Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7177-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
23 Sep 2022
Development and technical paper |
| 23 Sep 2022
| 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
CORRESPONDING AUTHOR
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Alessio Ciullo
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Laura Otth
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Simona Meiler
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
Arun Rana
Frankfurt School of Finance and Management Gemeinnützige GmbH, Adickesallee 32–34, 60322 Frankfurt am Main, Germany
Emanuel Schmid
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Jamie W. McCaughey
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
David N. Bresch
Institute for Environmental Decisions, ETH Zurich, Universitätstr. 16, 8092 Zurich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Operation Center 1, P.O. Box 257, 8058 Zurich Airport, Switzerland
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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
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
Bloemendaal, N., Haigh, I. D.,de Moel, H., Muis, S.,
Haarsma, R. J., and Aerts, J. C. J. H.: Generation of a Global Synthetic
Tropical Cyclone Hazard Dataset Using STORM, Sci. Data, 7, 40,
https://doi.org/10.1038/s41597-020-0381-2, 2020. a
Borgonovo, E.: A New Uncertainty Importance Measure, Reliab. Eng.
Syst. Safe., 92, 771–784, https://doi.org/10.1016/j.ress.2006.04.015, 2007. a
Borgonovo, E., Lu, X., Plischke, E., Rakovec, O., and Hill, M. C.: Making the
Most out of a Hydrological Model Data Set: Sensitivity Analyses to Open
the Model Black-Box, Water Resour. Res., 53, 7933–7950,
https://doi.org/10.1002/2017WR020767, 2017. a
Bradley, R. and Drechsler, M.: Types of Uncertainty, Erkenn, 79,
1225–1248, https://doi.org/10.1007/s10670-013-9518-4, 2014. a, b
Bradley, R. and Steele, K.: Making Climate Decisions, Philosophy Compass,
10, 799–810, https://doi.org/10.1111/phc3.12259, 2015. a, b
Center for International Earth Science Information Network
(CIESIN): Documentation for the Gridded Population of the
World, Version 4 (GPWv4), Revision 10 Data Sets [data set], https://doi.org/10.7927/H4D50JX4, 2017. a
Ceola, S., Laio, F., and Montanari, A.: Satellite Nighttime Lights Reveal
Increasing Human Exposure to Floods Worldwide, Geophys. Res. Lett.,
41, 7184–7190, https://doi.org/10.1002/2014GL061859, 2014. a
Ciullo, A., Kwakkel, J. H., Bruijn, K. M. D., Doorn, N., and Klijn, F.:
Efficient or Fair? Operationalizing Ethical Principles in Flood
Risk Management: A Case Study on the Dutch-German Rhine, Risk
Anal., 40, 1844–1862, https://doi.org/10.1111/risa.13527, 2020. a
Ciullo, A., Martius, O., Strobl, E., and Bresch, D. N.: A Framework for
Building Climate Storylines Based on Downward Counterfactuals: The Case
of the European Union Solidarity Fund, Climate Risk Management, 33,
100349, https://doi.org/10.1016/j.crm.2021.100349, 2021. a
Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., and Schaibly,
J. H.: Study of the Sensitivity of Coupled Reaction Systems to Uncertainties
in Rate Coefficients. I Theory, J. Chem. Phys., 59, 3873–3878,
https://doi.org/10.1063/1.1680571, 1973. a
de Moel, H., Asselman, N. E. M., and Aerts, J. C. J. H.: Uncertainty and sensitivity analysis of coastal flood damage estimates in the west of the Netherlands, Nat. Hazards Earth Syst. Sci., 12, 1045–1058, https://doi.org/10.5194/nhess-12-1045-2012, 2012. a
Doorn, N.: The Blind Spot in Risk Ethics: Managing Natural Hazards, Risk Anal.,
35, 354–360, https://doi.org/10.1111/risa.12293, 2015. a
Dottori, F., Di Baldassarre, G., and Todini, E.: Detailed Data Is Welcome, but
with a Pinch of Salt: Accuracy, Precision, and Uncertainty in Flood
Inundation Modeling, Water Resour. Res., 49, 6079–6085,
https://doi.org/10.1002/wrcr.20406, 2013. a, b
Douglas-Smith, D., Iwanaga, T., Croke, B. F. W., and Jakeman, A. J.: Certain
Trends in Uncertainty and Sensitivity Analysis: An Overview of Software
Tools and Techniques, Environ. Modell. Softw., 124, 104588,
https://doi.org/10.1016/j.envsoft.2019.104588, 2020. a, b
Ehre, M., Papaioannou, I., and Straub, D.: A Framework for Global Reliability
Sensitivity Analysis in the Presence of Multi-Uncertainty, Reliab.
