Sustainable Stormwater Management: Runoff Impact of Urban Land Layout with Multi-Level Impervious Surface Coverage

Abstract

The expansion of urban impervious surfaces exacerbates flooding risks, influenced by both impervious surface coverage (ISC) and its spatial distribution. To investigate the impact of urban land use layouts on stormwater runoff, this study examined the current land use conditions in the Xinling Bay watershed of Xiamen, China, and generalized land use into three ISC classes: impervious (I, ISC = 100%), semi-pervious (S, ISC = 50%), and pervious (P, ISC = 0%). Six spatial layouts (ISP, IPS, SIP, PIS, SPI, and PSI) were modeled using SWMM under varying rainfall intensities and land unit scales. The influence of ISC layouts on peak runoff, peak time, and total runoff was simulated. The results indicate: (1) The IPS spatial layout yields the most effective stormwater mitigation; (2) Prioritizing impervious land upstream while avoiding pervious units upstream minimizes runoff; (3) Layout effects weaken with higher rainfall intensity but strengthen with larger scales. These findings provide actionable strategies for sustainable urban planning to enhance flood resilience through spatial distribution optimization.

1. Introduction

Urbanization has brought the expansion of impervious surfaces such as buildings and roads, triggering severe urban flooding [1,2]. Various measures have been proposed to combat urban flooding, such as “Sponge City” and Low-Impact Development (LID) [3], which primarily focus on reducing Impervious Surface Coverage (ISC) to optimize urban hydrological effects [4]. However, the growth of impervious surfaces during urbanization is challenging to avoid, necessitating a combination of strategies for effective mitigation. Urban flooding is influenced not only by the quantity of impervious surfaces but also by their spatial distribution [5,6,7]. Therefore, revealing the relationship between ISC layouts and urban stormwater runoff effects is crucial. By strategically controlling the spatial layout of ISC, the adverse runoff effects resulting from increased ISC can be minimized.

Extensive research has established a statistically significant positive correlation between ISC and urban surface runoff generation [8,9], identifying ISC as a pivotal determinant of total runoff and runoff coefficients. This influence persists consistently across diverse storm intensity scenarios [10,11]. The spatiotemporal dynamics of urban impervious surfaces and their hydrological impacts have been systematically investigated through both qualitative and quantitative methodologies [12]. For instance, Miller et al. reported a 400% amplification in peak flow when the impervious surface ratio escalated from 11% to 44% within their study area [13]. Although these studies offer foundational insights into the quantitative nexus between impervious surfaces and runoff, their reliance on coarse-resolution remote sensing data and broad urban watershed scales limits their applicability to spatially refined land unit layout frameworks.

Recent research has transitioned from total impervious area to spatial configuration analysis, emphasizing how the arrangement of impervious surfaces modulates runoff dynamics [14]. For instance, Zhang et al. identified pronounced spatial heterogeneity in runoff regulation mechanisms at the neighborhood scale [15]. Empirical studies consistently affirm that interspersing impervious surfaces with permeable patches enhances hydrological performance, with impervious-pervious runoff redirection emerging as the optimal binary layout strategy. Ban et al. employed 10 distinct runoff routing scenarios to quantify infiltration gains, revealing that permeable interruptions could amplify total infiltration by up to 100% in their study area [16]. Ferreira et al. conducted controlled rainfall experiments to compare clustered, regularly dispersed, and irregularly dispersed impervious layouts, with the latter exhibiting superior runoff attenuation [17]. Silva et al. established that reduced hydraulic connectivity between impervious and pervious zones exacerbates surface runoff, underscoring the critical role of spatial fragmentation [18]. Zhou et al. demonstrated that green space interrupting impervious areas achieves substantial reductions in both total runoff and peak runoff, particularly for short-duration, low-peak-coefficient storms [19]. It is widely confirmed that the efficacy of spatial layout interventions on peak flow magnitude and timing attenuates with prolonged rainfall events [20].

Scholars have further focused on different urban land use types and statistically found that there are specific correlations between urban land use types and their runoff quantity or quality [21]. This is mainly attributed to ISC and its spatial configuration within land parcels, as well as the human activities generated in the built environments [22]. For example, empirical analyses indicate that commercial and industrial zones typically exhibit clustered impervious surface distributions, whereas residential areas demonstrate fragmented spatial patterns [23]. Recent frameworks for equitable coastal resilience emphasize that disaster prevention strategies should prioritize hydraulic justice, particularly through spatially optimized land use allocations in flood-prone informal settlements [24]. These insights offer actionable principles for optimizing site-specific stormwater management through site design and building layout.

