15h00 axel

I Simpósio da Bacia Hidrográfica do rio São Francisco, 5/9 Junho, PETROLINA (PE) e JUAZEIRO (BA), Brazil

Estudo dos Processos de Erosão e Transporte Sedimentos em Regiões SemiÁridas *

Studies of Erosion and Sediment Transport Processes in Semi-Arid Regions 1 & José C. de Araújo 2 Axel Bronstert Ramon J. Batalla3, Alexandre C. Costa 6, Till Francke1, Andreas Güntner4, Jose Lopez-Tarazon3, George L. Mamede6, Eva Paton1

1 University of Potsdam, Germany

2

Univ. Federal do Ceará, Fortaleza, Brazil

3 Univ. of

Lleida, Spain

4

Deutsches Geo ForschungsZentrum Potsdam, Germany

5

Forestry Technology Center of Catalonia

6

University of Intern.

Integration of the Afro-Brazilian Lusophony, Fortaleza, 1 Brazil

Assessment and modelling of erosion and sediment transport in dryland river basins: from the Hillslopes via the River System to Reservoirs

Outline Hydrological Peculiarities of drylands II. An integrated hydro-sedimentological model III. Results from Research Catchments IV. Applications examples to Large River Basins V. Conclusions and outlook I.

2

I Hydrological Peculiarities of drylands Episodic runoff processes trigger rather sudden sediment mobilisation and deposition

Peculiarities of drylands …

Soil losses and sediment deposition is the major threat for sustainable landscape and water resources functions

Sedimentation in the Barasona reservoir, Isábena/Èsera River [Bronstert, 200

Peculiarities of drylands ‌ water & sediment events: highly variable in time

and space

5

II An integrated hydrosedimentological model: WASASED WASA-SED: A meso-scale hydro-sedimentological model: Ø Catchment

River System

Reservoirs

Ø spatially distributed and catena-based, Ø process-oriented, n Spatial resolution:

hillslope to meso-scale

n Temporal resolution:

hourly or daily time steps

n Model Availability: Code freely available http://uni-

potsdam.de/sesam/reports.php#software Manual http://unipotsdam.de/sesam/download/wasa/WASA_SED_Manual.pdf

Code in Fortran90, currently ca. 50 sub-routines

6

Catchment Processes: multi-scale structure of modelling units

Landscape Units (LU)

Terrain components (TC)

Profile

WASA-Sed: Hydrological model approaches at the Hillslope Scale Process Interception

Evapotranspiration Infiltration unsaturated soil water flow Lateral surface/ subsurface flow Groundwater percolation

Spatial scale/ model unit

Approach / equation

Reference

Plot scale / soil- vegetation comp.

Plot scale / soil- vegetation comp.

Plot scale / soil profile Plot scale / soil profile

overflow model

e.g. Güntner (2002)

Extended (2 layer) Penman-Monteith

Shuttleworth and Wallace (1985)

Modified GreenAmpt approach Unsat. Darcy equation

Peschke (1987)

Hillslope scale / terrain comp.

empirical distribution

Güntner (2002)

e.g. Bronstert and Plate (1997)

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Plot scale / vertical Darcy soil- vegetation equation

e.g. Güntner (2002)

WASA-Sed: Process Representation at the channel scale and in the reservoir

A : C o n n e c t iv it y b e t w e e n h ills lo p e s , v a lle y b o t t o m s a n d t h e r iv e r

B : T r a n s m is s io n lo s s e s a n d t e m p o r a r y s t o r a g e in t h e r iv e r

C : R e t e n t io n in a n d t r a n s f e r t h r o u g h r e s e r v o ir s

I n t e g r a t e d m e s o - s c a le c a t c h m e n t m o d e l

12

WASA-Sed: Model approaches for the river system and the reservoirs

Process

River discharge Water balance of small reservoirs Water balance of large reservoirs

Spatial Approach / scale/ model equation unit River system Kinematic wave / extended Muskingum Generalized Aggregated water reservoir balance for classes reservoir classes Reservoir

Full water balance for each individual reservoir, incl. operation rules

Reference

e.g. Chow et al.

(1988) GĂźntner et al.

(2004)

GĂźntner et al.

