The Caylor group has received support from the Princeton Global Collaborative Network Fund to develop an integrated approach to investigating climate change impacts on food security in sub-Saharan Africa and China
Our group, along with Eric Wood and Justin Sheffield, has received support from the Princeton Global Collaborative Network Fund to develop an integrated approach to investigating climate change impacts on food security in sub-Saharan Africa and China. The announcement can be found here. Here’s a description of the network and its rationale:
Much of the vulnerability of agriculturalists within sub-Saharan Africa and China is driven by surface hydrological dynamics; both directly through rainfall variability and indirectly through additional human- or climate-induced land and water degradation. This tight coupling between social-ecological and hydrological systems in the developing regions make them an ideal setting to conduct fully integrated research between social and physical sciences, where transformative research in either domain necessarily depends on fundamental contributions in the other. Vulnerability to variations in precipitation is controlled by the manner by which meteorological drought propagates into agricultural and ecological drought in many dryland regions. For example, recent work has begun to show that in many cases agricultural drought can be quite substantial (i.e. complete crop failure) even when meteorological drought (i.e. rainfall deficit) is mild.
Although much progress has been made in trying to understand changes in food security under the threat of climate variability and change, there are significant uncertainties and lack of knowledge about interactions between climate, hydrology and agriculture. Uncertainties across crop models (empirical and process based) are large. Empirical-based models tend to have large uncertainties (often giving poor representations of historic yields) and little explanatory power, whilst process-based models are unwieldy in terms of data requirements and have potential for errors in their representation of finer scale processes. Crucially there are large difference among process-based models due to the wide range of approaches, key assumptions and often localized development.
Our network will provide a common framework for testing and implementing models developed and implemented by network participants. An example of the value of these sorts of inter-comparison activities is the experience of global climate model inter-comparisons, which have been used to define and constrain uncertainty regarding temperature changes during the 21st century. We envision our network as seeding a similar set of integrative activities, which will bring together models, managers, and observers to address uncertainties and opportunities related to climate change impacts on food security.