Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continental United States
Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continental United States
This study performs three experiments to calibrate the drought area percentages in the continental United States (CONUS), six U.S. Drought Monitor (USDM) regions, and 48 states downloaded from the USDM archive website. The corresponding three experiments are named CONUS, Region, and State, respectively. The data sets used in these experiments are from the North American Land Data Assimilation System Phase 2 (NLDAS-2). The main purpose is to develop an automated USDM-based approach to objectively generate and reconstruct USDM-style drought maps using NLDAS-2 data by mimicking 10 year (2000–2009) USDM statistics. The results show that State and Region have larger correlation coefficients and smaller root-mean-square error (RMSE) and bias than CONUS when compared to the drought area percentages derived from the USDM, indicating that State and Region perform better than CONUS. In general, State marginally outperforms Region in terms of RMSE, bias, and correlation. Analysis of normalized optimal weight coefficients shows that soil moisture percentiles (top 1 m and total column) play the dominant role in most of the 48 states. The optimal blended NLDAS drought index (OBNDI) has higher simulation skills (correlation coefficient and Nash-Sutcliffe efficiency) in the South, Southeast, High Plains, and Midwest regions when compared to those in the West and Northeast. The highest simulation skills appear in TX and OK. By using optimal equations, we can reconstruct the long-term drought area percentages and OBNDI over the continental United States for the entire period of the NLDAS-2 data sets (January 1979 to present).
2947-2965
Xia, Youlong
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Ek, Michael B.
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Peters-Lidard, Christa D.
ec52af50-c979-4df9-a8ee-0232a9d79878
Mocko, David
ab17d245-4d17-4334-b3f6-bbc287b3262e
Svoboda, Mark
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Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric F.
ee59ebb9-367e-48ce-beab-22666be5095d
27 March 2014
Xia, Youlong
dd51d092-f162-4643-adf3-f1b48f1a53af
Ek, Michael B.
ce2724ad-0b64-4802-85c5-ad556f31b1e8
Peters-Lidard, Christa D.
ec52af50-c979-4df9-a8ee-0232a9d79878
Mocko, David
ab17d245-4d17-4334-b3f6-bbc287b3262e
Svoboda, Mark
2b26012e-af6c-4825-bcb5-23c04a9e45a2
Sheffield, Justin
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
Wood, Eric F.
ee59ebb9-367e-48ce-beab-22666be5095d
Xia, Youlong, Ek, Michael B., Peters-Lidard, Christa D., Mocko, David, Svoboda, Mark, Sheffield, Justin and Wood, Eric F.
(2014)
Application of USDM statistics in NLDAS-2: Optimal blended NLDAS drought index over the continental United States.
Journal of Geophysical Research, 119 (6), .
(doi:10.1002/2013JD020994).
Abstract
This study performs three experiments to calibrate the drought area percentages in the continental United States (CONUS), six U.S. Drought Monitor (USDM) regions, and 48 states downloaded from the USDM archive website. The corresponding three experiments are named CONUS, Region, and State, respectively. The data sets used in these experiments are from the North American Land Data Assimilation System Phase 2 (NLDAS-2). The main purpose is to develop an automated USDM-based approach to objectively generate and reconstruct USDM-style drought maps using NLDAS-2 data by mimicking 10 year (2000–2009) USDM statistics. The results show that State and Region have larger correlation coefficients and smaller root-mean-square error (RMSE) and bias than CONUS when compared to the drought area percentages derived from the USDM, indicating that State and Region perform better than CONUS. In general, State marginally outperforms Region in terms of RMSE, bias, and correlation. Analysis of normalized optimal weight coefficients shows that soil moisture percentiles (top 1 m and total column) play the dominant role in most of the 48 states. The optimal blended NLDAS drought index (OBNDI) has higher simulation skills (correlation coefficient and Nash-Sutcliffe efficiency) in the South, Southeast, High Plains, and Midwest regions when compared to those in the West and Northeast. The highest simulation skills appear in TX and OK. By using optimal equations, we can reconstruct the long-term drought area percentages and OBNDI over the continental United States for the entire period of the NLDAS-2 data sets (January 1979 to present).
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Published date: 27 March 2014
Additional Information:
Funding Information:
The NLDAS project is sponsored by the Modeling, Analysis, Predictions, and Projections (MAPP) Program within NOAA?s Climate Program Office. The authors thank Weizheng Zhen, Helin Wei, and three anonymous reviewers whose edits and comments greatly improved the quality and readability of this manuscript. Y.X. also thanks Kingtse Mo from Climate Prediction Center who helped compute spi3 and spi6.
Publisher Copyright:
© 2014. American Geophysical Union. All Rights Reserved.
Identifiers
Local EPrints ID: 480780
URI: http://eprints.soton.ac.uk/id/eprint/480780
ISSN: 0148-0227
PURE UUID: 2c1c0f05-340b-4bd7-9ceb-65e66bd3a373
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Date deposited: 09 Aug 2023 17:13
Last modified: 18 Mar 2024 03:33
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Contributors
Author:
Youlong Xia
Author:
Michael B. Ek
Author:
Christa D. Peters-Lidard
Author:
David Mocko
Author:
Mark Svoboda
Author:
Eric F. Wood
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