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18 results on '"permafrost modeling"'

1. Simulating the Thermal Regime and Surface Energy Balance of a Permafrost‐Underlain Forest in Mongolia.

Author

Zweigel, Robin B., Dashtseren, Avirmed, Temuujin, Khurelbaatar, Aalstad, Kristoffer, Webster, Clare, Stuenzi, Simone M., Aas, Kjetil S., Lee, Hanna, and Westermann, Sebastian

Subjects
SURFACE energy, FOREST soils, HEAT adaptation, EARTH temperature, HEAT storage, TUNDRAS, CHERNOZEM soils
Abstract

Forests overlap with large parts of the northern hemisphere permafrost area, and representing canopy processes is therefore crucial for simulating thermal and hydrological conditions in these regions. Forests impact permafrost through the modulation of radiative fluxes and exchange of turbulent fluxes, precipitation interception and regulation of transpiration. Forests also feature distinct soil layers of litter and organic matter, which play central roles for the infiltration and evaporation of water, while also providing thermal insulation for deeper ground layers. In this study, we present a new module within the CryoGrid community model to simulate forest ecosystems and their impact on the surface water and energy balance. The module includes a big‐leaf vegetation scheme with adaptations for canopy heat storage and transpiration. Furthermore, we account for the effect of surface litter layers on water and energy transfer. We show that the model is capable of simulating radiation, snow cover and ground temperatures below a deciduous needleleaf forest on a north‐facing slope in the Khentii Mountains in Central Mongolia. A sensitivity analysis of topographic aspect and ecosystem configuration confirms the important role of the litter layers for the energy and water balance of the ground. Furthermore, it suggests that the presence of permafrost is primarily linked to topographic aspect rather than the presence of forest at this site. The presented model scheme can be used to study the development of the ground thermal regime in forests, including the state of permafrost, under different climate, ecosystem, and land use scenarios. Plain Language Summary: Large parts of the permafrost in the Northern Hemisphere are covered by forest, which impact the transfer of water and energy between the atmosphere and the ground. In this study we include single‐layer vegetation scheme in an established permafrost model, and test this for a site in Mongolia at the southern limit of Eurasian permafrost and boreal forest. We find that forest soils have smaller variations in ground temperature than on the nearby steppe, and that surface litter layers provide substantial insulation and soil water retention. Our simulations also suggest that the local distribution of permafrost in our study area is linked to topography rather than forest cover. The vegetation scheme is based on a global land surface model and represents a new modeling tool that readily can be used for further studies of permafrost‐ecosystem interactions. Key Points: Novel developments in the CryoGrid Community Model reproduce how forests dampen the seasonal ground surface temperature signalSurface litter layers are essential for forest ground hydrothermal regime as they provide thermal insulation and retain soil moisturePermafrost in our simulations is confined to north‐facing slopes in the study region regardless of ecosystem setup [ABSTRACT FROM AUTHOR]

Published
2024
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3. Simulating Snow Redistribution and its Effect on Ground Surface Temperature at a High‐Arctic Site on Svalbard.

Author

Zweigel, R. B., Westermann, S., Nitzbon, J., Langer, M., Boike, J., Etzelmüller, B., and Vikhamar Schuler, T.

Subjects
SNOW analysis, METAMORPHISM (Geology), PERCOLATION, SNOW accumulation, SNOW density, SURFACE temperature
Abstract

