Articles | Volume 23, issue 4
https://doi.org/10.5194/nhess-23-1453-2023
© 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/nhess-23-1453-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Apr 2023
Research article |
| 20 Apr 2023
| 20 Apr 2023
Hydrological drought forecasting under a changing environment in the Luanhe River basin
Min Li
CORRESPONDING AUTHOR
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300072, China
Mingfeng Zhang
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
Runxiang Cao
College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
Yidi Sun
College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, 225000, China
Xiyuan Deng
CORRESPONDING AUTHOR
Nanjing Hydraulic Research Institute, Nanjing, 210029, China
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, 210029, China
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This study proposes an innovative method for predicting drought in the Huaihe River Basin of China using advanced machine learning and interpretable artificial intelligence techniques. By analyzing more than 50 years of data, the model successfully predicted four drought categories with an accuracy of 79.9 %. It used explanatory methods to analyze the contribution of different drought influencing factors, providing key insights for early warning systems and water resources planning.
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The purpose of this study is to analyse the effects of climate change and watershed characteristics on drought transmission. Based on the GAMLSS framework and the Copula model, the probabilities and thresholds for the spread of drought in different seasons are calculated without the influence of climate factors. The results show that the spatio-temporal changes in the spread of drought are influenced by climate change and watershed characteristics.
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This study proposes an innovative method for predicting drought in the Huaihe River Basin of China using advanced machine learning and interpretable artificial intelligence techniques. By analyzing more than 50 years of data, the model successfully predicted four drought categories with an accuracy of 79.9 %. It used explanatory methods to analyze the contribution of different drought influencing factors, providing key insights for early warning systems and water resources planning.
Min Li, Zilong Feng, Mingfeng Zhang, Lijie Shi, and Yuhang Yao
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-174, https://doi.org/10.5194/nhess-2024-174, 2025
Revised manuscript accepted for NHESS
Short summary
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The purpose of this study is to analyse the effects of climate change and watershed characteristics on drought transmission. Based on the GAMLSS framework and the Copula model, the probabilities and thresholds for the spread of drought in different seasons are calculated without the influence of climate factors. The results show that the spatio-temporal changes in the spread of drought are influenced by climate change and watershed characteristics.
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To improve the hydrometric methods in the high-altitude area, and learn more about the hydrological mechanism in the Qinghai-Tibet Plateau, a series of observation research were carried out in the Niyang River watershed, a tributary of the Yarlung Zangbo River. The applicability of the hydrometric methods and instruments were discussed according to the monitoring situation. Based on the δD, δ18O, and the geochemical observed dataset, the runoff composition characteristics were analyzed.
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Short summary
It is an important disaster reduction strategy to forecast hydrological drought. In order to analyse the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. The conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in the Luanhe River basin.
It is an important disaster reduction strategy to forecast hydrological drought. In order to...
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