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Article Planning Strategy for the Reduction of Runoff Using

Byungsun Yang 1 and Dong Kun Lee 2,3,*

1 Korea Institute of Civil Engineering and Building Technology, Gyeonggi-Do 10223, Korea; [email protected] 2 Research Institute of and Life Sciences, Seoul National University, Seoul 08826, Korea 3 Department of Landscape Architecture and Rural System Engineering, Seoul National University, Seoul 08826, Korea * Correspondence: [email protected]

Abstract: Urban green space plays an important role in treating . In a highly dense urban environment, it is difficult to create large areas of green space. To utilize green space in urban areas effectively, locating an effective green space type is important. In this study, we examined the effect of green space on runoff reduction by comparing different green space setting scenarios. By changing the green space area ratio, green space structure, tree type, and rainfall duration and amount, we compared the runoff rates. The results showed that the green space area ratio was more effective when more than 10% of the area was green space, and the runoff reduction rate was decreased more effectively when the tree canopy LAI (leaf area index) value increased from 2 to 2.5 than when the LAI value was higher. Green space was more effective at lower intensities of rainfall events. Different green space structures cause other effects on evaporation and infiltration. Each strategy needs to be implemented correctly for policy purposes.

Keywords: landscape planning; green infrastructure; stormwater treatment; spatial modeling; street  tree; LAI 

Citation: Yang, B.; Lee, D.K. Planning Strategy for the Reduction of Runoff Using Urban Green Space. 1. Introduction Sustainability 2021, 13, 2238. https:// Rapid has caused many issues by increasing impervious surfaces [1,2]. doi.org/10.3390/su13042238 Urban floods are an important issue because both their frequency and their intensity and impact have increased recently, since rainfall patterns have changed due to climate Academic Editor: Jinhyung Chon change [3,4]. As most infrastructure and populations are located in urban areas [5], ur- Received: 19 January 2021 ban floods directly impact most of the infrastructure used for urban living, including Accepted: 11 February 2021 Published: 19 February 2021 transportation, electricity, and communication networks [6,7]. To reduce urban floods, traditional stormwater systems use sewer systems that collect stormwater and convey it along with other forms of surface [8]. Unfortunately, this sewer system in the gray Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in infrastructure only solves the issues of the increasing stormwater runoff that occurs during published maps and institutional affil- urban floods [9]. Instead of gray infrastructure, green infrastructure has emerged as a iations. complementary strategy to the gray infrastructure in treating stormwater [10]. Green space in urban areas has a role in balancing the system, reducing heat, providing habitats for , and controlling the local climate [11–13]. This recent interest in green space and the function of green infrastructure has emerged [14] due to the multiple functions of ecosystem services [15,16]. Traditionally, green infrastructure refers to Copyright: © 2021 by the authors. large green spaces or connected liner green spaces that are used for stormwater treatment Licensee MDPI, Basel, Switzerland. This article is an open access article and other management issues [8]. However, in highly dense urban areas where most of the distributed under the terms and area is covered with impervious surfaces, it is difficult to use a large amount of green space. conditions of the Creative Commons The indiscriminate expansion of urban areas is exacerbating the urban environment [17], Attribution (CC BY) license (https:// and the importance of green space is increasing. An increase in imperviousness is directly creativecommons.org/licenses/by/ related to an increase in stormwater runoff and damage to the urban water-cycle system [18]. 4.0/).

Sustainability 2021, 13, 2238. https://doi.org/10.3390/su13042238 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 2238 2 of 13

The increase in impervious surfaces in urban areas relates to the deterioration of the water- cycle system and to urban sustainability and resilience. Most studies on urban stormwater treatment have addressed issues on flooding or nonpoint source due to runoff in urban areas, and recently, issues of the urban water-cycle system have increased. In addition, traditional facilities to treat stormwater have primarily included such structures as barrels or detention tanks, whereas recent studies have focused on green infrastructure, such as rain gardens, detention , and the whole system of green space. However, related studies have focused on the capacity and size of green space to reduce runoff [19–21]. Thus, few studies have investigated the effectiveness of using green infrastructure for runoff reduction. In highly dense urban areas, it is hard to plan and create large green space. Most of the large green space in urban areas is located in the outskirts and not in the center of the urban core. However, urban core areas still have small patches of green space and street trees under certain regulations mandating the creation of green space. To use these green spaces and street trees effectively to reduce runoff, knowledge of the effective green space amount, structure, and street-tree type may be important for providing guidance to strategy and policy makers regarding management. In this study, we designed a simplified to compare each scenario with different green space settings that affect the runoff rate. This study examines the effects of urban green space and street-tree type on runoff reduction, evaluates the factors affecting runoff rate, and explores the most effective green-space planning strategy in the reduction of runoff by urban green space.

