Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1531-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1531-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2023
Research article |
| 14 Apr 2023
| 14 Apr 2023
Diagnosing modeling errors in global terrestrial water storage interannual variability
Max Planck Institute for Biogeochemistry, Jena, Germany
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
Martin Jung
Max Planck Institute for Biogeochemistry, Jena, Germany
Nuno Carvalhais
Max Planck Institute for Biogeochemistry, Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Costa da Caparica, Portugal
ELLIS Unit Jena, Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena, Germany
Tina Trautmann
Max Planck Institute for Biogeochemistry, Jena, Germany
Basil Kraft
Max Planck Institute for Biogeochemistry, Jena, Germany
Markus Reichstein
Max Planck Institute for Biogeochemistry, Jena, Germany
ELLIS Unit Jena, Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena, Germany
Matthias Forkel
Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Dresden, Germany
Sujan Koirala
Max Planck Institute for Biogeochemistry, Jena, Germany
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We introduce BuRNN, a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models. However, the model tends to underestimate burned area in parts of Africa and Australia. We identify the extent of bare ground, the presence of grasses, and fire weather conditions (long periods of warm and dry weather) as key regional drivers of fire activity in BuRNN.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
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Laura Nadolski, Tarek S. El-Madany, Jacob Nelson, Arnaud Carrara, Gerardo Moreno, Richard Nair, Yunpeng Luo, Anke Hildebrandt, Victor Rolo, Markus Reichstein, and Sung-Ching Lee
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Min Feng, Joseph O. Sexton, Panshi Wang, Paul M. Montesano, Leonardo Calle, Nuno Carvalhais, Benjamin Poulter, Matthew J. Macander, Michael A. Wulder, Margaret Wooten, William Wagner, Akiko Elders, Saurabh Channan, and Christopher S. R. Neigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-2268, https://doi.org/10.5194/egusphere-2025-2268, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
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The boreal forest, warming fastest among forested biomes, shows a northward shift in tree cover. Using the longest, highest-resolution satellite maps, we found an 0.844 million km² increase in tree cover and a 0.45° northward shift from 1985–2020, especially in northern latitudes. Stable disturbance rates suggest climate-driven growth. Young forests' biomass may help reduce global CO2, despite uncertainties in carbon balance, disturbance, and respiration.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
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An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos
EGUsphere, https://doi.org/10.5194/egusphere-2025-2097, https://doi.org/10.5194/egusphere-2025-2097, 2025
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We create a hybrid ecosystem-level carbon flux model using both eddy-covariance observations and observations of the atmospheric mole fraction of CO2 at three tall-tower observatories. Our study uses an atmospheric transport model (STILT) to connect the atmospheric signal to the ecosystem-level model. We show that this inclusion of atmospheric information meaningfully improves the model's representation of the interannual variability of the global net flux of CO2.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
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We estimate CO2 fluxes in semiarid southern Africa from 2009 to 2018 based on satellite CO2 measurements and atmospheric inverse modeling. By selecting process-based vegetation models, which agree with the satellite CO2 fluxes, we find that soil respiration mainly drives the seasonality, whereas photosynthesis substantially influences the interannual variability. Our study emphasizes the need for better representation of the response of semiarid ecosystems to soil rewetting in vegetation models.
Javier Pacheco-Labrador, Ulisse Gomarasca, Daniel E. Pabon-Moreno, Wantong Li, Mirco Migliavacca, Martin Jung, and Gregory Duveiller
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Measuring biodiversity is necessary to assess its loss, evolution, and role in ecosystem functions. Satellites image the whole terrestrial surface and could cost-efficiently map plant diversity globally. However, developing the necessary methods lacks consistent and sufficient field measurements. Thus, we propose using a simulation tool that generates virtual ecosystems, with species properties and functions varying in response to meteorology and the respective remote sensing imagery.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
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The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
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.
