International Journal of

Environmental Research and Public Health Article

Exploring the Linkage between Urban Flood Risk and Spatial Patterns in Small Urbanized Catchments of Beijing, China Lei Yao 1,2,3 , Liding Chen 2 and Wei Wei 2, * 1 2 3

*

College of Geography and Environment, Shandong Normal University, Ji’nan 250014, China; [email protected] State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; [email protected] University of Chinese Academy of Sciences, Beijing 100049, China Corresponding: [email protected]; Tel./Fax: +86-10-6294-3840

Academic Editor: Miklas Scholz Received: 15 January 2017; Accepted: 25 February 2017; Published: 28 February 2017

Abstract: In the context of global urbanization, urban flood risk in many cities has become a serious environmental issue, threatening the health of residents and the environment. A number of hydrological studies have linked urban flooding issues closely to the spectrum of spatial patterns of urbanization, but relatively little attention has been given to small-scale catchments within the realm of urban systems. This study aims to explore the hydrological effects of small-scaled urbanized catchments assigned with various landscape patterns. Twelve typical residential catchments in Beijing were selected as the study areas. Total Impervious Area (TIA), Directly Connected Impervious Area (DCIA), and a drainage index were used as the catchment spatial metrics. Three scenarios were designed as different spatial arrangement of catchment imperviousness. Runoff variables including total and peak runoff depth (Qt and Qp ) were simulated by using Strom Water Management Model (SWMM). The relationship between catchment spatial patterns and runoff variables were determined, and the results demonstrated that, spatial patterns have inherent influences on flood risks in small urbanized catchments. Specifically: (1) imperviousness acts as an effective indicator in affecting both Qt and Qp ; (2) reducing the number of rainwater inlets appropriately will benefit the catchment peak flow mitigation; (3) different spatial concentrations of impervious surfaces have inherent influences on Qp . These findings provide insights into the role of urban spatial patterns in driving rainfall-runoff processes in small urbanized catchments, which is essential for urban planning and flood management. Keywords: connectivity; urban flood risk; spatial pattern; imperviousness; rainfall-runoff; rainfall simulation

1. Introduction Rapid urban sprawl brings significant landscape modifications, of which the most pervasive hallmark is considered to be the transformation from natural lands to imperviousness [1,2]. This alteration leads to negative hydrologic impacts that result in enhanced hydraulic efficiency and can thus increase stormwater runoff volumes, flow rates and peak flows and flow-time reductions in urban catchments [3,4]. These unwanted side effects of rapid urbanization can increase the susceptibility towards urban flooding which endangers life, private property and public infrastructures and poses substantial threats to urban environmental development [5]. Furthermore, urban flooding risk will possibly intensify as a result of inadequate urban landscape planning and weather extremes, particularly in the context of global climate change [6,7]. Consequently, urban areas are increasingly Int. J. Environ. Res. Public Health 2017, 14, 239; doi:10.3390/ijerph14030239

www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2017, 14, 239

2 of 16

losing the capacity to cope with the complicated hydrological alterations, and this complexity poses significant challenges for urban flood management. How to mitigate excess runoff risks in urban areas is an important question to address, which thus requires a better understanding of hydrological alterations and their linkages to the spatial patterns of urbanization [8,9]. Imperviousness has been recommended as an effective spatial metric to indicate the urbanization level and reflect hydrological alternation [4,10]. For example, Schueler et al. [11] proposed an impervious cover model to diagnose the severity of future urban hydrological problems. Quantitative analysis also was conducted to determine the relationship between imperviousness and total runoff depth [12,13]. However, the significance of imperviousness in predicting other important runoff variables (e.g., peak runoff), has been estimated to be lower than that of total runoff depth, and varied with different rainfall conditions [14], indicating other spatial factors exert influence on urban runoff. A number of studies have stressed that the spatial arrangement of the impervious area and drainage network structures (e.g., drainage density, width function) have additional influence on surface hydrology, particularly on peak flow [8,9,15]. Most of the studies on this focus were conducted in large urban basins (>1 km2 , with natural stream channels) rather than the realm of the urban system [7]. However, those small-scaled catchments, with the similar size of neighborhood (several hectare), flat terrain, closed hydrological boundary and discharge overland flows mainly through artificial drainage networks [16], were facing increasingly pluvial and overland floods due to drainage blockage [17,18] and unreasonable landscape planning [19,20]. Potential downscaling from large basin to small urban catchment may intensify or weaken the hydrological effects of spatial patterns [21,22]. Due to such complexity, it is still unclear regarding the roles of spatial patterns in runoff responses at small urban catchments. Therefore, it is necessary to reconsider the principles linking hydrological processes to spatial patterns for small urban catchments. Hydrological models have advantages to assess the impacts of spatial patterns on urban rainfall-runoff processes at multiple spatial scales. Geophysical factors that affect catchment runoff, such as soil condition, climate and land cover, can be easily configured by altering the input parameters in hydrological models [21,23]. Furthermore, hydrological models provide a reductionist way of characterizing various spatial scenarios in detail without considering the complexity of the real world [24–26]. As such, model-based analysis is necessary to generalize the specific hydrological process, and thus provides guidance for future urban storm water management efforts [27]. In this study, several typical residential sites with hypothesized drainage systems were selected as the study catchments. The spatial characteristics of these catchments, including imperviousness and land cover types, were identified based on remote sensing methods. Moreover, three different scenarios with different spatial arrangements of the impervious areas were developed. A semi-distributed hydrological model was used to simulate the catchment hydrological responses (i.e., total and peak runoff). The detailed objectives of this paper are posed as follows: (1)

