Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3291-2023
© Author(s) 2023. 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-16-3291-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Jun 2023
Development and technical paper |
| 13 Jun 2023
| 13 Jun 2023
An improved subgrid channel model with upwind-form artificial diffusion for river hydrodynamics and floodplain inundation simulation
School of Geographical Sciences, University of Bristol, Bristol, BS8 1QU, UK
Paul Bates
School of Geographical Sciences, University of Bristol, Bristol, BS8 1QU, UK
Jeffrey Neal
School of Geographical Sciences, University of Bristol, Bristol, BS8 1QU, UK
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Laurence Hawker, Jeffrey Neal, James Savage, Thomas Kirkpatrick, Rachel Lord, Yanos Zylberberg, Andre Groeger, Truong Dang Thuy, Sean Fox, Felix Agyemang, and Pham Khanh Nam
Nat. Hazards Earth Syst. Sci., 24, 539–566, https://doi.org/10.5194/nhess-24-539-2024, https://doi.org/10.5194/nhess-24-539-2024, 2024
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We present a global flood model built using a new terrain data set and evaluated in the Central Highlands of Vietnam.
Leanne Archer, Jeffrey Neal, Paul Bates, Emily Vosper, Dereka Carroll, Jeison Sosa, and Daniel Mitchell
Nat. Hazards Earth Syst. Sci., 24, 375–396, https://doi.org/10.5194/nhess-24-375-2024, https://doi.org/10.5194/nhess-24-375-2024, 2024
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We model hurricane-rainfall-driven flooding to assess how the number of people exposed to flooding changes in Puerto Rico under the 1.5 and 2 °C Paris Agreement goals. Our analysis suggests 8 %–10 % of the population is currently exposed to flooding on average every 5 years, increasing by 2 %–15 % and 1 %–20 % at 1.5 and 2 °C. This has implications for adaptation to more extreme flooding in Puerto Rico and demonstrates that 1.5 °C climate change carries a significant increase in risk.
Solomon Hailu Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-251, https://doi.org/10.5194/hess-2023-251, 2023
Revised manuscript under review for HESS
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Our global precipitation data evaluation for hydrological modelling revealed variations in dataset accuracy. The Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP) followed by ERA5 performed well in some areas but had limitations in others. This informs dataset choice for river discharge modelling and highlights the need for improved global precipitation data quality, especially for daily and extreme values.
Thomas P. Collings, Niall D. Quinn, Ivan D. Haigh, Joshua Green, Izzy Probyn, Hamish Wilkinson, Sanne Muis, William V. Sweet, and Paul D. Bates
EGUsphere, https://doi.org/10.5194/egusphere-2023-2267, https://doi.org/10.5194/egusphere-2023-2267, 2023
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Coastal areas are at risk of flooding from rising sea levels and extreme weather events. This study uses a new way to figure out how likely coastal flooding is around the world. The method uses data from observations and computer models to create a detailed map of where these floods might happen at the coast. The approach can predict flooding in areas where there is little or no data. The results can be used to help get ready for and prevent this type of flooding.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Paul D. Bates, James Savage, Oliver Wing, Niall Quinn, Christopher Sampson, Jeffrey Neal, and Andrew Smith
Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, https://doi.org/10.5194/nhess-23-891-2023, 2023
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We present and validate a model that simulates current and future flood risk for the UK at high resolution (~ 20–25 m). We show that UK flood losses were ~ 6 % greater in the climate of 2020 compared to recent historical values. The UK can keep any future increase to ~ 8 % if all countries implement their COP26 pledges and net-zero ambitions in full. However, if only the COP26 pledges are fulfilled, then UK flood losses increase by ~ 23 %; and potentially by ~ 37 % in a worst-case scenario.
Yinxue Liu, Paul D. Bates, and Jeffery C. Neal
Nat. Hazards Earth Syst. Sci., 23, 375–391, https://doi.org/10.5194/nhess-23-375-2023, https://doi.org/10.5194/nhess-23-375-2023, 2023
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In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
Maria Pregnolato, Andrew O. Winter, Dakota Mascarenas, Andrew D. Sen, Paul Bates, and Michael R. Motley
Nat. Hazards Earth Syst. Sci., 22, 1559–1576, https://doi.org/10.5194/nhess-22-1559-2022, https://doi.org/10.5194/nhess-22-1559-2022, 2022
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The interaction of flow, structure and network is complex, and yet to be fully understood. This study aims to establish rigorous practices of computational fluid dynamics (CFD) for modelling hydrodynamic forces on inundated bridges, and understanding the consequences of such impacts on the surrounding network. The objectives of this study are to model hydrodynamic forces as the demand on the bridge structure, to advance a structural reliability and network-level analysis.
