28 Jun 2022
28 Jun 2022
Status: this preprint is currently under review for the journal HESS.

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

Sangchul Lee1, Dongho Kim1, Gregory W. McCarty2, Martha Anderson2, Feng Gao2, Fangni Lei2, Glenn E. Moglen2, Xuesong Zhang2, Haw Yen3, Junyu Qi4, Wade Crow2, In-Young Yeo5, and Liang Sun6 Sangchul Lee et al.
  • 1School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of Korea
  • 2USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
  • 3Crop Science, Bayer U.S., 700 W Chesterfield Pkwy W, Chesterfield, MO 63017, USA
  • 4Earth System Science Interdisciplinary Center, University of Maryland, College Park, 5825 University Research Ct, College Park, MD 20740, USA
  • 5School of Engineering, the University of Newcastle, Callaghan NSW 2308, Australia
  • 6Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture / Institute of 14 Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, 15 Beijing 100081, China

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.

Sangchul Lee et al.

Status: open (until 04 Sep 2022)

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  • RC1: 'Comment on hess-2022-187', Anonymous Referee #1, 08 Aug 2022 reply

Sangchul Lee et al.

Sangchul Lee et al.


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Short summary
Watershed modeling is important to protect water resources. However, errors are involved in watershed modeling. To reduce errors, remotely sensed evapotranspiration data are widely used. However, the use of remotely sensed evapotranspiration data still includes errors. This study applied two remotely sensed data (evapotranspiration and leaf area index) into watershed modeling to reduce errors. The results showed advancement of watershed modeling by two remotely sensed data.