| 摘要: |
| 城市的扩张建设会影响自然水文循
环，极端化气候造成建成环境的内涝现象逐
年增多。剖析城市建设与内涝风险之间的内
在耦合联系，揭示城市建成环境的内涝根
源，具有重要的研究价值，是填补多学科支
撑下城市防涝规划中理论空缺的重要环节。
本文以沈阳市建成区为例，系统研究了城市
地表基底环境对内涝风险的影响机制。通过
对建成区的绿化覆盖率、水体比例、不透水
面比例、林型分布、高差分布、客土分布、
地下建设分布及排涝设施标准等地表基底环
境因素进行详细统计，采用多因素线性回归
分析方法，量化各因素的权重系数及其相关
性。研究结果表明，排涝设施标准是内涝风
险强度的最显著影响因子，硬化比例和地下
建设分布对内涝风险强度具有较显著的正向
影响，其余因子则由于干扰因素过多并不显
著相关。 |
| 关键词: 城市建成环境 地表基底环境 内
涝风险强度 影响机制分析 |
| DOI：10.13791/j.cnki.hsfwest.20250311001 |
| 分类号: |
| 基金项目:国家自然科学基金青年基金项目（52308070） |
|
| Evaluation of the influence mechanism of urban area base environment on waterloggingrisk: Taking the main urban area of Shenyang City as an example |
|
CAO Xiaoyan,WANG Xi,CHU Yaqi,SHI Tiemao
|
| Abstract: |
| Urban expansion and construction significantly affect natural hydrological cycles, with
extreme climate conditions leading to an increasing frequency of waterlogging phenomena in built
environments year by year. Analyzing the internal coupling relationship between urban construction
and waterlogging risk while revealing the root causes of waterlogging in urban built environments
holds important research value and represents a crucial step in filling theoretical gaps in urban flood
prevention planning supported by multiple disciplines. This study systematically investigates the
influence mechanism of urban surface basement environment on waterlogging risk, taking the built-up
area of Shenyang City as an example. Through comprehensive statistical analysis of surface basement
environmental factors including green coverage rate, water body proportion, impervious surface
proportion, forest type distribution, elevation difference distribution, exotic soil distribution,
underground construction distribution, and drainage facility standards, this research employs multifactor
linear regression analysis methods to quantify the weight coefficients and correlations of each
factor. The methodology encompasses digital elevation model (DEM) construction for hydrological
and hydrodynamic modeling, utilizing equal volume method for inundation analysis and calculating
submersion depths of land patches within catchment areas. Geographic Information System (ArcGIS)
spatial analysis platform modules were employed for reclassification tools to divide factors according
to risk levels, adopting 500-meter grid statistics and natural breaks method to classify waterlogging
intensity into four levels corresponding to 50, 20, 10, and 5-year return period rainfall scenarios. The
research framework incorporates SPSS statistical analysis including likelihood ratio Omnibus testing,
correlation analysis, standardized ridge regression trace analysis, Vector Autoregression (VAR) model
variance decomposition, and multidimensional scaling analysis distance matrix to comprehensively
evaluate the dynamic contributions of different basement environment factors to waterlogging risk
prediction error variance. Results demonstrate that drainage facility standards constitute the most
significant influencing factor on waterlogging risk intensity, with a standardized coefficient of 0.260
and significance level of p=0.000, indicating that higher drainage facility standards correspond to
lower waterlogging risk intensity. The likelihood ratio chi-square value of 26.633 with eight degrees
of freedom and p=0.001 confirms the overall model significance, reflecting strong explanatory
capability of basement environment factors as a collective influence on waterlogging risk. Hardening
proportion and underground construction distribution exhibit relatively significant positive impacts on
waterlogging risk intensity, while other factors including green coverage rate, water body proportion,
forest type distribution, elevation difference distribution, and exotic soil distribution show nonsignificant
correlations due to excessive interference factors. VAR model variance decomposition
reveals that with increasing prediction periods, artificial environment factors demonstrate significantly
increased contributions, reflecting long-term cumulative effects, with drainage facility standardscontributing 6.568% and hardening proportion contributing 5.866% as dominant factors, while underground construction distribution contributes 2.531% as a
secondary factor. Natural regulation factors such as green coverage rate and water body proportion consistently maintain contributions below 1.9%, reflecting
insufficient utilization of urban green spaces and water bodies’ storage and regulation functions, possibly due to inadequate scale, scattered distribution, and
degraded ecological functions of urban green spaces in the old urban core areas within the first and second ring roads of Shenyang’s built-up area. The
correlation analysis reveals that although green coverage rate and water body proportion theoretically provide natural waterlogging mitigation capabilities, their
actual effectiveness remains limited in high-intensity urban development areas, suggesting threshold effects where natural factors only demonstrate effective
flood prevention when reaching certain scales. Distance matrix analysis indicates relatively small distances between green coverage rate and water body
proportion (8.79), and between hardening proportion and green coverage rate (8.109), suggesting synergistic effects in spatial distribution or functionality, while
forest type distribution maintains large distances from other factors (43.392 with green coverage rate), indicating independent influence mechanisms. The study
fills theoretical gaps in urban flood prevention planning under multidisciplinary support, providing scientific evidence for urban waterlogging risk management
and demonstrating that while drainage facility standards currently dominate waterlogging risk management, flood prevention strategies relying solely on gray
infrastructure present limitations. The multi-factor synergistic mechanism of urban basement environment reveals that future urban flood prevention should
adopt comprehensive “gray-green integration” strategies, optimizing drainage systems while fully utilizing natural patches’ storage and regulation potential.
This research establishes a “data-model-mechanism” three-element driving framework that can guide future urban built environment waterlogging risk studies
through integration of multi-source data and interdisciplinary methods, enhancing the robustness of risk prediction and decision support value for urban surface
environment renewal and resilience enhancement strategies. |
| Key words: urban construction environment surface base environment waterlogging risk intensity analysis of impact mechan |
