add additional protection to the urbanised north. This

partments and neighbourhoods; ﬂood-proof planning and

strengthening is to be combined with envisaged nature devel-

building of public spaces and buildings; and improved risk

opment along the dyke that will be designed to break ﬂood

and crisis communication.

waves, in addition to enriching the landscape. Incremental

Integration across temporal scales is inherent to the self-

investments over time, as proposed by the IFRM plan, provide

reliance strategy and is included in the FRM plan in two

the ﬂexibility to adjust the implementation and timing of

ways: to deal with various potential futures and for a ﬂexible

measures based on new insights, e.g. from updated climate

implementation strategy over time. Firstly, the plan is robust

and socio-economic scenarios.

and ﬂexible for various potential long-term futures, taking a

Integration in objectives is a direct consequence of the

range of climate and socio-economic scenarios into account.

three-layered MLS approach that aims for multiple ﬂood

As self-reliance increases the capacity to cope with and

risk objectives by reducing the probability and consequences

recover from an extreme ﬂood event, it is less dependent on

of ﬂoods, but has also been achieved by incorporating socio-

a design standard for the dyke protection system that is

economic objectives. The dyke reinforcement planned in the

designed to handle an expected return period and future cli-

East of the Island is to be combined with nature develop-

mate scenarios. For example, the entire dyke ring, with the

ment on the riverside of the dyke. The dyke reinforcement

exception of dyke section 13 (Voorstraat), has been designed

in the West supports the redevelopment of an industrial

with a residual height in order to accommodate climate

area that is intersected by the dyke, providing a safe elevated

change and sea level rise under the projected KNMI climate

transport route. The Stadswerven, an unembanked old indus-

change scenarios for 2100 (van den Hurk et al. ). The

trial area in the North of the island is being redeveloped into

Voorstraat, which is the primary dyke through the historical

a residential and commercial area. Spatial and ﬁnancial

inner city, is not high enough to withstand higher water

objectives have prevailed in the related master planning,

levels than those accounted for by the current design stan-

but ﬂood risk was used as a design variable. The masterplan

dard. This dyke section is difﬁcult to heighten without

includes lower lying, attractive water-rich areas to appeal to

affecting cultural heritage buildings (Gersonius et al. ).

potential house buyers. The public spaces and dwellings are

However, it is virtually unbreakable because of its very ﬂat

ﬂood-proofed by elevated construction, dry-proof building

inner slope and stone cover. For this dyke section the IFRM

design or by selection of materials, pavements and veg-

plan proposes the construction of dry-canals behind the

etation. Transport infrastructures are also elevated so that

dyke to accommodate ﬂoodwater in case overtopping and

they can serve as escape routes for evacuation. The construc-

overﬂow occurs more frequently in the future as expected

tion of dry-canals to guide ﬂoodwater overtopping the De

due to climate change. Secondly, the investment planning

Voorstraat dyke, requires integration of these ﬂows into plan-

for the proposed measures is optimised over time to reduce

ning and maintenance of public roads in the embanked

investment costs by exploiting the many opportunities for

polder area. Regional businesses have been invited to explore

mainstreaming and integrating ﬂood risk management into

business opportunities related to the implementation of the

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IFRM plan in Dordrecht so as to support economic develop-

elevation maps, led to the understanding that the unem-

ment and also any exploitation potential internationally.

banked areas are situated on higher ground (on average

Examples include advanced sensors and ICT systems for

around 3 meters higher than the embanked areas) and thus

ﬂood monitoring and management, as well as engineering

less vulnerable for ﬂooding than the embanked areas as

and construction of delta dykes and ﬂood-proof dwellings.

