Articles | Volume 26, issue 12
https://doi.org/10.5194/hess-26-3241-2022
© Author(s) 2022. 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-26-3241-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2022
Research article |
| 24 Jun 2022
| 24 Jun 2022
Analysis of flash droughts in China using machine learning
Linqi Zhang
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Hydrology and Quantitative Water Management Group, Wageningen
University, Wageningen 6708PB, the Netherlands
Yi Liu
CORRESPONDING AUTHOR
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Liliang Ren
CORRESPONDING AUTHOR
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering, Hohai University, Nanjing 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Adriaan J. Teuling
Hydrology and Quantitative Water Management Group, Wageningen
University, Wageningen 6708PB, the Netherlands
Ye Zhu
College of Hydrology and Water Resources, Nanjing University of
Information Science & Technology, Nanjing 210044, China
Linyong Wei
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Linyan Zhang
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Shanhu Jiang
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Xiaoli Yang
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Xiuqin Fang
College of Hydrology and Water Resources, Hohai University, Nanjing
210098, China
Hang Yin
Institute of Water Resources for Pastoral Area, Ministry of Water
Resources, Inner Mongolia 010020, China
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Cited
11 citations as recorded by crossref.
- The patterns, magnitude, and drivers of unprecedented 2022 mega-drought in the Yangtze River Basin, China Y. Liu et al. 10.1088/1748-9326/acfe21
- A pathway analysis method for quantifying the contributions of precipitation and potential evapotranspiration anomalies to soil moisture drought C. Wang et al. 10.1016/j.jhydrol.2023.129570
- The prevalent life cycle of agricultural flash droughts M. Lovino et al. 10.1038/s41612-024-00618-0
- A New Evapotranspiration-Based Drought Index for Flash Drought Identification and Monitoring P. Li et al. 10.3390/rs16050780
- A Complementary Streamflow Attribution Framework Coupled Climate, Vegetation and Water Withdrawal S. Jiang et al. 10.1007/s11269-023-03582-1
- Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China J. Wen et al. 10.3390/su15043865
- Spatiotemporal evolution of agricultural drought and its attribution under different climate zones and vegetation types in the Yellow River Basin of China Y. Ding et al. 10.1016/j.scitotenv.2023.169687
- A spatiotemporal deconstruction-based approach for identifying flash drought expansion: Normalized Area-Time Accumulation curve R. Chen et al. 10.1016/j.jhydrol.2023.129509
- Evaluation of flash drought under the impact of heat wave events in southwestern Germany M. Wang et al. 10.1016/j.scitotenv.2023.166815
- Identification and analysis of evolution characteristics of flash drought based on three-dimensional perspective: A case study in the Jialing River basin C. Meng et al. 10.1016/j.uclim.2023.101803
- Technical note: A stochastic framework for identification and evaluation of flash drought Y. Li et al. 10.5194/hess-27-1077-2023
11 citations as recorded by crossref.
- The patterns, magnitude, and drivers of unprecedented 2022 mega-drought in the Yangtze River Basin, China Y. Liu et al. 10.1088/1748-9326/acfe21
- A pathway analysis method for quantifying the contributions of precipitation and potential evapotranspiration anomalies to soil moisture drought C. Wang et al. 10.1016/j.jhydrol.2023.129570
- The prevalent life cycle of agricultural flash droughts M. Lovino et al. 10.1038/s41612-024-00618-0
- A New Evapotranspiration-Based Drought Index for Flash Drought Identification and Monitoring P. Li et al. 10.3390/rs16050780
- A Complementary Streamflow Attribution Framework Coupled Climate, Vegetation and Water Withdrawal S. Jiang et al. 10.1007/s11269-023-03582-1
- Probabilistic Forecast and Risk Assessment of Flash Droughts Based on Numeric Weather Forecast: A Case Study in Zhejiang, China J. Wen et al. 10.3390/su15043865
- Spatiotemporal evolution of agricultural drought and its attribution under different climate zones and vegetation types in the Yellow River Basin of China Y. Ding et al. 10.1016/j.scitotenv.2023.169687
- A spatiotemporal deconstruction-based approach for identifying flash drought expansion: Normalized Area-Time Accumulation curve R. Chen et al. 10.1016/j.jhydrol.2023.129509
- Evaluation of flash drought under the impact of heat wave events in southwestern Germany M. Wang et al. 10.1016/j.scitotenv.2023.166815
- Identification and analysis of evolution characteristics of flash drought based on three-dimensional perspective: A case study in the Jialing River basin C. Meng et al. 10.1016/j.uclim.2023.101803
- Technical note: A stochastic framework for identification and evaluation of flash drought Y. Li et al. 10.5194/hess-27-1077-2023
Latest update: 19 Apr 2024
Short summary
In this study, three machine learning methods displayed a good detection capacity of flash droughts. The RF model was recommended to estimate the depletion rate of soil moisture and simulate flash drought by considering the multiple meteorological variable anomalies in the adjacent time to drought onset. The anomalies of precipitation and potential evapotranspiration exhibited a stronger synergistic but asymmetrical effect on flash droughts compared to slowly developing droughts.
In this study, three machine learning methods displayed a good detection capacity of flash...
