Copula-based frequency analysis of drought with identified characteristics in space and time: a case study in Huai River basin, China
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Droughts are regional phenomena that dynamic varied along the time; however, the spatiotemporal dynamic processes of drought were usually ignored in the drought identification methods. To better understand the space-time structure of drought, a space-time continuum drought identification method was used to identify drought intensity, duration, and area in Huai River basin, and their multiple dependences were modeled by copulas. The Intensity-Area-Frequency (IAF) curves were used to model the regional variation of drought in this study. Without identified space-time structure of drought, the construction of IAF curves is usually focusing on the decreasing drought intensity with increasing areal extent. However, stronger drought intensity was found to be followed by larger areal extent when considering the space-time structure of drought, and a copula-based method was introduced to better constructive IAF curves. Conditional probabilities of several drought characteristics taking extreme values were further analyzed. Compared to probabilities estimated by univariate frequency analysis, results indicate that occurrence frequency of drought was underestimated when neglecting any natural characteristic of drought, and underestimation was more serious for the more extreme situations of drought. Furthermore, owing to stronger dependences of drought characteristics from short-term to long-term, more serious underestimations of occurrence frequency of drought were found for the drought from short-term to long-term when neglecting any natural characteristic of drought.
This work is supported by the National Key R&D Program of China (2016YFC0402706, 2016YFC0402710); “the Fundamental Research Funds for the Central Universities” (2017B15514); China Postdoctoral Science Foundation (2017M610292); and the National Natural Science Foundation of China (41323001, 51539003, 41605043). The observed meteorological data was provided by the National Meteorological Information Center of China, at the website of http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY_V3.0.html. The last but not the least, we cordially thank the editor, Prof. Dr. Hartmut Graßl, and an anonymous reviewer for their professional comments and constructive suggestions which are greatly helpful for further improvement of the quality of this manuscript.
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