Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-6957-2022
© 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-15-6957-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Sep 2022
Model description paper |
| 16 Sep 2022
| 16 Sep 2022
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Anke Hildebrandt
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Friedrich Schiller University Jena, Institute of Geoscience, Terrestrial Ecohydrology, Burgweg 11, 07745 Jena, Germany
Stephan Thober
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Corinna Rebmann
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Rico Fischer
Department of Ecological Modelling, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany
Luis Samaniego
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Oldrich Rakovec
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague-Suchdol 16500, Czech Republic
Rohini Kumar
Department of Computational Hydro-system, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
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Ehsan Modiri, Oldrich Rakovec, Pallav Kumar Shrestha, Almudena García-García, Leandro Avila, Katie Blackford, Elizabeth Cooper, Bram Droppers, Paolo Filippucci, Milan Fischer, Matěj Orság, Pietro Stradiotti, Luca Brocca, Douglas B. Clark, Wouter Dorigo, Stefan Kollet, Jian Peng, Niko Wanders, and Luis Samaniego
EGUsphere, https://doi.org/10.5194/egusphere-2026-1012, https://doi.org/10.5194/egusphere-2026-1012, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Drought impacts water supply, agriculture, and ecosystems, yet hydrological models often disagree on when and where drought occurs. This study tested whether satellite observations can improve how models represent soil moisture drought in the Rhine River basin. Using several models and major drought events, we show that satellite data improve spatial realism and reveal important differences among models, helping to better understand uncertainty in drought monitoring and early warning.
Xueyan Cheng, Chunhui Zhan, Martin G. De Kauwe, Anke Hildebrandt, and Rene Orth
EGUsphere, https://doi.org/10.5194/egusphere-2026-1199, https://doi.org/10.5194/egusphere-2026-1199, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Using long-term satellite greenness data and a machine-learning attribution approach, we assessed vegetation responses to wet and dry soil moisture extremes and identified their dominant drivers. Dry extremes caused widespread negative greenness anomalies mainly controlled by pre-extreme conditions and seasonal timing, while wet extremes showed more heterogeneous responses additionally shaped by climate and topography, highlighting contrasting ecosystem sensitivity to hydrological extremes.
Sadaf Nasreen, Oldrich Rakovec, Rohini Kumar, Manuela I. Brunner, Ujjwal Singh, Petr Maca, Yannis Markonis, and Martin Hanel
EGUsphere, https://doi.org/10.5194/egusphere-2026-973, https://doi.org/10.5194/egusphere-2026-973, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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European droughts threaten water and agriculture, but how distinct drought processes will change under warming is uncertain. We examine seven mechanisms across Europe using 1971 to 2000 observations and 2070 to 2099 projections. Changes are regional: the Mediterranean shifts to longer, more severe droughts, while Northern and Western Central Europe often improve. Temperature-driven mechanisms, especially rain to snow transitions, respond most, guiding targeted adaptation.
Tam V. Nguyen, Rohini Kumar, José L. J. Ledesma, Pia Ebeling, Jan Fleckenstein, and Andreas Musolff
EGUsphere, https://doi.org/10.5194/egusphere-2025-6246, https://doi.org/10.5194/egusphere-2025-6246, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Lumped and landscape-explicit dissolved organic carbon (DOC) models are commonly calibrated using stream DOC concentrations, while internal DOC dynamics in different model compartments are not given enough attention. Our study shows that stream DOC alone is insufficient to constrain DOC dynamics. Applying models calibrated in this way under changing boundary conditions may therefore lead to unrealistic results.
Pia Ebeling, Alexander Hubig, Alexander Wachholz, Ulrike Scharfenberger, Sarah Haug, Tam Nguyen, Fanny Sarrazin, Masooma Batool, Andreas Musolff, and Rohini Kumar
Earth Syst. Sci. Data, 18, 691–712, https://doi.org/10.5194/essd-18-691-2026, https://doi.org/10.5194/essd-18-691-2026, 2026
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The updated river water quality data set for Germany offers longer records, new variables such as water temperature and oxygen, and time series of pollution sources, and it adds more stations with both water quality and flow data. These improvements provide clearer insights into how stream water quality changes over time and how human activities affect aquatic ecosystems.
