Rapid urbanization and population growth are placing more demands on the world's natural water resources. New infrastructures are increasing the degree of surface sealing as well as the tendency for urban flooding and water quality degradation. These problems can be counteracted by nature-based solutions (NBS) for urban drainage in developed countries mostly having a temperate climate. Hence, there is a need to develop similar sustainable measures for tropical regions as currently there are no guidelines available. In this study, the multi-criteria decision analysis (MCDA) approach was utilized to identify the best site for NBS in the Asian Institute of Technology (AIT) in Bangkok, Thailand. Then, Personnel Computer Storm Water Management Model (PCSWMM) software was used to develop a numerical model. It was found that the MCDA approach is appropriate to determine the best site for NBS implementation considering different aspects including economic, environmental, and technical ones. The results strongly suggested that Site-1 is a suitable alternative to implement NBS in the AIT campus. It was found that a bioretention system can reduce runoff volume by at least 14% and pollutants by at least 14–20%. The present study will provide a guideline for site selection and development of the NBS model for urban water management in a tropical climate.

  • A comprehensive assessment of nature-based solutions performance was presented.

  • Multiple methods: numerical model, social survey, and economic analyses were implemented.

  • First detailed study in a developing country under a tropical climate.

The population over the globe is increasing at a rapid rate such that it is predicted that urban spaces will be occupied by 2.6 billion new residents in the year 2050 (UNDESA 2020). Fast and unplanned urbanizations, mainly in large urban centers, have caused an increase in impervious surfaces and, consequently, higher runoff volumes and rates, therefore amplifying the importance of sustainable utilization of water resources, particularly in rapidly developing urban areas (Koc & Işık 2020; Koc et al. 2021).

The extreme storm events induced by climate change can bring failure in stormwater drainage and water quality management, leading to extensive damage, economic loss, and risk to human life (Strauss et al. 2021; Li et al. 2022). The conventional urban stormwater systems mostly rely on gray infrastructures such as manholes, pipes, and outlets, which can cause stern non-point source pollution leading to water quality decline and eutrophication (Zhang & Chui 2019). In addition to that, pollutant accumulation during dry periods and runoff during storm events degrade the quality of surface waters such as rivers, lakes, and streams. The runoff from the urban stormwater accumulates and transports various pollutants, which include mainly nutrients, suspended solids, heavy metals, grease and oil, and other type of organic solids in the surface water (Muerdter et al. 2018). Traditional stormwater management practices mainly focus on risk mitigation through conveying the runoff towards drains and streams. Although they are helpful to reduce flooding problems, they are unable to improve water quality (Spahr et al. 2020). There are some sustainable land use and planning approaches known as nature-based solutions (NBS), which are aimed at tackling the adverse impacts of development on the environment (Pour et al. 2020). These approaches are aimed at imitating natural processes such as filtration and infiltration to reduce contaminants from effluents before they are released into the environment and also control stormwater runoff (Goh et al. 2019; Galleto et al. 2022).

These approaches have wide-ranging adoption in the world, particularly in the developed countries or regions under different names and terminologies including low impact developments (LIDs) and best management practices in North America (Fletcher et al. 2015), sustainable urban drainage (SUD) systems in the United Kingdom (CIRIA 2015), water sensitive urban designs (WSUDs) in Australia (Water by Design 2014), blue–green infrastructure in Europe (Wilbers et al. 2022), ABC Waters Programme in Singapore (PUB 2018), and sponge cities in China (Song 2022).

Amongst different NBS some well-known techniques include rain barrels, bioretention systems, green roofs, permeable pavements, and constructed wetlands (Li et al. 2019; Vijayaraghavan et al. 2021; Si et al. 2022). Bioretention systems are one of the most vital and efficient NBS techniques for managing the runoff generated from urban stormwater, and many other terms can be used for representing it including the rain gardens and the bio swales (Dagenais et al. 2018). The bioretention systems deliver benefits, which include improvement in runoff quality (Huang et al. 2022) along with managing the volume of runoff (Yang & Chui 2018). Moreover, the bioretention systems have the flexibility to combine with the urban infrastructure (Batalini de Macedo et al. 2022).

Multi-criteria decision analysis (MCDA) is a decision support tool which is used to systematically evaluate and rank different options according to a set of criteria to guide the decision makers to select best suitable alternatives. MCDA has been used in various fields including selection of an appropriate NBS for stormwater (Nguyen et al. 2019; Geronimo et al. 2021), flood risk assessment (Sharma et al. 2018; Linh et al. 2023), evaluation of the most significant flood risk parameters (Dang et al. 2011), flood hazard mapping (Fernández & Lutz 2010), and feasible locations for NBS (Yang et al. 2015). MCDA has been employed particularly for stormwater management using NBS or LIDs by many researchers (including Amorocho-Daza et al. 2019; Ahmed et al. 2023) who selected a best LID among three LIDs, namely, soak-away, leaky well, and infiltration trench under technical, economic, and social criteria and found that leaky well was the best option among other alternatives.

