Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-883-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-883-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Feb 2022
Development and technical paper |
| 01 Feb 2022
| 01 Feb 2022
Influence of modifications (from AoB2015 to v0.5) in the Vegetation Optimality Model
Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Jason Beringer
School of Agriculture and Environment, The University of Western Australia, Crawley, WA, 6909, Australia
Lindsay B. Hutley
Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT, 0909, Australia
Stanislaus J. Schymanski
Catchment and Ecohydrology Group (CAT), Environmental Research and Innovation (ERIN), Luxembourg Institute of Science and Technology (LIST), Belvaux, Luxembourg
Related authors
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025, https://doi.org/10.5194/gmd-18-1709-2025, 2025
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 6289–6309, https://doi.org/10.5194/hess-26-6289-2022, https://doi.org/10.5194/hess-26-6289-2022, 2022
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Most catchments plot close to the empirical Budyko curve, which allows for estimating the long-term mean annual evaporation and runoff. We found that a model that optimizes vegetation properties in response to changes in precipitation leads it to converge to a single curve. In contrast, models that assume no changes in vegetation start to deviate from a single curve. This implies that vegetation has a stabilizing role, bringing catchments back to equilibrium after changes in climate.
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Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 525–550, https://doi.org/10.5194/hess-26-525-2022, https://doi.org/10.5194/hess-26-525-2022, 2022
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Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
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Rivers and streams play a key role in how carbon moves through the environment, but we know little about this in Australia. To help close this gap, we compile the first national database of carbon data from rivers and streams, combining past studies, government records, and new data. The data show where and when carbon was measured and reveal major gaps in long-term monitoring. This new resource will help scientists understand carbon and water systems across Australia.
Clément Duvert, Vanessa Solano, Dioni I. Cendón, Francesco Ulloa-Cedamanos, Liza K. McDonough, Robert G. M. Spencer, Niels C. Munksgaard, Lindsay B. Hutley, Jean-Sébastien Moquet, and David E. Butman
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This study examines the age and composition of carbon in tropical streams. We find that dissolved organic carbon (DOC) is centuries to millennia old, while dissolved inorganic carbon (DIC) is consistently younger, indicating a decoupling between the two. DOC age varies seasonally, with rainforest streams exporting younger DOC during high flow, while agricultural streams mobilise older DOC. Our results suggest land conversion alters carbon export, potentially worsening with climate change.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
Geosci. Model Dev., 18, 1709–1736, https://doi.org/10.5194/gmd-18-1709-2025, https://doi.org/10.5194/gmd-18-1709-2025, 2025
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. Bayes’ factor is a tool that can be used to compare models; however, it is very difficult to compute Bayes' factor numerically. In our paper, we explore and develop highly efficient algorithms for computing Bayes’ factor of hydrological systems, which will introduce this useful tool for selecting models into everyday hydrological practice.
Samuele Ceolin, Stanislaus J. Schymanski, Dagmar van Dusschoten, Robert Koller, and Julian Klaus
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We investigated if and how roots of maize plants respond to multiple abrupt changes in soil moisture. We measured root lengths using a magnetic resonance imaging technique and calculated changes in growth rates after applying water pulses. The root growth rates increased in wetted soil layers within 48 hours and decreased in non-wetted layers, indicating fast adaptation of the root systems to moisture changes. Our findings could improve irrigation management and vegetation models.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 6289–6309, https://doi.org/10.5194/hess-26-6289-2022, https://doi.org/10.5194/hess-26-6289-2022, 2022
Short summary
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Most catchments plot close to the empirical Budyko curve, which allows for estimating the long-term mean annual evaporation and runoff. We found that a model that optimizes vegetation properties in response to changes in precipitation leads it to converge to a single curve. In contrast, models that assume no changes in vegetation start to deviate from a single curve. This implies that vegetation has a stabilizing role, bringing catchments back to equilibrium after changes in climate.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
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Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
Short summary
Short summary
Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
César Dionisio Jiménez-Rodríguez, Mauro Sulis, and Stanislaus Schymanski
Biogeosciences, 19, 3395–3423, https://doi.org/10.5194/bg-19-3395-2022, https://doi.org/10.5194/bg-19-3395-2022, 2022
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Vegetation relies on soil water reservoirs during dry periods. However, when this source is depleted, the plants may access water stored deeper in the rocks. This rock moisture contribution is usually omitted in large-scale models, which affects modeled plant water use during dry periods. Our study illustrates that including this additional source of water in the Community Land Model improves the model's ability to reproduce observed plant water use at seasonally dry sites.
Caitlyn A. Hall, Sheila M. Saia, Andrea L. Popp, Nilay Dogulu, Stanislaus J. Schymanski, Niels Drost, Tim van Emmerik, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 647–664, https://doi.org/10.5194/hess-26-647-2022, https://doi.org/10.5194/hess-26-647-2022, 2022
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Impactful open, accessible, reusable, and reproducible hydrologic research practices are being embraced by individuals and the community, but taking the plunge can seem overwhelming. We present the Open Hydrology Principles and Practical Guide to help hydrologists move toward open science, research, and education. We discuss the benefits and how hydrologists can overcome common challenges. We encourage all hydrologists to join the open science community (https://open-hydrology.github.io).
Remko C. Nijzink, Jason Beringer, Lindsay B. Hutley, and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 525–550, https://doi.org/10.5194/hess-26-525-2022, https://doi.org/10.5194/hess-26-525-2022, 2022
Short summary
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Most models that simulate water and carbon exchanges with the atmosphere rely on information about vegetation, but optimality models predict vegetation properties based on general principles. Here, we use the Vegetation Optimality Model (VOM) to predict vegetation behaviour at five savanna sites. The VOM overpredicted vegetation cover and carbon uptake during the wet seasons but also performed similarly to conventional models, showing that vegetation optimality is a promising approach.
Atbin Mahabbati, Jason Beringer, Matthias Leopold, Ian McHugh, James Cleverly, Peter Isaac, and Azizallah Izady
Geosci. Instrum. Method. Data Syst., 10, 123–140, https://doi.org/10.5194/gi-10-123-2021, https://doi.org/10.5194/gi-10-123-2021, 2021
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We reviewed eight algorithms to estimate missing values of environmental drivers and three major fluxes in eddy covariance time series. Overall, machine-learning algorithms showed superiority over the rest. Among the top three models (feed-forward neural networks, eXtreme Gradient Boost, and random forest algorithms), the latter showed the most solid performance in different scenarios.
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Short summary
The Vegetation Optimality Model (VOM) is a coupled water–vegetation model that predicts vegetation properties rather than determines them based on observations. A range of updates to previous applications of the VOM has been made for increased generality and improved comparability with conventional models. This showed that there is a large effect on the simulated water and carbon fluxes caused by the assumption of deep groundwater tables and updated soil profiles in the model.
The Vegetation Optimality Model (VOM) is a coupled water–vegetation model that predicts...
