Evapotranspiration Estimation and Influence on Water Change in the Weihe River Basin, China

Remote Sensing Science November 2015, Volume 3, Issue 4, PP.42-52

Evapotranspiration Estimation and Influence on Water Change in the Weihe River Basin, China Ronghua Zhang1,2, Rui Sun2† 1

Forestry College of Shandong Agricultural University, Key Laboratory of Soil Erosion and Ecological Restoration, Taishan

Forest Ecosystem Research Station, Taian 271018, China 2

State Key Laboratory of Remote Sensing Science, School of Geography and Remote Sensing Science of Beijing Normal

University, Beijing Key Laboratory of Environmental Remote Sensing and City Digitalization, Beijing 100875, China †

E-mail: sunrui@bnu.edu.cn

Abstract As the largest sub-basin of the middle reaches of the Yellow River with an obvious decreasing trend in annual runoff in recent years, the Weihe River basin is a significant region with regard to the protection and improvement of the environment in West China. Evapotranspiration (ET) is the loss of water from the Earth’s surface to the atmosphere and plays an important role in the regional water cycle, especially when considering water resource shortages. In this study, through analyzing the grid precipitation data after interpolation from 39 meteorological stations in and around the Weihe River basin from 1981 to 2011, certain periods during 1987, 1993, 1999, 2001, 2002 and 2009 with similar precipitation characteristics had been chosen for estimating the ET in the Weihe River basin. To illustrate ET’s influence on the water budget, these estimations are calculated based on an improved Penman-Monteith equation as well as remote sensing data and meteorological data. The results show that: (1) the annual ET in the Weihe River basin ranged from 350mm to 400mm in 1987, 1993, 1999, 2001, 2002 and 2009, accounting for more than 70% of the corresponding annual precipitation. There is a definite increasing trend in different decades that is primarily distributed during the summer. (2) The spatial distribution patterns of the ET in the six years mentioned area unique set, and the years are roughly identical with more than 500mm in the middle and lower reaches of the Weihe River in the southeastern region and less than 400mm in upper reaches of the Jinghe River in the northwestern area. (3) At the single-point scale, the coefficient of determination (R2) is 0.618 compared to the eddy correlation measurements in 2009 at the Changwu site, showing good agreement between the estimated ET and the observed ET. At the basin scale, the model-estimated ET is slightly lower than the actual ET with regard to the surface water budget. Additionally, the estimated ET in 2001, 2002 and 2009 is close to the MODIS ET product. (4) For similar precipitation conditions, the regional amount of water shows a decreasing tendency with increasing ET, which may result from the rise in NDVI and improvements in vegetation coverage caused by human activities. This research suggests the influence of ET on water change at the basin level, which can also explain the decreasing runoff and can provide necessary information for improved water resource management.

Keywords: Evapotranspiration; Remote Sensing; Penman-Montieth; Water Change; the Weihe River Basin

1 INTRODUCTION The hydrological cycle of a river basin is a complex process influenced by climate, physical characteristics of the river basin, and human activities. And evapotranspiration (ET) is the most significant component of the hydrologic budget apart from precipitation, and it plays an important role in the hydrological cycle as well as the energy exchange between land surfaces and the atmosphere. ET is a term used to describe the loss of water from the Earth’s surface to the atmosphere by the combined processes of evaporation from open water bodies, bare soil and plant surfaces, etc., and transpiration from vegetation or any other moisture-containing living surface [1]. Estimating ET - 42 http://www.ivypub.org/RSS

accurately is important for many fields such as geography, meteorology and ecology [2], and is especially important for regional water resource management research and the development of water-saving agriculture with a worsening of the water shortage problems [3]. In the last few decades, studies on the theory and application of ET have received much attention.

Conventional techniques for estimating ET, which are mainly dependent on point meteorological or climatological measurement data, describe the mechanisms present at the local scale and can accurately reflect the physical mechanisms of ET. However, these techniques become unreliable on a larger scale and with heterogeneous land surfaces. Although the water balance method can estimate ET at the basin scale, it works for long-term estimates, i.e., yearly estimates, but cannot meet the requirements of short-term studies. Since the 1970s, the rapid development of multi-sensor, multi-temporal and multispectral remote sensing has produced a wide range of quantitative and qualitative surface information; it is now possible to develop models for estimating regional ET with efficient temporal coverage and large spatial coverage using satellite observations from the last two decades. These advances provide a new perspective for ET estimates over larger and heterogeneous areas. Many researchers have systematically summarized the advances in estimating regional ET [4-6].

