SUDS selection methodology based on continuous modelling on EPA SWMM: a case study for Colombian and Spanish cities

doi:10.21203/rs.3.rs-1600939/v1

Download PDF

Research Article

SUDS selection methodology based on continuous modelling on EPA SWMM: a case study for Colombian and Spanish cities

https://doi.org/10.21203/rs.3.rs-1600939/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The development of computational tools based on urban drainage models is fundamental for the correct adoption of SUDS. The present study proposes a systematic approach based on continuous modeling on EPA SWMM to properly select SUDS, by evaluating several aspects related to their design and configuration. The analysis of the design parameters showed that only the surface variables evidenced a sensitivity of around 20% for some typologies. Likewise, the optimal area, number of structures, combination and interconnection of SUDS typologies were found for a case study in Santander, Spain. When applying the results to different geographic regions, the high dependence of the methodology on the performance variable, the region and the type of modeling used was evidenced. It is essential to develop tools that automate the SUDS selection process and, at the same time allow the inclusion of social and economic aspects, and the mediation with different stakeholders.

Urban drainage modeling

SUDS

LID

WSUD

Environmental Modelling

Runoff Quality

Runoff Quantity

SWMM

Sensitivity found for design parameters of SUDS was low, whereas variables regarding the distribution and disposition were significant
The optimal SUDS dispositions found for Santander Spain was a train conformed by green roofs, permeable pavement, vegetated swales, and rain gardens.
SUDS performance varies widely depending on the region and variables assessed, highlighting the importance of applying separately the methodology for each case study.

Urban water management has become a priority to ease the problems of runoff quantity and quality caused by urbanization. In recent years, different countries have implemented national and/or regional urban runoff management policies (Srishantha & Rathnayake, 2017) that promote the restoration of hydraulic and hydrological conditions prior to urbanization, through the implementation of green infrastructure that enhances the infiltration, storage, and evapotranspiration of rainwater (Stovin, 2010).

Sustainable Urban Drainage Systems (SUDS), also called Low Impact Development (LID), Green Infrastructure (GI), Best Management Practices (BMP), or Water Urban Sensitive Design (WSUD) are runoff control techniques that aim to solve the problems generated by urbanization related to water quality and quantity (Li et al., 2016; Perales-Momparler et al., 2015). The design philosophy of SUDS consists of reducing the dimensions and costs of traditional sewerage systems (gray infrastructure) by using structures that serve as a complement to the traditional paradigm of water management in cities (Sharma et al., 2016).

In recent years SUDS research has become highly relevant due to its potential for adapting to climate change and mitigating urbanization effects (Xu et al., 2020). Typologies such as infiltration trenches, green roofs, permeable pavements, storage tanks, detention basins, artificial wetlands, bioretention zones, among many others, 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 EPA SWMM (Rossman, 2010), MUSIC (eWater, 2020), Mike Urban (DHI, 2008), etc. allow this possibility.

Currently, there are case studies that have successfully developed urban drainage models that include SUDS (Kong et al., 2017; Maftuhah et al., 2018; Rujner et al., 2018). At the same time, there are specialized decision- making tools to support the optimal location and design of SUDS (Zubelzu et al., 2020; Xu et al., 2020). However, it is still necessary to develop a common framework to select SUDS models and tools to ultimately assist decision-making, applicable to different contexts (climatic, regional, spatial, temporal, etc.) (Ferrans et al., 2022). The present study proposes a standardized methodology to model SUDS using EPA SWMM, aiming to fill gaps related to the applicability of these models in the decision-making process, specifically in matters related to the design, selection, and location of SUDS for different regions.

2.1 SUDS Design Parameters Selection

The base scenario consisted of a hypothetical rectangular watershed with a total area of 80 hectares, with a continuous rainfall database of 10 years from Santander, Spain. The model included water quantity and quality modelling (Solids and Nutrients), the routing procedure was the dynamic wave, and the infiltration method was the Curve Number. Furthermore, the first stage of this study consisted of performing a sensitivity analysis to select the adequate design parameters for SUDS. In order to determine the ranges used for this analysis, a first review of the recommendations given by Ross- man (2010) was performed, as well as a literature search of case studies that included SUDS modeling using EPA SWMM; the values of the parameters reported were compiled in a database.