A decision-making framework was developed by Gogate et al. (2017) to choose the most suitable LID by using economic, environmental, social, and technical aspects and employed an analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) for evaluation. They found that the combination of leaky well and rain garden was the most preferred alternative. An evaluation was done by Li et al. (2017) using both single and combined LID scenarios, and they utilized the AHP method to determine criteria weights and concluded that the combination of the bioretention system and green roof was the most suitable alternative in terms of social and environmental aspects. Loc et al. (2017) used four methods of MCDA, namely, pairwise voting, Borda count, range of value, and AHP and found that the urban green space is the most suitable type of LID followed by green roof, pervious pavement, and rainwater harvesting. Furthermore, Koc et al. (2021) evaluated seven different LID scenarios considering social, economic, and environmental aspects using the AHP-TOPSIS framework and concluded that the combination of the bioretention system and green roof was the best alternative, while a combination of other LID such as green roof, bioretention system, permeable pavements, and infiltration trench depicted the best performance considering economic, social, and environmental aspects.

To predict the performance of bioretention systems for planning and evaluation purposes, there is an urgent need to develop a comprehensive process-based hydrologic model (Meng et al. 2014). For the adjustment of design standards to make it suitable for local characteristics, vegetation and local regional climate modeling can play a vital role. The effective performance of the bioretention system was reported by various monitoring research studies (Johnson & Hunt 2019; Vijayaraghavan et al. 2021; Tansar et al. 2022, 2023). The limitation of bioretention systems under different climate and scenarios of design can be identified by long-term modeling (Hathaway et al. 2014; Li & Lam 2015). In order to evaluate the performance of the bioretention system with respect to different parameters of design, modeling the water balance can be of use to researchers (Alikhani et al. 2020). To examine the effect of processes in a model, the storm water management model (SWMM) has been employed in various assessment studies regarding bioretention modeling (Loc et al. 2015; Tiveron et al. 2018; Tu et al. 2020). Moreover, to avoid errors, it is vital to perform modeling for bioretention systems at a site scale before modeling is performed at the catchment scale. For a wider investigation of the ecosystem, catchment scale modeling can be beneficial, whereas site scale modeling is useful when the effect of several parameters of design or the performance of a particular bioretention system is evaluated (Lisenbee et al. 2021).

The tropical regions normally have more fluctuation and intensity of rainfall as compared to temperate regions having precipitation spread out evenly throughout the year. These regions also have high rates of evapotranspiration, which may decrease the moisture available for infiltration, impacting the performance of NBS. The existing literature does not have thorough guidelines to implement NBS particularly fitted to regions having a tropical climate. This study showcases the utilization of the MCDA method in tropical urban settings and highlights the importance of filling this research gap. It aims to provide significant insights by focusing on tropical regions, enabling other regions across the globe facing similar issues to use the MCDA approach for effective NBS implementation. Moreover, there is limited literature which addresses the integration of NBS with existing gray infrastructure. The research emphasizes the requirement to fill this gap by demonstrating how NBS can be integrated with existing infrastructure in the premises of the Asian Institute of Technology (AIT) campus and showcases the insights for agencies and planners seeking to enhance urban water resilience by retrofitting NBS into existing infrastructure.

In this study, the AHP technique was utilized, which is a well-known MCDA method, in order to select the most suitable site for the implementation of the bioretention system in AIT considering environmental, social, and economic aspects. Furthermore, the SWMM was carried out to evaluate the performance of the bioretention system at the selected site for stormwater management in terms of both quality and quantity.
Figure 1

Study area along with proposed sites for the study.

Figure 1

Study area along with proposed sites for the study.

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Study area

The AIT is situated in Pathumthani Province, 40 km (25 miles) north of Bangkok, Thailand. AIT lies between the latitude of 14.0785°N and the longitude of 100.6140°E, as shown in Figure 1. The elevation is approximately 7 m above sea level. The catchment area is approximately 1.4 km2, which contributes to runoff. The climate is tropical, and the average high temperature is between 35 and 36°C. The wet period is from May to September, and the dry period is mostly from November to February. The atmosphere is humid, with a humidity of 60–70% approximately. The drainage of AIT is composed of pipelines, manholes, open channels (canals and ponds), and outlets as depicted in Figure 2. The stormwater is drained through the above-mentioned conduits and conveyed to perimeter ponds of AIT through outlets.
Figure 2

Stormwater drainage network of AIT (Source: Seanghak 2017).

Figure 2

Stormwater drainage network of AIT (Source: Seanghak 2017).

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The average area and volume of perimeter ponds are 97,647 and 98,034 m3, respectively. Being a green campus, the pervious areas around the AIT campus are substantially larger than impervious areas. The pervious areas are grassy swales and soccer fields, whereas the impervious areas mostly consist of roads, buildings, surface pavements, and parking lots. AIT was selected as the study area in this research because it was considered as a pilot study area for Thailand, especially for the implementation of bioretention systems for stormwater management. Furthermore, if the results advocate the implementation of bioretention in AIT, then it can be constructed and utilized for further research and monitoring, providing benefits to Water Engineering and Management (WEM) labs and students.

Three potential sites were proposed for the implementation of NBS in AIT. The proposed sites included Site-1 in front of the WEM building, Site-2 near the Industrial Systems Engineering (ISE) Department, and Site-3 at the parking near the AIT grocery. Site-1 was proposed because it is near the WEM building and it can benefit WEM students for further NBS research; also, it was near a building and a road, which will act as runoff contributing structures. Site-2 was proposed because it was located near the ISE building and a big parking lot will contribute to runoff. However, near Site-3, there was another big parking lot, which will contribute to its runoff. Therefore, all three sites were proposed based on the contributing structures and were near the drainage network of the AIT campus.

