# Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Sukanya Saikia, Paraic Ryan, Siegmund Nuyts, Paul Nolan, Eoghan Clifford

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Sukanya Saikia, Paraic Ryan, Siegmund Nuyts, Paul Nolan, Eoghan Clifford. Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems. Climate services, 2024, 35, pp.100511. 10.1016/j.cliser.2024.100511. hal-04807500

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Original research article

# Impacts of projected future changes in precipitation on wastewater treatment plant influent volumes connected by combined sewer collection systems

Sukanya D. Saikia<sup>a,b,*</sup>, Paraic Ryan<sup>c,d</sup>, Siegmund Nuyts<sup>e</sup>, Paul Nolan<sup>f</sup>, Eoghan Clifford<sup>a,b</sup>

<figure><img src="image_3.png" /></figure>

## HIGHLIGHTS

* Mid-century projections for the impacts of precipitation changes on influent volumes were estimated for 14 WWTPs in Ireland.

* Impacts were seasonally variable with decreases in influent volumes expected during the summer months and increases during winter months.

* The frequency of events exceeding the peak design capacity of WWTPs increase due to the increase in high and very high precipitation events by mid-century under RCP 4.5 and RCP 8.5.

* A new metric, percentage change in reserve capacity, is introduced to help assess the impact on WWTPs due to climate change.

* The research provides tools and recommendations to wastewater utilities, policy makers, the research community and practitioners to help analyse the impacts of precipitation changes on wastewater infrastructure and develop mitigation measures.

## ARTICLE INFO

### Keywords:

WWTPs

Combined sewerage systems

Wastewater influent volumes

Climate change

Peak design capacity

## ABSTRACT

Climate change induced precipitation changes can impact wastewater influent volumes and, in turn, the design and operation of wastewater treatment plants (WWTPs). As influent volumes approach or exceeds the design capacity of the WWTP, the likelihood of poor treatment or untreated discharges that result in environmental damage, increases. To date, there has been a lack of research analysing the impact changes in precipitation may have on influent volumes of WWTPs with combined sewerage systems. This study leverages data driven models of observed precipitation and influent volumes for 14 Irish WWTPs with combined sewerage systems, to project monthly wastewater influent volumes in 2041 – 2060 using Ireland’s most up-to-date high resolution multi-model RCM projections under RCPs 4.5 and 8.5. With changing monthly average daily precipitation, influent volumes for all the WWTPs demonstrated maximum decreases during summer months for both RCP 4.5 and RCP 8.5. Winter months showed increasing trends in influent volumes, particularly under RCP 8.5. The results indicate that with projected increase in high and very high precipitation days in Ireland (under CMIP5), the return periods for influent volumes that exceed the hydraulic capacity of 5 of the WWTPs will reduce. This work also presents a framework by which newer high-resolution downscaled climate models can be used to developed updated impact analysis of changing rainfall patterns on wastewater. It likely that wastewater treatment infrastructure will need to adapt to more intense precipitation to minimise the occurrence of combined sewer overflows.

<footnote><sup>a</sup> School of Engineering, College of Science and Engineering, University of Galway, H91 TK33, Ireland</footnote>

<footnote><sup>b</sup> Ryan Institute, University of Galway, H91 TK33, Ireland</footnote>

<footnote><sup>c</sup> Discipline of Civil, Structural & Environmental Engineering, School of Engineering, University College Cork, Ireland</footnote>

<footnote><sup>d</sup> Environmental Research Institute, University College Cork, Cork T23 XE10, Ireland</footnote>

<footnote><sup>e</sup> Géosciences Environnement Toulouse, GET, CNRS, UPS, Université de Toulouse, IRD, Toulouse, France</footnote>

<footnote><sup>f</sup> Irish Centre for High-end Computing, University of Galway, H91 TK33, Ireland</footnote>

<footnote>* Corresponding author at: School of Engineering, College of Science and Engineering, University of Galway, H91 TK33, Ireland.</footnote>

<footnote>E-mail addresses: s.saikia1@universityofgalway.ie (S.D. Saikia), paraic.ryan@ucc.ie (P. Ryan), siegmund.nuyts@get.omp.eu (S. Nuyts), paul.nolan@ichec.ie (P. Nolan), eoghan.clifford@universityofgalway.ie (E. Clifford).</footnote>
## Practical Implications

* Wastewater treatment plants (WWTPs) are critical infrastructure globally and are essential to protect public health and the natural environment. WWTPs are affected by changes in the intensity and frequency of precipitation events, particularly when connected with combined sewerage systems (CSSs). CSSs are prone to challenges such as combined sewer overflows and hydraulic overloading, both of which can result in untreated or poorly treated wastewater being released into the environment. Therefore, it is critical for wastewater utilities to build adaptation strategies to move towards climate-resilient wastewater infrastructure.

* A crucial parameter to consider in the resilience of a WWTP is the influent volume. Influent volumes have a significant impact on subsequent WWTP processes, and predicting how they might change can help prevent occurrences of overflows. Furthermore, as stricter environmental regulations towards releasing of treated and untreated wastewater are enforced, the resilience of WWTPs to climate change needs to be examined. To date, to the best knowledge of authors, there has been no research that analyses how changes in precipitation to mid-century will impact wastewater influent volumes. This article explores how WWTP influent volumes will be impacted by monthly average daily precipitation and extreme events as predicted under CMIP5 regional climate projections (RCP) 4.5 and 8.5 for 14 different WWTPs with CSSs in Ireland. These datasets are currently the most up to date peer-reviewed high-resolution datasets down-scaled for Ireland.

* This paper leverages data-driven models of 14 WWTPs in Ireland (Saikia et al., 2022) to predict future changes in influent volumes using RCM projections of monthly average daily precipitation (ensemble mean) under RCP 4.5 and RCP 8.5 (Nolan and Flanagan, 2020). The 33rd and the 66th percentiles of the ensemble of RCM projections of the monthly average daily precipitation are used to help analyse the uncertainty associated with climate projections. The projected percentage change in reserve capacity of the case study WWTPs under RCP 4.5 and RCP 8.5, is analysed. Exceedance curves derived from real data are linked to future precipitation projections of extreme events, i.e., high (> 20 mm/day) and very high (> 30 mm/day) precipitation days to investigate the impacts of high and very high rainfall days on the frequency of events exceeding the peak design capacity of WWTPs.

* For the case study WWTPs, the analysis showed that the influent volumes varied seasonally, with the winter months showing the highest increases in the examined years (2041–2060), particularly under RCP 8.5. In contrast, maximum decreases in influent volumes were projected to occur during the summer months, for both the RCP 4.5 and RCP 8.5 climate change emission scenarios. While 13 of the 14 WWTPs examined were found to have sufficient reserve hydraulic capacity under RCP 4.5 and RCP 8.5, one of the 14 WWTPs was found to be particularly vulnerable to climate change impacts. Lastly, with respect to the extreme events, the frequency of events exceeding the peak design capacity of WWTPs increased for 5 of the wastewater treatment plants and return periods decrease with change in high and very high precipitation days by mid-century under RCP 4.5 and RCP 8.5.

