Articles | Volume 21, issue 2
https://doi.org/10.5194/bg-21-605-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-605-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Jan 2024
Research article |
| 29 Jan 2024
| 29 Jan 2024
Microclimate mapping using novel radiative transfer modelling
Florian Zellweger
CORRESPONDING AUTHOR
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Eric Sulmoni
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Johanna T. Malle
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Andri Baltensweiler
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Tobias Jonas
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Niklaus E. Zimmermann
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Christian Ginzler
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Dirk Nikolaus Karger
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Pieter De Frenne
Forest and Nature Lab, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
David Frey
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Clare Webster
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland
Department of Geosciences, University of Oslo, Oslo, Norway
Related authors
No articles found.
Marlena Reil, Julia Kaltenborn, Donovan J. M. Allum, Francis Pelletier, Sebastian Roessler, Samip Shrestha, Zhibang Lv, John Truckenbrodt, Gabriele Schwaizer, Thomas Nagler, John W. Pomeroy, Christopher B. Marsh, Benoit Montpetit, Tobias Jonas, Gabriel Tseng, David Rolnick, Andreas J. Dietz, and Celia A. Baumhoer
EGUsphere, https://doi.org/10.5194/egusphere-2026-3833, https://doi.org/10.5194/egusphere-2026-3833, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Many communities rely on snowmelt from the mountains for their water supply. However, monitoring snow from space is challenging because clouds and other factors can obscure satellite observations. We present SnowGalileo, an AI model that combines data from multiple satellites to produce snow maps. The model can produce accurate maps even when clouds are present and high-resolution data are missing. This helps to improve our understanding of snow conditions and their impact on water resources.
Santiago José Elías Velazco, Natalie Briscoe, Lauren Buckley, Juliano Sarmento Cabral, Francisco Cervantes Peralta, Rob Cooke, Admir Cesar de Oliveira Junior, Simon Ferrier, Samuel J. L. Gascoigne, Lucas da Cunha Godoy, Gurutzeta Guillera-Arroita, Nick J. B. Isaac, Dirk Nikolaus Karger, Raya Keuth, Carolyn J. Lundquist, Cory Merow, Andy Purvis, Katrin Schifferle, Alice E. Semple, Josep M. Serra-Diaz, Justin M. J. Travis, Brendan A. Wintle, Damaris Zurell, Mark C. Urban, and Greta Bocedi
EGUsphere, https://doi.org/10.5194/egusphere-2026-5033, https://doi.org/10.5194/egusphere-2026-5033, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We propose a model intercomparison protocol to compare ecological models—from correlative to process-based models—at regional scales. Our protocol tests models against historical presence and abundance data while projecting future scenarios under land-use and climate shifts. This protocol advances regional biodiversity model intercomparisons and could reveal which models, processes, and drivers determine historical and future biodiversity dynamics.
Moritz Oberrauch, Bertrand Cluzet, Jan Magnusson, Giulia Mazzotti, Rebecca Mott, Louis Quéno, Clare Webster, Tobias Zolles, and Tobias Jonas
The Cryosphere, 20, 3387–3403, https://doi.org/10.5194/tc-20-3387-2026, https://doi.org/10.5194/tc-20-3387-2026, 2026
Short summary
Short summary
We present a snow dataset that provides daily information on snow depth, snow amount, and meltwater for Switzerland from 2016 to 2025. It combines weather data, computer simulations, and ground observations to give the most complete picture of how snow changes over time. Because mountain snow strongly affects avalanches, floods, water resources, and ecosystems, this freely available dataset supports better understanding and decision-making in these areas.
Emma Van de Walle, Félicien Meunier, Steven J. De Hertog, Louise Terryn, Pieter Sanczuk, Kim Calders, Francis wyffels, Pieter De Frenne, Michiel Stock, and Hans Verbeeck
Geosci. Model Dev., 19, 4661–4702, https://doi.org/10.5194/gmd-19-4661-2026, https://doi.org/10.5194/gmd-19-4661-2026, 2026
Short summary
Short summary
We present ForEdgeClim, a process-based model that simulates forest microclimate temperatures from edges to forest interiors. The model combines high-resolution forest structure, meteorological data, and a physically based energy balance that includes vertical and lateral radiation and heat exchange. Validation with field measurements shows that ForEdgeClim captures observed edge-to-core temperature gradients, supporting its use for studying forest fragmentation and climate impacts.
Aaron Cremona, Matthias Huss, Johannes Marian Landmann, Mauro Marty, Marijn van der Meer, Christian Ginzler, and Daniel Farinotti
The Cryosphere, 20, 3111–3130, https://doi.org/10.5194/tc-20-3111-2026, https://doi.org/10.5194/tc-20-3111-2026, 2026
Short summary
Short summary
Our study provides daily mass balance estimates for every Swiss glacier from 2010–2024 using modelling, remote sensing observations, and machine learning. Over the period, Swiss glaciers lost nearly a quarter of their ice volume. The approach enables investigating the spatio-temporal variability of glacier mass balance in relation to the driving climatic factors.
