Editorial Type:
Article
Article Type:
Research Article

Implementation of Snowpack Treatment in the CPC Water Balance Model and Its Impact on Drought Assessment

Jorge Arevalo aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona
bDepartamento de Meteorologia, Universidad de Valparaiso, Valparaiso, Chile

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https://orcid.org/0000-0002-6889-5395
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Josh Welty aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Yun Fan cNOAA/National Weather Service/National Centers for Environmental Protection/Climate Prediction Center, Camp Springs, Maryland

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Xubin Zeng aDepartment of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona

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Online Publication:
30 Apr 2021
Print Publication:
01 May 2021
DOI:
https://doi.org/10.1175/JHM-D-20-0201.1
Page(s):
1235–1247
Restricted access

Abstract

Droughts are a worldwide concern, thus assessment efforts are conducted by many centers around the world, mainly through simple drought indices, which usually neglect important hydrometeorological processes or require variables available only from complex land surface models (LSMs). The U.S. Climate Prediction Center (CPC) uses the Leaky Bucket (LB) water-balance model to postprocess temperature and precipitation, providing soil moisture (SM) anomalies to assess drought conditions. However, despite its crucial role in the water cycle, snowpack has been neglected by LB and most drought indices. Taking advantage of the high-quality snow water equivalent (SWE) data from The University of Arizona (UA), a single-layer snow scheme, forced by daily temperature and precipitation only, is developed for LB implementation and tested with two independent forcing datasets. Compared against the UA and SNOTEL SWE data over CONUS, LB outperforms a sophisticated LSM (Noah/NLDAS-2), with the median LB versus SNOTEL correlation (RMSE) about 40% (26%) higher (lower) than that from Noah/NLDAS-2, with only slight differences due to different forcing datasets. The changes in the temporal variability of SM due to the snowpack treatment lead to improved temporal and spatial distribution of drought conditions in the LB simulations compared to the reference U.S. Drought Monitor maps, highlighting the importance of snowpack inclusion in drought assessment. The simplicity but reasonable reliability of the LB with snowpack treatment makes it suitable for drought monitoring and forecasting in both snow-covered and snow-free areas, while only requiring precipitation and temperature data (markedly less than other water-balance-based indices).

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0201.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jorge Arevalo, jorgearevalo@email.arizona.edu

Abstract

Droughts are a worldwide concern, thus assessment efforts are conducted by many centers around the world, mainly through simple drought indices, which usually neglect important hydrometeorological processes or require variables available only from complex land surface models (LSMs). The U.S. Climate Prediction Center (CPC) uses the Leaky Bucket (LB) water-balance model to postprocess temperature and precipitation, providing soil moisture (SM) anomalies to assess drought conditions. However, despite its crucial role in the water cycle, snowpack has been neglected by LB and most drought indices. Taking advantage of the high-quality snow water equivalent (SWE) data from The University of Arizona (UA), a single-layer snow scheme, forced by daily temperature and precipitation only, is developed for LB implementation and tested with two independent forcing datasets. Compared against the UA and SNOTEL SWE data over CONUS, LB outperforms a sophisticated LSM (Noah/NLDAS-2), with the median LB versus SNOTEL correlation (RMSE) about 40% (26%) higher (lower) than that from Noah/NLDAS-2, with only slight differences due to different forcing datasets. The changes in the temporal variability of SM due to the snowpack treatment lead to improved temporal and spatial distribution of drought conditions in the LB simulations compared to the reference U.S. Drought Monitor maps, highlighting the importance of snowpack inclusion in drought assessment. The simplicity but reasonable reliability of the LB with snowpack treatment makes it suitable for drought monitoring and forecasting in both snow-covered and snow-free areas, while only requiring precipitation and temperature data (markedly less than other water-balance-based indices).

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0201.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Jorge Arevalo, jorgearevalo@email.arizona.edu

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