Subsequently, the parameters were classified into the following categories:

1) Study site-dependent characteristics (e.g. surface slope, type of soil, etc.),

2) Design parameters related to the construction materials (porosity of the substrate, hydraulic conductivity of the layers, etc.), and 3) Parameters from the design and constructive process (e.g. height of berms, height of the layers of the structure, offset and characteristics of the drains, etc.). Seven parameters from the third group were included in the analysis.

From the reported values in the database, the minimum and maximum values were identified for each parameter. Consequently, seven scenarios were developed (1 for each parameter), which were built using three different algorithms: one for the generation of the input files with the desired variations on the parameters, another for running the models, and another for analyzing the results. The latest algorithm automatically calculated variables of interest to assess the performance of SUDS, such as runoff volume, pollutants reductions, detention times, and peak flow reductions.

2.2 Decision Matrices, parameters redefinition and sensitivity levels definitions

For each scenario, a decision matrix was developed, that consisted of color scales that represented global performances and the sensitivity found for each of the parameters assessed. These matrices served as input to redefine the design parameters of SUDS.

In the event that the design parameter exhibited a low level of sensitivity (less than 10% variation in all performance variables), the lowest value of the parameter was maintained, considering that there would be no significant differences in the performance of the structure. If the sensitivity of the parameter showed a medium level (more than 10% and less than 50% variation), or that it was different depending on the performance variables, a manual analysis was performed, selecting values that offered a balance between the value of the parameter and the performance of the structure. Finally, in cases where the sensitivity level of the parameter was high (greater than 50%), the value of the parameter that optimized the performance was selected.

2.3 Distribution and Disposition Analysis

Having defined a unique design for each typology, the next step of the methodology consisted of analyzing the effect of other characteristics of SUDS regarding their distribution and disposition. For this, five different aspects were defined: 1) Area (expressed as the percentage of SUDS with respect to the total area of the watershed), 2) Number of structures, 3) Spatial distribution, 4) Combination of SUDS typologies in the same watershed, and 5) Interconnection of SUDS typologies (SUDS trains). For each of these aspects, the different possibilities were defined, and subsequently, one scenario was developed for each aspect. In all cases, the same analysis procedure from section 2.2 was developed to assess the performance and sensitivity of the aspects defined. The methodology was planned as a progressive process, in which each step fed back the next one.

2.4 Regional Effect

In order to assess the performance of SUDS in different regions, continuous rainfall databases from different regions in Colombia and Spain were gathered. Having as fixed variables the design, distribution, and disposition of SUDS, models were developed for 23 different cities. Results were analyzed using the same performance variables.

3.1 Global Performances

A first approximation was made, in order to evaluate the overall performance of SUDS, in terms of water quality and quantity. The values used for the design parameters were the mean values of those reported in the literature. A different model was developed for each typology, using as base-area 1% of SUDS area with respect to the total watershed area. The typologies included in the analysis are listed in Table 1, including the group of design parameters that each typology included.

The study consisted of continuous modelling on a hypothetical watershed, using a 10 years database from Santander, Spain. The variables used to assess the performance of SUDS were: total runoff volume, peak flow, maximum flows (average of the top 50 flows), pollutants loads, and nature of events; for the nature of events, the mean duration, volume, delay and detention times for all runoff events were calculated.

Table 1: Typologies and parameters groups for the structures modeled

Typology	Surface	Soil	Storage	Drainage
Bioretention Cell (BC)	X	X	X		X
Rain Garden (RG)	X	X	X
Green Roof (GR)	X	X			X
Infiltration Trench (IT)	X		X		X
Permeable Pavement (PP)	X	X	X		X
Rain Barrel (RB)			X		X
Vegetated Swale (VS)	X

The global analysis for the total runoff volume allowed us to identify good performances for the RG, BC, and PP (reductions between 10% to 30%), but quite limited for the rest of the typologies (lower than 5%). At the same time, the decrease in pollutant (TSS, NT, and TP) loads was high for all the typologies (higher than 90%). Regarding the peak flow, a good capacity was evidenced for the BC, GR, RG, IT, and PP, with reductions between 20% and 30%; at the same time, the reduction for the maximum flows did not evidence the same behavior, with no reductions, or even slight increments (IT and VS).

