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

Abstract. To improve the capacity of watershed modeling, remotely sensed products are frequently used to reduce the uncertainty resulting from data limitations. Although remotely sensed evapotranspiration (RS-ET) products are widely used, vegetation parameters governing spatial and temporal variations in evapotranspiration (ET) are often not constrained by benchmark data. Recently, remotely sensed leaf area index (RS-LAI) products are becoming increasingly available, providing an opportunity to assess and improve simulated vegetation dynamics. The objective of this study is to assess the role of the two remotely sensed products (i.e., RS-ET and RS-LAI) in improving the accuracy of watershed model predictions. Specifically, we investigated the role of RS-ET and RS-LAI products in 1) reducing parameter uncertainty and 2) improving model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level. The watershed-level assessment of the degree of equifinality (denoted as the number of parameter sets that produce equally acceptable model simulations) shows that less than half of the acceptable parameter sets for two constraints (streamflow and RS-ET; 14 parameter sets) are acceptable for three constraints (streamflow, RS-ET, and RS-LAI; six parameter sets). Among those six parameter sets, only three can satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level. Our results suggest that the use of multiple remotely sensed datasets holds great potential to reduce parameter uncertainty and increase the credibility of watershed modeling, particularly for characterizing spatial variability of hydrologic fluxes that are relevant to agricultural management.