The studies above primarily focus on the impact of impervious surfaces (ISC = 100%) and pervious surfaces (ISC = 0%) within individual land units on runoff. However, during urban planning and design, the microscale layout relationships between impervious surfaces (e.g., buildings and roads) and pervious surfaces (e.g., lawns) have not been addressed. Land use is typically divided into functional units such as parks, schools, residential, industrial, and commercial areas, and their permeability characteristics are comprehensively represented by the ISC index. Since ISC values can range from 0% to 100% across these units, a multi-level gradient emerges. While the hydrological interplay between entirely impervious (ISC = 100%) and entirely pervious (ISC = 0%) surfaces has been extensively characterized, the optimal spatial configuration when introducing intermediate imperviousness (e.g., ISC = 50%) remains a research gap. Consequently, two fundamental questions persist: (1) How do multi-level ISC gradients (0–50–100%) modulate runoff impacts? (2) What spatial configuration principles govern high-performance runoff impacts? These are essential for advancing urban hydrological science and sustainable stormwater management, especially in preliminary urban design phases [25].

This study aims to reveal the runoff impact of urban multi-level ISC layouts and is divided into two main parts: (1) Constructing a multi-level ISC land layout model that accounts for variations in rainfall return periods and land unit scales to simulate the effects of different land layouts on runoff. (2) Analyzing the relationships between land unit layout changes and runoff characteristics, including peak runoff, peak time, and total runoff. Xiamen was selected as a representative city due to its high urbanization, frequent flooding challenges, and comprehensive land-use diversity. This study identifies the influence patterns of land unit layouts on runoff and proposes optimal models for mitigating urban flooding, providing a scientific basis for urban land use planning.

2. Materials and Methods

2.1. Extraction and Classification of ISC

ISC is the key indicator that reflects the overall runoff behavior of land units; at the same time, it is significantly correlated with land use and development intensity indicators, such as construction density and the green ratio [26]. So, it could serve as an intermediary factor in establishing the relationship between urban land layouts and runoff impacts. Therefore, the first step is identifying and classifying the ISC of urban lands.

Xiamen is a renowned island city in China, and the Xinglin Bay watershed in Jimei District is a key area during its development. The region has experienced rapid urbanization and now features diverse land use types, showcasing the typical characteristics of modern urban development in southeast coastal areas.

Therefore, this research selected the Xinglin Bay watershed as the study area. It used Sentinel-2 data (downloaded from the European Space Agency’s L2A product) to analyze the current land use and eCognition 9.0 to extract land cover (Figure 1). ISC values for each land unit were then calculated by ArcGIS. According to Yan et al., when the total ISC ranges from 20% to 80%, total runoff in subwatersheds will increase rapidly [27]. So that urban land was classified into three categories based on ISC: 0–20%, 20–80%, and 80–100%, and then generalized into three classes: 0% (P), representing low development density sites; 50% (S), representing medium development density sites; and 100% (I), representing high development density sites (

Table 1. ISC classification of urban land.

Land Use	Green			Educational			Residential			Industrial			Commercial
Land unit number	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
ISC (%)	0	9	20	36	40	53	44	51	62	81	82	86	87	92	96
ISC classification	0% (P)			50% (S)						100% (I)
Representative land unit image
Land unit diagram

).

2.2. Land Unit Scale Classification

Since land use layouts exhibit different hydrological contributions at various spatial scales [28], this study aims to reveal how runoff impact varies across different scales. Drawing on common empirical metrics for road network spacing in China’s urban planning practice, combined with systematic analysis of actual land-use data within the study area, the arterial road, secondary road, and branch road network intervals were classified into the following three categories: 300 m × 300 m, 500 m × 500 m, and 1000 m × 1000 m. This study then examines how land use layouts at these scales influence urban stormwater runoff.

2.3. Model Construction and Parameter Settings

Given the challenges of adjusting land layouts in practice, this study employs the Storm Water Management Model (SWMM) [29], which is well-suited for simulating runoff processes under varying land use layouts, to conduct numerical experiments. The experimental setup consists of three land units on a hypothetical slope, each with an ISC of 0%, 50%, and 100%, respectively. The slope maintains a uniform gradient, with rainfall runoff naturally converging from the upper to the lower slope, ultimately flowing into a storage unit capable of accommodating the total runoff from all three land units to prevent overflow and ensure data accuracy. By altering the sequence of land unit connections, six different land use layout configurations are generated (Figure 2).

