Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-7133-2021
© Author(s) 2021. 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-14-7133-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
How biased are our models? – a case study of the alpine region
Denise Degen
CORRESPONDING AUTHOR
Computational Geoscience and Reservoir Engineering, RWTH Aachen University, Wüllnerstr. 2, 52062 Aachen, Germany
Cameron Spooner
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Telegrafenberg, 14473 Potsdam, Germany
Institute of Earth and Environmental Science, Potsdam University, 14476 Potsdam, Germany
Magdalena Scheck-Wenderoth
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Telegrafenberg, 14473 Potsdam, Germany
Department of Geology, Geochemistry of Petroleum and Coal, RWTH Aachen University, 52056 Aachen, Germany
Mauro Cacace
GFZ German Research Centre for Geosciences, Helmholtz Centre Potsdam, Telegrafenberg, 14473 Potsdam, Germany
Related authors
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.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
Short summary
Short summary
In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Ángela María Gómez-García, Álvaro González, Mauro Cacace, Magdalena Scheck-Wenderoth, and Gaspar Monsalve
Solid Earth, 15, 281–303, https://doi.org/10.5194/se-15-281-2024, https://doi.org/10.5194/se-15-281-2024, 2024
Short summary
Short summary
We compute a realistic three-dimensional model of the temperatures down to 75 km deep within the Earth, below the Caribbean Sea and northwestern South America. Using this, we estimate at which rock temperatures past earthquakes nucleated in the region and find that they agree with those derived from laboratory experiments of rock friction. We also analyse how the thermal state of the system affects the spatial distribution of seismicity in this region.
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.
Denise Degen and Mauro Cacace
Geosci. Model Dev., 14, 1699–1719, https://doi.org/10.5194/gmd-14-1699-2021, https://doi.org/10.5194/gmd-14-1699-2021, 2021
Short summary
Short summary
In this work, we focus on improving the understanding of subsurface processes with respect to interactions with climate dynamics. We present advanced, open-source mathematical methods that enable us to investigate the influence of various model properties on the final outcomes. By relying on our approach, we have been able to showcase their importance in improving our understanding of the subsurface and highlighting the current shortcomings of currently adopted models.
Cameron Spooner, Magdalena Scheck-Wenderoth, Mauro Cacace, and Denis Anikiev
Solid Earth Discuss., https://doi.org/10.5194/se-2020-202, https://doi.org/10.5194/se-2020-202, 2020
Revised manuscript not accepted
Short summary
Short summary
By comparing long term lithospheric strength to seismicity patterns across the Alpine region, we show that most seismicity occurs where strengths are highest within the crust. The lower crust appears largely aseismic due to energy being dissipated by ongoing creep from low viscosities. Lithospheric structure appears to exert a primary control on seismicity distribution, with both forelands display a different distribution patterns, likely reflecting their different tectonic settings.
Denis Anikiev, Adrian Lechel, Maria Laura Gomez Dacal, Judith Bott, Mauro Cacace, and Magdalena Scheck-Wenderoth
Adv. Geosci., 49, 225–234, https://doi.org/10.5194/adgeo-49-225-2019, https://doi.org/10.5194/adgeo-49-225-2019, 2019
Short summary
Short summary
We have developed a first Germany-wide 3D data-based density and temperature model integrating geoscientific observations and physical processes. The model can serve as a reference for local detailed studies dealing with temperature, pressure, stress, subsidence and sedimentation. Our results help to improve subsurface utilization concepts, reveal current geomechanical conditions crucial for hazard assessment and gather information on viable resources such groundwater and deep geothermal energy.
Cameron Spooner, Magdalena Scheck-Wenderoth, Hans-Jürgen Götze, Jörg Ebbing, György Hetényi, and the AlpArray Working Group
Solid Earth, 10, 2073–2088, https://doi.org/10.5194/se-10-2073-2019, https://doi.org/10.5194/se-10-2073-2019, 2019
Short summary
Short summary
By utilising both the observed gravity field of the Alps and their forelands and indications from deep seismic surveys, we were able to produce a 3-D structural model of the region that indicates the distribution of densities within the lithosphere. We found that the present-day Adriatic crust is both thinner and denser than the European crust and that the properties of Alpine crust are strongly linked to their provenance.
