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Runoff Simulation Under Future Climate Change and Uncertainty
Runoff is simulated using the Soil and Water Assessment Tool (SWAT) in the Shiyang River Basin and considering the precipitation and temperature obtained from five climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), including MRI-CGCM3, CanESM2, CNRM-CM5, GFDL-CM3, and FGOALS-g2. Precipitation and temperature were downscaled as the input for SWAT, and the uncertainty in runoff simulation under climate models was evaluated based on the Bayesian information criterion (BIC) and expectation-maximization (EM) algorithms of Bayesian model average (BMA) method, also runoff variation in the future was predicted. Results showed that, for the downscaled precipitation, the accuracy index R2 are mainly concentrated in 0.42–0.58. For the downscaled temperature, R2 is greater than 0.5. The predicted runoff using BMA is better than that using the single climate model in most sub-basins. MRI-CGCM3 and GFDL-CM3 have larger contribution than other three models using BMA based on either BIC or EM algorithms. Compared to the period of 1990–1999, temperature of each climate model is obviously increasing during 2018–2100. While for precipitation, CNRM-CM5 and FGOALS-g2 showed an increasing trend in all sub-basins. But GFDL-CM3, CanESM2, and MRI-CGCM3 vary across sub-basins. The trend in runoff is consistent with precipitation. To sum up, precipitation, temperature, and runoff under most climate models will increase in the future. The results will provide a reference for the utilization and management of water resources in the future.
Runoff Simulation Under Future Climate Change and Uncertainty
Runoff is simulated using the Soil and Water Assessment Tool (SWAT) in the Shiyang River Basin and considering the precipitation and temperature obtained from five climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), including MRI-CGCM3, CanESM2, CNRM-CM5, GFDL-CM3, and FGOALS-g2. Precipitation and temperature were downscaled as the input for SWAT, and the uncertainty in runoff simulation under climate models was evaluated based on the Bayesian information criterion (BIC) and expectation-maximization (EM) algorithms of Bayesian model average (BMA) method, also runoff variation in the future was predicted. Results showed that, for the downscaled precipitation, the accuracy index R2 are mainly concentrated in 0.42–0.58. For the downscaled temperature, R2 is greater than 0.5. The predicted runoff using BMA is better than that using the single climate model in most sub-basins. MRI-CGCM3 and GFDL-CM3 have larger contribution than other three models using BMA based on either BIC or EM algorithms. Compared to the period of 1990–1999, temperature of each climate model is obviously increasing during 2018–2100. While for precipitation, CNRM-CM5 and FGOALS-g2 showed an increasing trend in all sub-basins. But GFDL-CM3, CanESM2, and MRI-CGCM3 vary across sub-basins. The trend in runoff is consistent with precipitation. To sum up, precipitation, temperature, and runoff under most climate models will increase in the future. The results will provide a reference for the utilization and management of water resources in the future.
Runoff Simulation Under Future Climate Change and Uncertainty
Water Sci.,Technol.Library
Otazo-Sánchez, Elena María (editor) / Navarro-Frómeta, Amado Enrique (editor) / Singh, Vijay P. (editor) / Su, Xiaoling (author) / Guo, Jing (author) / Liang, Zheng (author) / Singh, Vijay P. (author)
2019-09-29
32 pages
Article/Chapter (Book)
Electronic Resource
English
Runoff , Uncertainty , Simulation , CMIP5 , Bayesian model average (BMA) Environment , Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution , Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences , Hydrogeology , Geographical Information Systems/Cartography , Earth and Environmental Science
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