Impact chains activated through hydrological changes

In this theme we address a fundamental category of climate policy impacts. Water supply and agricultural productivity in both China and India are affected or even dominated by monsoon systems; these monsoons may well weaken under global warming. Thus two of the countries whose CO2 emissions policies will crucially influence global emissions are also crucially influenced by climate change impacts.

Here we investigate the chain of societal consequences up to national security issues of those emerging markets (China, India), including the question of potential policy implications for these very countries. We start from ECHAM6/MPIOM global simulations, which represent well the salient features of the South and East Asian Monsoons (e.g., Saeed et al. 2010). We complement this global dynamical approach by both numerical and empirical downscaling methods merging (existing and newly performed) RCM-simulations, statistical downscaling (STAR, Orlowsky et al. 2010), and surface parameterization techniques (e.g., Böhner and Antonic 2008) within a comprehensive hierarchical downscaling scheme necessary to adequately drive our agro-economic model suite EPIC/GLOBIOM (Schneider et al. 2011), which explicitly captures hydrological and soil degradation effects. Extremes will greatly affect the economic health of the region, not only in the lowlands but also in the Himalayan region, given the particularly high vulnerability and adaptation needs of montane land use systems.

We finally ask under which conditions abrupt losses of harvest and sudden food price increases would lead to social unrest in lower income groups and increase the fragility of societies. The assessment will use a dynamic model (Scheffran et al., 2011), which relates the level of dissatisfaction of human actors to increasing food prices and the probability for resistance according to empirically validated response functions building on a statistical correlation between data on food prices, extreme weather events and social instability events such as riots.

For this impact chain we also utilize CliSAP’s competence on seasonal and decadal climate prediction (Pohlmann et al., 2009), merge it with statistics on threshold estimates and analyze the consequences of those for more short-term oriented economic optimizations of landuse practices and pollution policies under anticipated climate information.

In a final integrative section, the plethora of insights from the above – in part probabilistic – modeling will be condensed into a conceptual analysis from the point of view of a regional decision maker. Hereby we derive stylized policy options using modern methods of Knightian robust optimal control (Funke and Paetz 2010).

References

Böhner, J. an Antonic, O. (2008), Developments in Soil Science 33, Elsevier, 772 pp
Funke, M., Paetz, M. (2010), Climatic Change, doi: 10.1007/s10584-010-9943-1
Held, H. et al. (2009), Energy Economics, 31, S50–S61
Orlowsky, B. et al. (2010), J. Climate, 23, 3509-3524
Pohlmann, H. et al. (2009), J. Climate, 22, 3926-3938
Saeed, S. et al. (2010), Clim. Dyn., doi: 10.1007/s00382-00010-00888-x
Scheffran, J. et al. (2011), in: Scheffran, J., M. Brzoska et al. eds., Climate Change, Human Security and Violent Conflict, Springer, forthcoming
Schneider, U. et al. (2011), Agricultural Systems, 104: 204–215