In general, estimation models of the regional ET from remote sensing data can be classified into the following five categories: (a) Statistical models for estimating regional ET use an equation combining ET and surface parameters [7-8]. (b) Remote sensing models based on the traditional ET method with some surface parameters from remotesensing data allow the traditional single-point method to expand to the regional scale through the measurement of spatial patterns of the land surface properties. There has been much focus on this type of model in recent years.

Some examples of this are the Penman-Monteith approach [9], the Priestley-Taylor approach [10], the AdvectionAridity model [11], the complementary relationship areal evapotranspiration (CRAE) model [12] and the Granger model [13]. (c) The energy balance residual method calculates the net radiation, soil heat flux and sensible heat flux first and then obtains ET as the residual in the energy balance equation. This method type includes the single source model [14-15] and the double source model [16] in accordance with different land surface descriptions. (d) Vegetation indices estimated from remote sensing data, such as the normalized difference vegetation index (NDVI), are important tools for monitoring the fractional vegetation coverage and growth, and surface temperature (Ts), which are significant factors in describing the natural environment. The indices form a triangular or trapezoidal shape, and the dry edge must be determined to interpolate the value of the ET parameter [17-18]. (e) Finally, to determine the continuous changes in the water process of the soil-vegetation-atmosphere system, four dimensional data assimilation (4D) is combined with land-process models based on surface water and energy cycling processes [19-20]. In the above regional ET estimation methods, remote sensing models have a wide range of applications in recent years. Zhang et al. [21] have estimated canopy transpiration and soil evaporation using a modified PenmanMonteith approach and open water evaporation using a Priestley-Taylor approach in order to receive a relatively long term global ET record with well-quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long-term water cycle changes. However, it is unclear that how the application effects of the modified Penman-Monteith approach on the river basin scale.

There are lots of reported attempts to assess the impacts of climate change or human activities such as changes in vegetation cover on river basin scale hydrological cycle using different methods [22-27], but these studies have not considered the influence of ET. Meanwhile, regional impacts of climate change and human-induced factors on hydrology cycle vary from place to place and need to be investigated specifically. The studies by Gao et al. [28] and Liang et al. [29] on stream flow in different decades in the middle reaches of the Yellow River found that the annual runoff decreased significantly between 1950 and 2008; the annual runoff in the 1950s was the maximum runoff measured, and the runoff decreased gradually to less than half of the 1950s by the 2000s. The Weihe River is the largest tributary for the middle reaches of the Yellow River, and studies on the hydrologic variation of the Weihe River basin [30-31] play an important role in the protection and improvement of the environment in West China.

Therefore, it is important to determine how human activities, such as grain for green projects, soil and water conservation measures, and other ecological engineering affect ET and then runoff in Weihe River basin.

In this study, the modified Penman-Montieth equation with remote sensing data is used to estimate the monthly and - 43 http://www.ivypub.org/RSS

yearly ET in the Weihe River basin during periods of similar precipitation conditions, and the characteristics of the ET are analyzed and compared using measured fluxes and the MODIS ET product; the influence of ET on water change is discussed. Estimating the regional ET under similar precipitation conditions combined with years of remote sensing data is helpful to understand the reason for runoff reduction in the Weihe River basin and is also significant for integrated water resources management. The results will provide the foundation for improving the aquatic environment and managing the ecological environment.

2 MATERIALS AND METHODS 2.1 Study Area The Weihe River, which is 818km in the length and has a 2.2‰ gradient, is the largest and sandiest tributary to the Yellow River; its water and sediment come from different sources. The Weihe River basin (FIG. 1) is located in the northwest region of China between 33°40′-37°25′ N and 103°55′-110°20′ E and includes the Shanxi, Gansu and Ningxia provinces with a total area of 135000km2. The elevation decreases from west to east, with an obvious difference of more than 3000m in surface elevation. The basin is in the transition zone between an arid region and a humid region and is characterized by a continental monsoon climate with obvious vertical and horizontal climate zones. The average annual temperature decreases gradually from lowland to highland and from south to north, and it ranges from 5 to 14°C. Affected by geographic location and topography, the temporal and spatial precipitation distributions in the basin are limited and uneven with obvious seasonal and annual changes, and they show a decreasing trend from southeast to northwest. The average annual precipitation ranges from 450mm to 700mm, occurring mostly during the period from May to October. The annual potential evaporation is 700~1200mm, and the frost-free season is 120~240 days long. The surface runoff in the basin mainly comes from precipitation, and its distribution is consistent with precipitation: low in the Weihe River plain and northern Shanxi Loess Plateau, and high in the Qinling Mountains. The dominant soil type is loessial soil and the vegetation type can be characterized as warm-temperate deciduous broad-leaved forest. The ecosystem in the basin is fragile and vulnerable to natural disasters. The Weihe River basin is one of the more developed regions in history and is an important region for the production of food, cotton and oil. It is presently considered a strong industrial production base. Due to the rapid increase in water consumption on the national level, there is a lack of environmental consciousness with water consumption.