2. Materials and Methods 2.1. Simplified Hydrological Model The existing stormwater runoff models can calculate the exact amount of runoff, but they require a large amount of accurate data, as as time to simulate. To compare the runoff in different green-space distribution settings with limited data, we used a simplified hydrological model [22]. This model was designed to investigate the effect of different green space distributions on runoff reduction by comparing the total amount of stormwater runoff generated by using virtual watershed settings. The model can set and change the storm event setting, green space distribution and green space type, soil condition, and sewer capacity. The model has been designed to simulate the effectiveness of the different settings of green space on runoff reduction but not to predict the accurate amount of runoff generated from specific spaces and specific storm events.

2.2. Model Flow The model was designed in three parts to estimate the total amount of runoff (Figure1). In the first step, the runoff generated from each pervious and impervious cell is calculated. For impervious cells, runoff is calculated by sewer capacity. When the runoff generated in the cell is less than the sewer capacity, it is discharged out of the cell, and no runoff is generated. When the runoff generated in the cell exceeds the sewer capacity, it is calculated as runoff and it flows to the next cell. In the pervious cells, rainfall interception by the tree canopy and infiltration in the soil are employed to calculate the runoff amount. After calculating the runoff in each cell, runoff from one cell flows into the next cell based on the slope angle and direction of the surface. Runoff flowing into the next cell adds on the calculation of the next cell’s runoff (Figure2). Finally, the total runoff of the whole domain flows into the center bottom cell according to the topography, which is the total runoff amount of the virtual watershed. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 13

Sustainability 2021, 13, 2238 3 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 3 of 13

Figure 1. Flow chart of the simplified hydrological model.

After calculating the runoff in each cell, runoff from one cell flows into the next cell based on the slope angle and direction of the surface. Runoff flowing into the next cell adds on the calculation of the next cell’s runoff (Figure 2). Finally, the total runoff of the Figurewhole 1. domain Flow chart flows of the into simplifiedsimplified the center hydrologicalhydrological bottom cell model.model. according to the topography, which is the total runoff amount of the virtual watershed. After calculating the runoff in each cell, runoff from one cell flows into the next cell based on the slope angle and direction of the surface. Runoff flowing into the next cell adds on the calculation of the next cell’s runoff (Figure 2). Finally, the total runoff of the whole domain flows into the center bottom cell according to the topography, which is the total runoff amount of the virtual watershed.

FigureFigure 2. 2.Slope Slope setting setting for for the the model. model.