The SWMM modeling framework integrates three hydrological processes, mathematically described as follows:

a. Runoff Generation Process: The runoff generation process is simulated using the Horton infiltration model [30], which effectively describes the infiltration process in urban areas, where infiltration capacity decreases over time due to soil saturation and compaction. The infiltration process is calculated based on the Horton equation, as shown in Equation (1) as follows:

$$f_t=f_c+(f_0-f_c)e^{-kt}$$

(1)

where f_t is the infiltration rate at time t (mm/h); f₀ is the initial infiltration rate (mm/h); f_c is the steady-state infiltration rate (mm/h); t is time (hours); and k is the decay coefficient (h⁻¹).

b. Runoff Routing Process: The surface runoff routing process is simulated using the water balance equation, with runoff discharge calculated based on Manning’s equation, as shown in Equation (2) as follows:

$$Q=1.49W(d-d_p{)}^{5/3}\cdot S^{1/2}/n$$

(2)

where Q is the runoff discharge (m³/s), W is the width of the land unit (m), S is the average slope, n is the Manning’s roughness coefficient, d is the water depth (m), and d_p is the depression storage depth (m).

c. Water Conveyance Process: The dynamic wave method, suitable for short-term simulations, is used to solve the Saint-Venant equations, as shown in Equations (3) and (4) as follows:

$$\partial A/\partial t+\partial Q/\partial x=0$$

(3)

$$g\cdot A\cdot \partial H/\partial x+\partial (Q^2/A)/\partial x+\partial Q/\partial t+g\cdot A\cdot S_f=0$$

(4)

where Q is the discharge (m³/s), H is the water depth (m), A is the cross-sectional flow area (m²), g is the gravitational acceleration (9.8 m/s²), S_f is the friction slope; t is time (s), and x is the distance (m).

The calibrated data from Xiamen, China, was selected as the model simulation parameters [31,32], while other parameters were set based on empirical values provided in the SWMM5.2 manual. The relevant parameter settings are shown in

Table 2. Input parameters for subcatchment data.

Parameters	Value	Units
%Slope	2	%
N-Imperv (Manning’s N for impervious area)	0.01	-
N-Perv (Manning’s N for pervious area)	0.2	-
%Zero-Imperv	0	%
Subarea Routing	Pervious	-
Percent Routed	100	%
Max. Infil. Rate	89.3	mm/h
Min. Infil. Rate	2.5	mm/h
Decay Constant	8.1	1/h

.

2.4. Design of Rainfall Scenarios

To explore the impact of land layouts on runoff effects under different rainfall intensities, several rainfall scenarios with return periods (P) of 1 a, 2 a, 5 a, 10 a, 20 a, and 50 a were established based on the rainfall intensity formula of Xiamen (Equation (5)). The rainfall duration was set to 180 min, with a peak coefficient of r = 0.4. The designed rainfall data for each scenario is shown in

Table 3. Design rainfall data of 180 min for each return period in Xiamen, China.

	Rainfall Return Periods (P)
	1 a	2 a	5 a	10 a	20 a	50 a
Rainfall intensity/(mm/min)	2.52	3.06	3.77	4.32	4.86	5.58
Rainfall accumulations/(mm)	61.74	75.05	92.64	105.95	119.26	136.85

, and the time series diagrams of rainfall intensity and rainfall accumulations are shown in Figure 3.

$$q={\displaystyle \frac{928.15\times (1+0.716\lg P)}{{(t+4.4)}^{0.535}}}$$

(5)

where q represents the rainfall intensity (L/(s·hm²)), P is the rainfall return period (a), and t is the rainfall duration (min).

2.5. Data Processing and Analysis

In the SWMM model, a 3 h rainfall and 12 h simulation were conducted to observe the entire process of surface runoff, including its generation, peak, and recession phases. By modifying the input parameters to adjust the rainfall return periods and applying three ISCs (0%, 50%, and 100%) at different land unit scales and connection sequences, runoff data were simulated for each rainfall return period and land unit scale under various land use layouts. The runoff effects were comprehensively reflected through three key indicators: peak flow (Q_p), time to peak (T_p), and total runoff volume (Q_t).

