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A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought

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AghaKouchak, A.·2014·Hydrology and Earth System Sciences

Abstract

Abstract. The 2012 drought was one of the most extensive drought events in half a century, resulting in over USD 12 billion in economic loss in the United States and substantial indirect impacts on global food security and commodity prices. An important feature of the 2012 drought was rapid development and intensification in late spring/early summer, a critical time for crop development and investment planning. Drought prediction remains a major challenge because dynamical precipitation forecasts are highly uncertain, and their prediction skill is low. Using a probabilistic framework for drought forecasting based on the persistence property of accumulated soil moisture, this paper shows that the US drought of summer 2012 was predictable several months in advance. The presented drought forecasting framework provides the probability occurrence of drought based on climatology and near-past observations of soil moisture. The results indicate that soil moisture exhibits higher persistence than precipitation, and hence improves drought predictability.

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Metadata

Authors
AghaKouchak, A.
Venue
Hydrology and Earth System Sciences
Published
2014-07-04
Volume
18
Issue
7
Pages
2485-2492
ISSN
  • 1027-5606 (ISSN-L)
  • 1607-7938
OpenAlex
W2102441298
Cited by
218
References
0
FWCI
6.51
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought. https://app.cochat.ai/papers/10.5194/hess-18-2485-2014
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AghaKouchak, A. (2014). A baseline probabilistic drought forecasting framework using standardized soil moisture index: application to the 2012 United States drought. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-18-2485-2014

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.