“Vulnerability hotspots”: Integrating socio-economic and hydrological models to identify where cereal production may decline in the future due to climate change induced drought
Highlights
► Vulnerability to climate change depends on climatic and socio-economic factors. ► Socio-economic data are used to model adaptive capacity. ► Hydrological data is used to model exposure to drought. ► Wheat and maize vulnerability are high in SE S. America, S. Africa and E. Mediterranean.
Introduction
Crop models demonstrate that food production is vulnerable to climate change in many regions through a combination of temperature change, water stress and extreme weather (Challinor et al., 2009, Challinor et al., 2010, Lobell and Field, 2007). Although there is considerable uncertainty in these models, and some debate way that ozone pollution, carbon dioxide fertilization, and water shortages may interact with climate change to affect productivity, there is a general concern in the literature that these problems are likely to cause food production to fall over the next 100 years (Jaggard et al., 2010, Long et al., 2005, Royal Society, 2008, Sitch et al., 2007). These concerns sit alongside economic and demographic models that project a rising demand for food thanks to population growth (Foley et al., 2011, Lutz and KC, 2010), urbanization (Satterthwaite et al., 2010), and a shift towards more meat consumption (Kearney, 2010). This leads some to argue that global food security is threatened unless production increases by as much as 70% (Bruinsma, 2009, Godfray et al., 2010a, Godfray et al., 2010b). Therefore, new technologies (Brown and Funk, 2008), and in particular biotechnologies (Tester and Langridge, 2010), may be needed to create more productive crops and ensure food security during the 21st century.
In addition, the socio-economic, ecological, and institutional context of farming has a tremendous influence on whether a producer can adapt to environmental stressors and remain productive (Adger, 2006, Brooks et al., 2005, Patt et al., 2005, Smit and Skinner, 2002, Thomas et al., 2007, Watts and Bohle, 1993). For example, degraded soils, a lack of off-farm employment, social upheaval, and a dysfunctional government prevented the Ethiopian population from adapting to drought in the 1980s (Comenetz and Caviedes, 2002). As a result, it only took a very minor drought (measured in terms of rainfall) to trigger a famine (Fraser, 2007). By contrast, there are cases where even major climatic problems were adapted to without serious losses in agricultural productivity or human life (DeRose et al., 1998). The implication of this is that institutional reform, poverty reduction, and gender equality will help boost adaptive capacity and that this may be as important as developing new crops to meet the challenges of feeding future generations (Fraser et al., 2003, Paavola and Adger, 2006).
Overall, therefore, the vulnerability of crop production to climate change is seen by many scholars as a function of both an exposure to a climatic stress, such as a drought, as well as an ability to adapt to that stress (Fraser et al., 2011b, Intergovernmental Panel on Climate Change, 2001, Watts and Bohle, 1993). To date, however, most quantitative and global scale projections of how food crop production is vulnerable to climate change have focused on the ways in which new temperature and rainfall patterns will affect plant growth (Zhang and Cai, 2011). Those studies that do include socio-economic factors in future projections are most often based only on two socio-economic variables, GDP and population, and there is limited or no assessment as to whether, or under what context, these variables are significant (Diffenbaugh et al., 2007).
The aim of this paper is to better integrate socio-economic and meteorological data to conduct a global scale quantitative assessment that identifies which of the world's cereal producing regions may become vulnerable to climate change over the 21st century. We do this by identifying those regions that will be both exposed to climatic stresses and will not have the capacity to adapt to these problems. The climate impact we focus on is declining cereal harvests since these provide the world with approximately 90% of its calories and are likely to be affected by changing weather patterns (International Development Research Council, 1992). In terms of climate change exposure, we have chosen to focus our attention on droughts as many climate models project that droughts will be a major factor in limiting future crop growth (Intergovernmental Panel on Climate Change, 2007). To assess adaptive capacity, we use a range of socio-economic and ecological data and employ statistical methods to identify proxy indicators of adaptive capacity. We then use different socio-economic and climate projections to identify regions that, given current trends, are likely to be both exposed to worse droughts in the future as well as have a diminishing capacity to adapt. While this project does not provide conclusive results, this nonetheless represents an important step in the field of research devoted to better understanding when, where, and why food systems are likely to be vulnerable to climate change in the future.
Section snippets
Quantifying and modeling exposure to drought
To identify regions likely to be exposed to worse droughts in the future, we used soil moisture simulations from Mac-PDM.09, which is an established global hydrological model. Mac-PDM.09 simulates soil moisture and runoff across the world at a spatial resolution of 0.5° × 0.5°. A detailed description and validation of the model is provided by Gosling and Arnell (2011) and the model has been applied in several recent studies of the global hydrological cycle (e.g. Haddeland et al., 2011, Gosling et
Hydrological model
Fig. 1 presents changes from baseline (1990–2005) in two of the main climatic drivers of soil moisture (precipitation and temperature) for 2045–2060 and 2075–2090, under each scenario (A1B and B2). The climate projections show that changes in precipitation are slightly greater under the A1B emissions scenario than under the B2 emissions scenario. Likewise, warming is higher under A1B than under B2. The largest declines in precipitation with climate change are for northern Brazil, North Africa,
Discussion and conclusion
In terms of empirical results, the following observations stand out:
- (1)
Results from the hydrological model project significant drying in many parts of the world overt the 21st century.
- (2)
Results from the adaptive capacity models show that regions with the lowest overall adaptive capacity for wheat include much of western Russia, northern India, southeastern South America, and southeastern Africa. In terms of maize, regions with the lowest adaptive capacity include the northeastern USA, southeastern
Acknowledgements
This work was supported by a grant from the Natural Environment Research Council (NERC), under the QUEST program (grant # NE/E001890/1), the Economics and Social Research Council, under the Centre for Climate Change Economics and Policy, through support from the Canada Research Chair program and through a Rural Economy and Land Use Fellowship. The climate change scenarios that were used as input to Mac-PDM.09 were created using ClimGen, which was developed by Tim Osborn at the Climatic Research
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