Technical note: A stochastic framework for identification and evaluation of flash drought
Abstract The rapid development of droughts, referred to as flash droughts, can pose serious impacts on agriculture, the ecosystem, human health, and society. However, its definition, using pentad-averaged soil moisture, could result in low accuracy in assessing the drought occurrence, making it difficult to analyze various factors controlling the formation of flash droughts. Here we used a stochastic water balance framework to quantify the whole probability structure of the timing for soil moisture dropping from a higher level to a lower one. Based on this framework, we can theoretically examine the nonlinear relationship between the rapid decline rate of soil moisture and various hydrometeorological factors and identify possible flash drought risks caused by less rainfall (e.g., long dry spells), higher evapotranspiration (e.g., extreme heat waves), lower soil water storage capacity (e.g., deforestation), or a combination thereof. Applying this framework to the global datasets, we obtained global maps of the average time for drought development and the risks of flash drought. We found that possible flash drought development in humid regions, such as southern China and the northeastern United States, calls particular attention to the need for flash drought monitoring and mitigation.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Technical note: A stochastic framework for identification and evaluation of flash drought ; volume:27 ; number:5 ; year:2023 ; pages:1077-1087 ; extent:11
Hydrology and earth system sciences ; 27, Heft 5 (2023), 1077-1087 (gesamt 11)
- Urheber
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Li, Yuxin
Chen, Sisi
Yin, Jun
Yuan, Xing
- DOI
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10.5194/hess-27-1077-2023
- URN
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urn:nbn:de:101:1-2023033005465862418706
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:58 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Li, Yuxin
- Chen, Sisi
- Yin, Jun
- Yuan, Xing
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