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© Depositphotos.com | UTBP | New York | IN-SOURCE is based on three case studies in Ludwigsburg (Germany), New York (USA) and Vienna (Austria) and examines scenarios for integratedCO2-neutral and sustainable infrastructure planning, which can be transferred to other cities as prototype solutions. Background: Almost 80% of the world’s population is threatened by water shortage and an uncertain water supply.

How much drinking water do the cities need?

For the first time, a HFT research team has simulated the drinking water requirements of residential and non-residential buildings using a 3D data model for German cities and regions. Geometric building data as well as meteorological and socio-economic data were integrated. The team showed that the approach is feasible and resilient: the deviation from real drinking water consumption was less than seven percent.

The research team has thus solved a methodological problem: Until now, there was no tool that could simulate the water demand for all types of buildings, such as residential, office, school and industrial buildings, on the basis of a special 3D building model, the CityGML model.

The study in the “International Journal of Geo-Information” was developed within the IN-SOURCE project. It was funded by the German Federal Ministry of Education and Research and the European Horizon 2020 programme.

The aim is to develop digital solutions to analyse the vital areas of food, water and energy in different regions. Overall, urban regions around the world are facing major challenges in the future supply of food, water and energy, also in connection with climate change.

Drinking water requirements in individual buildings can be analyzed

“With the new approach, we can simulate and analyze water requirements at the level of individual buildings. Existing models tend to focus more on simulating entire communities,” explain Prof. Dr. Bastian Schröter, HFT Professor of Energy Technology, and Keyu Bao, doctoral student at the Center for Sustainable Energy Technology (zafh.net) at the HFT. The team used the existing simulation tool SimStadt, which produces energy analyses, and has now extended this tool with the function of analysing drinking water demand.

Local authorities in particular can benefit from such analyses: “Accurate modelling of urban water demand, covering residential and non-residential areas, can help local governments or infrastructure planners to better design local water supply infrastructures and improve the management of local resources,” the researchers explain. The tool is also ideal for simulating the water demand in a future construction area.

The water demand can be simulated on different scales: at the level of the building, the district, the city or the county. The buildings are the reference point. Instead of using the average per capita value of the water demand from superordinate scales, e.g. from a federal state, the new approach can now take into account data such as the local residential water demand per capita as well as the local climatic conditions and socio-economic factors in the area.

Feasibility test with data from three German regions

The research team simulated scenarios in three German districts with different climatic and socio-economic conditions to test the feasibility, accuracy and resilience of the new water demand workflow: Ludwigsburg. Cologne and Ilm district. The county of Ludwigsburg represents a typical southern German suburban area, the city of Cologne a densely populated urban area and the county of Ilm-Kreis a more rural region.

Every person needs 90 to 150 litres per day, depending on the federal state, age, type of house and income in residential buildings. For non-residential buildings, on the other hand, water consumption varies extremely between 0.05 cubic metres per square metre per year and 96 cubic metres per square metre per year.

Various scenarios were also analysed in the municipality of Rainau in the Ostalbkreis in Baden-Württemberg. In particular, the needs in terms of water prices, the ageing of the population and climate change were examined. The results: It was shown that the water demand per person varies. The water demand per person increases by 3.6 % if higher age and higher temperatures due to climate change are included in the calculation. This may be due to the fact that older people are more likely to be at home and have a garden to water. The warmer the temperatures, the more water is needed. In another future scenario, per capita water consumption is expected to decrease as water prices rise.

Industrial demand for water increases by 46% overall due to economic development measured in terms of gross domestic product.

Simulation can also be used worldwide

The determination of water demand with the CityGML model includes individual buildings as a basic element for calculation. Included are building types (residential, hotel, hospital, education, office, retail, sports and exhibition hall as well as industrial buildings), year of construction and building geometry. The building geometry is used to calculate the floor area. In combination with the year of construction, the number of residents in residential buildings can be simulated. The water demand value per area or per person is determined on the basis of building types, climate and socio-economic factors such as income.

The simulation can also be applied to other regions worldwide, provided that the 3D building model CityGML serves as a basis and the corresponding statistical data are available.

IN-SOURCE is based on three case studies in Ludwigsburg (Germany), New York (USA) and Vienna (Austria) and examines scenarios for integratedCO2-neutral and sustainable infrastructure planning, which can be transferred to other cities as prototype solutions. Background: Almost 80% of the world’s population is threatened by water shortage and an uncertain water supply.

  • Keyu Bao, Rushikesh Padsala, Daniela Thrän, Bastian Schröter: SPRS International Journal of Geo-Information. 2020, 9, 657. „Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGMLData Model“ | Link: https://www.mdpi.com/2220-9964/9/11/642/htm
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Hochschule für Technik – Stuttgart 2021

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