Robert Reinecke, Tanjila Akhter, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Wim Thiery, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
Geosci. Model Dev., 19, 523–542, https://doi.org/10.5194/gmd-19-523-2026, https://doi.org/10.5194/gmd-19-523-2026, 2026
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Malve Heinz, Annelie Holzkämper, Rohini Kumar, Sélène Ledain, Pascal Horton, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2025-5447, https://doi.org/10.5194/egusphere-2025-5447, 2025
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Droughts increasingly threaten agriculture. Improving soils to store more water, for example by increasing soil organic carbon, can help. We simulated this in a Swiss catchment and found that more soil carbon slightly increased soil water storage and evapotranspiration, modestly reduced floods, and shortened periods with very little streamflow. However in warmer, drier areas, these periods with little streamflow could sometimes last longer.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
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Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Katoria Lekarkar, Oldrich Rakovec, Rohini Kumar, Stefaan Dondeyne, and Ann van Griensven
EGUsphere, https://doi.org/10.5194/egusphere-2025-4526, https://doi.org/10.5194/egusphere-2025-4526, 2025
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Belgium has faced intense droughts in recent years, causing major losses across sectors. To assess their rarity, we used a hydrological model to reconstruct fifty years of soil moisture in the country. We show that 2011–2020 experienced the most severe droughts since 1971, with nearly 30 % of the decade under drought. We also show that rainfall-based indicators underestimate soil moisture droughts, so including soil moisture monitoring can give decision-makers a clearer picture of drought risks.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, https://doi.org/10.5194/hess-29-4395-2025, 2025
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Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ivan Alsina-Ferrer, Llorenç Lledó, Ehsan Sharifi, and Francisco Doblas-Reyes
Geosci. Model Dev., 18, 5873–5890, https://doi.org/10.5194/gmd-18-5873-2025, https://doi.org/10.5194/gmd-18-5873-2025, 2025
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We present One_Pass (v0.8.0), a Python package enabling computation of statistics from streamed global climate model output using one-pass algorithms. Users often need statistics covering periods longer than the stream duration, requiring algorithms that do not store full time series. One-pass methods address this need while avoiding full data archiving, offering memory-efficient, accurate results for high-performance computing (HPC) workflows and downstream applications like bias adjustment.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Omar Abdelazim Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2198, https://doi.org/10.5194/egusphere-2025-2198, 2025
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The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of climate projections. The system produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, Yannis Markonis, and Miroslav Trnka
Hydrol. Earth Syst. Sci., 29, 3341–3358, https://doi.org/10.5194/hess-29-3341-2025, https://doi.org/10.5194/hess-29-3341-2025, 2025
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, with clusters of 775 and 630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025, https://doi.org/10.5194/gmd-18-4483-2025, 2025
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This study presents FINAM (
FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
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Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
Mansi Nagpal, Jasmin Heilemann, Luis Samaniego, Bernd Klauer, Erik Gawel, and Christian Klassert
Nat. Hazards Earth Syst. Sci., 25, 2115–2135, https://doi.org/10.5194/nhess-25-2115-2025, https://doi.org/10.5194/nhess-25-2115-2025, 2025
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This study measures the direct effects of droughts in association with other extreme weather events on agriculture in Germany at the district level. Using a statistical yield model, we quantify the direct damage of extremes on crop yields and farm revenue. Extreme events during drought cause an average annual damage of EUR 781 million, accounting for 45 % of reported revenue losses. The insights herein can help develop better strategies for managing and mitigating the effects of future climate extremes.
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
Biogeosciences, 22, 2935–2958, https://doi.org/10.5194/bg-22-2935-2025, https://doi.org/10.5194/bg-22-2935-2025, 2025
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Semi-arid ecosystems are crucial for Earth's carbon balance and are sensitive to changes in nitrogen (N) and phosphorus (P) levels. Their carbon dynamics are complex and not fully understood. We studied how long-term nutrient changes affect carbon exchange. In summer, the addition of N+P changed plant composition and productivity. In transitional seasons, carbon exchange was less weather-dependent with N. The addition of N and N+P increases carbon-exchange variability, driven by grass greenness.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
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The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent decades and the most expensive flood in Germany. In this paper, we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
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Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Sven Armin Westermann, Anke Hildebrandt, Souhail Bousetta, and Stephan Thober
Biogeosciences, 21, 5277–5303, https://doi.org/10.5194/bg-21-5277-2024, https://doi.org/10.5194/bg-21-5277-2024, 2024
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Plants at the land surface mediate between soil and the atmosphere regarding water and carbon transport. Since plant growth is a dynamic process, models need to consider these dynamics. Two models that predict water and carbon fluxes by considering plant temporal evolution were tested against observational data. Currently, dynamizing plants in these models did not enhance their representativeness, which is caused by a mismatch between implemented physical relations and observable connections.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
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Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
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We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Sandra Raab, Karel Castro-Morales, Anke Hildebrandt, Martin Heimann, Jorien Elisabeth Vonk, Nikita Zimov, and Mathias Goeckede
Biogeosciences, 21, 2571–2597, https://doi.org/10.5194/bg-21-2571-2024, https://doi.org/10.5194/bg-21-2571-2024, 2024
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Water status is an important control factor on sustainability of Arctic permafrost soils, including production and transport of carbon. We compared a drained permafrost ecosystem with a natural control area, investigating water levels, thaw depths, and lateral water flows. We found that shifts in water levels following drainage affected soil water availability and that lateral transport patterns were of major relevance. Understanding these shifts is crucial for future carbon budget studies.
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.
Kingsley Nnaemeka Ogbu, Oldrich Rakovec, Luis Samaniego, Gloria Chinwendu Okafor, Bernhard Tischbein, and Hadush Meresa
Proc. IAHS, 385, 211–218, https://doi.org/10.5194/piahs-385-211-2024, https://doi.org/10.5194/piahs-385-211-2024, 2024
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In this study, the MPR-mHM technique was applied in four data-scarce basins in Nigeria. Remotely sensed rainfall datasets were used as model forcings to evaluate the mHM capability in reproducing observed stream discharge under single and multivariable model calibration frameworks. Overall, model calibration performances displayed satisfactory outputs as evident in the Kling-Gupta Efficiency (KGE) scores across most basins.