In high‐latitude and mountain regions, local processes such as redistribution by wind, snow metamorphism, and percolation of water produce a complex spatial distribution of snow depths and snow densities. With its strong control on the ground thermal regime, this snow distribution has pronounced effects on ground temperatures at small spatial scales which are typically not resolved by land surface models (LSMs). This limits our ability to simulate the local impacts of climate change on, for example, vegetation and permafrost. Here, we present a tiling approach combining the CryoGrid permafrost model with snow microphysics parametrizations from the CROCUS snow scheme to account for subgrid lateral exchange of snow and water in a process‐based way. We demonstrate that a simple setup with three coupled tiles, each representing a different snow accumulation class with a specific topographic setting, can reproduce the observed spread of winter‐time ground surface temperatures (GST) and end‐of‐season snow distribution for a high‐Arctic site on Svalbard. For the 3‐year study period, the three‐tile simulations showed substantial improvement compared to traditional single‐tile simulations which naturally cannot account for subgrid variability. Among others, the representation of the warmest and coldest 5% of the observed GST distribution was improved by 1–2°C, while still capturing the average of the distribution. The simulations also reveal positive mean annual GSTs at the locations receiving the greatest snow cover. This could be an indication for the onset of localized permafrost degradation which would be obscured in single‐tile simulations. Key Points: In high‐Arctic areas, wind redistribution of snow leads to a strong variability in snow depths and hence ground surface temperaturesA parametrization for lateral transport of snow between three model tiles is implemented in the CryoGrid 3 permafrost modelThe three‐tile setup reproduces the observed spatial variability of snow depths and ground surface temperatures in a process‐based fashion [ABSTRACT FROM AUTHOR]

Published
2021
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4. Modeling Present and Future Permafrost Distribution at the Seward Peninsula, Alaska.

Author

Debolskiy, M. V., Nicolsky, D. J., Hock, R., and Romanovsky, V. E.

Subjects
SPATIOTEMPORAL processes, PERMAFROST, EARTH temperature, BOREHOLES
Abstract

Permafrost, a key component of Arctic ecosystems, is currently affected by climate warming and anticipated to undergo further significant changes in this century. The most pronounced changes are expected to occur in the transition zone between the discontinuous and continuous types of permafrost. We apply a transient temperature dynamic model to investigate the spatiotemporal evolution of permafrost conditions on the Seward Peninsula, Alaska—a region currently characterized by continuous permafrost in its northern part and discontinuous permafrost in the south. We calibrate model parameters using a variational data assimilation technique exploiting historical ground temperature measurements collected across the study area. The model is then evaluated with a separate control set of the ground temperature data. Calibrated model parameters are distributed across the domain according to ecosystem types. The forcing applied to our model consists of historic monthly temperature and precipitation data and climate projections based on the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. Simulated near‐surface permafrost extent for the 2000–2010 decade agrees well with existing permafrost maps and previous Alaska‐wide modeling studies. Future projections suggest a significant increase (3.0°C under RCP 4.5 and 4.4°C under RCP 8.5 at the 2 m depth) in mean decadal ground temperature on average for the peninsula for the 2090–2100 decade when compared to the period of 2000–2010. Widespread degradation of the near‐surface permafrost is projected to reduce its extent at the end of the 21st century to only 43% of the peninsula's area under RCP 4.5 and 8% under RCP 8.5. Key Points: The ecosystem‐type approach is applied to simulate permafrost dynamics on the Seward PeninsulaImproved regional scale model is used to simulate snow interception and scouringEnhanced model calibration approach is implemented to allow for better use of historical borehole data [ABSTRACT FROM AUTHOR]

Published
2020
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5. Permafrost Favorability Index: Spatial Modeling in the French Alps Using a Rock Glacier Inventory

Author

Marco Marcer, Xavier Bodin, Alexander Brenning, Philippe Schoeneich, Raphaële Charvet, and Frédéric Gottardi

Subjects
permafrost modeling, French Alps, mountain permafrost, rock glaciers, statistical modeling, Science
Abstract

In the present study we used the first rock glacier inventory for the entire French Alps to model spatial permafrost distribution in the region. Climatic and topographic data evaluated at the rock glacier locations were used as predictor variables in a Generalized Linear Model. Model performances are strong, suggesting that, in agreement with several previous studies, this methodology is able to model accurately rock glacier distribution. A methodology to estimate model uncertainties is proposed, revealing that the subjectivity in the interpretation of rock glacier activity and contours may substantially bias the model. The model highlights a North-South trend in the regional pattern of permafrost distribution which is attributed to the climatic influences of the Atlantic and Mediterranean climates. Further analysis suggest that lower amounts of precipitation in the early winter and a thinner snow cover, as typically found in the Mediterranean area, could contribute to the existence of permafrost at higher temperatures compared to the Northern Alps. A comparison with the Alpine Permafrost Index Map (APIM) shows no major differences with our model, highlighting the very good predictive power of the APIM despite its tendency to slightly overestimate permafrost extension with respect to our database. The use of rock glaciers as indicators of permafrost existence despite their time response to climate change is discussed and an interpretation key is proposed in order to ensure the proper use of the model for research as well as for operational purposes.

