Continuous Quantity and Quality Modeling for Assessing the Effect of SUDS: Application on a Conceptual Urban Drainage Basin

Abstract

The development of computational tools based on urban drainage models is fundamental for the correct selection of SUDS. The present study proposes a systematic approach based on continuous modeling on USEPA SWMM. The objective was to select the most suitable Sustainable Urban Drainage Systems (SUDS) by evaluating several aspects related to their design and configuration. The proposed methodology was applied to a conceptual watershed with meteorological information from Santander, Spain. The analysis of SUDS design parameters showed that only the surface variables showed a sensitivity of ≈20% for berm height and vegetation volume. The optimal configuration for the case study was a SUDS train consisting of green roofs, permeable pavements, vegetated swales and rain gardens, with 1% of the total subcatchment area cover, one structure, and a semi-aggregated spatial distribution. The methodology proved efficient but also highly dependent on the case study parameters and the meteorological conditions. The SUDS proved to have different efficiencies (30%—90%) in reducing the total runoff volume, the peak flow, and the pollutant loads depending on the region where the conceptual watershed was modeled. The methodology proved to be efficient for studying the combinations and interconnection of seven different typologies, as well as the effect of SUDS configuration, design and distribution on their performance.

Full text

Environmental Processes (2022) 9:58
Vol.:(0123456789)
https://doi.org/10.1007/s40710-022-00609-4
1 3
ORIGINAL ARTICLE
Continuous Quantity andQuality Modeling forAssessing
theEffect ofSUDS: Application onaConceptual Urban
Drainage Basin
PascualFerrans1 · JavierTemprano2
Received: 5 May 2022 / Accepted: 2 November 2022
© The Author(s) 2022
Abstract
The development of computational tools based on urban drainage models is fundamental
for the correct selection of SUDS. The present study proposes a systematic approach based
on continuous modeling on USEPA SWMM. The objective was to select the most suitable
Sustainable Urban Drainage Systems (SUDS) by evaluating several aspects related to their
design and configuration. The proposed methodology was applied to a conceptual water-
shed with meteorological information from Santander, Spain. The analysis of SUDS design
parameters showed that only the surface variables showed a sensitivity of ≈20% for berm
height and vegetation volume. The optimal configuration for the case study was a SUDS
train consisting of green roofs, permeable pavements, vegetated swales and rain gardens,
with 1% of the total subcatchment area cover, one structure, and a semi-aggregated spa-
tial distribution. The methodology proved efficient but also highly dependent on the case
study parameters and the meteorological conditions. The SUDS proved to have different
efficiencies (30%—90%) in reducing the total runoff volume, the peak flow, and the pol-
lutant loads depending on the region where the conceptual watershed was modeled. The
methodology proved to be efficient for studying the combinations and interconnection of
seven different typologies, as well as the effect of SUDS configuration, design and distribu-
tion on their performance.
Highlights
• Most of the SUDS design parameters were found to be low-sensitive.
• The optimal SUDS conguration found was a train formed by GR, PP, VS, and RG.
• SUDS performance varies widely depending on the region and variables assessed.
* Pascual Ferrans
pferrans001@ikasle.ehu.eus
1 Doctoral School, University oftheBasque Country UPV/EHU, Leioa, Spain
2 Department ofWater andEnvironmental Sciences andTechniques, Civil Engineering School,
Canals andPorts Engineers, Cantabria University, Santander, Spain
Published online: 19 November 2022
/
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P. Ferrans, J. Temprano
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Keywords Urban drainage modeling· SUDS· LID· Environmental modeling· Runoff
quality· Runoff quantity· SWMM
1 Introduction
Urban water management has become a priority to resolve runoff quantity and quality
problems caused by urbanization. In recent years, different countries (e.g., China, Iran,
Australia) have implemented national and/or regional urban runoff management policies
(Srishantha and Rathnayake 2017). In these, they promote the restoration of hydraulic and
hydrological conditions prior to urbanization. These goals are achieved by implement-
ing green infrastructure and enhancing rainwater infiltration, storage, and evapotranspi-
ration (Stovin 2010). Sustainable Urban Drainage Systems (SUDS) are also called Low
Impact Development (LID), Green Infrastructure (GI), Best Management Practices (BMP),
or Water Urban Sensitive Design (WSUD). Mentioned runoff control techniques aim to
solve water quality and quantity problems caused by urbanization (Li etal. 2016; Perales-
Momparler etal. 2015). The SUDS are proposed to reduce the dimensions and costs of
traditional sewerage systems (grey infrastructure) by using structures that complement the
traditional paradigm of water management in cities (Sharma etal. 2016).
Furthermore, SUDS have become relevant to mitigate climate change and urbanization
effects (Xu etal. 2020). Typologies such as infiltration trenches, green roofs, permeable
pavements, storage tanks, detention basins, constructed wetlands and bioretention zones
have been studied in terms of their quantity, quality, and amenity performance (Wang etal.
2019; Galarza-Molina etal. 2015). One of the research topics has been the inclusion of
these structures in urban drainage models; nowadays programs such as USEPA SWMM
(Rossman 2010b), MUSIC (eWater 2020), Mike Urban (DHI 2008) allow this possibility.
When working with models, one of the critical factors is the correct definition of the
build-up and wash-off parameters for water quality. Since 1998, some authors (Tsihrintzis
and Hamid 1998) made efforts to report values of these parameters in calibrated models.
