[1]张思玲,郭晓军*,李 泳.统计模型在降雨型泥石流预报中的不确定性及优化[J].山地学报,2025,(3):438-452.[doi:10.16089/j.cnki.1008-2786.000903]
　ZHANG Siling,GUO Xiaojun*,LI Yong.Uncertainty and Optimization of Statistical Model for Rainfall-Triggered Debris Flow Forecast[J].Mountain Research,2025,(3):438-452.[doi:10.16089/j.cnki.1008-2786.000903]
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统计模型在降雨型泥石流预报中的不确定性及优化()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2025年第3期
页码:
438-452
栏目:
山地灾害
出版日期:
2025-06-20

文章信息/Info

Title:
Uncertainty and Optimization of Statistical Model for Rainfall-Triggered Debris Flow Forecast
文章编号:
1008-2786-(2025)3-438-15
作者:
张思玲1; 2; 郭晓军1*; 李 泳1
(1. 中国科学院、水利部成都山地灾害与环境研究所 山地灾害与地表过程重点试验室,成都 610213; 2.中国科学院大学,北京100049)
Author(s):
ZHANG Siling1; 2;  GUO Xiaojun1*;  LI Yong1
(1. Key Laboratory of Mountain Hazards and Surface Process, Chengdu Institute of Mountain Hazards and Environment, Chinese Academy of Sciences & Ministry of Water Resources, Chengdu 610213, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China)
关键词:
泥石流;  不确定性;  降雨阈值
Keywords:
debris flow;  uncertainty;  rainfall threshold
分类号:
P642. 23
DOI:
10.16089/j.cnki.1008-2786.000903
文献标志码:
A
摘要:
传统泥石流预报多依赖降雨-灾害事件的统计关联来确定降雨阈值。然而,此类方法受限于数据来源异质性、模型构建与分析等环节的复合不确定性,导致预报结果的可靠性存疑。本文系统解析基于统计模型的泥石流预警框架中不确定性的传递机制,并以具备高精度监测网络的典型小流域为实证对象,从数据采集、处理、模型参数化、阈值推导到结果验证的全流程,定量评估各环节不确定性的累积效应。研究揭示:(1)在数据处理阶段,雨量站空间代表性差异(误差放大系数1.2倍,平均雨量波动±35%)、降雨事件分割标准(误差放大系数4.1倍,临界雨量偏移20%～37%)及泥石流启动时间判定偏差(误差放大系数4.7倍,时间窗误差导致阈值失效率62%),可导致平均降雨强度计算值产生放大效应。(2)参数化建模阶段,降雨强度-降雨时间(I-D)模型在6 h/24 h降雨历时条件下最高与最低阈值诱发雨量比达7.0/8.1倍,诱发雨量-降雨时间-前期雨量(E-D-Ra)模型相应比值为3.4/2.8倍,这表明统计模型在人为主观决策与自然随机过程下显著放大阈值预测的不确定性。据此,本文基于误差溯源分析,从数据源优化、参数敏感性控制、模型结构改进三个维度,提出了降低不确定性的策略框架。研究成果可为构建物理机制与统计规律相融合的泥石流精细预报体系提供理论支撑。
Abstract:
Conventional debris flow forecasting primarily relies on statistical correlations between precipitation and disaster events to determine rainfall thresholds. However, such methods suffer from inherent uncertainties arising from heterogeneous data sources, model construction, and analytical processes, compromising the reliability of forecasting results.
In this study, it systematically dissected the uncertainty propagation mechanisms within statistical model-based debris flow anticipation frameworks. Using a small watershed equipped with a high-precision monitoring network as a case study, it quantitatively evaluated the cumulative effects of uncertainties across the entire workflow, including data acquisition and processing, model parameterization, threshold derivation, and result validation. Key findings include as below.
(1)During data preprocessing, spatial representativeness discrepancies among rainfall observation stations(error amplification factor of 1.2, average rainfall fluctuations ±35%), rainfall event segmentation criteria(error amplification factor of 4.1, critical rainfall deviations of 20%-35%), and biases in determining debris flow initiation time(error amplification factor of 4.7, time window errors causing 62% threshold failure rate)collectively made cascading amplification in calculating average rainfall intensity.
(2)During model parameterization, the maximum-to-minimum threshold precipitation ratios for the Intensity-Duration(I-D)model reached 7.0(6 h rainfall durations)and 8.1(24 h rainfall durations), while corresponding ratios for the Event-Duration-Antecedent rainfall(E-D-Ra)model were 3.4(6 h rainfall durations)and 2.8(24 h rainfall durations). This demonstrates significant uncertainty amplification in statistical forecasting resulting from subjective modeling decisions and inherent environmental stochasticity.
Building on error source tracing, this study develops a three-pillar uncertainty mitigation framework:(1)data source optimization,(2)parameter sensitivity control, and(3)model refinement. The integrated approach establishes theoretical foundations for advanced debris flow forecasting systems that synthesize physical mechanisms with statistical patterns, enhancing early warning reliability.

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备注/Memo

备注/Memo:
收稿日期(Received date): 2025- 04- 08; 改回日期(Accepted date):2025- 06-22
基金项目(Foundation item): 国家自然科学基金(42322703); 四川省科技厅项目(2022JDJQ0008); 西部之光青年学者项目。[National Natural Science Foundation of China(42322703); Sichuan Provincial Science and Technology Department Project(2022JDJQ0008); Western Light Youth Scholars Program]
作者简介(Biography): 张思玲(1999-),女,重庆人,硕士研究生,主要研究方向:泥石流形成和预报。[ZHANG Siling(1999- ), female, born in Chongqing, M.Sc. candidate, research on formation and forecast of debris flow] E-mail: zhangsiling@imde.ac.cn
*通讯作者(Corresponding author): 郭晓军(1985- ),男,博士,研究员,主要研究方向:泥石流形成和预报。[GUO Xiaojun(1985- ), male, Ph.D, professor, research on formation and forecast of debris flow] E-mail: aaronguo@imde.ac.cn
更新日期/Last Update: 2025-05-30