Published
2017
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7. Simulating Snow Redistribution and its Effect on Ground Surface Temperature at a High-Arctic Site on Svalbard

Author

Moritz Langer, Bernd Etzelmüller, T. Vikhamar Schuler, Jan Nitzbon, Julia Boike, Sebastian Westermann, R. B. Zweigel, and Earth and Climate

Subjects
010504 meteorology & atmospheric sciences, Microphysics, rain on snow, subgrid processes, Climate change, Vegetation, CryoGrid, 15. Life on land, Snow, Permafrost, Spatial distribution, Atmospheric sciences, 01 natural sciences, Svalbard, Geophysics, snow redistribution, Arctic, 13. Climate action, Percolation, Environmental science, permafrost modeling, 0105 earth and related environmental sciences, Earth-Surface Processes
Abstract

In high‐latitude and mountain regions, local processes such as redistribution by wind, snow metamorphism and percolation of water, produce a complex spatial distribution of snow depths and snow densities. With its strong control on the ground thermal regime, this snow distribution has pronounced effects on ground temperatures at small spatial scales which are typically not resolved by land surface models (LSMs). This limits our ability to simulate the local impacts of climate change on for example vegetation and permafrost. Here, we present a tiling approach combining the CryoGrid permafrost model with snow microphysics parametrizations from the CROCUS snow scheme to account for sub‐grid lateral exchange of snow and water in a process‐based way. We demonstrate that a simple setup with three coupled tiles, each representing a different snow accumulation class with a specific topographic setting, can reproduce the observed spread of winter‐time ground surface temperatures (GST) and end‐of‐season snow distribution for a high‐Arctic site on Svalbard. For the three‐year study period, the three‐tile simulations showed substantial improvement compared to traditional single‐tile simulations which naturally cannot account for sub‐grid variability. Amongst others, the representation of the warmest and coldest 5% of the observed GST distribution was improved by 1‐2°C, while still capturing the average of the distribution. The simulations also reveal positive mean annual GSTs at the locations receiving the greatest snow cover. This could be an indication for the onset of localized permafrost degradation which would be obscured in single‐tile simulations.

Published
2021

9. The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils.

Author

Kurylyk, Barret L. and Watanabe, Kunio

Subjects
*THAWING, *DEFORMATIONS (Mechanics), *SOIL freezing, *CLAUSIUS-Clapeyron relation, *HYDRAULICS, *PERMAFROST, *CLIMATE change, *MATHEMATICAL models
Abstract

Highlights: [•] Models for simulating heat and water processes in freezing soils are summarized. [•] The sources of existing variations in the Clapeyron equation are explained. [•] Various formulations for the soil freezing curve are reviewed. [•] Best practices to develop a frozen soil hydraulic conductivity model are detailed. [•] Several unresolved questions are highlighted and addressed. [ABSTRACT FROM AUTHOR]

Published
2013
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10. Modeling permafrost extension in a rock slope since the Last Glacial Maximum: Application to the large Séchilienne landslide (French Alps).

Author

Lebrouc, V., Schwartz, S., Baillet, L., Jongmans, D., and Gamond, J.F.