Nora Koltzer, Magdalena Scheck-Wenderoth, Mauro Cacace, Maximilian Frick, and Judith Bott
Adv. Geosci., 49, 197–206, https://doi.org/10.5194/adgeo-49-197-2019, https://doi.org/10.5194/adgeo-49-197-2019, 2019
Short summary
Short summary
In this study we investigate groundwater flow in the deep subsurface of the Upper Rhine Graben. We make use of a 3-D numerical model covering the entire Upper Rhine Graben. The deep hydrodynamics are characterized by fluid flow from the graben flanks towards its center and in the southern half of the graben from south to north. Moreover, local heterogeneities in the shallow flow field arise from the interaction between regional groundwater flow and the heterogeneous sedimentary configuration.
Guido Blöcher, Christian Kluge, Harald Milsch, Mauro Cacace, Antoine B. Jacquey, and Jean Schmittbuhl
Adv. Geosci., 49, 95–104, https://doi.org/10.5194/adgeo-49-95-2019, https://doi.org/10.5194/adgeo-49-95-2019, 2019
Short summary
Short summary
The focus of the paper is to evaluate the permeability change of a matrix-fracture systems under mechanical loading und to understand the processes behind. This evaluation is based on data from laboratory experiments in comparison to 3-D numerical modelling results.
Maximilian Frick, Magdalena Scheck-Wenderoth, Mauro Cacace, and Michael Schneider
Adv. Geosci., 49, 9–18, https://doi.org/10.5194/adgeo-49-9-2019, https://doi.org/10.5194/adgeo-49-9-2019, 2019
Short summary
Short summary
The study presented in this paper aims at reproducing findings from chemical and isotopic groundwater sample analysis along with quantifying the influence of regional (cross-boundary) flow for the area of Berlin, Germany. For this purpose we built 3-D models of the subsurface, populating them with material parameters (e.g. porosity, permeability) and solving them for coupled fluid and heat transport. Special focus was given to the setup of boundary conditions, i.e. fixed pressure at the sides.
Elisabeth Peters, Guido Blöcher, Saeed Salimzadeh, Paul J. P. Egberts, and Mauro Cacace
Adv. Geosci., 45, 209–215, https://doi.org/10.5194/adgeo-45-209-2018, https://doi.org/10.5194/adgeo-45-209-2018, 2018
Short summary
Short summary
Accuracy of well inflow modelling in different numerical simulation approaches was compared for a multi-lateral well with laterals of varying diameter. For homogeneous cases, all simulators generally were reasonably close in terms of the total well flow (deviations smaller than 4 %). The distribution of the flow over the different laterals in a well can vary significantly between simulators (> 20 %). In a heterogeneous case with a fault the deviations between the approaches were much larger.
Nasrin Haacke, Maximilian Frick, Magdalena Scheck-Wenderoth, Michael Schneider, and Mauro Cacace
Adv. Geosci., 45, 177–184, https://doi.org/10.5194/adgeo-45-177-2018, https://doi.org/10.5194/adgeo-45-177-2018, 2018
Short summary
Short summary
The main goal of this study was to understand how different realizations of the impact of groundwater pumping activities in a major urban center would affect the results of 3-D numerical models. In detail we looked at two model scenarios which both rely on the same geological structural model but differ in the realization of the groundwater boundary conditions. The results show, that it is necessary to use groundwater wells as an active parameter to reproduce local movement patterns.
Mauro Cacace and Antoine B. Jacquey
Solid Earth, 8, 921–941, https://doi.org/10.5194/se-8-921-2017, https://doi.org/10.5194/se-8-921-2017, 2017
Short summary
Short summary
The paper describes theory and numerical implementation for coupled thermo–hydraulic–mechanical processes focusing on reservoir (mainly related to geothermal energy) applications.