Among the three indicators, PSI demonstrated the poorest performance across all measures. Thus, PSI was established as the baseline reference group, and the characteristic indicator values for other groups were compared. The difference (∆) between each group’s value and the PSI value was calculated and divided by the baseline (∆), yielding the optimization rate of each parameter (∆Q_p%, ∆T_p%, and ∆Q_t%). The relationships between these indicators are graphically presented in Figure 4, with the mathematical formulation explicitly detailed in Equation (6).

$$\left\{\begin{array}{l}\Delta Q_t\%={\displaystyle \frac{\Delta Q_t}{Q_{PSIt}}}\times 100\%\\ \Delta Q_p\%={\displaystyle \frac{\Delta Q_p}{Q_{PSIp}}}\times 100\%\\ \Delta T_p\%={\displaystyle \frac{\Delta T_p}{T_{PSIp}}}\times 100\%\end{array}\right.$$

(6)

3. Results and Discussion

To evaluate the runoff effects of each land layout, the SWMM model simulated the following three key indicators: peak flow, peak time, and total runoff. The impacts of land layout on each of the three indicators were individually analyzed in this section.

3.1. Peak Flow Reduction Effect

The simulation results of peak flow values (

Table 4. Peak runoff (Q_p) of six layout modes under each rainfall and unit scale (m³/s).

Scale	300 m × 300 m						500 m × 500 m						1000 m × 1000 m
P	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a
ISP	1.54	2.13	2.98	3.67	4.38	5.36	3.22	4.44	6.22	7.67	9.19	11.30	8.52	12.02	16.81	20.71	24.82	30.56
IPS	1.50	2.09	2.93	3.60	4.31	5.29	3.14	4.36	6.12	7.55	9.06	11.15	8.58	11.84	16.58	20.44	24.52	30.22
SIP	1.53	2.12	2.98	3.67	4.39	5.38	3.20	4.43	6.22	7.67	9.21	11.34	8.72	12.03	16.84	20.77	24.92	30.71
PIS	2.15	2.85	3.83	4.61	5.42	6.54	4.65	6.23	8.46	10.24	12.10	14.65	12.81	17.28	23.71	28.89	34.33	41.85
SPI	1.99	2.55	3.39	4.09	4.83	5.87	4.79	6.11	8.02	9.57	11.20	13.47	15.37	19.64	25.68	30.50	35.50	42.39
PSI	2.22	2.93	3.92	4.71	5.52	6.64	5.15	6.73	8.99	10.81	12.69	15.28	15.94	20.70	27.48	32.91	38.56	46.43

) and peak flow reduction rates (Figure 5) for different land use layouts are summarized as follows:

Under all rainfall intensities, modes ISP, IPS, and SIP show significant advantages in peak flow values and reduction rates. Under the 50 a return period rainfall, the reduction rates across scales reach from 18.9% to 34.9%, indicating that even under higher rainfall intensities, these three layouts still have a notable peak flow reduction effect. Among these layout models, IPS exhibits the most effective peak flow reduction. The ranking of the peak flow reduction effect is IPS > ISP ≈ SIP > PIS/SPI > PSI.

On the rainfall intensity impacts, peak flow values increase with rainfall intensity while reduction rates decline for all modes except SPI.

On the land unit scale impacts, larger scales (e.g., 1000 m × 1000 m) amplify peak flow reduction rates for ISP, IPS, and SIP. SPI shows scale-dependent anomalies: At 300 m and 500 m scales, SPI outperforms PIS due to midstream permeability buffering upstream runoff. At the 1000 m scale, SPI’s reduction rates drop below PIS as infiltration capacity saturates.

3.2. Peak Time Delay Effect

The simulation results for peak time values (

Table 5. Peak time (Tp) of six layout modes under each rainfall and unit scale (min).

Scale	300 m × 300 m						500 m × 500 m						1000 m × 1000 m
P	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a
ISP	119	111	105	102	99	97	145	134	124	119	115	111	181	181	169	160	152	145
IPS	111	105	99	96	94	92	133	123	115	110	107	103	181	166	152	144	138	131
SIP	112	105	100	97	95	92	134	124	116	111	108	104	181	168	153	145	139	133
PIS	87	85	84	84	83	82	94	92	90	89	88	87	114	108	104	101	99	97
SPI	75	75	75	75	75	75	76	76	76	76	76	75	78	77	77	77	77	77
PSI	76	76	75	75	75	75	77	77	76	76	76	76	79	78	78	78	78	77

) and peak time delay rates (Figure 6) reveal the following key findings:

Under all rainfall return periods, land use layouts ISP, IPS, and SIP exhibit significant peak time delay advantages. ISP achieves maximum delays and reaches 88.3% at a 1000 m scale under 50 a rainfall. In contrast, SPI and PSI show minimal delays (75–78 min), aligning with rainfall peak time (72 min). The ranking of peak time delay effects is ISP > IPS ≈ SIP > PIS > SPI ≈ PSI.

