Enhanced Watershed Modeling by Incorporating Remotely Sensed Evapotranspiration and Leaf Area Index

Abstract. Remotely sensed evapotranspiration (RS-ET) products have been widely adopted as additional constraints on hydrologic modeling to enhance the model predictability while reducing predictive uncertainty. However, vegetation parameters, responsible for key time/space variation in evapotranspiration (ET), are often calibrated without the use of suitable constraints. Remotely sensed leaf area index (RS-LAI) products are increasingly available and provide an opportunity to assess vegetation dynamics and improve calibration of associated parameters. The goal of this study is to assess the Soil and Water Assessment Tool (SWAT) predictive uncertainty in estimates of ET using streamflow and two remotely sensed products (i.e., RS-ET and RS-LAI). We explore how the application of RS-ET and RS-LAI products contributes to 1) reducing the parameter uncertainty; 2) improving the model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level; and 3) assessing the model predictions of ET and LAI at the basic modeling unit (i.e., the hydrologic response unit [HRU]) under the assumption that ET and LAI are related in croplands. Our results suggest that most of the parameter sets with acceptable performances for two constraints (i.e., streamflow and RS-ET; 12 parameter sets) are also acceptable for three constraints (i.e., streamflow, RS-ET, and RS-LAI; 11 parameter sets) at the watershed level. This finding is likely because both the ET simulation algorithm and the RS-ET products consider LAI as an input variable. Relative to the watershed-level assessment, the number of parameter sets that satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level are reduced from 11 to 6. Among the 11 parameter sets acceptable for three constraints (i.e., streamflow, RS-ET and RS-LAI) at the sub-watershed level, two parameter sets appear to provide high spatial and temporal consistency between ET and LAI at the HRU level. These results suggested that use of multiple remotely sensed products as model constraints enables model evaluations at finer scales – thereby constraining acceptable parameter sets and accurately representing the spatial characteristics of hydrologic variables. As such, this study highlights the potential of remotely sensed data to increase the predictability and utility of hydrologic models.