Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China

Jinhua Wen, Yian Hua, Chenkai Cai (), Shiwu Wang, Helong Wang, Xinyan Zhou, Jian Huang and Jianqun Wang
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Jinhua Wen: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Yian Hua: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Chenkai Cai: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Shiwu Wang: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Helong Wang: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Xinyan Zhou: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Jian Huang: Zhejiang Institute of Hydraulics and Estuary, Hangzhou 310020, China
Jianqun Wang: College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China

Sustainability, 2023, vol. 15, issue 4, 1-20

Abstract: In recent years, flash droughts with a rapid onset and strong intensity have attracted extensive attention due to their impact on agriculture and ecosystems. However, there is still no feasible method for flash drought forecasting and early warning. This paper employs the thresholds of several meteorological variables to identify flash droughts in Zhejiang Province, China, and build a probabilistic flash drought forecasting model through numeric weather forecast (NWF) and the generalized Bayesian model (GBM). The results show that the northern part of Zhejiang Province has the highest risk of flash drought. The NWF is a viable method to provide future information for flash drought forecasting and early warning, but its forecasting accuracy tends to decline with the increase in the lead time and is very limited when the lead time is over 5 days, especially for the precipitation forecast. Due to the low performance of the NWF, the flash drought forecast based on the raw NWF may be unreliable when the lead time is over 5 days. To solve this problem, probabilistic forecasting based on GBM is employed to quantify the uncertainty in the NWF and is tested through an example analysis. In the example analysis, it was found that the probability of a flash drought exceeds 30% from the probabilistic forecasting when the lead time is 12 days, while the deterministic forecasting via the raw NWF cannot identify a flash drought when the lead time is over 5 days. In conclusion, probabilistic forecasting can identify a potential flash drought earlier and can be used to evaluate the risk of a flash drought, which is conducive for the early warning of flash droughts and the development of response measures.

Keywords: flash drought; numeric weather forecast model; probabilistic forecast; Bayesian theory; Zhejiang Province (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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