FIG.1 LOCATION AND DEM OF THE WEIHE RIVER BASIN

2.2 Data The main data sets in this study include remote sensing data, DEM (Digital Elevation Model), meteorological data, - 44 http://www.ivypub.org/RSS

hydrologic data and the latent heat fluxes measured by the eddy correlation technique. The remote sensing data contain the NDVI (Normalized Difference Vegetation Index), the Albedo, the Land Cover and the ET product and are mainly downloaded from public geographic information platforms such as MODIS (Moderate-resolution Imaging Spectroradiometer) with a spatial resolution of 1km (http://reverb.echo.nasa.gov/reverb/), GIMMS (Global Inventor Modeling and Mapping Studies) NDVI [32] and Pathfinder AVHRR (Advanced Very High Resolution Radiometers) with 8km spatial resolution [33]. To compare remote sensing data with different resolutions, a statistical method based on the MODIS data with relatively high resolution was adopted. This was used to establish the equation for the regional-scale relationship between the MODIS data and the GIMMS or AVHRR data in the same year to be used for the extrapolation from the GIMMS or AVHRR data to the MODIS data over time.

DEM data with 90m resolution are acquired from SRTM (Shuttle Radar Topography Mission) data, which is jointly measured by NASA (National Aeronautics and Space Administration) and NIMA (National Imagery and Mapping Agency) (http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp). The meteorological data, including temperature, precipitation, humidity and sunshine duration, from 39 meteorological stations in and around the Weihe River basin from 1981 to 2011 are derived from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/home.do). The runoff data are collected from hydrologic stations in the Weihe River basin.

The observed latent heat fluxes, derived from field measurements of the Coordinated Enhanced Observation Network, are measured by the eddy correlation technique in the underlying surface of wheat field farmland from 19, April to 30, September in 2009 at the Changwu site in the Weihe River basin.

2.3 Similar Precipitation Condition Selection

FIG.2 THE DISTRIBUTION MAPS IN SIMILAR PRECIPITATION CONDITIONS

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The precipitation data at the meteorological stations are interpolated to the grid format with 1km resolution using the Kriging method with the support of ArcGIS software. The variation characteristics of the precipitation in the Weihe River basin are analyzed according to precipitation statistics, such as the annual average precipitation, monthly average precipitation each year and the spatial distribution pattern in the basin. Years with similar precipitation conditions are selected for estimating monthly and yearly ET, and these include 1987 with 476.0mm, 1993 with 499.3mm, 1999 with 494.6mm, 2001 with 486.5mm, 2002 with 478.1mm and 2009 with 493.1mm (FIG. 2).

2.4 ET Estimation Based on Penman-Monteith In the ET algorithm, the surface energy at the Earth’s surface is governed by the surface energy balance equation as follows:

Rn=H+λE+G, where Rn (W·m ) is the net radiation flux, H (W·m-2) is the surface sensible heat flux, λE (W·m-2) is the surface latent heat flux (LE) and G (W·m-2) is the sum of the soil heat flux and heat storage in above-ground biomass (for vegetated areas) or in bodies of water. The equation to calculate Rn is given as: -2

Rn =Rns − Rnl =(1 − α ) Rs↓ − Rnl , -2

-2

where Rns (W·m ) and Rnl (W·m ) are the net shortwave radiation and outgoing net longwave radiation, respectively; Rs↓ (W·m-2) is the incoming shortwave radiation; α is the surface albedo.

According to Su et al. [34], the equation to calculate G is parameterized as follows for vegetated areas:

= G Rn [ Γ v + (Γ s − Γ v )(1 − f v )] , where Γv and Γs are the ratios of G to Rn for full vegetation canopy and bare soil, respectively, and fv is the vegetation coverage.

