Preprints
https://doi.org/10.5194/hess-2022-187
https://doi.org/10.5194/hess-2022-187
28 Jun 2022
 | 28 Jun 2022
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

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

Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun

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, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-187', Anonymous Referee #1, 08 Aug 2022
    • AC1: 'Reply on RC1', Sangchul Lee, 07 Sep 2022
    • AC2: 'Reply on RC1', Sangchul Lee, 07 Sep 2022
  • RC2: 'Comment on hess-2022-187', Anonymous Referee #2, 07 Sep 2022
    • AC3: 'Reply on RC2', Sangchul Lee, 07 Sep 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-187', Anonymous Referee #1, 08 Aug 2022
    • AC1: 'Reply on RC1', Sangchul Lee, 07 Sep 2022
    • AC2: 'Reply on RC1', Sangchul Lee, 07 Sep 2022
  • RC2: 'Comment on hess-2022-187', Anonymous Referee #2, 07 Sep 2022
    • AC3: 'Reply on RC2', Sangchul Lee, 07 Sep 2022
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun
Sangchul Lee, Dongho Kim, Gregory W. McCarty, Martha Anderson, Feng Gao, Fangni Lei, Glenn E. Moglen, Xuesong Zhang, Haw Yen, Junyu Qi, Wade Crow, In-Young Yeo, and Liang Sun

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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.