Eng. Syst. Safe., 195, 106726,
https://doi.org/10.1016/j.ress.2019.106726, 2020. a
Emanuel, K.: A Fast Intensity Simulator for Tropical Cyclone Risk Analysis, Nat.
Hazards, 88, 779–796, https://doi.org/10.1007/s11069-017-2890-7, 2017. a
Étoré, P., Prieur, C., Pham, D. K., and Li, L.: Global Sensitivity
Analysis for Models Described by Stochastic Differential Equations,
Methodol. Comput. Appl. Probab., 22, 803–831, https://doi.org/10.1007/s11009-019-09732-6,
2020. a
Funtowicz, S. O. and Ravetz, J. R.: Uncertainty and Quality in Science
for Policy, Springer Science & Business Media, https://doi.org/10.1007/978-94-009-0621-1, 1990. a
Gettelman, A., Bresch, D. N., Chen, C. C., Truesdale, J. E., and Bacmeister,
J. T.: Projections of Future Tropical Cyclone Damage with a High-Resolution
Global Climate Model, Climatic Change, 146, 575–585,
https://doi.org/10.1007/s10584-017-1902-7, 2017. a
Ghanem, R., Higdon, D., and Owhadi, H.: Handbook of Uncertainty
Quantification, Springer, New York, NY, 1st Edn., https://doi.org/10.1007/978-3-319-11259-6, 2017. a
Hall, J. W., Tarantola, S., Bates, P. D., and Horritt, M. S.: Distributed
Sensitivity Analysis of Flood Inundation Model Calibration, J.
Hydraul. Eng., 131, 117–126, https://doi.org/10.1061/(ASCE)0733-9429(2005)131:2(117),
2005. a
Hammersley, J. M.: Monte Carlo Methods for Solving Multivariable
Problems, Ann. NY Acad. Sci., 86, 844–874,
https://doi.org/10.1111/j.1749-6632.1960.tb42846.x, 1960. a
Hillger, D., Seaman, C., Liang, C., Miller, S., Lindsey, D., and Kopp, T.:
Suomi NPP VIIRS Imagery Evaluation, J. Geophys. Res.-Atmos., 119, 6440–6455, https://doi.org/10.1002/2013JD021170, 2014. a
Hinkel, J. and Bisaro, A.: Methodological Choices in Solution-Oriented
Adaptation Research: A Diagnostic Framework, Reg. Environ. Change, 16, 7–20,
https://doi.org/10.1007/s10113-014-0682-0, 2016. a, b
Holland, G.: A Revised Hurricane Pressure–Wind Model, Mon.
