Climate Change Impacts on Water Sensitive Urban Design Technologies

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Climate Change Impacts on Water Sensitive Urban Design Technologies


1. Introduction

Future climate projections indicate that there will be an increase in the frequency of extreme weather events, causing drought and flooding conditions [1]. Meanwhile, rapid urbanization further aggravates this scenario by increasing the impervious surface in urban areas [2]. Water Sensitive Urban Design (WSUD) approaches can provide a sustainable solution to assist in urban stormwater management and ease the burden of conventional drainage systems [3]. There are several types of WSUD technologies available, and the common trait among them is the principle of restoring the natural balance as far as practicable in urban scenarios [4].
According to Ahammed [3], one of the most popular infiltration-based WSUD devices in Australia is the soak-away, an underground system. This structure can contain stormwater runoff, reduce peak surface runoff and assist in the flood management of urban catchments [5]. Those benefits can have a fundamental role in building climate change resilience in populated areas [6]. To evaluate climate risks, the utilization of models can be a useful tool to assess the reliability of systems considering different future scenarios [7].

Research Background and Problem Statement

The Intergovernmental Panel on Climate Change (IPCC) has recently released its Sixth Assessment Report called ‘AR6’, predicting a global average warming of 1.5 °C or higher levels that will affect rainfall patterns and intensify precipitation events and associated flooding [8]. Australia is already experiencing worsening weather events because of climate change, causing environmental disasters, risking the wellbeing of the population and challenging the agriculture sector [9]. The WSUD practices have shown great potential to solve some of those problems by decentralizing the water management systems and improving a series of aspects related to water [10].
In fact, WSUD technologies may provide additional climate change resilience in terms of increasing water demands and a greater need for soil moisture retention within urban areas, which points to a necessity for further evaluation of WSUD assets in view of climate change impacts [11]. It is known that climate conditions and catchment characteristics influence the hydrologic design and, ultimately, the overall pollutant removal effectiveness of water management devices [12]. However, to the authors’ knowledge, very few studies have investigated the impact of climate change on WSUD systems. Ahammed et al. [3] investigated a case of stormwater management with climate change impacts for Dhaka City and demonstrated how leaky well-based WSUDs can transform Dhaka’s unsatisfactory drainage network into one that is sustainable. This research aimed to contribute to this overlooked aspect by investigating the influence of future changes in rainfall patterns on a soak-away device, considering a residential allotment at Aldinga, South Australia. According to the Council’s Community Plan 2030 [13], approximately 77% of Aldinga’s community is concerned with climate change impacts. The region has been suffering due to extreme rainfall patterns and long periods of drought [14]. Some studies regarding rainfall analysis have been developed for this area [15,16], but there is still a gap in knowledge involving the re-design of WSUD technologies considering the future climate. This study aimed to understand the climate change impacts on the design of a hypothetical soak-away technology installed in the area.

3. Methodology

Previous studies regarding WSUD systems used a variety of modelling software, such as SWIMM 5.2, DRAINS W8, MIKE 21, and MUSIC 6.3, to evaluate the infrastructure’s performance under a set of different conditions [30,31,32,33,34]. A couple of published articles also analyzed climate change impacts on WSUDs [30,31]. However, the methodology of this study focuses on the applications of various models, such as MUSIC, source control principle, Climate Future tool and life cycle cost analysis together.

According to the ARR guidelines, the CSIRO’s Climate Futures web tool provides guidance in the consensus of various Global Climate Models (GCMs) developed to predict future climatic conditions. For this reason, this decision-support tool was employed in this study to determine the projected rainfall intensity for the three future time periods (2030, 2060 and 2090) at different scenarios of greenhouse gas concentrations—RCPs, RCP 4.5 and RCP 8.5.

The City of Onkaparinga Council supplied their own approved climate dataset for use in the MUSIC model, which is the recommended source of climate data indicated by a Water Sensitive SA [11]. This climate dataset was projected and then imported to the MUSIC model for further analysis of the impacts of the projections on the peak flow and consequently, the surface runoff volume that might be directed to the soak-away system, considering the catchment area of a house allotment at Green Village, Aldinga.
Concurrently with the climate data projection, the Argue et al. [35] model was executed to determine the soak-away dimensions and emptying time for the design flows of 5-year ARI and 20-year ARI. The calculation of the soak-away dimensions also required the consultation of the 2016 IFDs dataset from the Bureau of Meteorology (BOM), which are the most current IFDs, to collect the rainfall intensities for the study area considering those average recurrence intervals and the critical storm duration for the study area (20 min).
Afterwards, the combinations of the preliminary soak-away dimensions obtained by the Argue et al. [35] model were reviewed and adjusted in view of the increase in the peak flow calculated from the results of the MUSIC model. In other words, the percentage of increase of the peak flows due to the climate change impact was ‘transferred’ to the Argue et al. model [35], which led to the re-dimensioning of the soak-away systems. Finally, a hydraulic and hydrological comparison between the soak-way designs was performed, considering several aspects, such as dimensions, financial costs and technical efficiency.