On the rainfall intensity impacts, peak time advances with higher rainfall intensity. Delay rates decline but remain effective for ISP, IPS, and SIP (e.g., ISP maintains 29.3–88.3% delays under 50-year storms). Conversely, SPI and PSI delays remain insensitive to rainfall intensity. This phenomenon is primarily attributed to the 100% impervious land being situated at the lowest downstream point, where surface runoff rapidly and directly discharges into the storage unit, resulting in earlier peak times.

On the land unit scale impacts, larger scales enhance peak time delays. When the scale reaches 1000 m × 1000 m, the peak times for ISP, IPS, and SIP under the 1 a return period occur at 181 min, which is later than the designed rainfall cessation time (180 min). This indicates that the land unit scale has a positive correlation effect on peak time delay.

3.3. Total Runoff Reduction Effect

The simulation results for the total runoff volume (

Table 6. Total runoff (Q_t) of six layout modes under each rainfall and unit scale (10³ m³).

Scale	300 m × 300 m						500 m × 500 m						1000 m × 1000 m
P	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a	1 a	2 a	5 a	10 a	20 a	50 a
ISP	11.6	15.1	19.8	23.3	26.9	31.6	30.4	40.2	53.2	63.0	72.9	86.0	109.0	147.0	199.0	238.0	277.0	330.0
IPS	11.5	15.0	19.7	23.3	26.8	31.5	30.3	40.0	52.9	62.8	72.6	85.7	109.0	147.0	198.0	237.0	276.0	328.0
SIP	11.6	15.1	19.8	23.4	26.9	31.7	30.6	40.3	53.3	63.2	73.1	86.2	110.0	149.0	200.0	239.0	278.0	330.0
PIS	11.8	15.2	19.9	23.4	27.0	31.7	31.3	40.8	53.6	63.4	73.2	86.2	117.0	153.0	203.0	241.0	280.0	331.0
SPI	11.7	15.2	19.9	23.4	27.0	31.7	31.0	40.6	53.6	63.4	73.3	86.3	113.0	151.0	202.0	240.0	279.0	331.0
PSI	11.8	15.3	19.9	23.4	27.0	31.7	31.5	41.0	53.7	63.5	73.3	86.3	118.0	154.0	204.0	242.0	281.0	332.0

) and total runoff reduction rates (Figure 7) under varying rainfall return periods and land unit scales reveal the following key findings:

Under all rainfall return periods, the total runoff reduction effects of different land use layout modes follow the order of IPS > ISP > SIP > SPI > PIS > PSI, indicating that IPS consistently achieves the most significant runoff reduction.

On the rainfall intensity impacts, the total runoff increases with higher rainfall intensity, while the runoff reduction rate gradually decreases. This trend suggests that more intense rainfall leads to greater runoff generation, thereby reducing the effectiveness of runoff reduction measures.

On the land unit scale impacts, both the total runoff volume and the runoff reduction rate increase as the land unit scale expands under the same rainfall intensity. Notably, when the land unit scale reaches 1000 m × 1000 m, the changes in runoff volume and reduction rate become more pronounced. This demonstrates that larger land unit scales amplify the impact of land use layout on runoff volume dynamics.

3.4. Runoff Process Optimization Effect Analysis

The comprehensive optimization of the runoff process shows a consistent pattern across different land unit scales and layout modes. Figure 8 presents the runoff process curves for the six layout modes at the 1000 m × 1000 m land unit scale, revealing the following key observations: ISP, IPS, and SIP exhibit characteristics of later peak times, slower rise rates, lower peak flows, and slower recession. In contrast, PIS, SPI, and PSI display features of earlier peak times, faster rise rates, higher peak flows, faster recession, and shorter periods of intense runoff accumulation.

Considering the combined effects of peak flow, peak time, and total runoff, the runoff optimization effects of ISP, IPS, and SIP are relatively better. Among these, IPS demonstrates superior performance in terms of peak flow reduction and total runoff control, making it the optimal land layout for comprehensive runoff effect optimization.