Moritz Oberrauch, Bertrand Cluzet, Jan Magnusson, Gabriele Schwaizer, and Tobias Jonas
EGUsphere, https://doi.org/10.5194/egusphere-2026-2725, https://doi.org/10.5194/egusphere-2026-2725, 2026
Short summary
Short summary
In mountain regions like the Swiss Alps, knowing how much snow is on the ground is important for managing water resources and natural hazards. Computer simulations are needed to estimate snow conditions across the terrain, and can be improved when combined with observations. This study uses snow-station measurements and satellite images to correct errors in both the weather input data and the model's representation of terrain effects, thereby reducing the snow model errors by around half.
Charis Moana Gretler, Daniel Kükenbrink, Mauro Marty, Christian Ginzler, and Felix Morsdorf
EGUsphere, https://doi.org/10.5194/egusphere-2026-1689, https://doi.org/10.5194/egusphere-2026-1689, 2026
Short summary
Short summary
Repeated airborne laser surveys were used to study how forest structure can be tracked over time in Switzerland. By comparing different years and methods, canopy height estimates were found to be consistent, while vegetation density results varied depending on how the data were collected. This highlights the need for consistent methods and data collection for reliable long-term analysis.
Vincent Haagmans, Giulia Mazzotti, Clare Webster, and Tobias Jonas
Hydrol. Earth Syst. Sci., 30, 1691–1717, https://doi.org/10.5194/hess-30-1691-2026, https://doi.org/10.5194/hess-30-1691-2026, 2026
Short summary
Short summary
In the Central European Alps, forests store about 20–30 % of midwinter snow. The effect of forests on snow cover varies greatly with topography, forest structure, weather, and regions. Forests usually decrease snow accumulation and decelerate melt, often leading to a later snow disappearance, especially on sunny slopes. But annual variations are considerable and can even reverse such effects. Environmental shifts will further complicate snow cover dynamics in these mountain forests.
Michael Zehnder, Annegret Udke, Katrin Meusburger, Frank Hagedorn, Andri Baltensweiler, and Christian Rixen
EGUsphere, https://doi.org/10.5194/egusphere-2026-835, https://doi.org/10.5194/egusphere-2026-835, 2026
Short summary
Short summary
Most organic carbon in alpine ecosystems is stored belowground, yet its distribution is poorly known. By combining new soil measurements with existing data from across the Swiss Alps, we mapped soil carbon storage from the treeline to permafrost-affected regions. We found that alpine grasslands store large amounts of soil carbon, explained mainly by vegetation productivity, temperature, and terrain. The results provide a baseline to assess how alpine soil carbon may respond to climate change.
Marit van Tiel, Matthias Huss, Massimiliano Zappa, Tobias Jonas, and Daniel Farinotti
Hydrol. Earth Syst. Sci., 30, 23–43, https://doi.org/10.5194/hess-30-23-2026, https://doi.org/10.5194/hess-30-23-2026, 2026
Short summary
Short summary
The summer of 2022 was extremely warm and dry in Europe, severely impacting water availability. We calculated water balance anomalies for 88 glacierized catchments in Switzerland, showing that glaciers played a crucial role in alleviating the drought situation by melting at record rates, partially compensating for the lack of rain and snowmelt. By comparing 2022 with past extreme years, we show that while glacier meltwater remains essential during droughts, its contribution is declining.
Joren Janzing, Niko Wanders, Marit van Tiel, Barry van Jaarsveld, Dirk N. Karger, and Manuela I. Brunner
Hydrol. Earth Syst. Sci., 29, 7041–7071, https://doi.org/10.5194/hess-29-7041-2025, https://doi.org/10.5194/hess-29-7041-2025, 2025
Short summary
Short summary
Process representation in hyper-resolution large-scale hydrological models (LHMs) limits model performance, particularly in mountain regions. Here, we update mountain process representation in an LHM and compare different meteorological forcing products. Structural and parametric changes in snow, glacier, and soil processes improve discharge simulations, while meteorological forcing remains a major control on model performance. Our work can guide future development of LHMs.
Mauro Marty, Livia Piermattei, Lars T. Waser, and Christian Ginzler
Earth Syst. Sci. Data, 17, 5811–5832, https://doi.org/10.5194/essd-17-5811-2025, https://doi.org/10.5194/essd-17-5811-2025, 2025
Short summary
Short summary
Millions of aerial photographs represent an enormous resource for geoscientists. In this study, we used freely available historical stereo images covering Switzerland (1979–2006) to derive four countrywide digital elevation models (DSMs) at a 1 m spatial resolution. Our DSMs achieved sub-metric accuracy compared to reference data and high image matching completeness, demonstrating the feasibility of capturing surface change at a high spatial resolution over different land cover classes.
Christoph Marty, Adrien Michel, Tobias Jonas, Cynthia Steijn, Regula Muelchi, and Sven Kotlarski
The Cryosphere, 19, 4391–4407, https://doi.org/10.5194/tc-19-4391-2025, https://doi.org/10.5194/tc-19-4391-2025, 2025
Short summary
Short summary
This work presents the first long-term (since 1962), daily, 1 km gridded dataset of snow depth and water storage for Switzerland. Its quality was assessed by comparing yearly, monthly, and weekly values to a higher-quality model and in situ measurements. Results show good overall performance, though some limitations exist at low elevations and short timescales. Despite this, the dataset effectively captures trends, offering valuable insights for climate monitoring and elevation-based changes.