Authors such as Temprano etal. (2006), Hossain etal. (2012), Marek and Blazej (2018),
Modugno etal. (2015), Chen and Adams (2007) and Wicke etal. (2012) calibrated urban
drainage models, reporting the performance and values achieved in their calibrations. Fur-
thermore, Leutnant et al. (2018), and Baffaut and Delleur (1990) proposed novel model
calibration methodologies that included water quality modules. At the same time, Chow
etal. (2012) and Hood etal. (2007) began to make isolated efforts to report values for cali-
brated models in poorly studied areas, such as tropical and desertic regions.
Regarding SUDS modeling, the first authors to address the subject were Li etal. (2016),
Tobio etal. (2015), Rosa etal. (2015), Jia etal. (2012), Rossman (2010a), Kourtis etal.
(2018), and Gulbaz and Kazezyılmaz-Alhan (2018). Initially, they reported simplified
models, focused on small study areas, which included SUDS performances and calibra-
tion procedures for certain, but limited, SUDS typologies (mainly GR, BC and PP). Fur-
thermore, research interest began to expand its thematic focus. Baek etal. (2020), Kourtis
etal. (2021), and Sarminingsih et al. (2019) began to investigate the potential of SUDS
to mitigate the effect of climate change, and Kourtis etal. (2020) focused on the flood
risk reduction. Zhang etal. (2019) and Tu and Smith (2018) included groundwater interac-
tion modules within their models. At the same time, studies such as Macro etal. (2019)
and Zhu etal. (2019) proposed the development of tools for the inclusion of hydrological-
hydraulic models in decision-making processes with SUDS. Bai etal. (2018) emphasized
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the importance of including different combinations of SUDS typologies within the models.
Likewise, Cipolla etal. (2016) is one of the few studies that has reported SUDS mod-
els with long-term continuous temporal resolution. Furthermore, Hamouz and Muthanna
(2019) made isolated efforts to study SUDS under poorly studied climatic and hydromete-
orological conditions, but a rigorous study on this issue is still needed.
Although the previous, and some other case studies have successfully implemented and
validated SUDS urban models (e.g., Kong etal. 2017; Maftuhah etal. 2018; Rujner etal.
2018; Garbanzos and Maniquiz-Redillas 2022; Arjenaki etal. 2021; Ma etal. 2022), there
are still gaps regarding modeling technicalities and aspects that have not been addressed so
far, such as trains modeling, continuous-long term modeling, effect of temporal and spatial
scale, or performance of different typologies. Previous studies have highlighted the impor-
tance of systematically studying the sensitivity of the models to specific SUDS design
parameters. Leimgruber etal. (2018) and Peng etal. (2021) addressed this issue for three
different typologies (green roofs, infiltration trenches and bioretention cells), but there is
still a need to include different parameters and typologies. Furthermore, previous studies
have identified the current need to fully comprehend the effect of specific aspects on SUDS
performance, such as the spatial distribution (Guo etal. 2019), temporal scales (Bai etal.
2018), SUDS trains (Beltran etal. 2019), and water quality (Baek etal. 2020). However,
these studies only apply to the specific case studies and have not proposed a standardized
methodology to address some of the previously mentioned gaps. Furthermore, these stud-
ies have only implemented event-based modeling, and have excluded capturing the long-
term performances of SUDS using continuous rainfall databases from different regions.
Concerning this last aspect, studies such as Goncalves etal. (2018) and Andrés-Doménech
etal. (2018) have made isolated efforts to study the performance of SUDS in tropical and
Mediterranean regions, but there is still a need to identify the potential of SUDS for key
regions such as these two.
To address these issues, the present study proposes a novel methodology based on con-
tinuous long-term modeling to analyze and select seven different SUDS typologies sys-
tematically. In the process, the effect of the following SUDS aspects will be analyzed for
seven different typologies: 1) design parameters; 2) area; 3) number of structures; 4) spatial
distribution; and 5) combinations and trains. The methodology is applied to a case study
based on a conceptual watershed with meteorological information from Santander, Spain.
To address the lack of information for tropical and semi-arid regions, rainfall databases
from these regions were used for the selected SUDS configuration to assess the effect of
meteorological information on their performance.
2 Materials andMethods
2.1 SWMM Conceptual Model
As this study was based on a conceptual watershed, no calibration-validation procedure
was performed, and all parameters of the model were defined based on previous stud-
ies (Tu and Smith 2018; Rosa etal. 2015; Modugno et al. 2015; Li et al. 2016; Hos-
sain etal. 2012; Hood etal. 2007; Chow etal. 2012; Chen and Adams 2007; Temprano
etal. 2006) and the recommendations of the user manual by USEPA SWMM (Rossman
2010b). The watershed (Figure1) developed consisted of a rectangular area of 80 ha,
distributed in 8 equal subcatchments (A1—A8). Subcatchments A5 to A8 discharged
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P. Ferrans, J. Temprano
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to nodes J5 to J8 respectively, and runoff was transported to nodes J1 to J4 through
conduits C1 to C4. At the same time, subcatchments A1 to A4 discharged to nodes J1 to
J4, and runoff was then transported using pipes C5 to C8; the final outlet node was O1.
All conduits of the sewer system consisted of pipelines dimensioned using the rational
method. For this matter, the return period was fixed as 20 years, and the concentration
time was averaged as the maximum transport time in the watershed. The IDF curves
were gathered and used for all regions with available information. All conduits were
circular, and the maximum capacity allowed was fixed as 0.85.
Based on the SWMM user manual recommendations, the mean slope defined for
the watershed was 1%, the impervious coverage was 50%, and the storage depths were
2.45 mm and 7.62 mm for impervious and pervious areas, respectively. The surface
roughness values were defined as 0.01 and 0.1s/m1/3 for impervious and pervious areas,
respectively, and the fraction of impervious areas without depression storage was set as
50%.