Gökben Demir, Andrew J. Guswa, Janett Filipzik, Johanna Clara Metzger, Christine Römermann, and Anke Hildebrandt
Hydrol. Earth Syst. Sci., 28, 1441–1461, https://doi.org/10.5194/hess-28-1441-2024, https://doi.org/10.5194/hess-28-1441-2024, 2024
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Experimental evidence is scarce to understand how the spatial variation in below-canopy precipitation affects root water uptake patterns. Here, we conducted field measurements to investigate drivers of root water uptake patterns while accounting for canopy induced heterogeneity in water input. We found that tree species interactions and soil moisture variability, rather than below-canopy precipitation patterns, control root water uptake patterns in a mixed unmanaged forest.
Mijael Rodrigo Vargas Godoy, Yannis Markonis, Oldrich Rakovec, Michal Jenicek, Riya Dutta, Rajani Kumar Pradhan, Zuzana Bešťáková, Jan Kyselý, Roman Juras, Simon Michael Papalexiou, and Martin Hanel
Hydrol. Earth Syst. Sci., 28, 1–19, https://doi.org/10.5194/hess-28-1-2024, https://doi.org/10.5194/hess-28-1-2024, 2024
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The study introduces a novel benchmarking method based on the water cycle budget for hydroclimate data fusion. Using this method and multiple state-of-the-art datasets to assess the spatiotemporal patterns of water cycle changes in Czechia, we found that differences in water availability distribution are dominated by evapotranspiration. Furthermore, while the most significant temporal changes in Czechia occur during spring, the median spatial patterns stem from summer changes in the water cycle.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Christine Fischer-Bedtke, Johanna Clara Metzger, Gökben Demir, Thomas Wutzler, and Anke Hildebrandt
Hydrol. Earth Syst. Sci., 27, 2899–2918, https://doi.org/10.5194/hess-27-2899-2023, https://doi.org/10.5194/hess-27-2899-2023, 2023
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Canopies change how rain reaches the soil: some spots receive more and others less water. It has long been debated whether this also leads to locally wetter and drier soil. We checked this using measurements of canopy drip and soil moisture. We found that the increase in soil water content after rain was aligned with canopy drip. Independently, the soil storage reaction was dampened in locations prone to drainage, like hig-macroporosity areas, suggesting that canopy drip enhances bypass flow.
Carolin Winter, Tam V. Nguyen, Andreas Musolff, Stefanie R. Lutz, Michael Rode, Rohini Kumar, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, https://doi.org/10.5194/hess-27-303-2023, 2023
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The increasing frequency of severe and prolonged droughts threatens our freshwater resources. While we understand drought impacts on water quantity, its effects on water quality remain largely unknown. Here, we studied the impact of the unprecedented 2018–2019 drought in Central Europe on nitrate export in a heterogeneous mesoscale catchment in Germany. We show that severe drought can reduce a catchment's capacity to retain nitrogen, intensifying the internal pollution and export of nitrate.
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.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
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In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Ralf Loritz, Maoya Bassiouni, Anke Hildebrandt, Sibylle K. Hassler, and Erwin Zehe
Hydrol. Earth Syst. Sci., 26, 4757–4771, https://doi.org/10.5194/hess-26-4757-2022, https://doi.org/10.5194/hess-26-4757-2022, 2022
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In this study, we combine a deep-learning approach that predicts sap flow with a hydrological model to improve soil moisture and transpiration estimates at the catchment scale. Our results highlight that hybrid-model approaches, combining machine learning with physically based models, are a promising way to improve our ability to make hydrological predictions.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
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This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
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Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
Ulrike Hiltner, Andreas Huth, and Rico Fischer
Biogeosciences, 19, 1891–1911, https://doi.org/10.5194/bg-19-1891-2022, https://doi.org/10.5194/bg-19-1891-2022, 2022
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Quantifying biomass loss rates due to stem mortality is important for estimating the role of tropical forests in the global carbon cycle. We analyse the consequences of long-term elevated stem mortality for tropical forest dynamics and biomass loss. Based on simulations, we developed a statistical model to estimate biomass loss rates of forests in different successional states from forest attributes. Assuming a doubling of tree mortality, biomass loss increased from 3.2 % yr-1 to 4.5 % yr-1.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Swamini Khurana, Falk Heße, Anke Hildebrandt, and Martin Thullner
Biogeosciences, 19, 665–688, https://doi.org/10.5194/bg-19-665-2022, https://doi.org/10.5194/bg-19-665-2022, 2022
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In this study, we concluded that the residence times of solutes and the Damköhler number (Da) of the biogeochemical reactions in the domain are governing factors for evaluating the impact of spatial heterogeneity of the domain on chemical (such as carbon and nitrogen compounds) removal. We thus proposed a relationship to scale this impact governed by Da. This relationship may be applied in larger domains, thereby resulting in more accurate modelling outcomes of nutrient removal in groundwater.