ﬂood levels would be lower. Subsequently, the options have been explored to use the unembanked areas as safe havens

Collaborative planning process

and to host shelters and use them for the provision of critical infrastructure functions as part of the self-reliant strategy. The

In this section, the collaborative planning process will be

Statutory Assessment for the Island of Dordrecht found that

described using: (i) system analysis; (ii) planning design

28% of its ﬂood defences do not comply with the legal stan-

and engineering; and (iii) governance as given in the frame-

dard and require reinforcement before 2015 (PZH ),

work of van Herk et al. (a).

whilst the Voorstraat dyke was expected to be found to be inadequate during the next Assessment round. The reinforce-

System analysis

ment projects required (I&M ) were later inventoried to determine if they could provide opportunities to combine

Extant analysis results from continuous monitoring of system

investments for cost-effective implementation of the IFRM

performance were inventoried. The Dutch Environmental

strategy. Many other analyses were also conducted, for

Assessment Agency had conducted a risk zoning analysis

example, vulnerability assessments of the cultural heritage

for Dordrecht and surrounding areas to classify risk areas

buildings in the unembanked areas, of the critical infrastruc-

in terms of maximum water depth and lead-time, variables

ture, and of the access and egress routes. See Gersonius et al.

which have the greatest inﬂuence on casualties in the Nether-

() for an elaborate presentation of the methods applied,

lands (Pieterse et al. ). The dyke ring area of Dordrecht is

and results of all analyses.

at high risk, where ﬂooding would either occur that would be

In a second phase of analyses, the various measures that

‘deep and slow’ or ‘deep and fast’. These insights brought

had been developed were assessed and compared to support

attention to the problem of residual risk beyond the design

dialogue on political feasibility and funding. These measures

standard: what if a dyke breaches? The east side of the

were compared in terms of their effects on the Expected

island was found to be one of the most dangerous breaching

Annual Damage and Expected Annual Number of Casual-

locations. From ﬂood modelling, such a ﬂood has been esti-

ties.

mated to cause damage of 4.6 billion Euro (approximately

comprising a set of measures, the graduality of ﬂood risk

one third of the total assets in Dordrecht), cause 566 casual-

was studied for different return periods (see Figure 2) follow-

ties and would affect 96% of the population living in the

ing De Bruijn (). Graduality refers to the relative

embanked areas. As a result, the ambition was set to avoid

increase in losses with increasingly severe ﬂood events.

a dyke breach at this location. This stimulated the exploration

This provided insight into the effectiveness of the strategy

of alternative options that led to the proposals for the

for various return periods, and thus indirectly for alternative

‘unbreachable’ delta dyke and the compartmentalizing of

climate change scenarios that would decrease return

the dyke ring area into subdivisions (as shown in Figure 1).

periods, i.e. ﬂood events would occur more frequently.

Evacuation of the entire population from the island was not

Structural assessment of protection measures (Den Hengst

considered feasible, or effective to reduce casualties. Because

) and prevention measures (Blom et al. ) that sup-

of a short warning time (less than 24 hours) only 15% of the

ported the engineering work was carried out in the EU

population could be evacuated and adjacent areas would

FP7 Floodprobe project (FloodProBE ). A new evacua-

Also,

for

the

combined

self-reliance

strategy

potentially also be ﬂooded, with the four main egress routes

tion strategy has been evaluated for the ‘evacuation

becoming inaccessible due to the storm conditions. This con-

fraction’ that has been increased from 15% of the population

clusion prompted the development of the self-reliant strategy.

leaving the island to 80%; because 65% of the population

Flood modelling, later conﬁrmed by an analysis of ground

can evacuate from compartments 2 and 3 to the safer

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from the emergency services listed the various water levels that would impede circulation of their vehicles, such as ambulances, or army vehicles and would thus hamper emergency response. Urban planners made a visual inspection of the dyke ring to inventory the characteristics of various dyke sections and their surroundings. This comprised an analysis of available physical space adjacent to dykes that could possibly be used for the widening of their foundations required for dyke strengthening. They found that certain dyke sections already have characteristics of a delta-dyke, because the ground level of adjacent land was as high as the dyke’s Figure 2