TheThe process process of of the the total total runoff runoff calculation calculation of of the the whole whole watershed watershed using using each each cell’s cell’s sewersewer capacity, capacity, interception interception and and storage storage by by the the tree tree canopy, canopy, and and soil soil infiltration is is consid- consid- eredered at at each each time time step. step. The The time time step step continues continues when when the the storm storm event event ends ends and and no no more more runoffFigurerunoff 2. flows flows Slope out settingout from from for any anythe cell model. cell [23 [23].]. ToTo apply apply the the urban urban green green spacespace environmentenvironment type to the model, model, the the common common type type of ofstreet-tree street-treeThe process character character of the was wastotal included includedrunoff in calculation the in themodel model. of. Trees the Trees whole with with different watershed different LAI using (leaf LAI eacharea (leaf index)cell’s area index)sewervalues capacity, values were set were interception to simulate set to simulate andthe effectstorage the of effect bytree the ofcanopy tree tree canopy, canopy interception. and interception. soil The infiltration CN The (serve CN is number)consid- (serve number)eredvalue at ofeach valuethe timesoil of was thestep. soilcalculated The was time calculated bystep using contin bya detailed usingues when a soil detailed the character storm soil charactereventmap andends mapland-cover and and no land-more map coverrunoffof Seoul mapflows to ofcalculate out Seoul from to the any calculate infiltration cell [23]. the rate infiltration of the soil. rate The of AMC the soil. (antecedent The AMC moisture (antecedent condi- moisturetion)To I conditionapply condition) the ofurban the I condition soilgreen was space ofused the environmen to soil apply was the usedt typeurban to to apply environment the model, the urban the to common environmentthe simulation. type toof thestreet-tree simulation. character was included in the model. Trees with different LAI (leaf area index) valuesThe were urban set settingsto simulate of buildings,the effect of , tree canopy and interception. used The the typicalCN (serve form number) of the Seoulvalue of urban the soil environment. was calculated The by domain using a sizedetailed was soil set ascharacter 200 m bymap 200 and m land-cover with a 2-m map by 2-mof Seoul cell size,to calculate and the the slope infiltration was set torate 2.5% of the evenly soil. overThe AMC the whole (antecedent domain. moisture The condi- wastion) set I condition at a 4-m width,of the soil and was one used lane to of apply wasthe urban set at aenvironment 3-m width. Theto the whole simulation. domain was divided into four blocks by street with sidewalks and roads that made each block Sustainability 2021, 13, x FOR PEER REVIEW 4 of 13

The urban settings of buildings, roads, and sidewalks used the typical form of the Seoul urban environment. The domain size was set as 200 m by 200 m with a 2-m by 2-m cell size, and the slope was set to 2.5% evenly over the whole domain. The sidewalk was set at a 4-m width, and one lane of road was set at a 3-m width. The whole domain was divided into four blocks by street with sidewalks and roads that made each block 40 m square, and street trees were placed every 5 m on the sidewalk (Error! Reference source not found., Error! Reference source not found.). Buildings were set as 40% of the total area, while roads and sidewalks were set as 35.7%, which made the total impervious sur- face 75.7%, the average impervious-area rate of the Seoul commercial area. The storm- water sewer was set to cover 200 m2 with a 120-L size and 1 m3/min intake capacity (Error! Reference source not found.).

Sustainability 2021, 13, 2238 Table 1. Landscape and parameter settings for the model. 4 of 13

Variable Value

40 m square, andTotal street area trees were placed every 5 m on the 200 sidewalk m by 200 (Table m 1, Figure3). Buildings were setCell as 40% size of the total area, while roads 2 m and by 2 sidewalks m (total 10,000 were setcells) as 35.7%, which made the total 75.7%, the average impervious-area rate of the Seoul commercialLandscape area. The slope stormwater sewer was set to cover 200 2.5% m 2 with a 120-L size and 1 m3/min intake capacity (Table2). Sewer size 40 × 50 × 60 cm (120 L) Table 1. Landscape and parameter settings for the model. Sewer intake 1 m3/min Variable Value Table 2. Built-environmentTotal area settings for the simulation. 200 m by 200 m Cell size 2 m by 2 m (total 10,000 cells) Feature Imperviousness (%) Size (m) Total Area (%) Landscape slope 2.5% Road Sewer 100size 6 (2 lanes), 12 (4 40 lanes)× 50 ×width60 cm (120 L) 22.56 Sidewalk Sewer intake100 4 width 1 m3/min 13.14 Building 100 - 40.00

Figure 3. Base land-use setting for the simulation (dark gray is road, light gray is sidewalk, light Figure 3. Base land-use setting for the simulation (dark gray is road, light gray is sidewalk, light green is sidewalk with street trees, and yellow is building). green is sidewalk with street trees, and yellow is building).

Table 2. Built-environment settings for the simulation.

