Uncertainties in Prediction of Streamflows Using SWAT Model—Role of Remote Sensing and Precipitation Sources

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-11, Vol.14 (21), p.5385
• Main Authors: Chordia, Jay, Panikkar, Urmila R., Srivastav, Roshan, Shaik, Riyaaz Uddien
• Format: Article
• Language: English
• Subjects: Agricultural management; Algorithms; Calibration; Climate change; Climate system; Contamination; Datasets; Decision making; Digital Elevation Models; Elevation; Geological surveys; Groundwater; Hydrologic models; hydrological modelling; Hydrology; Impact analysis; Land cover; Land use; Modelling; multi-criteria decision making (MCDM); Performance prediction; Precipitation; Predictions; Rain; Rainfall; Remote sensing; Runoff; Soil water; Stream discharge; Stream flow; streamflow prediction; Sustainability management; SWAT; Terrestrial ecosystems; Thermal emission; TRMM satellite; Uncertainty; Water balance; Water management; Watershed management; Watersheds
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14215385

Watershed modelling is crucial for understanding fluctuations in water balance and ensuring sustainable water management. The models’ strength and predictive ability are heavily reliant on inputs such as topography, land use, and climate. This study mainly focuses on quantifying the uncertainty associated with the input sources of the Digital Elevation Model (DEM), Land Use Land Cover (LULC), and precipitation using the Soil and Water Assessment Tool (SWAT) model. Basin-level modelling is being carried out to analyze the impact of source uncertainty in the prediction of streamflow. The sources for DEM used are National Elevation Dataset (NED)-United States Geological Survey (USGS), Shuttle Radar Topographic Mission (SRTM), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), whereas for LULC the sources were the National Land Cover Database (NLCD), Continuous Change Detection Classification (CCDC), and GAP/LANDFIRE National Terrestrial Ecosystems dataset. Observed monitoring stations (Gage), Climate Forecast System Reanalysis (CFSR), and Tropical Rainfall Measuring Mission (TRMM) satellites are the respective precipitation sources. The Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Percent Bias (PBIAS), and the ratio of Root Mean Square Error to the standard deviation (RSR) are used to assess the model’s predictive performance. The results indicated that TRMM yielded better performance compared to the CFSR dataset. The USGS DEM performs best in all four case studies with the NLCD and CCDC LULC for all precipitation datasets except Gage. Furthermore, the results show that using a DEM with an appropriate combination can improve the model’s prediction ability by simulating streamflows with lower uncertainties. TheVIKOR MCDM method is used to rank model combinations. It is observed from MCDM analysis that USGS DEM combinations with NLCD/CCDC LULC attained top priority with all precipitation datasets. Furthermore, the rankings obtained from VIKOR MCDM are in accordance with the validation analysis using SWAT.