Calibration and Validation of Swat Model for Kunthipuzha  Basin Using SUFI-2 Algorithm

Tejaswini, V.; Sathian, K. K.

doi:10.20546/ijcmas.2018.701.260

Cited by 29 publications

(9 citation statements)

References 7 publications

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“…Sequential Uncertainty Fitting Algorithm (SUFI-2) is very advantageous since it combines optimization with uncertainty analysis and can handle large number of parameter to achieve good prediction uncertainty ranges for the period of 6 years (2002 to 2007). Responded to parameter set more sensitive than any others [23,24].…”

Section: Swat -Cupmentioning

confidence: 99%

Analysis of Streamflow Response to Changing Climate Conditions Using SWAT Model

2020

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The understanding of climate change is curial for the security of hydrologic conditions of river basins and it is very important to study the climate change impacts on streamflow by analyzing the different climate scenarios with the help of the hydrological models. The main purpose of this study is to project the future climate impact on streamflow by using the SWAT model. The multi-model projections indicated that Upper Ayeyarwady River Basin is likely to become hotter in dry season under low rainfall intensity with increasing temperature and likely to become wetter but warmer in both rainy and winter season because of high rainfall intensity with increased temperature in future. The impact of climate change scenarios is predicted to decrease the annual streamflow by about 0.30 to 1.92% under RCP2.6, 5.59 to 7.29% under RCP4.5 and 10.43 to 11.92% under RCP8.5. Based on the change in high and low flow percentage with respect to the baseline period, the difference between high and low flow variation range will increase year by year based on future scenarios. Therefore, it can be concluded that it may occur more low flow in the dry season which leads to increase in water scarcity and drought and more high flow in the wet season which can cause flooding, water insecurity, stress, and other water-related disasters.

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Section: Swat -Cupmentioning

confidence: 99%

Analysis of Streamflow Response to Changing Climate Conditions Using SWAT Model

2020

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“…Based on generally acceptable performance criteria [68,69], the SWAT model was well calibrated (R 2 = 0.67 and NSE = 0.61) and validated (R 2 = 0.58 and NSE = 0.55). The performance outcomes represent uncertainty in the model and are probably related to significant variation in topography and rainfall [70].…”

Section: Swat Model Calibration and Validationmentioning

confidence: 99%

Climate Change Impact on Flood Frequency and Source Area in Northern Iran under CMIP5 Scenarios

Maghsood

Moradi

Bavani

et al. 2019

Water

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This study assessed the impact of climate change on flood frequency and flood source area at basin scale considering Coupled Model Intercomparison Project phase 5 General Circulation Models (CMIP5 GCMs) under two Representative Concentration Pathways (RCP) scenarios (2.6 and 8.5). For this purpose, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated and validated for the Talar River Basin in northern Iran. Four empirical approaches including the Sangal, Fill–Steiner, Fuller, and Slope-based methods were used to estimate the Instantaneous Peak Flow (IPF) on a daily basis. The calibrated SWAT model was run under the two RCP scenarios using a combination of twenty GCMs from CMIP5 for the near future (2020–40). To assess the impact of climate change on flood frequency pattern and to quantify the contribution of each subbasin on the total discharge from the Talar River Basin, Flood Frequency Index (FFI) and Subbasin Flood Source Area Index (SFSAI) were used. Results revealed that the projected climate change will likely lead to an average discharge decrease in January, February, and March for both RCPs and an increase in September and October for RCP 8.5. The maximum and minimum temperature will likely increase for all months in the near future. The annual precipitation could increase by more than 20% in the near future. This is likely to lead to an increase of IPF. The results can help managers and policy makers to better define mitigation and adaptation strategies for basins in similar climates.

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“…The value is considered acceptable when it ranges from zero to one (Pechlivanidis et al 2011). The average tendency of the simulated result becoming smaller or larger than the recorded one is measured by PBIAS of which the magnitude of zero represents optimum (Tejaswini & Sathian 2018).…”

Section: Calibration and Validationmentioning

confidence: 93%

Modeling the hydrological characteristics of Hangar Watershed, Ethiopia

Galata

2022

Water Supply

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The hydrologic characteristics of the Hangar watershed were modeled by using the Soil and Water Assessment Tool (SWAT) model. Digital elevation model, land use/land cover data, soil map, weather, and hydrological data were among the data used for this study. The measured streamflow data of (1990–2002) and (2003–2011) years were used for calibration and validation of the model respectively and its performance was well for both calibration and validation. The sensitivity analysis identified that the watershed is characterized by 13 sensitive parameters. The watershed receives around, 9.6%, 59.9%, and 30.5% precipitation during dry, wet and short rainy seasons respectively. The received precipitation was lost by 9.6%, 40.5%, and 41.3% in the form of evapotranspiration for each season correspondingly. The surface runoff contribution to the Watershed was 3.8%, and 79.2% during dry and wet seasons respectively, whereas, it contributes by 17.0% during short rainy seasons.

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Calibration and Validation of Swat Model for Kunthipuzha Basin Using SUFI-2 Algorithm

Cited by 29 publications

References 7 publications

Analysis of Streamflow Response to Changing Climate Conditions Using SWAT Model

Analysis of Streamflow Response to Changing Climate Conditions Using SWAT Model

Climate Change Impact on Flood Frequency and Source Area in Northern Iran under CMIP5 Scenarios

Modeling the hydrological characteristics of Hangar Watershed, Ethiopia

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