Jasper M. C. Denissen, Adriaan J. Teuling, Sujan Koirala, Markus Reichstein, Gianpaolo Balsamo, Martha M. Vogel, Xin Yu, and René Orth
Earth Syst. Dynam., 15, 717–734, https://doi.org/10.5194/esd-15-717-2024, https://doi.org/10.5194/esd-15-717-2024, 2024
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Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly introduced by large-scale atmospheric circulation but can be modulated by vegetation. Vegetation with access to water uses solar energy to evaporate water into the atmosphere. Under dry conditions, water may not be available, suppressing evaporation and heating the atmosphere. Using climate projections, we show that regionally less water is available for vegetation, intensifying future heat extremes.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
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Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
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This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
Martin Jung, Jacob Nelson, Mirco Migliavacca, Tarek El-Madany, Dario Papale, Markus Reichstein, Sophia Walther, and Thomas Wutzler
Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, https://doi.org/10.5194/bg-21-1827-2024, 2024
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We present a methodology to detect inconsistencies in perhaps the most important data source for measurements of ecosystem–atmosphere carbon, water, and energy fluxes. We expect that the derived consistency flags will be relevant for data users and will help in improving our understanding of and our ability to model ecosystem–climate interactions.
Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, and Ana Bastos
Atmos. Chem. Phys., 24, 2555–2582, https://doi.org/10.5194/acp-24-2555-2024, https://doi.org/10.5194/acp-24-2555-2024, 2024
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Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Hui Yang, Krzysztof Stereńczak, Zbigniew Karaszewski, and Nuno Carvalhais
EGUsphere, https://doi.org/10.5194/egusphere-2023-2691, https://doi.org/10.5194/egusphere-2023-2691, 2023
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Wood density is crucial for ecological and carbon stock assessment, yet its labor-intensive analysis limits studies across species and spaces. Our study, based on 48,000 samples from Central Europe, reveals that, even without species information, 91% of inter-tree variations can be predicted by vegetation indexes, topography, and soil texture. Importantly, we highlight neglected intra-tree variation, showing substantial variations vertically along the height and radially from the center to bark.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
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We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
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The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
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Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
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A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
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The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Sinikka Jasmin Paulus, Tarek Sebastian El-Madany, René Orth, Anke Hildebrandt, Thomas Wutzler, Arnaud Carrara, Gerardo Moreno, Oscar Perez-Priego, Olaf Kolle, Markus Reichstein, and Mirco Migliavacca
Hydrol. Earth Syst. Sci., 26, 6263–6287, https://doi.org/10.5194/hess-26-6263-2022, https://doi.org/10.5194/hess-26-6263-2022, 2022
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In this study, we analyze small inputs of water to ecosystems such as fog, dew, and adsorption of vapor. To measure them, we use a scaling system and later test our attribution of different water fluxes to weight changes. We found that they occur frequently during 1 year in a dry summer ecosystem. In each season, a different flux seems dominant, but they all mainly occur during the night. Therefore, they could be important for the biosphere because rain is unevenly distributed over the year.
Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Earth Syst. Dynam., 13, 1505–1533, https://doi.org/10.5194/esd-13-1505-2022, https://doi.org/10.5194/esd-13-1505-2022, 2022
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Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
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Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
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Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Sophia Walther, Simon Besnard, Jacob Allen Nelson, Tarek Sebastian El-Madany, Mirco Migliavacca, Ulrich Weber, Nuno Carvalhais, Sofia Lorena Ermida, Christian Brümmer, Frederik Schrader, Anatoly Stanislavovich Prokushkin, Alexey Vasilevich Panov, and Martin Jung
Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, https://doi.org/10.5194/bg-19-2805-2022, 2022
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Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
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The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
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We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
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We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
J. Pacheco-Labrador, U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, F. J. Bohn, G. Kraemer, U. Heiden, FunDivEUROPE members, and M. Migliavacca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 49–55, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, 2022
Simon Besnard, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Jacob Nelson, Jonas Gütter, Bruno Herault, Justin Kassi, Anny N'Guessan, Christopher Neigh, Benjamin Poulter, Tao Zhang, and Nuno Carvalhais
Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021, https://doi.org/10.5194/essd-13-4881-2021, 2021
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Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
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Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Maurizio Santoro, Oliver Cartus, Nuno Carvalhais, Danaë M. A. Rozendaal, Valerio Avitabile, Arnan Araza, Sytze de Bruin, Martin Herold, Shaun Quegan, Pedro Rodríguez-Veiga, Heiko Balzter, João Carreiras, Dmitry Schepaschenko, Mikhail Korets, Masanobu Shimada, Takuya Itoh, Álvaro Moreno Martínez, Jura Cavlovic, Roberto Cazzolla Gatti, Polyanna da Conceição Bispo, Nasheta Dewnath, Nicolas Labrière, Jingjing Liang, Jeremy Lindsell, Edward T. A. Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M. Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, and Simon Willcock
Earth Syst. Sci. Data, 13, 3927–3950, https://doi.org/10.5194/essd-13-3927-2021, https://doi.org/10.5194/essd-13-3927-2021, 2021
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Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
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In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
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Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
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Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Christopher Krich, Mirco Migliavacca, Diego G. Miralles, Guido Kraemer, Tarek S. El-Madany, Markus Reichstein, Jakob Runge, and Miguel D. Mahecha
Biogeosciences, 18, 2379–2404, https://doi.org/10.5194/bg-18-2379-2021, https://doi.org/10.5194/bg-18-2379-2021, 2021
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Ecosystems and the atmosphere interact with each other. These interactions determine e.g. the water and carbon fluxes and thus are crucial to understand climate change effects. We analysed the interactions for many ecosystems across the globe, showing that very different ecosystems can have similar interactions with the atmosphere. Meteorological conditions seem to be the strongest interaction-shaping factor. This means that common principles can be identified to describe ecosystem behaviour.
Milan Flach, Alexander Brenning, Fabian Gans, Markus Reichstein, Sebastian Sippel, and Miguel D. Mahecha
Biogeosciences, 18, 39–53, https://doi.org/10.5194/bg-18-39-2021, https://doi.org/10.5194/bg-18-39-2021, 2021
