Study on probabilistic forecasting of flash flood events based on critical rainfall uncertainty

梁峰铭; 王洁; 林诚杰; 刘淼; 李姣; 陈峨印

doi:10.11660/slfdxb.20240904

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Study on probabilistic forecasting of flash flood events based on critical rainfall uncertainty

更新时间：2026-06-29

- Study on probabilistic forecasting of flash flood events based on critical rainfall uncertainty
- Journal of Hydroelectric Engineering Vol. 43, Issue 9, (2024)
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- DOI：10.11660/slfdxb.20240904
  CLC：
- Online First：25 September 2024，
  
  Published：2024
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梁峰铭, 王洁, 林诚杰, et al. Study on probabilistic forecasting of flash flood events based on critical rainfall uncertainty[J]. Journal of Hydroelectric Engineering, 2024, 43(9).
DOI：

梁峰铭, 王洁, 林诚杰, et al. Study on probabilistic forecasting of flash flood events based on critical rainfall uncertainty[J]. Journal of Hydroelectric Engineering, 2024, 43(9). DOI： 10.11660/slfdxb.20240904.

摘要

水文模型作为洪水预报和水资源管理的有效途径，其参数对山洪临界雨量的确定产生显著影响。本文以河北柳林、西台峪和坡底3个小流域为研究对象，利用径流曲线水文模型、广义似然不确定性估计法和Sobol分析法，探究在不同模型参数组下临界雨量的变化区间，并提出一种山洪概率预报方法。结果表明：水文模型的平均纳什效率系数高于0.7，大部分洪水场次的洪峰相对误差低于12%；临界雨量的不确定性区间随着降雨历时和预警等级的增加而逐渐变宽，当土壤湿度较低时影响更为显著；水文模型的曲线数和初损率是影响临界雨量的主要参数，平均贡献率分别为46.23%和14.72%；相比于临界雨量法，概率预报法的综合评价指标提高了9.8%，可为山洪灾害预警提供更多的风险信息。

Abstract

Hydrological modeling is an effective approach to flood forecasting and water resources management

significantly impacting the determination of critical rainfall in flash flood warnings. Focusing on three small watersheds in Hebei province

namely Liulin

Xitaiyu and Podi

this study examines the ranges of variations in the critical rainfalls under different sets of hydrological model parameters

using the Soil Conservation Service Curve Number (SCS-CN) hydrological model

a generalized likelihood uncertainty estimation method

and the Sobol method. A probability forecast method for flash flood events is developed. The results indicate this SCS-CN model performs well in three case studies

achieving an average Nash-Sutcliffe efficiency coefficient exceeding 0.7 and a relative error below 12% in its peak discharge calculations for most of the flood events. The uncertainty of critical rainfalls gradually increases with rainfall duration or warning level

especially under the conditions of low soil moisture. The curve number and the initial loss ratio in the hydrological model are two primary parameters that affect critical rainfall uncertainty and contribute 46.2% and 14.7% on average

respectively. Compared to the critical rainfall method

our probability forecast method enhances the comprehensive evaluation index by 9.8%

offering additional risk information for flash flood warnings.

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