* Overall, this research provides an insight into the degree to which the wastewater influent volumes might be impacted by precipitation. The methodologies can be utilised by researchers, engineers, and utilities in their geographic context to examine potential impacts of rainfall changes in a changing climate. The findings of this research provide a climate service which will help inform decision towards establishing resilient wastewater infrastructure under climate change conditions at regional and local WWTP scales. Promising future research in this area could examine additional temporally dynamic parameters such as urbanization and demographics. Future research should

leverage more recent climate projections as they become available.

# 1. Introduction

Wastewater treatment plants (WWTPs) and their associated sewerage networks are a priority infrastructure globally (Hawchar et al., 2020). These networks are often connected with combined sewerage systems (CSSs) to collect wastewater and stormwater for treatment. The amount of municipal wastewater generated can be impacted by changes related to climate change but also demographics and industrial activity (Shakeri et al., 2021); this additional wastewater has many potential impacts on WWTPs (Zouboulis and Tolkou, 2015). However, infrastructure in many cases, will not have been designed for these changes (Meyer et al., 2013). For example, increased rainfall intensity and frequency of extreme rainfall events will result in increased wastewater influent volumes due to flow from CSSs and inflow and infiltration, whereas, decreased rainfall can lead to reduced inflows and also reduced infiltration, resulting in increased contaminant concentrations (Hughes et al., 2021; Zouboulis and Tolkou, 2015). For CSSs there can be strong relationships between flows entering WWTPs and precipitation (Saikia et al., 2022). Extreme weather events such as floods or droughts can decrease the efficiency and increase the costs of treatment processes in WWTPs (Reznik et al., 2020). In addition, a combination of extreme events such as extended dry periods followed by storms can intensify pollutant loading and result in overflow of excess water. Such overflows are known as combined sewer overflows (CSOs) (Langeveld et al., 2013). CSOs are a major challenge that threaten the environment and put public health at risk (Mailhot et al., 2015). It is thought that these impacts are likely to be exacerbated as wastewater networks become increasingly vulnerable to the intensifying impacts of climate change (Gooré Bi et al., 2015, 2017; Pachauri, 2014; Willems, 2013).

While there have been detailed studies identifying precipitation variables impacting wastewater influent volumes in CSSs (for example Gooré Bi et al., 2015; Langeveld et al., 2013; Li et al., 2016; Mines et al., 2007; Saikia et al., 2022), the response characteristics of influent volumes with respect to future changes in precipitation induced by climate change have, to the best knowledge of the authors, yet to be analysed. Kirchhoff & Watson (2019), revealed that a majority of wastewater managers are implementing changes in the wastewater networks to build climate-resilience, based on the historical storm events rather than future precipitation events induced by climate change. Therefore, it is likely that, if future projections are not considered, new and existing infrastructure may not be sufficiently resilient (Vogel et al., 2016).

There has been some previous work investigating the impact of potential changes in rainfall patterns on the hydraulic response characteristics of wastewater infrastructure (Hlodversdottir et al., 2015; Hussain et al., 2022; Jung et al., 2015; Kleidorfer et al., 2009). These studies consider sewer hydraulics and use hydraulic models/software packages to simulate hydraulic variables that help in the performance assessment of sewer systems. A number of studies (Abdellatif et al., 2014; Arnbjerg-Nielsen, 2012; Butler et al., 2007; Fortier & Mailhot, 2015; Gooré Bi et al., 2015; Hlodversdottir et al., 2015; Jung et al., 2015; Kleidorfer et al., 2009; Shakeri et al., 2021; Willems, 2013; Zhou et al., 2019) have also leveraged model projections of General Circulation Models (GCMs) and Regional Climate Models (RCMs) to establish the influence of climate change on the design and operation of sewer systems in future. Through the findings of these studies, the importance of climate change adaptation strategies has been emphasized. There has typically been less of a focus on the study of CSSs which would be particularly vulnerable to precipitation and furthermore the direct impacts on wastewater volumes entering treatment facilities has not been studied.

In terms of projected wastewater influent volumes, Shakeri et al.,
(2021) analysed the potential change in wastewater production in Tehran based on the relationship between change in climate variables and human behaviour. The authors used climate projections from CMIP5 models downscaled using a Statistical DownScaling Model (SDSM), and finally forecasted wastewater production volume under RCPs 2.6, 4.5 and 8.5 using a multi-layer perceptron network and fuzzy model. Wastewater networks in Tehran feature separate storm and wastewater sewerage systems, the research found temperature, relative humidity, sunshine hours and population as significant variables influencing wastewater production volumes with rainfall having relatively low influence.

Previous work considering the future impacts of climate change on the hydraulic response characteristics of wastewater infrastructure take into account one case-study at a time. Considering the spatial and temporal scales of climate-related impacts on water infrastructure is vital. Such analysis can inform policy, investment decisions and identify vulnerable infrastructure (Rubio-Martin et al., 2023).

Saikia et al., (2022) analysed the impact of precipitation on wastewater influent volumes to 14 WWTPs of varying treatment capacities, connected to CSSs, and spatially distributed across Ireland. They identified monthly average daily precipitation (ADP) as a statistically significant variable at 95 % level of confidence and developed linear regression models linking this to monthly average daily influent volumes. Saikia et al., (2022) also presented a methodology by which the likelihood, on a site-specific basis, of influent volumes exceeding any given limit under different precipitation intensity categories was developed and presented as probability of exceeding curves or PoE curves. As an example, the PoE curves for any given rainfall amount, show the likelihood of influent flow exceeding a certain value (e.g. exceeding it's peak hydraulic capacity). This study leverages the work presented in Saikia et al., (2022) to analyse the evolution of wastewater influent volume characteristics in the mid-21st century (2041–2060) as compared to measured data collected between 2005 and 2018 based on future precipitation projections under the two climate change scenarios of RCP 4.5 and RCP 8.5. The study also provides a spatiotemporal analysis of influent volume characteristics analysis by evaluating 14 WWTPs of varying size and location. Such analysis can provide a climate service for wastewater utility managers as end-users (Swart et al., 2021), to help them towards better understanding of the problems and challenges that WWTPs are likely to face with the changing climate at a regional and local scale, with particular focus on CSSs.

## 2. Materials and methods

### 2.1. Data used

#### 2.1.1. Measured data

The observed precipitation and wastewater influent volumes for each of the 14 WWTPs (located across Ireland) was obtained from Met Éireann (Irish Meteorological Service) and Uisce Éireann (Ireland's National Water Utility) respectively. The time period for which influent volume data was available differed for the 14 WWTPs, with the temporal range of data ranging from 3–14 years. This time period is referred to as the “current period” in this study. For example, the current period is 2005–2018 for WWTP 2 and the data for the remaining WWTPs lie within the ranges shown in Table 1. Details of the WWTPs studied are also shown in Table 1.

