**3. Role of Climate Modelling Outputs in Stormwater Quality Modelling**

Climate modelling entails the mathematical formulation and integration of energy and matter transfer on the land, atmosphere and ocean. The outcomes of climate models (general circulation models (GCMs)) indicate the probability of a given area having relatively warmer/cooler or wetter/drier climate, which is different from typical weather reports of daily wet/dry conditions.

With the objective of broadening the understanding of climate change in the historical, current, and future contexts, the Coupled Model Intercomparison Project (CMIP) brings together multiple climate modelling groups from across the world. The CMIP makes standardised output of climate models available to other researchers who are involved in climate change impact assessments. Detailed information about CMIP phases can be accessed via World Climate Research Program (WCRP) and the Program for Climate Model Diagnosis and Intercomparison (PCMDI) of the US Department of Energy. Meanwhile, the most recent phase, CMIP6, provides information that may be useful for the mathematical formulation of stormwater pollution processes under the effects of climate change.

CMIP6 addresses three issues broadly related to the Earth's climate system under 12 scientific themes (clouds/circulation, regional phenomena, ocean/land/ice, impacts, scenarios, decadal prediction, geo-engineering, land use, carbon cycle, chemistry/aerosols, characterising forcing, and paleo) [52]:

**Issue 1: Response of Earth's climate system to radiative forcing**: The radiative forcing (difference between energy absorbed by and radiated back from the Earth) changes due to natural/anthropogenic emissions. Consequently, land, ocean and atmospheric temperatures could change, and in turn, variables such as precipitation could also change.

Stormwater quality perspective: Changes to dry and wet weather patterns are the two key responses that need to be incorporated into the mathematical formulations of stormwater pollution processes such as build-up and wash-off. Fundamental changes to the structure of current mathematical functions are necessary due to changes to pollution processes (such as lengthy equilibrium periods during build-up and intense first flush events that lasts for shorter periods during wash-off, see Figures 2 and 3).

**Issue 2: Systematic model biases:** Climate models are expected to replicate complex climate processes, which can exhibit substantial inherent variability, particularly at the regional scale. As such, model biases are likely to arise, leading to over/under estimations of future climate.

Stormwater quality perspective: Current stormwater models already have deficiencies relating to model structure, input and calibration data, and model parameters specific to underlying processes such as coefficients of pollutant build-up and wash-off [53,54]. Understanding systematic biases in climate models and minimising their effects are necessary when using climate projections for stormwater quality modelling.

**Issue 3: Assessment of future changes in climate under climate variability, predictability and uncertainty in future scenarios:** The future climate scenarios are developed based on the expected changes to socio-economic systems that contribute to emissions. These scenarios are associated with uncertainty, which are accounted for when undertaking climate impact assessments.

Stormwater quality perspective: Stormwater pollution processes can be influenced by the variability in climate processes (precipitation and dry period), which leads to creating uncertainty in stormwater quality projections. Therefore, it is also necessary to address the effects of such uncertainty when projecting stormwater quality into the future.

Accordingly, five themes can be proposed (out of the 12 listed above) that have potential implications for stormwater quality modelling. These include cloud/circulation, regional phenomena, land use, scenarios and impacts. The outputs of the model intercomparison projects (MIPs) under clouds/circulation theme [55,56] are a guide to identifying the appropriate climate models that produce precipitation/dry period projections, which can contribute to stormwater quality modelling (see Figure 4).

**Figure 4.** Framework for incorporating various climate modelling outputs of Model Intercomparison Projects (MIPs) under the proposed five themes into specific elements of stormwater quality modelling. Note: CFMIP—Cloud Feedback Model Intercomparison Project [55]; DynVarMIP—Dynamics and Variability Model Intercomparison Project [56]; GMMIP—Global Monsoons Model Inter-comparison Project [57]; HighResMIP—High Resolution Model Intercomparison Project [58]; LUMIP—Land Use Model Intercomparison Project [59]; ScenarioMIP—Scenario Model Intercomparison Project [60]; VIACS—Vulnerability, Impacts, Adaptation and Climate Services [61]; CORDEX—Coordinated Regional Downscaling Experiment [62].

The outputs of the MIPs under Regional Phenomena theme [57,58] provide key information on the variability in monsoon system, which could substantially alter the length of dry periods (mega droughts) and result in heavy storms and large flood events. These significantly exacerbate the quality of stormwater, while flooding contributes to disperse pollutants across multiple areas.

In a recent study conducted in the North Australia region [13], the likely impacts of the climate change-driven variations in dry and wet weather events have been demonstrated. Due to an increase in atmospheric temperature by 1.5 ◦C (around year 2030–2052) above pre-industrial levels, North Australia region is projected to experience (compared to year 2007) dry periods that are extended by 4.72 days (range of variation at RCP 8.5 warming scenario: 3.67–11.93 days) and increase in rainfall by 0.31 mm (range of variation at RCP 8.5 warming scenario: 0–4.50 mm). These projections are set to worsen due to an additional 0.5 ◦C increase in temperature (i.e., 2 ◦C above pre-industrial levels), such that dry periods in North Australia can be 7.34 days longer (range of variation at RCP 8.5 warming scenario: 5.11–17.96 days) and the region can expect to receive 1.40 mm more rainfall (range of variation at RCP 8.5 warming scenario: 0–5.58 mm). As a result of these projected changes in dry and wet

weather, Wijesiri et al. [13] estimated (compared to year 2007) a more than 90% increase in the build-up of particle-bound toxicants such as heavy metals and nearly a 50% increase in those pollutants in stormwater runoff.

Given that extreme weather events mostly occur at smaller spatial scales, reliable climate projections under this Regional Phenomena theme could be crucial for stormwater quality modelling (see Figure 4), as stormwater quality exhibits substantial variability at catchment scale as well as between geographic regions [12].

Furthermore, land use is a key determinant of the quantity and type of pollutants released into stormwater runoff, although the effects of land use change have not been adequately accounted for in current stormwater models [63]. On the other hand, anthropogenic activities specific to different types of land use contribute to the emission of aerosols and greenhouse gases. This has implications to the climate such as extreme droughts and heavy precipitation [64]. The outputs of the MIPs under Land Use theme [59] play a key role in influencing the relationships between spatial and temporal changes in land use/land cover that contribute to both, climate change (and its effects on dry and wet weather) and pollutant accumulation on catchment surfaces (see Figure 4).

Various scenarios of how physical and human systems are expected to change in the future and their impacts on the climate system are important for projecting future climate change. The MIPs outputs under scenarios theme [60] provides climate projections of future scenarios of emissions and land use change. These scenarios are developed based on future pathways of inherently uncertain socio-economic developments, which also contribute to stormwater pollution, including pollutant generation, deposition and subsequent re-distribution during dry weather periods and wash-off during rainfall events. Therefore, the future climate scenarios could be the basis for projecting stormwater quality into the future (see Figure 4) and in turn for designing robust measures to enhance stormwater reuse.

Regarding climate impact assessment, usually a two-way dialogue is established between the climate modelling community and those who expect to utilise model outcomes for assessing the impacts on various human-environmental systems [61]. Further, one of the crucial elements of impact assessment is downscaling of regional climate projections. This plays a key role in stormwater quality modelling due to the regional variability in the factors that influence pollutant generation and subsequent distribution during dry and wet weather events. Accordingly, the outputs of MIPs under the impacts theme [62] provide a common framework to produce downscaled regional climate projections and to assess associated uncertainties, which can be considered for stormwater quality modelling to ensure the robustness in the projection of regional scale stormwater quality (see Figure 4).