The Penman-Monteith equation [21] is used to calculate vegetation transpiration and soil evaporation and is given as:

λ ECanopy =

∆ACanopy + ρ C pVPDg a ∆ + γ (1 + g a g s )

λ ESoil = RH (VPD K )

,

∆ASoil + ρ C pVPDg a ∆ + γ × g a gtotc

,

where λECanopy and λESoil (W·m-2) are the latent heat flux of the canopy and soil, respectively; λ (J·kg-1) is the latent heat of vaporization; Δ (Pa·K-1) is the slope of the curve relating the saturated water-vapor pressure to the air temperature; ACanopy and ASoil (W·m-2) are the available energy components for the canopy and soil, respectively; ρ (kg·m-3) is the air density; Cp (J·kg-1·K-1) is the specific heat capacity of air; VPD (Pa) is the vapor-pressure deficit; ga (m·s-1) is the aerodynamic conductance; γ (Pa·K-1) is the psychrometric constant; gs (m·s-1) in the original Penman-Monteith equation is the surface conductance, which is identical to the canopy conductance; RH is the relative humidity of air with values between 0 and 1; RH(VPD/K) is a moisture constraint on soil evaporation [35]; gtotc (m·s-1) is the corrected value of the total aerodynamic conductance.

The Priestley-Taylor equation [10] is used to calculate water evaporation given by:

λ EWater = α

∆A , ∆+γ

where α is a coefficient set to 1.26.

3 RESULTS AND DISCUSSION 3.1 ET Estimation The annual ET in the Weihe River basin during periods of similar precipitation lies between 350mm and 400mm in - 46 http://www.ivypub.org/RSS

ET(mm)

1987, 1993, 1999, 2001, 2002 and 2009, as shown in FIG. 3, and the difference between the maximum and the minimum is 46.1mm and 12.4% compared with the annual average. The annual ET accounts for more than 70% of the corresponding annual precipitation, which is consistent with research from L'Vovich and White [36], indicating that ET limits the regional availability of surface water resources. For the past three decades, the annual ET was 362.9mm in the 1980s, 368.4mm in the 1990s and 378.7mm in the 2000s, showing an obvious increasing trend. This trend can be explained by the rise in the NDVI in recent years caused by increases in vegetation coverage due to human activities such as projects returning farmland to forest and grassland, comprehensive management measures for reducing soil erosion and sediment, and other ecological rehabilitation projects aimed at improving the environment.

410 400 390 380 370 360 350 340 330 320 1987

1993

1999 2001 Year

2002

2009

FIG.3 ANNUAL ET IN SIMILAR PRECIPITATION CONDITIONS

In FIG. 4, the monthly ET values for the six years used for this study show that the yearly ET is mainly distributed between April and October; the maximum appears in July or August, and the minimum appears in December or January each year. Seasonally, the maximum ET occurs in summer, accounting for over 35% of the yearly ET, which is almost three times as much as that for winter.

1987

60

1993

ET(mm)

50

1999

40

2001

30

2002 2009

20 10 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month

FIG.4 MONTHLY ET IN SIMILAR PRECIPITATION CONDITIONS

Furthermore, the estimation results demonstrate that the ET spatial distribution differs; however, the same spatial distribution appears in each of the six years of this study. As FIG. 5 shows, the highest ET value (>500mm) is located in the middle and lower reaches of the Weihe River in the southeastern region, and the lowest ET value (<400mm) is located in the upper reaches of the Jinghe River in the northwestern area. Furthermore, there is a distinct variation in the northern slope of the Qinling Mountains; that is, the ET value is higher in 1987, 1993, 1999 and lower in 2000, 2001 and 2009. This difference may result from the applied MODIS Land Cover product; after 2000 the MODIS Land Cover product included more subdivisions and refinement for the forest land classification than before 2000.

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FIG.5 SPATIAL DISTRIBUTION OF ET IN SIMILAR PRECIPITATION CONDITIONS

3.2 Validation To validate the ET algorithm, we compare the estimated results in 2009 with the observed ET by the eddy correlation measurements at the Changwu site from 19, April to 30, September in 2009, and the results (FIG. 6) show good agreement between the satellite and field measurements with a coefficient of determination (R2) of 0.618 for the estimated ET and observed ET.

Observed ET(mm)

5 y = 0.8818x + 0.6971 R² = 0.6187

4 3 2 1 0 0

1

2 3 Estimated ET(mm)

4

5

FIG.6 COMPARISON OF ESTIMATED ET WITH OBSERVED ET BY CHANGWU SITE

Due to the limited availability of observed data at the Changwu site, the estimation results are compared to the - 48 http://www.ivypub.org/RSS

calculated ET based on the principle of surface water balance to validate the estimated results (FIG. 7). The results from the model-estimated ET are slightly lower than the ET calculated by the water balance at the basin scale.