Robert Schweppe, Stephan Thober, Sebastian Müller, Matthias Kelbling, Rohini Kumar, Sabine Attinger, and Luis Samaniego
Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, https://doi.org/10.5194/gmd-15-859-2022, 2022
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The recently released multiscale parameter regionalization (MPR) tool enables
environmental modelers to efficiently use extensive datasets for model setups.
It flexibly ingests the datasets using user-defined data–parameter relationships
and rescales parameter fields to given model resolutions. Modern
land surface models especially benefit from MPR through increased transparency and
flexibility in modeling decisions. Thus, MPR empowers more sound and robust
simulations of the Earth system.
Josephin Kroll, Jasper M. C. Denissen, Mirco Migliavacca, Wantong Li, Anke Hildebrandt, and Rene Orth
Biogeosciences, 19, 477–489, https://doi.org/10.5194/bg-19-477-2022, https://doi.org/10.5194/bg-19-477-2022, 2022
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Plant growth relies on having access to energy (solar radiation) and water (soil moisture). This energy and water availability is impacted by weather extremes, like heat waves and droughts, which will occur more frequently in response to climate change. In this context, we analysed global satellite data to detect in which regions extreme plant growth is controlled by energy or water. We find that extreme plant growth is associated with temperature- or soil-moisture-related extremes.
Michael Peichl, Stephan Thober, Luis Samaniego, Bernd Hansjürgens, and Andreas Marx
Hydrol. Earth Syst. Sci., 25, 6523–6545, https://doi.org/10.5194/hess-25-6523-2021, https://doi.org/10.5194/hess-25-6523-2021, 2021
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Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, https://doi.org/10.5194/hess-25-6437-2021, 2021
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Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low-frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
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We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Erwin Rottler, Axel Bronstert, Gerd Bürger, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, https://doi.org/10.5194/hess-25-2353-2021, 2021
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The mesoscale hydrological model (mHM) forced with an ensemble of climate projection scenarios was used to assess potential future changes in flood seasonality in the Rhine River basin. Results indicate that future changes in flood characteristics are controlled by increases in precipitation sums and diminishing snowpacks. The decreases in snowmelt can counterbalance increasing precipitation, resulting in only small and transient changes in streamflow maxima.
Cited articles
Ariza-Carricondo, C., Mauro, F., Op de Beeck, M., Roland, M., Gielen, B., Vitale, D., Ceulemans, R., and Papale, D.: A comparison of different methods for assessing leaf area index in four canopy types, Central European Forestry Journal, 65, 67–80, https://doi.org/10.2478/forj-2019-0011, 2019. a, b, c
Arora, V.: Modeling vegetation as dynamic component in soil-vegetation-atmosphere transfer schemes and hydrological models, Rev. Geophys., 40, 1–26, https://doi.org/10.1029/2001RG000103, 2002. a, b, c
Arsenault, K., Nearing, G., Wang, S., Yatheendradas, S., and Peters-Lidard, C.: Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation, J. Hydrometeorol., 19, 815–830, https://doi.org/10.1175/JHM-D-17-0205.1, 2018. a, b
Asaadi, A., Arora, V. K., Melton, J. R., and Bartlett, P.: An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework, Biogeosciences, 15, 6885–6907, https://doi.org/10.5194/bg-15-6885-2018, 2018. a, b, c, d, e
Balzarolo, M., Valdameri, N., Fu, Y. H., Schepers, L., Janssens, I. A., and Campioli, M.: Different determinants of radiation use efficiency in cold and temperate forests, Global Ecol. Biogeogr., 28, 1649–1667, https://doi.org/10.1111/geb.12985, 2019. a
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rodenbeck, C., Arain, M., Baldocchi, D., Bonan, G., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K., Roupsard, O., and Papale, D.: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010. a, b
Boeing, F., Rakovech, O., Kumar, R., Samaniego, L., Schrön, M., Hildebrandt, A., Rebmann, C., Thober, S., Müller, S., Zacharias, S., Bogena, H., Schneider, K., Kiese, R., and Marx, A.: High-resolution drought simulations and comparison to soil moisture observations in Germany, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-402, in review, 2021. a