The results for the nature of the runoff events are presented in Table 2. First, a decrease in the total number of events due to the presence of SUDS (20% to 40% reductions) was noticed, which in turn generated increases in the mean duration and volume, with increases of 50% to 100% depending on the typology. Additionally, the analysis for the delay times allowed us to identify that the storage structures (RG, BC, and PP) had the potential to delay an average of 4 hours the start of runoff, by storing the first part of the events; however, they did not generate delays at the end of the runoff events. On the other hand, transport and source control structures (VS, IT, and GR) behaved differently, delaying the end of the runoff by an average of 2 hours, through low flow velocities or temporary ponding, but in the long term, they did not store large runoff volumes.

Table 2: Nature of runoff events results by typologies

Typology	Reduction Number of Events (%)	Reduction Mean Duration (%)	Delay Start Runoff (h)	Delay End Runoff (h)
Typology	Reduction Number of Events (%)	Reduction Mean Duration (%)	Delay Start Runoff (h)	Delay End Runoff (h)
BC	58	-20	4.5	0
GR	0	-80	0.5	5.8
RG	67	-40	4.2	0
IT	42	-10	3.9	0
PP	60	0	6	0
RB	0	-25	5.8	9.2
VS	23	-100	0	5.5

3.2 Design parameters analysis

Due to space limitations, the full decision matrices for each parameter are not presented in this manuscript but will be supplied if needed. These matrices present a complete summary of the ranges used for each parameter, as well as the results found for the performance variables; according to this, a scale color is presented to easily identify the global performance of typologies and the sensitivity of the parameters. Nevertheless, the following sub-sections make a brief description of the main findings made for each parameter.

3.2.1 Surface Variables

The surface variables included 2 parameters: the berm height and the vegetation volume fraction. For the berm height, the values ranged between 0 to 500 mm, depending on the typology assessed. In general terms, the sensitivity found for the volume reduction, and the peak flow was low, with variations of no more than 5%. However, when analyzing the pollutants loads, results showed a clear positive correlation, with increases between 10% to 50% on the reductions, depending on the typology; this means that the height of the berm is a relevant parameter, by allowing higher levels of surface ponding, and promoting settlement and filtering phenomena (Bastien et al., 2010; Charlesworth et al., 2003; Napier et al., 2009). The analysis of the nature of runoff events allowed us to identify a high potential for reducing the total number of runoff events, which can be explained by the fact that the detention and delay times increased; this last fact also evidenced increased mean volume and duration for the runoff events due to the presence of SUDS.

The sensitivity for the vegetation volume presented variations between 0% to 100%; regarding the total runoff reduction, the BC started with reductions of 20%, which decreased as the vegetation cover increased. At the same time, the RG presented constant values of 25% reduction, with low sensitivity with respect to the design parameter. Both the GR and the VS (including modifying their vegetation cover) showed not to be efficient strategies to reduce the total volumes, with constant values of 0% reduction.

The variables of pollutants load, peak flow, and detention times presented similar results. In all cases the sensitivity yielded for the vegetation coverage was low. The net changes were small (quite a few 0%, some 5%, and some 10%). Due to this, it is suggested that the percentage of surface vegetation cover does not have a direct influence on the runoff quality and quantity. However, it is considered that the greatest potential of the vegetation cover of SUDS regards the aesthetic and landscape dimension, so is recommended to focus on these aspects when defining the percentage and type of vegetation cover, as highlighted previously by Monberg et al. (2018).

3.2.2 Soil Parameters

The soil variables analysis included the depth of this soil layer, with variations between 30 to 500 mm. In general terms, the sensitivity of the parameter was low for peak and maximum flow, runoff volume, and nature of events, with variations less than 5% in most of the cases. Only the mean duration and volume of runoff events presented a 10% increment due to the increase of the 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 the reduction of contaminants when increasing the soil depth. This fact can be explained by the fact that the greater the total volume of soil, the adsorption and filtering processes of contaminants are favored (Schlu¨ter & Jefferies, 2005). For the rest of the performance variables, the depth of the soil did not show significant repercussions on the functioning of the structures.

