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The LIS-platform, a BIM-based software platform, is being developed to monitor various aspects of buildings. This platform provides a comprehensive overview of structu...
Climate change will lead to more extreme precipitation events for many cities in the world. In support of climate adaptation planning, there is a need for a flood hazard analysis at local scale before and after climate scenarios. Such analysis has, however, difficulties for several reasons: climate model outputs are available at a scale that is too coarse for urban hydrological impact analysis; uncertainties in the future climate projections have to be accounted for; detailed 2-D urban flood modelling, mapping and analysis is time and resource demanding; requires high resolution data that is not always available; hydrologists and water system experts do not have the knowledge and/or capacity to deal with climate scenario development.
See more information about this level and the TRL and SRL levels.
The service consists of the following steps: (1) Statistical downscaling of the results of a large ensemble of available global and regional climate models, in order to obtain climate scenarios on extreme precipitation for different time scales and return periods T at the local scale of a specific city or municipality; (2) Rainfall intensity – duration – frequency (IDF) curves and synthetic design storms (storms for given return periods) obtained based on local time series of precipitation data; (3) IDF curves and T-year storms after climate scenarios; (4) High resolution urban pluvial flood model set up based on dual drainage approach: 1-D sewer network model integrated with a 2-D surface inundation model; (5) Detailed 2-D pluvial flood hazard maps obtained for different return periods before and after climate change, and taking climate change uncertainties into account.
Limitations/conditions under which this innovation does not work or is less effective
Added value
The approach can be based on detailed data, leading to a higher accuracy of the pluvial flood impact results, but does also work in data-scarce environments based on proxies for data lacking on the underground sewer network and/or subdaily rainfall data.
Climate change will lead to more extreme precipitation events for many cities in the world. In support of climate adaptation planning, there is a need for a flood hazard analysis at local scale before and after climate scenarios. Such analysis has, however, difficulties for several reasons: climate model outputs are available at a scale that is too coarse for urban hydrological impact analysis; uncertainties in the future climate projections have to be accounted for; detailed 2-D urban flood modelling, mapping and analysis is time and resource demanding; requires high resolution data that is not always available; hydrologists and water system experts do not have the knowledge and/or capacity to deal with climate scenario development.
Developed by KU Leuven
Service that provides information about the extent of pluvial flood hazard for different probabilities of occurrence and at high resolution (street level) in the city, before and after climate change.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
The service consists of the following steps: (1) Statistical downscaling of the results of a large ensemble of available global and regional climate models, in order to obtain climate scenarios on extreme precipitation for different time scales and return periods T at the local scale of a specific city or municipality; (2) Rainfall intensity – duration – frequency (IDF) curves and synthetic design storms (storms for given return periods) obtained based on local time series of precipitation data; (3) IDF curves and T-year storms after climate scenarios; (4) High resolution urban pluvial flood model set up based on dual drainage approach: 1-D sewer network model integrated with a 2-D surface inundation model; (5) Detailed 2-D pluvial flood hazard maps obtained for different return periods before and after climate change, and taking climate change uncertainties into account.
Limitations/conditions under which this innovation does not work or is less effective
Added value
The approach can be based on detailed data, leading to a higher accuracy of the pluvial flood impact results, but does also work in data-scarce environments based on proxies for data lacking on the underground sewer network and/or subdaily rainfall data.
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