Estimation of evapotranspiration and its relationship with environmental factors in Jinghe River Basin

Abstract. The Jinghe River Basin is an important grain-producing area in northwest China; yet, serious soil erosion and water shortage problems have greatly limited the development of agriculture in the region. Evapotranspiration (ET) is a key component of rational water resources planning and water cycle mechanism exploration; yet, studies in this region have mainly focused on rainfall and runoff sand transport, neglecting the role of ET. Therefore, to understand the characteristics of ET in the Jinghe River basin, we estimated the ET during the vegetation growing season (April to October) in 2018 using the surface energy balance system (SEBS) based on remote sensing images and meteorological data, and analyzed the characteristics of its spatio-temporal distribution and its relationship with environmental factors. The results showed that (1) SEBS has good applicability in the study area, with R1 between 0.57 and 0.77, RMSE between 0.98 and 1.15  mm  /  d, and MRE between 24% and 36% as verified with the lysimeter and ET products; (2) on the basin scale, the average daily ET showed a unimodal distribution with time, with variations ranging from 1.6 to 4.1 mm while spatially it showed that the mountainous forest and river catchment areas were overall higher than the loess plateau and hilly gully areas; (3) on the landscape scale, the vegetation with the highest average daily ET was broad-leaved forest, and the lowest was grassland. The vegetation with the highest ecological water demand was cultivated vegetation, and the lowest was meadow; (4) correlation analysis showed that rainfall, temperature, sunshine hours, and vegetation cover were positively correlated with ET. Among them, rainfall and temperature have the strongest correlation with ET and maybe the main factors affecting ET in the Jinghe River Basin.

[1]  Yuei-An Liou,et al.  Evapotranspiration Estimation with Remote Sensing and Various Surface Energy Balance Algorithms—A Review , 2014 .

[2]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[3]  Zhang Xue-li,et al.  Vegetation Coverage Distribution and Its Changes in Plan Blue Banner Based on Remote Sensing Data and Dimidiate Pixel Model , 2012 .

[4]  Bo-Hui Tang,et al.  Evaluating the SEBS‐estimated evaporative fraction from MODIS data for a complex underlying surface , 2012 .

[5]  S. Running,et al.  A review of remote sensing based actual evapotranspiration estimation , 2016 .

[6]  Chong-yu Xu,et al.  Reference evapotranspiration changes in China: natural processes or human influences? , 2011 .

[7]  Matthew F. McCabe,et al.  Modeling Evapotranspiration during SMACEX: Comparing Two Approaches for Local- and Regional-Scale Prediction , 2005 .

[8]  Minha Choi,et al.  Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach , 2018 .

[9]  Zhou Wei-feng,et al.  Estimation of Vegetation Fraction in the Upper Basin of Miyun Reservoir by Remote Sensing , 2004 .

[10]  Z. Su The Surface Energy Balance System ( SEBS ) for estimation of turbulent heat fluxes , 2002 .

[11]  Ioannis Manakos,et al.  Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta , 2011 .

[12]  José A. Sobrino,et al.  Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Wei Xia,et al.  Distribution of Actual Evapotranspiration over Qaidam Basin, an Arid Area in China , 2013, Remote. Sens..

[14]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[15]  K. Oleson,et al.  Modeling stomatal conductance in the earth system: linking leaf water-use efficiency and water transport along the soil–plant–atmosphere continuum , 2014 .

[16]  Zhang Ru A Study of the Validation Method of Remotely Sensed Evapotranspiration based on Observation Data , 2010 .

[17]  Hui Liu,et al.  Comparison of the Vegetation Effect on ET Partitioning Based on Eddy Covariance Method at Five Different Sites of Northern China , 2018, Remote. Sens..

[18]  Gao Yan-chun Progress in Models for Evapotranspiration Estimation Using Remotely Sensed Data , 2008 .

[19]  Valeriy Kovalskyy,et al.  Evapotranspiration Variability and Its Association with Vegetation Dynamics in the Nile Basin, 2002-2011 , 2014, Remote. Sens..

[20]  Prasanna H. Gowda,et al.  Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach , 2013 .

[21]  G. Senay,et al.  A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: Using point and gridded FLUXNET and water balance ET , 2013 .

[22]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[23]  P. Gowda,et al.  Performance of five surface energy balance models for estimating daily evapotranspiration in high biomass sorghum , 2017 .

[24]  R. Dickinson,et al.  A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic variability , 2011 .

[25]  D. She,et al.  Climate explanation of the potential evapotranspiration changes in Weihe River Basin , 2020, 资源科学.

[26]  T. McVicar,et al.  Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 , 2019, Remote Sensing of Environment.

[27]  X. Calbet,et al.  Validation practices for satellite‐based Earth observation data across communities , 2017 .

[28]  Gabriel B. Senay,et al.  Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model , 2011 .

[29]  Chunlin Huang,et al.  Improving Estimation of Evapotranspiration under Water-Limited Conditions Based on SEBS and MODIS Data in Arid Regions , 2015, Remote. Sens..

[30]  Zoltán Vekerdy,et al.  Surface Energy Balance of Fresh and Saline Waters: AquaSEBS , 2016, Remote. Sens..

[31]  A. Fares,et al.  Estimating reference crop evapotranspiration under limited climate data in West Texas , 2020, Journal of Hydrology: Regional Studies.

[32]  Ran Da-chuan Effect of soil-retaining dams on flood and sediment reduction in middle reaches of Yellow River , 2004 .

[33]  Zheng Hong-xing,et al.  On concepts of ecological water demand , 2004 .

[34]  W. Brutsaert,et al.  On the Use of the Term “Evapotranspiration” , 2020, Water resources research.

[35]  Wei Deng,et al.  A Quantitative Inspection on Spatio-Temporal Variation of Remote Sensing-Based Estimates of Land Surface Evapotranspiration in South Asia , 2015, Remote. Sens..

[36]  Xiaomang Liu,et al.  Assessing the Impacts of Vegetation Greenness Change on Evapotranspiration and Water Yield in China , 2020, Water Resources Research.

[37]  Yongqiang Zhang,et al.  Verification and comparison of three high-resolution surface evapotranspiration products in North China , 2020, 资源科学.

[38]  Alfred Stein,et al.  Validation of ETWatch using field measurements at diverse landscapes: A case study in Hai Basin of China , 2012 .