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, https://doi.org/10.5194/hess-25-5981-2021, 2021
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Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
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We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, https://doi.org/10.5194/gmd-14-3577-2021, 2021
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LISFLOOD-FP has been extended with new shallow-water solvers – DG2 and FV1 – for modelling all types of slow- or fast-moving waves over any smooth or rough surface. Using GPU parallelisation, FV1 is faster than the simpler ACC solver on grids with millions of elements. The DG2 solver is notably effective on coarse grids where river channels are hard to capture, improving predicted river levels and flood water depths. This marks a new step towards real-world DG2 flood inundation modelling.
Oliver E. J. Wing, Andrew M. Smith, Michael L. Marston, Jeremy R. Porter, Mike F. Amodeo, Christopher C. Sampson, and Paul D. Bates
Nat. Hazards Earth Syst. Sci., 21, 559–575, https://doi.org/10.5194/nhess-21-559-2021, https://doi.org/10.5194/nhess-21-559-2021, 2021
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Global flood models are difficult to validate. They generally output theoretical flood events of a given probability rather than an observed event that they can be tested against. Here, we adapt a US-wide flood model to enable the rapid simulation of historical flood events in order to more robustly understand model biases. For 35 flood events, we highlight the challenges of model validation amidst observational data errors yet evidence the increasing skill of large-scale models.
Thomas O'Shea, Paul Bates, and Jeffrey Neal
Nat. Hazards Earth Syst. Sci., 20, 2281–2305, https://doi.org/10.5194/nhess-20-2281-2020, https://doi.org/10.5194/nhess-20-2281-2020, 2020
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Outlined here is a multi-disciplinary framework for analysing and evaluating the nature of vulnerability to, and capacity for, flood hazard within a complex urban society. It provides scope beyond the current, reified, descriptors of
flood riskand models the role of affected individuals within flooded areas. Using agent-based modelling coupled with the LISFLOOD-FP hydrodynamic model, potentially influential behaviours that give rise to the flood hazard system are identified and discussed.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
Hydrol. Earth Syst. Sci., 22, 5629–5637, https://doi.org/10.5194/hess-22-5629-2018, https://doi.org/10.5194/hess-22-5629-2018, 2018
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One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
Keith J. Beven, Susana Almeida, Willy P. Aspinall, Paul D. Bates, Sarka Blazkova, Edoardo Borgomeo, Jim Freer, Katsuichiro Goda, Jim W. Hall, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, David B. Stephenson, Thorsten Wagener, Matt Watson, and Kate L. Wilkins
Nat. Hazards Earth Syst. Sci., 18, 2741–2768, https://doi.org/10.5194/nhess-18-2741-2018, https://doi.org/10.5194/nhess-18-2741-2018, 2018
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This paper discusses how uncertainties resulting from lack of knowledge are considered in a number of different natural hazard areas including floods, landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. As every analysis is necessarily conditional on the assumptions made about the nature of sources of such uncertainties it is also important to follow the guidelines for good practice suggested in Part 2.
Keith J. Beven, Willy P. Aspinall, Paul D. Bates, Edoardo Borgomeo, Katsuichiro Goda, Jim W. Hall, Trevor Page, Jeremy C. Phillips, Michael Simpson, Paul J. Smith, Thorsten Wagener, and Matt Watson
Nat. Hazards Earth Syst. Sci., 18, 2769–2783, https://doi.org/10.5194/nhess-18-2769-2018, https://doi.org/10.5194/nhess-18-2769-2018, 2018
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Part 1 of this paper discussed the uncertainties arising from gaps in knowledge or limited understanding of the processes involved in different natural hazard areas. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities. A conceptual framework for good practice in dealing with epistemic uncertainties is outlined and implications of applying the principles to natural hazard science are discussed.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
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We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Jannis M. Hoch, Jeffrey C. Neal, Fedor Baart, Rens van Beek, Hessel C. Winsemius, Paul D. Bates, and Marc F. P. Bierkens
Geosci. Model Dev., 10, 3913–3929, https://doi.org/10.5194/gmd-10-3913-2017, https://doi.org/10.5194/gmd-10-3913-2017, 2017
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To improve flood hazard assessments, it is vital to model all relevant processes. We here present GLOFRIM, a framework for coupling hydrologic and hydrodynamic models to increase the number of physical processes represented in hazard computations. GLOFRIM is openly available, versatile, and extensible with more models. Results also underpin its added value for model benchmarking, showing that not only model forcing but also grid properties and the numerical scheme influence output accuracy.