Site investigation

The site investigation was done by examining different parameters including soil properties, stormwater quality, physical restrictions, contributing structures, and catchment areas. For soil properties and storm water quality, samples were collected from the field and testing was done in the lab. For physical restrictions and other parameters, site visits were conducted. Hydraulic conductivity and soil porosity were measured for soil investigation at the WEM lab. For hydraulic conductivity, three measurements were performed at one site and the falling head method was adapted. Similarly, three measurements were done for porosity at a single site.

Stormwater samples were collected from all three sites at the end of three storm events, and maximum values of pollutants were utilized for three sites in MCDA among all samples. The pollutants included total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), biochemical oxygen demand (BOD), and chemical oxygen demand (COD). The tests were performed using standard operating procedures in the Environment Engineering and Management (EEM) lab.

Site visits for physical restrictions and other parameters

Site visits were conducted, and physical restrictions along with other parameters including contributing structures were identified. The catchment area was calculated by using Google Earth, which included all contributing structures to the bioretention system. The physical restrictions included trees, tree roots, and other wooded areas, which could be a source of hindrance when constructing the bioretention system and can be seen in Figure 3.
Figure 3

Physical restrictions (tree roots, etc.) at different sites depicting lower restrictions at Site-1 and medium to high restrictions at Site-2 and Site-3, respectively.

Figure 3

Physical restrictions (tree roots, etc.) at different sites depicting lower restrictions at Site-1 and medium to high restrictions at Site-2 and Site-3, respectively.

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Cost analysis

The cost analysis for each site was carried out using the LID costing tool developed by Sustainable Technologies Evaluation Program (STEP), Toronto, Canada. The tool is based on local costs and design for that area; however, it may be useful from a comparison standpoint as it features several stormwater management practices that can be compared, and there are separate costing sheets for bioretention, grass swales, green roof, infiltration chambers, infiltration trench, and rainwater harvesting. Costs are calculated based on the drainage area contributing to the bioretention system and thus determining the size of bioretention system. The cost analysis for each site is shown in Table 1.

Table 1

Cost analysis for each site using local rates in Thailand

ItemsSite-1Site-2Site-3
Cost (THB)
EXCAVATION 
Excavator 14,000 10,000 14,000 
Hauling 3,500 3,500 3,500 
MATERIALS & INSTALLATION 
Impermeable membrane 84,240 34,108 39,434 
Underdrain (200 mm) 18,948 5,830 6,996 
Clean out pipes (150 mm) 550 550 550 
Overflow pipes (200 mm) 899 899 899 
Pipe to sewer (200 mm) 2,725 3,270 3,270 
Fittings (materials & labor) 40,000 40,000 40,000 
Manhole adaptor (200 mm) 1,000 1,000 1,000 
Stone (50 mm clear) 16,000 6,000 7,200 
Pea gravel 32,500 12,500 15,000 
Geotextile 5,985 2,394 2,713 
Filter media (includes delivery) 88,000 33,750 40,500 
Backfill excavation 1,410 527 597 
Curbs & gutter with curb inlets 182,500 75,000 85,000 
Vegetation 87,000 33,333 40,000 
Wood mulch 18,600 6,600 8,100 
Stone inlets (50 mm clear) 1,500 1,500 1,500 
Total Cost 599,357 270,762 310,259 
Cost per m2 4,610 5,415 5,171 
ItemsSite-1Site-2Site-3
Cost (THB)
EXCAVATION 
Excavator 14,000 10,000 14,000 
Hauling 3,500 3,500 3,500 
MATERIALS & INSTALLATION 
Impermeable membrane 84,240 34,108 39,434 
Underdrain (200 mm) 18,948 5,830 6,996 
Clean out pipes (150 mm) 550 550 550 
Overflow pipes (200 mm) 899 899 899 
Pipe to sewer (200 mm) 2,725 3,270 3,270 
Fittings (materials & labor) 40,000 40,000 40,000 
Manhole adaptor (200 mm) 1,000 1,000 1,000 
Stone (50 mm clear) 16,000 6,000 7,200 
Pea gravel 32,500 12,500 15,000 
Geotextile 5,985 2,394 2,713 
Filter media (includes delivery) 88,000 33,750 40,500 
Backfill excavation 1,410 527 597 
Curbs & gutter with curb inlets 182,500 75,000 85,000 
Vegetation 87,000 33,333 40,000 
Wood mulch 18,600 6,600 8,100 
Stone inlets (50 mm clear) 1,500 1,500 1,500 
Total Cost 599,357 270,762 310,259 
Cost per m2 4,610 5,415 5,171 

Multi-criteria decision analysis by the analytic hierarchy process

The AHP is a method for organizing and analyzing complex decisions using math and psychology and was utilized in this study to select the best site for NBS application. It contained three parts: the goal, which is the most suitable site for NBS, all the possible solutions, called alternatives, which were the proposed sites (Sites-1, -2, and -3), and the criteria we used to judge the alternatives. For the criteria weight, calculations were done by giving equal importance to all aspects of NBS (soil properties, location, catchment size, economic factors, and stormwater quality). The AHP provides a rational framework for a needed decision by quantifying its criteria and alternative options and for relating those elements to the overall goal.

Local priorities and criteria weights in the AHP

The relative priorities are derived with respect to the decided criteria for all alternatives. As the local priorities are calculated with respect to specific criteria, they are called local priority rather than overall priority, which is calculated by using criteria weights. A pairwise comparison is done by using the numeric scale for all alternatives with respect to all criteria included in the AHP model. After finding the local priorities, the consistency test is performed. The test is performed if our answer is consistent, and different parameters are calculated including the consistency ratio, the consistency index, and the Principal Eigen value.