#### 2.1.2. Projected data

The projected Irish precipitation climate data were obtained from (Nolan and Flanagan, 2020), who dynamically downscaled five CMIP5 GCM datasets using RCMs. The choice of CMIP5 data for downscaling was informed by CMIP5 validation studies (e.g., Sweeney et al., 2015) and a careful review conducted by Euro-CORDEX partners (COSMO-CLM team pers. comm). This study identified an initial set of CMIP5 datasets based on many factors, e.g., model level data availability, RCP

<table><caption>**Table 1**

Characteristics of the 14 WWTPs.</caption><thead><tr><th>WWTPs</th><th>Treatment Capacity (PE)</th><th>Network Category</th><th>Temporal Range of data availability of observed influent volume (Current period)</th><th>Spatial Distribution in Ireland</th><th>Peak Design Capacity (m³/day)</th></tr></thead><tbody><tr><td>WWTP 1</td><td>1,86,000</td><td>Large</td><td>2014–2018</td><td>West</td><td>88,500</td></tr><tr><td>WWTP 2</td><td>4,13,200</td><td>Large</td><td>2005–2018</td><td>East</td><td>3,59,592</td></tr><tr><td>WWTP 3</td><td>28,000</td><td>Small</td><td>2012–2018</td><td>West</td><td>23,814</td></tr><tr><td>WWTP 4</td><td>25,000</td><td>Small</td><td>2008–2018</td><td>East</td><td>14,760</td></tr><tr><td>WWTP 5</td><td>3,000</td><td>Small</td><td>2014–2018</td><td>West</td><td>2,250</td></tr><tr><td>WWTP 6</td><td>40,000</td><td>Medium</td><td>2015–2018</td><td>North</td><td>32,400</td></tr><tr><td>WWTP 7</td><td>55,000</td><td>Medium</td><td>2014–2018</td><td>Central</td><td>37,125</td></tr><tr><td>WWTP 8</td><td>1,70,000</td><td>Large</td><td>2011–2018</td><td>West</td><td>1,35,000</td></tr><tr><td>WWTP 9</td><td>39,000</td><td>Medium</td><td>2012–2018</td><td>Central</td><td>23,400</td></tr><tr><td>WWTP 10</td><td>16,40,000</td><td>Very large</td><td>2015–2018</td><td>East</td><td>9,59,040</td></tr><tr><td>WWTP 11</td><td>1,86,000</td><td>Large</td><td>2015–2018</td><td>East</td><td>1,08,000</td></tr><tr><td>WWTP 12</td><td>20,000</td><td>Small</td><td>2008–2018</td><td>East</td><td>12,000</td></tr><tr><td>WWTP 13</td><td>24,834</td><td>Small</td><td>2016–2018</td><td>West</td><td>5,063</td></tr><tr><td>WWTP 14</td><td>45,000</td><td>Medium</td><td>2014–2018</td><td>Central</td><td>33,000</td></tr></tbody></table>

coverage. model resolution, Equilibrium Climate Sensitivity (ECS; low, medium & high), model skill, e.g., JJA/DJF Extratropical Storm Track, circulation and sea surface temperature validations. Informed by these studies, the following CMIP5 datasets were chosen by Nolan and Flanagan (2020) for downscaling over the Irish domain; the UK Met Office's Hadley Centre Global Environment Model version 2 Earth System configuration (HadGEM2-ES) GCM (Collins et al., 2011), the EC-Earth consortium GCM (Hazeleger et al., 2012), the CNRM-CM5 GCM developed by CNRM-GAME (Centre National de Recherches Météorologiques—Groupe d'études de l'Atmosphere Météorologique) and Cerfacs (Centre Européen de Recherche et de Formation Avancée) (Voldoire et al., 2013), the Model for Interdisciplinary Research on Climate (MIROC5) GCM developed by the MIROC5 Japanese research consortium (Watanabe et al., 2010) and the MPI-ESM-LR Earth System Model developed by the Max Planck Institute for Meteorology (Giorgetta et al., 2013).

Nolan and Flanagan (2020) dynamically downscaled the CMIP5 GCM data using the Consortium for Small-scale Modelling – Climate Limited-area Modelling (COSMO-CLM) and Weather Research Forecasting (WRF) RCMs. The RCMs were initially driven by global boundary conditions with the following nesting strategies; CMIP5 global dataset to 50 km to 18 km to ~ 4 km (for lower resolution CMIP5 data) and CMIP5 to 18 km to ~ 4 km (for higher resolution CMIP5 data). The higher resolution data allows sharper estimates of the regional variations of climate projections. The multi-model ensemble approach mentioned above, was employed to address the issue of uncertainty, which can be partially quantified, thus providing a measure of confidence in the predictions. To account for the uncertainty arising from the estimation of future global emission of greenhouse gases, downscaled GCM simulations based on two Representative Concentration Pathways (RCP 4.5 and RCP 8.5) were used herein to simulate the future climate of
Ireland (Moss et al., 2010; van Vuuren et al., 2011). A full description of the model setup, model validations and future projections is given by Nolan et al. (2017) and Nolan and Flanagan (2020).

Nolan and Flanagan (2020) produced simulations for the future period 2041 – 2060 (mid-century) with reference to 1981 – 2000. The differences between the two periods provided a measure of climate change. The simulations produced by Nolan and Flanagan (2020) were the first projected precipitation datasets, at this scale, available for Ireland. These datasets (mid-century projections under RCP 4.5 and RCP 8.5) were the most recent peer-reviewed and published datasets down-scaled to Ireland available for this research. The results from Nolan and Flanagan (2020) show that by mid-century, substantial decreases in precipitation are expected for Ireland in the summer months, with reductions ranging from ≈ 0% to 11% for the RCP 4.5 scenario and from 2% to 17% for the RCP 8.5 scenario. In contrast, precipitation in other seasons, and overall annual precipitation, show relatively small projected increases or decreases in precipitation depending on location and RCP scenario. However, the mid-century precipitation climate is projected to become more variable (as demonstrated by projected increases in the standard deviation of 3-hour precipitation) with substantial projected increases in both dry periods and short but heavy precipitation events (Nolan and Flanagan, 2020).

For this study, three aspects of future precipitation projections were of interest and are summarised in Table 2. The location-specific data for projected changes (increase or decrease) corresponding to each of the 14 WWTPs were obtained through collaboration with the Irish Centre for High-end Computing [authoring institution of Nolan and Flanagan, (2020)], with a focus on three different datasets as described in Table 2 for RCP 4.5 and RCP 8.5. The description of the data used, in addition to the purpose of the data in this paper are also included in Table 2.