3.2.3 Storage Variables

The analysis for the storage variables was also made for the height of this layer. Overall, the analysis yielded low levels of sensitivity (less than 3% for all the performance variables). One aspect to highlight is the inclusion of the RB in the analysis, which did not happen in the previous sections, as it did not have any of the design parameters that had been discussed so far. However, the performance of this structure, both in the sensitivity of the parameter and in the global reductions, 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).

3.2.4 Drainage Variables

In this case, the offset of the drainage orifice was analyzed, and the results obtained are similar to the previous sections: low levels of sensitivity, with less than 2% variations with changes in the offset between 0 to 150 mm. These results indicate that this parameter might not have a clear influence on the long-term performance of the structures.

3.2.5 Parameters Definition

Using the decision matrices, the design parameters were re-defined, and results from this process are presented in Table 3. For this matter, three different levels of sensitivity were defined, depending on the net differences detected in the performance variables comparing the results between the lowest and the highest value of the parameter. The levels were defined as follows: 1) High, for net changes above 50%, 2) Medium, corresponding to net changes between 10% and 50%, and 3) Low, for changes below 10%.

From the parameters analyzed, none presented high sensitivity levels. Furthermore, four parameters presented a medium sensitivity level (orange); in these cases, a manual selection of the value of the parameters was made, finding a balance between 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.

3.3 Distribution and Disposition Analysis

3.3.1 Area

Having defined a unique design for each typology, an analysis was carried out in which progressive increases were made in the area of the SUDS. The minimum and maximum areas were 0.1% and 1% of the total area (80 ha). In general terms, the analysis allowed us to identify that the sensitivity in the performance variables was high for changes in the area. For the specific case of total runoff volume, maximum flow, and pollutants loads, the performances of SUDS increased in values between 20% to 50%. The nature of the runoff events (number, mean volume, and duration) also showed high correlations with the total area of SUDS. 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.

Considering that 1% area offered a fair balance between performance and implementation complexity, and also being in accordance with the recommendations of multiple design guidelines (Strecker et al., 2010; Till & Torre, 2004; Valley, 2010; Woods-Ballard et al., 2007), it was decided to fix this value for the SUDS area.

3.3.2 Number of Structures

In this phase, the effect of modifying the number of SUDS structures in which the already fixed area was distributed was assessed. For this purpose, different models were generated in which the area of SUDS in each subcatchment was distributed in 1 to 10 structures of the same typology.

Results obtained showed low sensitivity for most of the parameters analyzed. Except for some specific cases, the variation in the performance of the SUDS when modifying the number of structures was practically imperceptible. The two highest sensitivities observed were 25% variations (for GR and VS), always being the best performed, the scenario with 1 structure per subcatchment. Due to this, this was the option selected.

3.3.3 Spatial Distribution

The objective of this section was to define the optimal spatial distribution of SUDS structures. For this purpose, different spatial configurations were developed. As a general fact, the runoff from each subcatchment was managed with 1 structure, which is why the number of subcatchments always coincided with the number of SUDS (to see the detailed spatial distributions proposed, refer to Figure 1). Distribution 1 (D1) had a total of 8 subcatchments; from distribution 2 onward, the number of subcatchments decreased progressively, making the spatial distribution more aggregated, until reaching distribution 4 (D4), with only 1 SUDS managing the runoff of the entire catchment.

The spatial analysis allowed us to identify that the sensitivity changed depending on the performance variable. For the total runoff volume and pollutants loads, the variations were minimal depending on the spatial distributions; however, for the peak flow, there were significant differences. Due to this, additional analyses were performed for this variable, to guarantee that the spatial distribution selected was the one that maximized its performance.

Figure 2 presents the results for the peak flow analysis in the 7 typologies assessed. In all cases, D4 was the best-performed distribution, however, it was discarded due to its lack of real-life implementation (no pipelines or additional runoff transport infrastructure). Subsequently, it was identified that, depending on the typology, D2 and D3 had better performances than D1. Giving priority to IT, RB, and VS, and also to ease the applicability in real cases (less complex and expensive sewage systems), D3 was chosen as the spatial distribution for SUDS.

