Climate change vulnerability mapping for regional application: North Rhine-Westphalia




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This study forms part of an integrated and transferable climate change vulnerability assessment for the German Federal state of North Rhine-Westphalia (NRW) (Holsten & Kropp 2012). NRW is located in the west of Germany and comprises 396 municipalities (see Figure 1). We define vulnerability as "the degree to which a system is susceptible to [...] adverse effects of climate change [...] as a function of the character, magnitude, and rate of climate variation to which a system is exposed, its sensitivity, and its adaptive capacity" (IPCC 2001). Vulnerabilities are quantified for all municipalities in NRW across physical, social, economic and environmental dimensions (see Figure 2). These dimensions comprise 'sectors' such as human health, tourism, agriculture, forestry, settlements and protected areas.

For data on future climate change we apply results based on two regional dynamical climate models, CCLM (Lautenschlager et al., 2009) and REMO (Jacob, Mahrenholz and Keup-Thiel, 2006) to allow for a comparison of our results between models.



Figure 1: The study region North Rhine-Westphalia and its location within the European Union. The hatched area indicates the metropolitan Rhine-Ruhr region. Municipalities are delineated by white borders.


In this multi-sector analysis sensitivity is quantified through bespoke approaches for specific sectors. These are directly related to relevant exposure variables, defined as projected relative climatic changes until the end of this century. Sensitivity and exposure values are rescaled according to the available data of the municipalities and these rescaled sensitivity values (relating to specific hazards such as flash floods and pluvial flooding) are then multiplied by the rescaled exposure vales (e.g. changes in days with heavy precipitation).

The sector-specific impact values are then averaged across all dimensions to express the total impacts. The final values of the total impacts are again rescaled to the data set of the region.

The measurement of adaptive capacity comprises data on private and public economic resources and on knowledge and awareness levels. This index therefore captures the cross-sectoral capacity of a region and describes the generic context within which the individual municipality or sector could adapt. The data has been normalised, creating a scale for values between 0 and 1. This assessment of regional adaptive capacity may be visually combined with the total impacts (using hue and transparency) to express the vulnerability. A graphical representation of the vulnerability components and the structure of their aggregation is provided in Figure 2.

The results show regional impact "hot-spots" in the metropolitan area, the foothills of the mountains and in the east of NRW.



Figure 2: Structural overview over the components and dimensions of the vulnerability analysis. Sensitivity indicators (S) are combined with relevant exposure indicators (E) expressing specific impacts (I). These are aggregated to the physical, social, environmental and economic dimension, which are aggregated further to produce the total potential impacts. Total Ptential impacts are compared with the generic adaptive capacity to create an understanding of vulnerability.


Holsten A., & Kropp J.P.: An integrated and transferable climate change vulnerability assessment for regional application, Natural Hazards, DOI 10.1007/s11069-012-0147-z

Data

Climate data: CCLM, all available runs, periods 1961-1900 and 2071-2100, scenario A1B CCLM simulations: Lautenschlager M, Keuler K, Wunram C, Keup-Thiel E, Schubert M, Will A, Rockel B and Boehm U (2009), Climate Simulation with CLM, Climate of the 20th Century run no.1,2 and 3 and Scenario A1B runs no.1,2, Data Stream 3 , Tech. rep., European region MPI-M/MaD, World Data Center for Climate.
Climate data: REMO, all available runs, periods 1961-1900 and 2071-2100, scenario A1B REMO simulations: Jacob D, Mahrenholz P and Keup-Thiel E (2006), REMO A1B Scenario Run "REMO UBA A1B D3 and REMO Climate off the 20th century run "REMO UBA C20 D3", Datastream 3, UBA project, CERA-DB, Tech. rep., World Data Center for Climate
Elevation (DEM, 50 m resolution) Agency for Nature, Environment and Consumer Protection NRW (LANUV)
Regional soil map (BK50, 1:50,000) Geological Survey NRW
Landuse data, highly resolved (ATKIS25, Authoritative Topographic-Cartographic Information System, 1:25,000), converted to the same resolution as the DEM State Office for Ecology, Soil and Forestry NRW (LÖBF)
CORINE Land Cover data (CLC 2006) CORINE Land Cover data (CLC 2006)
Demographic data, population density and sealed surface on municipal level Statistical Agency NRW
Lake characteristics, elevation (DEM, 50 m resolution) and regional characteristics of habitat composition of Natura 2000 sites Agency for Nature, Environment and Consumer Protection NRW (LANUV)
Information on Special Areas of Conservation EU Natura 2000 database
Damaged forest area during the storm event ''Kyrill'' in 2007 State Office for Forest and Timber NRW, see also Klaus et al. (2011)
Forest fire statistics (1993-2009) Federal Agency for Agriculture and Food (BLE)
Length of ski runs for Sauerland and Eifel mountains Roth et al. (2001) and websites of the municipalities
Status of financial budget of municipalities Ministry of Home and Municipal Affairs NRW (MIK)
Municipal initiatives regarding climate change or sustainability Energy Agency NRW (EnergyAgency.NRW 2009), Agenda 21 Forum (Agenda 21 Forum 2005), Environmental Ministry NRW (MUNLV)
Education and income level Statistical Agency NRW