To find local priorities, we are first required to derive by pairwise comparisons the relative priority of each criterion with respect to each of the others using a numerical scale developed by Saaty. The scale comprises verbal judgments with the highest numeric value of 9 for extremely important to the lowest value of 1 for equally important. To perform the pairwise comparison, we need to create a comparison matrix of the criteria involved in the decision.

For each criterion, priorities were calculated, and after that, the consistency test was performed to check the consistency of priorities. The value of the consistency ratio was less than 0.1 for each priority, which is the main requirement of the consistency test. The weights were decided based on a questionnaire sent to WEM students, alumni, and faculty. The priorities were decided based on the criteria given in Table 2.

Table 2

Priority decision for each criterion

Sr. noCriteriaPriority decision based on
Water quality Water quality tests (max. values are preferred) 
Cost Minimum cost 
Physical restrictions and contributing structures Minimum physical restrictions and maximum contributing structures 
Catchment size Maximum catchment size 
Sr. noCriteriaPriority decision based on
Water quality Water quality tests (max. values are preferred) 
Cost Minimum cost 
Physical restrictions and contributing structures Minimum physical restrictions and maximum contributing structures 
Catchment size Maximum catchment size 

The soil parameters (porosity and hydraulic conductivity) were excluded from MCDA because of much lower values than the recommended values. Weights for each criterion were calculated using a social survey. A questionnaire was developed and sent to the experts including faculty and alumni of WEM at AIT. The experts were asked to give priority for each criterion according to a five-level Likert scale. The weights were calculated from the opinion of 14 experts using a scoring scheme of 5 (Essential) having the highest priority and 1 (Not a priority) being the lowest priority. The average score was calculated for each criterion after getting opinions from 14 experts and the scores for physical restrictions, catchment size, cost, TSS, COD, and BOD were 3.71, 3.86, 3.93, 3.71, 3.64, and 3.71, respectively, while for TN and TP, it was 3.57. After obtaining the average score, the criteria weights were calculated.

Design of the NBS physical model

The bioretention system was designed based on the site investigations conducted and by using the LID costing tool developed by the STEP, Toronto, Canada. The preliminary design for the selected site included the native soil infiltration rate of 200 mm/h, the shape of the bioretention system was rectangular with no infiltration, while the ponding, filter media, mulch, pea gravel, and gravel storage depths were 0.20, 0.675, 0.075, 0.10, and 0.15 m, respectively.

Simulation of the designed NBS model using numerical modeling

The drainage model of AIT developed by Seanghak (2017) was utilized and transferred from MIKE urban to PCSWMM. The geo-referencing of the model was performed by using Google Earth and PCSWMM. The WGS84 coordinate system was used in geo-referencing to obtain the required coordinates of the sites.

Model calibration and validation were performed by Seanghak (2017) in a previous study done in the AIT campus. Model calibration involves the adjustment of the model parameter so that the computed discharge hydrographs agree closely with observed data from the field experiment. The field experiments were performed in that study and were calibrated and validated by simulations done in MIKE URBAN software. Only one catchment, namely, 8-1 was calibrated in this study. The catchment area of the present study (4-1) is near the calibrated catchment (8-1). The calibrated parameters included an impervious area having a value of 30%, time of concentration was 3 min, the time–area curve type was rectangular, the Manning coefficient was in the range of 0.0118–0.0147, while the initial infiltration was having the value of 0.0006 m.

Hydrometeorological data

The precipitation data were obtained from 1 January 2021 to 31 December 2021. The data consist of average daily rainfall (mm/day). The maximum value (70 mm/day) was selected for the simulation. The daily average rainfall depth of AIT for the year 2021 is shown in Figure 4.
Figure 4

Daily average rainfall depth at AIT for the year 2021.

Figure 4

Daily average rainfall depth at AIT for the year 2021.

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Development of the model

The SWMM was developed in PCSWMM software by using different parameters including the parameters for sub-catchment, land use, and LIDs. The values of the parameters were obtained from literature or from the guidelines. The simulation of the model was done for the duration of 24 h. The design storm was developed using the United States Soil Conservation Service (US SCS) method using the rainfall depth of 70 mm, which was the maximum daily average rainfall depth for the year 2021. Two sub-catchments were developed for the model including one adjacent building and some portion of road and green area, as these structures contribute towards the site. Similarly, the parameter values were obtained from literature or the guidelines. The sub-catchment parameters for the SWMM included the Horton method for infiltration, dynamic wave method for routing, the design storm method was SCS, the simulation duration was 24 h, rain interval was 6 min, whereas the number of antecedent days was 5. The SCS hyetograph having all mentioned parameters (rainfall depth, interval, and duration) had a steep rising limb having a high intensity and short duration rainfall event, which may lead to a substantial volume of runoff. Therefore, the design of the bioretention model should be developed in a way that accommodates the generated runoff effectively.

The sub-catchment parameters for the model are depicted in Table 3.

