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Published on: August 7, 2017
How is the risk of hydrological drought in the Tarim River Basin, Northwest China?
Peng Yang1, Jun Xia2, Yongyong Zhang3
1School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; Key Laboratory of Water Cycle & Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
Abstract:
Drought is an extremely widespread and common natural disaster that significantly impacts both the socio-economic activities of a community and the natural environment. A comprehensive and accurate understanding of hydrological drought is important for the drought prediction and risk management. In this study, a discussion of the characteristics of the historical and future hydrological drought in the Tarim River Basin (TRB) is presented. The research was conducted by modeling the relationship between the ecological water supply and the irrigation water supply using the Community Land Model-Distributed Time Variant Gain Model (CLM-DTVGM) and a copula function. The conclusions are as follows: (1) the Pearson-III probability distribution is the optimal marginal probability distribution for calculating the historical and future runoff from the mountainous region and the Alaer hydrological station; (2) the AMH is the optimal copula function for calculating the joint probability for joint between the ecological and irrigation flows, while the Arch 12, from Bayesian theory, is the optimal copula function under future scenarios (i.e., RCP 2.6, RCP 4.5, and RCP 8.5); and (3) the probability and recurrence period are 0.25 and 4 years, respectively, for the historical hydrological drought, when a multi-year runoff average is used as the threshold. In comparison, the probabilities for the future hydrological drought under the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios are 0.23, 0.15, and 0.18, respectively, which are related to the recurrence periods of 4.3, 7, and 5.6 years, respectively. These results can be used to significantly improve water saving awareness and drought prediction ability in the TRB.
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