3.1. Study Area

The study area is situated in the Aldinga suburb of the City of Onkaparinga Council, which is the largest metropolitan council in South Australia. This area is mainly rural and an important source of water supply for Adelaide, also providing water for farming activities and for the natural environment [15]. For this study, residential allotments of the Village Green townhouse complex were selected as the study site, being located approximately 40 km from Adelaide CBD.
The site sits in the proximity of Willunga Creek (as seen in Appendix A) and is part of the Onkaparinga Water catchment and the Willunga Basin. According to Beecham and Chowdhury [16], the Onkaparinga catchment has a median annual rainfall of approximately 770 mm and an intense rainfall variability, with less rainfall during the summer and heavier rains during winter [16]. The soil’s region is mainly composed of sandy and silty loam soil.

3.2. Data Collection

The Council provided a MUSIC template with the meteorological dataset from 2001 to 2010, including the evapotranspiration data relevant to the study area, which was used as the base in the data analysis process for the climate change projections. To represent the catchment area in MUSIC, the area for the source nodes was determined by utilizing the QGIS Desktop 3.20.0 ‘Measure Area’ tool. Through that tool, it was possible to estimate roof, road, paved and pervious areas. With both that information, the areas of the source nodes and the rainfall and evapotranspiration dataset, it was possible to set the preliminary MUSIC model that was used as the base for modelling different scenarios.

The 2016 IFD relationships from the Bureau of Meteorology (BoM) website were also consulted by imputing the geographic coordinates of the Green Village house allotments. The rainfall intensity for the design flows of ARI = 1 in 5 years and ARI = 1 in 20 years were collected, considering the site’s critical storm duration of 20 min. The average recurrence intervals of 5-year ARI and 20-year ARI were selected as they are commonly used as design storms in SA.

In addition, the 3 months ARI (4EY) was collected to estimate the high flow bypass value for the soak-away structure, as recommended by the Water Sensitive SA [11]. Those rainfall intensities were applied in Argue et al.’s source control principles [35] for soak-away design techniques to determine its preliminary dimensions and emptying times. The soak-away dimensions were later modified, considering MUSIC’s peak flow increments, for the projected scenarios.

3.3. Data Analysis

In sequence with the collection of data, several procedures were performed which started with the definition of the climate change projections for 2030, 2060 and 2090 at 4.5 and 8.5 RCPs. Then, it was also discussed how the soak-away dimensions were determined and re-dimensioned considering the climate change projection on the rainfall data. The three main components discussed in this section are focused on the Climate Future web tool from CSIRO, MUSIC modelling and Argue’s stormwater source control method.

3.4. Calibration of the IFDs Considering the Climate Change Factor

The climate change projections for the rainfall dataset collected from the City of Onkaparinga Council were estimated using the procedures set out in ‘Interim Guideline for Climate Change’, from ARR’s book A Guide for Flood Estimation [36]. Firstly, the guidelines outline a six-step decision procedure to determine if climate change projections should be incorporated into the flood design [36]. It is important to highlight that this procedure is subjected to professional judgement and is up to the designer to define the climate change impacts on the asset and its consequences on the surroundings. By performing this decision-making procedure, it was concluded that, because there is no statutory requirement for the current study, a cost-effectiveness analysis might be appropriate to assist in the decision on how to proceed with the design requirements.
Secondly, the Climate Futures web tool developed by CSIRO and the Bureau of Meteorology is utilized to assist in applying the Interim Guideline. This tool can inform about the projected changes in temperature and rainfall for certain regions, based on the latest GCM and the RCPs scenarios [21]. Through this tool, it is possible to identify a ‘maximum consensus’ case and the resultant classification of future weather, which can include:
  • Annual Mean Surface Temperature: ‘slightly warmer’ (<+0.5 °C), ‘warmer’ (+0.5 to +1.5 °C), ‘hotter’ (+1.5 to +3.0 °C) and ‘much hotter’ (>+3.0 °C).

