Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8765-2022
© 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-15-8765-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 Dec 2022
Methods for assessment of models |
| 06 Dec 2022
| 06 Dec 2022
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
Jason Goetz
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
Alexander Brenning
Department of Geography, Friedrich Schiller University Jena,
Loebdergraben 32, 07743 Jena, Germany
Related authors
No articles found.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
Short summary
Short summary
We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Raphael Knevels, Helene Petschko, Herwig Proske, Philip Leopold, Aditya N. Mishra, Douglas Maraun, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 23, 205–229, https://doi.org/10.5194/nhess-23-205-2023, https://doi.org/10.5194/nhess-23-205-2023, 2023
Short summary
Short summary
In summer 2009 and 2014, rainfall events occurred in the Styrian Basin (Austria), triggering thousands of landslides. Landslide storylines help to show potential future changes under changing environmental conditions. The often neglected uncertainty quantification was the aim of this study. We found uncertainty arising from the landslide model to be of the same order as climate scenario uncertainty. Understanding the dimensions of uncertainty is crucial for allowing informed decision-making.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
Short summary
Short summary
Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Jason Goetz, Robin Kohrs, Eric Parra Hormazábal, Manuel Bustos Morales, María Belén Araneda Riquelme, Cristián Henríquez, and Alexander Brenning
Nat. Hazards Earth Syst. Sci., 21, 2543–2562, https://doi.org/10.5194/nhess-21-2543-2021, https://doi.org/10.5194/nhess-21-2543-2021, 2021
Short summary
Short summary
Debris flows are fast-moving landslides that can cause incredible destruction to lives and property. Using the Andes of Santiago as an example, we developed tools to finetune and validate models predicting likely runout paths over large regions. We anticipate that our automated approach that links the open-source R software with SAGA-GIS will make debris-flow runout simulation more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.
Milan Flach, Alexander Brenning, Fabian Gans, Markus Reichstein, Sebastian Sippel, and Miguel D. Mahecha
Biogeosciences, 18, 39–53, https://doi.org/10.5194/bg-18-39-2021, https://doi.org/10.5194/bg-18-39-2021, 2021
Short summary
Short summary
Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. We study the impact of droughts and heatwaves on the uptake of CO2 of different vegetation types at the global scale. We find that agricultural areas are generally strongly affected. Forests instead are not particularly sensitive to the events under scrutiny. This implies different water management strategies of forests but also a lack of sensitivity to remote-sensing-derived vegetation activity.
Nora Linscheid, Lina M. Estupinan-Suarez, Alexander Brenning, Nuno Carvalhais, Felix Cremer, Fabian Gans, Anja Rammig, Markus Reichstein, Carlos A. Sierra, and Miguel D. Mahecha
Biogeosciences, 17, 945–962, https://doi.org/10.5194/bg-17-945-2020, https://doi.org/10.5194/bg-17-945-2020, 2020
Short summary
Short summary
Vegetation typically responds to variation in temperature and rainfall within days. Yet seasonal changes in meteorological conditions, as well as decadal climate variability, additionally shape the state of ecosystems. It remains unclear how vegetation responds to climate variability on these different timescales. We find that the vegetation response to climate variability depends on the timescale considered. This scale dependency should be considered for modeling land–atmosphere interactions.
Marco Marcer, Charlie Serrano, Alexander Brenning, Xavier Bodin, Jason Goetz, and Philippe Schoeneich
The Cryosphere, 13, 141–155, https://doi.org/10.5194/tc-13-141-2019, https://doi.org/10.5194/tc-13-141-2019, 2019
Short summary
Short summary
This study aims to assess the occurrence of rock glacier destabilization in the French Alps, a process that causes a landslide-like behaviour of permafrost debris slopes. A significant number of the landforms in the region were found to be experiencing destabilization. Multivariate analysis suggested a link between destabilization occurrence and permafrost thaw induced by climate warming. These results call for a regional characterization of permafrost hazards in the context of climate change.
Milan Flach, Sebastian Sippel, Fabian Gans, Ana Bastos, Alexander Brenning, Markus Reichstein, and Miguel D. Mahecha
Biogeosciences, 15, 6067–6085, https://doi.org/10.5194/bg-15-6067-2018, https://doi.org/10.5194/bg-15-6067-2018, 2018
Short summary
Short summary
Northern forests enhanced their productivity during and before the 2010 Russian mega heatwave. We scrutinize this issue with a novel type of multivariate extreme event detection approach. Forests compensate for 54 % of the carbon losses in agricultural ecosystems due to vulnerable conditions in spring and better water management in summer. The findings highlight the importance of forests in mitigating climate change, while not alleviating the consequences of extreme events for food security.
