Comparison of Four Different Energy Balance Models for Estimating Evapotranspiration in the Midwestern United States

The development of different energy balance models has allowed users to choose a model based on its suitability in a region. We compared four commonly used models—Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) model, Surface Energy Balance Algorithm for Land (SEBAL) model, Surface Energy Balance System (SEBS) model, and the Operational Simplified Surface Energy Balance (SSEBop) model—using Landsat images to estimate evapotranspiration (ET) in the Midwestern United States. Our models validation using three AmeriFlux cropland sites at Mead, Nebraska, showed that all four models captured the spatial and temporal variation of ET reasonably well with an R2 of more than 0.81. Both the METRIC and SSEBop models showed a low root mean square error ( 0.80), whereas the SEBAL and SEBS models resulted in relatively higher bias for estimating daily ET. The empirical equation of daily average net radiation used in the SEBAL and SEBS models for upscaling instantaneous ET to daily ET resulted in underestimation of daily ET, particularly when the daily average net radiation was more than 100 W·m−2. Estimated daily ET for both cropland and grassland had some degree of linearity with METRIC, SEBAL, and SEBS, but linearity was stronger for evaporative fraction. Thus, these ET models have strengths and limitations for applications in water resource management.

[1]  Huayi Wu,et al.  Improving temporal extrapolation for daily evapotranspiration using radiation measurements , 2013 .

[2]  James P. Verdin,et al.  Actual Evapotranspiration (Water Use) Assessment of the Colorado River Basin at the Landsat Resolution Using the Operational Simplified Surface Energy Balance Model , 2013, Remote. Sens..

[3]  J. A. Tolk,et al.  ET mapping for agricultural water management: present status and challenges , 2008, Irrigation Science.

[4]  Aliasghar Montazar,et al.  Advances in ET-based landscape irrigation management , 2015 .

[5]  W. Oechel,et al.  Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants , 2015 .

[6]  Dennis D. Baldocchi,et al.  A comparison of methods for determining forest evapotranspiration and its components: sap-flow, soil water budget, eddy covariance and catchment water balance , 2001 .

[7]  Assefa M. Melesse,et al.  Global Daily Reference Evapotranspiration Modeling and Evaluation 1 , 2008 .

[8]  Zheng Duan,et al.  Earth Observation Based Assessment of the Water Production and Water Consumption of Nile Basin Agro-Ecosystems , 2014, Remote. Sens..

[9]  W. Bastiaanssen Regionalization of surface flux densities and moisture indicators in composite terrain. A remote sensing approach under clear skies in Mediterranean climates. , 1995 .

[10]  M. Mccabe,et al.  Multi-site evaluation of terrestrial evaporation models using FLUXNET data , 2014 .

[11]  Ayse Irmak,et al.  Treatment of anchor pixels in the METRIC model for improved estimation of sensible and latent heat fluxes , 2011 .

[12]  Vijaya Gopal Kakani,et al.  Evapotranspiration partitioning and water use efficiency of switchgrass and biomass sorghum managed for biofuel , 2015 .

[13]  A. Monta Evapotranspiration deficit controls net primary production and growth of silver fir: Implications for Circum-Mediterranean forests under forecasted warmer and drier conditions , 2015 .

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

[15]  D. Vanella,et al.  Comparisons of satellite-based models for estimating evapotranspiration fluxes , 2014 .

[16]  Di Long,et al.  Assessing the impact of end‐member selection on the accuracy of satellite‐based spatial variability models for actual evapotranspiration estimation , 2013 .

[17]  Terri S. Hogue,et al.  Distributed Hydrologic Modeling Using Satellite-Derived Potential Evapotranspiration , 2015 .

[18]  Ge Sun,et al.  A COMPARISON OF SIX POTENTIAL EVAPOTRANSPIRATION METHODS FOR REGIONAL USE IN THE SOUTHEASTERN UNITED STATES 1 , 2005 .

[19]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[20]  R. Bradstock,et al.  Trends in evapotranspiration and streamflow following wildfire in resprouting eucalypt forests , 2015 .

[21]  K. Jensen,et al.  Effect of a high-end CO2-emission scenario on hydrology , 2015 .

[22]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

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

[24]  William P. Kustas,et al.  Daily evapotranspiration estimates from extrapolating instantaneous airborne remote sensing ET values , 2008, Irrigation Science.

[25]  E. Noordman,et al.  SEBAL model with remotely sensed data to improve water-resources management under actual field conditions , 2005 .

[26]  P. Gowda,et al.  Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08 , 2013 .

[27]  Ayse Irmak,et al.  Satellite‐based ET estimation in agriculture using SEBAL and METRIC , 2011 .

[28]  Andrew E. Suyker,et al.  Annual carbon dioxide exchange in irrigated and rainfed maize-based agroecosystems , 2005 .

[29]  Luis S. Pereira,et al.  Crop evapotranspiration estimation with FAO56: Past and future , 2015 .

[30]  Terry A. Howell,et al.  Comparison of five models to scale daily evapotranspiration from one-time-of-day measurements , 2006 .

[31]  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 .

[32]  Kelly R. Thorp,et al.  Remote sensing of evapotranspiration over cotton using the TSEB and METRIC energy balance models , 2015 .

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

[34]  Zhuguo Ma,et al.  Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China , 2014 .

[35]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

[36]  Vijay P. Singh,et al.  Comparison of Methods for Estimation of Regional Actual Evapotranspiration in Data Scarce Regions: Blue Nile Region, Eastern Sudan , 2012 .

[37]  Zhongbo Su,et al.  Quantifying the uncertainty in estimates of surface-atmosphere fluxes through joint evaluation of the SEBS and SCOPE models , 2011 .

[38]  William P. Kustas,et al.  Upscaling of evapotranspiration fluxes from instantaneous to daytime scales for thermal remote sensing applications , 2013 .

[39]  Ayse Irmak,et al.  An Evaluation of Evapotranspiration Model Complexity Against Performance in Comparison with Bowen Ratio Energy Balance Measurements , 2008 .

[40]  Martha C. Anderson,et al.  An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications , 2013 .

[41]  Martha C. Anderson,et al.  A comparison of operational remote sensing-based models for estimating crop evapotranspiration , 2009 .

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

[43]  A. Huete,et al.  Vegetation Index Methods for Estimating Evapotranspiration by Remote Sensing , 2010 .

[44]  Shaozhong Kang,et al.  Comparison of several surface resistance models for estimating crop evapotranspiration over the entire growing season in arid regions , 2015 .

[45]  Massimiliano Zappa,et al.  Does model performance improve with complexity? : A case study with three hydrological models , 2015 .

[46]  Alfred Stein,et al.  Integrating super resolution mapping and SEBS modeling for evapotranspiration mapping at the field scale , 2015, Precision Agriculture.