Graduality of ﬂood risk in terms of damage (millions of Euros) for the Island of

crest height and thus added strength to the dyke. Critical

Dordrecht, with the current FRM strategy (in black) and the IFRM strategy (in

infrastructure providers highlighted that water supply,

grey).

energy plants and chemical factories are based in the unembanked areas on higher ground, giving rise to the idea that if

compartment 1 in the North East (Figure 1). Also story lines, a narrative of a series of events and actions during a ﬂood event, have been developed (Lips ) to support the organisational preparation and improve the emergency response (third ﬂood safety layer).

properly protected, they could serve the population during a flood event and thus enable the self-reliant strategy. The police and the fire brigades realised that they have their headquarters in a flood vulnerable area and that their command centre is at the lowest level of that building. If flooded, communication could be hampered, jeopardising the emer-

Planning, design and engineering

gency response operations during a ﬂood event. Future renovation or newly planned building of the headquarters

This activity started with an inventory of possible measures

and command centre can address these vulnerabilities.

to reduce ﬂood risk based on existing literature (e.g. van de

Different options were combined in an IFRM plan using

Ven et al. ) and examples from other cities intended to

the new insights as regards their costs and effectiveness

inspire participants. Subsequently, a stakeholder workshop

(system analysis) and potential political support among sta-

was held with 42 representatives of police, ﬁre brigade,

keholders (governance). The former supported the idea

Red Cross, national, regional and local government, utility

that a combination of options from various scale levels of

companies, construction companies, engineering ﬁrms,

the ﬂooding system can effectively deliver upon multiple

research institutes and universities. Initially and without

objectives. A holistic strategy was proposed, consisting of:

prior instruction, participants started discussing the existing

a protection strategy with a delta dyke and compartmentali-

levee system and possible improvements framed in the con-

sation dykes; a self-reliant strategy with ﬂood-proofed

text of the traditional approach of ﬂood protection. Later,

critical infrastructure, shelters and area-speciﬁc evacuation;

the participants brainstormed options from the second and

and a development strategy coupling FRM measures with

third safety layers using the knowledge from their respective

spatial measures over time. The latter is crucially inﬂuenced

domains and considering their means and current practice.

by the mandates of different authorities and the available

This led to increased understanding of each other’s means

funding. For example, the transformation of a dyke section

and interests and an inventory of constraints, needs and

to a delta dyke proved not viable under the current govern-

research questions. For example, the representative of the

ance framework, because there is no national assessment

gas utility company stressed that water pressure from ﬂood-

framework for the prioritisation of dyke sections and thus

water can damage the underlying gas lines. This insight

for funding and implementation of delta dykes. Coupling

might have to be converted into a design requirement for

with existing (re)development and maintenance works is

new developments in the unembanked areas. Professionals

necessary to reduce costs, and, therefore, planners looked

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for such opportunities. As a result the self-resilient strategy

other policy domains. In parallel, separate workshops were

can only be implemented over a long period of time, with

organised to discuss the need for policy change. An exemplary

some redevelopment projects in the unembanked areas

discussion was held on the solution proposed for the Voor-

not being foreseen for the next 20 years. This poses an

straat dyke with dry canals to accommodate ﬂood water in

important challenge to decision making and governance

case of overtopping. A shift of responsibilities and budgets is

regarding the political feasibility to sustain ambitions and

required to fund, build and operate the dry canals. A political

commitments for a long-term strategy with multiple objec-

discussion document has been produced by policy advisors,

tives for various stakeholders each or any one of which

but is yet to be discussed by elected politicians. The policy

could have a shorter time horizon.

advisors indicated in an interview that the Water Board is interested in the approval of the dyke section without heigh-

Governance

tening, but considers that the decision for funding the construction and maintenance of dry canals can be post-