The routing and infiltration methods were the dynamic wave and the curve number,
respectively. Following the recommendations given by Rossman (2010b), the routing time
step was 30s, and the curve numbers were calculated by interpolation for the different
impermeability coverages. The rainfall database used was from Santander, Spain, and had
a total length of 10years, with temporal resolutions of 10min. The basic statistics for the
rainfall characteristics are presented in Table1. The evaporation was modeled using mean
multiannual averages for the different regions.
For the water quality modeling, the exponential function was selected to simulate the
pollutant build-up and wash-off. The parameters used (Table2) were defined using as ref-
erence 17 previous studies (Table3) which had previously reported parameters for cali-
brated models. These articles were selected because they had conditions similar to those of
the case study. Build up (B) was calculated as
B
=C
1
(1−e
−C2
), where C1 was the maxi-
mum build up, and C2 the constant. Washoff (W) was calculated as
W
=
C1qC
2
B
, where C1
was the coefficient, C2 the exponent, and q the runoff rate.
Fig. 1 Conceptual watershed developed on USEPA SWMM for the case study
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2.2 Methodological Framework andScenario Analysis
The methodological approach proposed for SUDS selections consisted of five stages
(Fig.2). The first stage consisted of defining the optimum values of the design parameters
of the typologies. Subsequently, the area of each structure was determined. The third stage
consisted of defining the number of structures in which the fixed area would be allocated.
Table 1 Mean, Min, Max and
Std for main indicators of rainfall
characteristics for Santander,
Spain
Variable Mean Standard
deviation
Min Max
Annual rainfall depth (mm) 1108 - - -
Number of annual rainfall events (#) 210 - - -
Mean intensity (mm/h) 1.54 2.4 0.1 57.6
Maximum intensity 10min (mm/h) 5.34 7.0 0.6 78.0
Antecedent dry period (days) 1.72 3.0 0.0 65.2
Duration (min) 5.13 7.93 0.16 450.8
Table 2 SWMM parameters used
for water quality modeling Pollutant Parameter Selected value
TSS Max. Build Up (C1) 71.06
Constant 0.74
Coefficient 4.03
Exponent 2.45
TP Max. Build Up 0.04
Constant 0.16
Coefficient 1.95
Exponent 2.05
TN Max. Build Up 0.15
Constant 2.02
Coefficient 25.22
Exponent 3.96
Table 3 Reference values used for defining the water quality parameters
# Article # Article
1 Baffaut and Delleur (1990) 10 Modugno etal. (2015)
2 Barco etal. (2004) 11 Qin etal. (2013)
3 Chen and Adams (2007) 12 Tu and Smith (2018)
4 Chow etal. (2012) 13 Temprano etal. (2006)
5 Gülbaz and Kazezyılmaz-Alhan (2018) 14 Tobio etal. (2015)
6 Hood etal. (2007) 15 Tsihrintzis and Hamid (1998)
7 Hossain etal. (2012) 16 Wicke etal. (2012)
8 Leutnant etal. (2018) 17 Marek and Blazej (2018)
9 Li etal. (2016)
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P. Ferrans, J. Temprano
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The next step was defining the spatial distribution of the SUDS typologies. Finally, the
optimal combination of structures and the possibility of interconnecting the SUDS struc-
tures with each other (trains) were assessed. Sections2.2.1 to 2.2.6 describe the method-
ology and the scenarios defined at each stage. Furthermore, Table4 summarizes the sce-
narios that were defined at each step.
2.2.1 Design Parameters
The typologies assessed in the analysis were bioretention cells, rain gardens, green roofs,
infiltration trenches, permeable pavements, rain barrels, and vegetated swales. Based on
previous studies (Table5), the minimum and maximum reported values for each parameter
are known (Table6). The groups of the parameters are presented using the same categories
that USEPA SWMM uses.
The minimum and maximum values for each parameter are defined as the ranges for the
sensitivity analysis. As the ranges for each typology differed, it was decided to generate
independent sets of scenarios for each typology. For computational and processing reasons,
Fig. 2 The proposed five-stage methodological framework. Each stage consists of an individual analysis
that yields decisions regarding SUDS configuration and design
Table 4 Variables assessed, options and decisions made at each step (green)
Area (%) Structures Distribution Combination Trains
0.1 1 Distributed Yes GR-PP-IT-BC
0.2 2 GR-PP-VS-BC
0.3 3
0.4 4 Semi-Distributed GR-PP-IT-RG
0.5 5
0.6 6 Semi-Aggregated No GR-PP-VS-RG
0.7 7
0.8 8 Aggregated GR-PP-IT-RB
0.9 9
1.0 10 GR-PP-VS-RB
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five scenarios were developed for each typology, consisting of progressive changes in the
design parameters, keeping the rest of the parameters fixed in the model.
2.2.2 Area andNumber ofStructures
The second stage of the analysis consists of assessing the effect of the total area of the
SUDS. Based on previous recommendations (Table5), the ranges for the analysis were
fixed as 0.1%—1% of SUDS areas, with respect to the total watershed. Subsequently,
twenty different scenarios were developed for each typology, which consisted of progres-
sive increases in the SUDS total area.
Furthermore, the effect of modifying the number of fixed SUDS distributed area was
assessed. For this purpose, ten scenarios were generated for each typology. Each scenario
distributed the total SUDS area in one, and up to ten structures.
