Articles | Volume 13, issue 2
https://doi.org/10.5194/gmd-13-537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-537-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
11 Feb 2020
Model description paper |
| 11 Feb 2020
| 11 Feb 2020
FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics
Matthias J. R. Speich
CORRESPONDING AUTHOR
Dynamic Macroecology, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Hydrological Forecasts, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
Biometry and Environmental Systems Analysis, University of Freiburg, 79085 Freiburg im Briesgau, Germany
Institute of Sustainable Development, Zurich University of Applied Sciences (ZHAW), 8401 Winterthur, Switzerland
Massimiliano Zappa
Hydrological Forecasts, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Marc Scherstjanoi
Dynamic Macroecology, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Institute of Climate-Smart Agriculture, Johann Heinrich von Thünen Institute, 38116 Braunschweig, Germany
Heike Lischke
Dynamic Macroecology, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
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Lieke Melsen, Adriaan Teuling, Paul Torfs, Massimiliano Zappa, Naoki Mizukami, Martyn Clark, and Remko Uijlenhoet
Hydrol. Earth Syst. Sci., 20, 2207–2226, https://doi.org/10.5194/hess-20-2207-2016, https://doi.org/10.5194/hess-20-2207-2016, 2016
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Michal Jenicek, Jan Seibert, Massimiliano Zappa, Maria Staudinger, and Tobias Jonas
Hydrol. Earth Syst. Sci., 20, 859–874, https://doi.org/10.5194/hess-20-859-2016, https://doi.org/10.5194/hess-20-859-2016, 2016
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M. Zappa, N. Andres, P. Kienzler, D. Näf-Huber, C. Marti, and M. Oplatka
Proc. IAHS, 370, 235–242, https://doi.org/10.5194/piahs-370-235-2015, https://doi.org/10.5194/piahs-370-235-2015, 2015
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The most severe threat for the city of Zürich (Switzerland) are flash-floods from the small Sihl river. An assessment using a rainfall-runoff model evaluated more than 40000 extreme flood scenarios. These scenarios identified deficits for the safety of Zürich. The combination of different structural and flood management measures can lead to an optimal safety also in case of unfavorable initial conditions. Pending questions concern the costs, political decisions and the environmental matters.
M. Zappa, T. Vitvar, A. Rücker, G. Melikadze, L. Bernhard, V. David, M. Jans-Singh, N. Zhukova, and M. Sanda
Proc. IAHS, 369, 25–30, https://doi.org/10.5194/piahs-369-25-2015, https://doi.org/10.5194/piahs-369-25-2015, 2015
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A research effort involving Switzerland, Georgia and the Czech Republic has been launched to evaluate the relation between snowpack and summer low flows. Two rainfall-runoff models will simulate over 10 years of snow hydrology and runoff in nested streams. Processes involved will be also evaluated by mean by means of high frequency sampling of the environmental isotopes 18O and 2H. The paper presents first analysis of available datasets of 18O, 2H, discharge, snowpack and modelling experiments.
P. Ronco, M. Bullo, S. Torresan, A. Critto, R. Olschewski, M. Zappa, and A. Marcomini
Hydrol. Earth Syst. Sci., 19, 1561–1576, https://doi.org/10.5194/hess-19-1561-2015, https://doi.org/10.5194/hess-19-1561-2015, 2015
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The aim of the paper is the application of the KULTURisk regional risk assessment (KR-RRA) methodology, presented in the companion paper (Part 1), to the Sihl River basin, in northern Switzerland. Flood-related risks have been assessed for different receptors lying in the Sihl river valley including the city of Zurich, which represents a typical case of river flooding in an urban area, by means of a calibration process of the methodology to the site-specific context and features.