Judith Sippel, Christian Meeßen, Mauro Cacace, James Mechie, Stewart Fishwick, Christian Heine, Magdalena Scheck-Wenderoth, and Manfred R. Strecker
Solid Earth, 8, 45–81, https://doi.org/10.5194/se-8-45-2017, https://doi.org/10.5194/se-8-45-2017, 2017
Short summary
Short summary
The Kenya Rift is a zone along which the African continental plate is stretched as evidenced by strong earthquake and volcanic activity. We want to understand the controlling factors of past and future tectonic deformation; hence, we assess the structural and strength configuration of the rift system at the present-day. Data-driven 3-D numerical models show how the inherited composition of the crust and a thermal anomaly in the deep mantle interact to form localised zones of tectonic weakness.
Y. Cherubini, M. Cacace, M. Scheck-Wenderoth, and V. Noack
Geoth. Energ. Sci., 2, 1–20, https://doi.org/10.5194/gtes-2-1-2014, https://doi.org/10.5194/gtes-2-1-2014, 2014
Related subject area
Numerical methods
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
Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding
An automatic mesh generator for coupled 1D/2D hydrodynamic models
HETerogeneous vectorized or Parallel (HETPv1.0): An updated inorganic heterogeneous chemistry solver for metastable state NH4+–Na+–Ca2+–K+–Mg2+–SO42––NO3––Cl– based on ISORROPIA II
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
3D geological modelling of igneous intrusions in LoopStructural v1.5.10
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1), part I: 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 II: a semi-discrete error analysis framework for assessing coupling schemes
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
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modelling using VolcanicAshInversion v1.2.1, within the operational eEMEP volcanic plume forecasting system (version rv4_17)
Fast approximate Barnes interpolation: illustrated by Python-Numba implementation fast-barnes-py v1.0
ParticleDA.jl v.1.0: A real-time data assimilation software platform
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
Assessing Effects of Climate and Technology Uncertainties in Large Natural Resource Allocation Problems
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
ISMIP-HOM benchmark experiments using Underworld
spyro: a Firedrake-based wave propagation and full-waveform-inversion finite-element solver
Spatial filtering in a 6D hybrid-Vlasov scheme to alleviate adaptive mesh refinement artifacts: a case study with Vlasiator (versions 5.0, 5.1, and 5.2.1)
A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
Characterizing uncertainties of Earth system modeling with heterogeneous many-core architecture computing
Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models
Impact of the numerical solution approach of a plant hydrodynamic model (v0.1) on vegetation dynamics
Islet: interpolation semi-Lagrangian element-based transport
Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains
Assessing the robustness and scalability of the accelerated pseudo-transient method
Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation
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.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, Jose Antolínez, Tim Leijnse, and Dano Roelvink
EGUsphere, https://doi.org/10.5194/egusphere-2023-2341, https://doi.org/10.5194/egusphere-2023-2341, 2023
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-1434, https://doi.org/10.5194/egusphere-2023-1434, 2023
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.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-159, https://doi.org/10.5194/gmd-2023-159, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This work outlines a new solver written in Fortran 90 to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the 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.
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.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-85, https://doi.org/10.5194/gmd-2023-85, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Previous work have demonstrated how adding geological knowledge to modelling methods create 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 test the method in 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.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
EGUsphere, https://doi.org/10.48550/arXiv.2306.05377, https://doi.org/10.48550/arXiv.2306.05377, 2023
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
EGUsphere, https://doi.org/10.48550/arXiv.2306.04929, https://doi.org/10.48550/arXiv.2306.04929, 2023
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 those groups of combined 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.
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.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
EGUsphere, https://doi.org/10.5194/egusphere-2023-51, https://doi.org/10.5194/egusphere-2023-51, 2023
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.
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.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-38, https://doi.org/10.5194/gmd-2023-38, 2023
Revised manuscript accepted for GMD
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. One of the current challenges is the optimal use of real-time observations. 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.