2.2.3 Spatial Distribution
To define the effect of the spatial distribution on SUDS performances, four different sce-
narios with different spatial configurations were developed (Figure 3). Distribution one
(D1) had a total of eight SUDS structures evenly distributed on the subcatchments; from
D2—D4, the spatial distribution was progressively centralized.
2.2.4 Combination ofTypologies andTrains
In this stage, the effect of combining different typologies in the same catchment was
assessed. The urban drainage guide by Butler etal. (2018) was utilized to propose logi-
cal combinations for the structures. The suggested stages/processes and the recommended
typologies for each stage (Fig.4) were used. Furthermore, two basic criteria were estab-
lished for proposing the combinations in which: (a) each scenario included all stages of
the drainage process, and (b) each scenario included one, and only one typology for each
process.
Following these criteria, all possible combinations were reached, leading to an inde-
pendent scenario for each combination (Table7). Also, two control scenarios (not mixing
typologies) were proposed.
The same combinations from Table7 represented SUDS trains in the watershed. For
this matter, it was necessary to manage each SUDS typology as independent subcatch-
ments, that were subsequently connected. By these means, one for each combination of
typologies, resulting in six scenarios.
Table 5 Articles referenced
for defining the SUDS design
parameters ranges
# Article # Article
1 Baek etal. (2020) 6 Jia etal. (2012)
2 Bai etal. (2018) 7 Rosa etal. (2015)
3 Zhang etal. (2018) 8 Rossman (2010a) 2010
4 Cipolla etal. (2016) 9 Sarminingsih etal. (2019)
5 Gulbaz and
Kazezyılmaz-Alhan
(2018)
10 Zhu etal. (2019)
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P. Ferrans, J. Temprano
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Table 6 SUDS minimum and maximum design parameters reported in the literature
Variable Units Bioretention cell Green roof Rain garden Infiltration
trench
Permeable pave-
ment
Rain barrel Vegetated
swale
Min Max Min Max Min Max Min Max Min Max Min Max Min Max
Surface
Berm Height mm 3 300 10 500 150 1000 0 300 0 25 1 400
Vegetation Volume mm 0.05 0.8 0.1 0.2 0 0.2 0 - 0 0 0 0.1
n Manning mm 0.03 0.3 0.05 0.13 0.1 0.12 0.24 - 0.01 0.05 0.13 0.24
Slope % 0.1 3 0.3 2 0 2 1 - 0.5 4 0.8 1.3
Soil
Thickness mm 100 800 30 300 500 1200 150 200
Porosity % 0.45 0.65 0.4 4 0.3 0.5 0.35 0.5
Field Capacity % 0.1 0.35 0.11 4 0.11 0.2 0.1 0.1
Wilting Point % 0.05 0.15 0.05 4 0.05 0.2 0.02 0.024
Conductivity mm/h 12 300 25 72 2.2 500 100 130
Conductivity Slope - 5 10 5 15 10 32.2
Suction Head mm/h 15 115 20 110 24.6 50
Storage
Thickness mm 10 380 500 600 10 750 800 914
Void Ratio % 0.3 0.8 0.41 0.75 0.75 - 0.3 0.78
Seepage Rate mm/h 0 80 2.2 200 24 - 0 600
Bottom Area (RB) m2 0.31 0.58
Clogging Factor - 0 0 0 0 0 0
Drain
Flow Coefficient* - 0 3.33 0.69 - 0.15 1 0.68 294
Flow Exponent - 0.5 2.1 0.5 - 0.5 1.6 0.5 0.5
Offset mm 0 150 0 150 0 150 0 150
Drain Delay (RB) h 5.8 6
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Table 6 (continued)
Variable Units Bioretention cell Green roof Rain garden Infiltration
trench
Permeable pave-
ment
Rain barrel Vegetated
swale
Min Max Min Max Min Max Min Max Min Max Min Max Min Max
Surface
Pavement
Thickness mm 50 150
Void Ratio % 0.13 0.7
Impervious Surface Fraction % 0 0.3
Permeability mm/h 50 3000
Drainage Mat
Thickness mm 10 75
Void Fraction % 0.43 3
n Manning mm 0.03 3
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P. Ferrans, J. Temprano
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2.3 Sensitivity Analysis
The metrics used to assess the performance of SUDS were total runoff volume, peak
flow, maximum flows (average of the top 50 flows), and pollutant loads (total sus-
pended solids, total phosphorus and total nitrogen). The reduction was calculated for
all the scenarios by comparing the values with the base scenario (no SUDS implemen-
tation). In all cases, the water quantity and quality measures were made at the outfall
(O1) of the watershed, after modeling the ten-years rainfall database.
The sensitivity analysis consisted on a One at a Time (OAT) approach (Fig.5); dif-
ferent Global Sensitivity Analysis methods were tested but failed to be implemented
due to the computational cost involved in these approaches (considering that each
model consisted on a ten-years continuous data-base). Thus, a model was developed
for each scenario defined in Section2.2. Subsequently, the effect of these scenarios on
the performance metrics was analyzed. To summarize the results, sensitivity graphs for
the performance variables were developed. Furthermore, the absolute changes in these
metrics were calculated as the percentage with respect to the base scenario and a color-
coded matrix was developed.
2.4 Decision Making
For each of the stages presented in Figure2, and based on the results of the sensitivity
analysis, a decision regarding SUDS design and configuration was made at each step. To
Fig. 3 SWMM model configuration for spatial distributions D1 (distributed) to D4 (Aggregated)
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avoid bias in the decision-making process, each decision was based on the sensitivity lev-
els, and the performances found during the analysis.