S. Jörg-Hess, F. Fundel, T. Jonas, and M. Zappa
The Cryosphere, 8, 471–485, https://doi.org/10.5194/tc-8-471-2014, https://doi.org/10.5194/tc-8-471-2014, 2014
K. Liechti, L. Panziera, U. Germann, and M. Zappa
Hydrol. Earth Syst. Sci., 17, 3853–3869, https://doi.org/10.5194/hess-17-3853-2013, https://doi.org/10.5194/hess-17-3853-2013, 2013
F. Fundel, S. Jörg-Hess, and M. Zappa
Hydrol. Earth Syst. Sci., 17, 395–407, https://doi.org/10.5194/hess-17-395-2013, https://doi.org/10.5194/hess-17-395-2013, 2013
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Juliette Bernard, Marielle Saunois, Elodie Salmon, Philippe Ciais, Shushi Peng, Antoine Berchet, Penélope Serrano-Ortiz, Palingamoorthy Gnanamoorthy, and Joachim Jansen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1331, https://doi.org/10.5194/egusphere-2024-1331, 2024
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Despite their importance, uncertainties remain in estimating methane emissions from wetlands. Here, a simplified model that operates at a global scale is developed. Taking advantage of advances in remote sensing data and in situ observations, the model effectively reproduces the spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights for sensitivity analyses.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-181, https://doi.org/10.5194/gmd-2023-181, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change the storage of carbon in sediments slows down carbon cycling and influences feedbacks in the atmosphere-ocean-sediment system. Here we coupled a sediment model to an ocean biogeochemistry model and found a shift of carbon storage from the atmosphere to the ocean-sediment system.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Cited articles
Allen, C. D., Macalady, A. K., Chenchouni, H., Bachelet, D., McDowell, N.,
Vennetier, M., Kitzberger, T., Rigling, A., Breshears, D. D., Hogg, E. H.,
Gonzalez, P., Fensham, R., Zhang, Z., Castro, J., Demidova, N., Lim, J. H.,
Allard, G., Running, S. W., Semerci, A., and Cobb, N.: A global overview of
drought and heat-induced tree mortality reveals emerging climate change risks
for forests, Forest Ecol. Manag., 259, 660–684, 2010. a
Anderegg, L. D. L., Anderegg, W. R. L., and Berry, J. A.: Not all droughts are
created equal: translating meteorological drought into woody plant mortality,
Tree Physiol., 33, 672–683, https://doi.org/10.1093/treephys/tpt044,
2013. a
Andréassian, V.: Waters and forests: from historical controversy to
scientific debate, J. Hydrol., 291, 1–27,
https://doi.org/10.1016/j.jhydrol.2003.12.015,
2004. a, b
Bachofen, H., Brändli, U., Brassel, P., Kasper, H., Lüscher, P.,
Mahrer, F., Riegger, W., Stierlin, H., Strobel, T., Sutter, R., Wenger, C.,
Winzeler, C., and Zingg, A.: Schweizerisches
Landesforstinventar – Ergebnisse der Erstaufnahme 1982–1986, Tech.
rep., Eidgenössische Anstalt für das Forstliche Versuchswesen,
Birmensdorf, available at:
https://www.lfi.ch/publikationen/publ/LFI1_Ergebnisbericht.pdf (last access: 10 February 2020),
1988. a, b, c
Badoux, A., Witzig, J., Germann, P. F., Kienholz, H., Lüscher, P.,
Weingartner, R., and Hegg, C.: Investigations on the runoff generation at the
profile and plot scales, Swiss Emmental, Hydrol. Process., 20,
377–394, https://doi.org/10.1002/hyp.6056, 2006. a
Bartholomeus, R. P., Stagge, J. H., Tallaksen, L. M., and Witte, J. P. M.: Sensitivity of potential evaporation estimates to 100 years of climate variability, Hydrol. Earth Syst. Sci., 19, 997–1014, https://doi.org/10.5194/hess-19-997-2015, 2015. a
Begert, M., Schlegel, T., and Kirchhofer, W.: Homogeneous temperature and
precipitation series of Switzerland from 1864 to 2000, Int. J. Climatol., 25, 65–80, https://doi.org/10.1002/joc.1118, 2005. a
Bosch, J. and Hewlett, J.: A review of catchment experiments to determine the
effect of vegetation changes on water yield and evapotranspiration, J. Hydrol., 55, 3–23, https://doi.org/10.1016/0022-1694(82)90117-2,
1982. a
Bréda, N., Huc, R., Granier, A., and Dreyer, E.: Temperate forest trees and
stands under severe drought: a review of ecophysiological responses,
adaptation processes and long-term consequences, Ann. Forest Sci.,
63, 625–644, https://doi.org/10.1051/forest:2006042, 2006. a
Brunner, M. I., Björnsen Gurung, A., Zappa, M., Zekollari, H., Farinotti, D.,
and Stähli, M.: Present and future water scarcity in Switzerland:
Potential for alleviation through reservoirs and lakes, Sci.