  • Annual rainfall: ‘much wetter’ (>+15.0%), ‘wetter’ (+5.0 to +15.0%), ‘little change’ (−5.0 to +5.0%), ‘drier’ (−15.0 to −5.0%) and ‘much drier’ (<−15.0%).

Ball et al. [21] recommend the selection of RCP 4.5 as the lowest concentration pathway and RCP 8.5 as the highest, considering the best GCM consensus GCM cases. Table 1 summarizes the projections from the Climate Futures tool for the scenarios being analyzed.
From Table 1, it can be noticed that in the scenarios that involve the lowest concentration pathway, RCP4.5, the consensus among the GCMs is the same (‘warmer’ and ‘drier’) for the 2030 and 2060 years of projection. In contrast, for the 2090 scenario, the temperature prediction reaches the ‘hotter’ condition, where temperatures may vary from 1.5 °C to 3 °C. Regarding the RCP 8.5 pathway, the predictions show great variability for the chosen scenarios, especially when considering the temperature, which seems to keep spiking over the years.
In practice, to calculate the rainfall intensity projections, Ball et al. [36] explain that, because of the uncertainties surrounding rainfall projections due to the regional variation, a 5% increase in rainfall intensity per °C is recommended. This is translated in a formula for temperature scaling, given by:

  I p = I A R R × 1.05 T m

where:

I p is the projected rainfall intensity (mm/h).

I A R R is the current design rainfall intensity (mm/h).

Tm is the temperature at the mid-point of the class interval (°C).

3.5. Soak-Away Design

Soak-away is a small-scale stormwater management system that aims to increase the infiltration into the soil [27]. According to Ahammed et al. [24] and Argue et al. [35], the basic formula to design a soak-away is:

A s o a k a w a y = V e × H + 60 × K h × τ × U

where:

A s o a k a w a y = area of soak-away;

V = critical stormwater runoff volume;

e = void space ratio: t o t a l   s p a c e   a v a i l a b l e t o t a l   v o l u m e   o f   d e v i c e ;

H = height of the soak-away;

K h = soil hydraulic conductivity;

τ = critical storm duration ( t c ) + site time of concentration ( t s );

U = moderation factor.

3.6. MUSIC Modelling

For the MUSIC modelling, the Water Sensitive SA [4] guidance on modelling approaches was followed. According to the Water Sensitive SA [4], since pollutant concentrations may substantially differ depending on surface types, the application of the split approach on source nodes can provide a more accurate modelling. Therefore, to determine the existing runoff conditions of the study area, the split surface type approach was undertaken, which mainly involved:
  • Splitting the house allotments by surface types (e.g., roofs, pervious areas, roads, etc.).

  • Defining the percentage of imperviousness for each surface type.

  • Selecting parameters for pervious and impervious areas properties, groundwater properties and pollutant export parameters.

In the study area, there is a total of 21 land lots, 5 of which were empty lots. Through the QGIS measure area tool, the lot size, roof area, driveway area and pervious area were collected from each of the 16 allotments with constructed houses. An average area from those different surface types was taken and applied to the five empty lots, assuming that the construction over these empty lots might soon be undertaken. Figure 1 shows the house allotments from the Green Village site.
The values from the areas collected for each surface type are depicted in Appendix B. To select the appropriate properties for the surface types, a Water Sensitive SA [4] was consulted, being the critical information to determine those parameters, as follows:
  • Soil type: Sandy Clay Loam.

  • Land use: Road, Roof or ‘All other urban’.

  • Lot size: approximately 600 m2.

The soak-away node on MUSIC was represented by the generic ‘Infiltration System’ node, as it is a known WSUD infiltration system and there is no available node specifically for soak-aways in the software.

3.7. Re-Dimensioning to the Soak-Away Systems

The parameters of the source nodes that represent one house allotment at Village Green were defined, and the rainfall intensity projections were entered into the MUSIC model. With those two elements, it was possible to run the MUSIC model again and determine the peak flows for each scenario, as shown in Table 2.
With the preliminary soak-away dimensions obtained by the Argue et al. [35] model (Equation (1)) and the peak flows from Table 2, the re-dimensioning of the soak-away systems was carried out.
Lastly, an optimized design of the soak-away was also modelled using a trial-and-error method from the Argue et al. [35] source control principle, considering the treatment effectiveness of the device. When the size of the soak-away allowed the treatment effectiveness to reach at least 45% of Total Nitrogen (TN) removal, 60% of Total Phosphorus (TP) removal and 80% of Total Suspended Solids (TSS) removal, that size was considered the optimal soak-away area for each scenario analyzed.

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