Bohn, F. J., Frank, K., and Huth, A.: Of climate and its resulting tree growth: Simulating the productivity of temperate forests, Ecol. Model., 278, 9–17, https://doi.org/10.1016/j.ecolmodel.2014.01.021, 2014. a
Bonhomme, R. and Chartier, P.: The interpretation and automatic measurement of hemispherical photographs to obtain sunlit foliage area and gap frequency, Israel J. Agr. Res., 22, 53–61, 1972. a
Boons-Prins, E.: Grassland simulation with the LPJmL model: version 3.4.018, no. 172 in WOt-werkdocument, Wettelijke Onderzoekstaken Natuur and Milieu, 2010. a
Buras, A., Rammig, A., and Zang, C. S.: Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003, Biogeosciences, 17, 1655–1672, https://doi.org/10.5194/bg-17-1655-2020, 2020. a, b
Che, M.-L., Chen, B.-Z., Wang, Y., and Guo, X.-Y.: Review of dynamic global vegetation models (DGVMs), J. Appl. Ecol., 25, 263–271, 2014. a
Cheng, Y.-B., Zhang, Q., Lyapustin, A., Wang, Y., and Middleton, E.: Impacts of light use efficiency and fPAR parameterization on gross primary production modeling, Agr. Forest Meteorol., 189–190, 187–197, https://doi.org/10.1016/j.agrformet.2014.01.006, 2014. a, b, c
Chiesi, M., Fibbi, L., Genesio, L., Gioli, B., Magno, R., Maselli, F., Moriondo, M., and Vaccari, F.: Integration of ground and satellite data to model Mediterranean forest processes, Int. J. Appl. Earth Obs., 13, 504–515, https://doi.org/10.1016/j.jag.2010.10.006, 2011. a
Cholet, C., Houle, D., Sylvain, J.-D., Doyon, F., and Maheu, A.: Climate Change Increases the Severity and Duration of Soil Water Stress in the Temperate Forest of Eastern North America, Frontiers in Forests and Global Change, 5, 879382, https://doi.org/10.3389/ffgc.2022.879382, 2022. a
Collatz, G., Ribas-Carbo, M., and Berry, J.: Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants, Funct. Plant Biol., 19, 519–538, https://doi.org/10.1071/PP9920519, 1992. a
Coops, N. C., Waring, R. H., and Law, B. E.: Assessing the past and future distribution and productivity of ponderosa pine in the Pacific Northwest using a process model, 3-PG, Ecol. Model., 183, 107–124, https://doi.org/10.1016/j.ecolmodel.2004.08.002, 2005. a
Cox, P., Betts, R., Bunton, C., Essery, R., Rowntree, P., and Smith, J.: The impact of new land surface physics on the GCM simulation of climate and climate sensitivity, Clim. Dynam., 15, 183–203, https://doi.org/10.1007/s003820050276, 1999. a
Cueva, A., Bullock, S. H., Méndez-Alonzo, R., López-Reyes, E., and Vargas, R.: Foliage Senescence as a Key Parameter for Modeling Gross Primary Productivity in a Mediterranean Shrubland, J. Geophys. Res.-Biogeo., 126, e2020JG005839, https://doi.org/10.1029/2020JG005839, 2021. a
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 10676–10700, https://doi.org/10.1002/2016JD025097, 2016. a, b
Durand, M., Murchie, E. H., Lindfors, A. V., Urban, O., Aphalo, P. J., and Robson, T. M.: Diffuse solar radiation and canopy photosynthesis in a changing environment, Agr. Forest Meteorol., 311, 108684, https://doi.org/10.1016/j.agrformet.2021.108684, 2021. a
Dyderski, M. K., Chmura, D., Dylewski, Ł., Horodecki, P., Jagodziński, A. M., Pietras, M., Robakowski, P., and Woziwoda, B.: Biological Flora of the British Isles: Quercus rubra, J. Ecol., 108, 1199–1225, https://doi.org/10.1111/1365-2745.13375, 2020. a
Estoque, R., Dasgupta, R., Winkler, K., Avitabile, V., Johnson, B., Myint, S., Gao, Y., Ooba, M., Murayama, Y., and Lasco, R.: Spatiotemporal pattern of global forest change over the past 60 years and the forest transition theory, Environ. Res. Lett., 17, 084022, https://doi.org/10.1088/1748-9326/ac7df5, 2022. a
Fang, H., Baret, F., Plummer, S., and Schaepman-Strub, G.: An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications, Rev. Geophys., 57, 739–799, https://doi.org/10.1029/2018RG000608, 2019. a, b, c
Farquhar, G. D., von Caemmerer, S., and Berry, J. A.: A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980. a, b
Fibbi, L., Moriondo, M., Chiesi, M., Bindi, M., and Maselli, F.: Impacts of climate change on the gross primary production of Italian forests, Ann. For. Sci., 76, 59, https://doi.org/10.1007/s13595-019-0843-x, 2019. a
Fischer, R., Armstrong, A., Shugart, H. H., and Huth, A.: Simulating the impacts of reduced rainfall on carbon stocks and net ecosystem exchange in a tropical forest, Environ. Modell. Softw., 52, 200–206, https://doi.org/10.1016/j.envsoft.2013.10.026, 2014. a