Specifically for the design parameters analysis, the data distribution was processed by
defining the values of each quartile. Subsequently, two different levels of sensitivity were
defined. The low sensitivity level was assigned to those values below quartile 2; for these
values, the minimum value for the parameter was selected. Furthermore, the values above
quartile 2 were assigned as high sensitivity, and a manual parameter selection was made.
For the rest of the stages of the analysis, the best-performed scenarios were selected.
Fig. 4 SUDS trains design process. Blue boxes refer to the drainage-control stages suggested. Orange
arrows refer to the typologies recommended (green boxes) for each stage. Adapted from Butler etal. (2018)
Table 7 SUDS combinations
selected for green roofs (GR),
permeable pavements (PP),
infiltration trenches (IT),
bioretention cells (BC), vegetated
swales (VS), rain gardens (RG),
and rain barrels (RB)
# Input control Source control Conduction Local control
1 Control (GR)
2 GR PP IT BC
3 GR PP VS BC
4 GR PP IT RG
5 GR PP VS RG
6 GR PP IT RB
7 GR PP VS RB
8 Control (RG)
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P. Ferrans, J. Temprano
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2.5 Effect ofRainfall Regime
Finally, the SUDS scheme selected from the previous analysis was evaluated with different
rainfall regimes to quantify the effect of this variable on SUDS performance. 23 continu-
ous rainfall and evaporation (mean multi-annual averages) databases from cities in Colom-
bia and Spain were used to develop the models. They were all developed using the same
conceptual watershed described in section2.1, and the results for total runoff, peak flow,
and pollutants reductions were compared.
3 Results andDiscussion
3.1 Overall Performances
In order to graphically summarize the performances (reduction percentages) found for
the different metrics in the seven typologies, Table8 presents a summary of the values
found. The analysis for the total runoff volume reductions (10%—30%) identified similar
performances for the RG, BC, and PP as those reported in previous studies (Arjenaki etal.
2021; Garbanzos and Maniquiz-Redillas 2022; Jato-Espino etal. 2016; Bai etal. 2018), but
somewhat limited for the rest of the typologies (< 5%). At the same time, the decrease in
pollutant (TSS, NT, and TP) loads was high for all the typologies (> 90%). Regarding the
peak flow, a similar capacity was evidenced for the BC, GR, RG, IT, and PP, with reduc-
tions of 20%—30%. Simultaneously, the reduction for the maximum flows did not show
the same behavior, with no reductions or even slight increments (IT and VS).
3.2 Design Parameter Analysis
Table9 presents the sensitivity (expressed as % of change with respect to the base sce-
nario) for each parameter included in the analysis. In addition, the design parameters of
Fig. 5 Step-By-Step methodological framework proposed for the sensitivity analysis
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SUDS included in the analysis are presented. The novel aspect of the results presented
consists mainly in the inclusion of 7 typologies. For each typology, the sensitivity to the
Table 8 Color-coded average reductions found for the four performance metrics (runoff volume, peak and
extreme flows and pollutant loads reductions) for the seven typologies analyzed. The color scale is pre-
sented at the bottom of the table. Typologies included are green roofs (GR), permeable pavements (PP),
infiltration trenches (IT), bioretention cells (BC), vegetated swales (VS), rain gardens (RG), and rain barrels
(RB)
Average Reducons (%)
Volume BC
GR
RG
IT
PP
VS
RB
Peak FlowBC
GR
RG
IT
PP
VS
RB
Extreme FlowsBC
GR
RG
IT
PP
VS
RB
Pollutant Loads BC
GR
RG
IT
PP
VS
RB
0255075100
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P. Ferrans, J. Temprano
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different performance metrics is presented independently. In general terms, the predom-
inance of the red and yellow colors in Table9 allowed to identify the low sensitivity
found for most of the parameters. Only some specific sensitivities reported values above
40%. These latest cases will be discussed in Sections.3.2.1—3.2.4.
3.2.1 Surface Variables
According to the procedure described in section 2.4, the berm height sensitivity found
for the volume reduction and peak flow was low, with no more than 5% variations for all
typologies. However, when analyzing the pollutant loads, results showed a clear positive
correlation, with increases of 10%—50% in the reductions. This confirms that berm height
is a relevant parameter by allowing higher levels of surface ponding and promoting settle-
ment and filtering of pollutants (Bastien etal. 2010; Charlesworth etal. 2003; Napier etal.
2009).
Table 9 Color-coded sensitivity found for the four performance metrics (runoff volume, peak and extreme
flows and pollutant loads reductions) for the five design parameters analyzed. The color scale is presented
at the bottom of the table. Typologies included are green roofs (GR), permeable pavements (PP), infiltration
trenches (IT), bioretention cells (BC), vegetated swales (VS), rain gardens (RG), and rain barrels (RB)
Berm Height Vegetaon Volume Soil DepthStorage DepthDrainage offset
Volume BC
GR
RG
IT
PP
VS
RB
Peak Flow BC
GR
RG
IT
PP
VS
RB
Extreme FlowsBC
GR
RG
IT
PP
VS
RB
Pollutant LoadsBC
GR
RG
IT
PP
VS
RB
60 50 40
30 20 10
0
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The sensitivity for the vegetation volume presented variations of 0%—60%. In the case
of the total runoff reduction, the BC started with reductions of 20% and decreased as the
vegetation cover increased. Simultaneously, the RG presented constant reduction values
(25%) with low sensitivity for the design parameter. Both the GR and the VS (including
modifying their vegetation cover) showed ineffective strategies to reduce the total volumes,
with constant values of 0% reduction. Previous studies (Leimgruber etal. 2018) had identi-
fied the low sensitivity of this parameter when assessing the performance of the structures.