Subjects
*PERMAFROST, *ROCK slopes, *GLACIAL landforms, *LANDSLIDES, *LAST Glacial Maximum
Abstract

Abstract: Recent dating performed on large landslides in the Alps has revealed that the initiation of instability did not immediately follow deglaciation but occurred several thousand years after ice down-wastage in the valleys. This result indicates that debuttressing is not the immediate cause of landslide initiation. The period of slope destabilization appears to coincide with the wetter and warmer Holocene Climatic Optimum, indicating a climatic cause of landslide triggering, although the role of seismic activity cannot be ruled out. A phenomenon which may partly explain the delay between valley deglaciation and gravitational instability is the temporal persistence of thick permafrost layers developed in the Alps since the Last Glacial Maximum (LGM). This hypothesis was tested through 2D thermal numerical modeling of the large Séchilienne landslide (Romanche valley, French Alps) using plausible input parameter values. Simulation results suggest that permafrost vanished in the Séchilienne slope at 10 to 11ka, 3000 to 4000years following the total ice down-wastage of the Romanche valley at 14.3ka. Permafrost persistence could have contributed to the failure delay by temporally strengthening the slope. Numerical simulations also show that the permafrost depth expansion approximately fits the thickness of ground affected by gravitational destabilization, as deduced from geophysical investigations. These results further suggest that permafrost development, associated with an ice segregation mechanism, damaged the rock slope and influenced the resulting landslide geometry. [Copyright &y& Elsevier]

Published
2013
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11. Satellite-based modeling of permafrost temperatures in a tundra lowland landscape.

Author

Langer, Moritz, Westermann, Sebastian, Heikenfeld, Max, Dorn, Wolfgang, and Boike, Julia

Subjects
*PERMAFROST, *TUNDRAS, *LANDSCAPES, *REMOTE sensing, *LAND surface temperature, *MODIS (Spectroradiometer), *FREEZE-thaw cycles, *FEASIBILITY studies
Abstract

Remote sensing offers great potential for detecting changes of the thermal state of permafrost and active layer dynamics in the context of Arctic warming. This study presents a comprehensive feasibility analysis of satellite-based permafrost modeling for a typical lowland tundra landscape in the Lena River Delta, Siberia. We assessed the performance of a transient permafrost model which is forced by time series of land surface temperatures (LSTs) and snow water equivalents (SWEs) obtained from MODIS and GlobSnow products. Both the satellite products and the model output were evaluated on the basis of long-term field measurements from the Samoylov permafrost observatory. The model was found to successfully reproduce the evolution of the permafrost temperature and freeze-thaw dynamics when calibrated with ground measurements. Monte-Carlo simulations were performed in order to evaluate the impact of inaccuracies in the model forcing and uncertainties in the parameterization. The sensitivity analysis showed that a correct SWE forcing and parameterization of the snow's thermal properties are essential for reliable permafrost modeling. In the worst case, the bias in the modeled permafrost temperatures can amount to 5°C. For the thaw depth, a maximum uncertainty of about ±15cm is found due to possible uncertainties in the soil composition. [ABSTRACT FROM AUTHOR]

Published
2013
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12. Permafrost probability modeling above and below treeline, Yukon, Canada

Author

Bonnaventure, Philip P. and Lewkowicz, Antoni G.

Subjects
*PERMAFROST, *TIMBERLINE, *STATISTICS, *REGRESSION analysis
Abstract

Abstract: Empirical–statistical methods were used to model mountain permafrost probability above and below treeline for five areas distributed across the southern half of the Yukon (60°–64°N). The modeling addressed a key issue by incorporating the effects of inverted or weak surface lapse rates below treeline, as well as the interfingering of lowland and mountain permafrost in the region. The variables used to predict the Basal Temperature of Snow (BTS) by linear regression included equivalent elevation, Potential Incoming Solar Radiation (PISR), slope and Normalized Difference Vegetation Index (NDVI). Topographic Position Index (TPI) was also examined as an explanatory variable but was not statistically significant in any of the areas. Predicted BTS temperatures were compared using logistic regression to late-summer physical verification of permafrost presence or absence. The resulting models indicate that permafrost underlies 19–24% of the Johnson''s Crossing area, 30% of the Sa Dena Hes area, 63% of the Faro and Dawson study areas and 79% of the Keno area. In the most northerly study locations where surface lapse rates are inverted, predictions include high permafrost probabilities within valleys, and decreasing probabilities up to treeline. The methods should be transferable to other areas where they could be used as a tool for infrastructure route planning. [Copyright &y& Elsevier]

