Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here we present a new feature of the climate risk modelling platform CLIMADA which allows to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision makers with a fact-base to understand the impact of weather and climate on their economies, communities, and ecosystems, including appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analyses will enhance transparency and inter-comparison of studies among climate risk modellers and help focus future research. For decision-makers and other users of climate risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualise risk assessment and options appraisal, and might be used to inform the development of story-lines and climate adaptation narratives.
<p>Modelling societal, ecological, and economic costs of natural hazards in the context of climate change is subject to both strong aleatoric and ethical uncertainty. Dealing with these is challenging on several levels &#8211; from the identification and the quantification of the sources of uncertainty to their proper inclusion in the modelling, and the communication of these in a tangible way to both experts and non-experts. One particularly useful approach is global uncertainty and sensitivity analysis, which can help to quantify the confidence in the output values and identify the main drivers of the uncertainty while considering potential correlations in the model. Here we present applications of global uncertainty analysis, robustness quantification, and sensitivity analysis in natural hazard modelling using the new uncertainty module of the CLIMADA (CLIMate ADAptation) platform.</p><p>CLIMADA is a fully open-source Python program that implements a probabilistic multi-hazard global natural catastrophe damage model, which also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioral, etc.). With the new uncertainty module, one can directly and comprehensively inspect the uncertainty and sensitivity to input variables of various output metrics, such as the spatial distribution of risk exceedance probabilities, or the benefit-cost ratios of different adaptation measures. This global approach does reveal interesting parameter interplays and might provide valuable input for decision-makers. For instance, a study of the geospatial distribution of sensitivity indices for tropical cyclones damage indicated that the main driver of uncertainty in dense regions (e.g. cities) is the impact function (vulnerability), whereas in sparse regions it is the exposure (asset) layer.&#160;</p><p>CLIMADA: https://github.com/CLIMADA-project/climada_python&#160;</p><p>(1) Aznar-Siguan, G. et al., GEOSCI MODEL DEV. 12, 7 (2019) 3085&#8211;97<br>(2) Bresch, D. N. and Aznar-Siguan., G., &#160;GEOSCI MODEL DEV. (2020), 1&#8211;20.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.