Laurent Guillaume Courty, Adrián Pedrozo-Acuña, and Paul David Bates
Geosci. Model Dev., 10, 1835–1847, https://doi.org/10.5194/gmd-10-1835-2017, https://doi.org/10.5194/gmd-10-1835-2017, 2017
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This paper presents Itzï, a new free software for the simulation of floods. It is integrated with a geographic information system (GIS), which reduces the human time necessary for preparing the entry data and analysing the results of the simulation.
Itzï uses a simplified numerical scheme that permits to obtain results faster than with other types of models using more complex equations.
In this article, Itzï is tested with three cases that show its suitability to simulate urban floods.
Melissa Wood, Renaud Hostache, Jeffrey Neal, Thorsten Wagener, Laura Giustarini, Marco Chini, Giovani Corato, Patrick Matgen, and Paul Bates
Hydrol. Earth Syst. Sci., 20, 4983–4997, https://doi.org/10.5194/hess-20-4983-2016, https://doi.org/10.5194/hess-20-4983-2016, 2016
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We propose a methodology to calibrate the bankfull channel depth and roughness parameters in a 2-D hydraulic model using an archive of medium-resolution SAR satellite-derived flood extent maps. We used an identifiability methodology to locate the parameters and suggest the SAR images which could be optimally used for model calibration. We found that SAR images acquired around the flood peak provide best calibration potential for the depth parameter, improving when SAR images are combined.
K. J. Beven, S. Almeida, W. P. Aspinall, P. D. Bates, S. Blazkova, E. Borgomeo, K. Goda, J. C. Phillips, M. Simpson, P. J. Smith, D. B. Stephenson, T. Wagener, M. Watson, and K. L. Wilkins
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2015-295, https://doi.org/10.5194/nhess-2015-295, 2016
Preprint withdrawn
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Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. This is Part 2 of 2 papers reviewing these epistemic uncertainties and covers different areas of natural hazards including landslides and debris flows, dam safety, droughts, earthquakes, tsunamis, volcanic ash clouds and pyroclastic flows, and wind storms. It is based on the work of the UK CREDIBLE research consortium.
K. J. Beven, W. P. Aspinall, P. D. Bates, E. Borgomeo, K. Goda, J. W. Hall, T. Page, J. C. Phillips, J. T. Rougier, M. Simpson, D. B. Stephenson, P. J. Smith, T. Wagener, and M. Watson
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-3-7333-2015, https://doi.org/10.5194/nhessd-3-7333-2015, 2015
Preprint withdrawn
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Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. This is Part 1 of 2 papers reviewing these epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities. It is based on the work of the CREDIBLE research consortium on Risk and Uncertainty in Natural Hazards.
R. Hostache, C. Hissler, P. Matgen, C. Guignard, and P. Bates
Hydrol. Earth Syst. Sci., 18, 3539–3551, https://doi.org/10.5194/hess-18-3539-2014, https://doi.org/10.5194/hess-18-3539-2014, 2014
C. C. Sampson, T. J. Fewtrell, F. O'Loughlin, F. Pappenberger, P. B. Bates, J. E. Freer, and H. L. Cloke
Hydrol. Earth Syst. Sci., 18, 2305–2324, https://doi.org/10.5194/hess-18-2305-2014, https://doi.org/10.5194/hess-18-2305-2014, 2014
B. Jongman, H. Kreibich, H. Apel, J. I. Barredo, P. D. Bates, L. Feyen, A. Gericke, J. Neal, J. C. J. H. Aerts, and P. J. Ward
Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, https://doi.org/10.5194/nhess-12-3733-2012, 2012
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
EGUsphere, https://doi.org/10.5194/egusphere-2023-2060, https://doi.org/10.5194/egusphere-2023-2060, 2023
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Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated and its computational performance is now quasi-linear. VISIR-2 yields, for more than ten days in a year, two-digit savings for a ferry sailing in the Mediterranean Sea. Sailboat routes with wind and currents can be optimised as well.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernandez Banos, William C. Skamarock, and Michael G. Duda
EGUsphere, https://doi.org/10.5194/egusphere-2023-2299, https://doi.org/10.5194/egusphere-2023-2299, 2023
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To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the the incremental analysis update (IAU) in the Model for Prediction Across Scales for the Atmospheric component (MPAS-A), coupled with the Joint Effort for Data assimilation Integration (JEDI), through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS-JEDI cycling system.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
EGUsphere, https://doi.org/10.5194/egusphere-2023-1569, https://doi.org/10.5194/egusphere-2023-1569, 2023
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Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics becomes affordable.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-88, https://doi.org/10.5194/gmd-2023-88, 2023
Revised manuscript accepted for GMD
