
Compound Flooding in Halmstad: Common Causes, Interannual Variability and the Effects of Climate Change
References
- Adell, A, Almström, B, Kroon, A, Larson, M, Uvo, CB and Hallin, C. 2023. Spatial and temporal wave climate variability along the south coast of Sweden during 1959–2021. Regional Studies in Marine Science, 63: 103011. DOI: 10.1016/j.rsma.2023.103011
- Andrée, E, Su, J, Dahl Larsen, MA, Drews, M, Stendel, M and Skovgaard Madsen, K. 2023. The role of preconditioning for extreme storm surges in the western Baltic sea. Natural Hazards and Earth System Sciences, 23(5): 1817–1834. DOI: 10.5194/nhess-23-1817-2023
- Bamber, JL, Oppenheimer, M, Kopp, RE, Aspinall, WP and Cooke, RM. 2019. Ice sheet contributions to future sea-level rise from structured expert judgment. Proceedings of the National Academy of Sciences, 116(23): 11195–11200. DOI: 10.1073/pnas.1817205116
- Bellinghausen, K, Hünicke, B and Zorita, E. 2023. Short-term prediction of extreme sea-level at the Baltic sea coast by random forests. Natural Hazards and Earth System Sciences Discussions, 2023: 1–48. DOI: 10.5194/nhess-2023-21
- Berg, P, Döscher, R and Koenigk, T. 2013. Impacts of using spectral nudging on regional climate model RCA4 simulations of the Arctic. Geoscientific Model Development, 6(3): 849–859. DOI: 10.5194/gmd-6-849-2013
- Berg, P, Photiadou, C, Bartosova, A, Biermann, J, Capell, R, Chinyoka, S, Fahlesson, T, Franssen, W, Hundecha, Y, Isberg, K, Ludwig, F, Mook, R, Muzuusa, J, Nauta, L, Rosberg, J, Simonsson, L, Sjökvist, E, Thuresson, J and van der Linden, E. 2021a. Hydrology related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.73237ad6
- Berg, P, Photiadou, C, Simonsson, L, Sjökvist, E, Thuresson, J and Mook, R. 2021b. Temperature and precipitation climate impact indicators from 1970 to 2100 derived from European climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.9eed87d5
- Bevacqua, E, Maraun, D, Hobæk Haff, I, Widmann, M and Vrac, M. 2017. Multivariate statistical modelling of compound events via pair-copula constructions: Analysis of floods in Ravenna (Italy). Hydrology and Earth System Sciences, 21(6): 2701–2723. DOI: 10.5194/hess-21-2701-2017
- Capellán-Pérez, I, Arto, I, Polanco-Martínez, JM, González-Eguinob, M and Neumann, MB. 2016. Likelihood of climate change pathways under uncertainty on fossil fuel resource availability. Energy and Environmental Science, 9: 2482–2496. DOI: 10.1039/C6EE01008C
- DeConto, RM, Pollard, D, Alley, RB, Velicogna, I, Gasson, E, Gomez, N, Sadai, S, Condron, A, Gilford, DM, Ashe, EL, Kopp, RE, Li, D and Dutton, A. 2021. The Paris Climate Agreement and future sea-level rise from Antarctica. Nature, 593: 83–89. DOI: 10.1038/s41586-021-03427-0
- Dieterich, C, Gröger, M, Arneborg, L and Andersson, HC. 2019. Extreme sea levels in the Baltic Sea under climate change scenarios – part 1: Model validation and sensitivity. Ocean Science, 15(6): 1399–1418. DOI: 10.5194/os-15-1399-2019
- Donnelly, C, Andersson, JCM and Arheimer, B. 2016. Using flow signatures and catchment similarities to evaluate the e-hype multi-basin model across Europe. Hydrological Sciences Journal, 61(2): 255–273. DOI: 10.1080/02626667.2015.1027710
- Dubois, K, Larsen, MAD, Drews, M, Nilsson, E and Rutgersson, A. 2023. Influence of data source and copula statistics on estimates of compound extreme water levels in a river mouth environment. Natural Hazards and Earth System Sciences Discussions, 2023: 1–28. DOI: 10.5194/nhess-2023-176
- Dupuis, DJ. 2007. Using copulas in hydrology: Benefits, cautions, and issues. Journal of Hydrologic Engineering, 12(4): 381–393. DOI: 10.1061/(ASCE)1084-0699(2007)12:4(381)
- Feng, PN, Lin, H, Derome, J and Merlis, TM. 2021. Forecast skill of the NAO in the subseasonal-to-seasonal prediction models. Journal of Climate, 34(12): 4757–4769. DOI: 10.1175/JCLI-D-20-0430.1
- Finnish Meteorological Institute. 2023. Baltic Sea wave hindcast. DOI: 10.48670/moi-00014
- Fox-Kemper, B, Hewitt, HT, Xiao, C, Adalgeirsdóttir, G, Drijfhout, SS, Edwards, TL, Golledge, NR, Hemer, M, Kopp, RE, Krinner, G, Mix, A, Notz, D, Nowicki, S, Nurhati, IS, Ruiz, L, Sallée, JB, Slangen, ABA and Yu, Y. 2021. Ocean, cryosphere and sea level change. Tech. rep. In: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, in press.