Fischer, R., Bohn, F., Dantas de Paula, M., Dislich, C., Groeneveld, J., Gutiérrez, A. G., Kazmierczak, M., Knapp, N., Lehmann, S., Paulick, S., Pütz, S., Rödig, E., Taubert, F., Köhler, P., and Huth, A.: Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests, Ecol. Model., 326, 124–133, https://doi.org/10.1016/j.ecolmodel.2015.11.018, 2016. a, b, c, d, e, f
Fleischer, K., Rebel, K. T., van der Molen, M. K., Erisman, J. W., Wassen, M. J., van Loon, E. E., Montagnani, L., Gough, C. M., Herbst, M., Janssens, I. A., Gianelle, D., and Dolman, A. J.: The contribution of nitrogen deposition to the photosynthetic capacity of forests, Global Biogeochem. Cy., 27, 187–199, https://doi.org/10.1002/gbc.20026, 2013. a, b, c, d
Foley, J. and Ramankutty, N.: A primer on the terrestrial carbon cycle: What we don't know but should, in: The global carbon cycle: integrating humans, climate, and the natural world, edited by: Field, C. B. and Raupach, M. R., Island Press, Washington, D.C., 279–294, 2003. a
Forzieri, G., Girardello, M., Ceccherini, G., Spinoni, J., Feyen, L., Hartmann, H., Beck, P. S. A., Camps-Valls, G., Chirici, G., Mauri, A., and Cescatti, A.: Emergent vulnerability to climate-driven disturbances in European forests, Nat. Commun., 12, 1081, https://doi.org/10.1038/s41467-021-21399-7, 2021. a
Francés, F., Velez, J., and Velez, J.: Split-parameter structure for the automatic calibration of distributed hydrological models, J. Hydrol., 332, 226–240, https://doi.org/10.1016/j.jhydrol.2006.06.032, 2007. a, b
Friedlingstein, P., Joel, G., Field, C. B., and Fung, I. Y.: Toward an allocation scheme for global terrestrial carbon models, Glob. Change Biol., 5, 755–770, https://doi.org/10.1046/j.1365-2486.1999.00269.x, 1999. a
Frost-Christensen, H. and Sand-Jensen, K.: The quantum efficiency of photosynthesis in macroalgae and submerged angiosperms, Oecologia, 91, 377–384, https://doi.org/10.1007/BF00317627, 1992. a
Gamon, J. A.: Reviews and Syntheses: optical sampling of the flux tower footprint, Biogeosciences, 12, 4509–4523, https://doi.org/10.5194/bg-12-4509-2015, 2015. a
Gim, H.-J., Park, S. K., Kang, M., Thakuri, B. M., Kim, J., and Ho, C.-H.: An improved parameterization of the allocation of assimilated carbon to plant parts in vegetation dynamics for Noah-MP, J. Adv. Model. Earth Sy., 9, 1776–1794, https://doi.org/10.1002/2016MS000890, 2017. a, b, c, d
Göhler, M., Mai, J., and Cuntz, M.: Use of eigendecomposition in a parameter sensitivity analysis of the Community Land Model, J. Geophys. Res.-Biogeo., 118, 904–921, https://doi.org/10.1002/jgrg.20072, 2013. a
Gower, S. and Norman, J.: Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations, Ecology, 72, 1896–1900, https://doi.org/10.2307/1940988, 1991. 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, b, c, d
Granier, A., Ceschia, E., Damesin, C., Dufrêne, E., Epron, D., Gross, P., Lebaube, S., Le Dantec, V., Le Goff, N., Lemoine, D., Lucot, E., Ottorini, J. M., Pontailler, J. Y., and Saugier, B.: The carbon balance of a young Beech forest, Funct. Ecol., 14, 312–325, https://doi.org/10.1046/j.1365-2435.2000.00434.x, 2000. a
Guan, X., Chen, J., Shen, H., and Xie, X.: A modified two-leaf light use efficiency model for improving the simulation of GPP using a radiation scalar, Agric. Forest Meteorol., 307, 108546, https://doi.org/10.1016/j.agrformet.2021.108546, 2021. a
Gupta, H., Kling, H., Yilmaz, K., and Martinez, G.: 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
Harper, A. B., Williams, K. E., McGuire, P. C., Duran Rojas, M. C., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L., Marthews, T., Breder Eller, C., Mathison, C., Nobrega, R. L. B., Gedney, N., Vidale, P. L., Otu-Larbi, F., Pandey, D., Garrigues, S., Wright, A., Slevin, D., De Kauwe, M. G., Blyth, E., Ardö, J., Black, A., Bonal, D., Buchmann, N., Burban, B., Fuchs, K., de Grandcourt, A., Mammarella, I., Merbold, L., Montagnani, L., Nouvellon, Y., Restrepo-Coupe, N., and Wohlfahrt, G.: Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, 2021. a, b, c
Hartge, K. H.: Feddes, R. A., Kowalik, P. I. und Zaradny, H.: simulation of field water use and crop yield. Pudoc (Centre for agricultural publishing and documentation) Wageningen, Niederlande, 195 Seiten, 13 Abbildungen, Paperback. Preis: hfl 30,–, Z. Pflanz. Bodenkunde, 143, 254–255, https://doi.org/10.1002/jpln.19801430219, 1980. a