Seasonal mid-century projected changes for 2041 – 2060 (Future Period) as compared to 1981 – 2000 (Reference Period) were available for Ireland across 4 seasons (i) December, January and February (DJF – winter); (ii) March, April and May (MAM – spring), (iii) June, July and August (JJA – summer) and (iv) September, October and November (SON – autumn) as shown in Fig. 1 (Nolan and Flanagan, 2020).

<table><caption>**Table 2**
Rainfall projections from (Nolan and Flanagan, 2020) used in this study.</caption><thead><tr><th>Data from (Nolan and Flanagan, 2020)</th><th>Description</th><th>Purpose in this research</th></tr></thead><tbody><tr><td>Projected % change in seasonal mean precipitation (Fig. 1) 33rd and 66th percentiles of the ensemble of RCM projections of mean precipitation for each season<br>Projected % change in the annual number of high and very high precipitation days (Fig. 2)</td><td>Change in seasonal mean precipitation in 2041 – 2060 compared to 1981 – 2000 33rd (P33) and 66th (P66) percentiles are calculated from the ensemble of RCM projections for each season. For each RCM ensemble member, the mean change in precipitation is calculated for the period 2041–2060 with respect to 1981–2000<br>Change in the annual number of high precipitation days (days with > 20 mm/day rainfall) and very high precipitation days (days with > 30 mm/day rainfall) in 2041 – 2060 as compared to 1981 – 2000</td><td>To understand the nature of influent volume variations on a monthly scale as a result of changes in monthly average daily precipitation<br>To help capture the impact of climate projection uncertainty on wastewater influent volumes and analyse variability of the findings of the ensembles<br>To understand the evolution of influent volume characteristics as an effect of extreme events (defined as high and very high precipitation days for this research)</td></tr></tbody></table>

## 2.2. Methodology

### 2.2.1. Estimate of model-based present-climate precipitation characteristics

This study uses monthly-scale models, which link precipitation to influent wastewater volumes based on measured data between 2005 and 2018 (Table 1; Saikia et al., 2022). The projection of mid-century precipitation characteristics were available compared to the reference period of 1981 – 2000, changes in projected precipitation characteristics were analysed with respect to the current time-period (2005 – 2018), in order to match the time period of the available influent volume observations (as described below). Thus the methodology adopted in this study included the following assumptions due to the nature of the available climate change projection data: (i) the seasonal projected data were representative of each month of the season; for example, the value of the projected % change in the winter season was applied to the months of December, January and February, which constitutes that season; (ii) change was assumed to occur linearly from the reference period (1981 – 2000) to the current period (2005 – 2018) and onto the future period (2041 – 2060) (Fig. 3) to enable interpolation of precipitation characteristics in the 2005 – 2018 period; and (iii) the projected wastewater influent volume characteristics did not account for demographic and other changes within the urban areas – thus the impacts are those that may be induced by changes in rainfall patterns only.

It is to be noted that, if the climate change variable of interest is shown to decrease in future, the linear interpolation plot can be produced similarly. As shown in Fig. 3, with the assumption of linear change in precipitation variables between the reference period (1981 – 2000) and the future period (2041 – 2060), projected precipitation changes for the current period could be estimated using linear interpolation. This is discussed in sections 2.2.2 and 2.2.3.

### 2.2.2. Impact of projected changes in mean precipitation on influent volumes

The linear regression model developed by Saikia et al., (2022) that described the relationship between monthly average daily influent volume and monthly average daily precipitation is presented in Equation (1):

$$Q_m = \beta ADP + c + \epsilon \quad (1)$$

where, $Q_m$ represents the observed monthly average daily influent volume, $\beta$ is the coefficient of the predictor variable ADP (monthly average daily precipitation), $c$ is the intercept and $\epsilon$ is the residual. Saikia et al., (2022) found a statistically significant (95% level of confidence) positive relationship between monthly average daily precipitation and monthly average daily influent volume for each WWTP analysed with model errors ranging between 9% and 29%. The projected percentage change in monthly average daily precipitation between the current and future time periods, computed on the basis of linear interpolation (Fig. 3) for emission scenarios RCP 4.5 and RCP 8.5, were incorporated into the existing linear regression models (Equation (1)) for each WWTPs to give a new projected monthly average daily influent volume $Q_{m,p}$ for each time period based on each emission scenario. The methodology to derive future changes in monthly average daily influent volumes as a function of monthly average daily precipitation is depicted in Fig. 4.

In order to analyse the variability in the projected influent volume findings, as mentioned in Table 2, the 33rd (P33) and 66th (P66) percentile values of the projected % change in monthly average daily influent volumes were also estimated using the methodology described above and in Fig. 4.

The impact of climate change on each WWTP was also assessed in terms of the change in the reserve capacity of a WWTP. The reserve capacity of a WWTP was defined as the portion of the peak design capacity which is not used under normal circumstances and can be affected by climate change induced change in precipitation events (Equation (2)).
<figure><img src="image_4.png" /><figcaption>Fig. 1. Spatial variation in the projected change in ensemble mean seasonal precipitation in Ireland in 2041 – 2060 as compared to 1981 – 2000 under a) RCP 8.5 and b) RCP 4.5. The numbers shown on each plot are the minimum and the maximum projected changes in mean seasonal precipitation displayed at their respective locations.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Mid-century projected % change in annual number of days with a) greater than 20 mm/day rainfall and b) greater than 30 mm/day rainfall under RCP 4.5 and RCP 8.5 with reference to 1981 – 2000.</figcaption></figure>

$$RC_{O,m} = PD - Q_m \qquad (2)$$

Where $RC_{O,m}$ is the observed reserve capacity for the month m, PD is the peak design capacity (a fixed value for each WWTP) which is obtained from the Annual Environmental Reports (Environmental Protection Agency, Ireland) and $Q_m$ is the observed average daily influent volume for month m as mentioned in Equation (1). If the projected precipitation changes impact upon the calculated influent volumes, resulting in a new monthly average daily influent volume (defined as $Q_{m,p}$), it will alter the observed reserve capacity for that WWTP. This % change in reserve capacity under RCP 4.5 and RCP 8.5, is expressed as a new metric introduced in this paper which provides an insight into the impact of

climate change, and can be calculated as (Equation (3)):

$$\%changeinreservecapacity = \frac{(PD - Q_{m,p}) - RC_{O,m}}{RC_{O,m}} \times 100 \qquad (3)$$

### 2.2.3. Impact of high and very high precipitation days on influent volumes

In order to assess the impact of extreme events on the influent volume response characteristics, the projected % change in the annual number of days with high (>20 mm/day) and very high precipitation (>30 mm/day) in 2041 – 2060 as compared to the current period were also computed on the basis of the assumption of linear interpolation as shown in Fig. 3.
<figure><img src="image_6.png" /><figcaption>Fig. 3. Estimation of present-climate precipitation characteristics with the assumption of linear change in climate variables.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Methodology depicting the estimation of projected % change in monthly average daily influent volume based on change in monthly average daily precipitation for the 14 WWTPs to 2041 – 2060.</figcaption></figure>

Using the observed daily influent volume data under high and very high precipitation days, the proportion of times a high or very high precipitation day led to influent volumes of greater than 25 %, > 50 %, > 75 % and > 100 % of the peak design capacity was calculated annually for each of the 14 WWTPs. It is to be noted that the daily influent volumes, and thereby the peak design capacities were unique to each WWTP (Table 1). Hence the nature of response characteristics of the influent volumes to the individual precipitation categories were also unique to each of the WWTP. The change in the number of influent flow incidences resulting from high or very high precipitation events that exceed particular level of design capacity, during the future period, would be directly related to the change in the number of occurrences in high and very high precipitation days under the RCP 4.5 and RCP 8.5 scenarios. The methodology for calculating this change is summarised in Fig. 5.