- Gupta, HV, Kling, H, Yilmaz, KK and Martinez, GF. 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1): 80–91. DOI: 10.1016/j.jhydrol.2009.08.003
- Hausfather, Z and Peters, GP. 2020. Emissions – the ‘business as usual’ story is misleading. Nature, 577: 618–620. DOI: 10.1038/d41586-020-00177-3
- Hieronymus, M. 2021. A yearly maximum sea level simulator and its applications: A Stockholm case study. Ambio, 51: 1263–1274. DOI: 10.1007/s13280-021-01661-4
- Hieronymus, M. 2023. The sea level simulator v1.0: A model for integration of mean sea level change and sea level extremes into a joint probabilistic framework. Geoscientific Model Development, 16(9): 2343–2354. DOI: 10.5194/gmd-16-2343-2023
- Hieronymus, M and Hieronymus, F. 2021. Southern Baltic sea level extremes: Tide gauge data, historic storms and confidence intervals. Boreal Environmental Research, 26: 79–87.
- Hieronymus, M and Hieronymus, F. 2023. A novel machine learning based bias correction method and its application to sea level in an ensemble of downscaled climate projections. Tellus A: Dynamic Meteorology and Oceanography, 75(1): 129–144. Available at: DOI: 10.16993/tellusa.3216
- Hieronymus, M, Hieronymus, J and Arneborg, L. 2017. Sea level modelling in the Baltic and the North Sea: The respective role of different parts of the forcing. Ocean Modelling, 118: 59–72. DOI: 10.1016/j.ocemod.2017.08.007
- Hieronymus, M, Hieronymus, J and Hieronymus, F. 2019. On the application of machine learning techniques to regression problems in sea level studies. Journal of Atmospheric and Oceanic Technology, 36(9): 1889–1902. DOI: 10.1175/JTECH-D-19-0033.1
- Hieronymus, M and Kalén, O. 2020. Sea-level rise projections for Sweden based on the new IPCC special report: The ocean and cryosphere in a changing climate. Ambio, 49: 1587–1600. DOI: 10.1007/s13280-019-01313-8
- Hieronymus, M and Kalén, O. 2022. Should Swedish sea level planners worry more about mean sea level rise or sea level extremes? Ambio. DOI: 10.1007/s13280-022-01748-6
- Hordoir, R, Axell, L, Höglund, A, Dieterich, C, Fransner, F, Gröger, M, Liu, Y, Pemberton, P, Schimanke, S, Andersson, H, Ljungemyr, P, Nygren, P, Falahat, S, Nord, A, Jönsson, A, Lake, I, Döös, K, Hieronymus, M, Dietze, H, Löptien, U, Kuznetsov, I, Westerlund, A, Tuomi, L and Haapala, J. 2019. Nemo-Nordic 1.0: A NEMO based ocean model for Baltic & North Seas, research and operational applications. Geoscientific Model Development, 12(1): 363–386. DOI: 10.5194/gmd-12-363-2019
- Huard, D, Fyke, J, Capellán-Pérez, I, Matthews, HD and Partanen, AI. 2022. Estimating the likelihood of ghg concentration scenarios from probabilistic integrated assessment model simulations. Earth’s Future, 10(10):
e2022EF002715 . DOI: 10.1029/2022EF002715 - Hundecha, Y, Arheimer, B, Donnelly, C and Pechlivanidis, I. 2016. A regional parameter estimation scheme for a pan-European multi-basin model. Journal of Hydrology: Regional Studies, 6: 90–111. DOI: 10.1016/j.ejrh.2016.04.002
- Hundecha, Y, Arheimer, B, Berg, P, Capell, R, Musuuza, J, Pechlivanidis, I and Photiadou, C. 2020. Effect of model calibration strategy on climate projections of hydrological indicators at a continental scale. Climatic Change, 163: 1287–1306. DOI: 10.1007/s10584-020-02874-4
- Hydrohazards Research Team. 2024. Hydrohazards. Available at
https://www.sei.org/projects/hydrohazards/ , accessed: 2024-06-17. - IPCC. 2013. Summary for Policymakers. Cambridge, UK: Cambridge University Press, 1–30. Available at
www.climatechange2013.org . DOI: 10.1017/CBO9781107415324.004 - IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. Available at
https://report.ipcc.ch/ar6/wg1/IPCC_AR6_WGI_FullReport.pdf . DOI: 10.1017/9781009157896 - Johansson, L. 2018. Extremvattenstånd i Halmstad. MSB report, SMHI, MSB, 651 81 KARLSTAD.
