Journal
REMOTE SENSING
Volume 14, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/rs14061511
Keywords
hydrological modeling; SWAT model; hydrological remote sensing observables; ETMonitor evapotranspiration; African Sahel; limited calibration
Categories
Funding
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19030203]
- National Natural Science Foundation of China (NSFC) [41661144022, 42090014]
- Chinese Academy of Sciences President's International Fellowship Initiative [2020VTA0001]
- Most High-Level Foreign Expert Program [GL20200161002]
- CAS-TWAS President's Fellowship Programme
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This study developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data in the Lake Chad Basin in Africa. Through sensitivity analysis and optimization of influential parameters, the model performance was significantly improved. The new approach of using remote sensing ETa for a limited period of time showed robustness and good performance.
Model calibration and validation are challenging in poorly gauged basins. We developed and applied a new approach to calibrate hydrological models using distributed geospatial remote sensing data. The Soil and Water Assessment Tool (SWAT) model was calibrated using only twelve months of remote sensing data on actual evapotranspiration (ETa) geospatially distributed in the 37 sub-basins of the Lake Chad Basin in Africa. Global sensitivity analysis was conducted to identify influential model parameters by applying the Sequential Uncertainty Fitting Algorithm-version 2 (SUFI-2), included in the SWAT-Calibration and Uncertainty Program (SWAT-CUP). This procedure is designed to deal with spatially variable parameters and estimates either multiplicative or additive corrections applicable to the entire model domain, which limits the number of unknowns while preserving spatial variability. The sensitivity analysis led us to identify fifteen influential parameters, which were selected for calibration. The optimized parameters gave the best model performance on the basis of the high Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and determination coefficient (R-2). Four sets of remote sensing ETa data products were applied in model calibration, i.e., ETMonitor, GLEAM, SSEBop, and WaPOR. Overall, the new approach of using remote sensing ETa for a limited period of time was robust and gave a very good performance, with R-2 > 0.9, NSE > 0.8, and KGE > 0.75 applying to the SWAT ETa vs. the ETMonitor ETa and GLEAM ETa. The ETMonitor ETa was finally adopted for further model applications. The calibrated SWAT model was then validated during 2010-2015 against remote sensing data on total water storage change (TWSC) with acceptable performance, i.e., R-2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R-2 and NSE greater than 0.85.
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