Wea. Rev., 136, 3432–3445, https://doi.org/10.1175/2008MWR2395.1, 2008. a
Hyde, K. M.: Uncertainty Analysis Methods For Multi-Criteria Decision
Analysis, PhD thesis, The University of Adelaide School of Civil and
Environmental Engineering, Adelaide, 2006. a
IFRC: World Disasters Rreport: Come Heat or High Water., INTL FED OF RED
CROSS, GENEVA, ISBN 978-2-9701289-5-3,
https://media.ifrc.org/ifrc/world-disaster-report-2020 (last access: 28 August 2022), 2020. a
Iooss, B. and Lemaître, P.: A Review on Global Sensitivity Analysis
Methods, in: Uncertainty Management in Simulation-Optimization of
Complex Systems: Algorithms and Applications, edited by: Dellino,
G. and Meloni, C., Operations Research/Computer Science Interfaces
Series, Springer US, Boston, MA, 101–122,
https://doi.org/10.1007/978-1-4899-7547-8_5, 2015. a
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer, L. A., IPCC, Geneva, Switzerland, 151 pp., https://www.ipcc.ch/report/ar5/syr/ (last access: 28 August 2022), 2014a. a, b, c
IPCC: 2014: Summary for policymakers, in: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Field, C. B., Barros, V. R., Dokken, D. J., Mach, K. J., Mastrandrea, M. D.,
Bilir, T. E., Chatterjee, M., Ebi, K. L., Estrada, Y. O., Genova, R. C., Girma, B., Kissel, E. S., Levy, A. N., MacCracken, S.,
Mastrandrea, P. R., and White, L. L., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 1–32, https://www.ipcc.ch/report/ar5/wg2/ (last access: 28 August 2022), 2014b. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp., https://www.ipcc.ch/report/ar6/wg1/ (last access: 28 August 2022), 2021. a
Kam, P. M., Aznar-Siguan, G., Schewe, J., Milano, L., Ginnetti, J., Willner,
S., McCaughey, J. W., and Bresch, D. N.: Global Warming and Population Change
Both Heighten Future Risk of Human Displacement Due to River Floods, Environ.
Res. Lett., 16, 044026, https://doi.org/10.1088/1748-9326/abd26c, 2021. a
Kasprzyk, J. R., Nataraj, S., Reed, P. M., and Lempert, R. J.: Many Objective
Robust Decision Making for Complex Environmental Systems Undergoing Change,
Environ. Modell. Softw., 42, 55–71,
https://doi.org/10.1016/j.envsoft.2012.12.007, 2013. a
Katzav, J., Thompson, E. L., Risbey, J., Stainforth, D. A., Bradley, S., and
Frisch, M.: On the appropriate and inappropriate uses of probability
distributions in climate projections and some alternatives, Climatic Change,
169, 15, https://doi.org/10.1007/s10584-021-03267-x, 2021. a
Kleppek, S., Muccione, V., Raible, C. C., Bresch, D. N., Köllner-Heck,
P., and Stocker, T. F.: Tropical Cyclones in ERA-40: A Detection and
Tracking Method, Geophys. Res. Lett., 35, L10705,
https://doi.org/10.1029/2008GL033880, 2008. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.:
The International Best Track Archive for Climate Stewardship
(IBTrACS): Unifying Tropical Cyclone Data, B. Am.
Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010. a
Knüsel, B.: Epistemological Issues in Data-Driven Modeling in
Climate Research, Doctoral Thesis, ETH Zurich,
https://doi.org/10.3929/ethz-b-000399735, 2020. a, b
Knutson, T. R., Sirutis, J. J., Zhao, M., Tuleya, R. E., Bender, M., Vecchi,
G. A., Villarini, G., and Chavas, D.: Global Projections of Intense
Tropical Cyclone Activity for the Late Twenty-First Century from
Dynamical Downscaling of CMIP5/RCP4.5 Scenarios, J.
Climate, 28, 7203–7224, https://doi.org/10.1175/JCLI-D-15-0129.1, 2015. a, b
Koks, E. E., Bočkarjova, M., de Moel, H., and Aerts, J.
