Articles | Volume 24, issue 11
https://doi.org/10.5194/hess-24-5579-2020
© Author(s) 2020. 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-24-5579-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rapid reduction in ecosystem productivity caused by flash droughts based on decade-long FLUXNET observations
Miao Zhang
Key Laboratory of Regional Climate-Environment for Temperate East Asia (RCE-TEA), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
School of Hydrology and Water Resources, Nanjing University of
Information Science and Technology, Nanjing 210044, China
Related authors
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Yuxin Li, Sisi Chen, Jun Yin, and Xing Yuan
Hydrol. Earth Syst. Sci., 27, 1077–1087, https://doi.org/10.5194/hess-27-1077-2023, https://doi.org/10.5194/hess-27-1077-2023, 2023
Short summary
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.
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
We identify flash drought events by considering the decline rate of soil moisture and the drought persistency, and we detect the response of ecosystem carbon and water fluxes to flash droughts based on FLUXNET observations. We find rapid declines in carbon assimilation within 16–24 d of flash drought onset, where savannas show the highest sensitivity. Water use efficiency increases for forests but decreases for herbaceous ecosystems during the recovery stage of flash droughts.
We identify flash drought events by considering the decline rate of soil moisture and the...