Advances in computational modelling of natural hazards and geohazards
Advances in computational modelling of natural hazards and geohazards
Editor(s): A. J. Kettner, G. E. Tucker, R. LeVeque, and N. Kerle
Natural hazards impact thousands of people every year. Floods, droughts, extreme storms, landslides, wildfires, and permafrost erosion all change the Earth's surface and can inflict tremendous damage on human infrastructure. Numerical models of Earth surface processes are one tool to simulate natural hazard processes and provide quantitative pre-event risk assessment. Yet such assessments are only appropriate when the models capture all important physical processes, when the models are tested and well-vetted, when they are usable and proven accurate.

The aim of the special issue is to identify (a) the current state of the art of our current natural hazard process understanding, both fundamentally in the Earth surface processes and in the modelling approaches and technology; (b) important gaps and shortcomings; (c) improvements in natural hazard modelling for risk assessment, with a special focus on building a next-generation cyberinfrastructure and a community of modern modelling and data analysis practices; (d) modeling and conveying uncertainty in numerical risk assessments; and (e) case studies in which numerical models have increased resilience by reducing vulnerability to disasters. This proposal for a NHESS special issue arises from a 3-day international natural hazards conference, Geoprocesses, Geohazards – CSDMS 2018, held during May 22–24th at the University of Colorado, Boulder, USA. See also: https://csdms.colorado.edu/wiki/CSDMS_meeting_2018.

The call for the special issue is open for all submissions within the given scope.

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03 Dec 2020
Forecasting flood hazards in real time: a surrogate model for hydrometeorological events in an Andean watershed
María Teresa Contreras, Jorge Gironás, and Cristián Escauriaza
Nat. Hazards Earth Syst. Sci., 20, 3261–3277, https://doi.org/10.5194/nhess-20-3261-2020,https://doi.org/10.5194/nhess-20-3261-2020, 2020
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18 Aug 2020
The 1958 Lituya Bay tsunami – pre-event bathymetry reconstruction and 3D numerical modelling utilising the computational fluid dynamics software Flow-3D
Andrea Franco, Jasper Moernaut, Barbara Schneider-Muntau, Michael Strasser, and Bernhard Gems
Nat. Hazards Earth Syst. Sci., 20, 2255–2279, https://doi.org/10.5194/nhess-20-2255-2020,https://doi.org/10.5194/nhess-20-2255-2020, 2020
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27 Feb 2020
Improved accuracy and efficiency of flood inundation mapping of low-, medium-, and high-flow events using the AutoRoute model
Michael L. Follum, Ricardo Vera, Ahmad A. Tavakoly, and Joseph L. Gutenson
Nat. Hazards Earth Syst. Sci., 20, 625–641, https://doi.org/10.5194/nhess-20-625-2020,https://doi.org/10.5194/nhess-20-625-2020, 2020
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05 Feb 2020
The impact of topography on seismic amplification during the 2005 Kashmir earthquake
Saad Khan, Mark van der Meijde, Harald van der Werff, and Muhammad Shafique
Nat. Hazards Earth Syst. Sci., 20, 399–411, https://doi.org/10.5194/nhess-20-399-2020,https://doi.org/10.5194/nhess-20-399-2020, 2020
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20 Jan 2020
Modeling the effects of sediment concentration on the propagation of flash floods in an Andean watershed
María Teresa Contreras and Cristián Escauriaza
Nat. Hazards Earth Syst. Sci., 20, 221–241, https://doi.org/10.5194/nhess-20-221-2020,https://doi.org/10.5194/nhess-20-221-2020, 2020
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12 Nov 2019
A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA
Ronda Strauch, Erkan Istanbulluoglu, and Jon Riedel
Nat. Hazards Earth Syst. Sci., 19, 2477–2495, https://doi.org/10.5194/nhess-19-2477-2019,https://doi.org/10.5194/nhess-19-2477-2019, 2019
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12 Nov 2019
Reconstructing patterns of coastal risk in space and time along the US Atlantic coast, 1970–2016
Scott B. Armstrong and Eli D. Lazarus
Nat. Hazards Earth Syst. Sci., 19, 2497–2511, https://doi.org/10.5194/nhess-19-2497-2019,https://doi.org/10.5194/nhess-19-2497-2019, 2019
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04 Nov 2019
An integrated evaluation of the National Water Model (NWM)–Height Above Nearest Drainage (HAND) flood mapping methodology
J. Michael Johnson, Dinuke Munasinghe, Damilola Eyelade, and Sagy Cohen
Nat. Hazards Earth Syst. Sci., 19, 2405–2420, https://doi.org/10.5194/nhess-19-2405-2019,https://doi.org/10.5194/nhess-19-2405-2019, 2019
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22 Oct 2019
Ensemble models from machine learning: an example of wave runup and coastal dune erosion
Tomas Beuzen, Evan B. Goldstein, and Kristen D. Splinter
Nat. Hazards Earth Syst. Sci., 19, 2295–2309, https://doi.org/10.5194/nhess-19-2295-2019,https://doi.org/10.5194/nhess-19-2295-2019, 2019
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26 Sep 2019
The Floodwater Depth Estimation Tool (FwDET v2.0) for improved remote sensing analysis of coastal flooding
Sagy Cohen, Austin Raney, Dinuke Munasinghe, J. Derek Loftis, Andrew Molthan, Jordan Bell, Laura Rogers, John Galantowicz, G. Robert Brakenridge, Albert J. Kettner, Yu-Fen Huang, and Yin-Phan Tsang
Nat. Hazards Earth Syst. Sci., 19, 2053–2065, https://doi.org/10.5194/nhess-19-2053-2019,https://doi.org/10.5194/nhess-19-2053-2019, 2019
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12 Aug 2019
Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework
Jannis M. Hoch, Dirk Eilander, Hiroaki Ikeuchi, Fedor Baart, and Hessel C. Winsemius
Nat. Hazards Earth Syst. Sci., 19, 1723–1735, https://doi.org/10.5194/nhess-19-1723-2019,https://doi.org/10.5194/nhess-19-1723-2019, 2019
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11 Jul 2019
Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation
Jing Wang, Guigen Nie, Shengjun Gao, and Changhu Xue
Nat. Hazards Earth Syst. Sci., 19, 1387–1398, https://doi.org/10.5194/nhess-19-1387-2019,https://doi.org/10.5194/nhess-19-1387-2019, 2019
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09 Apr 2019
Development and validation of the terrain stability model for assessing landslide instability during heavy rain infiltration
Alfonso Gutiérrez-Martín, Miguel Ángel Herrada, José Ignacio Yenes, and Ricardo Castedo
Nat. Hazards Earth Syst. Sci., 19, 721–736, https://doi.org/10.5194/nhess-19-721-2019,https://doi.org/10.5194/nhess-19-721-2019, 2019
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25 Mar 2019
Application of the Levenburg–Marquardt back propagation neural network approach for landslide risk assessments
Junnan Xiong, Ming Sun, Hao Zhang, Weiming Cheng, Yinghui Yang, Mingyuan Sun, Yifan Cao, and Jiyan Wang
Nat. Hazards Earth Syst. Sci., 19, 629–653, https://doi.org/10.5194/nhess-19-629-2019,https://doi.org/10.5194/nhess-19-629-2019, 2019
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