Calibration and Validation of SWAT Model by Using Hydrological Remote Sensing Observables in the Lake Chad Basin

Abstract

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 (R2). 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 R2 > 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., R2 = 0.57 and NSE = 0.55, and remote sensing soil moisture data with R2 and NSE greater than 0.85.

Assessing the effectiveness of potential best management practices for science-informed decision support at the watershed scale: The case of the Mar Menor coastal lagoon, Spain

“…In areas characterised by a semi-arid climate and an agricultural nature such as CC, AET is the main driver of the water balance ( Odusanya et al, 2019 ). Remote sensing data allow to deal with the limited availability of hydrological data ( Bennour et al, 2022 ). In this study, it was assumed that water balance components and nutrient and sediment loads were well estimated and a soft-validation was carried out to validate these estimates.…”

“…GLEAM v3b is an AET dataset generated by a combination of remote sensing observations from several satellites, highly validated with eddy-covariance towers and in-situ sensors ( Martens et al, 2017 ), covering a data period from 2003 to 2015 at a spatial resolution of 0.25° regular grid. In recent years, several studies have satisfactorily applied and validated the satellite-based AET calibration and validation process with GLEAM ( Bennour et al, 2022 ; Odusanya et al, 2021 ; Puertes et al, 2021 ; López-Ballesteros et al, 2019 ).…”

Analysis of Detailed Lake Variations and Associated Hydrologic Driving Factors in a Semi-Arid Ungauged Closed Watershed

“…This study aimed to solve the contradiction between data shortage and the requirement for detailed hydrological information in semi-arid and ungauged closed watersheds. Previous studies have confirmed the effectiveness of utilizing remote-sensed ET data for model evaluation in such areas [42][43][44]. Based on the hydrological characteristics of lakes in semi-arid regions [13,30,58], in addition to the remote-sensed ET data, we also used remotely sensed lake areas and the results from previous studies for model evaluation.…”

“…In some gauged watersheds, observed streamflow data and remotely sensed data have been utilized for model calibration and validation [40][41][42], and their combination indeed improved the performance of hydrologic models. For ungauged watersheds, several studies have verified the possibilities of using remote-sensing data (e.g., evapotranspiration, leaf area index, and soil moisture data) for model evaluation [43,44], and their results demonstrated that, without observed streamflow, the model calibrated by remotely sensed data can still simulate hydrologic processes appropriately. Overall, the above studies provide a feasible option for the evaluation of hydrologic models in data-poor watersheds and make the applications of the physically based models to semi-arid and ungauged closed watersheds possible.…”

Enhancing hydrological model calibration through hybrid strategies in data‐scarce regions

“…Several attempts have been made to calibrate the hydrological model based on the satellite remote sensing evapotranspiration datasets. Bennour et al (2022) have used Global Land Evaporation Amsterdam Model (GLEAM) and Water Productivity Open Access Portal, whereas, Immerzeel and Droogers (2008) and Emam et al (2017) have used moderate resolution imaging spectroradiometer (MODIS) remote sensing evapotranspiration datasets to calibrate the hydrological model. In this study, the simulated evapotranspiration of SWAT hydrological model was compared with the SSEBop actual evapotranspiration data derived from MODIS on monthly time step.…”