Inquiring about the reason why the spatial distribution of the SUDS was relevant, multiple authors were found stating the same fact (Tedoldi et al., 2017; Joshi et al., 2021; Fenner, 2017). In all cases, it was concluded that, because the SUDS modify the hydraulic and hydrological patterns of the catchment, how they are located in the space will be a determining variable in these modifications, which is why it is essential to adequately define what is the best distribution.

3.3.4 Combination of Typologies

3 So far, independent models were used for each SUDS typology. In this section, it was decided to evaluate the effect of mixing typologies within the same catchment. For this purpose, the urban drainage guide by Butler et al. (2018) was used, to propose logical combinations of structures. Figure 3 shows each of the stages or processes that the guide suggests, as well as the recommended typologies for each stage. Furthermore, two basic criteria were established for proposing the combinations: 1) each scenario included all stages of the drainage process, and 2) each scenario included one, and only one typology for each process.

Following the previous criteria, 6 possible combinations were reached (Table 4). To contrast, the results of combining SUDS with the previous results, the worst and best-performanced scenarios without combining structures were included, which were GR and RG, respectively. It is worth clarifying that, for all the scenarios, the runoff from each sub-catchment was divided into 4 equal parts, and each part was directed independently to each structure; furthermore, each structure drained to the outlet point of its corresponding subcatchment.

Table 4: SUDS combinations selected

#	Input Control	Source Control	Conduction	Local Control
1	(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	(RG)

Results for total runoff volume, peak flow, maximum flows, and pollutants loads are presented in Figure 4. For the total volume of runoff and the pollutants loads, Figure 4(a) and Figure 4(d) showed that in none of the combined cases (scenarios 2 to 7), results were better than the best-uncombined scenario (Scenario 8), nor worse than the worst uncombined scenario (Scenario 1); however, results for the peak flow (Figure 4(b)) and the extreme flows (Figure 4(c)) 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 both cases, the local control strategy was the RB, which suggests that this typology has a strong effect on reducing peak flows.

In general terms, this analysis allowed us to identify that it is very important to clearly define the objective variable (volume, peak flow, or contaminant load), and from that definition, determine which combination is the most appropriate, taking into account that in some cases combining typologies might not be the most appropriate.

3.3.5 SUDS Trains

Making use of the same combinations defined in the previous section, it was proceeded to evaluate the effect of having SUDS trains (interconnected with each other sequentially). For analyzing the results, it was decided to quantify the performance, using and not using SUDS trains. Due to the above, Figure 5 presents the results with and without SUDS trains (blue and green bars), and also the percentage of reduction implied by the fact of including the trains (red dots).

Results yielded that the performances for the interconnected structures improved substantially. For the total volume of runoff (Figure 5(a)), the fact of including SUDS trains causes the total volume of runoff to decrease significantly (between 40% and 60%). Something similar occurred with the pollutants loads (reductions around 65% to 85%). However, for the peak and maximum flows SUDS trains did not show to be an efficient strategy, as it can be evidenced in Figures 5.b and 5.c, with overall higher values for the scenarios with trains.

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, which is why it was decided to continue using SUDS trains. Based on the results, the combination 5 was the best option, and it was selected for the subsequent analysis.

3.3.6 Optimal SUDS scheme definition

To summarize the decisions procedure followed, Table 5 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 methodology 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 based on the specific objectives, it is recommended to apply this methodology to each specific case study.

Table 5: Variables assessed, options and decisions made at each step (green)

% Area	Num. 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	10			GR-PP-VS-RB

3.4 National and Regional Effect of SUDS

Finally, the SUDS scheme selected was tested with different rainfall regimes, in order to quantify the effect of the region on the SUDS performance. 23 continuous databases from cities in Colombia and Spain were used to develop the models, and Figure 6 summarizes the main results. For each city, the main Y-axe presents the performance variables for the model with SUDS (blue) and without SUDS (green). Furthermore, the secondary Y-axe represents the percentage of reduction due to the presence of SUDS; for this last case, results are differentiated between Colombian and Spanish cities, with rounded red indicators for the first group of cities, and pink quadrangle indicators for the second group. In all cases, the International Air Transport Association (IATA) code was used to represent the cities.