Feature Imperviousness (%) Size (m) Total Area (%) 6 (2 lanes), 12 (4 lanes) Road 100 22.56 width Sidewalk 100 4 width 13.14 Building 100 - 40.00

2.3. Scenario Setting To compare the effect of green space type on runoff reduction, different green space structures were set (Table3). Different green space area ratios, green space structures (which are components of plants in the green space), types of street trees simulated by different LAI values of tree canopies, and rainfall events with different durations and total amounts were used for scenario setting. The green space area ratio setting was from 5% to 15% at an interval of 2.5%. The green space structure had two types, grass only and tree with grass. The type of street tree was set with different LAIs from 2 to 4 at an interval of 0.5. The storm event was set by using the probable of Seoul’s 30-year storm event record (Table4). The storm event duration was set from 1 h to 4 h at an interval of Sustainability 2021, 13, 2238 5 of 13

30 min with a total precipitation amount of 94.3 mm. The setting of different total amounts of storm events was set from 94.3 mm to 173.1 mm in two hours at 10-mm intervals. The base scenario was a 10% green space area ratio with grass and trees, street trees with 3 LAI values, and 134-mm storm events for two hours. By changing each parameter, the effects of the green space area ratio, green space type, and street tree type on runoff amount reduction under different storm events were compared. Two parameters changed while the other two parameters were fixed on the base scenario. Finally, to compare best and worst cases, a high green space scenario, which is 15% of green space ratio, street trees with LAI value 4, and trees with grass, was compared with a low green space scenario, which is 5% of green space ratio, street trees with LAI value 2, and only grass in green space while using high intense and low intense rainfall event, set as 173.1 mm precipitation in 1 h and 94.3 mm precipitation in 4 h, respectively.

Table 3. Variable settings for the simulation. LAI: leaf area index.

Number of Variable Minimum Maximum Interval Scenarios Green space ratio (%) 5 15 2.5 5 Green space structure Grass only Grass with trees - Street tree type (LAI) 2 4 0.5 5 Rainfall duration (hour) 1 4 0.5 7 Rainfall amount (mm) 94.3 173.1 10 9

Table 4. Probable precipitation in Seoul by storm event time from 30 years of records (Ministry of the Interior and Safety, Korea, 2017).

Storm Event Time Precipitation (mm) 1 h 94.3 2 h 136.0 3 h 173.1

3. Results The runoff rate was calculated by comparing the base scenario with a 10% green space area ratio with grass and trees, street trees with a LAI value of 3, and 134-mm storm events for two hours. After changing the green space area ratio and green space structure, the runoff rate decreased more effectively when the green space ratio was over 10% (Figure4a). The green space structure with grass and trees was more effective as the green space area ratio increased and the runoff rate decreased to 71.6% under 15% of green space area ratio and green space structure with grass and trees. For the scenario with a street-tree type change, while the tree canopy LAI increased, a decrease in the runoff rate was more effective when the green space area ratio was high (Figure4b). While changing the street-tree type and green space structure, the runoff rate decreased more effectively when street-tree LAI increased from 2 to 2.5 than the other range commonly used for both green space structures. Regardless of the green space structure with grass and trees, the runoff rate decreased more effectively than that with grass only when the LAI value was higher than 3 (Figure4c). Changing the storm event duration commonly resulted in a high runoff rate for short- duration and high-intensity storm events. However, the runoff rate decreased effectively as the rainfall duration increased until the duration was 2 h, and it was more effective under a higher green space area ratio (Figure5a). The scenario under different green space structures showed similar runoff rates during the high-intensity storm event, but the green space structure with grass and trees showed more effective runoff reduction under lower rainfall intensities, which was a 4-h storm event (Figure5b). The different street-tree types showed greater differences in runoff rate, while the LAI value changed. The runoff rate Sustainability 2021, 13, 2238 6 of 13

decreased sharply from LAI values 1 to 2; however, when the LAI value exceeded 2.5, the runoff rate reduction was not very effective as the value increased (Figure5c). The scenarios with different rainfall amounts and green space area ratios of 94 mm in the two-hour storm event showed similar runoff rates of 69.1%, 68.5%, and 67.8%, while the green space area ratios were 5, 7.5, and 10, respectively. However, the runoff rate decreased effectively to 64.4% and 57.9% when the green space area ratio increased to 12.5% and 15%, respectively (Figure6a). As the rainfall amount increased, the runoff rate increased steeply under the low green space area ratio, while the higher green space area ratio scenario increased gradually. The simulation comparing different green space Sustainability 2021, 13, x FOR PEER REVIEW 6 of 13 structures showed that grass with trees reduced the runoff rate by 3.7% under the 94-mm storm event simulation, but as the rainfall amount increased, the gap decreased to 0.9% (Figure6c). A similar pattern was observed for the different street-tree types with a 7.7% differencethat with in grass the runoff only ratewhen between the LAI high value and was low higher LAI values than under 3 (Error! 94-mm Reference storm events source not and a 3.0% difference under 174-mm storm events (Figure6c). found.c).