Total Environ., 666, 1033–1047, https://doi.org/10.1016/j.scitotenv.2019.02.169,
2019. a, b
Bugmann, H., Brang, P., Elkin, C., Henne, P., Jakoby, O., Lévesque, M.,
Lischke, H., Psomas, A., Rigling, A., Wermelinger, B., and Zimmermann, N.:
Climate change impacts on tree species, forest properties, and ecosystem
services, in: Toward Quantitative Scenarios of Climate Change
Impacts in Switzerland, 79–88, Bern, Switzerland,
http://www.ch2014-impacts.ch/ (last access: 10 February 2020), 2014. a, b, c, d
Büntgen, U., Bellwald, I., Kalbermatten, H., Schmidhalter, M., Frank,
D. C., Freund, H., Bellwald, W., Neuwirth, B., Nüsser, M., and Esper, J.:
700 years of settlement and building history in the Loetschental,
Switzerland, Erdkunde, 2, 96–112, https://doi.org/10.3112/erdkunde.2006.02.02,
2006. a
Burga, C. A.: Swiss vegetation history during the last 18 000 years, New
Phytol., 110, 581–662, https://doi.org/10.1111/j.1469-8137.1988.tb00298.x,
1988. a, b
Camacho, F., Sánchez, J., and Latorre, C.: GIO Global Land
Component – Lot I “Operation of the Global Land Component”.
Quality Assessment Report LAI, FAPAR, FCover Collection 300 m.
Version 1, Issue I1.10, Tech. rep., GIO-GL Lot1 Consortium, Molderdijk, Belgium, 2016. a
Chakroun, H., Mouillot, F., Nasr, Z., Nouri, M., Ennajah, A., and Ourcival,
J. M.: Performance of LAI-MODIS and the influence on drought simulation
in a Mediterranean forest, Ecohydrology, 7, 1014–1028,
https://doi.org/10.1002/eco.1426,, 2014. a
Choat, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R.,
Bucci, S. J., Feild, T. S., Gleason, S. M., Hacke, U. G., Jacobsen, A. L.,
Lens, F., Maherali, H., Martínez-Vilalta, J., Mayr, S., Mencuccini,
M., Mitchell, P. J., Nardini, A., Pittermann, J., Pratt, R. B., Sperry,
J. S., Westoby, M., Wright, I. J., and Zanne, A. E.: Global convergence in
the vulnerability of forests to drought, Nature, 491, 752–755, https://doi.org/10.1038/nature11688, 2012. a
Copernicus Service Information: Leaf Area Index, available at: https://land.copernicus.eu/global/products/lai (last access: 10 February 2020), 2017. a
Creed, I. F., Spargo, A. T., Jones, J. A., Buttle, J. M., Adams, M. B., Beall,
F. D., Booth, E. G., Campbell, J. L., Clow, D., Elder, K., Green, M. B.,
Grimm, N. B., Miniat, C., Ramlal, P., Saha, A., Sebestyen, S., Spittlehouse,
D., Sterling, S., Williams, M. W., Winkler, R., and Yao, H.: Changing forest
water yields in response to climate warming: results from long-term
experimental watershed sites across North America, Glob. Change Biol.,
20, 3191–3208, https://doi.org/10.1111/gcb.12615, 2014. a
De Cáceres, M., Martínez-Vilalta, J., Coll, L., Llorens, P., Casals,
P., Poyatos, R., Pausas, J. G., and Brotons, L.: Coupling a water balance
model with forest inventory data to predict drought stress: the role of
forest structural changes vs. climate changes, Agr. Forest
Meteorol., 213, 77–90, https://doi.org/10.1016/j.agrformet.2015.06.012,
2015. a
Defila, C. and Clot, B.: Phytophenological trends in Switzerland, Int, J, Biometeorol,, 45, 203–207, https://doi.org/10.1007/s004840100101, 2001. a
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé,
J., and François, C.: Modelling interannual and spatial variability of
leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, https://doi.org/10.1016/j.agrformet.2008.11.014,
2009. a, b
Dobbertin, M., Eilmann, B., Bleuler, P., Giuggiola, A., Graf Pannatier, E.,
Landolt, W., Schleppi, P., and Rigling, A.: Effect of irrigation on needle
morphology, shoot and stem growth in a drought-exposed Pinus sylvestris
forest, Tree Physiol., 30, 346–360, https://doi.org/10.1093/treephys/tpp123, 2010. a
Du, E., Link, T. E., Wei, L., and Marshall, J. D.: Evaluating hydrologic
effects of spatial and temporal patterns of forest canopy change using
numerical modelling: Spatial and Temporal Patterns of Forest Canopy
Change, Hydrol. Process., 30, 217–231, https://doi.org/10.1002/hyp.10591, 2016. a
Elkin, C., Giuggiola, A., Rigling, A., and Bugmann, H.: Short- and long-term
efficacy of forest thinning to mitigate drought impacts in mountain forests
in the European Alps, Ecol. Appl., 25, 1083–1098,
https://doi.org/10.1890/14-0690.1, 2015. a
Ewers, B. E., Gower, S. T., Bond-Lamberty, B., and Wang, C. K.: Effects of
stand age and tree species on canopy transpiration and average stomatal
conductance of boreal forests, Plant Cell Environ., 28, 660–678,
https://doi.org/10.1111/j.1365-3040.2005.01312.x, 2005. a
Farley, K. A., Jobbagy, E. G., and Jackson, R. B.: Effects of afforestation on
water yield: a global synthesis with implications for policy, Glob. Change
Biol., 11, 1565–1576, https://doi.org/10.1111/j.1365-2486.2005.01011.x, 2005. a
Fatichi, S., Ivanov, V. Y., and Caporali, E.: A mechanistic ecohydrological
model to investigate complex interactions in cold and warm water-controlled
environments: 1. Theoretical framework and plot-scale analysis, J.