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Drought and heat events affect the uptake and sequestration of carbon in terrestrial ecosystems. We study the impact of droughts and heatwaves on the uptake of CO2 of different vegetation types at the global scale. We find that agricultural areas are generally strongly affected. Forests instead are not particularly sensitive to the events under scrutiny. This implies different water management strategies of forests but also a lack of sensitivity to remote-sensing-derived vegetation activity.
Naixin Fan, Sujan Koirala, Markus Reichstein, Martin Thurner, Valerio Avitabile, Maurizio Santoro, Bernhard Ahrens, Ulrich Weber, and Nuno Carvalhais
Earth Syst. Sci. Data, 12, 2517–2536, https://doi.org/10.5194/essd-12-2517-2020, https://doi.org/10.5194/essd-12-2517-2020, 2020
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The turnover time of terrestrial carbon (τ) controls the global carbon cycle–climate feedback. In this study, we provide a new, updated ensemble of diagnostic terrestrial carbon turnover times and associated uncertainties on a global scale. Despite the large variation in both magnitude and spatial patterns of τ, we identified robust features in the spatial patterns of τ which could contribute to uncertainty reductions in future projections of the carbon cycle–climate feedback.
Cited articles
Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M.,
Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E.,
Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., and Zeng, N.: The dominant role of semi-arid ecosystems in
the trend and variability of the land CO2 sink, Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668, 2015. a
Awange, J. L., Anyah, R., Agola, N., Forootan, E., and Omondi, P.: Potential
impacts of climate and environmental change on the stored water of Lake
Victoria Basin and economic implications, Water Resour. Res., 49, 8160–8173, https://doi.org/10.1002/2013WR014350, 2013. a
Awange, J. L., Saleem, A., Sukhadiya, R. M., Ouma, Y. O., and Kexiang, H.:
Physical dynamics of Lake Victoria over the past 34 years (1984–2018): Is the lake dying?, Sci. Total Environ., 658, 199–218, https://doi.org/10.1016/j.scitotenv.2018.12.051, 2019. a, b
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., v. Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 Global 3-Hourly
0.1∘ Precipitation: Methodology and Quantitative Assessment, B. Am. Meteorol. Soc., 100, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1, 2019. a, b
Bolaños, S., Salazar, J. F., Betancur, T., and Werner, M.: GRACE reveals
depletion of water storage in northwestern South America between ENSO extremes, J. Hydrol., 596, 125687, https://doi.org/10.1016/j.jhydrol.2020.125687, 2021. a
Bolaños Chavarría, S., Werner, M., Salazar, J. F., and Betancur Vargas, T.: Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin, Hydrol. Earth Syst. Sci. 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022, 2022. a
Chapin, F. S., Matson, P. A., and Vitousek, P. M.: Water and Energy Balance, in: Principles of Terrestrial Ecosystem Ecology, edited by: Chapin, F. S., Matson, P. A., and Vitousek, P. M., Springer, New York, NY, 93–122, https://doi.org/10.1007/978-1-4419-9504-9_4, 2011. a
Chen, J. L., Wilson, C. R., and Tapley, B. D.: The 2009 exceptional Amazon
flood and interannual terrestrial water storage change observed by GRACE,
Water Resour. Res., 46, W12526, https://doi.org/10.1029/2010WR009383, 2010. a, b
Decharme, B., Alkama, R., Douville, H., Becker, M., and Cazenave, A.: Global
Evaluation of the ISBA-TRIP Continental Hydrological System. Part II: Uncertainties in River Routing Simulation Related to Flow Velocity and Groundwater Storage, J. Hydrometeorol., 11, 601–617, https://doi.org/10.1175/2010JHM1212.1, 2010. a
Decharme, B., Martin, E., and Faroux, S.: Reconciling soil thermal and
hydrological lower boundary conditions in land surface models, J. Geophys. Res.-Atmos., 118, 7819–7834, https://doi.org/10.1002/jgrd.50631, 2013. a
Famiglietti, J. S.: Remote Sensing of Terrestrial Water Storage, Soil Moisture and Surface Waters, in: The State of the Planet: Frontiers and Challenges in Geophysics, AGU – American Geophysical Union, 197–207, https://doi.org/10.1029/150GM16, 2004. a
