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Article

Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model

1
Center for Sustainable Energy Technology, Hochschule für Technik Stuttgart, 70174 Stuttgart, Germany
2
Center for Geodesy and Geoinformatics, Hochschule für Technik Stuttgart, 70174 Stuttgart, Germany
3
Department of Bioenergy, Helmholz Center for Environmental Research, 04247 Leipzig, Germany
4
Chair of Bioenergy System, Faculty of Economic Sciences, University of Leipzig, 04109 Leiopzig, Germany
5
Unit Bioenergy System, Deutsches Biomasseforschungszentrum GmbH, 04347 Leiopzig, Germany
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 642; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110642
Received: 20 September 2020 / Revised: 13 October 2020 / Accepted: 23 October 2020 / Published: 28 October 2020
(This article belongs to the Special Issue The Applications of 3D-City Models in Urban Studies)
Humans’ activities in urban areas put a strain on local water resources. This paper introduces a method to accurately simulate the stress urban water demand in Germany puts on local resources on a single-building level, and scalable to regional levels without loss of detail. The method integrates building geometry, building physics, census, socio-economy and meteorological information to provide a general approach to assessing water demands that also overcome obstacles on data aggregation and processing imposed by data privacy guidelines. Three German counties were used as validation cases to prove the feasibility of the presented approach: on average, per capita water demand and aggregated water demand deviates by less than 7% from real demand data. 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%. View Full-Text
Keywords: CityGML (Geography Markup Language); occupant estimation; urban water demand; urban energy and water system modelling CityGML (Geography Markup Language); occupant estimation; urban water demand; urban energy and water system modelling
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MDPI and ACS Style

Bao, K.; Padsala, R.; Thrän, D.; Schröter, B. Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model. ISPRS Int. J. Geo-Inf. 2020, 9, 642. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110642

AMA Style

Bao K, Padsala R, Thrän D, Schröter B. Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model. ISPRS International Journal of Geo-Information. 2020; 9(11):642. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110642

Chicago/Turabian Style

Bao, Keyu, Rushikesh Padsala, Daniela Thrän, and Bastian Schröter. 2020. "Urban Water Demand Simulation in Residential and Non-Residential Buildings Based on a CityGML Data Model" ISPRS International Journal of Geo-Information 9, no. 11: 642. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110642

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