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# IN-SOURCE: Food-Water-Energy Simulation and Visualization

# Urban Water Demand Simulation

by Keyu Bao

Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%.

Water plays a fundamental role in sustaining human life and the Earth's ecosystems. However, almost 80% of the world's population face a high-level threat of water security, and the water stress will increase between today and the 2050s in around 70% of the world’s river basins. A precise modeling of urban water demands, covering residential and non-residential areas, can help local governments or infrastructure planners to better design local water supply infrastructures and improve management of local resource potentials.

So far there is no tool that allows one to simulate water demand for all building types (i.e., residential, office, school, industry, etc.), based on 3D building model (CityGML). With this method it is possible to simulate the water demand at different scales, e.g., city quarter, city or county, with a flexible boundary, e.g., nearby houses in different administration districts can be simulated together. This method is ideal for simulating water demand in to-be-built area. Instead of applying average residential water demand per capita value from higher scales, e.g., federal state, this method determines per capita residential water demand value from local climate and socio-economic factors.

The water demand assessment method is based on a geoinformatics CityGML model with individual buildings as the base element to calculate the water demand of each household/building. CityGML model contents the information including: building types (residential, hotel, hospital, education, office, retail, sport hall, exhibition hall and industry), year of construction and building geometry. Building geometry is used to calculate floor area. Combined with year of construction, occupant number in residential buildings can be further simulated. Water demand value per area or per person is generated based on building types, climate and socio-economic factors, such as income. Therefore, the water demand in each building can be emitted. Water demands in non-residential buildings vary dramatically from 0.05 m3 per square meter per year in exhibition hall to 96 m3 per square meter per year. Each person needs around 90 to 150 liter per day depending on the federal state, age, house type, income and so on.

As mentioned earlier, three German counties with different climatic and socio-economic conditions were used to validate the feasibility, accuracy and resilience of the established water demand workflow. The city of Cologne represents a densely populated urban area, while the county of Ludwigsburg represents a typical southern suburban area and the county of Ilm-Kreis a more rural region that is in many dimensions close to the average German county outside of large metropolitan areas. In terms of water demand per capita in residential buildings, numbers calculated by the newly established workflow deviate by between 1% and 7% from the statistical values.

Scenarios applied to a case region Ludwigsburg in Germany, which takes the increment of water price, aging of the population and the climate change into account, show that the residential water demand has the change of −2%, +7% and −0.4% respectively. The industrial water demand increases by 46% due to the development of economy indicated by GDP per capita. The rise of precipitation and temperature raise the water demand in non-residential buildings (excluding industry) of 1%.

The detailed descripotion and findings of this workflow can be find in the following open-sourced paper, which are funded by IN-Source project.

- Bao K, Padsala R, Thrän D, Schröter B (2020). Urban Water Demand Simulation in Residential and Non-Residential Buildings based on a CityGML Data Model. ISPRS Int. J. Geo-Inf. 2020, 9(11), 642; https://doi.org/10.3390/ijgi9110642