PIK Report No . 127 FOR POTSDAM INSTITUTE CLIMATE IMPACT RESEARCH ( PIK ) UNDERSTANDING CHANGE IN PATTERNS OF VULNERABILITY

A methodology to assess future development in patterns of vulnerability is presented which can support the assessment of global policies with regard to their impacts on specific vulnerabilities on the regional or local scale. Patterns of vulnerability, formalized by vulnerability profiles (e.g. for the livelihoods of dryland smallholder farmers) were investigated under different consistent indicator scenarios reflecting different global policies. After unfolding several principal possibilities to do such an analysis of temporal change in vulnerability patterns we could conclude that the concept of " Clusters of Change " (CoCs) is the most straight forward and promising approach. The main arguments are that each interpretation has necessarily to consider both, the starting situation and it's change over time (" poor and heavily improving " , " rich and stagnating " etc.). This implies that we are looking for patterns which represent typical combinations of present states AND expected future changes. An application of the CoC-concept to the drylands vulnerability patterns considering the indicator set for the present situation and the same indicator set for 2050 under a baseline scenario was performed as a test. Comparison of the present vulnerability cluster partition with the spatial distribution of the CoCs revealed that most of these clusters are separated into an improving and a deteriorating part which shows where winners and losers of the baseline scenario are – an interesting result which illustrates the appropriateness of the CoC – method. To explore the potential of CoCs for the dryland vulnerability we applied the method to two different sets of scenarios until 2050: a baseline vs. Climate policy scenario (OECD, 2012) and a " policy first " scenario vs. " security first " scenario (UNEP, 2007). The first one serves as an example for a policy assessment while the second compares the vulnerability consequences of two scenarios based on different story-lines of further global development. The main conclusion to be drawn from these calculations is that the CoCs are rather insensitive with regard to the small differences between the scenarios. Regarding the first set of scenarios the relatively short time horizon of relevant influences of climate policies on climate change impacts and several indicators which are not influenced at all generate only a very small difference. The only significant change in the resulting vulnerability profiles was in the values of change in water scarcity: it was lower for all profiles in the climate policy case. …

[1]  P. Lucas,et al.  Quantitative analysis of patterns of vulnerability to global environmental change. , 2010 .

[2]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[3]  Carsten Walther,et al.  Categorisation of typical vulnerability patterns in global drylands , 2011 .

[4]  Executive Summary World Urbanization Prospects: The 2018 Revision , 2019 .

[5]  P. Lucas,et al.  Downscaling drivers of global environmental change Enabling use of global SRES scenarios at the national and grid levels , 2007 .

[6]  Rob Alkemade,et al.  GLOBIO3: A Framework to Investigate Options for Reducing Global Terrestrial Biodiversity Loss , 2009, Ecosystems.

[7]  M. Lüdeke,et al.  Bridging Qualitative and Quantitative Methods in Foresight , 2013 .

[8]  P. Lucas,et al.  Armed conflict distribution in global drylands through the lens of a typology of socio-ecological vulnerability , 2014, Regional Environmental Change.

[9]  Alexander Gorobets,et al.  Vulnerability of People and the Environment : Challenges and Opportunities , 2007 .

[10]  P. Lucas,et al.  Towards a global Integrated sustainability model : GISMO1.0 status report , 2008 .

[11]  Dieter Gerten,et al.  Effects of Precipitation Uncertainty on Discharge Calculations for Main River Basins , 2009 .

[12]  Tom Kram,et al.  Intergrated modelling of global environmenthal change : An overview of IMAGE 2.4 , 2006 .

[13]  Kees Klein Goldewijk,et al.  Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1 , 2010 .

[14]  Diana SietzSabino,et al.  Typical patterns of smallholder vulnerability to weather extremes with regard to food security in the Peruvian Altiplano , 2012 .

[15]  J. Alcamo,et al.  Global modeling and scenario analysis for the World Commission on Water for the 21st Century , 2017 .

[16]  E. Lambin,et al.  Dynamic Causal Patterns of Desertification , 2004 .

[17]  Eric F. Lambin,et al.  What drives tropical deforestation?: a meta-analysis of proximate and underlying causes of deforestation based on subnational case study evidence , 2001 .

[18]  鳥居 泰彦,et al.  世界経済・社会統計 = World development indicators , 1998 .

[19]  R. Leemans,et al.  Modelling land degradation in IMAGE 2 , 2001 .

[20]  Carsten Walther,et al.  Cluster Analysis to Understand Socio-ecological Systems: a Guideline , 2012 .

[21]  Robert V. O'Neill,et al.  An Overview of Data Integration Methods for Regional Assessment , 2004, Environmental monitoring and assessment.

[22]  Alexei G. Sankovski,et al.  Geographical Distributions of Temperature Change for Scenarios of Greenhouse Gas and Sulfur Dioxide Emissions , 2000 .