Abstract
Currently available risk preparedness and prevention tools are increasingly lagging behind the growing threat of climate change, with multiple hazards often compounded causing cascaded and intertwined damages to society and the environment. Transborder mountainous areas are especially vulnerable to such threats, given the susceptibility to natural hazards as well as remoteness of some settlements and the related importance of connectivity and accessibility. At the same tim e mountains have experienced above average warming (Adler et al., 2022) and potentially more climate extremes than other areas. An effective risk management requires transnational collaborative efforts. We hereby propose an integrated approach to risk assessment, based on a homogeneous planar tessellation of hexagonal cells and an innovative dual graph-based simplification, where multiple spatial datasets, such as climate and natural hazard forecasts, exposure datasets and projections, impacts and vulnerability indicators converge. Cell- and edge-based data aggregations at user-defined spatial scales allow to find a balance between the complexity of the model and its computational efficiency for risk-related applications. By keeping a maximum resolution of 250m we ensure a sufficient degree of anonymity of potentially sensitive data at a scale still useful for risk management purposes.
For an effective assessment of risk, dynamic multi-temporal modelling is critical, in particular for the most volatile exposure component of all: the population. Rooted in the mentioned tessellated spatial support, we propose a border-independent population flow model that estimates dynamic intra-day concentrations of residents, where cell-based attraction coefficients allow for incremental refinements depending on the availability of auxiliary datasets. What-if scenarios for the simulation of changes in the underlying topology (e.g., roads interruptions due to landslides or floods) can be efficiently explored to provide decision support to local authorities. With the aim of fostering the discussion on exposure modelling for complex climate-related events, the hereby proposed modelling approach can be viewed as an open and flexible platform on top of which additional data sets and processes can be superimposed and plugged-in, enabling higher level applications including quantitative risk analysis and numeric simulations, probabilistic risk assessment, impact forecasting and early warning.
As a preliminary testbed of this platform, in the frame of the EU-co-funded TransAlp project, we generated an exposure model addressing the trans-national area that comprises South Tyrol in Italy, East Tyrol in Austria and the mountain community of Agordino in the Italian Veneto region. Overall the ca. 10’000〖km〗^2 study area was covered with a total of 165'000 hexagons at 250m horizontal spatial resolution. Exposure information covering the cross-border area under consideration sourced from authoritative sources as well as global datasets when necessary were harmonised onto the common tessellation and successfully used to showcase the potential impact of the presented multi-temporal modelling to the local stakeholders.
Literature
Pittore, M., Campalani, P., Renner, K., Plörer, M., Tagliavini, F., 2023. Cross-border multi-functional, multi-hazard exposure modelling in Alpine regions. Preprint. https://doi.org/10.22541/essoar.167397431.14519069/v1
Adler, C., Wester, P., Bhatt, I., Huggel, C., Insarov, G.E., Morecroft, M.D., Muccione, V., Prakash, A., 2022. Cross-Chapter Paper 5: Mountains. Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://doi.org/10.1017/9781009325844.022.2273