Adv. Model. Earth Syst., 4, M05002, https://doi.org/10.1029/2011MS000086, 2012. a
Fatichi, S., Pappas, C., and Ivanov, V. Y.: Modeling plant-water interactions:
an ecohydrological overview from the cell to the global scale: Modeling
plant-water interactions, WIRES Water, 3,
327–368, https://doi.org/10.1002/wat2.1125, 2016. a
Federer, C. A., Vörösmarty, C., and Fekete, B.: Sensitivity of Annual
Evaporation to Soil and Root Properties in Two Models of
Contrasting Complexity, J. Hydrometeorol., 4, 1276–1290,
https://doi.org/10.1175/1525-7541(2003)004<1276:SOAETS>2.0.CO;2, 2003. a
Ford, C. R., Hubbard, R. M., and Vose, J. M.: Quantifying structural and
physiological controls on variation in canopy transpiration among planted
pine and hardwood species in the southern Appalachians, Ecohydrology, 4,
183–195, https://doi.org/10.1002/eco.136, 2011. a
Fuhrer, J., Beniston, M., Fischlin, A., Frei, C., Goyette, S., Jasper, K., and
Pfister, C.: Climate Risks and Their Impact on Agriculture and
Forests in Switzerland, Climatic Change, 79, 79–102,
https://doi.org/10.1007/s10584-006-9106-6, 2006. a, b
Garrigues, S., Lacaze, R., Baret, F., Morisette, J. T., Weiss, M., Nickeson,
J. E., Fernandes, R., Plummer, S., Shabanov, N. V., Myneni, R. B.,
Knyazikhin, Y., and Yang, W.: Validation and intercomparison of global Leaf
Area Index products derived from remote sensing data, J.
Geophys. Res.-Biogeo., 113, G02028,
https://doi.org/10.1029/2007JG000635, 2008. a
Gaudard, L., Romerio, F., Dalla Valle, F., Gorret, R., Maran, S., Ravazzani,
G., Stoffel, M., and Volonterio, M.: Climate change impacts on hydropower in
the Swiss and Italian Alps, Sci. Total Environ., 493,
1211–1221, https://doi.org/10.1016/j.scitotenv.2013.10.012,
2014. a
Gehrig-Fasel, J., Guisan, A., and Zimmermann, N. E.: Tree line shifts in the
Swiss Alps: Climate change or land abandonment?, J. Veg.
Sci., 18, 571, https://doi.org/10.1658/1100-9233(2007)18[571:TLSITS]2.0.CO;2,
2007. a, b
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.:
Terrestrial vegetation and water balance – hydrological evaluation of a
dynamic global vegetation model, J. Hydrol., 286, 249–270, 2004. a
Gimmi, U., Bürgi, M., and Stuber, M.: Reconstructing Anthropogenic
Disturbance Regimes in Forest Ecosystems: A Case Study from the
Swiss Rhone Valley, Ecosystems, 11, 113–124,
https://doi.org/10.1007/s10021-007-9111-2, 2008. a
Ginzler, C. and Hobi, M. L.: Das aktuelle Vegetationshöhenmodell der
Schweiz: spezifische Anwendungen im Waldbereich, Schweiz.