Meanwhile, the observed runoff is relatively lower than the natural runoff data in the water balance equation, resulting in an overestimation of the ET. precipitation water balance ET estimated ET

600

P/ET(mm)

500 400 300 200 100 0 1987

1993

1999 2001 Year

2002

2009

FIG.7 COMPARISON OF ESTIMATED ET WITH WATER BALANCE ET

P-M ET(mm)

Additionally, the estimated ET values in 2001, 2002 and 2009 are compared with the MODIS product (MOD16A2).

As shown in FIG. 8, the monthly Penman-Monteith ET is in accordance with the MODIS ET and more consistent with the 1:1 line. The R2 of the two estimation results is 0.664, showing good correlation.

4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0

y = 1.4967x - 1.2818 R² = 0.6641

0.0

0.5

1.0

1.5

2.0 2.5 3.0 MODIS ET(mm)

3.5

4.0

4.5

FIG.8 COMPARISON OF ESTIMATED ET WITH MODIS PRODUCT

Annual runoff(0.1billion

m3)

3.3 Influence on Water Change 80 70 60 50 40 30 20 10 0 340

y = -0.6091x + 274.3 R² = 0.4916

360

380 400 Estimated ET(mm)

420

FIG.9 RELATIONSHIP BETWEEN ET AND RUNOFF IN SIMILAR PRECIPITATION CONDITIONS - 49 http://www.ivypub.org/RSS

The relationship between the estimated ET and the annual runoff clearly shows an inverse relationship in FIG. 9.

Therefore, under similar precipitation conditions and with an increase in the regional ET, the water amount notably decreases.

It is clear that a decrease in precipitation is the primary cause for the reduction in the water amount; however, when the rainfall factor is eliminated in this study, ET is the main influencing factor, indicating that it is an important component of the terrestrial water cycle. With a rise in the NDVI and increases in vegetation coverage due to human factors in recent years, regional ET increases and causes a noticeable decrease in the water amount.

4 CONCLUSIONS With the combined remote sensing data and meteorological data, the annual and monthly ET in the Weihe River basin under similar precipitation conditions between 1981 and 2011 are estimated using an improved PenmanMonteith equation, and the relationship between ET and changes in the water amount is analyzed. The results show that: (1) The annual ET ranged from 350mm to 400mm for similar precipitation conditions in 1987, 1993, 1999, 2001, 2002 and 2009 and accounted for more than 70% of the corresponding annual precipitation, showing a certain increasing trend on the decadal scale. ET is mainly distributed between June and October each year, and summer ET values nearly three times greater than winter values. The ET spatial distribution over the six years used in this study is relatively consistent and clear with more than 500mm in the middle and lower reaches of the Weihe River in the southeastern region and less than 400mm in the upper reaches of the Jinghe River in the northwestern area.

(2) The results have been validated by comparing the estimated ET with the observed data at the Changwu site in 2009, which are in good agreement. On the basin scale, the model-estimated ET is slightly lower than the calculated ET in the view of the principle of surface water balance. The estimated ET in 2001, 2002 and 2009 is consistent with the MODIS ET product.

(3) In similar precipitation conditions, with a rise in the NDVI and the improvements of vegetation coverage due to human factors, regional ET increases, leading to a noticeable reduction in the water amount.

The preliminary results indicate that the study on ET in the Weihe River basin can adequately address the decrease in runoff and meet the basic requirements of water resource management at the basin scale. However, the key parameters such as net radiation and surface conductance in the process of estimating ET need to be further optimized to improve the accuracy in the next study. Besides, when estimating ET using remote sensing data with different spatial resolutions, it is important to develop an improved and more accurate scaling protocol in the future.

ACKNOWLEDGMENTS The research was funded by the National Natural Science Foundation of China (41471349) and the Fundamental Research Funds for the Central Universities (2014kJJCA02). The authors wish to thank the field measurements of the Coordinated Enhanced Observation Network which provides the observed latent heat fluxes in 2009 at Changwu site.

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AUTHORS 1

2

Han nationality, doctor’s degree, lecturer,

professor, major in GIS and RS, 07/1998-, Beijing Normal

major in GIS and RS, 09/2010-06/2013,

University, Beijing. Email: sunrui@bnu.edu.cn

Ronghua Zhang (1984-), female, the

Beijing 07/2013,

Normal

University,

Shandong

Rui Sun (1970-), male, the Han nationality, doctor’s degree,

Beijing,

Agricultural

University, Tai’an.

Email: zrhua5766@163.com

- 52 http://www.ivypub.org/RSS