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Details zur Publikation

Referenztyp Zeitschriften
DOI / URL Link
Creative Commons Lizenz creative commons licence
Titel (primär) Urban water demand simulation in residential and non-residential buildings based on a CityGML data model
Autor Bao, K.; Padsala, R.; Thrän, D.; Schröter, B.;
Journal / Serie ISPRS International Journal of Geo-Information
Erscheinungsjahr 2020
Department BIOENERGIE;
Band/Volume 9
Heft 11
Sprache englisch;
POF III (gesamt) Y11;
Keywords CityGML (Geography Markup Language); occupant estimation; urban water demand; urban energy and water system modelling
Abstract 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%.
ID 23842
dauerhafte UFZ-Verlinkung https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23842
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. 9 (11), art. 642