In order to highlight the impact of extreme events on the WWTP influent volumes, the return period of events that would cause exceedance of 100% of peak design capacities were also estimated for each of the WWTPs under RCP 4.5 and RCP 8.5. Any difference between the current and the future return period of events characterized by exceedance of 100% of the peak design capacity would reflect the impact of changes in the number of high and very high precipitation events.

## 3. Results

### 3.1. Response of influent volumes to projected change in monthly mean precipitation

The changes in monthly average daily influent volumes in the future period (2041 – 2060) compared to the current period (somewhere between 2005 and 2018 depending on WWTP) under RCP 8.5 and RCP 4.5 are summarised in Figs. 6 and 7 respectively. The shades in blue denote reductions in influent volume to the WWTP in the future period (2041 – 2060) with respect to the current period, green denotes no change in influent volume and the shades in orange represent increase in influent volume. During the winter months, the high emission RCP 8.5 scenario showed increases in monthly average daily influent volumes (due to changes in precipitation) across all WWTPs, ranging from + 0.3% to + 3%. Under RCP 4.5 the monthly average daily influent volume showed mixed trends in winter months across the 14 WWTPs, with projected increases in monthly average daily influent volumes ranging from + 0.2 % to + 0.6% for 6 WWTPs with the remaining showing decreases ranging from –0.9% to –0.2%. For the spring months, 13 out of the 14 WWTPs showed a decrease in monthly average daily influent volumes, with the exception showing an almost negligible increase of + 0.1% under both RCPs 8.5 and 4.5. As seen in Figs. 6 and 7, these decreases are marginal, ranging from –1% to –0.1% for the 13 WWTPs across the RCP 8.5 and RCP 4.5 scenarios. In the summer months, projected decreases in
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Methodology depicting the estimation of mid-century projected annual number of occurrences exceeding any given proportion of peak design capacity under high and very high precipitation days as compared to current period for each of the 14 WWTPs.</figcaption></figure>

<table><caption>**Fig. 6.** Projected % change in monthly average daily influent volume as compared to current period under RCP 8.5 based on monthly average daily precipitation projections (Equation (1)).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Winter</th><th colspan="3">Spring</th><th colspan="3">Summer</th><th colspan="3">Autumn</th></tr><tr><th>December</th><th>January</th><th>February</th><th>March</th><th>April</th><th>May</th><th>June</th><th>July</th><th>August</th><th>September</th><th>October</th><th>November</th></tr></thead><tbody><tr><td>WWTP 1</td><td>0.5</td><td>0.5</td><td>0.5</td><td>-0.1</td><td>-0.1</td><td>-0.1</td><td>-0.8</td><td>-1.1</td><td>-1.3</td><td>0.7</td><td>0.5</td><td>0.9</td></tr><tr><td>WWTP 2</td><td>1.6</td><td>1.6</td><td>1.2</td><td>-0.6</td><td>-0.5</td><td>-0.6</td><td>-2.1</td><td>-2.1</td><td>-1.8</td><td>0.3</td><td>0.3</td><td>0.3</td></tr><tr><td>WWTP 3</td><td>0.9</td><td>0.8</td><td>0.8</td><td>-1.0</td><td>-0.9</td><td>-0.9</td><td>-3.0</td><td>-3.0</td><td>-3.4</td><td>0.6</td><td>0.6</td><td>0.7</td></tr><tr><td>WWTP 4</td><td>1.4</td><td>1.3</td><td>1.1</td><td>-0.4</td><td>-0.4</td><td>-0.3</td><td>-1.2</td><td>-1.2</td><td>-1.1</td><td>0.1</td><td>0.2</td><td>0.2</td></tr><tr><td>WWTP 5</td><td>1.8</td><td>1.7</td><td>1.6</td><td>-0.7</td><td>-0.5</td><td>-0.6</td><td>-1.7</td><td>-2.1</td><td>-2.1</td><td>0.5</td><td>0.5</td><td>0.6</td></tr><tr><td>WWTP 6</td><td>0.8</td><td>0.9</td><td>0.8</td><td>-0.5</td><td>-0.4</td><td>-0.4</td><td>-1.5</td><td>-1.9</td><td>-2.0</td><td>-0.1</td><td>-0.1</td><td>-0.1</td></tr><tr><td>WWTP 7</td><td>1.2</td><td>1.2</td><td>1.1</td><td>-0.6</td><td>-0.5</td><td>-0.7</td><td>-2.7</td><td>-2.5</td><td>-3.1</td><td>0.7</td><td>0.8</td><td>0.9</td></tr><tr><td>WWTP 8</td><td>0.9</td><td>0.8</td><td>0.6</td><td>-0.1</td><td>-0.1</td><td>-0.1</td><td>-1.0</td><td>-1.0</td><td>-1.0</td><td>0.1</td><td>0.1</td><td>0.1</td></tr><tr><td>WWTP 9</td><td>0.3</td><td>0.3</td><td>0.3</td><td>-0.2</td><td>-0.2</td><td>-0.2</td><td>-1.1</td><td>-0.9</td><td>-1.1</td><td>-0.4</td><td>-0.4</td><td>-0.5</td></tr><tr><td>WWTP 10</td><td>0.7</td><td>0.6</td><td>0.4</td><td>0.1</td><td>0.1</td><td>0.1</td><td>-1.2</td><td>-0.9</td><td>-0.9</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>WWTP 11</td><td>1.4</td><td>1.2</td><td>0.9</td><td>-0.4</td><td>-0.3</td><td>-0.4</td><td>-1.5</td><td>-1.7</td><td>-1.7</td><td>-0.1</td><td>-0.1</td><td>-0.2</td></tr><tr><td>WWTP 12</td><td>3.0</td><td>2.7</td><td>2.3</td><td>-0.2</td><td>-0.2</td><td>-0.2</td><td>-3.1</td><td>-3.2</td><td>-3.0</td><td>0.3</td><td>0.3</td><td>0.4</td></tr><tr><td>WWTP 13</td><td>1.6</td><td>1.6</td><td>1.7</td><td>-0.9</td><td>-0.5</td><td>-0.6</td><td>-2.1</td><td>-1.9</td><td>-2.5</td><td>0.9</td><td>0.8</td><td>0.9</td></tr><tr><td>WWTP 14</td><td>0.6</td><td>0.6</td><td>0.5</td><td>-0.3</td><td>-0.3</td><td>-0.4</td><td>-1.7</td><td>-1.5</td><td>-1.8</td><td>-0.3</td><td>-0.3</td><td>-0.4</td></tr></tbody></table>