(2) (3)

By using regression analysis, this study attempts to quantify the potential relationships between spatial characteristics and urban flood variables under different rainfall conditions for small urbanized catchments; Comparing the catchment runoff outputs of different scenarios and investigating whether different catchment patterns affect runoff process; and We discussed the potential implications of our findings and the relevance of the study to urban catchment design and flood management.

2. Materials and Methods 2.1. Study Areas The residential sites selected for study are located in Beijing, China (Figure 1). These twelve residential sites, ranging from 1.39 to 6.84 ha in area, show typical building layouts (including linear, interspersed, and semi-enclosed building forms), and are composed of both impervious surfaces

Int. J. Environ. Res. Public Health 2017, 14, 239

3 of 16

(roofs and roads) and pervious surfaces (trees and lawns). Figure 2 presents an overview of Int. J. Environ. Res. Public Health 2017, 14, 239 3 ofthe 16 twelve residential sites. Table 1 lists the values of different landscape variables for each of the twelve residential sites. Land Land cover cover and and Total Total Impervious Impervious Area (TIA) of each site were obtained by visual interpretation based on an IKONOS image with 1-m 1-m spatial spatial resolution resolution (Table (Table1). 1).

Figure Figure 1. 1. Locations Locations of of the the selected selected residential residential sites sites in in Beijing. Beijing.

Each residential site is considered as one drainage catchment and occupies an independent Each residential site is considered as one drainage catchment and occupies an independent drainage system. Meierdiercks et al. [8] and Ogden et al. [15] found that different drainage densities drainage system. Meierdiercks et al. [8] and Ogden et al. [15] found that different drainage densities play an important role on discharge alterations in urbanized catchments. In order to emphasize the play an important role on discharge alterations in urbanized catchments. In order to emphasize the hydrological role of catchment spatial patterns, drainage densities of the twelve residential hydrological role of catchment spatial patterns, drainage densities of the twelve residential catchments catchments were hypothesized with the similar drainage density. Catchment drainage pipelines were were hypothesized with the similar drainage density. Catchment drainage pipelines were outlined outlined mainly along the roads according to Goldshleger et al. [28], as shown in Figure 2. The mainly along the roads according to Goldshleger et al. [28], as shown in Figure 2. The drainage drainage densities of the twelve catchments range from 296.15 to 316.86 m/ha (Table 1). Drainage densities of the twelve catchments range from 296.15 to 316.86 m/ha (Table 1). Drainage network network morphologies were simplified to share the same values, including the pipeline geometry morphologies were simplified to share the same values, including the pipeline geometry (circle, 1-m in (circle, 1-m in diameter), minimum slope (2%), and maximum water depth of inlet (2-m). In addition, diameter), minimum slope (2%), and maximum water depth of inlet (2-m). In addition, following the following the method presented by Lee and Heaney [29] and Yao et al. [30], the Directly Connected method presented by Lee and Heaney [29] and Yao et al. [30], the Directly Connected Impervious Area Impervious Area (DCIA) of each catchment was determined as the part of impervious surfaces (DCIA) of each catchment was determined as the part of impervious surfaces (including roads and a (including roads and a portion of the roofs) which drained the overland flows directly into the portion of the roofs) which drained the overland flows directly into the drainage networks (Figure 3). drainage networks (Figure 3).

