| 摘要: |
| 面对全球气候变化与快速城市化双
重挑战，解析建成环境对水文过程的影响是
提升城市韧性的关键。既有研究多侧重于宏
观尺度风险评估、绿地系统防涝效能，或仅
关注建筑单一增流效应，较为缺乏针对街区
尺度，三维建筑形态对地表径流“增流与抑
流”双重空间效应的定量解析。研究以沈阳
市6 个典型内涝街区为例，构建三维建筑形
态指标体系，通过多元线性回归和RF-SHAP
模型，确立各指标对地表径流的线性基准与
非线性阈值。结果表明：空间拥挤度SCD、
建筑结构指数BSI 为主导性增流指标，而建
筑平均高度AH与迎风面密度FAI 则为关键
性抑流指标； 当SCD＞30% 后径流激增，
BSI 在7~20 m区间增流效应显著，而AH在
30~60 m区间或FAI＜0.5 时，对径流的空间
阻滞效应最强。基于此，提出街区尺度防控
空间形态优化路径，以期为韧性城市的防涝
空间协同优化提供定量依据。 |
| 关键词: 城市内涝 三维建筑形态 地表径
流 街区尺度 非线性解析 |
| DOI：10.13791/j.cnki.hsfwest.20260216001 |
| 分类号: |
| 基金项目:国家自然科学基金青年项目（52308070）；国家自然科学基金面上项目（52378063）；辽宁省土木建筑学会科研项目（24LTJK23） |
|
| The impact of three-dimensional urban building forms at the block scale on surfacerunoff: A case study of the typical flood-prone blocks in Shenyang |
|
CHU Yaqi,LI Yating,SHI Yu,ZHAN Mingsong,LIU Chong
|
| Abstract: |
| Against the backdrop of escalating global climate change and rapid urbanization, enhancing
urban flood resilience and mitigating the impacts of extreme precipitation have become critical
research topics in urban planning and stormwater management. Buildings are dominant components
of the urban underlying surface, and their spatial configurations directly influence local hydrological
processes. However, existing studies have largely focused on macro-scale risk assessments or the
flood mitigation effectiveness of green space systems, generally treating buildings merely as a single
factor that increases runoff. Consequently, there has been a relative lack of systematic quantitative
analysis regarding the dual spatial effects, both runoff enhancement and suppression of threedimensional
building forms on surface runoff at the block scale. Taking six typical flood-prone blocks
in Shenyang as case studies, this research explores the nonlinear relationships and multidimensional
coupling effects between three-dimensional building spatial forms and surface runoff. The study
analyzes hydrological processes within the study area based on extreme rainfall scenarios.
Specifically, surface runoff data were simulated using a GIS-based Soil Conservation Service Curve
Number (SCS-CN) model. To ensure simulation accuracy, the model parameters were localized by
calculating comprehensive CN values through an area-weighting method based on block-specific land
use, soil properties, and vegetation cover. The hydrological response was then simulated under a 50-
year return period design rainstorm scenario, generating the spatial distribution of grid-level surface
runoff as the dependent variable for subsequent analyses. To comprehensively quantify the impact of
three-dimensional building forms, this study developed a three-dimensional building form index
system derived from the intrinsic transmission mechanism of “building form – land surface – surface
runoff”. These indicators encompass three dimensions: horizontal constraints, vertical regulation, and
spatial patterns. After multicollinearity testing and optimization using Pearson’s correlation
coefficient, seven core predictive indicators were retained. This study aimed to develop a
comprehensive analytical framework that integrates linear benchmarking with nonlinear analysis.
First, a multivariate linear regression benchmark model was employed to quantitatively analyze the
overall linear correlation and influence trends of various indicators on surface runoff. To overcome the
“black box” nature of complex models and precisely quantify the threshold effects of dominant
variables, the study introduced a nonlinear interpretation framework combining Random Forests (RF)
with the Shapley Additive Explanation (SHAP), incorporating feature importance ranking and partial
dependency plots for in-depth analysis. The results of the feature analysis indicate that multiple linear
regression and the RF-SHAP model can systematically analyze the influence of three-dimensional
building morphology on surface runoff. SHAP analysis revealed the relative importance and dual
attributes of various three-dimensional building morphological indicators. Spatial CongesDegree (SCD) and Building Structure Index (BSI) were identified as the primary runoff-enhancing indicators. The next most influential features were the
Frontal Area Index (FAI) and Average Height (AH), which serve as key runoff-suppressing indicators. Crucially, SHAP dependency plots generated through
nonlinear analysis revealed the multidimensional coupled characteristics of three-dimensional building forms on surface runoff, encompassing “horizontal
constraints, vertical regulation, and spatial blocking”. For instance, in the horizontal dimension, the impact of spatial density shifts abruptly around a threshold
of 30%; exceeding this value causes impermeable patches to infiltrate, leading to a sharp increase in runoff. BSI exhibits a significant runoff-enhancing effect
within the 7-20 range. The influence of Building Surface Area Average (BSA) exhibits a clear positive correlation with runoff, particularly becoming more
pronounced beyond 1,500 m2, as it triggers a strong local catchment effect causing instantaneous flow rates to far exceed the surface infiltration limit. In the
vertical dimension, an average building height between 30 and 60 m produces an optimal flow-suppressing effect by effectively intercepting wind and rain and
promoting evaporation. In contrast, when building height exceeds 60 m or windward density surpasses 0.5, building facades force high-altitude rainwater to
concentrate at the base, which, combined with near-surface wind resistance suppressing evaporation, actually exacerbates water accumulation. Additionally,
regarding spatial patterns, the building Height Variation Coefficient (HVC) exhibits a unique property: low values enhance runoff, whereas high values suppress
it. Specifically, positive flow-suppression effects occur when HVC falls within the 0.3-1.0 range, as the staggered arrangement of buildings significantly
increases three-dimensional spatial roughness and hydraulic resistance, thereby generating a spatial retention effect. In summary, this study demonstrates the
effectiveness of the RF-SHAP framework in analyzing the complex nonlinear effects of three-dimensional building forms on surface runoff. The findings
confirm that enhancing the three-dimensional morphological heterogeneity of buildings in both vertical and spatial dimensions effectively reduces the risk of
urban flooding caused by horizontal sprawl. These findings provide a solid quantitative foundation for developing coordinated optimization strategies for flood
prevention spaces at the block scale under extreme rainfall scenarios (e.g., mandating SCD of high-density blocks to remain ≤30%, subdividing large-scale
ground-floor commercial spaces, and vertically expanding low-rise, aging residential communities to 30-60 meters). |
| Key words: urban flooding three-dimensional building geometry surface runoff block scale nonlinear analys |