Table 3

Sub-catchment parameters for the SWMM

Sub-catchment parametersValueSource
No. of sub-catchments – 
1. Building B1 – 
2. Green area and road G1 + R1 – 
Slope (%) 0.5 Default 
Impervious percentage 
B1 80 From Google earth 
G1 + R1 50 From Google earth 
Impervious Manning's n 0.011 (Chow et al. 2012
Pervious Manning's n 0.03 
Impervious depression storage (mm) 0.2 
Pervious depression storage (mm) 2.5 
Horton's maximum infiltration rate (mm/h) 150 
Horton's minimum infiltration rate (mm/h) 15 
Horton's decay rate (1/h) 0.0012 
Sub-catchment parametersValueSource
No. of sub-catchments – 
1. Building B1 – 
2. Green area and road G1 + R1 – 
Slope (%) 0.5 Default 
Impervious percentage 
B1 80 From Google earth 
G1 + R1 50 From Google earth 
Impervious Manning's n 0.011 (Chow et al. 2012
Pervious Manning's n 0.03 
Impervious depression storage (mm) 0.2 
Pervious depression storage (mm) 2.5 
Horton's maximum infiltration rate (mm/h) 150 
Horton's minimum infiltration rate (mm/h) 15 
Horton's decay rate (1/h) 0.0012 

Pollutant buildup is the rate at which a pollutant is added on the surface of land in the dry weather periods, while the pollutant washoff is the process of mobilization and erosion of pollutants from the surface of sub-catchments in the wet weather periods or events. In the SWMM, there are three buildup functions, namely, exponential, power, and saturation; external time series can also be used. However, for washoff, there are three functions, namely, exponential, event mean concentration, and rating curve (Rossman 2010). For the pollutant buildup and washoff, the parameters for exponential buildup and washoff functions are reported mostly in literature and hence these parameters were utilized in the model. Equations (1) and (2) calculate the pollutant buildup and washoff, respectively.
formula
(1)
where Buildup is the pollutant buildup (mass/unit area or unit curb length); t is the number of antecedent dry days; C1 is the maximum buildup possible; and C2 is the buildup rate constant (1/days).
formula
(2)

where Washoff is the washoff load (unit mass/h); Runoff is the runoff rate/unit area (inches/h or mm/h); Buildup is the pollutant buildup; C3 is the washoff coefficient; and C4 is the washoff exponent.

The LID parameters used in this model were obtained from literature, guidelines, and the proposed design of this model. The land use parameters used in this model are shown in Table 4, whereas the LID parameters including bioretention layers depth are shown in Table 5.

Table 4

Land use parameters for the SWMM

Land use parametersValueSource
Land use type Residential – 
Buildup 
Function Exponential – 
Maximum buildup (C1
TSS 0.003 (kg/m curb) Chow et al. (2012)  
TP 0.003 (kg/m curb) 
TN 0.0001 (kg/m curb) Temprano et al. (2007)  
COD 0.0027 (kg/m curb) 
Buildup exponent (C2
TSS 0.8 Chow et al. (2012)  
TP 0.05 
TN 0.3 Temprano et al. (2007)  
COD 0.3 
Washoff 
Function Exponential – 
Wash off coefficient (C3
TSS 0.2 Chow et al. (2012)  
TP 0.41 
TN 8.661 Temprano et al. (2007)  
COD 3.937 
Wash off exponent (C4
TSS 1.4 Chow et al. (2012)  
TP 1.46 
TN Temprano et al. (2007)  
COD 
Land use parametersValueSource
Land use type Residential – 
Buildup 
Function Exponential – 
Maximum buildup (C1
TSS 0.003 (kg/m curb) Chow et al. (2012)  
TP 0.003 (kg/m curb) 
TN 0.0001 (kg/m curb) Temprano et al. (2007)  
COD 0.0027 (kg/m curb) 
Buildup exponent (C2
TSS 0.8 Chow et al. (2012)  
TP 0.05 
TN 0.3 Temprano et al. (2007)  
COD 0.3 
Washoff 
Function Exponential – 
Wash off coefficient (C3
TSS 0.2 Chow et al. (2012)  
TP 0.41 
TN 8.661 Temprano et al. (2007)  
COD 3.937 
Wash off exponent (C4
TSS 1.4 Chow et al. (2012)  
TP 1.46 
TN Temprano et al. (2007)  
COD 
Table 5

LID parameters for the SWMM

LID parametersValueSource
Surface 
Berm height (mm) 200 Design 
Vegetation volume (fraction) 0.1 William et al. (2011)  
Surface roughness (Mannings n0.3 William et al. (2011)  
Surface slope (%) Design 
Soil 
Thickness (mm) 750 Design 
Porosity (volume fraction) 0.5 Chaosakul et al. (2013)  
Field capacity (volume fraction) 0.121 Rossman & Huber (2015)  
Wilting point (volume fraction) 0.057 Rossman & Huber (2015)  
Conductivity (mm/h) 200 SUDS, WSUDs, LID guidelines 
Conductivity slope (%) 44 Rossman & Huber (2016)  
Suction head (mm) 50 Rossman & Huber (2015)  
Storage 
Thickness (mm) 250 Design 
Void ratio (voids/solids) 0.4 Rossman & Huber (2015)  
Conductivity (mm/h) 25 Rossman & Huber (2015)  
Clogging factor Not considered as per literature 
Underdrain 
Drain coefficient (mm/h) Rossman & Huber (2015)  
Drain exponent 0.5 Rossman & Huber (2015)  
Drain offset height (mm) 125 Design 
LID parametersValueSource
Surface 
Berm height (mm) 200 Design 
Vegetation volume (fraction) 0.1 William et al. (2011)  
Surface roughness (Mannings n0.3 William et al. (2011)  
Surface slope (%) Design 
Soil 
Thickness (mm) 750 Design 
Porosity (volume fraction) 0.5 Chaosakul et al. (2013)  
Field capacity (volume fraction) 0.121 Rossman & Huber (2015)  
Wilting point (volume fraction) 0.057 Rossman & Huber (2015)  
Conductivity (mm/h) 200 SUDS, WSUDs, LID guidelines 
Conductivity slope (%) 44 Rossman & Huber (2016)  
Suction head (mm) 50 Rossman & Huber (2015)  
Storage 
Thickness (mm) 250 Design 
Void ratio (voids/solids) 0.4 Rossman & Huber (2015)  
Conductivity (mm/h) 25 Rossman & Huber (2015)  
Clogging factor Not considered as per literature 
Underdrain 
Drain coefficient (mm/h) Rossman & Huber (2015)  
Drain exponent 0.5 Rossman & Huber (2015)  
Drain offset height (mm) 125 Design 