Milan Flach, Fabian Gans, Alexander Brenning, Joachim Denzler, Markus Reichstein, Erik Rodner, Sebastian Bathiany, Paul Bodesheim, Yanira Guanche, Sebastian Sippel, and Miguel D. Mahecha
Earth Syst. Dynam., 8, 677–696, https://doi.org/10.5194/esd-8-677-2017, https://doi.org/10.5194/esd-8-677-2017, 2017
Short summary
Short summary
Anomalies and extremes are often detected using univariate peak-over-threshold approaches in the geoscience community. The Earth system is highly multivariate. We compare eight multivariate anomaly detection algorithms and combinations of data preprocessing. We identify three anomaly detection algorithms that outperform univariate extreme event detection approaches. The workflows have the potential to reveal novelties in data. Remarks on their application to real Earth observations are provided.
Guillermo F. Azócar, Alexander Brenning, and Xavier Bodin
The Cryosphere, 11, 877–890, https://doi.org/10.5194/tc-11-877-2017, https://doi.org/10.5194/tc-11-877-2017, 2017
Short summary
Short summary
We present in this work a new statistical permafrost distribution model that provided a more detailed, locally adjusted insights into mountain permafrost distribution in the semi-arid Chilean Andes. The results indicate conditions favorable for permafrost presence, can be present in up to about 6.8 % of the study area (1051 km2), especially in the Elqui and Huasco watersheds. This kind of methodological approach used in this research can be replicable in another parts of the Andes.
Stefan Steger, Alexander Brenning, Rainer Bell, and Thomas Glade
Nat. Hazards Earth Syst. Sci., 16, 2729–2745, https://doi.org/10.5194/nhess-16-2729-2016, https://doi.org/10.5194/nhess-16-2729-2016, 2016
Short summary
Short summary
This study investigates the propagation of landslide inventory-based positional errors into statistical landslide susceptibility models by artificially introducing such spatial inaccuracies. The findings highlight that (i) an increasing positional error is related to increasing distortions of modelling and validation results, (ii) interrelations between inventory-based errors and modelling results are complex, and (iii) inventory-based errors can be counteracted by adapting the study design.
A. Brenning, M. Schwinn, A. P. Ruiz-Páez, and J. Muenchow
Nat. Hazards Earth Syst. Sci., 15, 45–57, https://doi.org/10.5194/nhess-15-45-2015, https://doi.org/10.5194/nhess-15-45-2015, 2015
Short summary
Short summary
Mountain roads in developing countries often increase landslide occurrence. In this study, landslide initiation frequency along interurban highways was investigated in the Ecuadorian Andes across different climates. Using statistical models, landslides were found to be about 20 times more likely to occur in close proximity to highways compared to areas in 200m distance from highways while accounting for other environmental factors. Road effects appear to be enhanced in some geological units.