(2004)

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Sedimentological model approaches and spatial scales in the WASA-SED model

Process Surface soil erosion

Spatial scale/ model unit Hillslope scale (catena) / LU

Soil deposition

Field scale / TC

Suspended sediment transport (incl. deposition or degradation) in the river Bedload transport in the river

River reach

River reach

5 transport formulae (for optional use)

Sediment transport in small reservoirs Sediment transport in large reservoirs

Generalized reservoir classes

Aggregated sediment budget for reservoir classes Full sediment budget for each individual reservoir, incl. sediment

Reservoir

Approach / equation

Reference

USLE + 3 modifications: MUSLE, Onstad-Foster, MUST (for optional use) sediment transport capacity limitation Suspended sediment transport capacity assessment

Wischmeier and Smith (1978), Williams (1995)

Meyer-Peter and MĂźller (1948); Schoklitsch (1950); Bagnold (1956); Smart and Jaeggi (1983); Mamede (2008); Mamede et al. (2014)

Everaert (1991) e.g. Arnold (1995) / Neitsch et al. (2002)

Mamede (2008) 14

Hillslope erosion approaches in the WASA-SED model c = EI

(USLE)

c = 0.646 EI + 0.45 (Qsurf qp)0.33 E = c K LS C P ROKF A Â

(Onstad-Foster)

c = 1.586 (Qsurf qp)0.56 A0.12

(MUSLE)

c = 2.5 (Qsurf qp)0.5 (MUST) EI rainfall energy factor (MJ.mm.ha-1.hr-1)

E (gross) erosion (t) K soil erodibility factor (t.ha.hr.ha-1.MJ-1.mm-1) Qsurf LS length-slope factor (-) C vegetation and crop qp management factor (-) P erosion control factor (-) ROKF coarse fragment factor (-) A area of the scope (ha)

surface runoff volume (mm) peak runoff rate (mm/h) (after Williams, 1995)

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Erosion and sediment transport in the river Erosion/ depositio n

RSEDdep = (Cs,max – Cactual ) · V

Transport capacity

C s ,max

5 different transport formulae

Susp. sedi ment

Bedload

RSEDero = (Cs,max–C actual)·V·K·C

a v peak

b

RSEDdep amount of sediment deposited (t) RSEDero amount of sediment in the reach segment (t) V water in the reach (m3) K channel erodibility (cm/hr/Pa) C channel cover factor (-) vpeak peak channel velocity (m/s) Cs,max maximum sediment conc. for river stretch (t/m3) A, b user-definrd coefficients Meyer-Peter and Müller 1948, Schoklitsch 1950, Bagnold 1956, Smart and Jaeggi 1983, Rickenmann 2001 16

Reservoir Processes: three conecptual sediment layers Stage (m)

350

Top Layer Deposition

345

Original bed material 50

100

Erosion Intermediate Layer Lowest layer

Compaction 150

200

250

300

350

Width (m)

Sediment laden flow

Sediment laden flow

Top Layer

(from cross section 6)

(to cross section 8)

Intermediate Layer

Deposition

Erosion

Lowest Layer Compaction

6

7

Original Bed Material

8

17

Reservoir Module: Spatial representation of the reservoir

River subreach

Reservoir subreach

Stage (m)

Inflow

1 2 3

4 5

6 7

8

460 455 450 Outflow 445 440 435 430

9 10 11 12 13 14

Distance (km) 14 13 12 11 10

9

8

7

6

5

4

3

2

1

0

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Connectivity: the special challenge of dryland regions Connectivity: transfer, storage and re/ entrainment processes of water and sediments among different landscape components A: Connectivity between hillslopes, valley bottoms and the river

B: Transmission losses and temporary storage in the river

C: Retention in and transfer through reservoirs

D: Integrated meso-scale catchment model 19

III

Results from Research Catchments

Ø Northeast Brazil (Ceará): semi-arid climate with

a pronounced seasonality Ø Northeast Spain (Catalonia and Aragón): subhumid or semi-arid climate These research regions include of a set of individual (but nested) catchments of different spatial scales

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Uppe Yaguaribe (NE Brazil) Vรกrzea do Boi

Bengue hillslopes

Aiuaba

exp.

Upper Jaguaribe

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Hydrologic and sediment measurements in the Upper Jaguaribe Basin, Brazil

No.