Soil investigation

The values of hydraulic conductivity for Sites-1, -2, and- 3 were 0.0028, 0.0195, and 0.0493 m/day, respectively. According to Smedema & Rycroft (1983), the texture of the soil is poorly structured clay loam and clay. The values depict that for all sites in AIT, soil cannot transmit water properly; hence, it needs to be replaced by engineered soil having good permeability. Hence, the hydraulic conductivity was excluded from MCDA analysis.

The porosity values were 42.3, 44.63, and 49.95% for Sites-1, -2, and -3, respectively. The results are acceptable according to the guidelines. Therefore, the soil tests show that soil at all sites can store water but cannot transmit it.

Ullah et al. (2017) conducted research in AIT and found out the soil-based filter media properties, which include TN, TP, potassium, etc. All sites have the same pH values, which means that at sites, the values of all parameters will be same; therefore, they were also excluded from the analysis. The properties are shown in Figure 5.
Figure 5

Soil-based filter media properties (Ullah et al. 2017).

Figure 5

Soil-based filter media properties (Ullah et al. 2017).

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Water quality

Water quality tests were performed in EEM labs. Three trials were performed for three different storm events. Maximum values of pollutants were obtained from trials for MCDA. The water quality results for each site are presented in Table 6.

Table 6

Water quality results for all sites

PollutantTrialRainfall depth (mm)Site-1Site-2Site-3
TSS (mg/l) 9.5 9.5 65.33 134.67 
TN (mg O2/l) 4.1 8.96 15.8 11.59 
TP (mg O2/l) 9.5 0.15 0.57 0.55 
COD (mg N/l) 31.8 7.87 78.69 39.34 
BOD (mg P/l) 9.5 Not Detected (N.D.) 16.5 14.4 
PollutantTrialRainfall depth (mm)Site-1Site-2Site-3
TSS (mg/l) 9.5 9.5 65.33 134.67 
TN (mg O2/l) 4.1 8.96 15.8 11.59 
TP (mg O2/l) 9.5 0.15 0.57 0.55 
COD (mg N/l) 31.8 7.87 78.69 39.34 
BOD (mg P/l) 9.5 Not Detected (N.D.) 16.5 14.4 

MCDA criteria weights and AHP results

The survey was carried out to obtain responses for calculating criteria weights. The results depict that the maximum weight was obtained for cost. Hence, cost is the most important criteria for the analysis as compared to others. The summary of results is shown in Table 7.

Table 7

Summary of results for criteria weights

Suitable site for NBSPS + CSCatchment sizeCostTSSTNTPCODBODPriority
Physical Restrictions + Contributing Structures (PS + CS) 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 
Catchment size 0.26 0.18 0.14 0.26 0.16 0.16 0.22 0.26 0.20 
Cost 0.43 0.54 0.42 0.43 0.22 0.22 0.31 0.43 0.37 
TSS 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 
TN 0.02 0.04 0.06 0.02 0.03 0.03 0.01 0.02 0.03 
TP 0.02 0.04 0.06 0.02 0.03 0.03 0.01 0.02 0.03 
COD 0.03 0.04 0.06 0.03 0.09 0.09 0.04 0.03 0.05 
BOD 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 
Suitable site for NBSPS + CSCatchment sizeCostTSSTNTPCODBODPriority
Physical Restrictions + Contributing Structures (PS + CS) 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 
Catchment size 0.26 0.18 0.14 0.26 0.16 0.16 0.22 0.26 0.20 
Cost 0.43 0.54 0.42 0.43 0.22 0.22 0.31 0.43 0.37 
TSS 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 
TN 0.02 0.04 0.06 0.02 0.03 0.03 0.01 0.02 0.03 
TP 0.02 0.04 0.06 0.02 0.03 0.03 0.01 0.02 0.03 
COD 0.03 0.04 0.06 0.03 0.09 0.09 0.04 0.03 0.05 
BOD 0.09 0.06 0.08 0.09 0.16 0.16 0.13 0.09 0.11 

The MCDA results obtained from the AHP process are presented in Table 8. The ranking was then done according to the results, and rank 1 was given to Site-1 and ranks 2 and 3 for Sites-2 and -3, respectively. The best site was Site-1, and this may be due to the minimum cost, big catchment area, and minimum physical restrictions at the site.