Richard Essery, Giulia Mazzotti, Sarah Barr, Tobias Jonas, Tristan Quaife, and Nick Rutter
Geosci. Model Dev., 18, 3583–3605, https://doi.org/10.5194/gmd-18-3583-2025, https://doi.org/10.5194/gmd-18-3583-2025, 2025
Short summary
Short summary
How forests influence accumulation and melt of snow on the ground is of long-standing interest, but uncertainty remains in how best to model forest snow processes. We developed the Flexible Snow Model version 2 to quantify these uncertainties. In a first model demonstration, how unloading of intercepted snow from the forest canopy is represented is responsible for the largest uncertainty. Global mapping of forest distribution is also likely to be a large source of uncertainty in existing models.
Jan Magnusson, Yves Bühler, Louis Quéno, Bertrand Cluzet, Giulia Mazzotti, Clare Webster, Rebecca Mott, and Tobias Jonas
Earth Syst. Sci. Data, 17, 703–717, https://doi.org/10.5194/essd-17-703-2025, https://doi.org/10.5194/essd-17-703-2025, 2025
Short summary
Short summary
In this study, we present a dataset for the Dischma catchment in eastern Switzerland, which represents a typical high-alpine watershed in the European Alps. Accurate monitoring and reliable forecasting of snow and water resources in such basins are crucial for a wide range of applications. Our dataset is valuable for improving physics-based snow, land surface, and hydrological models, with potential applications in similar high-alpine catchments.
Adrien Michel, Johannes Aschauer, Tobias Jonas, Stefanie Gubler, Sven Kotlarski, and Christoph Marty
Geosci. Model Dev., 17, 8969–8988, https://doi.org/10.5194/gmd-17-8969-2024, https://doi.org/10.5194/gmd-17-8969-2024, 2024
Short summary
Short summary
We present a method to correct snow cover maps (represented in terms of snow water equivalent) to match better-quality maps. The correction can then be extended backwards and forwards in time for periods when better-quality maps are not available. The method is fast and gives good results. It is then applied to obtain a climatology of the snow cover in Switzerland over the past 60 years at a resolution of 1 d and 1 km. This is the first time that such a dataset has been produced.
Robin Benjamin Zweigel, Avirmed Dashtseren, Khurelbaatar Temuujin, Anarmaa Sharkhuu, Clare Webster, Hanna Lee, and Sebastian Westermann
Biogeosciences, 21, 5059–5077, https://doi.org/10.5194/bg-21-5059-2024, https://doi.org/10.5194/bg-21-5059-2024, 2024
Short summary
Short summary
Intense grazing at grassland sites removes vegetation, reduces the snow cover, and inhibits litter layers from forming. Grazed sites generally have a larger annual ground surface temperature amplitude than ungrazed sites, but the net effect depends on effects in the transitional seasons. Our results also suggest that seasonal use of pastures can reduce ground temperatures, which can be a strategy to protect currently degrading grassland permafrost.
Giulia Mazzotti, Jari-Pekka Nousu, Vincent Vionnet, Tobias Jonas, Rafife Nheili, and Matthieu Lafaysse
The Cryosphere, 18, 4607–4632, https://doi.org/10.5194/tc-18-4607-2024, https://doi.org/10.5194/tc-18-4607-2024, 2024
Short summary
Short summary
As many boreal and alpine forests have seasonal snow, models are needed to predict forest snow under future environmental conditions. We have created a new forest snow model by combining existing, very detailed model components for the canopy and the snowpack. We applied it to forests in Switzerland and Finland and showed how complex forest cover leads to a snowpack layering that is very variable in space and time because different processes prevail at different locations in the forest.
Dylan Reynolds, Louis Quéno, Michael Lehning, Mahdi Jafari, Justine Berg, Tobias Jonas, Michael Haugeneder, and Rebecca Mott
The Cryosphere, 18, 4315–4333, https://doi.org/10.5194/tc-18-4315-2024, https://doi.org/10.5194/tc-18-4315-2024, 2024
Short summary
Short summary
Information about atmospheric variables is needed to produce simulations of mountain snowpacks. We present a model that can represent processes that shape mountain snowpack, focusing on the accumulation of snow. Simulations show that this model can simulate the complex path that a snowflake takes towards the ground and that this leads to differences in the distribution of snow by the end of winter. Overall, this model shows promise with regard to improving forecasts of snow in mountains.
Johanna Teresa Malle, Giulia Mazzotti, Dirk Nikolaus Karger, and Tobias Jonas
Earth Syst. Dynam., 15, 1073–1115, https://doi.org/10.5194/esd-15-1073-2024, https://doi.org/10.5194/esd-15-1073-2024, 2024
Short summary
Short summary
Land surface processes are crucial for the exchange of carbon, nitrogen, and energy in the Earth system. Using meteorological and land use data, we found that higher resolution improved not only the model representation of snow cover but also plant productivity and that water returned to the atmosphere. Only by combining high-resolution models with high-quality input data can we accurately represent complex spatially heterogeneous processes and improve our understanding of the Earth system.