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.
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
Short summary
Short summary
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
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.
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
Short summary
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
Short summary
Short summary
To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
Short summary
Short summary
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
Short summary
Short summary
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Geosci. Model Dev., 15, 6285–6310, https://doi.org/10.5194/gmd-15-6285-2022, https://doi.org/10.5194/gmd-15-6285-2022, 2022
Short summary
Short summary
Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, the Islet method. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. The Islet method performs well on a number of verification problems and achieves high performance in the E3SM Atmosphere Model version 2.
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
Geosci. Model Dev., 15, 6085–6113, https://doi.org/10.5194/gmd-15-6085-2022, https://doi.org/10.5194/gmd-15-6085-2022, 2022
Short summary
Short summary
This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Geosci. Model Dev., 15, 5757–5786, https://doi.org/10.5194/gmd-15-5757-2022, https://doi.org/10.5194/gmd-15-5757-2022, 2022
Short summary
Short summary
Continuum mechanics-based modelling of physical processes at large scale requires huge computational resources provided by massively parallel hardware such as graphical processing units. We present a suite of numerical algorithms, implemented using the Julia language, that efficiently leverages the parallelism. We demonstrate that our implementation is efficient, scalable and robust and showcase applications to various geophysical problems.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, https://doi.org/10.5194/gmd-15-4941-2022, 2022
Short summary
Short summary
We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
Cited articles
Aretz-Nellesen, N., Grepl, M. A., and Veroy, K.: 3D-VAR for parameterized
partial differential equations: a certified reduced basis approach, Adv. Comput. Math., 45, 2369–2400, 2019. a
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for
uncertainty and global sensitivity analysis of deterministic models: A
hydrological case study, Environ. Modell. Softw., 51, 26–34, 2014. a
Baş, D. and Boyacı, I. H.: Modeling and optimization I: Usability of response surface methodology, J. Food Eng., 78, 836–845, 2007. a
Bezerra, M. A., Santelli, R. E., Oliveira, E. P., Villar, L. S., and Escaleira,
L. A.: Response surface methodology (RSM) as a tool for optimization in
analytical chemistry, Talanta, 76, 965–977, 2008. a
Böhm, R., Auer, I., Schöner, W., Ganekind, M., Gruber, C., Jurkovic,
A., Orlik, A., and Ungersböck, M.: Eine neue Webseite mit instrumentellen
Qualitäts-Klimadaten für den Grossraum Alpen zurück bis 1760,
Wiener Mitteilungen, 216, 7–20, 2009. a
Box, G. E.: Robustness in the Strategy of Scientific Model Building, in:
Robustness in statistics, Elsevier, Amsterdam, The Netherlands, 201–236, 1979. a
Cannavó, F.: Sensitivity analysis for volcanic source modeling quality
assessment and model selection, Comput. Geosci., 44, 52–59, 2012. a
Cherpeau, N. and Caumon, G.: Stochastic structural modelling in sparse data situations, 21, 233–247, 2015. a
Cloke, H., Pappenberger, F., and Renaud, J.-P.: Multi-Method Global
Sensitivity Analysis (MMGSA) for modelling floodplain hydrological
processes, Hydrological Processes: An International Journal, 22, 1660–1674, 2008. a