monthly average daily influent volumes across all WWTPs under both RCP 8.5 and RCP 4.5, ranged from -3.2% to -0.8% and -2.7% to -0.5% respectively. During the autumn months under RCP 8.5, the majority of the WWTPs (9 out of 14) showed an increase in influent volume (+0.1% to +0.9%), 4 WWTPs showed negligible decreases ranging from -0.4% to -0.1%, and the remaining one showed no change. Under RCP 4.5, a reduction was projected ranging from -1.9% to -0.1% across all WWTPs. Overall, the winter months showed the maximum increases and summer months showed maximum decreases in the monthly average daily influent volumes under both RCP 4.5 and RCP 8.5. These trends in these cases could be expected given the positive correlation between precipitation and influent volume and the projected precipitation datasets in Nolan and Flanagan (2020) that showed a

reduction in projected precipitation during summer months and increase during winter months.

No spatial trend was apparent with respect to the projected change in influent volumes. However, the data used to produce the influent volumes results, i.e., the projected change in seasonal mean precipitation, showed a strong spatial trend as demonstrated in Fig. 1. This strong spatial signal did not translate into the influent volumes findings due to the following reasons: i) The precipitation projections are obtained at the seasonal scale as opposed to the influent volume projections which are estimated at the monthly scale, ii) some of the WWTPs studied in this paper are not precisely located in the areas where a strong spatial trend in seasonal mean precipitation is observed, and iii) in addition to changes in precipitation in future, other variables such as urbanization
<table><caption>**Fig. 7.** Projected % change in monthly average daily influent volume as compared to current period under RCP 4.5 based on monthly average daily precipitation (Equation (1)).</caption><thead><tr><th rowspan="2"></th><th colspan="3">Winter</th><th colspan="3">Spring</th><th colspan="3">Summer</th><th colspan="3">Autumn</th></tr><tr><th>December</th><th>January</th><th>February</th><th>March</th><th>April</th><th>May</th><th>June</th><th>July</th><th>August</th><th>September</th><th>October</th><th>November</th></tr></thead><tbody><tr><td>WWTP 1</td><td>-0.2</td><td>-0.2</td><td>-0.1</td><td>-0.2</td><td>-0.1</td><td>-0.2</td><td>-0.5</td><td>-0.6</td><td>-0.8</td><td>-0.2</td><td>-0.1</td><td>-0.2</td></tr><tr><td>WWTP 2</td><td>0.4</td><td>0.4</td><td>0.3</td><td>-0.4</td><td>-0.3</td><td>-0.3</td><td>-1.3</td><td>-1.3</td><td>-1.2</td><td>-1.2</td><td>-1.2</td><td>-1.2</td></tr><tr><td>WWTP 3</td><td>-0.3</td><td>-0.2</td><td>-0.2</td><td>-0.9</td><td>-0.8</td><td>-0.8</td><td>-1.1</td><td>-1.2</td><td>-1.3</td><td>-0.8</td><td>-0.7</td><td>-0.9</td></tr><tr><td>WWTP 4</td><td>0.3</td><td>0.3</td><td>0.2</td><td>-0.1</td><td>-0.1</td><td>-0.1</td><td>-0.7</td><td>-0.7</td><td>-0.6</td><td>-0.6</td><td>-0.9</td><td>-1.0</td></tr><tr><td>WWTP 5</td><td>0.6</td><td>0.6</td><td>0.5</td><td>-0.8</td><td>-0.6</td><td>-0.7</td><td>-1.2</td><td>-1.5</td><td>-1.5</td><td>-0.8</td><td>-0.8</td><td>-1.0</td></tr><tr><td>WWTP 6</td><td>-0.7</td><td>-0.7</td><td>-0.6</td><td>-0.1</td><td>-0.1</td><td>-0.1</td><td>-1.1</td><td>-1.4</td><td>-1.4</td><td>-0.1</td><td>-0.1</td><td>-0.1</td></tr><tr><td>WWTP 7</td><td>-0.5</td><td>-0.5</td><td>-0.4</td><td>-0.4</td><td>-0.3</td><td>-0.4</td><td>-2.4</td><td>-2.2</td><td>-2.7</td><td>-0.9</td><td>-1.0</td><td>-1.2</td></tr><tr><td>WWTP 8</td><td>0.3</td><td>0.3</td><td>0.2</td><td>-0.1</td><td>-0.1</td><td>-0.2</td><td>-0.5</td><td>-0.5</td><td>-0.6</td><td>-0.6</td><td>-0.6</td><td>-0.7</td></tr><tr><td>WWTP 9</td><td>-0.6</td><td>-0.5</td><td>-0.4</td><td>-0.1</td><td>-0.1</td><td>-0.1</td><td>-0.9</td><td>-0.7</td><td>-0.9</td><td>-0.2</td><td>-0.3</td><td>-0.3</td></tr><tr><td>WWTP 10</td><td>-0.3</td><td>-0.3</td><td>-0.2</td><td>0.1</td><td>0.1</td><td>0.1</td><td>-0.7</td><td>-0.5</td><td>-0.5</td><td>-0.7</td><td>-0.5</td><td>-0.8</td></tr><tr><td>WWTP 11</td><td>-0.9</td><td>-0.7</td><td>-0.6</td><td>-0.2</td><td>-0.2</td><td>-0.2</td><td>-0.6</td><td>-0.7</td><td>-0.7</td><td>-1.4</td><td>-1.2</td><td>-1.6</td></tr><tr><td>WWTP 12</td><td>0.5</td><td>0.4</td><td>0.4</td><td>-0.2</td><td>-0.2</td><td>-0.2</td><td>-1.3</td><td>-1.4</td><td>-1.3</td><td>-1.5</td><td>-1.8</td><td>-1.9</td></tr><tr><td>WWTP 13</td><td>0.3</td><td>0.3</td><td>0.3</td><td>-1.0</td><td>-0.6</td><td>-0.7</td><td>-1.4</td><td>-1.3</td><td>-1.8</td><td>-0.9</td><td>-0.8</td><td>-0.9</td></tr><tr><td>WWTP 14</td><td>-0.6</td><td>-0.5</td><td>-0.5</td><td>-0.4</td><td>-0.3</td><td>-0.4</td><td>-1.6</td><td>-1.5</td><td>-1.8</td><td>-0.5</td><td>-0.4</td><td>-0.6</td></tr></tbody></table>

and demographics which might have an impact on wastewater influent volumes, were not considered. The only exceptions were observed under RCP 8.5, when i) WWTP 12, located in the southeast of Ireland showed the maximum increase in influent volumes during the winter months, coinciding with Winter map shown in Fig. 1 a and ii) WWTPs 2 (southeast), 7 (midlands), 12 (southeast) and 13 (midlands), showed decreases in the summer months coinciding with the Summer map shown in Fig. 1 b.