Louis Quéno, Rebecca Mott, Paul Morin, Bertrand Cluzet, Giulia Mazzotti, and Tobias Jonas
The Cryosphere, 18, 3533–3557, https://doi.org/10.5194/tc-18-3533-2024, https://doi.org/10.5194/tc-18-3533-2024, 2024
Short summary
Short summary
Snow redistribution by wind and avalanches strongly influences snow hydrology in mountains. This study presents a novel modelling approach to best represent these processes in an operational context. The evaluation of the simulations against airborne snow depth measurements showed remarkable improvement in the snow distribution in mountains of the eastern Swiss Alps, with a representation of snow accumulation and erosion areas, suggesting promising benefits for operational snow melt forecasts.
Livia Piermattei, Michael Zemp, Christian Sommer, Fanny Brun, Matthias H. Braun, Liss M. Andreassen, Joaquín M. C. Belart, Etienne Berthier, Atanu Bhattacharya, Laura Boehm Vock, Tobias Bolch, Amaury Dehecq, Inés Dussaillant, Daniel Falaschi, Caitlyn Florentine, Dana Floricioiu, Christian Ginzler, Gregoire Guillet, Romain Hugonnet, Matthias Huss, Andreas Kääb, Owen King, Christoph Klug, Friedrich Knuth, Lukas Krieger, Jeff La Frenierre, Robert McNabb, Christopher McNeil, Rainer Prinz, Louis Sass, Thorsten Seehaus, David Shean, Désirée Treichler, Anja Wendt, and Ruitang Yang
The Cryosphere, 18, 3195–3230, https://doi.org/10.5194/tc-18-3195-2024, https://doi.org/10.5194/tc-18-3195-2024, 2024
Short summary
Short summary
Satellites have made it possible to observe glacier elevation changes from all around the world. In the present study, we compared the results produced from two different types of satellite data between different research groups and against validation measurements from aeroplanes. We found a large spread between individual results but showed that the group ensemble can be used to reliably estimate glacier elevation changes and related errors from satellite data.
Benjamin Bouchard, Daniel F. Nadeau, Florent Domine, François Anctil, Tobias Jonas, and Étienne Tremblay
Hydrol. Earth Syst. Sci., 28, 2745–2765, https://doi.org/10.5194/hess-28-2745-2024, https://doi.org/10.5194/hess-28-2745-2024, 2024
Short summary
Short summary
Observations and simulations from an exceptionally low-snow and warm winter, which may become the new norm in the boreal forest of eastern Canada, show an earlier and slower snowmelt, reduced soil temperature, stronger vertical temperature gradients in the snowpack, and a significantly lower spring streamflow. The magnitude of these effects is either amplified or reduced with regard to the complex structure of the canopy.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
Earth Syst. Sci. Data, 15, 5121–5133, https://doi.org/10.5194/essd-15-5121-2023, https://doi.org/10.5194/essd-15-5121-2023, 2023
Short summary
Short summary
Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
Short summary
Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Johannes Aschauer, Adrien Michel, Tobias Jonas, and Christoph Marty
Geosci. Model Dev., 16, 4063–4081, https://doi.org/10.5194/gmd-16-4063-2023, https://doi.org/10.5194/gmd-16-4063-2023, 2023
Short summary
Short summary
Snow water equivalent is the mass of water stored in a snowpack. Based on exponential settling functions, the empirical snow density model SWE2HS is presented to convert time series of daily snow water equivalent into snow depth. The model has been calibrated with data from Switzerland and validated with independent data from the European Alps. A reference implementation of SWE2HS is available as a Python package.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
Short summary
Short summary
We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary
Short summary
This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Tobias Siegfried, Aziz Ul Haq Mujahid, Beatrice Sabine Marti, Peter Molnar, Dirk Nikolaus Karger, and Andrey Yakovlev
EGUsphere, https://doi.org/10.5194/egusphere-2023-520, https://doi.org/10.5194/egusphere-2023-520, 2023
Preprint archived
Short summary
Short summary
Our study investigates climate change impacts on water resources in Central Asia's high-mountain regions. Using new data and a stochastic soil moisture model, we found increased precipitation and higher temperatures in the future, leading to higher water discharge despite decreasing glacier melt contributions. These findings are crucial for understanding and preparing for climate change effects on Central Asia's water resources, with further research needed on extreme weather event impacts.
Dirk Nikolaus Karger, Michael P. Nobis, Signe Normand, Catherine H. Graham, and Niklaus E. Zimmermann
Clim. Past, 19, 439–456, https://doi.org/10.5194/cp-19-439-2023, https://doi.org/10.5194/cp-19-439-2023, 2023
Short summary
Short summary
Here we present global monthly climate time series for air temperature and precipitation at 1 km resolution for the last 21 000 years. The topography at all time steps is created by combining high-resolution information on glacial cover from current and Last Glacial Maximum glacier databases with the interpolation of an ice sheet model and a coupling to mean annual temperatures from a global circulation model.
