Preprints
https://doi.org/10.5194/nhess-2022-244
https://doi.org/10.5194/nhess-2022-244
 
18 Oct 2022
18 Oct 2022
Status: a revised version of this preprint is currently under review for the journal NHESS.

Hydrological Drought forecasting under changing environment in Luanhe River basin

Min Li1,2, Mingfeng Zhang2, Runxiang Cao3, Yidi Sun2, and Xiyuan Deng4,5 Min Li et al.
  • 1State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, China
  • 2College of Hydraulic Science and Engineering, Yangzhou University, JiangSu, China
  • 3College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
  • 4Nanjing Hydraulic Research Institute, Nanjing, 210029, China
  • 5State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing, 210029, China

Abstract. Hydrological drought forecasting can mitigate the socio-economic and ecological impacts of drought. It is an important disaster reduction strategy to forecast the occurrence of hydrological drought according to the forecasting system. In this paper, the conditional distribution model with human activity factor as exogenous variable was constructed to forecast the hydrological drought based on meteorological drought, and then compared with the traditional normal distribution model and conditional distribution model. The results show that the runoff series of Luanhe River Basin from 1961 to 2010 was non-stationary. For the traditional conditional probability models, the transition probabilities of drought were affected by SPI time scales and forecasting periods. In order to analyze the impact of human activities on hydrological drought, we constructed the human activity factor based on the method of restoration. Subsequently, the conditional distribution models involving human index were constructed and the influence of human activities on drought transition probability was analyzed. With the increase of human index (HI) value, hydrological droughts tend to transition to more severe droughts. Finally, a scoring mechanism was applied to evaluate the performance of three drought forecasting models. According to the scores of the three drought forecasting models, the conditional distribution model involving of human activity factor can further improve the forecasting accuracy of drought in Luanhe River Basin.

Min Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-244', fawen li, 25 Oct 2022
    • AC1: 'Reply on RC1', Mingfeng Zhang, 17 Nov 2022
  • RC2: 'Comment on nhess-2022-244', Anonymous Referee #2, 02 Nov 2022
    • AC3: 'Reply on RC2', Mingfeng Zhang, 17 Nov 2022
  • RC3: 'Comment on nhess-2022-244', fawen li, 16 Nov 2022
    • AC2: 'Reply on RC3', Mingfeng Zhang, 17 Nov 2022

Min Li et al.

Min Li et al.

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Short summary
It is an important disaster reduction strategy to forecast hydrological drought.In order to analyze 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 Luanhe River Basin.
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