Degen, D., Veroy, K., and Wellmann, F.: cgre-aachen/DwarfElephant:
DwarfElephant 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4074777, 2020b. a, b
Doherty, J. E. and Hunt, R. J.: Approaches to highly parameterized inversion: a
guide to using PEST for groundwater-model calibration, US Department of the
Interior, US Geological Survey Reston, 2010. a
Elison, P., Niederau, J., Vogt, C., and Clauser, C.: Quantification of thermal conductivity uncertainty for basin modeling, AAPG Bull., 103, 1787–1809, 2019. a
Fan, Y. and Van den Dool, H.: A global monthly land surface air temperature
analysis for 1948 – present, J. Geophys. Res.-Atmos., 113, https://doi.org//10.1029/2007JD008470, 2008. a
Feng, L., Palmer, P. I., Parker, R. J., Deutscher, N. M., Feist, D. G., Kivi, R., Morino, I., and Sussmann, R.: Estimates of European uptake of CO2 inferred from GOSAT X retrievals: sensitivity to measurement bias inside and outside Europe, Atmos. Chem. Phys., 16, 1289–1302, https://doi.org/10.5194/acp-16-1289-2016, 2016. a
Fernández, M., Eguía, P., Granada, E., and Febrero, L.: Sensitivity
analysis of a vertical geothermal heat exchanger dynamic simulation:
Calibration and error determination, Geothermics, 70, 249–259, 2017. a
Feyen, L. and Caers, J.: Quantifying geological uncertainty for flow and
transport modeling in multi-modal heterogeneous formations, Adv. Water Resour., 29, 912–929, 2006. a
Floris, F., Bush, M., Cuypers, M., Roggero, F., and Syversveen, A. R.: Methods
for quantifying the uncertainty of production forecasts: a comparative study, Petrol. Geosci., 7, S87–S96, 2001. a
Frangos, M., Marzouk, Y., Willcox, K., and van Bloemen Waanders, B.: Surrogate and reduced-order modeling: A comparison of approaches for large-scale statistical inverse problems, in: Large–Scale Inverse Problems and Quantification of Uncertainty, John Wiley and Sons, Hoboken, New Jersey, USA, 7, 123–149. https://doi.org/10.1002/9780470685853.ch7, 2010. a
Fuchs, S. and Balling, N.: Improving the temperature predictions of subsurface thermal models by using high-quality input data. Part 1: Uncertainty analysis of the thermal-conductivity parameterization, Geothermics, 64, 42–54, 2016. a
Ghasemi, M. and Gildin, E.: Model order reduction in porous media flow
simulation using quadratic bilinear formulation, Computat. Geosci.,
20, 723–735, 2016. a
Gosses, M., Nowak, W., and Wöhling, T.: Explicit treatment for Dirichlet, Neumann and Cauchy boundary conditions in POD-based reduction of groundwater models, Adv. Water Resour., 115, 160–171, 2018. a
Grepl, M.: Reduced-basis Approximation and A Posteriori Error Estimation for
Parabolic Partial Differential Equations, Ph.D. thesis, Massachusetts
Institute of Technology, 2005. a
Hill, M. C. and Tiedeman, C. R.: Effective groundwater model calibration: with analysis of data, sensitivities, predictions, and uncertainty, John Wiley and Sons, Hoboken, New Jersey, USA, 2006. a
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J.,
Dai, X., Maskell, K., and Johnson, C.: Climate change 2001: the scientific
basis, The Press Syndicate of the University of Cambridge, Cambridge, UK, 2001. a
Iglesias, M. and Stuart, A. M.: Inverse Problems and Uncertainty
Quantification, SIAM News, https://homepages.warwick.ac.uk/~masdr/BOOKCHAPTERS/stuart19c.pdf, 2–3, 2014. a
Jülich Supercomputing Centre: JUWELS: Modular Tier-0/1 Supercomputer, Jülich Supercomputing Centre, JLSRF., 5, A135, https://doi.org/10.17815/jlsrf-5-171, 2019. a
Kärcher, M., Boyaval, S., Grepl, M. A., and Veroy, K.: Reduced basis
approximation and a posteriori error bounds for 4D-Var data assimilation,
