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.

Highlights

  • An improved understanding of water resource dynamics aids the development of effective adaptation strategies [1]

  • Hydrologic models are often used as a means to design and manage water resource systems [2,3,4]

  • The results from this study demonstrate the value of remotely sensed evapotranspiration (RS-ET) in assessing improved model structures, suggesting the added potential of RS-ET in advancing hydrologic modeling

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Summary

Introduction

Water resource management is important for resource allocation in agricultural and mixed land-use watersheds, to accommodate for the rising water demand as a result of population increase. An improved understanding of water resource dynamics aids the development of effective adaptation strategies [1]. Hydrologic models are often used as a means to design and manage water resource systems [2,3,4]. The use of these models for operational purposes may provide practical solutions, there is a need for continuous effort to reduce uncertainty involved in operational applications of hydrologic models [5,6]

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