Therefore, it is highly recommendable for future work to further investigate the perfor-
mance of the structure.
The metrics of pollutants load and peak flow presented similar results. In all cases, the
sensitivity yielded for the vegetation coverage was low, as reported by Leimgruber et al.
(2018). The net changes were small (0% — 10%). Therefore, it is suggested that the per-
centage of surface vegetation cover does not directly influence the runoff quality and quan-
tity. However, it is considered that the greatest potential of the vegetation cover of SUDS
regards the aesthetic and landscape dimension. Monberg etal. (2018) highlighted that it is
recommended to focus on these aspects when defining the percentage and type of vegeta-
tion cover.
3.2.2 Soil Parameters
The sensitivity found for the depth of the soil layer was low for peak and maximum flows
and runoff volume (< 5% in most cases). Only the mean duration and volume of runoff
events presented a 10% increment due to increased soil depth for the BC.
Results for pollutants loads were different. In this case, the variable sensitivity was high
for BC, GR, and PP, with increases of up to 10% in reducing contaminants when increasing
the soil depth. This fact can be explained by the fact as the total volume of soil increases,
the adsorption and filtering processes of contaminants are favored (Schlu¨ter and Jefferies
2005). For the rest of the performance metrics, the depth of the soil did not show signifi-
cant repercussions on the functioning of the structures, confirming the conclusions made
by Leimgruber etal. (2018).
3.2.3 Storage andDrainage Parameters
The analysis for the storage variables was also made for the height of this layer. Over-
all, the analysis yielded low levels of sensitivity (< 3%). This was the first analysis that
included the RB. However, the sensitivity of the parameter and the global reductions of this
structure, behaves similarly to the rest of the typologies. In this case, only the BC presented
increases of 30% in the mean average duration of runoff events while the storage layer
increased.
A possible reason why the storage height of the structures was not a relevant parameter
is that most of the relevant phenomena that intervene in the long-term modification of the
patterns of runoff quality and quantity (seepage, infiltration, evapotranspiration, etc.) are
not directly related to this parameter (Charlesworth etal. 2012).
In this case, the offset of the drainage orifice was analyzed, and the results obtained are
similar to the previous sections: low sensitivity levels, with less than 2% variations with
changes in the offset between 0—150mm. These results indicate that this parameter might
not clearly influence the structures’ long-term performance.
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P. Ferrans, J. Temprano
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3.2.4 Parameters Re‑definition
The design parameters were re-defined depending on their sensitivity levels, and results
from this process are presented in Table10 Four parameters presented a high sensitivity
level (orange), in which a manual selection of the value of the parameters was made. This
balances the performance and the constructive facilities of the structure. The rest of the
parameters presented a low level of sensitivity (green), which is why the lowest values for
the parameters were assigned. The rest of the parameters were fixed as the average values
from those reported in the studies in Table6.
3.3 Distribution andDisposition Analysis
3.3.1 Area
The analysis of the area indicates that the sensitivity in the performance metrics for changes
in the area was high for SUDS. For the specific cases of total runoff volume, maximum
flow, and pollutants loads, the performances of SUDS increased in values (20%—50%). As
a general fact, the performance of the structures improved when the area of the SUDS was
higher; the above occurred indistinctly for all typologies.
The SUDS area value was chosen based on two criteria; considering that 1% area
offered a fair balance between performance and implementation complexity and following
the design guidelines (Strecker etal. 2010; Till and Torres 2004; Valley 2010; Woods-
Ballard etal. 2007).
3.3.2 Number ofStructures
Results showed low sensitivity for most of the analyzed parameters. The variation in the
performance of the SUDS when modifying the number of structures was practically imper-
ceptible. The two highest sensitivities observed were 25% variations (for GR and VS). In
both cases, the best-performed scenario was the one-structure (pollutant reductions of 75%,
in comparison with reductions of 50% for the rest scenarios). Due to this, this was the cho-
sen scenario.
Table 10 Design parameters values selected and sensitivity levels. Low level (green, < 10%) and high level
(orange 10%—50%)
Variable UnitsBCGRRGITPPRBVS
Surface
Berm Height
mm 300120150 0 0 100
Vegetaon Volume %0.40.1 0 0
Soil
Thickness mm 10030 500150
Storage
Thickness mm 10 20050010 800
Drain
Offset
mm 0 0 0 0
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3.3.3 Spatial Distribution
This analysis allowed to identify the sensitivity changes of the performance metrics in
response to the spatial distribution of SUDS. The variations were minimal for the total
runoff volume and pollutant loads depending on the spatial distributions. However, for the
peak flow, there were higher differences. Therefore, to guarantee that the spatial distribu-
tion selected was the one that maximized its performance, additional analyses were per-
formed for this variable.
The results for the peak flow analysis in the seven typologies were assessed (Figure6).
In all cases, D4 was the distribution that best-performed. However, it was discarded due
to its lack of real-life implementation (no pipelines or additional runoff transport infra-
structure). Subsequently, depending on the typology, D2 and D3 had better performances
than D1. Previous authors had already highlighted the importance of having decentralized
managing strategies to improve the performance of SUDS (Guo etal. 2019). Therefore,
giving priority to IT, RB, and VS, and also to ease the applicability in real cases (less com-
plex and expensive sewage systems), D3 was chosen as the spatial distribution for SUDS.
Overall, it was concluded that because the SUDS modify the hydraulic and hydrological
patterns of the catchment, their spatial distribution will be a determining variable in these
modifications, and for this reason it is essential to define adequately what is the best distri-
bution, as also stated by other authors (Tedoldi etal. 2017; Joshi etal. 2021; Fenner 2017).