Related subject area
Numerical methods
Numerical stabilization methods for level-set-based ice front migration
Modelling chemical advection during magma ascent
Consistent point data assimilation in Firedrake and Icepack
A computationally efficient parameterization of aerosol, cloud and precipitation pH for application at global and regional scale (EQSAM4Clim-v12)
Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)
Assessing effects of climate and technology uncertainties in large natural resource allocation problems
VISIR-2: ship weather routing in Python
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments
MinVoellmy v1: a lightweight model for simulating rapid mass movements based on a modified Voellmy rheology
Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets
Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model
CHONK 1.0: landscape evolution framework: cellular automata meets graph theory
Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
An improved subgrid channel model with upwind-form artificial diffusion for river hydrodynamics and floodplain inundation simulation
A model instability issue in the National Centers for Environmental Prediction Global Forecast System version 16 and potential solutions
A comparison of 3-D spherical shell thermal convection results at low to moderate Rayleigh number using ASPECT (version 2.2.0) and CitcomS (version 3.3.1)
LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations
Measurement Error Proxy System Models: MEPSM v0.2
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
Strategies for conservative and non-conservative monotone remapping on the sphere
Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)
A mixed finite-element discretisation of the shallow-water equations
Multifidelity Monte Carlo estimation for efficient uncertainty quantification in climate-related modeling
Massively parallel modeling and inversion of electrical resistivity tomography data using PFLOTRAN
Parallelized domain decomposition for multi-dimensional Lagrangian random walk mass-transfer particle tracking schemes
The Intelligent Prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
ISMIP-HOM benchmark experiments using Underworld
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
Short summary
Short summary
We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Hugo Dominguez, Nicolas Riel, and Pierre Lanari
Geosci. Model Dev., 17, 6105–6122, https://doi.org/10.5194/gmd-17-6105-2024, https://doi.org/10.5194/gmd-17-6105-2024, 2024
Short summary
Short summary
Predicting the behaviour of magmatic systems is important for understanding Earth's matter and heat transport. Numerical modelling is a technique that can predict complex systems at different scales of space and time by solving equations using various techniques. This study tests four algorithms to find the best way to transport the melt composition. The "weighted essentially non-oscillatory" algorithm emerges as the best choice, minimising errors and preserving system mass well.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024, https://doi.org/10.5194/gmd-17-5369-2024, 2024
Short summary
Short summary
Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating quantities that are hard to measure. We highlight an approach that ensures accurate results from point data sources (e.g. height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, aids communication between scientists, and is well-suited for integration with specialised software that automates processes.
Swen Metzger, Samuel Rémy, Jason E. Williams, Vincent Huijnen, and Johannes Flemming
Geosci. Model Dev., 17, 5009–5021, https://doi.org/10.5194/gmd-17-5009-2024, https://doi.org/10.5194/gmd-17-5009-2024, 2024
Short summary
Short summary
EQSAM4Clim has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. It is based on an analytical concept that is sufficiently fast and free of numerical noise, which makes it attractive for air quality forecasting. Version 12 allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases, including the acidity of the aerosols.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
Geosci. Model Dev., 17, 4943–4959, https://doi.org/10.5194/gmd-17-4943-2024, https://doi.org/10.5194/gmd-17-4943-2024, 2024
Short summary
Short summary
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 become affordable.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
Short summary
Short summary
This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
Short summary
Short summary
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 quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component 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.
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., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
Short summary
Short summary
We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
Short summary
Short summary
This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
Short summary
Short summary
In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
Short summary
Short summary
The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
Short summary
Short summary
Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
Short summary
Short summary
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
Short summary
Short summary
Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
Short summary
Short summary
It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Matt J. Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2023-643, https://doi.org/10.5194/egusphere-2023-643, 2023
Short summary
Short summary
In paleoclimate research, proxy system models or PSMs model chemical or biological systems which receive environmental inputs, and output e.g. geochemical signals. The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs, and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Cited articles
Ai, X., Sun, B., and Chen, X.: Construction of small sample seismic
landslide susceptibility evaluation model based on transfer learning: a case
study of Jiuzhaigou earthquake, B. Eng. Geol. Environ., 81, 116, https://doi.org/10.1007/s10064-022-02601-6, 2022.
Baktashmotlagh, M., Harandi, M. T., Lovell, B. C., and Salzmann, M.:
Unsupervised domain adaptation by domain invariant projection, IEEE
I. Conf. Comp. Vis., 1–8 December, 769–776,
https://doi.org/10.1109/ICCV.2013.100, 2013.
Bannour, W., Maalel, A., and Ben Ghezala, H. H.: Emergency management
case-based reasoning systems: a survey of recent developments, J.
Exp. Theor. Artif. In., 1–24,
https://doi.org/10.1080/0952813x.2021.1952654, 2021.
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and
Vaughan, J. W.: A theory of learning from different domains, Mach.
Learn., 79, 151–175, https://doi.org/10.1007/s10994-009-5152-4, 2010.
Bordoni, M., Galanti, Y., Bartelletti, C., Persichillo, M. G., Barsanti, M.,
Giannecchini, R., Avanzi, G. D., Cevasco, A., Brandolini, P., Galve, J. P.,
and Meisina, C.: The influence of the inventory on the determination of the
rainfall-induced shallow landslides susceptibility using generalized
additive models, Catena, 193, 104630,
https://doi.org/10.1016/j.catena.2020.104630, 2020.