The 33rd and 66th percentiles of the projected % change in monthly average daily influent volume for each of the 14 WWTPs in future period as compared to the current period were also reported to help analyse the climate projection related uncertainty associated with the future influent volume findings (Figs. 8 and 9).

The variability or spread in the projected findings was determined by the range between the 33rd and the 66th percentile values. As observed from Fig. 8, under RCP 8.5, summer months showed the least variability in monthly average daily influent volumes with spread (difference between P66 and P33) ranging from 0.6% (WWTPs 5 and 9) to 2.8% (WWTP 7). Winter months showed maximum spread in the projected findings ranging from 0.7% (WWTP 4) to 4.5% (WWTP 13). The variability in the spring and autumn months was observed to lie between the variability in the winter and summer months. This might be because extreme events such as storms and dry spells are more likely to occur in winter and summer months resulting in increased uncertainty associated with the projected % change in monthly average daily influent volumes. Similarly, Fig. 9 shows the variability associated with the influent volume projections under RCP 4.5. Under RCP 4.5, a maximum spread of 4.4% was observed for the spring months and minimum spread of 3.1% for autumn months. Variability in the influent volume findings of

summer and winter months lied between that of spring and autumn months.

The full results showing the range of the projected influent volume findings for all the 14 WWTPs are included in Supplementary Information Table SI 1.

In addition to the above results, the % change in reserve capacity of these WWTPs was estimated from Equation (3) for RCP 8.5 and RCP 4.5. Among all the 14 WWTPs, influent volumes of WWTP 13 were observed to be most susceptible to the effects of precipitation changes induced by climate change, with a reduction in reserve capacity of 18.1% under RCP 4.5 and 108.4% under RCP 8.5 for the month of January. In the span of three years of observed data for WWTP 13 from 2016 to 2018 (Table 1), the observed monthly average daily influent volume in the month of January exceeded the peak design capacity for two years in 2016 and in 2018. Hence this WWTP has in recent times occasionally been operating at maximum capacity and the additional of precipitation related changes could provide additional data for any future upgrades. Results for all the other WWTPs are presented in Supplementary Information Table SI 2.

## 3.2. Response of influent volumes to projected change in number of high and very high precipitation days

Table 3 summarises the change in the return period of each WWTP exceeding its peak design capacity (PD) due to high or very high rainfall events under RCP 4.5 and RCP 8.5 (as outlined in Section 2.2.3).

As shown in Table 3, the return periods of events exceeding 100% of peak design capacity in the period 2041–2060 under RCPs 4.5 and 8.5 decreased as compared to the current period across all 7 of the 14 WWTPs for which the return periods could be computed. For the

<figure><img src="image_9.png" /><figcaption>**Fig. 8.** The 33rd and the 66th percentile values of the projected % change in monthly average daily influent volumes based on the projected % change in monthly average daily precipitation in 2041–2060 as compared to current period across all the 14 WWTPs under RCP 8.5.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 9.** The 33rd and the 66th percentile values of the projected % change in monthly average daily influent volumes based on the projected % change in monthly average daily precipitation in 2041 – 2060 as compared to current period across all the 14 WWTPs under RCP 4.5.</figcaption></figure>

<caption>**Table 3**</caption>

Return periods of exceedance of 100% of the peak design capacity under high and very high precipitation days.

<table><thead><tr><th rowspan="2">WWTPs</th><th colspan="3">High precipitation days</th><th colspan="3">Very high precipitation days</th></tr><tr><th>Current (years)</th><th>RCP 4.5 (years)</th><th>RCP 8.5 (years)</th><th>Current (years)</th><th>RCP 4.5 (years)</th><th>RCP 8.5 (years)</th></tr></thead><tbody><tr><td>WWTP 1</td><td>5</td><td>4.6</td><td>4.4</td><td>5</td><td>4.2</td><td>4.1</td></tr><tr><td>WWTP 2</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 3</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 4</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 5</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 6</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 7</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 8</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 9</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 10</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 11</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 12</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 13</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>WWTP 14</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td></tr></tbody></table>

remaining 7 WWTPs, there were no observed occurrences of such events in the current period and hence projected return periods could not be estimated. The maximum decrease in the return period of events that result in exceedances of peak daily WWTP hydraulic design capacities occurred in WWTP9 under RCP8.5 (from one event every 7 years in the current period to an event every 5.67 years by mid-century – an almost 20% decrease in return period). WWTP12 could be considered particularly vulnerable both with a return period of 0.21 years for exceedances in WWTP hydraulic capacity by mid-century due to high rainfall events. However, this is only a slight increase in the current return period for such exceedances. To illustrate the response of the WWTPs to the projected change in the number of high and very high precipitation days, the results for WWTP 11 are shown as an example. The observed exceedance of 25%, 50%, 75% and 100% of the peak design capacity of WWTP 11 under high and very high precipitation conditions is shown in Fig. 10. For example, on 30% of occasions where there is very high precipitation on a given day, the WWTP capacity is exceeded. For high

precipitation days this drops to 20%. Factors such as previous days rainfall, soil moisture content and water table (which can impact infiltration), seasonal issues which influent demographics may all impact, on any given day, whether a particular precipitation event will result in WWTP hydraulic capacity being exceeded.

Fig. 11 shows the location-specific projected % change in the number of high and very high precipitation days from current period to 2041 – 2060 (future period). From this the number of occurrences of events exceeding 25%, 50%, 75% and 100% of the peak design capacity for each of the 14 WWTPs and are provided in Supplementary Information Table SI 3. The results for WWTP 11, as an example are shown in Table 4.

The results indicate that with the projected increase in high and very high precipitation days in Ireland, the operations and maintenance of some WWTPs with CSSs will be challenged as the frequency of days exceeding a given proportion of peak design capacity increases under both the medium and high emission scenarios RCP 4.5 and RCP 8.5. WWTP infrastructure will need to adapt to handling more intense precipitation to avoid occurrence of combined sewer overflows. It is notable that the responses of influent volume at a monthly average daily scale and under extreme precipitation events show different characteristics. While at a monthly level resolution, the changes in influent volumes demonstrate relatively minor increases or decreases, the return period of events exceeding peak design capacity under extreme events decrease significantly during mid-century for many WWTPs.

