Preprints
https://doi.org/10.5194/hess-2020-669
https://doi.org/10.5194/hess-2020-669

  12 Jan 2021

12 Jan 2021

Review status: this preprint was under review for the journal HESS. A final paper is not foreseen.

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

Sangchul Lee1, Gregory W. McCarty2, Glenn E. Moglen2, Haw Yen3, Fangni Lei2, Martha Anderson2, Feng Gao2, Wade Crow2, In-Young Yeo4, and Liang Sun5 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, United States
  • 3Blackland Research and Extension Center, Texas A&M University, 720 East Blackland Road, Temple, TX 76502, United States
  • 4School of Engineering, the University of Newcastle, Callaghan NSW 2308, Australia
  • 5Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China

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.

This preprint has been withdrawn.

Sangchul Lee et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-669', Anonymous Referee #1, 28 Jan 2021
  • RC2: 'Comment on hess-2020-669', Anonymous Referee #2, 07 Feb 2021

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2020-669', Anonymous Referee #1, 28 Jan 2021
  • RC2: 'Comment on hess-2020-669', Anonymous Referee #2, 07 Feb 2021

Sangchul Lee et al.

Sangchul Lee et al.

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This preprint has been withdrawn.