4.7 Article

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

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EARTHS FUTURE
卷 10, 期 1, 页码 -

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2021EF002417

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资金

  1. National Science Foundation (NSF) [EAR-1856054]
  2. National Oceanic and Atmospheric Administration (NOAA) Modeling, Analysis, Predictions, and Projections (MAPP) program [NA18OAR4310319]

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This study examines the coincidence of agricultural droughts and tropical storms in the contiguous United States, and evaluates the impact of tropical storms on drought mitigation. High-resolution soil moisture data and hurricane database are used to analyze the offset of drought conditions. The findings provide valuable information for mitigating drought and tropical storms in the future.
Drought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive data set generated at the Center for Complex Hydrosystems Research at the University of Alabama, and Hurricane Database 2nd generation to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the contiguous United States from 2015 to 2019. The Cumulative Distribution Function (CDF) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS-2), and then weekly Standardized Soil Moisture Index is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high-resolution data set and provide potential information in terms of mitigating drought and TS for the future.

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