面向煤基能源产业气象服务需求的灾害预警阈值优化研究

doi:10.12006/j.issn.1673-1719.2025.107

摘要/Abstract

摘要：

以陕西榆林煤基能源产业为研究对象，构建“致灾因子—工艺参数—经济影响—服务需求”四维调研框架，基于413份企业调研问卷，系统分析了极端天气对煤基全产业链的影响机制。结果表明：气温、降水和风速是影响煤基能源产业运行的三大关键因子，其中强降水、低温冰冻和雷电为主要灾害类型。进一步建立了包括高温、强降水、大风等要素的动态阈值体系，揭示了复合灾害的协同放大效应，并据此提出分级预警机制。研究成果为煤基能源产业应对气候变化提供了科学依据。

关键词: 煤基能源产业, 极端天气, 阈值分析, 复合灾害, 预警系统

Abstract:

Against the backdrop of global warming, the frequent occurrence of extreme weather events poses severe challenges to the safe production and economic operation of energy industries, particularly coal-based industries. This study constructs a four-dimensional research framework of “hazard factor-process parameter-economic impact-service demand”, collecting 413 survey questionnaires and conducting field visits to nine typical enterprises. Through quantitative analysis of the correlation between meteorological elements and production parameters, a threshold system for the impact of meteorological disasters on the Yulin coal-based energy industry was established. The main conclusions are as follows. The three dominant meteorological factors affecting the coal-based energy industry are temperature (29.0%), precipitation (24.4%), and wind speed (19.3%). The three major meteorological disasters are heavy precipitation (14.8%), low-temperature freezing (14.2%), and lightning (13.6%). By integrating process parameters and equipment status indicators from different coal-based industries, dynamic thresholds for temperature, precipitation, relative humidity, strong winds, and air pressure variation were proposed. A tiered early-warning mechanism was established, and emergency plans were optimized based on regional climate characteristics to enhance resilience against extreme weather. The synergistic amplification mechanism of compound meteorological disasters was elucidated. The combination of “low temperature-high humidity-calm wind” significantly increased haze pollution concentration compared to individual factors, while the “dry heat-strong wind” combination sharply elevated the risk index of explosion and fire. The threshold system established in this study, based on data from Yulin, has regional limitations. When extended to bases in Shanxi, Inner Mongolia, and other regions, differences in precipitation distribution and wind speed characteristics must be carefully considered for determining critical threshold values. Although this threshold system possesses the capacity for dynamic evolution with technological advancements, climate change, and policy adjustments, future efforts should focus on establishing an update mechanism coupled with climate predictions to achieve proactive adaptive adjustments.

Key words: Coal-based energy industries, Extreme weather, Threshold analysis, Compound hazards, Early warning system

魏娜, 任琦, 孙娴, 王旭红, 杨晓春. 面向煤基能源产业气象服务需求的灾害预警阈值优化研究[J]. 气候变化研究进展, 2026, 22(1): 110-120.

WEI Na, REN Qi, SUN Xian, WANG Xu-Hong, YANG Xiao-Chun. Research on optimization of early warning thresholds for meteorological disasters in coal-based energy industry[J]. Climate Change Research, 2026, 22(1): 110-120.

图/表 13

图1 榆林市煤基产业气象服务需求调研的层次化框架图

Fig. 1 Hierarchical framework diagram of meteorological service demand survey for Yulin’s coal-based industry

表1 实地勘探能源企业列表

Table 1 Inventory of energy enterprises investigated in the field

图2 融合工艺参数与非线性机制的产业气象风险多级集成评估框架

Fig. 2 Multi-level integration framework for industrial meteorological risk assessment incorporating process parameters and nonlinear mechanisms

表2 煤基产业阈值温度数据

Table 2 Temperature threshold data for coal-based industries

图3 温度阈值分布与聚类中心

Fig. 3 Temperature threshold distribution and cluster centers

表3 五类企业分段回归参数估计表

Table 3 Parameter estimates for segmented regression of five enterprise types

表4 夏季日最高气温阈值等级

Table 4 Threshold classification of summer daily maximum temperature

表5 11月至次年4月日平均气温降温幅度阈值等级

Table 5 Threshold classification of daily mean temperature decreases from November to April of the following year

表6 降水阈值等级

Table 6 Threshold classification of precipitation

表7 大风阈值等级

Table 7 Threshold classification of gale wind speed

表8 相对湿度阈值等级

Table 8 Threshold classification of relative humidity

表9 气压阈值等级

Table 9 Threshold classification of barometric pressure

表10 复合型气象灾害阈值

Table 10 Threshold classification of compound extreme weather event

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