The analysis for the total runoff volume (Figure 6(a)) allowed to identify high variability in the efficiency of reduction, with overall values varying between 30% and 80%. Except for Pasto (PSO), the cities with the lowest reduction percentages were located in Colombia, with values between 30% to 55%. Consequently, Spanish cities showed higher reduction rates (above 60%). Based on the above, it is suggested that SUDS have high variability in their efficiency and effectiveness depending on the region; for tropical regions, with more intense rainfall regimes, the SUDS effectiveness decreases, while for regions with longer but less intense rainfall events, the effectiveness of these structures increases considerably, as previously identified by Zhang et al. (2019).

Analyzing the inner-country variability, a similar trend was identified. In the case of Colombia, in cities with large rainfall events, such as Inírida (PDA), Quibdó (UIB), and Mitu´ (MVP), SUDS efficiency was lower than in the rest of the cities. The same happened in Spain, showing that the variability is also observed at the regional (inner-country) level.

The analysis for the peak flow (Figure 6(b)) allowed to identify reductions between 5% and 30%. Regarding the trans-national and inner-region analysis, a similar (but inverse) variability was observed. 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 6(a).

Finally, Figure 6(c) allowed us to identify that for the contaminants loads the variability, both national and regional, decreased significantly. Colombian cities such as In´ırida (PDA), Quibd´o (UIB), and Mitu´ (MVP), which are those with the highest annual rainfall, had lower rates of reductions, 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 suggests that SUDS have a high potential to reduce pollutants, and this potential does not depend directly on the region.

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 satisfactory since the performance of SUDS was good for this city. At the same time, this confirms the need to apply the proposed methodology to each case study, since SUDS did not perform equally in all cities.

The analysis for the design parameters allowed us to identify that the sensitivity levels reported were medium, low, or very low. In general, the surface parameters (berm height and vegetation cover) had a slightly higher sensitivity than the rest, without reporting significant values (maximum 50%, but overall above 20%). On the other hand, the soil, storage, and drainage parameters presented lower levels of sensitivity (many times imperceptible). This sensitivity depended largely on the typology.

It is possible that the low sensitivity levels found were due to the high amount of SUDS parameters. Because the way to approach the problem was to select the parameters that were directly related to the design process of the structures, an analysis of the rest of the parameters were not included. However, it is possible that variables related to the nature of the construction materials or the study site (hydraulic conductivity of the substrate, soil porosity, field capacity, surface roughness, etc.) are highly relevant in the process. In this case, it was considered that these variables are fixed and dependent on external factors, that are not easily defined by the designer of the structures, which is why they were excluded from the analysis.

Additionally, it is highly likely that aspects related to the nature and conception of the model also mediate the results found. For example, it is considered that the modeling time scale can be fundamental to the results found. In this particular case, the objective was to evaluate the effect of SUDS in the long term, which is why it was decided to do continuous modeling of a 10 years database. However, it is highly likable that if the database is shorter, or even modeling by events, the results found will be different. Thus, it is very 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 scheme of the 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%; concerning the spatial distribution, the semi-aggregated option proved to be the most efficient, specifically for managing peak flows. SUDS trains proved to have high efficiency for managing the total runoff volume and the reduction of pollutants. Finally, the number of structures and the combinations proved not to be decisive variables.

The different results depending on the performance variables highlight the importance of clearly defining them, including the desired goals. For this study, all the variables were included, however, for future exercises, the modeler needs to define the main performance 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 with respect to the total catchment area, with a semi-aggregated spatial distribution, combining and interconnecting the following typologies: GR-PP-VS-RG.

Finally, the analysis of the regional effect showed variability in the performance of the SUDS for the different cities evaluated. The fact that the results were satisfactory for the city of Santander indicates that the proposed methodology was adequate, but it also reaffirms the need to apply this methodology for each specific case study. It is highly likely that the optimal SUDS configuration obtained by applying this methodology will be different depending on the city. For future work, it is recommended to perform structured analyzes including more regions, in order to find regional, national, or even continental patterns, regarding how certain typologies or configurations adapt more easily to certain contexts. Additionally, it is essential to include the economic dimension in the decision-making process, as well as the possibility to mediate and negotiate between multiple stakeholders in the decision-making process. For the above, it is proposed as future work to automate, through a software tool, the methodology exposed in this manuscript, in order to facilitate the decision-making process.