Philipp Brun, Niklaus E. Zimmermann, Chantal Hari, Loïc Pellissier, and Dirk Nikolaus Karger
Earth Syst. Sci. Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022, https://doi.org/10.5194/essd-14-5573-2022, 2022
Short summary
Short summary
Using mechanistic downscaling, we developed CHELSA-BIOCLIM+, a set of 15 biologically relevant, climate-related variables at unprecedented resolution, as a basis for environmental analyses. It includes monthly time series for 38+ years and 30-year averages for three future periods and three emission scenarios. Estimates matched well with station measurements, but few biases existed. The data allow for detailed assessments of climate-change impact on ecosystems and their services to societies.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
Short summary
Short summary
Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Robert Pazúr, Nica Huber, Dominique Weber, Christian Ginzler, and Bronwyn Price
Earth Syst. Sci. Data, 14, 295–305, https://doi.org/10.5194/essd-14-295-2022, https://doi.org/10.5194/essd-14-295-2022, 2022
Short summary
Short summary
We mapped the distribution of cropland and permanent grassland across Switzerland, where the agricultural land is considerably spatially heterogeneous due to strong variability in topography and climate, thus presenting challenges to mapping. The resulting map has high accuracy in lowlands as well as in mountainous areas. Thus, we believe that the presented mapping approach and resulting map will provide a solid ground for further research in agricultural land cover and landscape structure.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Ionuț Iosifescu Enescu, David Hanimann, Dominik Haas-Artho, Marius Rüetschi, Dirk Nikolaus Karger, Gian-Kasper Plattner, Martin Hägeli, Rebecca Kurup Buchholz, Lucia de Espona, Niklaus E. Zimmermann, and Loïc Pellissier
Abstr. Int. Cartogr. Assoc., 3, 120, https://doi.org/10.5194/ica-abs-3-120-2021, https://doi.org/10.5194/ica-abs-3-120-2021, 2021
Ionuț Iosifescu Enescu, David Hanimann, Dominik Haas-Artho, Marius Rüetschi, Dirk Nikolaus Karger, Gian-Kasper Plattner, Martin Hägeli, Rebecca Kurup Buchholz, Lucia de Espona, Niklaus E. Zimmermann, and Loïc Pellissier
Abstr. Int. Cartogr. Assoc., 3, 119, https://doi.org/10.5194/ica-abs-3-119-2021, https://doi.org/10.5194/ica-abs-3-119-2021, 2021
Cited articles
Ashcroft, M. B. and Gollan, J. R.: Moisture, thermal inertia, and the spatial distributions of near-surface soil and air temperatures: Understanding factors that promote microrefugia, Agr. Forest Meteorol., 176, 77–89, https://doi.org/10.1016/j.agrformet.2013.03.008, 2013.
Bates, D., Mächler, M., Bolker, B., and Walker, S.: Fitting Linear Mixed-Effects Models Using lme4, J. Stat. Softw., 67, 1–48, https://www.jstatsoft.org/article/view/v067i01 (last access: 19 January 2024), 2015.
Bode, C. A., Limm, M. P., Power, M. E., and Finlay, J. C.: Subcanopy Solar Radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models, Remote Sens. Environ., 154, 387–397, https://doi.org/10.1016/j.rse.2014.01.028, 2014.
Bramer, I., Anderson, B. J., Bennie, J., Bladon, A. J., De Frenne, P., Hemming, D., Hill, R. A., Kearney, M. R., Körner, C., Korstjens, A. H., Lenoir, J., Maclean, I. M. D., Marsh, C. D., Morecroft, M. D., Ohlemüller, R., Slater, H. D., Suggitt, A. J., Zellweger, F., and Gillingham, P. K.: Advances in Monitoring and Modelling Climate at Ecologically Relevant Scales, Adv. Ecol. Res., 58, 101–161, https://doi.org/10.1016/bs.aecr.2017.12.005, 2018.
Brändli, U.-B., Abegg, M., and Allgaier Leuch, B.: Schweizerisches Landesforstinventar, Ergebnisse der vierten Erhebung 2009–2017, Birmensdorf, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL, Bern, Bundesamt für Umwelt, 341 pp., https://doi.org/10.16904/envidat.146, 2020.
Chen, J., Saunders, S. C., Crow, T. R., Naiman, R. J., Brosofske, K. D., Mroz, G. D., Brookshire, B. L., and Franklin, J. F.: Microclimate in forest ecosystem and landscape ecology: Variations in local climate can be used to monitor and compare the effects of different management regimes, Bioscience, 49, 288–297, 1999.
Christiansen, D. M., Iversen, L. L., Ehrlén, J., and Hylander, K.: Changes in forest structure drive temperature preferences of boreal understorey plant communities, J. Ecol., 110, 631–643, https://doi.org/10.1111/1365-2745.13825, 2022.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008.
De Frenne, P., Zellweger, F., Rodriìguez-Saìnchez, F., Scheffers, B., Hylander, K., Luoto, M., Vellend, M., Verheyen, K., and Lenoir, J.: Global buffering of temperatures under forest canopies, Nat. Ecol. Evol., 3, 744–749, 2019.
