Evaluation of the SWAT model for water balance study of a mountainous snowfed river basin of Nepal

Dhami, Birsingh; Himanshu, Sushil Kumar; Pandey, Ashish; Gautam, Amar Kant

doi:10.1007/s12665-017-7210-8

Cited by 79 publications

(42 citation statements)

References 40 publications

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“…For example, the SWAT model was applied to study changes in the water budget caused by climate change [20][21][22][23]. The SWAT model was used to study hydrological elements in ice-and snow-covered mountainous area [24][25][26]. The SWAT model has also been used to study the main hydrological elements in agricultural areas [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

Cao

Zhang

Yang

et al. 2018

Water

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The China Meteorological Assimilation Driving Datasets for the Soil and Water AssessmentTool model (CMADS) have been widely applied in recent years because of their accuracy. An evaluation of the accuracy and efficiency of the Soil and Water Assessment Tool (SWAT) model and CMADS for simulating hydrological processes in the fan-shaped Lijiang River Basin, China, was carried out. The Sequential Uncertainty Fitting (SUFI-2) algorithm was used for parameter sensitivity and uncertainty analysis at the daily scale. The pair-wise correlation between parameters and the uncertainties associated with equifinality in model parameter estimation were investigated. The results showed that the SWAT model performed well in predicting daily streamflow for the calibration period (2009)(2010). The correlation coefficient (R 2 ) was 0.92, and the Nash-Sutcliffe model efficiency coefficient (NSE) was 0.89. For the validation period (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018), R 2 = 0.89, NSE = 0.88, and reasonable values for the P-factor, R-factor, and percent bias (PBIAS) were obtained. In addition, the spatial and temporal variation of evapotranspiration (ET), surface runoff, and groundwater discharge were analyzed. The results clearly showed that spatial variation in surface runoff and groundwater discharge are strongly related to precipitation, while ET is largely controlled by land use types. The contributions to the water budget by surface runoff, groundwater discharge, and lateral flow were very different in flood years and dry years.

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Section: Introductionmentioning

confidence: 99%

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

Cao

Zhang

Yang

et al. 2018

Water

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“…In this study, the SUFI-2 algorithm was selected for calibration. The SUFI2 method takes the uncertainty of data into consideration, and a group of parameters is selected systematically according to certain automatic regulations to render the objective function optimal [36]. Various hydrologic and water quality parameters are changed for the best fitting of the observed data within their ranges [22].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China

Liu

Cui

2020

Water

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Snowmelt is the main source of runoff in the alpine regions of northern China. When using the soil and water assessment tool (SWAT) to simulate snowmelt runoff, the snowmelt date and snowmelt factor parameters are set according to the North American values. To improve the accuracy of the runoff simulation in northern China, we innovatively used a baseflow segmentation method to determine the snowmelt time, taking temperature as a reference. The snowmelt period was extracted from statistical data, and the corresponding parameters in the source code of SWAT were optimized for the study area. After the calibration was completed, the modified simulation value was compared with the original code simulation value. The simulation accuracy of the daily runoff was improved, and we found that the greater the difference between the source code simulation value and the observed value was, the better the simulation accuracy. Therefore, modifying the source code in SWAT is an effective way to improve the accuracy of simulations of Alpine regions in Northern China. The results show that adjustments to the snowmelt modules of SWAT to reflect local conditions can be an effective way to improve the predictions.

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“…The climate in Nepal is dominated by a southwestern monsoon (June-September) that originates from the Bay of Bengal and about 80% of precipitation in Nepal occurs only in the monsoon season [30]. The months of October-November occasionally experience post-monsoon rainfall, November-March typically remains dry, and April-May experiences pre-monsoon rainfall [4,5,9,31]. The summer monsoon is more active in the eastern part of Nepal as the monsoon enters and departs from that region, and the winter monsoon is more active in the western region because of the influence of western disturbances [28].…”

Section: Study Area and Datamentioning

confidence: 99%

Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

et al. 2020

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Improving decision-making in various areas of water policy and management (e.g., flood and drought preparedness, reservoir operation and hydropower generation) requires skillful streamflow forecasts. Despite the recent advances in hydrometeorological prediction, real-time streamflow forecasting over the Himalayas remains a critical issue and challenge, especially with complex basin physiography, shifting weather patterns and sparse and biased in-situ hydrometeorological monitoring data. In this study, we demonstrate the utility of low-complexity data-driven persistence-based approaches for skillful streamflow forecasting in the Himalayan country Nepal. The selected approaches are: (1) simple persistence, (2) streamflow climatology and (3) anomaly persistence. We generated the streamflow forecasts for 65 stream gauge stations across Nepal for short-to-medium range forecast lead times (1 to 12 days). The selected gauge stations were monitored by the Department of Hydrology and Meteorology (DHM) Nepal, and they represent a wide range of basin size, from ~17 to ~54,100 km2. We find that the performance of persistence-based forecasting approaches depends highly upon the lead time, flow threshold, basin size and flow regime. Overall, the persistence-based forecast results demonstrate higher forecast skill in snow-fed rivers over intermittent ones, moderate flows over extreme ones and larger basins over smaller ones. The streamflow forecast skill obtained in this study can serve as a benchmark (reference) for the evaluation of many operational forecasting systems over the Himalayas.

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Evaluation of the SWAT model for water balance study of a mountainous snowfed river basin of Nepal

Cited by 79 publications

References 40 publications

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China

Optimization and Application of Snow Melting Modules in SWAT Model for the Alpine Regions of Northern China

Benchmarking Real-Time Streamflow Forecast Skill in the Himalayan Region

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