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We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel, which optimizes trajectories under weather conditions simulated by an atmospheric model (EMAC). This paper focuses on the ability of the module to identify eco-efficient trajectories, which reduce the flights climate impact at limited cost penalties.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
EGUsphere, https://doi.org/10.48550/arXiv.2304.06058, https://doi.org/10.48550/arXiv.2304.06058, 2023
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Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating hard-to-measure quantities. We highlight an approach that ensures accurate results from point data sources (such as height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, can aid communication between scientists, and is well suited for integration with specialised software that automates the processes.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
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Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
Bruno K. Zürcher
Geosci. Model Dev., 16, 1697–1711, https://doi.org/10.5194/gmd-16-1697-2023, https://doi.org/10.5194/gmd-16-1697-2023, 2023
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We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations with an algorithmic complexity that depends on the product of the number of sample points and the number of grid points, our approach reduces this dependency to their sum.
David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551, https://doi.org/10.5194/gmd-16-1537-2023, https://doi.org/10.5194/gmd-16-1537-2023, 2023
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Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343, https://doi.org/10.5194/gmd-16-1315-2023, https://doi.org/10.5194/gmd-16-1315-2023, 2023
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In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276, https://doi.org/10.5194/gmd-16-1265-2023, https://doi.org/10.5194/gmd-16-1265-2023, 2023
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This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229, https://doi.org/10.5194/gmd-16-1213-2023, https://doi.org/10.5194/gmd-16-1213-2023, 2023
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This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976, https://doi.org/10.5194/gmd-16-961-2023, https://doi.org/10.5194/gmd-16-961-2023, 2023
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We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849, https://doi.org/10.5194/gmd-16-833-2023, https://doi.org/10.5194/gmd-16-833-2023, 2023
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We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313, https://doi.org/10.5194/gmd-16-289-2023, https://doi.org/10.5194/gmd-16-289-2023, 2023
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In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
EGUsphere, https://doi.org/10.5194/egusphere-2022-863, https://doi.org/10.5194/egusphere-2022-863, 2023
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This paper applies cutting-edge numerical methods to show how uncertain climate change and technological progress affect the future utilization of the scarce world's land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The more uncertain the future crop yields are, the more forest conversion will be necessary to sustain human welfare. Some of that conversion takes place even when crop yields are not actually affected.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029, https://doi.org/10.5194/gmd-15-9015-2022, https://doi.org/10.5194/gmd-15-9015-2022, 2022
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While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784, https://doi.org/10.5194/gmd-15-8765-2022, https://doi.org/10.5194/gmd-15-8765-2022, 2022
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A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764, https://doi.org/10.5194/gmd-15-8749-2022, https://doi.org/10.5194/gmd-15-8749-2022, 2022
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Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022, https://doi.org/10.5194/gmd-15-8639-2022, 2022
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Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022, https://doi.org/10.5194/gmd-15-7903-2022, 2022
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Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator’s grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
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Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, https://doi.org/10.5194/gmd-15-7641-2022, 2022
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Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Cited articles
Al Baky, M. A., Islam, M., and Paul, S.: Flood hazard, vulnerability and
risk assessment for different land use classes using a flow model, Earth
Syst. Environ., 4, 225–244, 2020.
Alsdorf, D., Bates, P., Melack, J., Wilson, M., and Dunne, T.: Spatial and
temporal complexity of the Amazon flood measured from space, Geophys.
Res. Lett., 34, L08402, https://doi.org/10.1029/2007GL029447, 2007.
Ayog, J. L., Kesserwani, G., Shaw, J., Sharifian, M. K., and Bau, D.:
Second-order discontinuous Galerkin flood model: comparison with
industry-standard finite volume models, J. Hydrol., 594, 125924, https://doi.org/10.1016/j.jhydrol.2020.125924,
2021.