From the perspective of land use layout characteristics, ISP, IPS, and SIP, which exhibit superior overall runoff mitigation effects, share a common feature: the most impervious land unit (I, ISC = 100%) is consistently followed downstream by the most pervious land unit (P, ISC = 0%). This approach facilitates a sequential runoff attenuation: The 100% impervious unit (I) generates rapid peak flow, which is then buffered by the fully pervious unit (P), allowing gradual infiltration before reaching the semi-pervious (S) unit downstream. This cascading effect reduces flow velocity, enhances retention, and maximizes the infiltration capacity of pervious surfaces, which is the primary reason for the favorable runoff reduction performance of these layouts.

Among these three modes, ISP and IPS outperform SIP, mainly because they include a 50% impervious land unit downstream of the 100% impervious unit, which enhances the downstream unit’s capacity to receive and manage runoff. Moreover, IPS exhibits a slight advantage over ISP due to the relative positioning of the 0% and 50% impervious land units—placing the 0% impervious land unit directly downstream of the 100% impervious unit proves more effective than interspersing it with the 50% unit.

Conversely, PIS, SPI, and PSI exhibit inferior performance due to their inverse configuration: their common drawback lies in placing the most pervious land unit upstream, preventing it from fully utilizing its infiltration potential along the runoff path, which ultimately results in poorer overall runoff mitigation performance. Among these, PIS performs better than SPI and PSI but worse than SIP, highlighting the critical role of downstream land unit capacity in receiving runoff from highly impervious areas.

3.5. Discussion

The above findings provide actionable recommendations for urban planners: zone high-density developments in upstream areas; establish mandatory previous buffer zones downstream of impervious clusters; promote permeable materials in critical watershed zones; and, for urban renewal projects, prioritize increasing green spaces in downstream parcels adjacent to high-density developments and in terminal watershed areas.

ISC spatial planning may contribute to property protection and climate justice. High-intensity development areas, typically concentrated with populations and assets, should be prioritized in upstream zones less prone to flooding. This approach would enhance resilience for vulnerable populations and properties. Additionally, as low-income communities often reside in downstream flood-risk areas, optimizing their local impervious surface ratios could significantly improve living conditions and flood resilience.

Additionally, this study has several limitations that should be acknowledged. In this research, the ISC classification is simplified into three categories, which is useful but might overlook the complexity and variability in actual urban landscapes. Future research should focus on the following: a refined classification framework that incorporates both continuously varying ISC values and comprehensive integration of urban land-use functions; addressing multi-objective optimization challenges encompassing socioeconomic equity, extreme rainfall scenarios, and cost-benefit analyses; leveraging advanced algorithms to resolve computational complexities in large-scale spatial planning. Implementing these advancements requires robust datasets currently unavailable for the Xinglin Bay watershed. Collaborative efforts with municipal authorities will be prioritized to acquire longitudinal monitoring data and pilot IPS-based layouts in targeted flood-vulnerable communities.

4. Conclusions

This study investigates the runoff impact of multi-level ISC layouts (ISP, IPS, SIP, PIS, SPI, and PSI) along the runoff paths on rainfall under various rainfall return periods and land use unit scales. The following conclusions are drawn:

a. ISP, IPS, and SIP exhibit significant stormwater runoff mitigation effects, with IPS emerging as the most optimal mode of overall runoff performance. When land use constraints prevent the implementation of the IPS layout, the ISP and SIP layouts provide alternative options, offering flexibility in urban planning and design.

b. The key factor influencing runoff effects is ensuring the most pervious land is positioned downstream of the most impervious land. Urban land use planning should prioritize placing highly impervious areas in upstream locations while ensuring that the least impervious areas are directly downstream to maximize their retention and infiltration capacity. Avoiding layouts where the least impervious land is positioned upstream is crucial to optimizing runoff management.

c. The influence of impervious surface coverage land use layouts on regional stormwater runoff effects gradually weakens as rainfall intensity increases. However, even under the high-intensity storm of 50 a return period, the runoff mitigation effects induced by land use layouts remain effective to a certain extent.

d. The reductions in peak flow, delay in peak occurrence time, and decrease in total runoff volume all demonstrate that the impact of land use layout on runoff effects increases with land unit scale. However, due to the overall increase in runoff volume with larger land units, excessive unit scales may lead to diminishing returns in runoff mitigation efficiency. Therefore, optimizing stormwater management through layouts with land units smaller than 500 × 500 m is more practical and effective.

This study introduces a land use layout system incorporating three ISC gradients—0%, 50%, and 100%. By integrating a 50% impervious surface category. The research aligns with the multi-level impervious surface coverage characteristics of real urban landscapes, significantly enhancing the applicability of the findings to urban planning and design practices.