Int.J.J.Environ. Environ.Res. Res.Public PublicHealth Health2017, 2017,14, 14,239 239 Int.

16 44ofof16

Figure2.2.Impervious Imperviousand anddrainage drainagelayouts layoutsof ofthe thetwelve twelveresidential residentialcatchments. catchments. Figure Table1.1.Summary Summaryof ofspatial spatialand anddrainage drainagecharacteristics characteristicsof ofthe thetwelve twelvestudy studysites. sites. Table Catchment

Catchment

Layout Type 1

Impervious Impervious Fraction (%) Catchment Area Catchment 2 (%) 3 (ha) TIAFraction DCIA Area

Layout Type 1

Percent Land Cover (%)

Percent Land Cover (%) Roof

Road

Tree

Lawn

Average Average Drainage Area 4 Drainage (Ad , ha)

Drainage

Drainage Density 5 (m/ha) 5 Density 303.82 (m/ha)

4 Area 0.11 307.45 (Ad0.14 , ha) 0.11 316.86 0.11 303.82 0.09 303.08 0.14 307.45 0.09 307.83 0.09 302.35 0.11 316.86 0.07 305.60 0.09 303.08 0.18 304.24 0.12 296.15 0.09 307.83 0.16 305.39 0.09 302.35 0.16 302.13 0.08 304.17 0.07 305.60 1 Three CAT 8 6.18 72.65 on the 59.20 46.36of residential 27.29 27.35 0.18 Impervious 304.24 types Linear of site layout were defined based categories building form; 2 Total 3 Directly Connected Impervious Area, DCIA; 4 Average drainage area (A ) expresses the average Area, CAT 9 TIA; Interspersed 2.15 37.95 20.55 18.47 19.48 33.65 28.40 0.12 296.15 d 5 Drainage density expresses the total drainage pipe length drainage area (ha) dominated by each rainwater CAT 10 Semi-enclosed 6.84 74.50 inlet; 71.42 38.59 35.91 2.31 23.20 0.16 305.39 (m)11per unitInterspersed drainage area (ha).2.73 CAT 48.56 19.27 34.94 13.62 12.17 39.27 0.16 302.13 CAT 12 Semi-enclosed 5.88 72.73 65.01 53.64 19.10 27.27 0.08 304.17

CAT 1 CAT 2 CAT 3 CAT 1 CAT 4 CAT CAT 52 CAT 6 CAT 3 CAT 7 CAT CAT 84 CAT 95 CAT CAT 10 CAT 6 CAT 11 CAT 12 CAT 7

Linear Interspersed Semi-enclosed Linear Interspersed Interspersed Semi-enclosed Interspersed Semi-enclosed Linear Interspersed Linear Interspersed Semi-enclosed Semi-enclosed Interspersed Interspersed Semi-enclosed Linear

2.26 (ha) 4.17 4.81 2.26 1.39 3.74 4.17 5.07 4.81 2.67 6.18 1.39 2.15 3.74 6.84 2.73 5.07 5.88 2.67

58.28 TIA 2 68.18 73.68 58.28 54.38 68.18 77.60 53.03 73.68 77.79 54.38 72.65 37.95 77.60 74.50 53.03 48.56 72.73 77.79

40.843 DCIA 50.34 65.11 40.84 24.06 50.34 61.67 38.47 65.11 56.47 24.06 59.20 20.55 61.67 71.42 38.47 19.27 65.01 56.47

28.13 Roof

34.36 33.78 28.13 30.32 34.36 43.13 27.63 33.78 46.29 30.32 46.36 18.47 43.13 38.59 27.63 34.94 53.64 46.29

30.15 Road 33.82 39.89 30.15 24.06 33.82 34.46 25.39 39.89 31.50 24.06 27.29 19.48 34.46 35.91 25.39 13.62 19.10 31.50

41.72 Tree

0.83 22.03 41.72 45.62 0.83 9.56 34.57 22.03 13.92 45.62 33.65 9.56 2.31 34.57 12.17 27.27 13.92

Lawn 30.99 4.29 30.99 12.84 12.40 4.29 8.30 27.35 28.40 12.84 23.20 12.40 39.27 8.30

Three types of site layout were defined based on the categories of residential building form; 2 Total Impervious Area, TIA; 3 Directly Connected Impervious Area, DCIA; 4 Average drainage area (Ad) expresses the average drainage area (ha) dominated by each rainwater inlet; 5 Drainage density expresses the total drainage pipe length (m) per unit drainage area (ha). 1