De Frenne, P., Lenoir, J., Luoto, M., Scheffers, B. R., Zellweger, F., Aalto, J., Ashcroft, M. B., Christiansen, D. M., Decocq, G., De Pauw, K., Govaert, S., Greiser, C., Gril, E., Hampe, A., Jucker, T., Klinges, D. H., Koelemeijer, I. A., Lembrechts, J. J., Marrec, R., Meeussen, C., Ogée, J., Tyystjärvi, V., Vangansbeke, P., and Hylander, K.: Forest microclimates and climate change: Importance, drivers and future research agenda, Glob. Change Biol., 27, 2279–2297, https://doi.org/10.1111/gcb.15569, 2021.
Dobrowski, S. Z.: A climatic basis for microrefugia: the influence of terrain on climate, Glob. Change Biol., 17, 1022–1035, https://doi.org/10.1111/j.1365-2486.2010.02263.x, 2011.
Duffy, J. P., Anderson, K., Fawcett, D., Curtis, R. J., and Maclean, I. M. D.: Drones provide spatial and volumetric data to deliver new insights into microclimate modelling, Landsc. Ecol. Eng., 9, 685–702, https://doi.org/10.1007/s10980-020-01180-9, 2021.
FOEN: Federal Office for the Environment: Swiss Forest Ecoregeions https://opendata.swiss/en/dataset/waldstandortsregionen (last access: 15 January 2024), 2022.
Freeman, T. G.: Calculating catchment area with divergent flow based on a regular grid, Comput. Geosci., 17, 413–422, https://doi.org/10.1016/0098-3004(91)90048-I, 1991.
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances, Int. J. Climatol., 34, 1585–1605, https://doi.org/10.1002/joc.3786, 2014.
Frey, S. J. K., Hadley, A. S., Johnson, S. L., Schulze, M., Jones, J. A., and Betts, M. G.: Spatial models reveal the microclimatic buffering capacity of old-growth forests, Sci. Adv., 2, e1501392, https://doi.org/10.1126/sciadv.1501392, 2016.
Geiger, R., Aron, R. H., and Todhunter, P.: The climate near the ground, Rowman and Littlefield, Oxford, ISBN-10 0742518574, 2009.
Greiser, C., Meineri, E., Luoto, M., Ehrlén, J., and Hylander, K.: Monthly microclimate models in a managed boreal forest landscape, Agr. Forest Meteorol., 250–251, 147–158, https://doi.org/10.1016/j.agrformet.2017.12.252, 2018.
Haesen, S., Lembrechts, J. J., De Frenne, P., Lenoir, J., Aalto, J., Ashcroft, M. B., Kopecký, M., Luoto, M., Maclean, I., Nijs, I., Niittynen, P., van den Hoogen, J., Arriga, N., Brůna, J., Buchmann, N., Čiliak, M., Collalti, A., De Lombaerde, E., Descombes, P., Gharun, M., Goded, I., Govaert, S., Greiser, C., Grelle, A., Gruening, C., Hederová, L., Hylander, K., Kreyling, J., Kruijt, B., Macek, M., Máliš, F., Man, M., Manca, G., Matula, R., Meeussen, C., Merinero, S., Minerbi, S., Montagnani, L., Muffler, L., Ogaya, R., Penuelas, J., Plichta, R., Portillo-Estrada, M., Schmeddes, J., Shekhar, A., Spicher, F., Ujházyová, M., Vangansbeke, P., Weigel, R., Wild, J., Zellweger, F., and Van Meerbeek, K.: ForestClim – Bioclimatic variables for microclimate temperatures of European forests, Glob. Change Biol., 29, 2886–2892, https://doi.org/10.1111/gcb.16678, 2023a.
Haesen, S., Lenoir, J., Gril, E., De Frenne, P., Lembrechts, J. J., Kopecký, M., Macek, M., Man, M., Wild, J., and Van Meerbeek, K.: Microclimate reveals the true thermal niche of forest plant species, Ecol. Lett., 26, 2043–2055, https://doi.org/10.1111/ele.14312, 2023b.
Jones, H. G.: Plants and microclimate, A quantitative approach to environmental plant physiology, 3rd Edn., Cambridge, Cambridge University Press, ISBN: 10 0521279593, 2014.
Jucker, T., Hardwick, S. R., Both, S., Elias, D. M. O., Ewers, R. M., Milodowski, D. T., Swinfield, T., and Coomes, D. A.: Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes, Glob. Change Biol., 24, 5243–5258, 2018.
Kearney, M. R. and Porter, W. P.: NicheMapR – an R package for biophysical modelling: the microclimate model, Ecography, 40, 664–674, https://doi.org/10.1111/ecog.02360, 2017.
Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., and Maclean, I. M. D.: A method for computing hourly, historical, terrain-corrected microclimate anywhere on earth, Method. Ecol. Evol., 11, 38–43, https://doi.org/10.1111/2041-210X.13330, 2020.
Kükenbrink, D., Schneider, F. D., Schmid, B., Gastellu-Etchegorry, J. P., Schaepman, M. E., and Morsdorf, F.: Modelling of three-dimensional, diurnal light extinction in two contrasting forests, Agr. Forest Meteorol., 296, 108230, https://doi.org/10.1016/j.agrformet.2020.108230, 2021.