Haverd, V., Smith, B., Raupach, M., Briggs, P., Nieradzik, L., Beringer, J., Hutley, L., Trudinger, C. M., and Cleverly, J.: Coupling carbon allocation with leaf and root phenology predicts tree–grass partitioning along a savanna rainfall gradient, Biogeosciences, 13, 761–779, https://doi.org/10.5194/bg-13-761-2016, 2016. a
Hidy, D., Barcza, Z., Hollós, R., Dobor, L., Ács, T., Zacháry, D., Filep, T., Pásztor, L., Incze, D., Dencső, M., Tóth, E., Merganičová, K., Thornton, P., Running, S., and Fodor, N.: Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model, Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, 2022. a, b
Hirmas, D., Giménez, D., Nemes, A., Kerry, R., Brunsell, N., and Wilson, C.: Climate-induced changes in continental-scale soil macroporosity may intensify water cycle, Nature, 561, 100–103, https://doi.org/10.1038/s41586-018-0463-x, 2018. a
Holtmann, A., Huth, A., Pohl, F., Rebmann, C., and Fischer, R.: Carbon Sequestration in Mixed Deciduous Forests: The Influence of Tree Size and Species Composition Derived from Model Experiments, Forests, 12, 726, https://doi.org/10.3390/f12060726, 2021. a
Hou, Z., Huang, M., Leung, L. R., Lin, G., and Ricciuto, D. M.: Sensitivity of surface flux simulations to hydrologic parameters based on an uncertainty quantification framework applied to the Community Land Model, J. Geophys. Res.-Atmos., 117, D15108, https://doi.org/10.1029/2012JD017521, 2012. a
Huang, X., Zheng, Y., Zhang, H., Lin, S., Liang, S., Li, X., Ma, M., and Yuan, W.: High spatial resolution vegetation gross primary production product: Algorithm and validation, Science of Remote Sensing, 5, 100049, https://doi.org/10.1016/j.srs.2022.100049, 2022. a
Imadi, S., Gul, A., Dikilitas, M., Karakas, S., Sharma, I., and Ahmad, P.: Water stress: Types, causes, and impact on plant growth and development, John Wiley & Sons, 343–355, https://doi.org/10.1002/9781119054450.ch21, 2016. a
Iooss, B. and Lemaître, P.: A Review on Global Sensitivity Analysis Methods, Operations Research/Computer Science Interfaces Series, vol. 59, Springer, https://doi.org/10.1007/978-1-4899-7547-8_5, 2014. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L.,
Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L.,
Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, in press, https://doi.org/10.1017/9781009157896, 2021. a
Istanbulluoglu, E., Wang, T., and Wedin, D. A.: Evaluation of ecohydrologic model parsimony at local and regional scales in a semiarid grassland ecosystem, Ecohydrology, 5, 121–142, https://doi.org/10.1002/eco.211, 2012. a
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E., and Schulze, E. D.: A global analysis of root distributions for terrestrial biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996. a, b, c
Jolly, W. M., Nemani, R., and Running, S. W.: A generalized, bioclimatic index to predict foliar phenology in response to climate, Glob. Change Biol., 11, 619–632, https://doi.org/10.1111/j.1365-2486.2005.00930.x, 2005. a
Jung, M., Vetter, M., Herold, M., Churkina, G., Reichstein, M., Zaehle, S., Ciais, P., Viovy, N., Bondeau, A., Chen, Y., Trusilova, K., Feser, F., and Heimann, M.: Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogeochem. Cy., 21, GB4021, https://doi.org/10.1029/2006GB002915, 2007. a, b
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a
Kattge, J., Diaz, S., Lavorel, S., Prentice, I., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P., Wright, I., Cornelissen, J., Violle, C., Harrison, S., Bodegom, P., Reichstein, M., Enquist, B., Soudzilovskaia, N., Ackerly, D., Anand, M., and Wirth, C.: TRY – a global database of plant traits, Glob. Change Biol., 17, 2905–2935, https://doi.org/10.1111/j.1365-2486.2011.02451.x, 2011. a, b
Kennedy, D., Swenson, S., Oleson, K. W., Lawrence, D. M., Fisher, R., Lola da Costa, A. C., and Gentine, P.: Implementing Plant Hydraulics in the Community Land Model, Version 5, J. Adv. Model. Earth Sy., 11, 485–513, https://doi.org/10.1029/2018MS001500, 2019. a
Knohl, A. and Baldocchi, D.: Effects of diffuse radiation on canopy gas exchange processes in a forest ecosystem, J. Geophys. Res.-Biogeo., 113, G02023, https://doi.org/10.1029/2007JG000663, 2008. a, b
Knorr, W.: Annual and interannual CO2 exchanges of the terrestrial biosphere: process-based simulations and uncertainties, Global Ecol. Biogeogr., 9, 225–252, https://doi.org/10.1046/j.1365-2699.2000.00159.x, 2000. a
Konings, A. G., Williams, A. P., and Gentine, P.: Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation, Nat. Geosci., 10, 284–288, https://doi.org/10.1038/ngeo2903, 2017. a
Kuczera, G. and Parent, E.: Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm, J. Hydrol., 211, 69–85, https://doi.org/10.1016/S0022-1694(98)00198-X, 1998. a