A stakeholder analysis was conducted prior to engaging the

poned. This poses a risk to the implementation of the

relevant stakeholders. It followed the approach of Klijn &

measure. Similarly, for the dyke reinforcement project Kop

Van Twist () and Koppenjan & Klijn (): identify

van het Land, the project manager and responsible politicians

involved stakeholders, their perceptions, and their positions

of the Water Board, Municipality and National Government

and interdependencies. Inspired by the MLS concept, stake-

have been presented with the opportunity to convert that

holders from the second and third ﬂood safety layer were

dyke section in a delta-dyke, but no agreement has been

involved for the ﬁrst time in discussing water safety with tra-

reached yet on the funding of the additional costs that are

ditionally dominant stakeholders from the ﬁrst layer. During

not mandatory under current regulation. The political feasi-

the stakeholder workshop they got to know each other and

bility of the delta-dyke upgrade is decreasing, as the

discussed their roles and ambitions. As one survey respondent

preparation continues of the already planned and funded

stated: ‘We know of the existence of each organisation, but

dyke reinforcement project that is less ambitious. Vulner-

don’t know the people, let alone work together.’ And another:

ability analysis showed that the upgrade of this dyke

‘No organisation has a coordinating or steering role over the

segment is a prerequisite for a self-reliant strategy, because

three layers and every organisation is ﬁghting for its role …

the consequences of a breach here cannot be mitigated with

At a national level the three safety layers are decoupled in

the proposed measures on the second and third ﬂood safety

three ministries. Now you can still work on ﬂood safety

layers.

from your own policy sector without coordinating.’ During the workshop they sought for synergies by combining their means to explore integrated strategies and combinations of options. After further design work and analyses, the Munici-

DISCUSSION ON THE PROCESS FRAMEWORK AND ITS GENERAL APPLICABILITY

pality, Water Board, Safety Region and National Ministry, discussed possible implementation paths and investment

The contribution of the collaborative process to an IFRM

strategies for the IFRM plan. However, to date no decisions

plan is discussed below, and the process framework has

have been made on the investments for and implementation

been enriched by the analysis of the interaction between

of the IFRM plan, because of barriers imposed by existing gov-

activities and the phasing of the activities to develop an

ernment structures and policies. Current national regulation

IFRM plan.

merely sets design standards for levee systems based on return periods and thus national funding is merely provided

Contribution of the collaborative planning process

for defence structures. Implementation of measures from

to integrated outputs

the other safety layers is to be funded by regional stakeholders and without legal obligation (Kolen et al. ). This focused

Based on the case study analysis it can be concluded that

discussion on how to combine objectives and funding from

the collaborative planning process led to the development

S. van Herk et al.

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Journal of Water and Climate Change

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of an IFRM plan. The IFRM plan that was developed is

scenario analysis abounds to consider various temporal

integrated across spatial and time scales and aligned

scales in ‘system analysis’, such as related to the Assessment

multiple objectives; and the three activity groups in the col-

Reports of the Intergovernmental Panel on Climate Change.

laborative planning process have contributed to the

In terms of ‘planning, design and engineering’, Gersonius

integration in the IFRM plan. For example ‘system analysis’

() provides methods to devise strategies and portfolios of

assessed the performance of IFRM for different objectives

measures to be implemented over time horizons up to 50 to

(e.g. individual risk and economic risk) and for various cli-

100 years. Also many concepts have been proposed to

mate change scenarios. Planning, design and engineering

‘design’ with multiple objectives, such as ‘building with

devised options for various scale levels such as upgrading

nature’, combining coastal protection with land use planning

the dyke-ring around the island and converting individual

through land reclamation using sand from the sea for dunes

buildings into shelters for evacuees. ‘Governance’ involved

and beaches (Waterman ). The concept of ‘green infra-

a wide range of stakeholders ranging from private develo-

structure’ (Sandström ) aims to utilise green systems as

ﬂood

residential

much as possible in urban space, that if done properly can

neighbourhood whilst improving its value on the property

help reduce and manage ﬂood risk (Gill et al. ; Tzoulas

market, to the national government to discuss safety stan-

et al. ). ‘River widening’ that aims to increase river dis-

dards and funding for the dyke system. The contributions

charge capacity provides opportunities to increase spatial

of the planning activities to the integration elements of

quality (Opperman et al. ; Rijke et al. ). ‘Governance’