Availability of data and material: Datasets and material used to develop this research are available to readers, upon request to the corresponding author.

Competing interests: Authors declare they have no financial or competing interests of any kind.

Funding: This work was supported by the “Instituto Colombiano de Cr´edito Educativo y Estudios T´ecnicos en el Exterior” (ICETEX) under “Pasaporte a la Ciencia” program, granted to the first author under the grant number 5334506.

Authors Contributions: The writing, data analysis and conceptualization of the article were carried out by Pascual Ferrans. The revision and conceptualization of the article were carried out by Dr. Javier Temprano.

Acknowledgments: 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.

Bastien, N., Arthur, S., Wallis, S., & Scholz, M. (2010). The best management of suds treatment trains: a holistic approach. Water Science and technology , 61 , 263–272.
Butler, D., Digman, C. J., Makropoulos, C., & Davies, J. W. (2018). Urban drainage. Crc Press.
Charlesworth, S. M., Harker, E., & Rickard, S. (2003). A review of sustainable drainage systems (suds): A soft option for hard drainage questions? Geography , (pp. 99–107).
Charlesworth, S. M., Nnadi, E., Oyelola, O., Bennett, J., Warwick, F., Jack- son, R., & Lawson, D. (2012). Laboratory based experiments to assess the use of green and food based compost to improve water quality in a sustainable drainage (suds) device such as a swale. Science of the total environment , 424 , 337–343.
DHI (2008). Mike urban model user manual. Danish Hydraulic Institute (DHI), Denmark , 4 .
eWater (2020). MUSIC Software. URL: https://ewater.org.au/products/music/.
Fenner, R. (2017). Spatial evaluation of multiple benefits to encourage multi- functional design of sustainable drainage in blue-green cities. Water , 9 , 953.
Ferrans, P., Torres, M. N., Temprano, J., & Rodr´ıguez S´anchez, J. P. (2022). Sustainable urban drainage system (suds) modeling supporting decision-making: A systematic quantitative review. Science of The Total Environment , 806 , 150447. URL: https://www.sciencedirect.com/science/article/pii/S0048969721055248. doi:https://doi.org/10.1016/j.scitotenv.2021.150447.
Galarza-Molina, S., Torres, A., Lara-Borrero, J., M´endez-Fajardo, S., Solarte, L., & Gonz´alez, L. (2015). Towards a constructed- wetland/reservoir-tank system for rainwater harvesting in an experimental catchment in colombia. Ingeniería y Universidad, 19 , 415–421. doi:10.1114/javeriana.iyu19-2.tcws.
Joshi, P., Leit˜ao, J. P., Maurer, M., & Bach, P. M. (2021). Not all suds are created equal: Impact of different approaches on combined sewer overflows. Water Research, 191 , 116780. URL: https://www.sciencedirect.com/science/article/pii/S0043135420313130. doi:https://doi.org/10.1016/j.watres.2020.116780.
Kong, F., Ban, Y., Yin, H., James, P., & Dronova, I. (2017). Modeling stormwater management at the city district level in response to changes in land use and low impact development. Environmental Modelling and Software, 95 , 132–142. doi:10.1016/j.envsoft.2017.06.021.
Li, J., Li, Y., & Li, Y. (2016). SWMM-based evaluation of the effect of rain gardens on urbanized areas. Environmental Earth Sciences, 75 , 1–14. doi:10.1007/s12665-015-4807-7.
Maftuhah, D. I., Anityasari, M., & Sholihah, M. (2018). Model of ur- ban water management towards water sensitive city: a literature review. IOP Conference Series: Materials Science and Engineering, 337 , 12047. doi:10.1088/1757-899X/337/1/012047.
Monberg, R. J., Howe, A. G., Ravn, H. P., & Jensen, M. B. (2018). Explor- ing structural habitat heterogeneity in sustainable urban drainage systems (suds) for urban biodiversity support. Urban Ecosystems, 21 , 1159–1170.
Napier, F., Jefferies, C., Heal, K. V., Fogg, P., Arcy, B., & Clarke, R. (2009). Evidence of traffic-related pollutant control in soil-based sustainable urban drainage systems (suds). Water Science and Technology, 60 , 221–230.