Lembrechts, J. J., Lenoir, J., Roth, N., Hattab, T., Milbau, A., Haider, S., Pellissier, L., Pauchard, A., Ratier Backes, A., Dimarco, R. D., Nuñez, M. A., Aalto, J., and Nijs, I.: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing, Glob. Ecol. Biogeogr., 28, 1578–1596, https://doi.org/10.1111/geb.12974, 2019a.
Lembrechts, J. J., Nijs, I., and Lenoir, J.: Incorporating microclimate into species distribution models, Ecography, 42, 1267–1279, https://doi.org/10.1111/ecog.03947, 2019b.
Lembrechts, J. J., Aalto, J., Ashcroft, M. B., et al.: SoilTemp: a global database of near-surface temperature, Glob. Change Biol., 26, 6616–6629, https://doi.org/10.1111/gcb.15123, 2020.
Lembrechts, J. J., van den Hoogen, J., Aalto, J., et al.: Global maps of soil temperature, Glob. Change Biol., 28, 3110–3144, https://doi.org/10.1111/gcb.16060, 2022.
Liaw, A. and Wiener, M.: Classification and Regression by Random forest, R News, 2, 18–22, 2002.
Maclean, I. M. D. and Early, R.: Macroclimate data overestimate range shifts of plants in response to climate change, Nat. Clim. Change, 13, 484–490, https://doi.org/10.1038/s41558-023-01650-3, 2023.
Maclean, I. M. D. and Klinges, D. H.: Microclimc: A mechanistic model of above, below and within-canopy microclimate, Ecol. Modell., 451, 109567, https://doi.org/10.1016/j.ecolmodel.2021.109567, 2021.
Maclean, I. M. D., Mosedale, J. R., and Bennie, J. J.: Microclima: an R package for modelling meso- and microclimate, Method. Ecol. Evol., 10, 280–290, https://doi.org/10.1111/2041-210X.13093, 2018.
Maclean, I. M. D., Duffy, J. P., Haesen, S., Govaert, S., De Frenne, P., Vanneste, T., Lenoir, J., Lembrechts, J. J., Rhodes, M. W., and Van Meerbeek, K.: On the measurement of microclimate, Method. Ecol. Evol., 12, 1397–1410, https://doi.org/10.1111/2041-210X.13627, 2021.
Malkoç, E., Rüetschi, M., Ginzler, C., and Waser, L. T.: Countrywide mapping of trees outside forests based on remote sensing data in Switzerland, Int. J. Appl. Earth Obs. Geoinf., 100, 102336, https://doi.org/10.1016/j.jag.2021.102336, 2021.
Musselman, K. N., Margulis, S. A., and Molotch, N. P.: Estimation of solar direct beam transmittance of conifer canopies from airborne LiDAR, Remote Sens. Environ., 136, 402–415, https://doi.org/10.1016/j.rse.2013.05.021, 2013.
Potter, K. A., Arthur Woods, H., and Pincebourde, S.: Microclimatic challenges in global change biology, Glob. Change Biol., 19, 2932–2939, https://doi.org/10.1111/gcb.12257, 2013.
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F.: Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure, Ecography, 40, 913–929, https://doi.org/10.1111/ecog.02881, 2017.
Scherrer, D. and Körner, C.: Infra-red thermometry of alpine landscapes challenges climatic warming projections, Glob. Change Biol., 16, 2602–2613, https://doi.org/10.1111/j.1365-2486.2009.02122.x, 2010.
Senf, C. and Seidl, R.: Mapping the forest disturbance regimes of Europe, Nat. Sustain., 4, 63–70, https://doi.org/10.1038/s41893-020-00609-y, 2021.
Suggitt, A. J., Wilson, R. J., Isaac, N. J. B., Beale, C. M., Auffret, A. G., August, T., Bennie, J. J., Crick, H. Q. P., Duffield, S., Fox, R., Hopkins, J. J., Macgregor, N. A., Morecroft, M. D., Walker, K. J., and Maclean, I. M. D.: Extinction risk from climate change is reduced by microclimatic buffering, Nat. Clim. Change, 8, 713–717, https://doi.org/10.1038/s41558-018-0231-9, 2018.
Sulmoni, E., De Frenne, P., Zimmermann, N., Frey, D. J., Karger, D., Malle, J., Webster, C., Jonas, T., Ginzler, C., Baltensweiler, A., and Zellweger, F.: Monthly topsoil and near surface microclimate temperature data for Switzerland, EnviDat [data set], https://doi.org/10.16904/envidat.431, 2023.
Swisstopo: Height Models https://www.swisstopo.admin.ch/en/geodata/height.html (last access: 15 January 2024), 2020.
Swisstopo: VECTOR25, https://www.swisstopo.admin.ch/de/geodata/maps/smv/smv25.html (last access: 15 January 2024), 2022.
Tymen, B., Vincent, G., Courtois, E. A., Heurtebize, J., Dauzat, J., Marechaux, I., and Chave, J.: Quantifying micro-environmental variation in tropical rainforest understory at landscape scale by combining airborne LiDAR scanning and a sensor network, Ann. Forest Sci., 74, 32, https://doi.org/10.1007/s13595-017-0628-z, 2017.