Published
2012
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13. Water and energy transfer modeling in a permafrost‐dominated, forested catchment of Central Siberia: the key role of rooting depth

Author

Anatoly S. Prokushkin, Laurent Orgogozo, Oleg S. Pokrovsky, Jérôme Viers, Stéphane Audry, Michel Quintard, Christophe Grenier, Géosciences Environnement Toulouse (GET), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Sciences (SB RAS), Tomsk State University [Tomsk], Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Modélisation Hydrologique (HYDRO), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut de mécanique des fluides de Toulouse (IMFT), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Laboratoire des Mécanismes et Transfert en Géologie (LMTG), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Commissariat à l'Energie Atomique et aux énergies alternatives - CEA (FRANCE), Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut de Recherche pour le Développement - IRD (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université de Versailles Saint-Quentin-en-Yvelines -UVSQ (FRANCE), Bureau de Recherches Géologiques et Minières - BRGM (FRANCE), V. N. Sukachev Institute of Forest (RUSSIA), Tomsk State University - TSU (RUSSIA), Laboratoire des Sciences du Climat et de l'Environnement - LSCE (Gif-sur-Yvette, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), and Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP)

Subjects
Biogeochemical cycle, Watershed, 010504 meteorology & atmospheric sciences, Mécanique des fluides, [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, Drainage basin, Active layer dynamics, Permafrost, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, Hydrology (agriculture), Evapotranspiration, Fluid dynamics, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.5: Model Development, OpenFOAM, OpenFOAM®, [PHYS.MECA.MEFL]Physics [physics]/Mechanics [physics]/Fluid mechanics [physics.class-ph], [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Earth-Surface Processes, geography, geography.geographical_feature_category, Permafrost modeling, [SDE.IE]Environmental Sciences/Environmental Engineering, 15. Life on land, cryohydrogeology modeling, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 13. Climate action, Soil water, [PHYS.MECA.THER]Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], Massively parallel computation, Cryohydrogeology modelling, Geology
Abstract

International audience; In order to quantify the impact of evapotranspiration phenomena on active layer dynamics in a permafrost-dominated forested watershed in Central Siberia, we performed a numerical cryohydrological study of water and energy transfer using a new open source cryohydrogeology simulator, with two innovative features: the spatially distributed, mechanistic handling of evapotranspiration and the inclusion of the developed numerical tool in the high performance computing tool box for numerical simulation of fluid dynamics OpenFOAM®. In this region, the heterogeneity of solar exposure leads to strong contrasts in vegetation cover, which constitute the main source of variability of hydrological conditions at the landscape scale. The uncalibrated numerical results reasonably reproduce the measured soil temperature profiles and the dynamics of infiltrated waters revealed by previous biogeochemical studies. The impacts of the thermo-hydrological processes on the water fluxes from the soils to the stream are discussed through comparison between numerical results and field data. The impact of evapotranspiration on water fluxes is studied numerically, which highlights a strong sensitivity to the variability of rooting depth and corresponding evapotranspiration at the slopes of different aspects in the catchment.

Published
2019
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14. Analysis of the global ESA GlobPermafrost map for Scandinavia