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010.
Cook, A. and Merwade, V.: Effect of topographic data, geometric
configuration and modeling approach on flood inundation mapping, J. Hydrol., 377, 131–142, https://doi.org/10.1016/j.jhydrol.2009.08.015, 2009.
Cozzolino, L., Cimorelli, L., Della Morte, R., Pugliano, G., Piscopo, V.,
and Pianese, D.: Flood propagation modeling with the Local Inertia
Approximation: Theoretical and numerical analysis of its physical
limitations, Adv. Water Resour., 133, 103422, https://doi.org/10.1016/j.advwatres.2019.103422, 2019.
de Almeida, G. A. and Bates, P.: Applicability of the local inertial
approximation of the shallow water equations to flood modeling, Water Resour. Res., 49, 4833–4844, 2013.
de Almeida, G. A., Bates, P., Freer, J. E., and Souvignet, M.: Improving the
stability of a simple formulation of the shallow water equations for 2-D
flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570, 2012.
Edmonds, D. A., Caldwell, R. L., Brondizio, E. S., and Siani, S. M.: Coastal
flooding will disproportionately impact people on river deltas, Nat.
Commun., 11, 1–8, 2020.
Fewtrell, T. J., Neal, J. C., Bates, P. D., and Harrison, P. J.: Geometric
and structural river channel complexity and the prediction of urban
inundation, Hydrol. Process., 25, 3173–3186, https://doi.org/10.1002/hyp.8035, 2011.
Grimaldi, S., Li, Y., Walker, J. P., and Pauwels, V. R. N.: Effective
Representation of River Geometry in Hydraulic Flood Forecast Models, Water Resour. Res., 54, 1031–1057, https://doi.org/10.1002/2017wr021765, 2018.
Horritt, M. S., Bates, P. D., Fewtrell, T. J., Mason, D. C., and Wilson, M.
D.: Modelling the hydraulics of the Carlisle 2005 flood event, Proceedings
of the Institution of Civil Engineers – Water Management, 163, 273–281, https://doi.org/10.1680/wama.2010.163.6.273, 2010.
Hunter, N., Bates, P., Neelz, S., Pender, G., Villanueva, I., Wright, N.,
Liang, D., Falconer, R. A., Lin, B., and Waller, S.: Benchmarking 2D
hydraulic models for urban flooding, Proceedings of the Institution of Civil
Engineers-Water Management, 13–30, 2008.
Hunter, N. M., Horritt, M. S., Bates, P. D., Wilson, M. D., and Werner, M.
G. F.: An adaptive time step solution for raster-based storage cell
modelling of floodplain inundation, Adv. Water Resour., 28,
975–991, https://doi.org/10.1016/j.advwatres.2005.03.007, 2005.
Jongman, B., Ward, P. J., and Aerts, J. C.: Global exposure to river and
coastal flooding: Long term trends and changes, Global Environmental Change,
22, 823–835, 2012.
Kesserwani, G., Ayog, J. L., and Bau, D.: Discontinuous Galerkin formulation
for 2D hydrodynamic modelling: Trade-offs between theoretical complexity and
practical convenience, Comput. Method. Appl. M., 342, 710–741, https://doi.org/10.1016/j.cma.2018.08.003, 2018.
Leopold, L. B. and Maddock, T.: The hydraulic geometry of stream channels
and some physiographic implications, US Government Printing Office, 1953.
McMichael, C., Dasgupta, S., Ayeb-Karlsson, S., and Kelman, I.: A review of
estimating population exposure to sea-level rise and the relevance for
migration, Environ. Res. Lett., 15, 123005, https://doi.org/10.1088/1748-9326/abb398, 2020.
Merwade, V., Cook, A., and Coonrod, J.: GIS techniques for creating river
terrain models for hydrodynamic modeling and flood inundation mapping,
Environ. Model. Softw., 23, 1300–1311, https://doi.org/10.1016/j.envsoft.2008.03.005, 2008.
Neal, J., Schumann, G., and Bates, P.: A subgrid channel model for
simulating river hydraulics and floodplain inundation over large and data
sparse areas, Water Resour. Res., 48, W11506, https://doi.org/10.1029/2012WR012514, 2012.
Neal, J., Hawker, L., Savage, J., Durand, M., Bates, P., and Sampson, C.:
Estimating river channel bathymetry in large scale flood inundation models,
Water Resour. Res., 57, e2020WR028301, https://doi.org/10.1029/2020WR028301, 2021.
Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D.,
and Horritt, M. S.: Distributed whole city water level measurements from the
Carlisle 2005 urban flood event and comparison with hydraulic model
simulations, J. Hydrol., 368, 42–55, 2009.
Neal, J. C., Odoni, N. A., Trigg, M. A., Freer, J. E., Garcia-Pintado, J.,
Mason, D. C., Wood, M., and Bates, P. D.: Efficient incorporation of channel
cross-section geometry uncertainty into regional and global scale flood
inundation models, J. Hydrol., 529, 169–183, https://doi.org/10.1016/j.jhydrol.2015.07.026, 2015.
O'Loughlin, F. E., Neal, J., Schumann, G., Beighley, E., and Bates, P. D.: A
LISFLOOD-FP hydraulic model of the middle reach of the Congo, J. Hydrol., 580, 124203, https://doi.org/10.1016/j.jhydrol.2019.124203, 2020.
Paiva, R. C., Collischonn, W., and Tucci, C. E.: Large scale hydrologic and
hydrodynamic modeling using limited data and a GIS based approach, J. Hydrol., 406, 170–181, 2011.
Rajib, A., Liu, Z., Merwade, V., Tavakoly, A. A., and Follum, M. L.: Towards
a large-scale locally relevant flood inundation modeling framework using
SWAT and LISFLOOD-FP, J. Hydrol., 581, 124406, https://doi.org/10.1016/j.jhydrol.2019.124406, 2020.
Rong, Y.: An improved sub-grid channel model, Zenodo [code], https://doi.org/10.5281/zenodo.7064320, 2022.
Rudari, R., Campo, L., Rebora, N., Boni, G., and Herold, C.: Improvement of the global food model for the GAR 2015, United Nations, Geneva, 2015.
Schumann, G. J. P., Neal, J. C., Voisin, N., Andreadis, K. M., Pappenberger,
F., Phanthuwongpakdee, N., Hall, A. C., and Bates, P. D.: A first
large-scale flood inundation forecasting model, Water Resour. Res.,
49, 6248–6257, https://doi.org/10.1002/wrcr.20521, 2013.
Sehili, A., Lang, G., and Lippert, C.: High-resolution subgrid models:
background, grid generation, and implementation, Ocean Dynam., 64,
519–535, 2014.
Shaw, J., Kesserwani, G., Neal, J., Bates, P., and Sharifian, M. K.: LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs, Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, 2021.
Shustikova, I., Domeneghetti, A., Neal, J. C., Bates, P., and Castellarin,
A.: Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex
topography, Hydrol. Sci. J., 64, 1769–1782, 2019.
Sridharan, B., Gurivindapalli, D., Kuiry, S. N., Mali, V. K., Nithila Devi,
N., Bates, P. D., and Sen, D.: Explicit Expression of Weighting Factor for
Improved Estimation of Numerical Flux in Local Inertial Models, Water Resour. Res., 56, e2020WR027357, https://doi.org/10.1029/2020WR027357, 2020.
Sridharan, B., Bates, P. D., Sen, D., and Kuiry, S. N.: Local-inertial
shallow water model on unstructured triangular grids, Adv. Water
Resour., 152, 103930, https://doi.org/10.1016/j.advwatres.2021.103930, 2021.
Trigg, M. A., Wilson, M. D., Bates, P. D., Horritt, M. S., Alsdorf, D. E.,
Forsberg, B. R., and Vega, M. C.: Amazon flood wave hydraulics, J. Hydrol., 374, 92–105, 2009.
Trigg, M. A., Bates, P. D., Wilson, M. D., Schumann, G., and Baugh, C.:
Floodplain channel morphology and networks of the middle Amazon River, Water Resour. Res., 48, W10504, https://doi.org/10.1029/2012wr011888, 2012.
Yamazaki, D., Kanae, S., Kim, H., and Oki, T.: A physically based
description of floodplain inundation dynamics in a global river routing
model, Water Resour. Res., 47, W04501, https://doi.org/10.1029/2010WR009726, 2011.
Yamazaki, D., O'Loughlin, F., Trigg, M. A., Miller, Z. F., Pavelsky, T. M.,
and Bates, P. D.: Development of the Global Width Database for Large Rivers,
Water Resour. Res., 50, 3467–3480, https://doi.org/10.1002/2013wr014664, 2014.
Short summary
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing...
