Editorial Type:
Article
Article Type:
Research Article

A Statistical Method for Categorical Drought Prediction Based on NLDAS-2

Zengchao Hao * Green Development Institute, School of Environment, Beijing Normal University, Beijing, China

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Fanghua Hao * Green Development Institute, School of Environment, Beijing Normal University, Beijing, China

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Youlong Xia Environmental Modeling Center, National Centers for Environmental Prediction, College Park, Maryland
I. M. Systems Group, College Park, Maryland

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Vijay P. Singh Department of Biological and Agricultural Engineering, Texas A&M University, College Station, Texas
Zachry Department of Civil Engineering, Texas A&M University, College Station, Texas

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Yang Hong Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, Oklahoma
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma

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Xinyi Shen Civil and Environmental Engineering Department, School of Engineering, University of Connecticut, Storrs, Connecticut

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Wei Ouyang * Green Development Institute, School of Environment, Beijing Normal University, Beijing, China

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Print Publication:
01 Apr 2016
DOI:
https://doi.org/10.1175/JAMC-D-15-0200.1
Page(s):
1049–1061
Restricted access

Abstract

Drought is a slowly varying natural phenomenon and may have wide impacts on a range of sectors. Tremendous efforts have therefore been devoted to drought monitoring and prediction to reduce potential impacts of drought. Reliable drought prediction is critically important to provide information ahead of time for early warning to facilitate drought-preparedness plans. The U.S. Drought Monitor (USDM) is a composite drought product that depicts drought conditions in categorical forms, and it has been widely used to track drought and its impacts for operational and research purposes. The USDM is an assessment of drought condition but does not provide drought prediction information. Given the wide application of USDM, drought prediction in a categorical form similar to that of USDM would be of considerable importance, but it has not been explored thus far. This study proposes a statistical method for categorical drought prediction by integrating the USDM drought category as an initial condition with drought information from other sources such as drought indices from land surface simulation or statistical prediction. Incorporating USDM drought categories and drought indices from phase 2 of the North American Land Data Assimilation System (NLDAS-2), the proposed method is tested in Texas for 2001–14. Results show satisfactory performance of the proposed method for categorical drought prediction, which provides useful information to aid early warning for drought-preparedness plans.

Corresponding author address: Zengchao Hao, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China. E-mail: haozc@bnu.edu.cn

Abstract

Drought is a slowly varying natural phenomenon and may have wide impacts on a range of sectors. Tremendous efforts have therefore been devoted to drought monitoring and prediction to reduce potential impacts of drought. Reliable drought prediction is critically important to provide information ahead of time for early warning to facilitate drought-preparedness plans. The U.S. Drought Monitor (USDM) is a composite drought product that depicts drought conditions in categorical forms, and it has been widely used to track drought and its impacts for operational and research purposes. The USDM is an assessment of drought condition but does not provide drought prediction information. Given the wide application of USDM, drought prediction in a categorical form similar to that of USDM would be of considerable importance, but it has not been explored thus far. This study proposes a statistical method for categorical drought prediction by integrating the USDM drought category as an initial condition with drought information from other sources such as drought indices from land surface simulation or statistical prediction. Incorporating USDM drought categories and drought indices from phase 2 of the North American Land Data Assimilation System (NLDAS-2), the proposed method is tested in Texas for 2001–14. Results show satisfactory performance of the proposed method for categorical drought prediction, which provides useful information to aid early warning for drought-preparedness plans.

Corresponding author address: Zengchao Hao, No. 19, XinJieKouWai St., HaiDian District, Beijing 100875, China. E-mail: haozc@bnu.edu.cn
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Journal of Applied Meteorology and Climatology

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