Int. J. Environ. Res. Public Health 2017, 14, 239 Int. J. Environ. Res. Public Health 2017, 14, 239

5 of 16 5 of 16

Figure 3. 3. Illustration Illustrationof ofcatchment catchmentstructure structure and and flow flow pathways pathways in in SWMM. SWMM. Figure

2.2. 2.2. Model Model Implementation Implementation Complex Complex urban urban morphology morphology and and ungauged ungauged conditions conditions make make itit intrinsically intrinsically difficult difficult to to obtain obtain rainfall-runoff to to assess the the consequences of urbanization [27,31].[27,31]. In this study, thestudy, drainage rainfall-runoffdata dataand and assess consequences of urbanization In this the networks the catchments were assumed no monitoring rainfall-runoff data were available, a drainage of networks of the catchments wereand assumed and no monitoring rainfall-runoff data were hydrological model-basedmodel-based analysis thus analysis was conducted to accomplish our goals. All the available, a hydrological thus was conducted to accomplish ourrainfall-runoff goals. All the processes for this study were simulated the Strom Water Management Model (SWMM) [27,31]. rainfall-runoff processes for this study using were simulated using the Strom Water Management Model SWMM is [27,31]. a semi-distributed model which treats urban catchments as thecatchments “Catchment(SWMM) SWMM is ahydrological semi-distributed hydrological model which treats urban as Sub-basin-Sub-catchment” structure, as shown in Figure Each sub-catchment is characterized by the “Catchment-Sub-basin-Sub-catchment” structure, as3.shown in Figure 3. Each sub-catchment single land cover type and homogenous properties. As shown in properties. Figure 3, all the can3, is characterized by single land cover type and homogenous As sub-catchments shown in Figure be into threecan types identify theinto detailed path spatial disconnected allcategorized the sub-catchments betocategorized threeflow types to and identify thehierarchies: detailed flow path and impervious sub-catchments, pervious sub-catchments, and DCIA sub-catchments [30,32]. and The DCIA three spatial hierarchies: disconnected impervious sub-catchments, pervious sub-catchments, types of sub-catchments the drainage inlet constitute relatively independent sub-catchments [30,32]. assigned The threewith types ofsame sub-catchments assigned withathe same drainage inlet drainage of the sub-catchments withinAll each sub-basin are connected via the storm constitutesub-basin. a relativelyAll independent drainage sub-basin. of the sub-catchments within each sub-basin runoff flow pathways the flow drainage network in an urban catchment, including overland are connected via the associated storm runoff pathways associated the drainage network in an urban flow and flow throughoverland the drainage Rainfall-runoff of each sub-catchment is calculated catchment, including flowpipes. and flow through theprocess drainage pipes. Rainfall-runoff process of according to its hydrologic characteristics, such as depression and infiltration Generated each sub-catchment is calculated according to its hydrologic characteristics, such losses. as depression and overland flows are routed over the sub-catchments to their outlet (i.e., other sub-catchment or infiltration losses. Generated overland flows are routed over the sub-catchments to their outlet (i.e., drainage network) byorusing the network) nonlinearby reservoir equation, combination of theacontinuity and other sub-catchment drainage using the nonlineara reservoir equation, combination of Manning’s equation [33]. the continuity and Manning’s equation [33]. Twelve Twelve models models thus thus were were built built for the study catchments, catchments, and and the the three three types types of of sub-catchment sub-catchment for for each each model model were were delineated delineated based based on on the the catchment catchment land land cover cover and and impervious impervious characteristics. characteristics. In In these these models, models, infiltration infiltration losses losses of of pervious pervious surfaces surfaces were were estimated estimated using using Horton Horton equation, equation, and and the the Kinematic Kinematic Wave Wave method method was was used used for for runoff runoff routing routing computation computation for for drainage drainagenetworks networks[34]. [34]. Determination Determination of of model model parameters parameters are described as follows: (1) (1) Spatial Spatial parameters parameters in in SWMM SWMM model model including including drainage drainage area, area, flow flow width width [32], [32], impervious impervious coverage, and drainage pipeline length were calculated according to the geo-analysis coverage, and drainage pipeline length were calculated according to the geo-analysis of of the the GIS GIS data (Figure 2). data (Figure 2). (2) As previously described, drainage systems of the study catchments were designated to be (2) As previously described, drainage systems of the study catchments were designated to be similar similar in order to minimize the hydraulics interference from drainage structures. Therefore, no in order to minimize the hydraulics interference from drainage structures. Therefore, no real real life measurements were conducted in these sites, and no suitable rainfall-runoff datasets are available for model calibration/validation. To guarantee the model credibility, we conducted a

