Abstract
In this paper we develop a GIS-based multicriteria flood risk assessment and mapping approach. This approach includes flood risks which are not measured in monetary terms; it shows the spatial distribution of multiple risks, and it is able to deal with uncertainties in criteria values and to show their influence on the overall flood risk assessment. Additionally, the approach can be used to show the spatial allocation of the flood effects if risk reduction measures are implemented. The approach is applied to a pilot study for the River Mulde in Saxony, Germany, heavily affected by the hazardous flood in 2002. Therefore, a GIS database of economic, social and environmental risk criteria was created. Two different multicriteria decision rules, a disjunctive and an additive weighting approach, are utilised for an overall flood risk assessment in the area. For implementation, a software tool (FloodCalc) was developed supporting both, the risk calculation of the single criteria as well as the multicriteria analysis.
Notes
It should be noted that this risk formula is often criticised especially in social science for several reasons (see e.g. Banse and Bechmann 1998): Firstly, it implies that an “objective risk” exists and can be measured. This is often not the case because of large uncertainties in the data, variations in time and very complex perceptions and evaluations of risks among people. Secondly, the risk formula suggests that risk is somehow naturally given. In contrast to that sociologists like Renn (1998) argue that risk is always associated with human decisions or actions: “risks refer to the possibility that human actions or events lead to consequences that affect aspects of what humans value”. With regard to flood risk this means that this current risk situation (whether it can be quantified or not) is always a product of human actions or decisions, like for example to settle in the floodplain (or not), to build up protection measures (or not), etc. These aspects should be kept in mind when assessing risks. We nevertheless use the risk formula in the following as we believe that even an uncertain estimation of a risk measure can be a valuable information basis for new human decisions.
This hydrodynamic modelling was done by Gerald Wenk, Helmholtz Centre for Environmental Research, Department Aquatic Ecosystem Analysis and Management.
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Acknowledgements
We would like to thank Gerald Wenk for providing the inundation data, Frank Messner and Thilo Weichel for useful advice on an earlier version of this manuscript and Deborah Connolly for polishing the language of the paper. Further we thank two anonymous reviewers for their fruitful comments. The work described in this publication was supported by the European Community’s Sixth Framework Programme through the grant to the budget of the Integrated Project FLOODsite, Contract GOCE-CT-2004-505420. This paper reflects the authors’ views and not those of the European Community. Neither the European Community nor any member of the FLOODsite Consortium is liable for any use of the information in this paper.
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Annex 1: FloodCalc tool
Annex 1: FloodCalc tool
FloodCalc is a software tool written in Python to carry out various flood damage/risk assessment calculations. The utility is raster-based and allows the import and export of the ASCII grid file format that can be written and read by most GIS software. The usage of ASCII grid files enables the user to incorporate various data sources, i.e. raster output from non-GIS programs, into an analysis. The usage of Python makes it easy to migrate the program to different operating systems, as the language is available for all major OS. The source code can but must not be compiled, so that an adaptation of the application to specific questions, or enhancements in general, can easily be accomplished.
FloodCalc requires various input data in the mentioned raster format, primarily inundation depth, value of assets, social statistics data (i.e. a population distribution or socio-economic points of interest) and environmental values, as described earlier. By assigning a depth-damage function to each asset category during the raster import process, FloodCalc is able to compute the relative and absolute damage per raster cell for each asset category. All intermediate grids are stored internally, and can be exported to an ASCII grid file if needed.
In the current development stage, social data loaded into the program is analysed by intersecting the grid of inundation depth with each social data layer, thereby deducing inhabitants or social hotspots affected by a particular flood event. Environmental data is dealt with in a similar approach: In a first step, affected grid cells per environmental value layer are determined by intersecting each raster with the inundation depth, with the Boolean values 0 for non-affected pixels and 1 for affected pixels being assigned to temporary grids respectively. In a second step, all previous results are aggregated. These analysis capabilities could be extended in future versions of the program by implementing depth-damage functions for specific social or environmental value categories.
Furthermore, annual damage can be computed for a series of inundation events. To do so, the user has to provide a set of damage raster and the corresponding occurrence probabilities. The software will then compute the expected annual economic damage \( \ifmmode\expandafter\bar\else\expandafter\=\fi{D}, \) or the annually affected social and environmental units, respectively, using the Eq. 2.
FloodCalc also offers two basic approaches for a MCA: a simple additive weighting and a disjunctive approach. The implemented additive weighting procedure normalizes the input rasters, being annual damage for assets as well as affected environmental and social values, according to three normalization procedures the user can choose from: linearly over the whole range of values, per threshold defined in standard deviation units added to the mean value of a grid, or by providing a user-defined threshold value. All normalized grids are then weighted and summed up, the weights being previously allocated to each criterion by the user and normalized by the program. If an initial and final weights set and the desired number of steps are provided, FloodCalc automatically alternates the weights and exports the desired number of raster maps as ASCII grids. The utility also lets the user choose to export the normalized, intermediate grids.
The disjunctive approach allows the user to select specific raster cells by entering a threshold value for each grid that is analysed. Hence, FloodCalc automatically selects those pixels with a cell value higher than the chosen threshold for each layer, by assigning the Boolean value 1 to the respective cells. The selected cells are then aggregated. Like described above for the additive weighting approach, the user can also allocate weights to each layer prior to aggregation.
Further modifications of the utility should aim to improve the overall performance at first, and only later on implementing new features such as weighting functions for differently sized and functionally variable social hot spots or the improvement and extension of the implemented MCA approaches. It is suggested to refacture parts of the code in order to achieve the aforementioned points. This has already been done partially to increase the number of grid cells the software can handle. So far, the utility can handle about 4.2 million grid cells per raster, which covers an area of about 400 km² in a resolution of 10 × 10 m.
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Meyer, V., Scheuer, S. & Haase, D. A multicriteria approach for flood risk mapping exemplified at the Mulde river, Germany. Nat Hazards 48, 17–39 (2009). https://doi.org/10.1007/s11069-008-9244-4
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DOI: https://doi.org/10.1007/s11069-008-9244-4