Multiple Markov Chains for Categorial Drought Prediction on the U.S. Drought Monitor at Weekly Scale

Junjun Cao aKey Laboratory for Geographical Process Analysis and Simulation of Hubei Province, School of Urban and Environmental Sciences, Central China Normal University, Wuhan, China

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Fu Guan aKey Laboratory for Geographical Process Analysis and Simulation of Hubei Province, School of Urban and Environmental Sciences, Central China Normal University, Wuhan, China

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Xiang Zhang bNational Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China
cHubei Luojia Laboratory, Wuhan, China
dState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

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https://orcid.org/0000-0002-1017-742X
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Won-Ho Nam eSchool of Social Safety and Systems Engineering, Institute of Agricultural Environmental Science, National Agricultural Water Research Center, Hankyong National University, Anseong, South Korea

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Guoyong Leng fKey Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
gUniversity of Chinese Academy of Sciences, Beijing, China

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Haoran Gao hSchool of Public Administration China, China University of Geosciences (Wuhan), Wuhan, China

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Qingqing Ye iInstitute of Yangtze River Basin Economic Research, Hubei Academy of Social Sciences, Wuhan, China

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Xihui Gu jDepartment of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, China
kCentre for Severe Weather and Climate and Hydro-geological Hazards, Wuhan, China
lSchool of Geography and the Environment, University of Oxford, Oxford, United Kingdom

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Jiangyuan Zeng dState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

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Xu Zhang bNational Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China

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Tailai Huang bNational Engineering Research Center of Geographic Information System, School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, China

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Dev Niyogi mDepartment of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
nDepartment of Civil, Architecture, and Environmental Engineering, The University of Texas at Austin, Austin, Texas

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Abstract

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July–August) had the lowest PA while winter had the highest (January–February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiang Zhang, zhangxiang76@cug.edu.cn

Abstract

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July–August) had the lowest PA while winter had the highest (January–February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiang Zhang, zhangxiang76@cug.edu.cn
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