Fig. 6 Peak flow reduction (%) for spatial distributions D1, D2, D3 and D4. Starting from D1, the spatial
distributions are distributed, and progressively centralized
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P. Ferrans, J. Temprano
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3.3.4 Combination ofTypologies
Results for total runoff volume, peak and maximum flows, and pollutants loads are pre-
sented in Figure7. For the total volume of runoff (Figure7a) and the pollutant loads (Fig-
ure7d), it is shown that in none of the combined cases (scenarios 2—7) results are higher
than the best control scenario (Scenario 8), nor worse than the worst control scenario (Sce-
nario 1). However, results for the peak flow (Figure7b) and the extreme flows (Figure7c)
did show a tendency to improve performance for the combined scenarios. In both cases,
scenarios, 6 and 7 showed the best performance (expressed as the lowest flow). In addition,
the local control strategy was the RB, which suggests that this typology has a strong effect
on reducing peak flows.
Generally, this analysis allowed to identify that it is particularly important to clearly
define the objective variable (volume, peak flow, or contaminant load). In addition, from
that definition, determine which combination is the most appropriate, considering that in
some cases combining typologies might not be the most appropriate.
3.3.5 SUDS Trains
Figure8 presents the results with and without SUDS trains (blue and green bars), and the
percentage of reduction implied by including the trains (red dots).
Results yielded that the performances for the interconnected structures improved sub-
stantially. Furthermore, for the total volume of runoff (Figure8a), the fact of including
Fig. 7 Total Runoff Volume, Peak and Maximum Flows and Pollutants Loads for 8 possible combinations
of SUDS typologies
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SUDS trains causes the reduction of the total volume of runoff to increase significantly
(40%—60%), and similarly for the pollutant loads (65%—85%). Overall, suggestions made
by Beltran etal. (2019) were confirmed, highlighting the importance of structured meth-
odologies to include SUDS trains in the models, as their potential for improving SUDS
performances is high.
For this specific case, and because the positive effect of trains over runoff volume and
pollutants loads was comparatively higher than the negative effects over the peak flows,
it was decided to prioritize the first two variables, and for this, it was decided to continue
using SUDS trains. Based on the results, combination five was the best option and was
selected for the subsequent analysis.
3.3.6 Optimal SUDS Scheme Definition
To summarize the decision-making procedure followed, Table 11 presents each of the
stages of the analysis, along with the possibilities in each stage and the decision made (in
green).
It is worth clarifying that this configuration was selected for this specific case study.
Although it cannot be guaranteed that it applies to other case studies, this selection meth-
odology can be recommended as applicable and extrapolated to other cases. Because the
results can be highly variable, and also because of the variability of possible decisions
Fig. 8 Total Runoff Volume, Peak and Maximum Flows and Pollutant Loads for combinations with and
without SUDS trains
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P. Ferrans, J. Temprano
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Table 11 Variables assessed, options and decisions made at each step (green)
Area
Structures
Distribuon
Combinaon
Trains
0.11
Distributed
Yes
GR-PP-IT-BC
0.2
2
GR-PP-VS-BC
0.3
3
0.4
4
Semi-Distributed
GR-PP-IT-RG
0.5
5
0.6
6
Semi-Aggregated
No
GR-PP-VS-RG
0.7
7
0.8
8
Aggregated GR-PP-IT-RB
0.9
9
110
GR-PP-VS-RB
Fig. 9 Total Runoff Volume, Peak Flow and Pollutants Loads with and without SUDS trains for 23 differ-
ent Colombian and Spanish Cities. The main Y-axis represents the performance metrics for the model with
SUDS (blue) and without SUDS (green). The secondary Y-axis represents the percentage of reduction due
to the presence of SUDS in each case. Results are differentiated between rainfall information from Colom-
bian (Col) and Spanish (Spa) cities, with rounded red indicators for the first group and pink quadrangle
indicators for the second. The International Air Transport Association (IATA) code was used to represent
the cities
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based on the specific objectives, it is recommended to apply this methodology to each spe-
cific case study.
3.4 Effect ofRainfall Regime onSUDS Performance
Figure9 summarizes the main results for the effect of rainfall regimes. The analysis for the
total runoff volume (Figure 9a) identified high variability in the efficiency of reduction,
with overall values varying between 30%—80%. Except for Pasto (PSO), the scenarios
with the lowest reduction percentages were from Colombia, with values between 30%—
55%. At the same time, scenarios from Spanish cities showed higher reduction rates (above
60%). Similar trends were previously reported by authors that worked in Mediterranean
and tropical areas (Andrés-Doménech etal. 2018; Goncalves etal. 2018).
Based on the above, it is suggested that SUDS might have high variability in their effi-
ciency and effectiveness depending on the region; for tropical regions with more intense
rainfall regimes, the SUDS effectiveness decreases, while for regions with more prolonged
but less intense rainfall events, the effectiveness of these structures increases considerably,
as previously identified by Zhang etal. (2019).
Analyzing the inner-region variability, a similar trend was identified. For example, in
the case of Colombia, in cities with large rainfall events, such as Inirida (PDA), Quibdó
(UIB) and Mitú (MVP), SUDS efficiency was lower than for the rest of the cities. The
same happened for Spanish scenarios, showing that the variability in performance rates is
also observed at the regional (inner-country) level.