Int. J. Environ. Res. Public Health 2017, 14, 239

6 of 16

life measurements were conducted in these sites, and no suitable rainfall-runoff datasets are 6 of 16a To guarantee the model credibility, we conducted ◦ 0 ◦ hydrological monitoring task in a residential catchment of Beijing, China (40 2 N, 116 240 E), hydrological monitoring task in a residential catchment Beijing, China N, 116°24′ E), as as shown in Figure 4. This monitored catchment covers of a drainage area of(40°2′ 1.69 ha. We collected shown in Figure 4. This monitored catchment covers a drainage area of 1.69 ha. We collected detailed rainfall-runoff data in this site with runoff sensor and rain gauge during the rainy season detailed rainfall-runoff data in this site with runoff sensor rain gauge during thefor rainy in 2013. Following the modelling procedure mentioned above,and we built a detailed model this season in 2013. Following the modelling procedure mentioned above, we built a detailed model monitored catchment by using SWMM model. Then we obtained a group of calibrated SWMM for this monitored by using SWMM we obtained a group of monitored calibrated parameters based catchment on these rainfall-runoff datamodel. (TableThen 2). More details about this SWMM parameters based on these rainfall-runoff data (Table 2). More details about this catchment and its model calibration/validation process can refer to Yao et al. [14]. monitored catchment and its model calibration/validation process can refer to Yao et al. [14].

Int. J. Environ. Res. for Public Health calibration/validation. 2017, 14, 239 available model

Figure Figure 4. 4. Catchment Catchmentfor forrainfall-runoff rainfall-runoff monitoring monitoring in in this this study. study. Table Model (SWMM). (SWMM). Table 2. 2. Calibrated Calibrated parameter parameter values values for for the the Storm Storm Water Water Management Management Model LandCover Cover Land Roads

Roads Roofs Roofs Lawns Lawns Trees Pipeline Trees Pipeline

Manning’s Roughness 0.017 0.017 0.008 0.008 0.266 0.266 0.150 0.0123 0.150 0.0123

Manning’s Roughness

Depression Storage Depression Storage (mm) (mm) 0.675 0.675 0.100 0.100 1.540 1.540 1.540 1.540-

After side-walk investigations, we confirmed that this monitored catchment shared the similar investigations, we confirmed thattrees, this monitored shared the similar land After cover side-walk with our study catchments, i.e., roofs, roads, and lawns. catchment This study thus adopted these land cover with our study catchments, i.e., roofs, roads, trees, and lawns. This study thus adopted calibrated parameters in Table 2 for all these built models equally. This reductionist parameterization these calibrated parameters in hydrological Table 2 forconditions all theseamong built these models equally. This and reductionist approach can guarantee the same study catchments minimize parameterization approach can guarantee the same hydrological conditions among these study the significant hydrological disturbances from diverse model parameters. However, this of course catchments and minimize the significant hydrological disturbances from diverse model parameters. introduces a certain level of uncertainty to the results presented in this work. To handle this uncertainty, However, this of introduces a certain levelresults of uncertainty to the results inthe thissimilar work. it is necessary to course interpret the simulated model in an appropriate way.presented Following To handle this uncertainty, is necessary to interpret simulated model results in anhydrological appropriate purport presented by Vos etital. [35], this study mainlythe focus on explaining the general way. Following the similar purport presented by Vos et al. [35], this study mainly focus on explaining trends that show up from the multitude of study catchments rather than individual results. In this way, the general hydrological trends that show up from the multitude of study catchments rather than individual results. In this way, we believe that our modeling results do have a valuable role and thus guarantee a fair quantitative characterization on the hydrological effect of spatial patterns in small urbanized catchments.