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013. a
Felfelani, F., Wada, Y., Longuevergne, L., and Pokhrel, Y. N.: Natural and
human-induced terrestrial water storage change: A global analysis using
hydrological models and GRACE, J. Hydrol., 553, 105–118,
https://doi.org/10.1016/j.jhydrol.2017.07.048, 2017. a
Frappart, F. and Ramillien, G.: Monitoring Groundwater Storage Changes Using the Gravity Recovery and Climate Experiment (GRACE) Satellite Mission: A Review, Remote Sens., 10, 829, https://doi.org/10.3390/rs10060829, 2018. a
Frappart, F., Papa, F., d. Silva, J. S., Ramillien, G., Prigent, C., Seyler,
F., and Calmant, S.: Surface freshwater storage and dynamics in the Amazon
basin during the 2005 exceptional drought, Environ. Res. Lett., 7, 044010, https://doi.org/10.1088/1748-9326/7/4/044010, 2012. a
Getirana, A., Kumar, S., Girotto, M., and Rodell, M.: Rivers and Floodplains
as Key Components of Global Terrestrial Water Storage Variability: Water Storage in Rivers and Floodplains, Geophys. Res. Lett., 44, 10359–10,68, https://doi.org/10.1002/2017GL074684, 2017. a, b
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: G-RUN
ENSEMBLE: A Multi-Forcing Observation-Based Global Runoff Reanalysis, Water Resour. Res., 57, e2020WR028787, https://doi.org/10.1029/2020WR028787, 2021. a
Guan, K., Wood, E. F., Medvigy, D., Kimball, J., Pan, M., Caylor, K. K.,
Sheffield, J., Xu, X., and Jones, M. O.: Terrestrial hydrological controls on
land surface phenology of African savannas and woodlands: Hydrology controls on African phenology, J. Geophys. Res.-Biogeo., 119, 1652–1669, https://doi.org/10.1002/2013JG002572, 2014. a, b
Hosseini-Moghari, S.-M., Araghinejad, S., Ebrahimi, K., Tang, Q., and
AghaKouchak, A.: Using GRACE satellite observations for separating
meteorological variability from anthropogenic impacts on water availability,
Sci. Rep., 10, 15098, https://doi.org/10.1038/s41598-020-71837-7, 2020. a
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2, 36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001. a, b
Humphrey, V., Gudmundsson, L., and Seneviratne, S. I.: Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes, Surv. Geophys., 37, 357–395, https://doi.org/10.1007/s10712-016-9367-1 2016. a, b
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69,
https://doi.org/10.1038/s41586-021-03325-5, 2021. a
Huziy, O. and Sushama, L.: Impact of lake–river connectivity and interflow on the Canadian RCM simulated regional climate and hydrology for Northeast
Canada, Clim. Dynam., 48, 709–725, https://doi.org/10.1007/s00382-016-3104-9, 2017. a
Jensen, L., Eicker, A., Dobslaw, H., Stacke, T., and Humphrey, V.: Long-Term
Wetting and Drying Trends in Land Water Storage Derived From GRACE and CMIP5 Models, J. Geophys. Res.-Atmos., 124, 9808–9823, https://doi.org/10.1029/2018JD029989, 2019. a, b
Jensen, L., Eicker, A., Dobslaw, H., and Pail, R.: Emerging Changes in
Terrestrial Water Storage Variability as a Target for Future Satellite Gravity Missions, Remote Sens., 12, 3898, https://doi.org/10.3390/rs12233898, 2020. a
Jin, J., Wang, L., Yang, J., Si, B., and Niu, G.-Y.: Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5, Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, 2022. a
Jung, H. C., Hamski, J., Durand, M., Alsdorf, D., Hossain, F., Lee, H.,
Hossain, A. K. M. A., Hasan, K., Khan, A. S., and Hoque, A. Z.:
Characterization of complex fluvial systems using remote sensing of spatial
and temporal water level variations in the Amazon, Congo, and Brahmaputra Rivers, Earth Surf. Proc. Land., 35, 294–304, https://doi.org/10.1002/esp.1914, 2010. a, b
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S.,
Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P.,
Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly,
B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U.,
Zaehle, S., and Zeng, N.: Compensatory water effects link yearly global land
CO2 sink changes to temperature, Nature, 541, 516–520,
https://doi.org/10.1038/nature20780, 2017. a, b, c
Jung, M., Koirala, S., Weber, U., Ichii, K., Gans, F., Camps-Valls, G., Papale, D., Schwalm, C., Tramontana, G., and Reichstein, M.: The FLUXCOM ensemble of global land-atmosphere energy fluxes, Scient. Data, 6, 74,
