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Sustainability 2015, 7(8), 10789-10808; doi:10.3390/su70810789

SDI and Markov Chains for Regional Drought Characteristics

1
Department of Resources Engineering, National Cheng Kung University, Tainan 70101, Taiwan
2
Three Gorges Research Center for Geo-hazard, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 22 May 2015 / Revised: 21 July 2015 / Accepted: 5 August 2015 / Published: 7 August 2015
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Abstract

In recent years, global climate change has altered precipitation patterns, causing uneven spatial and temporal distribution of precipitation that gradually induces precipitation polarization phenomena. Taiwan is located in the subtropical climate zone, with distinct wet and dry seasons, which makes the polarization phenomenon more obvious; this has also led to a large difference between river flows during the wet and dry seasons, which is significantly influenced by precipitation, resulting in hydrological drought. Therefore, to effectively address the growing issue of water shortages, it is necessary to explore and assess the drought characteristics of river systems. In this study, the drought characteristics of northern Taiwan were studied using the streamflow drought index (SDI) and Markov chains. Analysis results showed that the year 2002 was a turning point for drought severity in both the Lanyang River and Yilan River basins; the severity of rain events in the Lanyang River basin increased after 2002, and the severity of drought events in the Yilan River basin exhibited a gradual upward trend. In the study of drought severity, analysis results from periods of three months (November to January) and six months (November to April) have shown significant drought characteristics. In addition, analysis of drought occurrence probabilities using the method of Markov chains has shown that the occurrence probabilities of drought events are higher in the Lanyang River basin than in the Yilan River basin; particularly for extreme events, the occurrence probability of an extreme drought event is 20.6% during the dry season (November to April) in the Lanyang River basin, and 3.4% in the Yilan River basin. This study shows that for analysis of drought/wet occurrence probabilities, the results obtained for the drought frequency and occurrence probability using short-term data with the method of Markov chains can be used to predict the long-term occurrence probability of drought/wet events. View Full-Text
Keywords: streamflow drought index; Markov chains; hydrological drought; drought prediction streamflow drought index; Markov chains; hydrological drought; drought prediction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yeh, C.-F.; Wang, J.; Yeh, H.-F.; Lee, C.-H. SDI and Markov Chains for Regional Drought Characteristics. Sustainability 2015, 7, 10789-10808.

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