Z. Forstw., 167, 128–135, https://doi.org/10.3188/szf.2016.0128, 2016. a
Granier, A., Bréda, N., Biron, P., and Villette, S.: A lumped water balance
model to evaluate duration and intensity of drought constraints in forest
stands, Ecol. Model., 116, 269–283,
https://doi.org/10.1016/S0304-3800(98)00205-1,
1999. a
Guan, H. and Wilson, J. L.: A hybrid dual-source model for potential
evaporation and transpiration partitioning, J. Hydrol., 377,
405–416, https://doi.org/10.1016/j.jhydrol.2009.08.037,
2009. a, b
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003,
2009. a
Gurtz, J., Zappa, M., Jasper, K., Lang, H., Verbunt, M., Badoux, A., and
Vitvar, T.: A comparative study in modelling runoff and its components in two
mountainous catchments, Hydrol. Process., 17, 297–311,
https://doi.org/10.1002/hyp.1125, 2003. a
Guswa, A. J.: The influence of climate on root depth: A carbon cost-benefit
analysis, Water Resour. Res., 44, W02427, https://doi.org/10.1029/2007WR006384, 2008. a, b
Guswa, A. J.: Effect of plant uptake strategy on the water-optimal root depth,
Water Resour. Res., 46, W09601, https://doi.org/10.1029/2010WR009122, 2010. a, b, c
Jarvis, P.: The interpretation of the variations in leaf water potential and
stomatal conductance found in canopies in the field, Philos. T. Roy. Soc. B, 273, 593–610, 1976. a
Johst, M., Uhlenbrook, S., Tilch, N., Zillgens, B., Didszun, J., and Kirnbauer,
R.: An attempt of process-oriented rainfall-runoff modeling using
multiple-response data in an alpine catchment, Loehnersbach, Austria,
Hydrol. Res., 39, 1–16, https://doi.org/10.2166/nh.2008.035, 2008. a, b
Kergoat, L.: A model for hydrological equilibrium of leaf area index on a
global scale, J. Hydrol., 212–213, 268–286,
https://doi.org/10.1016/S0022-1694(98)00211-X,
1998. a
Klok, E. J., Jasper, K., Roelofsma, K. P., Gurtz, J., and Badoux, A.:
Distributed hydrological modelling of a heavily glaciated Alpine river
basin, Hydrolog. Sci. J., 46, 553–570,
https://doi.org/10.1080/02626660109492850,
2001. a
Köplin, N., Schädler, B., Viviroli, D., and Weingartner, R.: The importance of glacier and forest change in hydrological climate-impact studies, Hydrol. Earth Syst. Sci., 17, 619–635, https://doi.org/10.5194/hess-17-619-2013, 2013. a
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014. a
Landsberg, J. and Waring, R.: A generalised model of forest productivity using
simplified concepts of radiation-use efficiency, carbon balance and
partitioning, Forest Ecol. Manag., 95, 209–228,
https://doi.org/10.1016/S0378-1127(97)00026-1,
1997. a, b
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson,
S. C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K.,
Bonan, G. B., and Slater, A. G.: Parameterization improvements and functional
and structural advances in Version 4 of the Community Land Model,
J. Adv. Model. Earth Syst., 3, M03001, https://doi.org/10.1029/2011MS00045, 2011. a
Lischke, H. and Zierl, B.: Feedback between structured vegetation and soil
water in a changing climate: A simulation study, in: Climatic Change:
Implications for the Hydrological Cycle and for Water Management,
edited by: Beniston, M., 349–377, Kluwer Academic Publishers, Dordrecht, the Netherlands, 2002. a, b, c, d, e
Lischke, H., Löffler, T. J., and Fischlin, A.: Aggregation of Individual
Trees and Patches in Forest Succession Models: Capturing
Variability with Height Structured, Random, Spatial
Distributions, Theor. Popul. Biol., 54, 213–226,
https://doi.org/10.1006/tpbi.1998.1378,
1998. a, b, c
Manusch, C., Bugmann, H., and Wolf, A.: Sensitivity of simulated productivity
to soil characteristics and plant water uptake along drought gradients in the
Swiss Alps, Ecol. Model., 282, 25–34,
https://doi.org/10.1016/j.ecolmodel.2014.03.006,
2014. a
Martin-Benito, D. and Pederson, N.: Convergence in drought stress, but a
divergence of climatic drivers across a latitudinal gradient in a temperate
broadleaf forest, J. Biogeogr., 42, 925–937,
https://doi.org/10.1111/jbi.12462, 2015. a
Mayor, J. R., Sanders, N. J., Classen, A. T., Bardgett, R. D., Clément,
J.-C., Fajardo, A., Lavorel, S., Sundqvist, M. K., Bahn, M., Chisholm, C.,
Cieraad, E., Gedalof, Z., Grigulis, K., Kudo, G., Oberski, D. L., and Wardle,
D. A.: Elevation alters ecosystem properties across temperate treelines
globally, Nature, 542, 91–95, https://doi.org/10.1038/nature21027, 2017. a
McDowell, N., Pockman, W. T., Allen, C. D., Breshears, D. D., Cobb, N., Kolb,
T., Plaut, J., Sperry, J., West, A., Williams, D. G., and Yepez, E. A.:
Mechanisms of plant survival and mortality during drought: why do some plants
survive while others succumb to drought?, New Phytol., 178, 719–739,
https://doi.org/10.1111/j.1469-8137.2008.02436.x, 2008. a
McLaughlin, D. L., Kaplan, D. A., and Cohen, M. J.: Managing Forests for
Increased Regional Water Yield in the Southeastern U.S.