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Figure 4. Cont. Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13

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Sustainability 2021, 13, 2238 85.0% 7 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 7 of 13

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80.0% (c) Streetrate Runoff tree LAI and green space structure. 75.0% Figure 4. Runoff rate under different scenarios. (a) Green space ratio and green space structure, (b) street-tree LAI70.0% and green space ratio, and (c) street-tree LAI and green space structure. 22.533.54 Changing the storm event duration commonly resulted in a high runoff rate for short- duration and high-intensity storm events.Street Howe treever, LAI the runoff rate decreased effectively as the rainfall duration increased until the duration was 2 h, and it was more effective under a higher green space area ratio (Error! Reference source not found.a). The scenario grass grass with tree under different green space structures showed similar runoff rates during the high-inten- sity storm event, but the green space structure with grass and trees showed more effective runoff(c) Street reduction tree LAI under and green lower space rainfall structure. intensities, which was a 4-h storm event (Error! Ref- erence source not found.b). The different street-tree types showed greater differences in FigurerunoffFigure 4. Runoff rate,4. Runoff while rate underrate the under differentLAI value different scenarios. changed. scenarios. (a) GreenThe (arunoff space) Green ratiorate space anddecreased ratio green and space sharply green structure, spacefrom ( b structure,LAI) (b) street-treevaluesstreet-tree LAI1 to andLAI2; however, greenand green space when space ratio, the and ratio, LAI (c) and street-tree value (c) street-treeexceeded LAI and green2.5,LAI the and space runoff gr structure.een ratespace reduction structure. was not very effective as the value increased (Error! Reference source not found.c). Changing the storm event duration commonly resulted in a high runoff rate for short- duration100.0% and high-intensity storm events. However, the runoff rate decreased effectively as the rainfall duration increased until the duration was 2 h, and it was more effective under a90.0% higher green space area ratio (Error! Reference source not found.a). The scenario under different green space structures showed similar runoff rates during the high-inten- sity storm event, but the green space structure with grass and trees showed more effective 80.0% runoff reduction under lower rainfall intensities, which was a 4-h storm event (Error! Ref-

erencerate Runoff source not found.b). The different street-tree types showed greater differences in runoff rate,70.0% while the LAI value changed. The runoff rate decreased sharply from LAI values 1 to 2; however, when the LAI value exceeded 2.5, the runoff rate reduction was not very60.0% effective as the value increased (Error! Reference source not found.c). 11.522.533.54

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FigureFigure 5. Runoff5. Runoff rate rate under under different different rainfall rainfall durations. durations. ((aa)) Duration and and green green space space ratio, ratio, (b (b) ) durationduration and and green green space space structure, structure, and and (c) duration(c) duration and and street-tree street-tree LAI. LAI.

The scenarios with different rainfall amounts and green space area ratios of 94 mm in the two-hour storm event showed similar runoff rates of 69.1%, 68.5%, and 67.8%, while the green space area ratios were 5, 7.5, and 10, respectively. However, the runoff rate de- creased effectively to 64.4% and 57.9% when the green space area ratio increased to 12.5% and 15%, respectively (Error! Reference source not found.a). As the rainfall amount in- creased, the runoff rate increased steeply under the low green space area ratio, while the higher green space area ratio scenario increased gradually. The simulation comparing dif- ferent green space structures showed that grass with trees reduced the runoff rate by 3.7% under the 94-mm storm event simulation, but as the rainfall amount increased, the gap decreased to 0.9% (Error! Reference source not found.c). A similar pattern was observed for the different street-tree types with a 7.7% difference in the runoff rate between high and low LAI values under 94-mm storm events and a 3.0% difference under 174-mm storm events (Error! Reference source not found.c). Sustainability 2021, 13, 2238 9 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 9 of 13