the IFRM plan have not been straightforward and depend

literature is emerging to integrate water and ﬂood manage-

on the involved practitioners and the coordination of

ment across various spatial scale levels under the concept of

their work. For example, the analysts were told to assess

multi-level governance (e.g. Lundqvist ; Kern et al.

pers

that

can

proof

to-be-built

strategies on more criteria than the reduction of potential

; Pahl-Wostl ). The validation interviews indicated

damage and casualties of different ﬂood events. Urban

that the MLS concept itself was an important catalyst to set

planners themselves looked to combine ﬂood measures

up an integrated process to deliver an IFRM plan. The MLS

with investment plans from other policy domains. Table 1

approach implicitly broadened the objective – from reducing

summarises how each activity group can contribute to inte-

ﬂood probability to (including) reducing potential conse-

gration, which provides an evaluation framework and

quences of ﬂood events – and explicitly spanned the

guidance to project managers of future IFRM projects.

boundaries and design freedom to develop alternative sol-

Much literature is available on how speciﬁc activities con-

utions with more integrative elements. The interviews,

tribute to the various elements of integration (in objectives

however, also conﬁrmed that the collaborative planning pro-

and across spatial and temporal scales), but not from a holistic

cess based on the framework has been instrumental to deliver

process-oriented perspective as analysed in this research.

the IFRM plan. As one interviewee appositely stated: The

Some examples are given here. Research on climate change

deliberate use of results (of analysis) and visualisations (of

Table 1

Evaluation framework for the contribution of a collaborative process to an IFRM plan

Activities

Contribution to elements of integration: multiple objectives, spatial scales and time scales.

System analysis

Analyse the performance of the flooding system and proposed strategies at all spatial scale levels (e.g. dyke ring, individual buildings), on various objectives (e.g. flood risk, cost-benefit); and for various time scales (e.g. under various climate change scenarios or investment scenarios).

Planning, design and engineering

Explore entire scope of ﬂooding system for options from all spatial scales to reduce ﬂood risk on various time scales; combine options from various domains delivering on multiple objectives; with various investment planning horizons (time scales).

Governance

Involve stakeholders from all spatial scales in the ﬂooding system to support decision making with their interests (objectives) and their means to be applied or invested over various time scales.

S. van Herk et al.

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designs) supported stakeholder dialogue towards integrated

deﬁne or reframe and address problems. Planning, design

solutions. Initially, however, stakeholders started working

and engineering mainly aims to create images: frame reﬂec-

with a traditional approach. The ﬁrst results of system analysis

tion in which parties identify their view of reality and

presented the potential consequences of dyke breaches that

discuss it, look for images or meanings that they share, and

directed initial design work towards options to upgrade the

create renewed and more creative images as a result of the

dyke system. Interviewees stressed that this has not been

interaction; and to develop, analyse, discuss and propose sol-

because of the process design, but rather is a result of the fea-

utions and strategies. The governance activity involves

tures of the ﬂooding system that is technically locked-in to

participants in discussing and deﬁning their ambitions to

ﬂood protection measures. Later during the process, options

create socially construed knowledge and discussing the ambi-

from the second and third ﬂood safety layer have been

tions and roles of the stakeholders towards implementation.

incorporated in the IFRM plan.

