Impact of climate change on runoff and uncertainty analysis

Jianxia Chang (), Hongxue Zhang, Yimin Wang and Lianpeng Zhang
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Jianxia Chang: Xi’an University of Technology
Hongxue Zhang: Xi’an University of Technology
Yimin Wang: Xi’an University of Technology
Lianpeng Zhang: Xi’an University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 88, issue 2, No 24, 1113-1131

Abstract: Abstract It is necessary to analyze the future runoff changes using a more realistic climate classification scheme. This paper investigates the climate changes and runoff variation by runoff simulation from global climate models participating in the CMIP5. This study also focuses on the uncertain effect of meteorological input data and hydrological model parameters on the runoff. TOPMODEL is used to simulate runoff, and the sensibility of runoff to precipitation and temperature variabilities is analyzed under the scenario of 25 hypothesis. Considering the uncertainty of input data of hydrological model, the predicted future precipitation and temperature data under RCP4.5 and RCP8.5 are input into the TOPMODEL model to simulate the future runoff under different scenarios, and the M-GLUE method is used to analyze the uncertainty of model parameters. Jinghe River basin is selected as the study area in this paper. The analytical results reveal decreasing trends for runoff and precipitation, and the influence of precipitation change on runoff is more sensitive than the change of temperature. Integrating the results of the two climate models, the annual average runoff will decrease by from 13.3 to 27.7% under RCP4.5 scenario and under RCP8.5 scenario the decrease interval is from 17.1 to 25.2%. The RV, SRmax and S zm are the most sensitive parameters for the TOPMODEL. M-GLUE method can effectively analyze the uncertainty of hydrological simulation, and the interval width and coverage rate of the 90% confidence interval are 19 and 74%, respectively.

Keywords: Runoff variation; Climate change; TOPMODEL; M-GLUE method; Sensitivity; Uncertainty (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-017-2909-0

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