Perales-Momparler, S., Andr´es-Dom´enech, I., Andreu, J., & Escuder-Bueno, I. (2015). A regenerative urban stormwater management methodology: the journey of a mediterranean city. Journal of Cleaner Production, 109 , 174–189. doi:10.1016/j.jclepro.2015.02.039.
Rossman, L. A. (2010). Storm water management model user’s manual, version 5.0 . Environmental Protection Agency (EPA), USA.
Rujner, H., Leonhardt, G., Marsalek, J., & Viklander, M. (2018). High-resolution modelling of the grass swale response to runoff inflows with mike she. Journal of Hydrology, 562 , 411–422. doi:10.1016/j.jhydrol.2018.05.024.
Schlu¨ter, W., & Jefferies, C. (2005). The real issues with in-ground suds in Scotland. In 10th International Conference on Urban Drainage.
Sharma, A. K., Pezzaniti, D., Myers, B., Cook, S., Tjandraatmadja, G., Chacko, P., Chavoshi, S., Kemp, D., Leonard, R., Koth, B., & Walton, A. (2016). Water sensitive urban design: An investigation of current systems, implementation drivers, community perceptions and potential to supplement urban water services. water , 8 , 272. doi:10.3390/w8070272.
Srishantha, U., & Rathnayake, U. (2017). Sustainable urban drainage systems (suds) - what it is and where do we stand today? Engineering and Applied Science Research, 44 , 235–241. doi:10.14456/easr.2017.36.
Stovin, V. (2010). The potential of green roofs to manage urban stormwater. Water and Environment Journal , 24 , 192–199.
Strecker, E., Sheffield, A., Cristina, C., & Leisenring, M. (2010). Stormwater BMP guidance tool & a stormwater best management practices guide for Orleans and Jefferson Parishes. Louisiana: the Louisiana Department of Environmental Quality.
Tedoldi, D., Chebbo, G., Pierlot, D., Kovacs, Y., & Gromaire, M.-C. (2017). Assessment of metal and pah profiles in suds soil based on an improved experimental procedure. Journal of environmental management , 202 , 151– 166.
Till, B., & Torre, A. (2004). Western Australia’s new stormwater management manual incorporating the principles and guidelines into council policies and design standards.
Valley, C. (2010). Low impact development stormwater management planning and design guide.
Wang, M., Zhang, D., Cheng, Y., & Tan, S. K. (2019). Assessing performance of porous pavements and bioretention cells for stormwater management in response to probable climatic changes. Journal of Environmental Management , 243 , 157–167. doi:10.1016/j.jenvman.2019.05.012.
Woods-Ballard, B., Kellagher, R., Martin, P., Jefferies, C., Bray, R., & Shaf- fer, P. (2007). The SUDS manual volume 697. Ciria London.
Xu, H., Ma, C., Xu, K., Lian, J., & Long, Y. (2020). Staged optimization of urban drainage systems considering climate change and hydrological model uncertainty. Journal of Hydrology, 587 , 124959. doi:10.1016/j.jhydrol.2020.124959.
Zhang, K., Manuelpillai, D., Raut, B., Deletic, A., & Bach, P. M. (2019). Evaluating the reliability of stormwater treatment systems under various future climate conditions. Journal of Hydrology, 568 , 57–66.
Zubelzu, S., Rodr´ıguez-Sinobas, L., Sordo-Ward, A., P´erez-Dur´an, A., & Cisneros-Almaz´an, R. (2020). Multi-objective approach for determining optimal sustainable urban drainage systems combination at city scale. the case of San Luis Potosí (México). Water , 12 . doi:10.3390/w12030835.

Table 3 is available in the Supplementary Files section.

Table3.docx

Download PDF

Reviewers agreed at journal
16 May, 2022
Reviewers invited by journal
14 May, 2022
Editor assigned by journal
13 May, 2022
First submitted to journal
09 May, 2022

You are reading this latest preprint version

SUDS selection methodology based on continuous modelling on EPA SWMM: a case study for Colombian and Spanish cities

Status:

Version 1

Abstract

Figures

Highlights

1. Introduction

2. Materials And Methods

3. Results And Discussion

4. Conclusions, Recommendations And Limitations

Declarations

References

Table 3

Supplementary Files

Status:

Version 1