Brenning, A., Schwinn, M., Ruiz-Páez, A. P., and Muenchow, J.: Landslide susceptibility near highways is increased by 1 order of magnitude in the Andes of southern Ecuador, Loja province, Nat. Hazards Earth Syst. Sci., 15, 45–57, https://doi.org/10.5194/nhess-15-45-2015, 2015.
Ciccarese, G., Mulas, M., and Corsini, A.: Combining spatial modelling and
regionalization of rainfall thresholds for debris flows hazard mapping in
the Emilia-Romagna Apennines (Italy), Landslides, 18, 3513–3529,
https://doi.org/10.1007/s10346-021-01739-w, 2021.
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015.
Courty, N., Flamary, R., Tuia, D., and Rakotomamonjy, A.: Optimal transport
for domain adaptation, IEEE T. Pattern Anal., 39, 1853–1865, https://doi.org/10.1109/Tpami.2016.2615921,
2017.
Dou, J., Chang, K. T., Chen, S. S., Yunus, A. P., Liu, J. K., Xia, H., and
Zhu, Z. F.: Automatic case-based reasoning approach for landslide detection:
integration of object-oriented image analysis and a genetic algorithm,
Remote Sensing, 7, 4318–4342, https://doi.org/10.3390/rs70404318, 2015.
Dou, J., Yunus, A. P., Merghadi, A., Shirzadi, A., Nguyen, H., Hussain, Y.,
Avtar, R., Chen, Y., Pham, B. T., and Yamagishi, H.: Different sampling
strategies for predicting landslide susceptibilities are deemed less
consequential with deep learning, Sci. Total Environ., 720,
137320, https://doi.org/10.1016/j.scitotenv.2020.137320, 2020.
European Union:
EU-DEM, https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem,
last access: 29 November 2022.
Fang, B., Chen, G., Pan, L., Kou, R., and Wang, L. Z.: GAN-based siamese
framework for landslide inventory mapping using bi-temporal optical remote
sensing images, IEEE Geosci. Remote Sens., 18, 391–395,
https://doi.org/10.1109/LGRS.2020.2979693, 2021.
Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.
Gasser, D., Gusterhuber, J., Krische, O., Puhr, B., Scheucher, L., Wagner,
T., and Stüwe, K.: Geology of Styria: an overview, Mitteilungen des
naturwissenschaftlichen Vereines für Steiermark, 139, 5–36, 2009.
Goetz, J. N., Brenning, A., Petschko, H., and Leopold, P.: Evaluating
machine learning and statistical prediction techniques for landslide
susceptibility modeling, Compu. Geosci., 81, 1–11,
https://doi.org/10.1016/j.cageo.2015.04.007, 2015.
Goetz, J. N., Guthrie, R. H., and Brenning, A.: Integrating physical and
empirical landslide susceptibility models using generalized additive models,
Geomorphology, 129, 376–386, https://doi.org/10.1016/j.geomorph.2011.03.001,
2011.
Gong, B., Grauman, K., and Sha, F.: Connecting the dots with landmarks:
discriminatively learning domain-invariant features for unsupervised domain
adaptation, International conference on machine learning (ICML), Proceedings of the 30th International Conference on Machine
Learning, 28,
222–230, https://proceedings.mlr.press/v28/gong13.html (last access: 2 December 2022), 2013.
Gong, B., Grauman, K., and Sha, F.: Geodesic flow kernel and landmarks:
kernel methods for unsupervised domain adaptation, in: Domain Adaptation in
Computer Vision Applications, Springer, 59–79,
https://doi.org/10.1007/978-3-319-58347-1_3, 2017.
Gretton, A., Borgwardt, K., Rasch, M., Schölkopf, B., and Smola, A.: A
kernel method for the two-sample-problem, Adv. Neur. In., 19, 513–520, https://doi.org/10.48550/arXiv.0805.2368,
2006.
Hammond, K. J.: Case-based planning: viewing planning as a memory task,
Academic Press, San Diego, Elsevier, ISBN 0-12-322060-2, 2012.
Haque, U., da Silva, P. F., Devoli, G., Pilz, J., Zhao, B. X., Khaloua, A.,
Wilopo, W., Andersen, P., Lu, P., Lee, J., Yamamoto, T., Keellings, D., Wu,
J. H., and Glass, G. E.: The human cost of global warming: deadly landslides
and their triggers (1995–2014), Sci. Total Environ., 682,
673–684, https://doi.org/10.1016/j.scitotenv.2019.03.415, 2019.
Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X.: Applied Logistic
Regression Third Edition Preface, John Wiley & Sons, 398 pp.,
https://doi.org/10.1002/9781118548387, 2013.
Huang, Y. and Zhao, L.: Review on landslide susceptibility mapping using
support vector machines, Catena, 165, 520–529,
https://doi.org/10.1016/j.catena.2018.03.003, 2018.
Humphreys, P., McIvor, R., and Chan, F.: Using case-based reasoning to
evaluate supplier environmental management performance, Expert Syst.
Appl., 25, 141–153, https://doi.org/10.1016/S0957-4174(03)00042-3,
2003.
Jiang, J. and Zhai, C.: Instance weighting for domain adaptation in NLP,
Proceedings of the 45th Annual Meeting of the Association Computational
Linguistics, 23–30 June, Prague, Czech Republic, 264–271,
https://ink.library.smu.edu.sg/sis_research/1253 (last access: 29 November 2022), 2007.
Kavzoglu, T., Colkesen, I., and Sahin, E. K.: Machine learning techniques
in landslide susceptibility mapping: a survey and a case study, Landslides, 50, 283–301,
https://doi.org/10.1007/978-3-319-77377-3_13, 2019.
Knevels, R., Petschko, H., Leopold, P., and Brenning, A.: Geographic
object-based image analysis for automated landslide detection using open
source GIS software, ISPRS Int. J. Geo-Inf., 8, 551,
https://doi.org/10.3390/ijgi8120551, 2019 (data available at: https://www.mdpi.com/2220-9964/8/12/551/s1, last access: 2 December 2022).
Knevels, R., Petschko, H., Proske, H., Leopold, P., Maraun, D., and
Brenning, A.: Event-based landslide modeling in the Styrian Basin, Austria:
accounting for time-varying rainfall and land cover, Geosciences, 10, 217,
https://doi.org/10.3390/geosciences10060217, 2020.
Knevels, R., Brenning, A., Gingrich, S., Heiss, G., Lechner, T., Leopold,
P., Plutzar, C., Proske, H., and Petschko, H.: Towards the use of land use
legacies in landslide modeling: current challenges and future perspectives
in an Austrian case study, Land, 10, 954,
https://doi.org/10.3390/land10090954, 2021 (data available at: https://www.mdpi.com/article/10.3390/land10090954/s1, last access: 2 December 2022).
Liang, P., Qin, C. Z., Zhu, A. X., Hou, Z. W., Fan, N. Q., and Wang, Y. J.:
A case-based method of selecting covariates for digital soil mapping,
J. Integr. Agr., 19, 2127–2136,
https://doi.org/10.1016/S2095-3119(19)62857-1, 2020a.
Liang, P., Qin, C. Z., Zhu, A. X., Zhu, T. X., Fan, N. Q., and Hou, Z. W.:
Using the most similar case method to automatically select environmental
covariates for predictive mapping, Earth Sci. Inf., 13, 719–728,
https://doi.org/10.1007/s12145-020-00466-5, 2020b.
Liang, P., Qin, C. Z., and Zhu, A. X.: Comparison on two case-based
reasoning strategies of automatically selecting terrain covariates for
digital soil mapping, T. GIS, 25, 2419–2437,
https://doi.org/10.1111/tgis.12831, 2021.
Lin, Q. G., Lima, P., Steger, S., Glade, T., Jiang, T., Zhang, J. H., Liu,
T. X., and Wang, Y.: National-scale data-driven rainfall induced landslide
susceptibility mapping for China by accounting for incomplete landslide
data, Geosci. Front., 12, 101248,
https://doi.org/10.1016/j.gsf.2021.101248, 2021.
Liu, D., Li, J., and Fan, F.: Classification of landslides on the
southeastern Tibet Plateau based on transfer learning and limited labelled
datasets, Remote Sens. Lett., 12, 286–295,
https://doi.org/10.1080/2150704X.2021.1890263, 2021.
Long, M., Wang, J., Ding, G., Sun, J., and Yu, P. S.: Transfer feature
learning with joint distribution adaptation, IEEE I. Conf.
Comp. Vis., 1–8 December, 2200–2207,
https://doi.org/10.1109/ICCV.2013.274, 2013.