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Figure 6. Cont. Sustainability 2021, 13, 2238 10 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 10 of 13 Sustainability 2021, 13, x FOR PEER REVIEW 10 of 13

95.0% 95.0% 85.0% 85.0% 75.0% 75.0% Runoff rate Runoff

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(c) Rainfall amount and street-tree LAI. (c) Rainfall amount and street-tree LAI. FigureFigure 6. Runoff6. Runoff rate rate under under different different rainfall rainfall amounts. amounts. (a) Rainfall(a) Rainfall amount amount and and green green space space ratio, ratio, (b) rainfallFigure(b) rainfall amount 6. Runoff amount and rate green and under spacegreen different structure,space rainfallstructure, and amounts. (c )and rainfall (c) (rainfalla amount) Rainfall amount and amount street-tree and and street-tree green LAI. space LAI. ratio, (b) rainfall amount and green space structure, and (c) rainfall amount and street-tree LAI. Finally,Finally, for thefor best-the best- and worst-caseand worst-case comparison, comparison, the high the green high space green scenario space showed scenario 82.9%showedFinally, of runoff 82.9% for rate of the runoff for best- a highrate and for intense worst-case a high rainfall intense comparison, event rainfall and event 56.8%the highand of runoff56.8%green of ratespace runoff for scenario a rate low for intenseshoweda low rainfall intense 82.9% event. ofrainfall runoff On event. therate low for On greena thehigh low space intense green scenario, rainfall space the scenario,event runoff and rate the 56.8% wasrunoff 98.2%of runoffrate on was arate high 98.2% for intensea onlow a high rainfallintense intense eventrainfall rainfall and event. 81.3% event On on theand a low low 81.3% intense green on a rainfallspace low intense scenario, event. rainfall The the high runoff event. green rate The space was high ratio98.2% green scenarioonspace a high ratio had intense 26.1% scenario rainfall of difference had event26.1% between and of difference 81.3% high on and abetween low low intense intense high rainfall and rainfall low event. eventintense The while rainfall high the green lowevent greenspacewhile space ratio the scenariolowscenario green hadhad space 16.9%26.1% scenario(Figure of difference had7). 16.9% between (Figure high 7). and low intense rainfall event while the low green space scenario had 16.9% (Figure 7). 100 100

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Figure 7. Runoff rate under high and low green space scenario with high and low intense rainfall FigureFigureevent. 7. 7. Runoff Runoff raterate under high and and low low green green space space scenario scenario with with high high and and low low intense intense rainfall rain- fallevent. event. Overall, the runoff rate decreased as the green space area ratio and LAI of street trees increasedOverall,Overall, or the thethe runoff runoffgreen rate spacerate decreased decreased had more as as structure the the green green with space space trees area area than ratio ratio grass and and LAIalone. LAI of of However, street street trees trees the increasedincreasedgreen space or or the the area green green ratio space space simulation had had more more showed structure structure that withthe with runoff trees trees thanreduction than grass grass effect alone. alone. was However, However, higher thewhen the green space area ratio simulation showed that the runoff reduction effect was higher when greenthe green space areaspace ratio area simulation ratio was showed higher thatthan the 10%, runoff and reduction the street-tree effect was type higher simulation when the green space area ratio was higher than 10%, and the street-tree type simulation Sustainability 2021, 13, 2238 11 of 13

the green space area ratio was higher than 10%, and the street-tree type simulation showed that it was more effective until the LAI value was lower than 2.5. The simulation under high rainfall intensity showed that green space was less effective in treating stormwater. The intensity of rainfall decreased the gap between low and high green space area ratios or increased the LAI value. This finding shows that the effectiveness of stormwater treatment by green space is better under low rainfall intensity, since more green space areas have more capacity to treat stormwater onsite and runoff does not flow to lower areas. For the best and worst scenario comparison, the result showed the high green space scenario still reduces runoff, however, the rate does not reduce effectively on low rainfall intense case. This is because the total amount of tree canopy storage and soil infiltration would be almost full to store or filter more water. It also showed the effectiveness of green space on low intense rainfall since the difference of high and low green space scenario was 15.3% for a high intense rainfall event while it was 24.5% for a low intense rainfall event. Under the condition of less green space area, stormwater is not treated onsite but flows into low areas, accumulating and overflowing, thereby resulting in urban floods.

