amount of water in severe rain events and the
intensity of the flow call for changing the
approach to water management and sewer renovation and design.
Designing Sewer Systems
Rain
As a basis for modelling, it is essential to have data
on how much rainwater or other (waste) water
needs handling. There are different approaches to
collect or obtain relevant information on rainwater.
In Denmark, rainfall is registered via rain gauges in
a national grid since the 1970s [9]. Rain gauges
measure the rain event volume, intensity, and duration of the rain at any given location. Long-term
monitoring of rain events by measuring the duration, intensity, and volume has generated a historical knowledge base of so-called rain events.
The amount of rain expected to precipitate and
drain to the sewer system needs to be known. It
also needs to be assessed whether the runoff from
the rain will be handled through a separate network or together with wastewater. Determining
the amount of rainfall is generally via utilizing
historical rain event data collected from local
rain gauges or climates/geographies that closely
match the catchment. Data on the frequency of
intensity and volume of the rain is the basis for
choosing relevant rain events for input data for the
modelling [10].
Recent developments on measuring frequency
and intensity of rain use information from radar
[2]. Radar measurements make it possible to measure the movement and extent of the rainfall over
an area and thus provide more information than a
rainfall measurement in a single point. When
modelling the impact of the rainfall in a specific
area, the understanding of the movement across
the area may be of significance since this might
increase the effect of the rainfall on the water
runoff systems. The usage of this type of data
enhances the ability to forecast certain events
and their impact and thus the ability of water
managers and engineers to best manage and control flow into the sewer system [2].
Modelling Surface Runoff
The approach on designing of sewer systems for
effective and efficient surface water management
requires advanced tools for analysis. Developments of advanced computer models for analyzing
water transport and flow on surfaces as well as in
the sewers have been very relevant. MIKE
URBAN is such a tool [11]. MIKE URBAN can
model an urban drainage system (urban drainage
modelling) as well as water transport on the surface
of a city. The model is an advanced model using
comprehensive dynamic hydraulic modelling [12].
Primary data for a model is information on
catchment areas size, slope, shape, and imperviousness shape. The model uses the data as characteristic values or input information for the
modelling of water flows from ground to sewer
system. The data are necessary parameterizations
to the dynamic modelling of water flow.
The development in urban areas has been
toward restricting percolation of surface water
into the ground and thus increases flow over the
ground. The decreased imperviousness of the surface cover is due to extended coverage of urban
terrain. The decreasing imperviousness potentially results in urban areas becoming more vulnerable to flooding from severe rain both due to
the change in weather and changes in imperviousness and percentage non-percolating surface area.
For effective modelling, it is therefore essential
for urban surface water management to be able to
estimate and map impervious surfaces in catchments efficiently. Data on impervious surfaces
and by association, runoff, is an indicator. The
data on imperviousness is an essential input factor
in hydrological models and urban drainage
modelling. To obtain imperviousness data for
urban drainage modelling, a comprehensive collection of information and data, as well as characterization of the examined urban area, must be
done. Traditionally, data from digital or other
maps are the basis for the calculation.
Conventional mapping techniques in complex
urban areas are often expensive and timeconsuming, particularly when the area in question
is large. Therefore, the development of more automated analysis and classification based on, e.g.,
aerial photos or satellite images, i.e., remote
Urban Drainage Modelling for Management of Urban Surface Water
231
intensity of the flow call for changing the
approach to water management and sewer renovation and design.
Designing Sewer Systems
Rain
As a basis for modelling, it is essential to have data
on how much rainwater or other (waste) water
needs handling. There are different approaches to
collect or obtain relevant information on rainwater.
In Denmark, rainfall is registered via rain gauges in
a national grid since the 1970s [9]. Rain gauges
measure the rain event volume, intensity, and duration of the rain at any given location. Long-term
monitoring of rain events by measuring the duration, intensity, and volume has generated a historical knowledge base of so-called rain events.
The amount of rain expected to precipitate and
drain to the sewer system needs to be known. It
also needs to be assessed whether the runoff from
the rain will be handled through a separate network or together with wastewater. Determining
the amount of rainfall is generally via utilizing
historical rain event data collected from local
rain gauges or climates/geographies that closely
match the catchment. Data on the frequency of
intensity and volume of the rain is the basis for
choosing relevant rain events for input data for the
modelling [10].
Recent developments on measuring frequency
and intensity of rain use information from radar
[2]. Radar measurements make it possible to measure the movement and extent of the rainfall over
an area and thus provide more information than a
rainfall measurement in a single point. When
modelling the impact of the rainfall in a specific
area, the understanding of the movement across
the area may be of significance since this might
increase the effect of the rainfall on the water
runoff systems. The usage of this type of data
enhances the ability to forecast certain events
and their impact and thus the ability of water
managers and engineers to best manage and control flow into the sewer system [2].
Modelling Surface Runoff
The approach on designing of sewer systems for
effective and efficient surface water management
requires advanced tools for analysis. Developments of advanced computer models for analyzing
water transport and flow on surfaces as well as in
the sewers have been very relevant. MIKE
URBAN is such a tool [11]. MIKE URBAN can
model an urban drainage system (urban drainage
modelling) as well as water transport on the surface
of a city. The model is an advanced model using
comprehensive dynamic hydraulic modelling [12].
Primary data for a model is information on
catchment areas size, slope, shape, and imperviousness shape. The model uses the data as characteristic values or input information for the
modelling of water flows from ground to sewer
system. The data are necessary parameterizations
to the dynamic modelling of water flow.
The development in urban areas has been
toward restricting percolation of surface water
into the ground and thus increases flow over the
ground. The decreased imperviousness of the surface cover is due to extended coverage of urban
terrain. The decreasing imperviousness potentially results in urban areas becoming more vulnerable to flooding from severe rain both due to
the change in weather and changes in imperviousness and percentage non-percolating surface area.
For effective modelling, it is therefore essential
for urban surface water management to be able to
estimate and map impervious surfaces in catchments efficiently. Data on impervious surfaces
and by association, runoff, is an indicator. The
data on imperviousness is an essential input factor
in hydrological models and urban drainage
modelling. To obtain imperviousness data for
urban drainage modelling, a comprehensive collection of information and data, as well as characterization of the examined urban area, must be
done. Traditionally, data from digital or other
maps are the basis for the calculation.
Conventional mapping techniques in complex
urban areas are often expensive and timeconsuming, particularly when the area in question
is large. Therefore, the development of more automated analysis and classification based on, e.g.,
aerial photos or satellite images, i.e., remote
Urban Drainage Modelling for Management of Urban Surface Water
231