Literature DB >> 28865269

Climate and drought risk regionalisation in China based on probabilistic aridity and drought index.

Zhiyong Wu1, Huating Xu2, Yuanyuan Li3, Lei Wen4, Jianqiang Li3, Guihua Lu2, Xiaoyan Li2.   

Abstract

The general approach to drought regionalisation regards the multi-year average values of drought indexes as regionalisation indicators, without taking long-term variability into account. This type of regionalisation is known as static regionalisation, or mean regionalisation, and does not consider possible variations over multiple years. In order to analyse the probability of climate aridity and drought, in this study, we firstly introduce the novel concept of a probabilistic aridity index for climate regionalisation and a drought index for drought risk regionalisation, as well as a methodology for estimating the frequency of the probabilistic aridity and drought index. Details of the approach used in the regionalisation of aridity and drought risk, and its associated characteristics, are then discussed. Finally, the value of our approach is demonstrated in China. The result shows that climate and drought risk regionalisation is able to provide enriched aridity and drought probability information compared with general climate and drought regionalisation, and can thus provide enhanced technical support for the rational allocation of water resources and the prevention and mitigation of drought disasters.
Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords:  Aridity and drought index; Climate and drought probability; Climate regionalisation; Drought regionalisation; Risk regionalisation

Year:  2017        PMID: 28865269     DOI: 10.1016/j.scitotenv.2017.08.078

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Transcriptome Analysis Reveals Long Intergenic Non-Coding RNAs Contributed to Intramuscular Fat Content Differences between Yorkshire and Wei Pigs.

Authors:  Qianqian Li; Ziying Huang; Wenjuan Zhao; Mengxun Li; Changchun Li
Journal:  Int J Mol Sci       Date:  2020-03-03       Impact factor: 5.923

  1 in total

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