4. Discussion This model compared different urban green space settings for runoff reduction using a simplified hydrological model. The model calculated runoff by applying the interception of the tree canopy, infiltration by the soil, and outflow by stormwater sewers. Urban environment setting parameters, including street trees, the green space ratio, and storm events, were determined by using data from Seoul. However, since the model did not use actual storm event data and the model itself was not designed for that purpose, the actual amount of accurate runoff was difficult to derive. Therefore, the results of this simulation were compared with those from previous studies concerning how much urban green space affects runoff reduction [23–27]. The results were consistent with previous studies and showed the effectiveness of urban green space on runoff reduction, especially from small storm events. The aim of this study is to assist municipal urban planners and landscape planners in designing and formulating policies for the implementation of green space planning. The results of the study suggest that the benefits of green infrastructure are effective for runoff reduction, especially for small storm events. Recently, there has been strong interest in green infrastructure, particularly for stormwater treatment. However, most municipalities still focus on large facilities and to remove or store stormwater, rather than treating it onsite. This strategy has already demonstrated its limitations through failure due to the dramatically changing rainfall trends due to climate change. Strategies to treat stormwater onsite by green infrastructure and using gray infrastructure to treat overflow may be the best solutions to treat higher levels of storm event intensity. In addition, for long-term planning, the growth of street trees that affects the LAI value, as well as the trimming of the street trees, needs to be considered. The green space structure also needs to be implemented differently based on its purpose. Where runoff reduction is more important than recharge, planting more trees with high LAI values may be more important, while creating more concave green space may be better where is the priority. The results obtained using the simplified hydrological model are slightly higher than those obtained from similar previous studies [25,27–29], since other external factors may be missing. This property also means that the model only considers direct factors of green space that affect the runoff rate. In the version of the model, which has a simple structure and input, the model could be transferred to simulate actual sites and urban environment settings by changing the parameters, including sewer capacity, urban structure setting, soil and green space structure, and rainfall event setting. This approach could also implement other LID (low impact development) practices, such as rain gardens, green roofs, and porous paving, by setting parameters by cell. Sustainability 2021, 13, 2238 12 of 13

5. Conclusions The object of this study was to examine green space on runoff reduction effect and find the most effective green space planning strategy. The results of this study show that a higher green space area rate is more effective, especially when the total rainfall amount and rainfall intensity are high. This finding is due to the green space that includes street trees treating stormwater onsite and generating less runoff, which flows and accumulates in the lowest area. The gap between the high and low green space area ratios increases because the runoff from each area (each cell in this simulation) increases and flows into the lower area (next cell in this simulation), which causes the lower area to be overwhelmed by the rainfall that onsite and the runoff from the higher area. This result shows the importance of onsite treatment to reduce runoff. As failures of traditional stormwater treatment systems using a sewer system have recently occurred more often, utilizing green spaces to treat stormwater onsite will reduce pressure on gray infrastructure and will restore urban water cycle systems close to their natural conditions by increasing evaporation, infiltration, and groundwater. Furthermore, green infrastructure also has limitations in that the effectiveness of reducing runoff under high-intensity rainfall is relatively low compared with small storm events. The traditional sewer system in which water rapidly flows out can compensate for the limitation of green infrastructure during short- and high-intensity rainfall events. By utilizing each advantage of green and gray infrastructure to reduce runoff, urban flash floods may be prevented, and the urban water cycle system may be restored. Restoration of urban water cycle systems can reduce flash floods in urban areas and can increase urban resilience. Urban green space planning benefits not only but also economic and social advantages by reducing floods, reducing the heat island effect, and providing social gathering areas [4,30].

Author Contributions: Conceptualization, B.Y. and D.K.L.; Data curation, D.K.L.; Formal analysis, B.Y.; Investigation, D.K.L.; Methodology, B.Y.; Resources, B.Y.; Writing–original draft, B.Y.; Writing– review & editing, B.Y. and D.K.L. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article. Conflicts of Interest: The authors declare no conflict of interest.

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