SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Impending hydrological regime of Lhasa River as subjected to hydraulic interventions—a SWAT model manifestation 
Authors:Yasir, M., T. Hu and S.A. Hakeem 
Year:2021 
Journal:Remote Sensing 
Volume (Issue):13(7) 
Pages: 
Article ID:1382 
DOI:10.3390/rs13071382 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:reservoirs, ponds, lakes and/or other impoundment effects  
Secondary Application Category:hydrologic assessment 
Watershed Description:32,321 km^2 Lhasa River, a tributary of the Yarlung Tsangpo River located in the autonomous Qinghai–Tibetan Plateau in southwest China. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:The damming of rivers has altered their hydrological regimes. The current study evaluated the impacts of major hydrological interventions of the Zhikong and Pangduo hydropower dams on the Lhasa River, which was exposed in the form of break and change points during the double-mass curve analysis. The coefficient of variability (CV) for the hydro-meteorological variables revealed an enhanced climate change phenomena in the Lhasa River Basin (LRB), where the Lhasa River (LR) discharge varied at a stupendous magnitude from 2000 to 2016. The Mann–Kendall trend and Sen’s slope estimator supported aggravated hydro-meteorological changes in LRB, as the rainfall and LR discharge were found to have been significantly decreasing while temperature was increasing from 2000 to 2016. The Sen’s slope had a largest decrease for LR discharge in relation to the rainfall and temperature, revealing that along with climatic phenomena, additional phenomena are controlling the hydrological regime of the LR. Reservoir functioning in the LR is altering the LR discharge. The Soil and Water Assessment Tool (SWAT) modeling of LR discharge under the reservoir’s influence performed well in terms of coefficient of determination (R2), Nash–Sutcliffe coefficient (NSE), and percent bias (PBIAS). Thus, simulation-based LR discharge could substitute observed LR discharge to help with hydrological data scarcity stress in the LRB. The simulated–observed approach was used to predict future LR discharge for the time span of 2017–2025 using a seasonal AutoRegressive Integrated Moving Average (ARIMA) model. The predicted simulation-based and observation-based discharge were closely correlated and found to decrease from 2017 to 2025. This calls for an efficient water resource planning and management policy for the area. The findings of this study can be applied in similar catchments. 
Language:English 
Keywords:SWAT; double-mass analysis; coefficient of variability; seasonal ARIMA; MK-S trend analysis