基于疏勒河数字孪生流域平台的洪水预测模型及应用

doi:10.13866/j.azr.2025.11.06

摘要/Abstract

摘要：

气候剧烈变化使得极端降水事件频发，流域防洪减灾能力面临前所未有的挑战。疏勒河流域气象、地形、水文和植被等要素的组合极易形成洪水，流域现有的防洪模型预测水平较低。为了解决疏勒河流域防洪需求，开发疏勒河流域数字孪生平台并提高平台的精细化预测与模拟水平，构建了以改进SCS产汇流模型和SRM融雪模型为核心，并以马斯京根河道演进模型耦合的洪水预测模型；在缺资料区采用SCS-CN扣损法计算净雨量，并以三角形概化单位线实现高效汇流；针对高寒融雪洪水，引入SRM并将日尺度细化为时段计算。将疏勒河上游划分为121个小流域单元，建立“数据底板-模型平台-知识平台”的数字孪生架构，实现模型注册、参数率定、实时驱动与滚动预报。以2023年“0711”洪水进行参数率定，洪峰流量误差6.8%，峰现时差2 h；2024年7月15日洪水实测应用中，平台提前72 h发布预警，预报洪峰438 m³·s^-1、峰现时间7月15日12:00，实测分别为491 m³·s^-1与14:00，误差满足《水文情报预报规范》SL250-2000要求。系统及时支撑水库预泄与下游避险，有效削减洪灾损失。研究提升了洪水预测精度，为智慧水利和韧性建设提供智能化解决方案。

关键词: 洪水预报模型, 产汇流模型, 融雪径流模型, 数字孪生平台, 疏勒河

Abstract:

Intensifying climate change has led to a surge in extreme precipitation events, posing unprecedented challenges to basin-wide flood prevention and mitigation. The unique combination of meteorological, topographical, hydrological and vegetation factors in the Shule River Basin makes it highly prone to flooding, yet existing forecasting models in the region exhibit low predictive skill. To address the flood control needs of the Shule River Basin，a digital twin platform was developed, and its precision in prediction and simulation was enhanced，this study establishes a coupled flood-prediction framework that centers on an improved SCS rainfall-runoff model and a Snowmelt Runoff Model (SRM), integrated with the Muskingum channel-routing scheme. In data-scarce areas, the SCS-CN loss method is employed to compute net rainfall, and a triangular unit hydrograph is adopted for efficient flow concentration. For alpine snowmelt-driven floods, the SRM is introduced and refined from a daily to a sub-daily time step. The upper Shule River is discretized into 121 sub-basins, and a digital-twin architecture comprising a “data foundation-model platform-knowledge platform” is built, enabling model registration, parameter calibration, real-time forcing and rolling forecasts. Model parameters were calibrated against the July 11, 2023 flood event; the simulated peak discharge showed an error of 6.8% and a timing lag of 2 h relative to observations. During the operational forecast for the July 15, 2024 flood, the platform issued an alert 72 h in advance, predicting a peak discharge of 438 m³·s^-1 arriving at 12:00 on July 15, while the observed values were 491 m³·s^-1 and 14:00, respectively. All errors meet the tolerance criteria specified in the Chinese national standard SL250-2000 for hydrological forecasting. The system provided timely support for reservoir pre-release and downstream evacuation, effectively reducing flood losses. The study advances flood-forecast accuracy and offers an intelligent solution for smart water management and resilient basin development.

Key words: flood forecasting model, rainfall-runoff model, snowmelt runoff model, digital-twin platform, Shule River Basin

安建民, 张鹏举, 张建新, 史永杰. 基于疏勒河数字孪生流域平台的洪水预测模型及应用[J]. 干旱区研究, 2025, 42(11): 2018-2030.

AN Jianmin, ZHANG Pengju, ZHANG Jianxin, SHI Yongjie. Development and application of a flood forecasting model based on the Shule River digital-twin basin platform[J]. Arid Zone Research, 2025, 42(11): 2018-2030.

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模型类型	模型参数名称	初始值	单位
河道演进模型	马斯京根法河道分段数	1	个
	马斯京根法计算初始流量	0.5	m³·s^-1
	河道汇流马斯京根法系数	0.4	-
	稳定河道洪水传播历时	3	h
径流曲线模型	CN值	75	个
	初损系数	0.2	-
	土壤蓄水能力SW	13	mm
	无因次单位线	1	-
	长度	62.477	m
	坡度	0.023	%
	流域当时的最大可能滞留量	0	m³·s^-1
融雪径流模型	融雪径流系数	1	-
	气温日融雪率	2	mm·(℃·d)^-1
	流域温度直减率	1	℃·(100m)^-1
	降雨融雪度系数	1	-
	融雪临界温度	1	℃
	流量衰减系数	1	-

洪号	实测					洪峰流量检验				模拟峰现时间
洪号	起涨时间	起涨流量 /( m³·s^-1 )	洪峰流量 /(m³·s^-1)	峰现时间		模拟洪峰 /(m³·s^-1)	许可误差/%	合格否		模拟时间	许可误差	合格否
20230711	2023-07-12 06:00	12.7	271.2	2023-07-12 11:00		291	6.8	√		2023-07-12 13:00	2 h	√

模型类型	输入	输出
河道演进模型	开始时间	开始时间结束时间流域出口断面流量过程
	结束时间
	第一断面第一时刻初始流量
	第一断面第二时刻初始流量
	第二断面第一时刻初始流量
	河道汇流马斯京根法系数
	稳定河道洪水传播历时
径流曲线模型	开始时间	开始时间结束时间流域出口断面流量过程
	结束时间
	流域面平均雨量
	CN值
	初损系数
	土壤湿润等级
	无因次单位线
	流域概化长度
	流域坡度
	流域当时的最大可能滞留量
融雪径流模型	开始时间	开始时间结束时间流域出口断面流量过程
	结束时间
	融雪径流系数
	气温日融雪率
	流域温度直减率
	降雨融雪度系数
	融雪临界温度
	流量衰减系数
	高度带
	面降雨量

	洪峰流量/（m³·s^-1）	洪峰到达时间	径流深/m
预测	438.03	7月15日12:00	13.01
实际	491	7月15日14:00	13.33
误差率/%	10.80	8.33	1.61
规范许可误差率/%	20	30	20