https://doi.org/10.1038/s41597-019-0076-8, 2019. a
Kim, H., Yeh, P. J.-F., Oki, T., and Kanae, S.: Role of rivers in the seasonal variations of terrestrial water storage over global basins, Geophys. Res. Lett., 36, L17402, https://doi.org/10.1029/2009GL039006, 2009. a, b, c
Koirala, S., Yeh, P. J.-F., Hirabayashi, Y., Kanae, S., and Oki, T.:
Global-scale land surface hydrologic modeling with the representation of
water table dynamics, J. Geophys. Res.-Atmos., 119, 75–89, https://doi.org/10.1002/2013JD020398, 2014. a
Kraft, B., Jung, M., Körner, M., and Reichstein, M.: Hybrid Modeling: Modeling Fusion Of A Deep Learning Approach And A Physics-Based Model For Global Hydrological Modeling, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.,, XLIII-B2-2020, 1537–1544,
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020, 2020. a
Law, B. E., Falge, E., Gu, L., Baldocchi, D. D., Bakwin, P., Berbigier, P.,
Davis, K., Dolman, A. J., Falk, M., Fuentes, J. D., Goldstein, A., Granier,
A., Grelle, A., Hollinger, D., Janssens, I. A., Jarvis, P., Jensen, N. O.,
Katul, G., Mahli, Y., Matteucci, G., Meyers, T., Monson, R., Munger, W.,
Oechel, W., Olson, R., Pilegaard, K., Paw U, K. T., Thorgeirsson, H., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Environmental controls over carbon dioxide and water vapor exchange of
terrestrial vegetation, Agr. Forest Meteorol., 113, 97–120,
https://doi.org/10.1016/S0168-1923(02)00104-1, 2002. a
Lee, H., Jung, M., Carvalhais, N., Trautmann, T., Kraft, B., Reichstein, M., Forkel, M., and Koirala, S.: Data sets used in Lee et al. (2023), Zenodo [data set], https://doi.org/10.5281/zenodo.7813179, 2023. a
Lee, H., Jung, M., Carvalhais, N., Trautmann, T., Kraft, B., Reichstein, M., Forkel, M., and Koirala, S.: Python scripts used in Lee et al. (2023), GitHub [code], https://github.com/hoontaeklee/hlee_tws_iav_hotspots (last access: 10 April 2023), 2023b. a
Liu, B., Zou, X., Yi, S., Sneeuw, N., Cai, J., and Li, J.: Identifying and
separating climate- and human-driven water storage anomalies using GRACE
satellite data, Remote Sens. Environ., 263, 112559, https://doi.org/10.1016/j.rse.2021.112559, 2021. a
Lo, M.-H., Famiglietti, J. S., Yeh, P. J.-F., and Syed, T. H.: Improving
parameter estimation and water table depth simulation in a land surface model
using GRACE water storage and estimated base flow data, Water Resour. Res., 46, W05517, https://doi.org/10.1029/2009WR007855, 2010. a, b
Luo, X. and Keenan, T. F.: Tropical extreme droughts drive long-term increase
in atmospheric CO2 growth rate variability, Nat. Commun., 13, 1193, https://doi.org/10.1038/s41467-022-28824-5, 2022. a
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Mortimer, C., Derksen, C., Mudryk, L., Moisander, M., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Salminen, M., Norberg, J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset, Scient. Data, 8, 163, https://doi.org/10.1038/s41597-021-00939-2, 2021. a
Madani, N., Kimball, J. S., Parazoo, N. C., Ballantyne, A. P., Tagesson, T.,
Jones, L. A., Reichle, R. H., Palmer, P. I., Velicogna, I., Bloom, A. A.,
Saatchi, S., Liu, Z., and Geruo, A.: Below-surface water mediates the response of African forests to reduced rainfall, Environ. Res. Lett., 15, 034063, https://doi.org/10.1088/1748-9326/ab724a, 2020. a, b, c
Meghwal, R., Shah, D., and Mishra, V.: On the Changes in Groundwater Storage Variability in Western India Using GRACE and Well Observations, Remote Sens. Earth Syst. Sci., 2, 260–272, https://doi.org/10.1007/s41976-019-00026-6, 2019. a, b, c
Munagapati, H., Tiwari, V. M., and Panda, D. K.: An analysis of spatio-temporal variability of terrestrial water storage in India, Water Secur., 14, 100099, https://doi.org/10.1016/j.wasec.2021.100099, 2021. a
Oki, T. and Sud, Y. C.: Design of Total Runoff Integrating Pathways (TRIP) – A Global River Channel Network, Earth Interact., 2, 1–37, https://doi.org/10.1175/1087-3562(1998)002<0001:DOTRIP>2.3.CO;2, 1999. a, b, c
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016. a, b, c, d
Peltier, W. R., Argus, D. F., and Drummond, R.: Space geodesy constrains ice