As climate science progresses and new peer-reviewed climate models are available (e.g. data from the CMIP 6 with high-resolution projections for Ireland are not yet available) the analysis carried out in this research can be repeated to enable more accurate predictions to mid-century and beyond. Given, required for the development and adaptation of infrastructure (e.g. securing government investment and navigating planning processes) medium to long term projections are essential. Alignment of policies such as implementation of water-efficiency measures in new and existing buildings, industry and houses will limit inputs from existing and increased populations and industry activity. The implementation of sustainable drainage systems can be a key step in mitigating the impacts of climate change on wastewater treatment infrastructure. In cases, where combined sewers are prevalent, retroactively separating storm water and wastewater may not be feasible, however, in newer developments, the separation of storm water and wastewater at source can further alleviate future impacts on wastewater treatment plants.

## 4. Conclusions

Precipitation is one of the strongest variables influencing influent volumes in combined sewerage systems. With climate change, changing precipitation patterns, may impact influent volume characteristics and present infrastructure operators and designers with new challenges. To date, research studying how precipitation changes predicted in climate
<figure><img src="image_11.png" /><figcaption>Fig. 10. Observed probability of exceedance curves showing the likelihood, in percentage, of influent volumes exceeding any given proportion of peak design capacity under high and very high precipitation days for WWTP 11.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 11. Projected mid-century (2041–2060) % change in the annual number of occurrences of a) high precipitation days marked by > 20 mm/day and b) very high precipitation days marked by > 30 mm/day across all the 14 WWTPs under RCP 4.5 and RCP 8.5.</figcaption></figure>

change models may impact wastewater influent volumes of combined sewerage systems is limited. This paper analyses the evolution of wastewater influent volume characteristics in the mid-21st century (2041 – 2060) as compared to recent data using (i) precipitation projections under two climate change scenarios, namely RCP 4.5 and RCP 8.5 and (ii) influent data from 14 WWTPs in Ireland.

The outputs of the analyses show that there is an expected increase (~0 – 3 %) in monthly average daily influent volumes during autumn and winter months, particularly under RCP 8.5. On the other hand, during the spring and summer months, some WWTPs may have reduced monthly average daily influent volumes (~0 – 3.5 % less than the reference period). The research presents a new metric to indicate the change in reserve hydraulic capacity of WWTPs from predicted changes to precipitation patterns. In this context most of the WWTPs examined, have sufficient reserve capacity for changes in influent volumes due to project precipitation changes in the period 2041–2060 with only one being particularly vulnerable. This research however does not account for potential changes in population, discharge limits and industrial activity that may also increase wastewater treatment demand. Furthermore, error in underlying models that predict influent flow will also be

subject to error (which may vary between WWTPs).

Projected impacts of extreme events such as high and very high precipitation days on the influent volumes in relation to the peak design capacities of the WWTPs were also quantified. The annual number of occurrences of exceedance of peak daily WWTP hydraulic design capacities was projected to increase in future under both RCP 4.5 and RCP 8.5 for 5 of the WWTPs examined. Nine WWTPs showed no change with WWTP10 showing a potential decrease in the number of annual events where influent flow would exceed WWTP capacity under RCP8.5. The maximum increase in exceedance of peak daily WWTP hydraulic design capacities was modelled to occur in WWTP13 under RCP8.5 (from 1 such events per year currently to 1.2 per year by mid-century). This analysis can support future investment decisions in prioritising which WWTPs are most vulnerable to changes in precipitation and enable informed decision towards establishing resilient wastewater infrastructure under climate change conditions. It can also inform investment and policy decisions which may involve considering the trade-offs involved in separation of storm and sewage collection systems with the potential need to expand existing wastewater treatment infrastructure.

Promising future research in this area could examine additional
<caption>**Table 4**</caption>

Estimation of number of events exceeding any given proportion of peak design capacity of WWTP 11 based on observed probability of exceedance and the projected % change in high and very high precipitation days in 2041 – 2060 as compared to current period under RCP 4.5 and RCP 8.5.

<table><thead><tr><th rowspan="2">Time period</th><th rowspan="2">Annual number of occurrences</th><th colspan="2">> 25% of PD</th><th colspan="2">> 50% of PD</th><th colspan="2">> 75% of PD</th><th colspan="2">> 100% of PD</th></tr><tr><th>Observed PoE</th><th>Annual no. of events exceeding 25% of PD</th><th>Observed PoE</th><th>Annual no. of events exceeding 50% of PD</th><th>Observed PoE</th><th>Annual no. of events exceeding 75% of PD</th><th>Observed PoE</th><th>Annual no. of events exceeding 100% of PD</th></tr></thead><tbody><tr><td colspan="10"><strong>High precipitation days</strong></td></tr><tr><td>Current</td><td>5.5</td><td>100%</td><td>5.5</td><td>77%</td><td>4.25</td><td>41%</td><td>2.25</td><td>18%</td><td>1</td></tr><tr><td>2041 – 2060 (RCP 4.5)</td><td>5.6</td><td></td><td>5.6</td><td></td><td>4.33</td><td></td><td>2.29</td><td></td><td>1.02</td></tr><tr><td>2041 – 2060 (RCP 8.5)</td><td>5.7</td><td></td><td>5.7</td><td></td><td>4.4</td><td></td><td>2.33</td><td></td><td>1.04</td></tr><tr><td colspan="10"><strong>Very high precipitation days</strong></td></tr><tr><td>Current</td><td>1.4</td><td>100%</td><td>1.4</td><td>86%</td><td>1.2</td><td>71%</td><td>1</td><td>29%</td><td>0.4</td></tr><tr><td>2041 – 2060 (RCP 4.5)</td><td>1.47</td><td></td><td>1.47</td><td></td><td>1.26</td><td></td><td>1.05</td><td></td><td>0.42</td></tr><tr><td>2041 – 2060 (RCP 8.5)</td><td>1.48</td><td></td><td>1.48</td><td></td><td>1.27</td><td></td><td>1.06</td><td></td><td>0.42</td></tr></tbody></table>

temporally dynamic parameters such as urbanization and demographics. In examining these, and with new datasets, underlying models that are used to predict influent flow can be improved (and calibrated against recent datasets). Given the ever-changing nature of climate projections, future work should include analysis of the impacts of projected precipitation datasets downscaled from CMIP 6 climate models as they become available for various countries.

### CRediT authorship contribution statement

**Sukanya D. Saikia:** Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. **Paraic Ryan:** Investigation, Methodology, Supervision, Validation, Writing – review & editing. **Siegmund Nuyts:** Formal analysis, Investigation, Methodology, Writing – original draft. **Paul Nolan:** Data curation, Methodology, Validation, Writing – review & editing. **Eoghan Clifford:** Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing – review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

The authors do not have permission to share data.

### Acknowledgments

This research has received financial support from Science Foundation Ireland Strategic Partnership Programme Grant Numbers: SFI/21/3756 (NexSys) and SFI/15/SPP/E3125 (ESIPP). The opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Science Foundation Ireland. The author(s) would like to thank Irish Water and Met Eireann for the data used in this paper.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.cliser.2024.100511.

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