Based on this research it can be concluded that the interaction and overlap between the activities relates to the exchange of

Enriched framework explaining interaction between

knowledge between them. The interaction between the activi-

activities

ties is a process of ‘convergence’ in the planning process where the options are reduced by feasibility checks (‘system

It has also been validated in this case study that the three

analysis’ checks on performance; ‘design’ on technical feasi-

activities are mutually dependent in developing an IFRM

bility; and ‘governance’ on political feasibility), rather than

plan. For example, insights that were gained through ‘system

the process of ‘divergence’ through the knowledge generation

analysis’ on the vulnerability of the Island of Dordrecht led

in each separate activity that explores the possible options.

to the reframing of the problem among stakeholders (‘govern-

Table 2 provides a generalised summary of the interaction

ance’) that the potential consequences, rather than the

between the activities through knowledge exchange.

probability of ﬂooding, need to be decreased. This provided a basis to develop new strategy in ‘planning, design and engin-

Enriched framework by including phasing

eering’, namely to become self-reliant, comprising measures such as an overtoppable dyke ring, compartments to

The analysis of the collaborative planning process shows a

manage overtopped ﬂood water, and shelters. The rationale

phasing of sub-activities that was not prescribed by the plan-

of van Herk et al. (a) behind the classiﬁcation of the activi-

ning framework. The observed phasing relates to the process

ties is related to the different knowledge that is generated and

of divergence and convergence explained by the interaction

its contribution to decision making in planning. System

between activities. Based on the above, the process frame-

analysis mainly aims to establish facts, coherent and not con-

work to design and manage collaborative processes, this

tradictory knowledge to reduce uncertainty, and to analyse,

can be further extended by introducing the interfaces

Table 2

Interaction between the activities through knowledge exchange

Activities

Interaction between activities through knowledge exchange.

System analysis

Contribute with new facts on the system performance and the effectiveness of the IFRM plan and its underlying strategy and constituent measures. These insights can change perceptions and support problem (re)framing and stimulate discussions on ambitions and the feasibility of various strategies and measures. The activity involves participants for analysis and interpretation.

Planning, design and engineering

Involve multiple stakeholders in planning, design and engineering to develop new solutions with their means and expertise, creating new perceptions on possible strategies and measures that stimulate discussion on their ambitions; and formulate research questions/information requirements to guide system analysis.

Governance

Involve all participants and discuss their ambitions. Explore solutions from the means and expertise of all stakeholders and discuss these based on their performance. Performance analysis is supported based on objectives and information provided by stakeholders.

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Journal of Water and Climate Change

05.1

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between activities and the phases. In Figure 3 the process is

the performance of the IFRM plan is to be analysed,

illustrated with circles representing the three activity types

which inherently is a feedback loop to the continuous

of the framework (system analysis, planning, design and

monitoring of system’s performance (phase 1) that will

engineering, and governance), and nine sub-activities that

change with the implementation of the IFRM plan due

have been conducted in three different phases, either

to exogenous factors (e.g. Milly et al. ). The set of

within or on the interface of the three activity types. In the

sub-activities in the ﬁrst phase can be conducted in paral-

ﬁrst phase (phase 1) of ‘divergence’, the (ﬂooding) system

lel, as they are independent. The sub-activities in phases 2

performance is analysed, e.g. in terms of the probability

and 3 have feedback loops, just as there can be feedback

and potential consequences of ﬂood events, stakeholders

loops and iterations between the phases. Hence, the

are brought together and set their objectives, and different

enriched framework, as presented in Figure 3, can pro-

strategies, options or measures to intervene in the ﬂooding

vide guidance for the design and coordination of the

system are explored.

collaborative planning process to develop an IFRM

Bringing the outcomes of these series of activities

plan, but is not a blueprint process design. Neither does

together on the interfaces between activity types leads to a

the framework guide further planning phases towards

new phase (phase 2) of problem (re)framing or joint goal-

implementation.

setting based on the discussion of the system performance and different objectives: to discuss strategies and options

Flaws in the framework: governance and

based on the combined means and objectives of all stake-

implementation

holders; to assess the performance of identiﬁed strategies and options, also using the objectives discussed.

