Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Tobin, Kenneth J.; Bennett, Marvin E.

doi:10.3390/w12072039

Cited by 5 publications

(8 citation statements)

References 41 publications

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“…SUFI-2 was found to be more convenient and simpler to use compared to other automatic calibration techniques. The SWAT model performance indicates good performance that fulfills the necessary criteria [53,70]. The Dhidhessa catchment satisfies the following criteria (Table 3): The coefficient of determination is best when R 2 > 0.60, and the Nash-Sutcliffe (1970) coefficient of efficiency is best when NS > 0.5.…”

Section: Surface Water Modellingmentioning

confidence: 99%

“…The SUFI-2 algorithm, which is included in the SWAT-CUP interface, was used to perform the SWAT model calibration and validation processes [67]. The performance of the model was evaluated by graphical comparison and using statistical indices [53,70]. The statistical criteria include the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of determination (R 2 ), Nash-Sutcliffe Coefficient (NSE), percent bias (PBIAS) [53,67,70].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…The performance of the model was evaluated by graphical comparison and using statistical indices [53,70]. The statistical criteria include the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of determination (R 2 ), Nash-Sutcliffe Coefficient (NSE), percent bias (PBIAS) [53,67,70]. The value of R 2 ranges from 0 to 1.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…The results of the calibration and validation were evaluated using graphical and statistical procedures based on the quality criteria [53,70]. Daily data were used for the SWAT model, where the first two years were considered as the warmup period.…”

Section: Model Performance Evaluationmentioning

confidence: 99%

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Assessment of Surface Irrigation Potential of the Dhidhessa River Basin, Ethiopia

et al. 2020

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Assessing available water resources and their potential for irrigation water use is vital for sustainable agricultural development and planning. This is particularly of interest in developing countries like Ethiopia, where a small portion of largely accessible land for surface irrigation applications has been utilized, despite the majority of the population relying on agricultural productivity. This study utilized the Dhidhessa River Basin (Ethiopia) as a case study and analyzed the main challenges to balance the sustainable water resources utilization and enhance agricultural productivity of the basin. The study mainly focused on estimating the available water resources and their potential for surface irrigation water use in the basin. This was achieved by utilizing Geographic Information System (GIS)-based tools, a hydrological Soil and Water Assessment Tool (SWAT) model, and a Crop Water and Irrigation Requirements Program of FAO (CROPWAT) model. While the SWAT estimated the water availability in the basin, GIS-tools such as Model Builder were used to map the irrigation potential of the basin. For irrigation water potential assessment, we selected six crops (cabbage, maize, tomato, pepper, groundnut and sugarcane) and estimated their irrigation water requirements using the CROPWAT model. We developed the SWAT model for the period from 1986 to 2012 using the available hydro-meteorological and geo-spatial data. Due to many parameters used in the model, we first performed a parameter sensitivity analysis and identified the most essential/sensitivity parameters via Sequential Uncertainty Fitting-II (SUFI-2). The identified sensitive parameters were subsequently used for model calibration (1989–2000) and validation (2001–2012) procedures achieved via SUFI-2. SWAT was able to reproduce the observed monthly streamflow values with a coefficient of determination (R2) and Nash-Sutcliffe Coefficient (NSE) of 0.85 and 0.87 for the calibration period and 0.91 and 0.89 for the validation period, respectively. The findings generally indicated a “good” performance of the model in simulating the hydrology. The annual available water of the basin is 9.26 billion cubic meters (BCM) whereas the 70% and 80% dependable flow is 7.56 and 6.97 BCM, respectively. Based on the Model Builder of ArcGIS, the SWAT estimated available water can potentially irrigate an area of 259,028 ha for slope less than 8%, 643,162 ha for slopes less than 15% and 1,023,581 ha for slopes less than 30%. Moreover, the irrigation water requirements were calculated by the CROPWAT model for the six selected crops indicated that although the need for irrigation water varies depending on the season, the potential irrigation area of the Dhidhessa River Basin is greater than its irrigated land. Therefore, it is concluded that the basin’s surface irrigation systems need to be expanded to enhance the agricultural productivity and improve the livelihood of the basin’s communities and similar basins elsewhere.

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Section: Surface Water Modellingmentioning

confidence: 99%

Section: Model Calibration and Validationmentioning

confidence: 99%

Section: Model Calibration and Validationmentioning

confidence: 99%

Section: Model Performance Evaluationmentioning

confidence: 99%

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Assessment of Surface Irrigation Potential of the Dhidhessa River Basin, Ethiopia

et al. 2020

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“…Furthermore, model calibration against satellite-driven vegetation data led to advanced prediction of vegetation growth and recovery from fire [17]. The addition of root zone soil moisture to model calibration was proven to reduce parameter sampling spaces as well as uncertainty [18].…”

Section: Introductionmentioning

confidence: 99%

Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

Lee

Kim

et al. 2021

Sustainability

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There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

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Developing SWAT‐S to strengthen the soil erosion forecasting performance of the SWAT model

Long,

Gao,

Shao

et al. 2023

Land Degrad Dev

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Soil erosion is an important cause of global land degradation, and accurate monitoring of it is essential. The Soil and Water Assessment Tool (SWAT), a distributed hydrological model, is an advanced technique for predicting soil erosion at watershed scale. However, as the erosion framework was established in gently sloping land, SWAT is limited in predicting soil erosion in some highland and mountainous regions. Therefore, this study suggested a method to integrate the sediment transport theoretical formula that can reflect the morphology of gully regions into SWAT to obtain SWAT‐S to enhance the calculation performance of sediment load, and the SWAT‐S was evaluated according to the coefficient of determination (R2), Nash‐Sutcliffe coefficient (NSE), Percent‐Bias (P‐BIAS) and root mean square errors (RMSE)‐observations SD ratio (RSR) in the Yanhe basin on the Chinese Loess Plateau. The results showed that SWAT‐S is more successful in reproducing the monthly sediment load, with R2, NSE, |P‐BIAS| and RSR were changed by 5.08%, 17.65%, −2.92% and −10.00% in the calibration, as well as by 1.18%, 10.39%, 45.45% and −18.75% in the validation of the SWAT‐S compared to SWAT. Meanwhile, SWAT‐S estimates 2.66 × 106 t more sediment than SWAT during the June–September flood season and better matches observed data. In total, the revised SWAT can improve the performance of sediment estimation, which is beneficial for the wider application of the model in more regions of the world.

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Improving SWAT Model Calibration Using Soil MERGE (SMERGE)

Cited by 5 publications

References 41 publications

Assessment of Surface Irrigation Potential of the Dhidhessa River Basin, Ethiopia

Assessment of Surface Irrigation Potential of the Dhidhessa River Basin, Ethiopia

Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

Developing SWAT‐S to strengthen the soil erosion forecasting performance of the SWAT model

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