Urban Green-space Pattern Optimization for Mitigation of Overland flow



Completion Date:

Associate Prof. Min WANG, Tongji University
Prof. Harald Zepp, Ruhr Universität Bochum

Building Strong Ecological Security Patterns through Elevating Green Infrastructure’s Level of Ecosystem Services (China National R&D Program No. 2017YFC0505705)

April 2020


Due to the global climate change, cities are more frequently stuck with flood and water-logging nowadays, but their own rainwater regulation capacity has been weakened, since urbanization has greatly changed the urban water cycles. Urban green space (UGS) is the primary permeable landcover type, which can assist urban drainage system in preventing uncontrollable overland-flows, and its increased quantities have been proved to result in strengthened water-regulation capacity and lowered logging risks in many studies. However, there has been little discussion about the contribution of UGS. It remains unclear how UGS are spatially distributed could help cities strengthen the capacity of overland-flow mitigation.

Research Questions

In response to the research gap, my dissertation aims to explore the key features of UGS spatial pattern that associate with its service of overland-flow regulation and therefore to provide theoretical support for optimization of UGS spatial pattern in planning practices. For completion of the research goal, three main research questions are addressed as follows:

(I) How can we quantify the UGS’s service of overland-flow mitigation?

(II) What features of UGS spatial pattern have association with the water overland-flow mitigation?

(III) How to optimize UGS spatial pattern following the confirmed UGS pattern features?


The study area is located in the central urban area of Kunshan, Jiangsu Province, China. Firstly, spatial distribution of overland-flows is finely simulated using ArcGIS as well as its hydro-tool plugins. Then Unit Hydrograph of each catchment within the study area is drawn and exports its peak, peak time lag, duration, which can be integrally used to measure the overland-flow mitigation service; meanwhile a total of 10 pattern feature indices can be applied to delineate UGS pattern from the perspectives of the scale, the shape, and the spatial distribution. Finally, with correlation and regression analysis will the association between the pattern and the service be revealed.

Methodology Flowchart


The study drew some maim conclusions as follows:

① UGS Overland-flow mitigation service involves multiple dimensions of impact on the urban water cycle, thus it is more reasonable to quantify the service from three different aspects with the help of Unit Hydrograph, that is, flow peak, peak lag time, and runoff duration. A catchment that consumes good overland-flow mitigation service manifests itself with relatively lower flow peak, later peak lag time, and longer flow duration.

② Flow peak is negatively correlated with greening rate (GR), mean patch index(MPI), shape index(SI),integral index of connectivity(IIC), and positively correlated with patch density(PD), mean Euclidean nearest-neighbor distance(mENN). The multiple linear regression model composed of GR, MPI, shape index and mENN has the best performance compared with others. It is revealed that the efficiency of flow peak reduction decreases when greening rate has exceeded 30%; Besides, with the increase of the adjacent distance of UGS would flow peak rise faster.

③ Peak lag time is negatively correlated with edge density (ED), shape index(SI), related circumscribing circle(RCC), patch density(PD), mean Euclidean Nearest-Neighbor Distance(mENN) and integral index of connectivity(IIC), while positively related to greening rate(GR), largest patch index(LPI) and mean patch index(MPI). The multiple linear regression model composed of MPI, RCC, and division index (DI) has the best performance. When the average size of UGS is small, overland peak would delay with the increase of MPI; otherwise, the marginal effect would sharply shrink. The situation is similar when the shape index increases to a certain degree.

④ Runoff duration is negatively correlated with its edge density (ED), shape index (SI), related circumscribing circle (RCC), patch density (PD), mean Euclidean Nearest-Neighbor Distance(mENN) and integral index of connectivity (IIC), while positively related to greening rate (GR), largest patch index (LPI) and mean patch index (MPI). The multiplex linear regression model composed of LPI, MPI, mENN and IIC is the best performance. There is likely a quadratic function relationship between duration and IIC. The results revealed that the improved connectivity of UGS could prolong runoff duration only when it could exceed a threshold.

⑤ Based on the revealed association between overland-flow mitigation of UGS and the UGS pattern features, I proposed some guidelines for UGS pattern optimization, whose proves goes through investigation, simulation, diagnosis, strategy, and feedback.

Results of Overland-flow Simulation
Fine Simulation of Overland-flow

Selective Results of Curve Fitting

Selective Results of 5 Types of Unit Hydrograph