Optim. Eng., 19, 663–695, 2018. a
Khuri, A. I. and Mukhopadhyay, S.: Response surface methodology, Wiley
Interdisciplinary Reviews: Computational Statistics, 2, 128–149, 2010. a
Lehmann, H., Wang, K., and Clauser, C.: Parameter identification and
uncertainty analysis for heat transfer at the KTB drill site using a 2-D
inverse method, Tectonophysics, 291, 179–194, 1998. a
Lerch, F. J.: Optimum data weighting and error calibration for estimation of
gravitational parameters, B. Geod., 65, 44–52, 1991. a
Linde, N., Ginsbourger, D., Irving, J., Nobile, F., and Doucet, A.: On
uncertainty quantification in hydrogeology and hydrogeophysics, Adv. Water Resour., 110, 166–181, 2017. a
Locarnini, M. M., Mishonov, A. V., Baranova, O. K., Boyer, T. P., Zweng, M. M., Garcia, H. E., Reagan, J. R., Seidov, D., Weathers, K. W., Paver, C. R., and Smolyar, I.: World ocean atlas 2013: Volume 1, Temperature, https://doi.org/10.7289/V55X26VD, 2013. a
Luijendijk, E., Winter, T., Köhler, S., Ferguson, G., von Hagke, C., and
Scibek, J.: Using thermal springs to quantify deep groundwater flow and its
thermal footprint in the Alps and North American Orogens, Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL090134, 2020. a
Magrin, A. and Rossi, G.: Deriving a new crustal model of Northern Adria: the
Northern Adria Crust (NAC) model, Front. Earth Sci., 8, 89, https://doi.org/10.3389/feart.2020.00089, 2020. a
Miao, T., Lu, W., Lin, J., Guo, J., and Liu, T.: Modeling and uncertainty
analysis of seawater intrusion in coastal aquifers using a surrogate model: a case study in Longkou, China, Arab. J. Geosci., 12, 1, https://doi.org/10.1007/s12517-018-4128-8, 2019. a
Mo, S., Shi, X., Lu, D., Ye, M., and Wu, J.: An adaptive Kriging surrogate
method for efficient uncertainty quantification with an application to
geological carbon sequestration modeling, Comput. Geosci., 125, 69–77, https://doi.org/10.1016/j.cageo.2019.01.012, 2019. a
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J.,
Collins, M., and Stainforth, D. A.: Quantification of modelling
uncertainties in a large ensemble of climate change simulations, Nature,
430, 768–772, 2004. a
Myers, R. H., Montgomery, D. C., and Anderson-Cook, C. M.: Response Surface
Methodology: Process and Product Optimization Using Designed Experiments,
John Wiley and Sons, Hoboken, New Jersey, USA, 2016. a
Navarro, M., Le Maître, O. P., Hoteit, I., George, D. L., Mandli, K. T.,
and Knio, O. M.: Surrogate-based parameter inference in debris flow model,
Comput. Geosci., 22, 1447–1463, 2018. a
Permann, C. J., Gaston, D. R., Andrš, D., Carlsen, R. W., Kong, F.,
Lindsay, A. D., Miller, J. M., Peterson, J. W., Slaughter, A. E., Stogner,
R. H., and Martineau, R. C.: MOOSE: Enabling massively parallel
multiphysics simulation, SoftwareX, 11, 100430, https://doi.org/10.1016/j.softx.2020.100430, 2020. a
Przybycin, A. M., Scheck-Wenderoth, M., and Schneider, M.: The 3D conductive
thermal field of the North Alpine Foreland Basin: influence of the deep
structure and the adjacent European Alps, Geothermal Energy, 3, 17, 2015. a
Refsgaard, J. C., van der Sluijs, J. P., Højberg, A. L., and Vanrolleghem,
P. A.: Uncertainty in the environmental modelling process – a framework and guidance, Environ. Modell. Softw., 22, 1543–1556, 2007. a
Rizzo, C. B., de Barros, F. P., Perotto, S., Oldani, L., and Guadagnini, A.:
Adaptive POD model reduction for solute transport in heterogeneous porous
media, Comput. Geosci., 22, 297–308, https://doi.org/10.1007/s10596-017-9693-5, 2018. a
Rousset, M. A., Huang, C. K., Klie, H., and Durlofsky, L. J.: Reduced-order