Coastal Plain, J. Am. Water Resour. As.,
49, 953–965, https://doi.org/10.1111/jawr.12073, 2013. a
Medlyn, B. E., Barton, C. V. M., Broadmeadow, M. S. J., Ceulemans, R.,
De Angelis, P., Forstreuter, M., Freeman, M., Jackson, S. B., Kellomaki, S.,
Laitat, E., Rey, A., Roberntz, P., Sigurdsson, B. D., Strassemeyer, J., Wang,
K., Curtis, P. S., and Jarvis, P. G.: Stomatal conductance of forest species
after long-term exposure to elevated CO2 concentration: a synthesis, New
Phytol., 149, 247–264, https://doi.org/10.1046/j.1469-8137.2001.00028.x, 2001. a, b, c
Medlyn, B. E., Duursma, R. A., and Zeppel, M. J. B.: Forest productivity under climate change: a checklist for evaluating model studies, WIRES Clim. Change, 2, 332–355,
https://doi.org/10.1002/wcc.108, 2011. a, b
Menzel, L.: Modelling canopy resistances and transpiration of grassland,
Phys. Chem. Earth, 21, 123–129,
https://doi.org/10.1016/S0079-1946(97)85572-3,
1996. a
MeteoSwiss: Climate normals Sion, Reference period 1981–2010, available at:
http://www.meteoswiss.admin.ch/product/output/climate-data/climate-diagrams-normal-values-station-processing/SIO/climsheet_SIO_np8110_e.pdf (last access: 10 February 2020),
2014. a
Milano, M., Reynard, E., Köplin, N., and Weingartner, R.: Climatic and
anthropogenic changes in Western Switzerland: Impacts on water stress,
Sci. Total Environ., 536, 12–24,
https://doi.org/10.1016/j.scitotenv.2015.07.049,
2015. a
Milly, P. C. D.: An analytic solution of the stochastic storage problem
applicable to soil water, Water Resour. Res., 29, 3755–3758,
https://doi.org/10.1029/93WR01934, 1993. a, b
Murray, M. B., Cannell, M. G. R., and Smith, R. I.: Date of Budburst of
Fifteen Tree Species in Britain Following Climatic Warming, J. Appl. Ecol., 26, 693, https://doi.org/10.2307/2404093, 1989. a, b
National Centre for Climate Services: CH2018 – Climate Scenarios for
Switzerland, Tech. rep., NCCS, Zurich, Switzerland, 2018. a
Niinemets, Ü. and Valladares, F.: Tolerance to Shade, Drought and
Waterlogging of Temperate Northern Hemisphere Trees and Shrubs,
Ecol. Monogr., 76, 521–547,
https://doi.org/10.1890/0012-9615(2006)076[0521:TTSDAW]2.0.CO;2,
2006. a
Nijzink, R., Hutton, C., Pechlivanidis, I., Capell, R., Arheimer, B., Freer, J., Han, D., Wagener, T., McGuire, K., Savenije, H., and Hrachowitz, M.: The evolution of root-zone moisture capacities after deforestation: a step towards hydrological predictions under change?, Hydrol. Earth Syst. Sci., 20, 4775–4799, https://doi.org/10.5194/hess-20-4775-2016, 2016. a, b, c
Pappas, C., Fatichi, S., Leuzinger, S., Wolf, A., and Burlando, P.: Sensitivity
analysis of a process-based ecosystem model: Pinpointing parameterization
and structural issues, J. Geophys. Res.-Biogeo., 118,
505–528, https://doi.org/10.1002/jgrg.20035, 2013. a, b
Proporato, A., Daly, E., and Rodriguez-Iturbe, I.: Soil Water Balance and
Ecosystem Response to Climate Change, Am. Nat., 164,
625–632, https://doi.org/10.2307/3473173, 2004. a
Price, B., Kaim, D., Szwagrzyk, M., Ostapowicz, K., Kolecka, N., Schmatz,
D. R., Wypych, A., and Kozak, J.: Legacies, socio-economic and biophysical
processes and drivers: the case of future forest cover expansion in the
Polish Carpathians and Swiss Alps, Reg. Environ. Change, 17, 2279–2291,
https://doi.org/10.1007/s10113-016-1079-z, 2016. a
Rasche, L., Fahse, L., Zingg, A., and Bugmann, H.: Enhancing gap model accuracy
by modeling dynamic height growth and dynamic maximum tree height, Ecol.
