Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

Begou, Jamilatou Chaibou; Jomaa, Seifeddine; Benabdallah, Sihem; Bazie, Pibgnina; Afouda, Abel; Rode, Michael

doi:10.3390/w8050178

Cited by 64 publications

(43 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Uncertainty was analysed using the Generalized Likelihood Uncertainty Estimation (GLUE) method [31,32]. GLUE is a Monte Carlo-based method for model calibration and uncertainty analysis.…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…The prior distributions of the selected parameters are assumed to follow a uniform distribution over their respective range since the real distribution of the parameter is unknown. By comparing the predicted and observed responses, each set of parameter values is assigned a likelihood value [32]. In this study, the number of model runs was set to 10,000 and the total sample of simulations was split into "behavioural" and "non-behavioural" simulations based on a threshold value of NSE ≥ 0.5 [32], 90% coverage of the observed values, and a GLUE quantile range of 0.05-0.95.…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…By comparing the predicted and observed responses, each set of parameter values is assigned a likelihood value [32]. In this study, the number of model runs was set to 10,000 and the total sample of simulations was split into "behavioural" and "non-behavioural" simulations based on a threshold value of NSE ≥ 0.5 [32], 90% coverage of the observed values, and a GLUE quantile range of 0.05-0.95. In line with the study of Chaibou Begou et al [32], GLUE prediction uncertainty was quantified by two indices referred to as the P-factor and R-factor [32,33].…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…In this study, the number of model runs was set to 10,000 and the total sample of simulations was split into "behavioural" and "non-behavioural" simulations based on a threshold value of NSE ≥ 0.5 [32], 90% coverage of the observed values, and a GLUE quantile range of 0.05-0.95. In line with the study of Chaibou Begou et al [32], GLUE prediction uncertainty was quantified by two indices referred to as the P-factor and R-factor [32,33]. The P-factor represents the percentage of observed data bracketed by the 90% predictive uncertainty band of the model calculated at the 5% and 95% levels of the cumulative distribution of an output variable obtained through random sampling.…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

“…The R-factor is the ratio of the average width of the 90% predictive uncertainty band and the standard deviation of the measured variable. For the uncertainty assessment, a value of P-factor >0.5 (i.e., more than half of the observed data should be enclosed within the 90% predictive uncertainty band) and R-factor <1 (i.e., the average width of the 90% predictive uncertainty band should be less than the standard deviation of the measured data) should be adequate for this study, especially considering the limited data availability [32].…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

See 4 more Smart Citations

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Oyerinde

Diekkrüger

2017

Climate

View full text Add to dashboard Cite

Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from eight Global Climate Models (GCM). The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J, and the Sacramento model were chosen for this study. During the model evaluation, 10,000 parameter sets were generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997)(1998)(1999)(2000)(2001)(2002)(2003) and the validation (2004)(2005)(2006)(2007)(2008)(2009)(2010) period, respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment, while the GR4J and IHACRES-CMD over and under estimated low flow, respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended, along with other methods for sustainable river basin management.

show abstract

“…Uncertainty was analysed using the Generalized Likelihood Uncertainty Estimation (GLUE) method [31,32]. GLUE is a Monte Carlo-based method for model calibration and uncertainty analysis.…”

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

Section: Uncertainty and Sensitivity Analysismentioning

confidence: 99%

See 3 more Smart Citations

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Oyerinde

Diekkrüger

2017

Climate

View full text Add to dashboard Cite

show abstract

Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations?

Myers

Ficklin

Robeson

et al. 2021

Hydrological Processes

View full text Add to dashboard Cite

The selection of calibration and validation time periods in hydrologic modelling is often done arbitrarily. Nonstationarity can lead to an optimal parameter set for one period which may not accurately simulate another. However, there is still much to be learned about the responses of hydrologic models to nonstationary conditions. We investigated how the selection of calibration and validation periods can influence water balance simulations. We calibrated Soil and Water Assessment Tool hydrologic models with observed streamflow for three United States watersheds (St. Joseph River of Indiana/Michigan, Escambia River of Florida/Alabama, and Cottonwood Creek of California), using time period splits for calibration/validation. We found that the choice of calibration period (with different patterns of observed streamflow, precipitation, and air temperature) influenced the parameter sets, leading to dissimilar simulations of water balance components. In the Cottonwood Creek watershed, simulations of 50-year mean January streamflow varied by 32%, because of lower winter precipitation and air temperature in earlier calibration periods on calibrated parameters, which impaired the ability for models calibrated to earlier periods to simulate later periods. Peaks of actual evapotranspiration for this watershed also shifted from April to May due to different parameter values depending on the calibration period's winter air temperatures. In the St. Joseph and Escambia River watersheds, adjustments of the runoff curve number parameter could vary by 10.7% and 20.8%, respectively, while 50-year mean monthly surface runoff simulations could vary by 23%-37% and 169%-209%, depending on the observed streamflow and precipitation of the chosen calibration period. It is imperative that calibration and validation time periods are chosen selectively instead of arbitrarily, for instance using change point detection methods, and that the calibration periods are appropriate for the goals of the study, considering possible broad effects of nonstationary time series on water balance simulations. It is also crucial that the hydrologic modelling community improves existing calibration and validation practices to better include nonstationary processes.

show abstract

Hydrological streamflow modeling at the inlet of Gilgel Gibe III dam of Ethiopia using soil and water assessment tool

Andiye,

Lohani

2024

World Water Policy

View full text Add to dashboard Cite

Comprehensive approach is a pre‐requisite to improve the estimation of variability in streamflow data for designing water supply systems, generating hydropower, determining environmental flows, allocating water, and conducting pollution studies that need a comprehensive approach. There is a need to model streamflow data to develop effective water policy plans when sufficient data are unavailable. This study aims at assessing the accuracy and applicability of the Soil and Water Assessment Tool (SWAT) model in predicting streamflow at the inlet of the Gilgel Gibe III dam watershed in Ethiopia. Historical meteorological data from nine available stations between 1987 and 2017 were used to predict the streamflow. The SWAT model was calibrated and validated for two different time periods (1997–1999 and 2003–2004) using SWATCUP program and SUFI‐2 technique. Calibration and validation were based on monthly observed discharges at the Abelti and Gojeb stations. The results showed acceptable values of R2, Nash–Sutcliffe modeling efficiency (NSE), and percent bias (PBIAS). At the Abelti station and Gojeb station, the calibration and validation results were 0.82, 0.79, and −10.1%; 0.88, 0.84, and −14.4%; and 0.86, 0.79, and 14.1% for calibration and 0.88, 0.8, and 11.1% for validation, respectively. The prediction uncertainty of 95% was consistent with the observed discharge. The mean annual runoff was 440.08 m3/s, demonstrating the model's effectiveness in simulating streamflow at the dam's inlet. The calibrated and validated model can be used to study the effects of climate and land use changes; analyze water quality and sediment yield; and inform future water policy plans, dam construction planning, and flood disaster risk management.

show abstract

Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

Cited by 64 publications

References 54 publications

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate

Choosing an arbitrary calibration period for hydrologic models: How much does it influence water balance simulations?

Hydrological streamflow modeling at the inlet of Gilgel Gibe III dam of Ethiopia using soil and water assessment tool

Contact Info

Product

Resources

About