Int. J. Environ. Res. Public Health 2017, 14, 239

7 of 16

Int. J. Environ.that Res. Public Health 2017, results 14, 239 do have a valuable role and thus guarantee a fair quantitative 7 of 16 we believe our modeling characterization on the hydrological effect of spatial patterns in small urbanized catchments. 2.3. Input Rainfall Conditions 2.3. Input Rainfall Conditions A group of rainfall events with various return periods were designated as the SWMM input data A on group rainfallintensity events with various return periods designated as the[26,36]. SWMMEventually, input data based the of rainfall formula in Beijing and thewere Chicago hyetograph based the rainfall intensity formulaofin Beijing and0.3, the0.5, Chicago hyetograph [26,36].had Eventually, rainfallonevents with the return periods 0.1, 0.15, 0.2, 1, 3, 5, and 10 year, which the same rainfall with theand return periods of 0.1, 0.3, 0.5, peak 1, 3, 5, andto10rainfall year, which had as theshown same rainfall events duration (2-h) peak ratio (0.4, the0.15, ratio0.2, of rainfall time duration, rainfall duration and peak (0.4,The thecorresponding ratio of rainfalltotal peakrainfall time toamounts rainfall duration, shown in Figure 5) were(2-h) adopted in thisratio study. were 8.8, as 10.9, 20.2, in Figure were adopted in this The corresponding rainfall amounts werecan 8.8,encompass 10.9, 20.2, 26.9, 35.3,5)46.6, 64.7, 73.1, and 84.5study. mm, respectively. A widetotal range of rainfall amounts 26.9, 46.6, 64.7, andstorm 84.5 mm, respectively. A wide range of were rainfall amounts encompass both 35.3, the minor and73.1, major conditions. All the rainfall data input to thecan built SWMM both the separately, minor and major storm conditions. All results the rainfall data inputfrom to thethe built SWMM models and the simulated runoff could be were obtained output filesmodels of the separately, models. and the simulated runoff results could be obtained from the output files of the models.

Figure 5. 5. Designated Designated rainfall rainfall hyetograph hyetograph by by using using the the Chicago Chicago hyetograph hyetograph method. method. Figure

2.4. Data Data Analysis Analysis 2.4. In this this study, thethe twelve catchments werewere characterized by using TIA, DCIA, In study, spatial spatialpatterns patternsof of twelve catchments characterized by using TIA, average drainage area (A d), and Impervious Area Curves (IAC). TIA can characterize the overall DCIA, average drainage area (Ad ), and Impervious Area Curves (IAC). TIA can characterize the urbanization level of the catchment, which is significantly related to therelated catchment runoff generation [13]. overall urbanization level of the catchment, which is significantly to the catchment runoff DCIA is an important attribute of TIA that is directly connected to the drainage systems. This metric generation [13]. DCIA is an important attribute of TIA that is directly connected to the drainage can represent hydrological efficiency of impervious surfacesofand usually contribute most of the systems. This the metric can represent the hydrological efficiency impervious surfaces and usually runoff to the whole urban catchments [29,37]. A d can be treated as an effective index representing contribute most of the runoff to the whole urban catchments [29,37]. Ad can be treated as an effective both the characteristics of characteristics catchment spatial segmentation drainage structure related to the index representing both the of catchment spatialand segmentation and drainage structure catchment size and drainage junctions. Higher A d represents larger drainage area and drainage loads related to the catchment size and drainage junctions. Higher Ad represents larger drainage area assigned withloads eachassigned rainwater which may exert potential influence oninfluence the catchment and drainage withinlet, each rainwater inlet, which may exert potential on the hydrograph. IAC depicts the accumulated TIA fraction as a function of flow distance from the catchment hydrograph. IAC depicts the accumulated TIA fraction as a function of flow distance catchment outlet, as presented by Meierdiercks et al. [8]. This type of curve can reflect the spatial from the catchment outlet, as presented by Meierdiercks et al. [8]. This type of curve can reflect the configuration of catchment impervious surfaces along with thethe drainage spatial configuration of catchment impervious surfaces along with drainagepathway. pathway. Specifically, Specifically, accumulated TIA faction was presented the curves, and the corresponding flow distance from the the accumulated TIA faction was presented the curves, and the corresponding flow distance from outlet was shown on the X-axis, as shown in Figure 6. outlet was shown on the X-axis, as shown in Figure 6. Runoff volumes volumes and impacts from urbanization on Runoff and peaks peaks are are commonly commonlyused usedtotostudy studythethe impacts from urbanization watersheds [9,38]. Two key indicators, total runoff depth (Q t, mm) and peak runoff depth (Qp, on watersheds [9,38]. Two key indicators, total runoff depth (Qt , mm) and peak runoff depth mm/min), were used to represent the catchment runoff responses. Qt and Qp of each catchment under (Q p , mm/min), were used to represent the catchment runoff responses. Qt and Qp of each all the designated rainfall conditionsrainfall were calculated model output files, and output the detailed catchment under all the designated conditionsfrom werethe calculated from the model files, calculation procedures for these two indicators were presented by Yao et al. [14]. Basic and the detailed calculation procedures for these two indicators were presented by Yaodescription et al. [14]. statistics was used to describe the details of spatial characteristics and runoff responses of the Basic description statistics was used to describe the details of spatial characteristics and runoff catchments.ofMultiple linear regression models were developed to describe significance spatial responses the catchments. Multiple linear regression models werethe developed to of describe patterns in runoff variables assigned with various rainfall conditions. Three scenarios were the significance of spatial patterns in runoff variables assigned with various rainfall conditions. designated to examine the potential with differentalterations imperviouswith locations. We Three scenarios were designated tohydrological examine thealterations potential hydrological different rearranged all the sub-basins with different rainwater inlets (Figure 3) of each catchment in impervious locations. We rearranged all the sub-basins with different rainwater inlets (Figure 3)the of SWMM model, in order to obtain different impervious concentration scenarios without changing impervious fraction. Different impervious location scenarios could be depicted with various IACs. The original catchment IAC was for the base scenario. Two additional scenarios were determined to downstream and upstream concentration of imperviousness, respectively, as shown in Figure 6.