modeling for thermal recovery processes, Comput. Geosci., 18, 401–415, 2014. a
Rozza, G., Huynh, D. B. P., and Patera, A. T.: Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive
partial differential equations, Arch. Comput. Methods E., 15, 229, https://doi.org/10.1007/s11831-008-9019-9, (2008). a
Schaeffer, A. and Lebedev, S.: Global shear speed structure of the upper mantle and transition zone, Geophys. J. Int., 194, 417–449, 2013. a
Schwarz, R., Pfeifer, N., Pfennigbauer, M., and Mandlburger, G.: Depth
Measurement Bias in Pulsed Airborne Laser Hydrography Induced by Chromatic
Dispersion, IEEE Geosci. Remote S., 18, 1332–1336, 2020. a
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, 2015. a
Spooner, C., Scheck-Wenderoth, M., Götze, H.-J., Ebbing, J., Hetényi, G., and the AlpArray Working Group: Density distribution across the Alpine lithosphere constrained by 3-D gravity modelling and relation to seismicity and deformation, Solid Earth, 10, 2073–2088, https://doi.org/10.5194/se-10-2073-2019, 2019a. a, b
Spooner, C., Scheck-Wenderoth, M., Götze, H.-J., Ebbing, J., and Hetényi, G.: 3D ALPS: 3D Gravity Constrained Model of Density Distribution Across the Alpine Lithosphere. V. 2.0., GFZ Data Services [data set], https://doi.org//10.5880/GFZ.4.5.2019.004, 2019b.
a
Spooner, C., Scheck-Wenderoth, M., Cacace, M., and Anikiev, D.: 3D-ALPS-TR: A 3D thermal and rheological model of the Alpine lithosphere, GFZ Data Services [data set], https://doi.org/10.5880/GFZ.4.5.2020.007, 2020a. a
Tang, Y., Reed, P., Van Werkhoven, K., and Wagener, T.: Advancing the
identification and evaluation of distributed rainfall-runoff models using
global sensitivity analysis, Water Resour. Res., 43, 6, https://doi.org/10.1029/2006WR005813, 2007. a
Trumpy, E. and Manzella, A.: Geothopica and the interactive analysis and
visualization of the updated Italian National Geothermal Database,
In. J. Appl. Earth Obs., 54, 28–37, 2017. a
Turcotte, D. L. and Schubert, G.: Geodynamics, 2nd edn, Cambridge University
Press, Cambridge, UK, 456 pp., 2002. a
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and
Srinivasan, R.: A global sensitivity analysis tool for the parameters of
multi-variable catchment models, J. Hydrol., 324, 10–23, 2006. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nature methods, 17, 261–272, 2020 (code available at: https://scipy.org, last access: 18 November 2021). a
Vogt, C., Mottaghy, D., Wolf, A., Rath, V., Pechnig, R., and Clauser, C.:
Reducing temperature uncertainties by stochastic geothermal reservoir
modelling, Geophys. J. Int., 181, 321–333, 2010. a
Wagner, R. and Clauser, C.: Evaluating thermal response tests using parameter
estimation for thermal conductivity and thermal capacity, J. Geophys. Eng., 2, 349–356, 2005. a
Wellmann, J. F. and Reid, L. B.: Basin-scale geothermal model calibration:
Experience from the Perth Basin, Australia, Energy Proced., 59,
382–389, 2014. a
Zehner, B., Watanabe, N., and Kolditz, O.: Visualization of gridded scalar data with uncertainty in geosciences, Comput. Geosci., 36, 1268–1275,
2010. a
Zhan, C.-S., Song, X.-M., Xia, J., and Tong, C.: An efficient integrated
approach for global sensitivity analysis of hydrological model parameters,
Environ. Modell. Softw., 41, 39–52, 2013. a
Zlotnik, S., Díez, P., Modesto, D., and Huerta, A.: Proper generalized
decomposition of a geometrically parametrized heat problem with geophysical
applications, Int. J. Numer. Meth. Eng., 103, 737–758, 2015. a
Short summary
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
In times of worldwide energy transitions, an understanding of the subsurface is increasingly...