Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

Sangchul Lee, Junyu Qi, Hyunglok Kim, Gregory W. McCarty, Glenn E. Moglen, Martha Anderson, Xuesong Zhang and Ling Du
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Sangchul Lee: School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Korea
Junyu Qi: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Hyunglok Kim: Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
Gregory W. McCarty: USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Glenn E. Moglen: USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Martha Anderson: USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Xuesong Zhang: Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740, USA
Ling Du: USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA

Sustainability, 2021, vol. 13, issue 4, 1-18

Abstract: There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

Keywords: hydrologic model; predictive uncertainty; model structure improvements; remotely sensed evapotranspiration products (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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