Int. J. Environ. Res. Public Health 2017, 14, 239

8 of 16

each catchment in the SWMM model, in order to obtain different impervious concentration scenarios without changing impervious fraction. Different impervious location scenarios could be depicted with various IACs. The original catchment IAC was for the base scenario. Two additional scenarios were determined to downstream and upstream concentration of imperviousness, respectively, as shown in 8 of 16 FigureInt.6.J. Environ. Res. Public Health 2017, 14, 239

Figure 6. Impervious Area Curves catchmentsforfor different scenarios. X-axis Figure 6. Impervious Area Curves(IAC) (IAC) of of the the twelve twelve catchments different scenarios. X-axis represents the fraction of flow distance to the catchment outlet; Y-axis represents the accumulate represents the fraction of flow distance to the catchment outlet; Y-axis represents the accumulate fraction of the TotalArea Impervious Areasolid (TIA).lines Greyrepresent solid linesthe represent the uniform distribution TIAstate. of the fraction Total Impervious (TIA). Grey uniform distribution of TIA in of ideal in ideal state.

3. Results

3. Results

3.1. Spatial Details of the Study Catchments

3.1. Spatial Details of the Study Catchments

As shown in Table 1, Ad of twelve catchments rangedranged between 0.07 and Theha.catchment As shown in Table 1, the Ad of the twelve catchments between 0.070.18 andha. 0.18 The fractions of TIAfractions and DCIA showed large variations, ranging from 37.95% 77.79% and 19.27% catchment of TIA and DCIA showed large variations, ranging from to 37.95% to 77.79% and to 71.42%, respectively. surfacesRoof contributed of the most TIA for all the except CAT 9, 19.27% to 71.42%,Roof respectively. surfaces most contributed of the TIAcatchments for all the catchments except 9, whereas roadthe surfaces contributed the major DCIA. The general distribution whereas roadCAT surfaces contributed major DCIA. The general distribution characteristics of impervious characteristics of impervious surfaces for the twelve catchments also showed great differences based surfaces for the twelve catchments also showed great differences based on the gaps between TIA and on the gaps between TIA and DCIA. Among the catchments, CAT 4 and 11 had the greatest difference DCIA. Among the catchments, CAT 4 and 11 had the greatest difference between TIA and DCIA, with between TIA and DCIA, with 30.32% and 29.29%, respectively; the least gap was found in CAT 10 30.32% and 29.29%, respectively; the least gap was found in CAT 10 and only 3.08% of the land surfaces and only 3.08% of the land surfaces were treated as disconnected impervious area, which equals to were treated as disconnected impervious area, which equals to the part of TIA excluding DCIA. the part of TIA excluding DCIA.

3.2. Simulated Runoff Variables

3.2. Simulated Runoff Variables

At the case,case, the the runoff responses varied significantly. example, Atbase the base runoff responsesamong among the the catchments catchments varied significantly. For For example, underunder rainfall with 0.10.1year period,thethe catchment Qt ranged from 1.59mm to and 7.46Qmm and Qp rainfall with yearreturn return period, catchment Qt ranged from 1.59 to 7.46 p ranged ranged from 0.04 to 0.16 mm/min. In addition, the changing range of Q and Q of each catchment from 0.04 to 0.16 mm/min. In addition, the changing range of Qt and Qp of each varied as the t catchment p rainfall amount increased from 8.8 to 84.5 mm, such as CAT 4 (Qt: 1.84–67.71 mm) and CAT 6 (Qt: 3.04– 64.75 mm). Detailed simulated runoff data are shown in Table 3.