Lu, H., Ma, L., Fu, X., Liu, C., Wang, Z., Tang, M. and Li, N.: Landslides
information extraction using object-oriented image analysis paradigm based
on deep learning and transfer learning, Remote Sensing, 12, 752,
https://doi.org/10.3390/rs12050752, 2020.
Mboga, N., D'Aronco, S., Grippa, T., Pelletier, C., Georganos, S.,
Vanhuysse, S., Wolff, E., Smets, B., Dewitte, O., Lennert, M., and Wegner,
J. D.: Domain adaptation for semantic segmentation of historical
panchromatic orthomosaics in Central Africa, ISPRS Int. J.
Geo-Inf., 10, 523, https://doi.org/10.3390/ijgi10080523, 2021.
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T.,
Avtar, R., and Abderrahmane, B.: Machine learning methods for landslide
susceptibility studies: a comparative overview of algorithm performance,
Earth-Sci. Rev., 207, 103225,
https://doi.org/10.1016/j.earscirev.2020.103225, 2020.
Mezaal, M. R. and Pradhan, B.: An improved algorithm for identifying shallow
and deep-seated landslides in dense tropical forest from airborne laser
scanning data, Catena, 167, 147–159,
https://doi.org/10.1016/j.catena.2018.04.038, 2018.
Muenchow, J.:
Geomorphic process rates of landslides along a humidity gradient in the tropical Andes, https://cran.r-project.org/package=sperrorest,
last access: 29 November 2022.
Muenchow, J., Brenning, A., and Richter, M.: Geomorphic process rates of
landslides along a humidity gradient in the tropical Andes, Geomorphology,
139, 271–284, https://doi.org/10.1016/j.geomorph.2011.10.029, 2012.
Pan, S. J.: Transfer learning, in: Data Classification: Algorithms and
Applications, Vol. 21, edited by: Aggarwal, C. C. and Reddy, C. K.,
CRC Press, Roca, Bosa, Italy, 537–570, ISBN 9780429102639, 2014.
Pan, S. J. and Yang, Q. A.: A survey on transfer learning, IEEE T.
Knowl. Data En., 22, 1345–1359,
https://doi.org/10.1109/TKDE.2009.191, 2010.
Patel, V. M., Gopalan, R., Li, R. N., and Chellappa, R.: Visual domain
adaptation, IEEE Signal Proc. Mag., 32, 53–69,
https://doi.org/10.1109/MSP.2014.2347059, 2015.
Petschko, H.: Challenges and solutions of modelling landslide susceptibility
in heterogeneous regions, PhD thesis, University of Vienna, Vienna, AC Nummer: AC12052251, 2014.
Petschko, H., Bell, R., Brenning, A., and Glade, T.: Landslide
susceptibility modeling with generalized additive models–facing the
heterogeneity of large regions, in: Landslides and Engineered Slopes,
Protecting Society through Improved Understanding, Vol. 1, edited by:
Eberhardt, E., Froese, C., Turner, A. K., and Leroueil, S., Taylor and
Francis, Banff, Alberta, Canada, 769–777, ISBN 0415621232, 9780415621236, 2012.
Petschko, H., Brenning, A., Bell, R., Goetz, J., and Glade, T.: Assessing the quality of landslide susceptibility maps – case study Lower Austria, Nat. Hazards Earth Syst. Sci., 14, 95–118, https://doi.org/10.5194/nhess-14-95-2014, 2014.
Petschko, H., Bell, R., and Glade, T.: Effectiveness of visually analyzing
LiDAR DTM derivatives for earth and debris slide inventory mapping for
statistical susceptibility modeling, Landslides, 13, 857–872,
https://doi.org/10.1007/s10346-015-0622-1, 2016.
Piacentini, D., Troiani, F., Daniele, G., and Pizziolo, M.: Historical
geospatial database for landslide analysis: the Catalogue of Landslide
OCcurrences in the Emilia-Romagna Region (CLOCkER), Landslides, 15, 811–822,
https://doi.org/10.1007/s10346-018-0962-8, 2018.
Qin, C. Z., Zhu, A. X., Shi, X., Li, B. L., Pei, T., and Zhou, C. H.:
Quantification of spatial gradation of slope positions, Geomorphology, 110,
152–161, https://doi.org/10.1016/j.geomorph.2009.04.003, 2009.
