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Abstract The significant impact of flash droughts (FDs) on society can vary based on a combination of FD characteristics (event counts, mean severity, and rate of intensification), which is largely unexplored. We employed root‐zone soil‐moisture for 1980–2018 to calculate the FD characteristics and integrated them to formulate a novel multivariate FD indicator for mapping the global FD hotspot regions. The potential influence of climate characteristics (i.e., anomalies, aridity, and evaporative fractions) and land‐climate feedbacks on the evolution of multivariate FD indicator is investigated. Our results indicate that precipitation is the primary driver of FD evolution, while the effect of temperature, vapor pressure deficit, and land‐climate interaction varies across the climate divisions after the onset of the events. The magnitude of multivariate FD indicator decreases with increased climate aridity, and it is significant in the global humid regimes, underscoring the importance of water and energy supply as limiting factors regulating FD‐risk.
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Causal Discovery Analysis Reveals Global Sources of Predictability for Regional Flash Droughts
Abstract Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2‐m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high‐pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low‐level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings.
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- Award ID(s):
- 2144293
- PAR ID:
- 10556381
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 60
- Issue:
- 11
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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