Name

Area (km²)

Annual rainfall (mm)

B1

exp. hillslopes (part of Aiuaba, in Benguê)

2.8

~600

B2

Aiuaba experimetal catchment

12

560

B3

Benguê

933

560

B4

Várzea do Boi

1,221

~500

B5

B6

Jaguaribe, upstream Orós reservoir Jaguaribe, downstream Orós reservoir

20,700

24,600

Main Main discharge sediment measuremen measuremen ts ts (outflux) ---

sed. deposition

by Cs137

Discharge flume and reservoir level Reservoir level sampling Reservoir level sensor

reservoir bathymetry reservoir bathymetry

River water level sensor

manual sampling

400 to 800 Reservoir level sensor

manual sampling

manual sampling

22

Results: Upper Jaguaribe Basin, Brazil: Sediment budgets at the nested catchments

23

ร sera (NE Spain, Ebro region) Isรกbena

Villacarli

Ball

exp. badlands

Villacarli

24

Hydrologic and sediment measurements in the Ésera Basin, Spain

No.

Name

Area (km²)

Annual rainfall (mm)

S1

Badland

~ 0.03

~750

S2

Ball

10

~750

S3

Villacarli

41

730

445

450-1,600

1,380

450-2,000 in the high mountains

S4

S5

Isábena (gauge Capella) Ésera (upstream Barasona reservoir

Main discharge sediment measuremen measureme ts nts (outflux) Discharge Tubidimeter; flume Isco sampling River water Tubidimeter; level sensor Isco sampling River water Tubidimeter; level sensor Isco sampling River water Tubidimeter; level sensor Isco sampling Water level sensor

Reservoir bathymetry

25

Results NE Spain: Assessment of water flows and sediment yields

sediment export occurs mainly during -late summer floods very high specific sediment for badland areas (Villacarli) gauge

SSY [t/km2]

Torrelaribera

6277

Villacarli

1971

Cabecera

139

Capella

409

highly episodic sediment fluxes from headwaters are strongly attenuated towards outlet

26

Results NE-Spain: Modeled Water hydrological fluxes at the Villacarli, Catchment

27 WASA-SED results for Villacarli catchment (Francke, 2009)

Results NE Spain: Modeled sediment yields

Observed and simulated sediment fluxes of the Villacarli sub-catchment Sept – Dec 2006 (Franke, 2009) 28

Results NE-Spain: Estimated Sediment transfer and deposition in the Isábena, Catchment,

Estimates of sediment transfer and deposition rates in the 29 Isábena catchment for the period 2007 – 2009 (Bronstert et al.

(2014)

RIVER Modelling

Temporary river storage of sediments

30

Results: Modelling of water and sediment fluxes in the river Sediment Transport in the River System Upstream River Stretch in the Mountains

30 km Downstream Reach in the Lowlands

12000

60

50

10000

50

10000

40

8000

40

8000

30

6000

30

6000

20

4000

20

4000

10

2000

10

2000

60

0 7000

7500

Time [h]

8000

0 8500

0 7000

7500

Time [h]

8000

12000

0 8500

31

Reservoir Sedimentation: Observations Barasona Reservoir (Spain) (ca. 1340 km2)

32

Reservoir Sedimentation: Observations

95 3

) 90 85 80 75 Storage capacity (Mm 70 1920

1940

1960

1980

2000

2020

Time (years)

comparison of bathymetric surveys of the Barasona Reservoir, Spain

RESERVOIR Monitoring & Modelling

Cross Section 40 470 a

Cross Section 50 470

initial observed modelled

460 450 440

Elevation (m)

430 100

300

500 x (m)

700

900

450 440 430 Elevation (m) 420 410 600

470 initial observed modelled

a 460

440 430 Elevation (m) 420 200

500

800 x (m)

1400

1800

Cross Section 59

470 450

1000 x (m)

Cross Section 45 a 460

initial observed modelled

a 460

1100

1400

450 440 430 Elevation (m) 420 410 250 300

initial observed modelled

350 400 x (m)

450

500

Bed elevation changes at four different cross sections of the Barasona reservoir, 1986-1993

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RESERVOIR Monitoring & Modelling

450.00 445.00 440.00 435.00 430.00 1986 1993 1993 1993 1993

425.00

m eas ured m eas ured calc. W u et al calc. T s inghua Equat ion calc. A s hida and M ichiue

Elevation (m)