Qin, C.-Z., Wu, X.-W., Jiang, J.-C., and Zhu, A.-X.: Case-based knowledge formalization and reasoning method for digital terrain analysis – application to extracting drainage networks, Hydrol. Earth Syst. Sci., 20, 3379–3392, https://doi.org/10.5194/hess-20-3379-2016, 2016.
Qin, S., Guo, X., Sun, J., Qiao, S., Zhang, L., Yao, J., Cheng, Q., and
Zhang, Y.: Landslide detection from open satellite imagery using distant
domain transfer learning, Remote Sensing, 13, 3383,
https://doi.org/10.3390/rs13173383, 2021.
Regione Emilia-Romagna public administration:
Geology, soil and seismic risk in the Emilia-Romagna region, https://ambiente.regione.emilia-romagna.it/it/geologia/cartografia/webgis-banchedati/cartografia-dissesto-idrogeologico#consulta-dati-shp,
last access: 29 November 2022.
Rossi, M., Witt, A., Guzzetti, F., Malamud, B. D., and Peruccacci, S.:
Analysis of historical landslide time series in the Emilia-Romagna region,
northern Italy, Earth Surf. Proc. Land., 35, 1123–1137,
https://doi.org/10.1002/esp.1858, 2010.
Rudy, A. C. A., Lamoureux, S. F., Treitz, P., and van Ewijk, K. Y.:
Transferability of regional permafrost disturbance susceptibility modelling
using generalized linear and generalized additive models, Geomorphology,
264, 95–108, https://doi.org/10.1016/j.geomorph.2016.04.011, 2016.
Segoni, S., Rosi, A., Fanti, R., Gallucci, A., Monni, A., and Casagli, N.: A
regional-scale landslide warning system based on 20 years of operational
experience, Water, 10, 1297, https://doi.org/10.3390/w10101297, 2018.
Segoni, S., Pappafico, G., Luti, T., and Catani, F.: Landslide
susceptibility assessment in complex geological settings: sensitivity to
geological information and insights on its parameterization, Landslides, 17,
2443–2453, https://doi.org/10.1007/s10346-019-01340-2, 2020.
Sequeira, A. M. M., Mellin, C., Lozano-Montes, H. M., Vanderklift, M. A.,
Babcock, R. C., Haywood, M. D. E., Meeuwig, J. J., and Caley, M. J.:
Transferability of predictive models of coral reef fish species richness,
J. Appl. Ecol., 53, 64–72,
https://doi.org/10.1111/1365-2664.12578, 2016.
SGSS (Servizio Geologico Sismico e dei Suoli): Carta Inventario delle frane
e Archivio storico delle frane,
https://ambiente.regione.emilia-romagna.it/it/geologia/cartografia/webgis-banchedati/cartografia-dissesto-idrogeologico
(last access: 28 March 2022), 2019.
Shi, X., Zhu, A. X., Burt, J. E., Oi, F., and Simonson, D.: A case-based
reasoning approach to fuzzy soil mapping, Soil Sci. Soc. Am.
J., 68, 885–894, https://doi.org/10.2136/sssaj2004.8850, 2004.
Shi, X., Long, R., Dekett, R., and Philippe, J.: Integrating different types
of knowledge for digital soil mapping, Soil Sci. Soc. Am.
J., 73, 1682–1692, https://doi.org/10.2136/sssaj2007.0158, 2009.
Shimodaira, H.: Improving predictive inference under covariate shift by
weighting the log-likelihood function, J. Stat. Plan. Infer., 90, 227–244,
https://doi.org/10.1016/S0378-3758(00)00115-4, 2000.
Steger, S., Brenning, A., Bell, R., and Glade, T.: The influence of
systematically incomplete shallow landslide inventories on statistical
susceptibility models and suggestions for improvements, Landslides, 14,
1767–1781, https://doi.org/10.1007/s10346-017-0820-0, 2017.
Van Den Eeckhaut, M., Hervas, J., Jaedicke, C., Malet, J. P., Montanarella,
L., and Nadim, F.: Statistical modelling of Europe-wide landslide
susceptibility using limited landslide inventory data, Landslides, 9,
357–369, https://doi.org/10.1007/s10346-011-0299-z, 2012.
Wang, D. L., Wan, K. D., and Ma, W. X.: Emergency decision-making model of
environmental emergencies based on case-based reasoning method, J.
