Agricultural drought in the light of Climate Change
A case study on the implied transborder climate change risks of international supply chains.
In Case Study 5, the case team (University of Salzburg, Eurac Research and Environmental Agency Austria) developed Impact Chains, trough stakeholder dialogues and workshops, explored system dynamic approaches and conducted a quantitative risk assessment. The assessment focused on the co-development of drivers of agricultural drought and discussed adaptation approaches on this basis.
The case study utilized Causal Loop Diagrams to depict the dynamic cause-effect relationships between risk drivers. It integrated another systems modelling approach by dividing the study area in two risk regimes based on statistical cluster analysis of significant correlations between risk factors throughout space. Another core element was the application of a spatial regionalisa-tion approach that is independent from administrative boundaries.
The case study aimed to get a better understanding of the factors that fuel drought risk in addi-tion to a negative water balance. This comprises procedural, financial, political and environmen-tal aspects. Furthermore, the case team wanted to understand these aspects’ relations to each other. Overall, the case aimed to assess where in the Salzburg municipality drought risk is high and where it is low, and why. Thus, we collected, integrated and analyzed a set of spatial da-tasets that are representatives of each influencing aspect. The assessment results are intended to raise awareness among the involved stakeholders for the drought topic. Results did raise awareness for the benefits of taking a holistic perspective on a particular problem, with a focus on the relationships between different risk aspects. The target audience for the assessment re-sults are the stakeholders. The assessment focuses on future risk. The case assessed, whether or not probability of very dry and very wet years will increase in 2021-2050 and 2071-2100 compared to 1981-2010.
Case study 5 resulted in the following innovations:
• We depicted the Impact Chain diagram using Causal Loop Diagram notation styles to better reflect the dynamic cause-effect relationships between risk drivers.
• We applied a spatial regionalization technique to identify regions that share a similar risk level.
• These spatial regions of similar risk levels were divided into two risk regimes based on their internal structures.
Province of Salzburg in Austria.
The case study involved stakeholders from governmental institutions (national, provincial and regional), farmer associations, farmers, insurance representatives and scientists.
Summary data collection
* Qualitative data was acquired through stakeholder dialogues, workshops and a literature review.
* Quantitative spatial data, such as socio-demographic, socio-economic and environmental data, has been partly
* collected from various open government data portals and other freely and openly available sources,
* provided by stakeholders
1) Final documented output:
• One Impact Chain validated by stakeholders;
• An interactive dashboard showing the applied workflow, used data sources, risk maps and other diagrams informing about spatially explicit drought risk factors;
• Documented R scripts that developed to process and analyze the spatial datasets, openly available through GitHub;
• A case study report detailing about the applied workflow.
2) Added value for stakeholders:
• A better understanding of the risk of drought for the agricultural sector now and un-der different climate change scenarios;
• A spatially explicit decision making basis showing the different risk profiles of differ-ent regions within the municipality of Salzburg.
3) Methodological improvements of the Impact Chain-based climate risk and vulnerability assessment:
• Improved depiction of cause-effect relationships between risk driver, utilizing causal Loop Diagrams;
• Improved differentiation between spatial regions with different risk profiles;
• Connecting Impact Chains with state-of-the-art geospatial data analysis techniques.
4) Good practices in stakeholder engagement, co-production of knowledge, and presenta-tion of results.
Case study responsible
Department of Geoinformatics Z_GIS, Paris Lodron University of Salzburg)
Funding was provided by the FFG, reference 872000