420.00 415.00 410.00

12000

10000

8000

6000

4000

2000

0

D is t ance from t he dam (m )

Deposition patterns of the Barasona Reservoir (1986-1993)

35

RESERVOIR Monitoring & Modelling: Storage volume changes by deposition and erosion

1st Simulation Period (1986-1993): no management

36

RESERVOIR Monitoring & Modelling: Storage volume changes by deposition and erosion

2nd Simulation Period (1995 -1997): flushing

37

IV

Applications examples to Large River Basins

ØUpper Jaguaribe Basin, Brazil Ø Upper Sao Francisco River Basin,

Brazil ØMahanadi River Basin, India

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Large-scale process-based hydrol. modelling in the Upper Sao Francisco River Basin n Study area: Alto Sao Francisco, ate Tres Marias n Description: Area de drenagem do Alto rio Sao

Francisco ate a barragem de Tres Marias n Applied hydro-sedimentological model: WASASED n Data: EMPRAPA, USGS, EU ‌

Sao Francisco River Basin

Division in sub-catchments

40

Data: SRTM, 2000 (USGS)

Data: EC, 2002

Vegetation Parameter derived from these data o Stomata_Resistance [s/m] o min_suction [hPa] o max_suction [hPa] o Height [m]* o Root-depth [m]* o LAI [-]* o Albedo [-]*

*seasonal

Data: EMPRAPA, 1999

Soil Parameter derived from these data

o Soil particle fraction* o Alluvium o Gravel o Organic Carbon

*for each soil horizon

Ways ahead‌ Seamless Prediction of hydrological extremes

Sources of uncertainty and concept of uncertainty analysis

Seamless Prediction Prediction of of hydrological hydrological extremes: extremes Seamless Example: Mahanadi Basin, India

Mahanadi Basin: •Area: 140 000 km •Monsoon climate: wet summers, dry winters •Annual rainfall: about 1500 mm •55% agricultural land (rice)

Mahanadi river at Mundali, June 2015

Seamless Prediction of hydrological extremes: Meteorological Forcing: 15 members of the multi-model seasonal forecasting system from the European Centre of Medium-Range Weather forecast To get regionalised meteorological forecasts the method of expanded downscaling is applied (BĂźrger et al., 2009) Era-Interim precipitatio n (black line) and ensemble simulations (gray shadow)

Seamless Prediction of hydrological extremes: Hydrological Modelling

Precipitation (black), observed discharge (blue dots) and simulated discharge (red) for Basantpur gauging station and Hirakud inflow

Station

Cal

Val

Basant pur

0.8 4

0.7 6

Hiraku d

0.8 9

0.8 6

NSE for calibration (Cal) and Validation (Val) period

Seamless Prediction of hydrological extremes Seamless Prediction of hydrological extremes: Work in progress Estimation of predictive uncertainty → Hydrological uncertainty processor (Krzysztofowicz, 1999) → Quantile regression (Weerts et al., 2011) Preliminary results for predictive uncertainty using quantile regression:

Residuals vs. simulated discharge with quantile regression (95%) in real space

Observed inflow (blue dots), simulated inflow (gray line) and 95% confidence interval (red shadow) for Hirakud reservoir

V Conclusions Ø

High relevance of “hot-spots” for sediment production

Ø

connectivity between the landscape compartments plays a very relevant role for both water and sediment transport

Ø

Varying relevance in different space-time scales !

Ø

Relative sediment retention in reservoirs is strongly dependent on the scale Reservoir sedimentation stronger affected by land use and water management than by climatic scenarios Integrated hydro-sedimentological modelling is essential to sustainable manage land and water resources in drylands New opportunities: drought forecasting; seamless

Ø

Ø

Ø

51

V Outlook Ø BHSF: important and interesting Ø Water management is highly important Ø Model system WASA-Sed: o mulit-scale and process based o tailored for semi-arid conditions o water and sediment fluxes o freely available Ø Currently in Brazil under application for: o Upper Sao Francisco Basin (Trés Marias) o Seasonal drought forecasting, incl. water management o climate change impacts for water and sediment management Ø Further ideas for application are very welcome

52

Studies of erosion and sediment transport in semi-arid Regions

Muito obrigado para atenção Projects funded by: DFG, BMBF (D) CAPES, CNPq (Br)

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