Int. J. Environ. Res. Public Health 2017, 14, 239

9 of 16

varied as the rainfall amount increased from 8.8 to 84.5 mm, such as CAT 4 (Qt : 1.84–67.71 mm) and CAT 6 (Qt : 3.04–64.75 mm). Detailed simulated runoff data are shown in Table 3. 3.3. Relationship Spatial Patterns and Runoff Responses Int. J. Environ.between Res. PublicCatchment Health 2017, 14, 239

9 of 16

Multiple linear regression analysis results are shown in Table 4. The results showed that the 3.3. Relationship between Catchment Spatial Patterns and Runoff Responses spatial pattern indicators could predict the runoff responses well (R2 > 0.75) under various rainfall linear regression analysis resultssolely are shown in Table 4. The resultsi.e., showed that the conditions.Multiple Particularly, Qt could be predicted by impervious metrics, TIA and DCIA, spatial pattern indicators could predict the runoff responses well (R2 > 0.75) under various rainfall whereas Ad acted as a significant indicator (negative) for predicting Qp besides TIA and DICA. conditions. Particularly, Qt could be predicted solely by impervious metrics, i.e., TIA and DCIA, However, the relative significances of TIA and DCIA in predicting runoff variables varied in whereas Ad acted as a significant indicator (negative) for predicting Qp besides TIA and DICA. different rainfall conditions. DCIA generally played a more important role in affecting both Qt and Q However, the relative significances of TIA and DCIA in predicting runoff variables varied in p than TIA when the rainfall return period less than 0.3 year, while TIA acted a more dominant role different rainfall conditions. DCIA generally played a more important role in affecting both Qt and in affecting variables DCIA forless larger In addition, the dominant rapid decreased Qpthe thantwo TIArunoff when the rainfallthan return period thanrainfall 0.3 year,events. while TIA acted a more role coefficient of Ad (from −0.511 to − 9.719) indicated that rolerainfall of the drainage in explaining in affecting the two runoff variables than DCIA forthe larger events. In area addition, the rapid the decreased coefficient of Aimportant d (from −0.511 to −9.719) indicated that the role of the drainage area in variations in Qp became more as rainfall increased. explaining the variations in Q p became more important as rainfall increased. Scenario design significantly altered the original IACs, as illustrated with red and blue lines in Scenario design significantly altered the original IACs, as illustrated bluecatchments, lines in Figure 6. IACs in scenarios 1 and 2 depicted curves above and below the greywith linered forand all the Figure 6. IACs in scenarios 1 and 2 depicted curves above and below the grey line for all the showing obvious impervious concentration down- and up-streams. catchments, showing obvious impervious concentration down- and up-streams. Detailed runoff variations of Qt and Qp in scenarios 1 and 2 are shown in Figures 6 and 7. Detailed runoff variations of Qt and Qp in scenarios 1 and 2 are shown in Figures 6 and 7. Compared to the base case, most of the catchments showed intuitively similar changing trends in both Compared to the base case, most of the catchments showed intuitively similar changing trends in Qt andboth Qp that increasing in scenario 1 and1decreasing in scenario thechanged changed IACs Qt and Qp that increasing in scenario and decreasing in scenario2.2.However, However, the IACs induced different alterations in Q and Q . For Q , catchments did not experience apparent changes t p t induced different alterations in Qt and Qp. For Qt, catchments did not experience apparent changes in in Qt under different IAC scenarios withthe thehighest highestvariation variation < 1%. Qt under different IAC scenariosand andrainfall rainfall conditions, conditions, with in in Qt Q < t1%. Changes in Qp were significant than that . Thehighest highest increment increment ininQQ p was found in CAT Changes in Qp were moremore significant than that of of QtQ. tThe found in CAT p was under scenario 1, nearly with nearly while largest decrementwas wasin in CAT CAT 66 under 2 (−8.8%). under scenario 1, with 4%, 4%, while thethe largest decrement underscenario scenario 2 (−8.8%). However, Qp in most the catchments experienced relativelylow low variation variation

Exploring the Linkage between Urban Flood Risk and Spatial Patterns in Small Urbanized Catchments of Beijing, China.

In the context of global urbanization, urban flood risk in many cities has become a serious environmental issue, threatening the health of residents a...
4MB Sizes 0 Downloads 8 Views