Table 8

AHP results

AspectsTechnical
EconomicEnvironmental
Overall priority
CriteriaPhysical R, contributing SCatchment sizeCostTSSTNTPCODBOD
Criteria weights 0.11 0.20 0.37 0.11 0.03 0.03 0.05 0.11  
Site-1 0.724 0.644 0.633 0.06 0.11 0.091 0.074 0.07 0.467 
Site-2 0.193 0.282 0.106 0.27 0.63 0.455 0.644 0.64 0.277 
Site-3 0.083 0.074 0.260 0.67 0.26 0.455 0.282 0.28 0.256 
AspectsTechnical
EconomicEnvironmental
Overall priority
CriteriaPhysical R, contributing SCatchment sizeCostTSSTNTPCODBOD
Criteria weights 0.11 0.20 0.37 0.11 0.03 0.03 0.05 0.11  
Site-1 0.724 0.644 0.633 0.06 0.11 0.091 0.074 0.07 0.467 
Site-2 0.193 0.282 0.106 0.27 0.63 0.455 0.644 0.64 0.277 
Site-3 0.083 0.074 0.260 0.67 0.26 0.455 0.282 0.28 0.256 

Design of the bioretention system

The bioretention system was designed by the following guidelines from SUDs, LIDs, and WSUDs and by using the LID tool developed by the STEP as mentioned in methodology. The proposed design for the bioretention system at Site-1 is shown in Figure 6. Furthermore, the design features of the bioretention system are shown in Table 9.
Table 9

Design features of the bioretention system at Site-1

ItemValue
Drainage area (m²) 1,906 
Native soil infiltration rate (mm/h) 200 
Bioretention shape Rectangular 
Bioretention design type No infiltration 
Underdrain diameter (m) 0.2 
Length of bioretention (m) 32.5 
Width of bioretention (m) 
Water storage volume (m352 
Surface area (m2130 
ItemValue
Drainage area (m²) 1,906 
Native soil infiltration rate (mm/h) 200 
Bioretention shape Rectangular 
Bioretention design type No infiltration 
Underdrain diameter (m) 0.2 
Length of bioretention (m) 32.5 
Width of bioretention (m) 
Water storage volume (m352 
Surface area (m2130 
Figure 6

Design of the bioretention system at Site-1.

Figure 6

Design of the bioretention system at Site-1.

Close modal

PCSWMM simulation

The PCSWMM simulation results are shown in Table 10. The results show a reduction in runoff as well as the pollutants; also, there is some reduction in catchment washoff. The performance of the bioretention system is compared with the performance in other regions of the world, and it is found that some reductions are similar while others are different. The reason is the difference in parameters including rainfall characteristics, land use, antecedent dry period, sub-catchment characteristics, soil type, etc. The results are compared with previous studies in Table 11. Furthermore, the hydrographs and pollutographs of the PCSWMM simulation results are shown in Figures 711.
Table 10

Pollutant and runoff reduction at outfall

ItemSite-1 without LIDSite-1 with LIDReduction (%)
Total volume (106 l) 0.086 0.074 14 
Maximum flow (CMS) 0.018 0.015 17 
TSS (kg) 1.067 0.903 15 
TP (kg) 0.07 0.056 20 
TN (kg) 0.777 0.67 14 
COD (kg) 0.852 0.719 16 
ItemSite-1 without LIDSite-1 with LIDReduction (%)
Total volume (106 l) 0.086 0.074 14 
Maximum flow (CMS) 0.018 0.015 17 
TSS (kg) 1.067 0.903 15 
TP (kg) 0.07 0.056 20 
TN (kg) 0.777 0.67 14 
COD (kg) 0.852 0.719 16 
Table 11

Comparison of results with previous studies

StudyMethodsGeographical regionReductions (%)
Runoff volumeTSSTNTPCOD
Present study Numerical modeling Thailand 14 15 14 20 16 
Tuomela et al. (2019)  Numerical modeling Finland 15–23 45–72 27–49 24–51 – 
Li et al. (2017)  Numerical modeling China 39 34 36 34 28 
Wang et al. (2017)  Monitoring/analysis Singapore – 53 25 46 – 
Ong et al. (2012)  Monitoring/analysis Singapore – 73 65 51 – 
Mangangka et al. (2015)  Monitoring/analysis Australia – 81 ± 8 48 ± 31 75 ± 14 – 
Brown & Hunt (2011)  Monitoring/analysis USA – 71 12 5.3 – 
Li et al. (2021)  Physical modeling China 12–29 – – – – 
StudyMethodsGeographical regionReductions (%)
Runoff volumeTSSTNTPCOD
Present study Numerical modeling Thailand 14 15 14 20 16 
Tuomela et al. (2019)  Numerical modeling Finland 15–23 45–72 27–49 24–51 – 
Li et al. (2017)  Numerical modeling China 39 34 36 34 28 
Wang et al. (2017)  Monitoring/analysis Singapore – 53 25 46 – 
Ong et al. (2012)  Monitoring/analysis Singapore – 73 65 51 – 
Mangangka et al. (2015)  Monitoring/analysis Australia – 81 ± 8 48 ± 31 75 ± 14 – 
Brown & Hunt (2011)  Monitoring/analysis USA – 71 12 5.3 – 
Li et al. (2021)  Physical modeling China 12–29 – – – – 
Figure 7

Total inflow hydrograph at the outfall of Site-1.

Figure 7

Total inflow hydrograph at the outfall of Site-1.

Close modal
Figure 8

TSS pollutograph at the outfall of Site-1.

Figure 8

TSS pollutograph at the outfall of Site-1.

Close modal
Figure 9

TP pollutograph at the outfall of Site-1.

Figure 9

TP pollutograph at the outfall of Site-1.

Close modal
Figure 10

TN pollutograph at the outfall of Site-1.

Figure 10

TN pollutograph at the outfall of Site-1.

Close modal
Figure 11

COD pollutograph at the outfall of Site-1.