age terminal deglaciation: The global ICE-6G_C (VM5a) model, J. Geophys. Res.-Solid, 120, 450–487, https://doi.org/10.1002/2014JB011176, 2015. a
Peltier, W. R., Argus, D. F., and Drummond, R.: Comment on “An Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model” by Purcell et al., J. Geophys. Res.-Solid, 123, 2019–2028, https://doi.org/10.1002/2016JB013844, 2018. a
Pokhrel, Y., Shin, S., Lin, Z., Yamazaki, D., and Qi, J.: Potential Disruption of Flood Dynamics in the Lower Mekong River Basin Due to Upstream Flow Regulation, Sci. Rep., 8, 17767, https://doi.org/10.1038/s41598-018-35823-4, 2018. a
Pokhrel, Y., Felfelani, F., Satoh, Y., Boulange, J., Burek, P., Gädeke, A., Gerten, D., Gosling, S. N., Grillakis, M., Gudmundsson, L., Hanasaki, N.,
Kim, H., Koutroulis, A., Liu, J., Papadimitriou, L., Schewe, J.,
Müller Schmied, H., Stacke, T., Telteu, C. E., Thiery, W., Veldkamp, T.,
Zhao, F., and Wada, Y.: Global terrestrial water storage and drought severity under climate change, Nat. Clim. Change, 11, 226–233, https://doi.org/10.1038/s41558-020-00972-w, 2021. a, b
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W., Sitch, S., and van der Werf, G. R.: Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 509, 600–603,
https://doi.org/10.1038/nature13376, 2014. a
Reager, J. T. and Famiglietti, J. S.: Global terrestrial water storage capacity and flood potential using GRACE, Geophys. Res. Lett., 36, L23402, https://doi.org/10.1029/2009GL040826, 2009. a
Reager, J. T., Gardner, A. S., Famiglietti, J. S., Wiese, D. N., Eicker, A.,
and Lo, M.-H.: A decade of sea level rise slowed by climate-driven hydrology,
Science, 351, 699–703, https://doi.org/10.1126/science.aad8386, 2016. a
Rodell, M., Velicogna, I., and Famiglietti, J. S.: Satellite-based estimates of groundwater depletion in India, Nature, 460, 999–1002, https://doi.org/10.1038/nature08238, 2009. a, b
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H. K., Landerer, F. W., and Lo, M.-H.: Emerging trends in global freshwater
availability, Nature, 557, 651–659, https://doi.org/10.1038/s41586-018-0123-1, 2018. a, b, c
Save, H., Bettadpur, S., and Tapley, B. D.: High-resolution CSR GRACE RL05 mascons, J. Geophys. Res.-Solid, 121, 7547–7569, https://doi.org/10.1002/2016JB013007, 2016. a
Sayre, R., Noble, S., Hamann, S., Smith, R., Wright, D., Breyer, S., Butler,
K., Van Graafeiland, K., Frye, C., Karagulle, D., Hopkins, D., Stephens, D.,
Kelly, K., Basher, Z., Burton, D., Cress, J., Atkins, K., Van Sistine, D. P.,
Friesen, B., Allee, R., Allen, T., Aniello, P., Asaad, I., Costello, M. J.,
Goodin, K., Harris, P., Kavanaugh, M., Lillis, H., Manca, E., Muller-Karger,
F., Nyberg, B., Parsons, R., Saarinen, J., Steiner, J., and Reed, A.: A new
30 meter resolution global shoreline vector and associated global islands
database for the development of standardized ecological coastal units, J. Operat. Oceanogr., 12, S47–S56, https://doi.org/10.1080/1755876X.2018.1529714, 2019. a
Scanlon, B. R., Zhang, Z., Save, H., Wiese, D. N., Landerer, F. W., Long, D.,
Longuevergne, L., and Chen, J.: Global evaluation of new GRACE mascon products for hydrologic applications: Global Analysis Of GRACE MASCON Products, Water Resour. Res., 52, 9412–9429, https://doi.org/10.1002/2016WR019494, 2016. a, b
Scanlon, B. R., Zhang, Z., Save, H., Sun, A. Y., Müller Schmied, H., van Beek, L. P. H., Wiese, D. N., Wada, Y., Long, D., Reedy, R. C., Longuevergne, L., Döll, P., and Bierkens, M. F. P.: Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data, P. Natl. Acad. Sci. USA, 115, E1080–E1089,
https://doi.org/10.1073/pnas.1704665115, 2018. a, b, c, d, e, f
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413,
https://doi.org/10.5194/essd-9-389-2017, 2017. a, b, c
Schrapffer, A., Sörensson, A., Polcher, J., and Fita, L.: Benefits of
representing floodplains in a Land Surface Model: Pantanal simulated with ORCHIDEE CMIP6 version, Clim. Dynam., 55, 1303–1323, https://doi.org/10.1007/s00382-020-05324-0, 2020. a
Seneviratne, S. I., Nicholls, N., Easterling, D., Goodess, C. M., Kanae, S.,
Kossin, J., Luo, Y., Marengo, J., McInnes, K., Rahimi, M., Reichstein, M.,
Sorteberg, A., Vera, C., Zhang, X., Rusticucci, M., Semenov, V., Alexander,