The collaborative planning process in the case study also

Ultimately (phase 3) the options are to be combined

revealed some ﬂaws in the process and the framework. All

into an IFRM plan comprising a portfolio of measures

six participants interviewed for validation stressed the impor-

and an investment and implementation plan that is sub-

tance of the shadow network and the possibility to freely

ject to formal decision-making processes between the

discuss problems and solutions, despite their organisation’s

stakeholders and within the democratically representative

formal position or regulatory constraints. The learning

bodies of the governmental organisations involved. Also,

environment attracted a wide range of participants, but some

Figure 3

Adapted framework for collaborative planning processes to deliver IFRM plans focusing on interfaces between activities.

112

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Journal of Water and Climate Change

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key stakeholders were missing. Project managers of construc-

prescriptively (Koukoui et al. ) and descriptively (van

tion and maintenance projects and politicians overseeing

Herk et al. ) to two other case studies in the Nether-

those projects and their funding were only involved at

lands. Initial results show that the framework is simple to

the end of the process. As a result no formal agreements

apply as it provides the overarching structure of project

have been made to date and thus, the implementation of the

activities, but can comprise different work tasks. Further

IFRM plan is uncertain. Provan & Kenis () stressed the

validation and international comparison between case

potential tension in networks related to internal and external

studies are necessary and recommended. This is beyond

legitimacy. Whilst the shadow network was effective in devel-

the scope of the research presented in this paper, but

oping an IFRM, its implementation is likely to be hampered by

some points of attention are presented here.

a lack of external legitimacy in the legislative and executive

Transferring best practices to other countries is likely to

bodies of the government organisations involved. The

be a major challenge as each country has its unique geo-

implementation of the IFRM can also be hampered as current

graphical,

regulation and funding schemes do not incentivise the incor-

features requiring customized approaches (Huntjens et al.

cultural,

institutional,

and

socio-economic

poration of measures from the second and third ﬂood safety

; Zevenbergen et al. a, b). In this case study, the exist-

layers (Kolen et al. ). The challenges to the implementation

ing ﬂooding system that relies on ﬂood protection has been a

of the IFRM plan posed by both the existing regulatory frame-

cornerstone to the IFRM plan. The incumbent cultures and

work and the legitimacy tensions of the network point towards

approaches initially inﬂuenced the collaborative planning

possible further improvements of the process framework pre-

process. The process framework can accommodate a range

sented. Namely, the governance activity can comprise

of options and approaches, but it is unlikely that it alone sup-

different aspects (as adopted from Voß & Bornemann ):

ports the delivery of integrated outputs without an integrated

‘policy’ (discussing problems and solutions); ‘polity’ (rules

concept such as MLS. Hence, we hypothesise that an IFRM

and structure); and ‘politics’ (interaction and process). Using

process requires an integrated concept and/or multiple objec-

these deﬁnitions, the case study focused mostly on policy

tives. Furthermore, validation interviews indicated the

and much less on polity and politics. In other words, govern-

importance of the Learning and Action Alliance or a

ance activities are not only to discuss and deﬁne joint

shadow network to explore options and the potential of the

ambitions, but also to explore mandates and willingness to

MLS approach, unhampered by existing regulations and

commit or combine resources (e.g. funding). Future research

mandates. In many countries, incumbent institutional struc-

is needed on if and how to manage these three governance

tures, regulation and policy, cultures and approaches in

activities explicitly and to deﬁne output indicators for them.

planning and ﬂood risk management hamper IFRM and

Also, the collaborative planning framework does not

require a transition or regime change (e.g. van der Brugge

initiation,

et al. ; Newman et al. ). We hypothesise that the

design, construction, and operation and maintenance. The

organizational structures that govern the collaborative pro-

case study can be placed in the initiation or design phase

cess are to be ﬁt-for-purpose and ﬁt-for-context, recognizing

when policy options have been drafted. Barriers towards

the state and transition of the regime.

differentiate

between

development

phases:

implementation can be the consequence of not organizing for activities that involve stakeholders and objectives that seem more relevant in later development phases.