Figure 11

COD pollutograph at the outfall of Site-1.

Close modal

Findings based on PCSWMM simulation results depict that the bioretention system reduced runoff and pollutants at the selected site. By employing the bioretention system, PCSWMM gives a total runoff volume and maximum flow reduction of 14 and 17%, respectively. However, the bioretention system reduced the pollutants including TSS, TP, TN, and COD by 15, 20, 14, and 16%, respectively. Finding the best location to install the bioretention system is vital to ensure optimum results, and MCDA was utilized to locate the most suitable sites for the bioretention system in the study area. The MCDA employed the AHP method, which included different criteria including water quality, cost, physical restrictions and contributing structures, and catchment size and three alternatives, namely, Sites-1, -2, and -3. The AHP method was found to be a suitable approach to locate the best site for NBS in the study area, and that was Site-1.

The patterns of the hydrographs and pollutographs are depicted in Figures 711. It can be seen that the runoff peak is reduced for the scenario with LID as compared to the scenario without LID at outfall; in addition to that, a similar pattern can be seen for pollutographs (TSS, TP, TN, and COD) as the peaks are reduced with the LID scenarios as compared to without LID scenarios at the outfall of Site-1. To reduce the volume and peaks further, it is recommended that the area of the bioretention system may be increased, or the number of units can be increased; however, it will consequently increase the cost of the NBS system. Moreover, the physical processes that are occurring in bioretention systems mimic the natural ecological processes, and improved estimation of their performance will help in meeting the water quality requirements in downstream surface water bodies. Also, continuous research should be done to further refine the design of the bioretention system and improve its performance.

Significance, practical implications, and added value of research

The study aims to tackle urban flooding and degradation of water quality and line up with the challenges of fast urbanization. The methodology employed in this study to determine appropriate sites for NBS can be utilized for areas that are facing similar issues. The tropical regions unlike temperate regions have a climate with high amounts of precipitation, mostly intense rainfall events, and the hyetographs exhibit a steep rising limb and short duration, which leads to fast generation of runoff. The high rainfall intensity challenges the capability of NBS to manage stormwater effectively. Also, tropical climate has high rates of evapotranspiration, which may decrease the moisture available for infiltration, impacting the performance of NBS. Hence, the need to assess the performance of NBS in tropical climate is required, and the emphasis on tropical areas fills up the vital gap in the guidelines of NBS, allowing other tropical regions to use the MCDA approach for NBS implementation in the urban context. The illustrated MCDA method, including economic, environmental, and technical aspects, serves as a thorough decision-making tool, and urban planners and municipalities can employ this approach to identify ideal sites for NBS application, encouraging urban water management sustainability.

AIT was selected as the study area because of its promising benefits to WEM labs and students. It proposes tangible insights for the people who are associated with campus planning or educational institutes. The study showcases the integration of NBS into the existing gray infrastructure for better research and education. The aim of this study is to identify potential locations to implement NBS that can be integrated with pre-existing structures and encourage continuous research and monitoring. Researchers interested in understanding the long-term performance and evolution of NBS applications can find this study useful as it offers pertinence that surpasses immediate results.

Worldwide collaboration and adoption of NBS strategies find motivation in the unique focus of this study in tropical climate. This approach compels researchers, experts, and policymakers to consider region-specific challenges and solutions, thus fostering a more inclusive approach to sustainable water management at a global scale. Moreover, the study's pioneering nature; strategic application within campus; considerate site selection method; development of guidelines for urban water management in tropical regions; applicability for further research; and its potential to encourage global collaboration and adaptation in addressing urban water management are the added value of this research.

The rainfall pattern is variable across the globe and depends on factors such as seasons, climate change, and geographical location. In tropical regions, there are short, frequent, and intense rainfall events, which are not common in temperate areas. Hence, in this study, MCDA has been found to be a suitable approach for the site selection of NBS projects in tropical climate regions.

Site-1 is the best site for the implementation of NBS project in AIT. The bioretention system at Site-1 can potentially reduce runoff by 14% and pollutants by 14–20%, respectively. Further reductions can be made by increasing the bioretention area or number of units, consequently increasing costs.

The performance of the bioretention system is compared with the performance in other regions of the world, and it is found that some reductions are similar while others are different. The reason is the difference in parameters including rainfall characteristics, land use, antecedent dry period, sub-catchment characteristics, soil type, etc. The research provides a comprehensive assessment of site suitability and development of the NBS model, a push forward for NBS implementation in Thailand.

The soil of AIT is not suitable for construction of a bioretention system. It is recommended that engineered soil should be used for this purpose. Different aspects or criteria can be used for MCDA other than those used in this study. The buildup and wash up parameters should be developed for Thailand as in this study, they were used from other studies (Malaysia, Spain). In this study, the daily precipitation data were utilized; however, hourly data may provide more accurate results in case of extreme rainfall events. The AIT weather station was unable to provide hourly data; therefore, utilization of regional climate models is recommended for future research to enhance accuracy.

The bioretention system should be constructed following the guidelines developed in this research and can be monitored for further study in the AIT Campus. The MCDA approach used in this study can be used for the site selection of other types of LIDs or NBS techniques. Moreover, other types of LIDs can be used for modeling and to evaluate their performance for managing stormwater quality and quantity.

The authors would like to acknowledge Mr Ky Le Gia (EEM Department, AIT) and Mr Ruel Francisco (WEM Department, AIT) for their support in data collection (soil and stormwater quality tests) for the research.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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