Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7177-2022
https://doi.org/10.5194/gmd-15-7177-2022
Development and technical paper
 | 
23 Sep 2022
Development and technical paper |  | 23 Sep 2022

Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0

Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch

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Cited articles

Anderson, W., Guikema, S., Zaitchik, B., and Pan, W.: Methods for Estimating Population Density in Data-Limited Areas: Evaluating Regression and Tree-Based Models in Peru, PLOS ONE, 9, e100037, https://doi.org/10.1371/journal.pone.0100037, 2014. a
Aznar-Siguan, G. and Bresch, D. N.: CLIMADA v1: a global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-12-3085-2019, 2019. a, b, c, d, e, f, g, h, i, j
Berger, L.: Leaving No One Off The Map: A Guide For Gridded Population Data For Sustainable Development, A Report by the Thematic Research Network on Data and Statistics (TReNDS) of the UN Sustainable Development Solutions Network (SDSN) in Support of the POPGRID Data Collaborative, https://www.unsdsn.org/leaving-no-one-off-the-map-a-guide-for-gridded-population-data-for-sustainable-development (last access: 28 August 2022), 2020. a
Beven, K. J., Almeida, S., Aspinall, W. P., Bates, P. D., Blazkova, S., Borgomeo, E., Freer, J., Goda, K., Hall, J. W., Phillips, J. C., Simpson, M., Smith, P. J., Stephenson, D. B., Wagener, T., Watson, M., and Wilkins, K. L.: Epistemic uncertainties and natural hazard risk assessment – Part 1: A review of different natural hazard areas, Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, 2018. a, b
Beven, K. J., Aspinall, W. P., Bates, P. D., Borgomeo, E., Goda, K., Hall, J. W., Page, T., Phillips, J. C., Simpson, M., Smith, P. J., Wagener, T., and Watson, M.: Epistemic uncertainties and natural hazard risk assessment – Part 2: What should constitute good practice?, Nat. Hazards Earth Syst. Sci., 18, 2769–2783, https://doi.org/10.5194/nhess-18-2769-2018, 2018b. a, b
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Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
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