Vanneste, T., Govaert, S., Spicher, F., Brunet, J., Cousins, S. A. O., Decocq, G., Diekmann, M., Graae, B. J., Hedwall, P. O., Kapás, R. E., Lenoir, J., Liira, J., Lindmo, S., Litza, K., Naaf, T., Orczewska, A., Plue, J., Wulf, M., Verheyen, K., and De Frenne, P.: Contrasting microclimates among hedgerows and woodlands across temperate Europe, Agr. Forest Meteorol., 281, 107818, https://doi.org/10.1016/j.agrformet.2019.107818, 2020.
von Arx, G., Graf Pannatier, E., Thimonier, A., Rebetez, M., and Gilliam, F.: Microclimate in forests with varying leaf area index and soil moisture: potential implications for seedling establishment in a changing climate, J. Ecol., 101, 1201–1213, https://doi.org/10.1111/1365-2745.12121, 2013.
Waser, L. T., Ginzler, C., and Rehush, N.: Wall-to-Wall tree type mapping from countrywide airborne remote sensing surveys, Remote Sens., 9, 1–24, https://doi.org/10.3390/rs9080766, 2017.
Webster, C., Rutter, N., Zahner, F., and Jonas, T.: Modeling subcanopy incoming longwave radiation to seasonal snow using air and tree trunk temperatures, J. Geophys. Res.-Atmos., 121, 1220–1235, https://doi.org/https://doi.org/10.1002/2015JD024099, 2016.
Webster, C., Mazzotti, G., Essery, R., and Jonas, T.: Enhancing airborne LiDAR data for improved forest structure representation in shortwave transmission models, Remote Sens. Environ., 249, 112017, https://doi.org/10.1016/j.rse.2020.112017, 2020.
Webster, C., Essery, R., Mazzotti, G., and Jonas, T.: Using just a canopy height model to obtain lidar-level accuracy in 3D forest canopy shortwave transmissivity estimates, Agr. Forest Meteorol., 338, 109429, https://doi.org/10.1016/j.agrformet.2023.109429, 2023 (model code available at https://github.com/c-webster/CanRad.jl, last access: 19 January 2024).
Wood, S. N.: Generalized Additive Models: An Introduction with R, 2nd Edn., Chapman and Hall/CRC, https://doi.org/10.1201/97813153702792017, 2017.
Zellweger, F., Frenne, P. De, Lenoir, J., Rocchini, D., and Coomes, D.: Advances in microclimate ecology arising from remote sensing, Trends Ecol. Evol., 34, 327–341, https://doi.org/10.1016/j.tree.2018.12.012, 2019a.
Zellweger, F., Baltensweiler, A., Schleppi, P., Huber, M., Küchler, M., Ginzler, C., and Tobias, J.: Estimating below-canopy light regimes using airborne laser scanning: an application to plant community analysis, Ecol. Evol., 9, 9149–9159, https://doi.org/10.1002/ece3.5462, 2019b.
Zellweger, F., Coomes, D., Lenoir, J., Depauw, L., Maes, S. L., Wulf, M., Kirby, K., Brunet, J., Kopecky, M., Malis, F., Schmidt, W., Heinrichs, S., Ouden, J. den, Jaroszewicz, B., Buyse, G., Spicher, F., Verheyen, K., and De Frenne, P.: Seasonal drivers of understorey temperature buffering in temperate deciduous forests across Europe, Glob. Ecol. Biogeogr., 28, 1774–1786, 2019c.
Zellweger, F., De Frenne, P., Lenoir, J., Vangansbeke, P., Verheyen, K., Bernhardt-Römermann, M., Baeten, L., Hédl, R., Berki, I., Brunet, J., Van Calster, H., Chudomelová, M., Decocq, G., Dirnböck, T., Durak, T., Heinken, T., Jaroszewicz, B., Kopecký, M., Máliš, F., Macek, M., Malicki, M., Naaf, T., Nagel, T. A., Ortmann-Ajkai, A., Petřík, P., Pielech, R., Reczyńska, K., Schmidt, W., Standovár, T., Świerkosz, K., Teleki, B., Vild, O., Wulf, M., and Coomes, D.: Forest microclimate dynamics drive plant responses to warming, Science, 368, 772–775, https://doi.org/10.1126/science.aba6880, 2020.
Zimmermann, N. E. and Roberts, D. W.: Final report of the MLP climate and biophysical mapping project, Swiss Federal Research Inst. WSL, Birmensdorf, Switzerland and Utah State Univ., Logan, USA, 2001.
Short summary
The microclimatic conditions experienced by organisms living close to the ground are not well represented in currently used climate datasets derived from weather stations. Therefore, we measured and mapped ground microclimate temperatures at 10 m spatial resolution across Switzerland using a novel radiation model. Our results reveal a high variability in microclimates across different habitats and will help to better understand climate and land use impacts on biodiversity and ecosystems.
The microclimatic conditions experienced by organisms living close to the ground are not well...
Altmetrics
Final-revised paper
Preprint