CONCLUSION

Transferability of framework and lessons

New ﬂood risk management policies account for climate and socio-economic change by embracing a more integrated

This case study research in itself comprises the validation

approach.

and enrichment of the framework that has been developed

collaboration between a group of stakeholders; combining

previously. Moreover, following the research presented in

objectives and funding from various policy domains; con-

this paper, the process framework is already being applied

sideration of a range of possible options at all spatial scale

Their

implementation

processes

require:

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Journal of Water and Climate Change

05.1

2014

levels and for various time horizons. Literature provides lim-

the reduction of the potential adverse consequences and

ited guidance on how to organise a collaborative planning

that embrace uncertainty regarding future scenarios.

process to devise IFRM plans. The collaboration process fra-

Hence, there is a growing need to share information and

mework presented in this paper has been found to be

best practices in the ﬁeld of IFRM across countries. This

instrumental in delivering an IFRM plan in the case study

also holds true for conceptual frameworks and approaches

Dordrecht. It comprises three types of activities: system analy-

that provide guidance on how to organize the process

sis, planning, design and engineering, and governance. Each

design and management to develop IFRM plans. The pro-

activity as proposed in the framework has contributed to inte-

cess framework presented in this paper will therefore have

gration by exploring possible options across spatial and

international relevance as it will help planners, engineers

temporal scales and various policy domains. They can be con-

and decision makers to better understand the requirements

ducted in parallel, but are mutually enriching through

to shape the underlying planning collaborative process. It

feedback loops and iterations. Knowledge exchange between

provides the overarching structure of and relation between

the activities comprised feasibility checks that furthered the

project activities that can comprise different work tasks. It

planning process into subsequent phases. The validation

presents how the activities can contribute to integration of

interviews indicated that the MLS approach itself, comprising

multiple objectives and across spatial and temporal scales.

ﬂood protection, spatial planning and emergency response,

Further research is recommended for further validation

was an important catalyst in the setting up of an innovative,

and international comparison between case studies.

integrative process to deliver an IFRM plan. The MLS approach implicitly broadened the objective – from flood probability to flood risk including reducing potential consequences of flood events – and explicitly spanned the design

ACKNOWLEDGEMENTS

freedom to develop alternative solutions with more integrative elements.

This paper has been written in the frameworks of the Interreg

The collaborative process delivered an IFRM plan, but

4b project MARE (MARE ) and of the ﬂood safety policy

its implementation is uncertain. Legislative and executive

pilot of the Dutch Delta Programme (DPRD ). The

stakeholders have not yet adopted the plan, because they

authors acknowledge the support of all MARE and Delta

were only involved at the end of the process. Moreover, cur-

Programme participants and those who were willing to

rent regulation and funding schemes do not incentivise the

share their knowledge on the Dordrecht MLS case study.

plan’s implementation. These challenges point towards possible further improvements of the process framework that has been developed and validated. We recommend further research into the management of governance activi-

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First received 17 May 2013; accepted in revised form 17 September 2013. Available online 22 November 2013

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Erratum: Journal of Water and Climate Change 4(4), 317–337: Evaluating the cumulative rainfall deviation approach for projecting groundwater levels under future climate, I. Emelyanova, R. Ali, W. Dawes, S. Varma, G. Hodgson and D. McFarlane

The publisher regrets that due to an error in production, the following sentence on page 321 was printed incorrectly: A The CDFMM t variable tends to have relatively low inter-annual ﬂuctuations while CDFMt has higher inter-annual ﬂuctuations due to the constant value A.

The sentence should have read: A The CDFMM t variable tends to have relatively low intra-annual ﬂuctuations while CDFMt has higher intra-annual ﬂuctuations due to the constant value of A.

The publisher apologises for any confusion caused by this error.

doi: 10.2166/wcc.2014.004