The analysis for the peak flow (Figure 9b) allowed to identify reductions between 5% and
30%. A similar (but inverse) variability was observed regarding the global and inner-region anal-
ysis. This means that in this case, the potential of the SUDS to reduce peak flows is greater for
cities with more intense rainfall regimes (Colombian cities), and gradually decreases for cities
with more stable rainfall regimes (Spanish cities). However, in this case, the dispersion between
the different performances was less perceptible than that of those observed in Figure9a.
Finally, Figure 9c allowed to identify that for the contaminant loads, the variability,
both regional and inner-regional, decreased significantly. Colombian cities such as Inirida
(PDA), Quibdo (UIB) and Mitu (MVP), which are those with the highest annual rainfall,
had lower rates of reduction, but in this case, the differences were less than 5% or 10%
within the cities. Overall, the pollutant reductions were between 90% and 99%. Data sug-
gests that SUDS have a high potential to reduce pollutants, and this potential does not
depend directly on the rainfall regimes.
Considering that the selection methodology was applied to the city of Santander (SDR),
it could be confirmed that the results obtained from the methodology proposed were satis-
factory since the performance of SUDS was good for this city. At the same time, this con-
firms the need to apply the proposed methodology to each case study, since SUDS did not
perform equally with all the different rainfall regimes analyzed.
4 Conclusions
A novel modeling-based methodology was proposed to assess and study SUDS perfor-
mance in a systematic manner. The combinations and interconnection of typologies was
successfully studied, as well as the effect of SUDS configuration, design and distribution
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P. Ferrans, J. Temprano
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on its performance. The study was successfully implemented using long-term modeling
with rainfall information from different and unstudied regions. The five-step methodology
is expected to be replicable for different case studies, in order to contribute in the develop-
ment of SUDS-tools based on models that will ultimately assist the decision-making pro-
cess for urban drainage projects involving SUDS.
The analysis for the design parameters identified the low sensitivity levels for most
of the parameters. Generally, the surface parameters (berm height and vegetation cover)
had a slightly higher sensitivity (maximum 50%). In contrast, the soil, storage, and
drainage parameters presented lower levels of sensitivity (many times imperceptible).
The low sensitivity levels found might be due to the high amount of SUDS parameters.
This study analyzed the most relevant parameters during the design process. However,
variables related to the nature of the construction materials and the study site (hydraulic
conductivity of the substrate, soil porosity, field capacity, surface roughness, etc.) may
be highly relevant in the process. In this case, it was considered that these variables are
fixed and dependent on external factors, which are not easily defined by the designer of
the structures, and for this reason, they were excluded from the analysis. Furthermore,
for future studies, it is highly recommended to assess the crossed-effect of changing
more than one parameter at a time, in order the evaluate the high complexity of the
parameters involved in the models.
Additionally, it is highly likely that aspects related to the nature and conception of
the model also mediate the results found. For example, the modeling time scale can
be fundamental to the results. In this study, the objective was to evaluate the effect of
SUDS in the long term, and for this reason, it was decided to do continuous modeling of
a ten years database. However, it is highly likely that if the database is shorter or even
modeling by event is done, the results will be different. Thus, it is particularly important
to define beforehand the objective of the study, to select the most appropriate time scale
for the model.
Regarding the effect of the distribution and configurations of SUDS, the variables
that proved to be of greater relevance were the area, as well as the spatial distribution,
and the presence of trains. For the area, the performance of SUDS increased with their
total area, and the percentage selected (with respect to the total area) was 1%; concern-
ing the spatial distribution, the semi-aggregated option proved to be the most efficient,
specifically for managing peak flows. Finally, SUDS trains proved to have high effi-
ciency in managing the total runoff volume and the reduction of pollutants.
The dependent result on the performance metrics (including the desired goals) high-
lights the importance of clearly defining them. For this study, all the metrics were
included. However, for future exercises, the modeler needs to define the main perfor-
mance variable and the desired objectives for this variable. Additionally, aspects such
as the economic, construction, maintenance, social or landscape dimensions might be
highly relevant and should be included in future studies.
For the specific case of Santander, Spain, it was found that the optimal configuration
of SUDS included a 1% area of SUDS concerning the total catchment area, with a semi-
aggregated spatial distribution, combining and interconnecting the following typolo-
gies: GR-PP-VS-RG.
Finally, the analysis of the effect of rainfall regimes showed variability in the per-
formance of the SUDS for the different scenarios evaluated. The fact that the results
were satisfactory for the city of Santander indicates that the proposed methodology was
adequate. Still, it also reaffirms the urgent need to apply this methodology to each case
study. The optimal SUDS configuration obtained by applying this methodology will
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Continuous Quantity and Quality Modeling for Assessing the…
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likely be different depending on the city. For future work, it is recommended to perform
structured analyses including more regions to find regional, national, or even continen-
tal patterns, regarding how certain typologies or configurations adapt more easily to
certain contexts. It is also essential to include the economic dimension in the decision-
making process and the possibility to mediate and negotiate among multiple stakehold-
ers in the decision-making process. In the latter, it is proposed as future work to auto-
mate, through a software tool, the methodology exposed in this study, to facilitate the
decision-making process.
Acknowledgements Rainfall and evaporation information used to develop the models was gathered thanks
to the public state agencies of AEMET in Spain and IDEAM in Colombia.
Authors Contributions The writing, data analysis and conceptualization of the article were conducted by
Pascual Ferrans. The revision of the article was conducted by Dr. Javier Temprano.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This
work was supported by the “Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exte-
rior” (ICETEX) under “Pasaporte a la Ciencia” program, granted to the first author under the grant number
5334506.
Data Availability Datasets and material used to develop this research are available to readers, upon request
to the corresponding author.
Declarations
Competing Interests Authors declare they have no financial or competing interests of any kind.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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