L. V., Allen, S., Benito, G., Cavazos, T., Clague, J., Conway, D., Della-Marta, P. M., Gerber, M., Gong, S., Goswami, B. N., Hemer, M., Huggel,
C., van den Hurk, B., Kharin, V. V., Kitoh, A., Tank, A. M. K., Li, G., Mason, S., McGuire, W., van Oldenborgh, G. J., Orlowsky, B., Smith, S., Thiaw, W., Velegrakis, A., Yiou, P., Zhang, T., Zhou, T., and Zwiers, F. W.:
Changes in Climate Extremes and their Impacts on the Natural Physical Environment, in: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, edited by: Field, C. B., Barros, V., Stocker, T. F., and Dahe, Q., Cambridge University Press, Cambridge, 109–230, https://doi.org/10.1017/CBO9781139177245.006, 2012. a, b
Soni, A. and Syed, T. H.: Diagnosing Land Water Storage Variations in Major Indian River Basins using GRACE observations, Global Planet. Change, 133, 263–271, https://doi.org/10.1016/j.gloplacha.2015.09.007, 2015. a
Stager, J. C., Ruzmaikin, A., Conway, D., Verburg, P., and Mason, P. J.:
Sunspots, El Niño, and the levels of Lake Victoria, East Africa, J. Geophys. Res.-Atmos., 112, D15106, https://doi.org/10.1029/2006JD008362, 2007. a
Swenson, S. C. and Lawrence, D. M.: A GRACE-based assessment of interannual
groundwater dynamics in the Community Land Model, Water Resour. Res., 51, 8817–8833, https://doi.org/10.1002/2015WR017582, 2015. a
Syed, T. H., Famiglietti, J. S., Rodell, M., Chen, J., and Wilson, C. R.:
Analysis of terrestrial water storage changes from GRACE and GLDAS:
Terrestrial Water Storage Changes, Water Resour. Res., 44, W02433, https://doi.org/10.1029/2006WR005779, 2008. a
Syed, T. H., Famiglietti, J. S., and Chambers, D. P.: GRACE-Based Estimates of Terrestrial Freshwater Discharge from Basin to Continental Scales, J. Hydrometeorol., 10, 22–40, https://doi.org/10.1175/2008JHM993.1, 2009. a, b
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S.,
Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P.,
Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C.,
Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and
Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019. a
van Beek, L. P. H., Wada, Y., and Bierkens, M. F. P.: Global monthly water
stress: 1. Water balance and water availability, Water Resour. Res., 47, W07517, https://doi.org/10.1029/2010WR009791, 2011.
a
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a
GIS‐based distributed model for river basin scale water balance and flood
simulation, Int. J. Geogr. Inform. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010. a
Van Dijk, A. and Warren, G.: The Australian Water Resources Assessment System, Technical Report 3, CSIRO: Water for a Healthy Country National
Research Flagship, https://doi.org/10.4225/08/5852dd9bb578c, 2010. a
Wada, Y., Wisser, D., and Bierkens, M. F. P.: Global modeling of withdrawal,
allocation and consumptive use of surface water and groundwater resources,
Earth Syst. Dynam., 5, 15–40, https://doi.org/10.5194/esd-5-15-2014, 2014. a
Wang, K., Bastos, A., Ciais, P., Wang, X., Rödenbeck, C., Gentine, P.,
Chevallier, F., Humphrey, V. W., Huntingford, C., O'Sullivan, M., Seneviratne, S. I., Sitch, S., and Piao, S.: Regional and seasonal partitioning of water and temperature controls on global land carbon uptake
variability, Nat. Commun., 13, 3469, https://doi.org/10.1038/s41467-022-31175-w, 2022. a
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Smith, G. L.,
and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment, B. Am. Meteorol. Soc., 77, 853–868,
https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996. a, b
Xiao, C., Lofgren, B. M., Wang, J., and Chu, P. Y.: Improving the lake scheme
within a coupled WRF-lake model in the Laurentian Great Lakes, J. Adv. Model. Earth Syst., 8, 1969–1985, https://doi.org/10.1002/2016MS000717, 2016. a
Zhang, L., Dobslaw, H., Stacke, T., Güntner, A., Dill, R., and Thomas, M.:
Validation of terrestrial water storage variations as simulated by different
global numerical models with GRACE satellite observations, Hydrol. Earth Syst. Sci., 21, 821–837, https://doi.org/10.5194/hess-21-821-2017, 2017. a, b, c, d
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
We spatially attribute the variance in global terrestrial water storage (TWS) interannual...
