Although Austria is a water-rich country, impacts of climate change on water supply are already noticeable. Some regions were affected by water scarcity in recent years. Due to climate change, an increase in peak water demand is expected in the future. Therefore, water demand prediction models that include climate indices are of interest. In this paper, we present a general multiple linear regression (GMLR) model that can be applied to selected study sites. We compared the performance of the GMLR model with different modeling approaches, i.e., stepwise multiple linear regression, support vector regression, random forest regression and a neural network approach. All models were trained with water demand and weather data reaching back several years and tested with the last available observation year. The applied modeling approaches achieved a similar performance. As a second step, the GMLR model was used to estimate the peak water demands for the time period 2025–2050. For the future water demand estimate, 16 different climate projections were used. These climate projections represent the worst-case climate change scenario (RCP 8.5). The expected increase in peak water demand could be confirmed with the modeling approach. An increase in peak water demand by 3.5% compared to the reference period was estimated.

Austria’s water utilities are facing new challenges due to advancing climate change. In recent years, changes in water demand have been observed. Water demand forecast models are required to assess these changes and react to them in a sustainable way. In this study, an existing modeling approach was extended with new climate indices. The multiple linear regression model was applied to different study sites. The model was trained with a training dataset and validated with a test dataset. The performance of the model was assessed using common parameters, such as the mean absolute percentage error. In a further step, the modeling approach was applied to climate projections to estimate the change in water demand for three different representative concentration pathways (RCPs). The change in water demand due to population growth was then considered and combined with the change due to climate change. RCP2.6 shows an average 14% increase in water demand for the period 2051–2070, with climate change (average increase of 0.7%) playing a negligible role. For RCP4.5, an increase of 16% is predicted, while the highest increase of 19% is observed in RCP8.5. Population growth is responsible for most of the increase.

To design sustainable water supply systems, utilities need to determine the average per capita water consumption along with factors that influence it. However, data on residents per building are often unavailable. This study introduces a GIS-based approach to estimate residents per building despite incomplete data. Building height and size were determined and spatially linked to water consumption data. These values were used to calculate the living space and the number of residents per building. A comparison with actual residents from Study Site 1 showed high agreement (correlation of 0.7), confirming the method’s reliability. With an accuracy of ±1 person, 66% of single-family homes and 17% of apartment buildings were correctly estimated. The analysis further showed that single-family home residents consume about 130 L per day, while those in apartment buildings use 105–114 L per day. The presence of swimming pools was identified as a significant factor. A resident in a single-family home with a pool consumes around 153 L per day, while one without a pool uses around 126 L per day. This approach provides a method to estimate per capita water consumption, enabling water utilities to plan sustainably, even in regions with limited register data.

Climate and demographic changes force water utilities to adapt to shifts in both water demand and water availability. The web-based EWA tool supports Austrian water utilities in addressing these problems. Based on water demand and availability forecasts from 2025 to 2055, it encourages robust planning by calculating different performance indicators based on hydraulic models. It provides a platform for assessing water distribution systems, integrating forecast and operational scenarios, and performance indicators. Users can assess long-term impacts, adjust planning approaches, and visualize results through specific graphs. Tutorials help users navigate the tool, while gamified challenges aim at testing problem-solving skills and motivating users to improve their performance and raise awareness. The EWA tool facilitates resilient and forward-looking planning, which is critical to adapting to climate change and demographic shifts while ensuring sustainable water resource management.

Dealing with the effects of urbanization and climate change has become a central task in urban water management. In this paper we present the web-based water distribution system modelling tool EWA, which was developed with the purpose to raise awareness for some of these tasks. We have linked gamification elements with modelling and have involved potential users of the tool into tool-development, following a participatory research approach. In EWA we provide tasks and challenges that have to be fulfilled by the user to guarantee a reliable water supply in future. Therefore, the UI provides a map view where model components can be added, removed, selected and are visualized. Forms are provided to edit selected components. For hydraulic modelling we use Epanet 2.2 (Rossmann, 2020) with OOPNet (Steffelbauer & Fuchs-Hanusch, 2015). Water demand prediction is based on regression models incorporating climate change projections and population development for Austria. To provide an overview of system performance, indicators like the resilience index (Creaco et al.,2016) or the number of unsatisfied nodes are used. The change of these indicators over time is visualized in graphs. To follow a participatory approach, we are testing the usability of the tool with a group of engineers from governmental institutions, water utilities and members of the Game Lab and the Institute of Urban Water Management at TU Graz. In these tests the participants have to fulfil challenges related to a) basics, like editing parameter of an existing hydraulic system and adding new components to the system b) add additional components representing an urban development area and c) simulate and interpret system performance under climate change for different conditions like component failure. Such challenges can be generated by using the “challenge editor”, which was created by a master student of the TU Graz Game Lab, mainly to allow quick and flexible adaption of challenges defined by the urban water management team. The usability tests with water engineers have shown that the “challenge-editor“ is also of interest for staff training at water utilities. Hence, we decided to put more effort in the design of the “challenge-editor” in a next step. Further a generation gap in user performance was identified, mainly in context of the time to fulfil the given challenges but also in preferences for structure and appearance of the UI. From this we derived to focus the adaptions of the UI on the feedback from the younger participants which we assumed to be our major target group.Acknowledgements: EWA is funded by the Federal Ministry for Agriculture, Forestry, Regions and Water Management of the Republic of AustriaCreaco, E., Franchini, M., & Todini, E. (2016). Generalized Resilience and Failure Indices for use with Pressure-Driven Modeling and Leakage. Journal of Water Resources Planning and Management, 142(8), Art. 8. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000656 Rossman, L., Woo, H., Tryby, M., Shang, F., Janke, R., & Haxton, T. (2020). EPANET 2.2 User Manual. U.S. Environmental Protection Agency.Steffelbauer, D., Fuchs-Hanusch, D., 2015. OOPNET: an object-oriented EPANET in Python. Procedia Eng. 119. 710e719 https://doi.org/10.1016/j.proeng.2015.08.924.

Due to the climate change, it is expected that there will be longer dry and hot episodes in the future in Central Europe. As a result, temporary water shortages are to be expected in certain parts of Austria. Due to these changes, it is assumed that the future water demand will increase, caused by a change in consumption behavior and the increase of garden irrigations. Furthermore, an increasing population is expected, which may also lead to a shortage, especially in small supply areas.For water supply companies it is especially important to know the future change in water demand in order to be able to adapt to these changes. Therefore, a water demand forecasting model is derived in this study. In a first step, this study analyses the change in water demand in recent years and the relationship between water demand and climate indices. Furthermore, the change of the future water demand depending on the different climate change scenarios (RCP 2.6, RCP4.5 and RCP8.5) is estimated. For this purpose, different modeling approaches (e.g. multiple linear regression, random forest,…) were tested and a suitable approach is selected. The water demand forecasting model is trained and tested with water demand and weather data reaching back several years. To estimate the future change in water demand, the model is applied to the climate projections and the change between the selected reference period and the two future periods is calculated. The change in demographic development is considered in the last step.So far, we found that for the selected study site peak water demand will increase by an average between 1.5% and 5.5%, depending on the different climate change scenario for the period 2051-2070 compared with the reference period (2001-2020). It was also determined that demographic development is responsible for the majority of the increase in water demand.Acknowledgements: The presented research is funded by the Federal Ministry for Agriculture, Forestry, Regions and Water Management of the Republic of Austria