Author

Nesterova, Nina and Nesterova, Nina

Abstract

Due to its high vulnerability, permafrost is one of the key features studied in the field of climate change impacts. Permafrost is widespread in the Arctic region. The majority of the area underlain by permafrost is however difficult to access for in-situ monitoring and it is difficult to get an overview of the current state of permafrost in many areas. Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some characteristics, i.e. ground temperatures over large remote areas. Temperature at the top of the permafrost (TTOP) is one of several permafrost modeling approaches which conceptually represents a steady-state equilibrium model. In this study, two TTOP-based models were used; the GlobPermafrost model which was used to produce the most recent global permafrost map (Alfred-Wegener-Institut) and a local Scandinavian model. The aim of this study was twofold; firstly, the performance of the GlobPermafrost model in Scandinavia was analyzed by comparing the model output with the output from the local Scandinavian model. Secondly, the role of land cover data as an input variable in the TTOP model was investigated. The TTOP-based GlobPermafrost model was run with different land cover input data to evaluate this. In general, the GlobPermafrost model underestimated permafrost occurrence in Scandinavia (overall r2 being 0.39). The lowest underestimation is located in the regions with little or no permafrost. The biggest underestimations are found in peatlands and mountainous areas with more likely permafrost occurrence. Unexpected underestimation of permafrost was observed in the forests. This exposed the weaknesses of regional permafrost model overestimating permafrost occurrence in forests. The rerun of the GlobPermafrost model with three times more detailed land cover input data did surprisingly not have a great effect on the model performance (r2 only changed by 8%). The small changes detected, Permafrost is a frozen ground which remains with a temperature of 0 degrees C or lower for 2 or more seasons. Permafrost is widespread in the Arctic region. Due to its high vulnerability to the temperature changes permafrost can degrade and lead to serious consequence such as infrastructure collapses, changes in the relief and vegetation. Nowadays climate change makes permafrost present even bigger interest for the researchers. To make correct projections it is important to explore the distribution of the permafrost and to study its parameters. Unfortunately, most of the area underlain by permafrost is however difficult to access for the field studies. Permafrost modeling provides a solution which overcomes this difficulty and allows studies on permafrost distribution as well as some of its characteristics. In this study we explored outcomes of two models: global (GlobPermafrost) and regional Scandinavian. The outcome we focused on was the map of permafrost occurrence probabilities. These values present on how likely it is that permafrost can be found in a certain cell of the map. Permafrost occurrence map produced by regional Scandinavian model is more accurate than the global one since the input data were more detailed. In fact, it is the most precise permafrost map for Scandinavia available, thus we assume that regional Scandinavian permafrost map represents the truth. The aim of this study was twofold. Firstly, we estimated the performance of the GlobPermafrost model in Scandinavia. To achieve it we compared the map of permafrost probabilities in Scandinavia produced by global (GlobPermafrost) model with the regional Scandinavian permafrost probability map which we assumed as truth. Secondly global (GlobPermafrost) model was rerun with a one input replaced. This input was a land cover data (the information about physical material at the surface). We replaced the land cover used with a more detailed one. The new outcome permafrost probability map was compared wit

Published
2018

15. Permafrost Favorability Index: Spatial Modeling in the French Alps Using a Rock Glacier Inventory

Author

Philippe Schoeneich, Frédéric Gottardi, Raphaële Charvet, Xavier Bodin, Marco Marcer, Alexander Brenning, Environnements, Dynamiques et Territoires de la Montagne (EDYTEM), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Department of Geography and Environmental Management [Waterloo], University of Waterloo [Waterloo], Pacte, Laboratoire de sciences sociales (PACTE), and Sciences Po Grenoble - Institut d'études politiques de Grenoble (IEPG)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
Mediterranean climate, Index (economics), 010504 meteorology & atmospheric sciences, statistical modeling, [SDE.MCG]Environmental Sciences/Global Changes, Rock glacier, Predictor variables, 010502 geochemistry & geophysics, Permafrost, 01 natural sciences, Precipitation, French Alps, lcsh:Science, 0105 earth and related environmental sciences, mountain permafrost, [SDE.IE]Environmental Sciences/Environmental Engineering, rock glaciers, [SHS.GEO]Humanities and Social Sciences/Geography, [SDE.ES]Environmental Sciences/Environmental and Society, Early winter, 13. Climate action, General Earth and Planetary Sciences, lcsh:Q, Physical geography, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, permafrost modeling, Geology, Snow cover
Abstract