Model., 232, 133–143, https://doi.org/10.1016/j.ecolmodel.2012.03.004,
2012. a, b
Reynard, E., Bonriposi, M., Graefe, O., Homewood, C., Huss, M., Kauzlaric, M.,
Liniger, H., Rey, E., Rist, S., Schädler, B., Schneider, F., and
Weingartner, R.: Interdisciplinary assessment of complex regional water
systems and their future evolution: how socioeconomic drivers can matter more
than climate: Interdisciplinary assessment of complex regional water
systems and their future evolution, WIRES Water, 1, 413–426, https://doi.org/10.1002/wat2.1032, 2014. a
Rickebusch, S., Gellrich, M., Lischke, H., Guisan, A., and Zimmermann, N. E.:
Combining probabilistic land-use change and tree population dynamics
modelling to simulate responses in mountain forests, Ecol. Model.,
209, 157–168, https://doi.org/10.1016/j.ecolmodel.2007.06.027,
2007. a
Rössler, O., Addor, N., Bernhard, L., Figura, S., Köplin, N.,
Livingstone, D., Schädler, B., Seibert, J., and Weingartner, R.:
Hydrological responses to climate change: river runoff and groundwater, in:
Toward Quantitative Scenarios of Climate Change Impacts in
Switzerland, OCCR, FOEN, MeteoSwiss, C2SM, Agroscope, and ProClim, Bern,
Switzerland, available at: http://www.ch2014-impacts.ch/ (last access: 10 February 2020), 2014. a
Schattan, P., Zappa, M., Lischke, H., Bernhard, L., Thürig, E., and
Diekkrüger, B.: An approach for transient consideration of forest change
in hydrological impact studies, in: Climate and Land Surface Changes in
Hydrology, edited by: IAHS, IAHS–IAPSO–IASPEI, Gothenburg,
Sweden, 311–319, 2013. a, b, c, d, e, f, g
Scherstjanoi, M., Kaplan, J. O., and Lischke, H.: Application of a computationally efficient method to approximate gap model results with a probabilistic approach, Geosci. Model Dev., 7, 1543–1571, https://doi.org/10.5194/gmd-7-1543-2014, 2014. a, b
Schleppi, P., Thimonier, A., and Walthert, L.: Estimating leaf area index of
mature temperate forests using regressions on site and vegetation data,
Forest Ecol. Manag., 261, 601–610,
https://doi.org/10.1016/j.foreco.2010.11.013,
2011. a
Schulla, J.: WaSiM Model description. Completely revised version of 2012 with 2013 to 2015 extensions, available at: http://www.wasim.ch/downloads/doku/wasim/wasim_2013_en.pdf (last access: 10 February 2020),
2015. a
SCNAT: Mountains, a priority for a planet under pressure and for Switzerland, Swiss Academies Factsheets, available at: https://naturalsciences.ch/organisations/scnat/publications/ factsheets/28388-mountains-a-priority-for-a-planet-under-pressure-and-for-switzerland? (last access: 10 February 2020),
2012. a
Seely, B., Welham, C., and Scoullar, K.: Application of a Hybrid Forest
Growth Model to Evaluate Climate Change Impacts on
Productivity, Nutrient Cycling and Mortality in a Montane Forest
Ecosystem, PLOS ONE, 10, e0135034, https://doi.org/10.1371/journal.pone.0135034, 2015. a
Seibert, J.: Regionalisation of parameters for a conceptual rainfall-runoff
model, Agr. Forest Meteorol., 98-99, 279–293,
https://doi.org/10.1016/s0168-1923(99)00105-7, 1999. a
Seidl, R., Rammer, W., Scheller, R. M., and Spies, T. A.: An individual-based
process model to simulate landscape-scale forest ecosystem dynamics,
Ecol. Model., 231, 87–100, https://doi.org/10.1016/j.ecolmodel.2012.02.015,
2012. a
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B.,
Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil
moisture–climate interactions in a changing climate: A review,
Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004,
2010. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model,
Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003. a
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space: Vegetation dynamics
in ecosystem models, Global Ecol. Biogeogr., 10, 621–637,
https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x,
2001. a