C. J. H.: Integrated Direct and Indirect Flood Risk Modeling:
Development and Sensitivity Analysis, Risk Anal., 35, 882–900,
https://doi.org/10.1111/risa.12300, 2015. a
Krauß, W. and Bremer, S.: The Role of Place-Based Narratives of Change in
Climate Risk Governance, Climate Risk Manage., 28, 100221,
https://doi.org/10.1016/j.crm.2020.100221, 2020. a
Kropf, C. M., Schmid, E., Aznar-Siguan, G., Eberenz, S., Vogt, T., Steinmann,
C. B., Röösli, T., Lüthi, S., Sauer, I. J., Mühlhofer, E.,
Hartman, J., Guillod, B. P., Stalhandske, Z., Ciullo, A., Fairless, C., Kam,
P. M. M., wjan262, Meiler, S., Bungener, R., Bozzini, V., Stocker, D., and
Bresch, D. N.: CLIMADA-project/Climada_python: V3.1.0, Zenodo [code],
https://doi.org/10.5281/zenodo.5947271, 2022a. a, b, c, d
Kropf, C. M., Rana, A., and Zhu, Q.: Probabilistic storm surge hazard event set
for Vietnam on 30 arcsecond resolution (2020 and 2050), ETH Research Collection [code and data set],
https://doi.org/10.3929/ethz-b-000566528, 2022b. a
Lemieux, C.: Monte Carlo and Quasi-Monte Carlo Sampling, Springer
Science & Business Media, https://doi.org/10.1007/978-0-387-78165-5, 2009. a, b, c
Leobacher, G. and Pillichshammer, F.: Introduction to Quasi-Monte Carlo
Integration and Applications, Springer, https://doi.org/10.1007/978-3-319-03425-6, 2014. a, b
Marelli, S. and Sudret, B.: UQLab: A Framework for Uncertainty
Quantification in Matlab, in: Second International Conference on
Vulnerability and Risk Analysis and Management (ICVRAM) and
the Sixth International Symposium on Uncertainty, Modeling, and
Analysis (ISUMA), American Society of Civil
Engineers, Liverpool, 2554–2563, https://doi.org/10.1061/9780784413609.257, 2014. a
Marrel, A., Iooss, B., Da Veiga, S., and Ribatet, M.: Global Sensitivity
Analysis of Stochastic Computer Models with Joint Metamodels, Stat. Comput.,
22, 833–847, https://doi.org/10.1007/s11222-011-9274-8, 2012. a
Matott, L. S., Babendreier, J. E., and Purucker, S. T.: Evaluating Uncertainty
in Integrated Environmental Models: A Review of Concepts and Tools, Water
Resour. Res., 45, W06421, https://doi.org/10.1029/2008WR007301, 2009. a, b
Mayer, L. A., Loa, K., Cwik, B., Tuana, N., Keller, K., Gonnerman, C., Parker,
A. M., and Lempert, R. J.: Understanding Scientists' Computational Modeling
Decisions about Climate Risk Management Strategies Using Values-Informed
Mental Models, Global Environ. Chang., 42, 107–116,
https://doi.org/10.1016/j.gloenvcha.2016.12.007, 2017. a, b
Merwade, V., Olivera, F., Arabi, M., and Edleman, S.: Uncertainty in Flood
Inundation Mapping: Current Issues and Future Directions, J.
Hydrol. Eng., 13, 608–620,
https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608), 2008. a, b
Morris, M. D.: Factorial Sampling Plans for Preliminary Computational
Experiments, Technometrics, 33, 161–174,
https://doi.org/10.1080/00401706.1991.10484804, 1991. a
Norton, J.: An introduction to sensitivity assessment of simulation models,
Environ. Modell. Softw., 69, 166–174,
https://doi.org/10.1016/j.envsoft.2015.03.020, 2015. a
Paleari, L. and Confalonieri, R.: Sensitivity Analysis of a Sensitivity
Analysis: We Are Likely Overlooking the Impact of Distributional
Assumptions, Ecol. Modell., 340, 57–63,
https://doi.org/10.1016/j.ecolmodel.2016.09.008, 2016. a, b
Pianosi, F. and Wagener, T.: A Simple and Efficient Method for Global
Sensitivity Analysis Based on Cumulative Distribution Functions,
Environ. Modell. Softw., 67, 1–11,
https://doi.org/10.1016/j.envsoft.2015.01.004, 2015. a
Plischke, E., Borgonovo, E., and Smith, C. L.: Global Sensitivity Measures from
given Data, Eur. J. Oper. Res., 226, 536–550,
https://doi.org/10.1016/j.ejor.2012.11.047, 2013. a
Saltelli, A.: Making Best Use of Model Evaluations to Compute Sensitivity
Indices, Comput. Phys. Commun., 145, 280–297,
https://doi.org/10.1016/S0010-4655(02)00280-1, 2002. a
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer, John Wiley & Sons, Ltd,
Chichester, England, Hoboken, NJ, ISBN 9780470059975, Online ISBN 9780470725184, https://doi.org/10.1002/9780470725184, 2008. a, b, c
Saltelli, A. and Annoni, P.: How to Avoid a Perfunctory Sensitivity Analysis,
Environ. Modell. Softw., 25, 1508–1517,
https://doi.org/10.1016/j.envsoft.2010.04.012, 2010. a, b, c
Saltelli, A., Guimaraes Pereira, Â., der Sluijs, J.
