Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data
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,
Liang Sun
Affiliations
Sangchul Lee
Division of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, Seoul 02841, Republic of Korea; Corresponding author.
Dongho Kim
Department of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul 02504, Republic of Korea
Gregory W. McCarty
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Martha Anderson
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Feng Gao
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Fangni Lei
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Glenn E. Moglen
Department of Civil and Environmental Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223, USA
Xuesong Zhang
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
Haw Yen
School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL 36849, USA
Junyu Qi
Earth System Science Interdisciplinary Center, University of Maryland, College Park, 5825 University Research Ct, College Park, MD 20740, USA
Wade Crow
USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA
In-Young Yeo
School of Engineering, The University of Newcastle, Callaghan NSW 2308, Australia
Liang Sun
Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.