Speich, M.: Quantifying and modeling water availability in temperate forests: a review of drought and aridity indices, iForest, 12, 1–16, https://doi.org/10.3832/ifor2934-011, 2019. a
Speich, M., Bernhard, L., Teuling, A., and Zappa, M.: Application of bivariate
mapping for hydrological classification and analysis of temporal change and
scale effects in Switzerland, J. Hydrol., 523, 804–821,
https://doi.org/10.1016/j.jhydrol.2015.01.086, 2015. a, b, c
Speich, M. J. R., Lischke, H., and Zappa, M.: Testing an optimality-based model of rooting zone water storage capacity in temperate forests, Hydrol. Earth Syst. Sci., 22, 4097–4124, https://doi.org/10.5194/hess-22-4097-2018, 2018b. a, b, c, d
Speich, M. J. R., Zappa, M., Scherstjanoi, M., and Lischke, H.: FORHYCS v. 1.0.0 model code, EnviDat, https://doi.org/10.16904/envidat.93, 2019. a
Tang, J., Pilesjö, P., Miller, P. A., Persson, A., Yang, Z., Hanna, E., and
Callaghan, T. V.: Incorporating topographic indices into dynamic ecosystem
modelling using LPJ-GUESS, Ecohydrology, 7, 1147–1162,
https://doi.org/10.1002/eco.1446, 2013. a
Tesemma, Z., Wei, Y., Peel, M., and Western, A.: The effect of year-to-year
variability of leaf area index on Variable Infiltration Capacity model
performance and simulation of runoff, Adv. Water Resour., 83,
310–322, https://doi.org/10.1016/j.advwatres.2015.07.002,
2015. a
Theurillat, J.-P. and Guisan, A.: Potential impact of climate change on
vegetation in the European Alps: a review, Climatic Change, 50, 77–109,
2001. a
Thornthwaite, C. and Mather, J.: Instructions and tables for computing
potential evapotranspiration and the water balance, Publ. Climatol., 10,
185–311, 1957. a
Trancoso, R., Larsen, J. R., McVicar, T. R., Phinn, S. R., and McAlpine, C. A.: CO2-vegetation feedbacks and other climate changes implicated in reducing base flow, Geophys. Res. Lett., 44, 2310–2318, https://doi.org/10.1002/2017GL072759, 2017. a, b
Viviroli, D., Weingartner, R., and Messerli, B.: Assessing the Hydrological
Significance of the World's Mountains, Mt. Res. Dev., 23, 32–40,
https://doi.org/10.1659/0276-4741(2003)023[0032:ATHSOT]2.0.CO;2,
2003. a
Watson, B. M., McKeown, R. A., Putz, G., and MacDonald, J. D.: Modification of
SWAT for modelling streamflow from forested watersheds on the Canadian
Boreal Plain, J. Environ. Eng. Sci., 7,
145–159, https://doi.org/10.1139/S09-003, 2008. a
Wattenbach, M., Hattermann, F., Weng, R., Wechsung, F., Krysanova, V., and
Badeck, F.: A simplified approach to implement forest eco-hydrological
properties in regional hydrological modelling, Ecol. Model., 187, 40–59,
https://doi.org/10.1016/j.ecolmodel.2005.01.026, 2005. a, b
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology–vegetation model for complex terrain, Water Resour. Res.,
30, 1665–1679, 1994. a
Zappa, M. and Kan, C.: Extreme heat and runoff extremes in the Swiss Alps, Nat. Hazards Earth Syst. Sci., 7, 375–389, https://doi.org/10.5194/nhess-7-375-2007, 2007. a
Zappa, M., Pos, F., Strasser, U., Warmerdam, P., and Gurtz, J.: Seasonal
Water Balance of an Alpine Catchment as Evaluated by Different
Methods for Spatially Distributed Snowmelt Modelling, Nord.
Hydrol., 34, 179–202, 2003. a
Zierl, B.: A water balance model to simulate drought in forested ecosystems and
its application to the entire forested area in Switzerland, J.
Hydrol., 242, 115–136, https://doi.org/10.1016/S0022-1694(00)00387-5,
2001. a, b, c
Short summary
Climate change is expected to substantially affect natural processes, and simulation models are a valuable tool to anticipate these changes. In this study, we combine two existing models that each describe one aspect of the environment: forest dynamics and the terrestrial water cycle. The coupled model better described observed patterns in vegetation structure. We also found that including the effect of water availability on tree height and rooting depth improved the model.
Climate change is expected to substantially affect natural processes, and simulation models are...