P. V., and Funtowicz, S.: What Do I Make of Your Latinorumc
Sensitivity Auditing of Mathematical Modelling, International Journal of Foresight and Innovation Policy, 9, 213,
https://doi.org/10.1504/IJFIP.2013.058610, 2013. a
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst,
N., Li, S., and Wu, Q.: Why so Many Published Sensitivity Analyses Are False:
A Systematic Review of Sensitivity Analysis Practices, Environ.
Modell. Softw., 114, 29–39, https://doi.org/10.1016/j.envsoft.2019.01.012,
2019. a, b, c, d
Sarrazin, F. J., Pianosi, F., and Wagener, T.: Global Sensitivity Analysis
of Environmental Models: Convergence and Validation, Environ.
Modell. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005,
2016. a, b
Savage, J. T. S., Pianosi, F., Bates, P., Freer, J., and Wagener, T.:
Quantifying the Importance of Spatial Resolution and Other Factors through
Global Sensitivity Analysis of a Flood Inundation Model, Water Resour.
Res., 52, 9146–9163, https://doi.org/10.1002/2015WR018198, 2016. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S.,
Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O.,
Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth,
K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby,
R. L., and Zenghelis, D. A.: Storylines: An Alternative Approach to
Representing Uncertainty in Physical Aspects of Climate Change, Climatic
Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a
Sobol′, I. M. and Kucherenko, S.: Derivative Based Global Sensitivity Measures
and Their Link with Global Sensitivity Indices, Mathe. Comput.
Simulat., 79, 3009–3017, https://doi.org/10.1016/j.matcom.2009.01.023, 2009. a
Souvignet, M., Wieneke, F., Müller, L., and Bresch, D. N.: Economics of Climate Adaptation (ECA): Guidebook for Practitioners, KfW Group, KfW Development Bank, Frankfurt am Main, https://www.kfw-entwicklungsbank.de/PDF/Download-Center/Materialien/2016_No6_Guidebook_Economics-of-Climate-Adaptation_EN.pdf (last access: 28 August 2022), 2016. a
Sudret, B.: Global Sensitivity Analysis Using Polynomial Chaos Expansions,
Reliab. Eng. Syst. Safe., 93, 964–979,
https://doi.org/10.1016/j.ress.2007.04.002, 2008. a
Uusitalo, L., Lehikoinen, A., Helle, I., and Myrberg, K.: An Overview of
Methods to Evaluate Uncertainty of Deterministic Models in Decision Support,
Environ. Modell. Softw., 63, 24–31,
https://doi.org/10.1016/j.envsoft.2014.09.017, 2015. a
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace,
Scotts Valley, CA, ISBN 978-1-4414-1269-0, 2009. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman,
K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey,
C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D.,
Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R.,
Archibald, A. M., Ribeiro, A. H., Pedregosa, F., and
van Mulbregt, P.: SciPy 1.0: Fundamental
Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Wagenaar, D. J., de Bruijn, K. M., Bouwer, L. M., and de Moel, H.: Uncertainty in flood damage estimates and its potential effect on investment decisions, Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, 2016. a
Wagener, T., Reinecke, R., and Pianosi, F.: On the Evaluation of Climate Change
Impact Models, WIREs Clim. Change, 13, e772, https://doi.org/10.1002/wcc.772, 2022. a
Wilby, R. L. and Dessai, S.: Robust Adaptation to Climate Change, Weather, 65,
180–185, https://doi.org/10.1002/wea.543, 2010. a
Zhu, X. and Sudret, B.: Global Sensitivity Analysis for Stochastic Simulators
Based on Generalized Lambda Surrogate Models, Reliab. Eng.
Syst. Safe., 214, 107815, https://doi.org/10.1016/j.ress.2021.107815, 2021. a
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.
Mathematical models are approximations, and modellers need to understand and ideally quantify...
