Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1077-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1077-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note: A stochastic framework for identification and evaluation of flash drought
Yuxin Li
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044 Nanjing, China
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, 210044 Nanjing, China
Sisi Chen
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044 Nanjing, China
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, 210044 Nanjing, China
Jun Yin
CORRESPONDING AUTHOR
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044 Nanjing, China
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, 210044 Nanjing, China
Xing Yuan
Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, 210044 Nanjing, China
School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, 210044 Nanjing, China
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Junjiang Liu, Xing Yuan, Junhan Zeng, Yang Jiao, Yong Li, Lihua Zhong, and Ling Yao
Hydrol. Earth Syst. Sci., 26, 265–278, https://doi.org/10.5194/hess-26-265-2022, https://doi.org/10.5194/hess-26-265-2022, 2022
Short summary
Short summary
Hourly streamflow ensemble forecasts with the CSSPv2 land surface model and ECMWF meteorological forecasts reduce both the probabilistic and deterministic forecast error compared with the ensemble streamflow prediction approach during the first week. The deterministic forecast error can be further reduced in the first 72 h when combined with the long short-term memory (LSTM) deep learning method. The forecast skill for LSTM using only historical observations drops sharply after the first 24 h.
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Short summary
Flash drought is referred to the rapid development of drought events with a fast decline of soil moisture, which has serious impacts on agriculture, the ecosystem, human health, and society. While flash droughts have received much research attention, there is no consensus on its definition. Here we used a stochastic water balance framework to quantify the timing of soil moisture crossing different thresholds, providing an efficient tool for diagnosing and monitoring flash droughts.
Flash drought is referred to the rapid development of drought events with a fast decline of soil...