- Knoben, WJM, Freer, JE and Woods, RA. 2019. Technical note: Inherent benchmark or not? comparing Nash–Sutcliffe and Kling–Gupta efficiency scores. Hydrology and Earth System Sciences, 23: 4323–4331. DOI: 10.5194/hess-23-4323-2019
- Lindström, G, Pers, C, Rosberg, J, Strömqvist, J and Arheimer, B. 2010. Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales. Hydrology Research, 41(3–4): 295–319. DOI: 10.2166/nh.2010.007
- Longuet-Higgins, MS and Stewart, RW. 1963. A note on wave set-up. Journal of Marine Research, 21(1). Available at:
https://elischolar.library.yale.edu/journal_of_marine_research/989 . - Madec, G and the NEMO team. (eds.) 2016.
NEMO ocean engine . Institut Pierre-Simon Laplace, Université Pierre et Marie Curie, 4 place Jussieu, Paris, ISSN No 1288-1619. - Nash, J and Sutcliffe, J. 1970. River flow forecasting through conceptual models part i — a discussion of principles. Journal of Hydrology, 10(3): 282–290. DOI: 10.1016/0022-1694(70)90255-6
- Phillips, RC, Samadi, S, Hitchcock, DB, Meadows, ME, Wilson, CAME. 2022. The devil is in the tail dependence: An assessment of multivariate copula-based frameworks and dependence concepts for coastal compound flood dynamics. Earth’s Future, 10(9):
e2022EF002705 . DOI: 10.1029/2022EF002705 - Pindsoo, K and Soomere, T. 2015. Contribution of wave set-up into the total water level in the Tallinn area. Proceedings of the Estonian Academy of Sciences, 64: 338–348. DOI: 10.3176/proc.2015.3S.03
- Rutgersson, A, Kjellström, E, Haapala, J, Stendel, M, Danilovich, I, Drews, M, Jylhä, K, Kujala, P, Larsén, XG, Halsnæs, K, Lehtonen, I, Luomaranta, A, Nilsson, E, Olsson, T, Särkkä, J, Tuomi, L and Wasmund, N. 2022. Natural hazards and extreme events in the Baltic sea region. Earth System Dynamics, 13(1): 251–301. DOI: 10.5194/esd-13-251-2022
- Samuelsson, P, Jones, C, Willén, U, Ullerstig, U, Golvik, S, Hansson, U, Jansson, C, Kjellström, E, Nikulin, G and Wyser, K. 2011. The Rossby centre regional climate model rca3: Model description and performance. Tellus, 63A: 4–23. DOI: 10.1111/j.1600-0870.2010.00478.x
- Taylor, KE, Stouffer, RJ and Meehl, GA. 2012. An overview of cmip5 and the experiment design. Bulletin of the American Meteorological Society, 93(4): 485–498. DOI: 10.1175/BAMS-D-11-00094.1
- Trenberth, K, Fasullo, J and Shepherd, T. 2015. Attribution of climate extreme events. Nature Climate Change, 5: 725–730. DOI: 10.1038/nclimate2657
- Vestøl, O, Ågren, J, Steffen, H, Kierulf, H and Tarasov, L. 2019. NKG2016LU: A new land uplift model for Fennoscandia and the Baltic region. Journal of Geodesy, 93(9): 1759–1779. DOI: 10.1007/s00190-019-01280-8
- Vousdoukas, MI, Mentaschi, L, Voukouvalas, E, Bianchi, A, Dottori, F and Feyen, L. 2018. Climatic and socioeconomic controls of future coastal flood risk in Europe. Nature Climate Change, 8: 776–780. DOI: 10.1038/s41558-018-0260-4
- Wang, L, Ting, M and Kushner, P. 2017. A robust empirical seasonal prediction of winter NAO and surface climate. Scientific Reports, 7(279). DOI: 10.1038/s41598-017-00353-y
DOI: https://doi.org/10.16993/tellusa.4068 | Journal eISSN: 3035-9554
Language: English
Page range: 148 - 165
Submitted on: Mar 25, 2024
Accepted on: Jun 22, 2024
Published on: Jul 5, 2024
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2024 Magnus Hieronymus, Peter Berg, Faisal Bin Ashraf, Karina Barquet, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.