Multiple Markov Chains for Categorial Drought Prediction on the US Drought Monitor at Weekly Scale
文献类型：期刊论文
| 作者 | Cao, Junjun; Guan, Fu; Zhang, Xiang12,13,14; Nam, Won-ho11; Leng, Guoyong9,10; Gao, Haoran8; Ye, Qingqing7; Gu, Xihui4,5,6; Zeng, Jiangyuan12; Zhang, Xu14 |
| 刊名 | JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 62期号:10页码:1415-1435 |
| ISSN号 | 1558-8424 |
| 关键词 | Drought Statistical techniques Time series Short-range prediction Statistical forecasting |
| DOI | 10.1175/JAMC-D-23-0061.1 |
| 通讯作者 | Zhang, Xiang(zhangxiang76@cug.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | SOIL-MOISTURE ; PROBABILISTIC DROUGHT ; INDEX ; FRAMEWORK ; CLIMATE ; PRECIPITATION ; CHALLENGE ; MODEL |
| 资助项目 | Open Fund of Hubei Luojia Laboratory[220100059] ; Open Fund of State Key Laboratory of Remote Sensing Science[OFSLRSS202114] ; Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences[2022KFJJ01] ; Fundamental Research Funds for the Central Universities[CCNU22JC022] ; Fundamental Research Funds for the Central Universities[30106220503] ; Youth Innovation Promotion Association CAS[Y2022050] ; Mr. Yu Hong of Ludong University |
| WOS研究方向 | Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| 出版者 | AMER METEOROLOGICAL SOC |
| WOS记录号 | WOS:001091936700001 |
| 资助机构 | Open Fund of Hubei Luojia Laboratory ; Open Fund of State Key Laboratory of Remote Sensing Science ; Open Fund of National Engineering Research Center of Geographic Information System, China University of Geosciences ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS ; Mr. Yu Hong of Ludong University |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199698]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Xiang |
| 作者单位 | 1.Univ Texas Austin, Dept Civil Architecture & Environm Engn, Austin, TX USA 2.Univ Texas Austin, Jackson Sch Geosci, Dept Geol Sci, Austin, TX USA 3.Univ Oxford, Sch Geog & Environm, Oxford, England 4.Ctr Severe Weather & Climate & Hydrogeol Hazards, Wuhan, Peoples R China 5.China Univ Geosci, Sch Environm Studies, Dept Atmospher Sci, Wuhan, Peoples R China 6.Hubei Acad Social Sci, Inst Yangtze River Basin Econ Res, Wuhan, Peoples R China 7.China Univ Geosci Wuhan, Sch Publ Adm, Wuhan, Peoples R China 8.Univ Chinese Acad Sci, Beijing, Peoples R China 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China 10.Hankyong Natl Univ, Inst Agr Environm Sci, Natl Agr Water Res Ctr, Sch Social Safety & Syst Engn, Anseong, South Korea |
| 推荐引用方式 GB/T 7714 | Cao, Junjun,Guan, Fu,Zhang, Xiang,et al. Multiple Markov Chains for Categorial Drought Prediction on the US Drought Monitor at Weekly Scale[J]. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY,2023,62(10):1415-1435. |
| APA | Cao, Junjun.,Guan, Fu.,Zhang, Xiang.,Nam, Won-ho.,Leng, Guoyong.,...&Niyogi, Dev.(2023).Multiple Markov Chains for Categorial Drought Prediction on the US Drought Monitor at Weekly Scale.JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY,62(10),1415-1435. |
| MLA | Cao, Junjun,et al."Multiple Markov Chains for Categorial Drought Prediction on the US Drought Monitor at Weekly Scale".JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY 62.10(2023):1415-1435. |
入库方式： OAI收割
来源：地理科学与资源研究所
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