Environ. Manage., 262, 110382,
https://doi.org/10.1016/j.jenvman.2020.110382, 2020.
Wang, H., Wang, L., and Zhang, L.: Transfer learning improves landslide
susceptibility assessment, Gondwana Res., 1–17,
https://doi.org/10.1016/j.gr.2022.07.008, online first, 2022.
Wang, K., Zhang, S. J., Delgado-Téllez, R., and Wei, F. Q.: A new slope
unit extraction method for regional landslide analysis based on
morphological image analysis, B. Eng. Geol. Environ., 78, 4139–4151, https://doi.org/10.1007/s10064-018-1389-0, 2019.
Wang, M. and Deng, W. H.: Deep visual domain adaptation: a survey,
Neurocomputing, 312, 135–153, https://doi.org/10.1016/j.neucom.2018.05.083,
2018.
Wang, Z., Hu, Z., Liu, H., Gong, H., Zhao, W., Yu, M., and Zhang, M.:
Application of the relief degree of land surface in landslide disasters
susceptibility assessment in China, 2010 18th International Conference on
Geoinformatics, 18–20 June, 1–5,
https://doi.org/10.1109/GEOINFORMATICS.2010.5567734, 2010.
Wang, Z. H. and Brenning, A.: Active-learning approaches for landslide
mapping using support vector machines, Remote Sensing, 13, 2588,
https://doi.org/10.3390/rs13132588, 2021.
Wenger, S. J. and Olden, J. D.: Assessing transferability of ecological
models: an underappreciated aspect of statistical validation, Methods
Ecol. Evol., 3, 260–267,
https://doi.org/10.1111/j.2041-210X.2011.00170.x, 2012.
Wilson, G. and Cook, D. J.: A survey of unsupervised deep domain adaptation,
ACM T. Intel. Syst. Tec., 11, 1–46,
https://doi.org/10.1145/3400066, 2020.
Wood, S. N.: Generalized additive models: an introduction with R, Chapman and Hall/CRC, New York, U.S., ISBN 9780429093159, https://doi.org/10.1201/9781420010404, 2006.
Xu, Q., Ouyang, C., Jiang, T., Yuan, X., Fan, X., and Cheng, D.: MFFENet and
ADANet: a robust deep transfer learning method and its application in high
precision and fast cross scene recognition of earthquake induced landslides,
Landslides, 19, 1617–1647, https://doi.org/10.1007/s10346-022-01847-1,
2022.
Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F.,
Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A., Dormann,
C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf,
R. F., Gregr, E. J., Halpin, P. N., Heikkinen, R. K., Heinanen, S., Jones,
A. R., Krishnakumar, P. K., Lauria, V., Lozano-Montes, H., Mannocci, L.,
Mellin, C., Mesgaran, M. B., Moreno-Amat, E., Mormede, S., Novaczek, E.,
Oppel, S., Crespo, G. O., Peterson, A. T., Rapacciuolo, G., Roberts, J. J.,
Ross, R. E., Scales, K. L., Schoeman, D., Snelgrove, P., Sundblad, G.,
Thuiller, W., Torres, L. G., Verbruggen, H., Wang, L., Wenger, S.,
Whittingham, M. J., Zharikov, Y., Zurell, D., and Sequeira, A. M. M.:
Outstanding challenges in the transferability of ecological models, Trends
Ecol. Evol., 33, 790–802,
https://doi.org/10.1016/j.tree.2018.08.001, 2018.
Wang, Z.: W-Zhihao/GMD_slidetransfer: scripts and test data for manuscript “Transfer learning for landslide susceptibility modelling using domain adaptation and case-based reasoning” (v0.1.0-landslideTL), Zenodo [code], https://doi.org/10.5281/zenodo.7376782, 2022.
Zhu, A.-X. and Band, L. E.: A knowledge-based approach to data integration
for soil mapping, Can. J. Remote Sens., 20, 408–418,
https://doi.org/10.1080/07038992.1994.10874583, 2014.
Zhu, Q., Chen, L., Hu, H., Pirasteh, S., Li, H., and Xie, X.: Unsupervised
feature learning to improve transferability of landslide susceptibility
representations, IEEE J. Sel. Top. Appl., 13, 3917–3930,
https://doi.org/10.1109/JSTARS.2020.3006192, 2020.
Short summary
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.
A lack of inventory data can be a limiting factor in developing landslide predictive models,...