International audience; In the present study we used the first rock glacier inventory for the entire French Alpsto model spatial permafrost distribution in the region. Climatic and topographic dataevaluated at the rock glacier locations were used as predictor variables in a GeneralizedLinearModel.Model performances are strong, suggesting that, in agreement with severalprevious studies, this methodology is able to model accurately rock glacier distribution. Amethodology to estimate model uncertainties is proposed, revealing that the subjectivityin the interpretation of rock glacier activity and contours may substantially bias the model.Themodel highlights a North-South trend in the regional pattern of permafrost distributionwhich is attributed to the climatic influences of the Atlantic and Mediterranean climates.Further analysis suggest that lower amounts of precipitation in the early winter and athinner snow cover, as typically found in the Mediterranean area, could contribute tothe existence of permafrost at higher temperatures compared to the Northern Alps. Acomparison with the Alpine Permafrost Index Map (APIM) shows no major differenceswith our model, highlighting the very good predictive power of the APIM despite itstendency to slightly overestimate permafrost extension with respect to our database. Theuse of rock glaciers as indicators of permafrost existence despite their time response toclimate change is discussed and an interpretation key is proposed in order to ensure theproper use of the model for research as well as for operational purposes.

Published
2017
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16. Soil Moisture Data for the Validation of Permafrost Models Using Direct and Indirect Measurement Approaches at Three Alpine Sites

Author

Pellet Cécile, Hilbich Christin, Marmy Antoine, and Hauck Christian

Subjects
four-phase model, General Earth and Planetary Sciences, lcsh:Q, Earth Science, Electrical Resistivity Tomography (ERT), soil moisture, permafrost modeling, lcsh:Science, Permafrost modelling, mountain permafrost
Abstract

In regions affected by seasonal and permanently frozen conditions soil moisture influences the thermal regime of the ground as well as its ice content, which is one of the main factors controlling the sensitivity of mountain permafrost to climate changes. In this study, several well established soil moisture monitoring techniques were combined with data from geophysical measurements to assess the spatial distribution and temporal evolution of soil moisture at three high elevation sites with different ground properties and thermal regimes. The observed temporal evolution of measured soil moisture is characteristic for sites with seasonal freeze/thaw cycles and consistent with the respective site-specific properties, demonstrating the general applicability of continuous monitoring of soil moisture at high elevation areas. The obtained soil moisture data were then used for the calibration and validation of two different model approaches used in permafrost research in order to characterize the lateral and vertical distribution of ice content in the ground. Calibration of the geophysically based four-phase model (4PM) with spatially distributed soil moisture data yielded satisfactory two dimensional distributions of water-, ice-, and air content. Similarly, soil moisture time series significantly improved the calibration of the one-dimensional heat and mass transfer model COUP, yielding physically consistent soil moisture and temperature data matching observations at different depths.

Published
2016
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17. Permafrost extension modeling in rock slope since the Last Glacial Maximum: application to the large Séchilienne landslide (French Alps)

Author

Lebrouc, Vincent, Schwartz, Stéphane, Baillet, Laurent, Jongmans, Denis, Gamond, Jean-François, Risques, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF), Risques (Risques), Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
Last Glacial Maximum, Permafrost modeling, TTOP model, Séchilienne, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Large landslide, French Alps
Abstract

12 pages; International audience; Recent dating performed on large landslides in the Alps reveal that the initiation of instability did not immediately follow deglaciation but occurred several thousand years after ice down-wastage in the valleys. This result indicates that debuttressing is not the immediate cause of landslide initiation. The period of slope destabilization appears to coincide with the wetter and warmer Holocene Climatic Optimum, indicating a climatic cause of landslide triggering, although the role of seismic activity cannot be ruled out. A phenomenon which may partly explain the delay between valley deglaciation and gravitational instability is the temporal persistence of thick permafrost layers developed in the Alps since the Last Glacial Maximum (LGM). This hypothesis was tested through 2D thermal numerical modeling of the large Séchilienne landslide (Romanche valley, French Alps) using plausible input parameter values. Simulation results suggest that permafrost vanished in the Séchilienne slope at 10 to 11 ka, 3,000 to 4,000 years following the total ice down-wastage of the Romanche valley at 14.3 ka. Permafrost persistence could have contributed to the failure delay by temporally strengthening the slope. Numerical simulations also show that the permafrost depth expansion approximately fits the thickness of ground affected by gravitational destabilization, as deduced from geophysical investigations. These results further suggest that permafrost development, associated with an ice segregation mechanism, damaged the rock slope and influenced the resulting landslide geometry.

Published
2012
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