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013. a, b
Lal, R. and Lorenz, K.: Carbon Sequestration in Temperate Forests, Springer, 187–202, https://doi.org/10.1007/978-94-007-4159-1_9, 2012. a
Law, B., Anthoni, P., and Aber, J.: Measurements of gross and net ecosystem productivity and water vapour exchange of a Pinus ponderosa ecosystem, and an evaluation of two generalized models, Glob. Change Biol., 6, 155–168, https://doi.org/10.1046/j.1365-2486.2000.00291.x, 2000. a
Lee, H., Park, J., Cho, S., Lee, M., and Kim, H.: Impact of leaf area index from various sources on estimating gross primary production in temperate forests using the JULES land surface model, Agr. Forest Meteorol., 276–277, 107614, https://doi.org/10.1016/j.agrformet.2019.107614, 2019. a, b
Li, J., Wang, Y., Duan, Q., Lu, X., Pak, B., Wiltshire, A., Robertson, E., and Ziehn, T.: Quantification and attribution of errors in the simulated annual gross primary production and latent heat fluxes by two global land surface models, J. Adv. Model. Earth Sy., 8, 1270–1288, https://doi.org/10.1002/2015MS000583, 2016. a, b, c
Li, Q., Lu, X., Wang, Y., Huang, X., Cox, P. M., and Luo, Y.: Leaf area index identified as a major source of variability in modeled CO2 fertilization, Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, 2018. a
Lloyd, J. and Taylor, J. A.: On the temperature dependence of soil respiration, Funct. Ecol., 8, 315–323, 1994. a
Luyssaert, S., Inglima, I., Jungs, M., Richardson, A., Reichsteins, M., Papale, D., Piao, S., Schulzes, E., Wingate, L., Matteucci, G., Aragaoss, L., Aubinet, M., Beers, C., Bernhofer, C., Black, K., Bonal, D., Bonnefonds, J., Chambers, J., Ciais, P., and Janssens, I.: CO2 balance of boreal, temperate, and tropical forests, Glob. Change Biol., 13, 2509–2537, https://doi.org/10.1111 j.1365-2486.2007.01439.x, 2007. a
Ma, H., Ma, C., Li, X., Yuan, W., Liu, Z., and Zhu, G.: Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation, Sustainability, 12, 7, https://doi.org/10.3390/su12072584, 2020. a
Ma, J., Yan, X., Dong, W., and Chou, J.: Gross primary production of global forest ecosystems has been overestimated, Sci. Rep.-UK, 5, 10820, https://doi.org/10.1038/srep10820, 2015. a, b, c
Madani, N., Kimball, J. S., Affleck, D. L. R., Kattge, J., Graham, J., van Bodegom, P. M., Reich, P. B., and Running, S. W.: Improving ecosystem productivity modeling through spatially explicit estimation of optimal light use efficiency, J. Geophys. Res.-Biogeo., 119, 1755–1769, https://doi.org/10.1002/2014JG002709, 2014. a, b, c
Malhi, Y., Doughty, C., and Galbraith, D.: The allocation of ecosystem net primary productivity in tropical forests, Philos. T. Roy. Soc. B, 366, 3225–3245, https://doi.org/10.1098/rstb.2011.0062, 2011. a
Malhi, Y., Franklin, J., Seddon, N., Solan, M., Turner, M., Field, C., and Knowlton, N.: Climate change and ecosystems: Threats, opportunities and solutions, Philos. T. Roy. Soc. B, 375, 20190104, https://doi.org/10.1098/rstb.2019.0104, 2020. a
Maselli, F., Pasqui, M., Chirici, G., Chiesi, M., L, F., Salvati, R., and Corona, P.: Modeling primary production using a 1 km daily meteorological data set, Clim. Res., 54, 271–285, https://doi.org/10.3354/cr01121, 2012. a
Melton, J. R. and Arora, V. K.: Sub-grid scale representation of vegetation in global land surface schemes: implications for estimation of the terrestrial carbon sink, Biogeosciences, 11, 1021–1036, https://doi.org/10.5194/bg-11-1021-2014, 2014. a
Meyer, L., Brinkman, S., van Kesteren, L., Leprince-Ringuet, N., and van Boxmeer, F. E.: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Geneva, Switzerland, ISBN 978-92-9169-143-2, 2014. a
Monteith, J. L.: Solar Radiation and Productivity in Tropical Ecosystems, J. Appl. Ecol., 9, 20, 1972. a
Monteith, J. L.: Climate and the Efficiency of Crop Production in Britain, Philos. T. Roy. Soc. B, 281, 277–294, https://doi.org/10.1098/rstb.1977.0140, 1977. a
Nathalie, B.: Ground-based measurements of leaf area index: A review of methods, instruments and current controversies, J. Exp. Bot., 54, 2403–2417, https://doi.org/10.1093/jxb/erg263, 2003. a
Nathalie, B., 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
Nigatu, M.: Review on Effect of Climate Change on Forest Ecosystem, International Journal of Environmental Sciences and Natural Resources, 17, 126–129, https://doi.org/10.19080/IJESNR.2019.17.555968, 2019. a
Nossent, J. and Bauwens, W.: Optimising the convergence of a Sobol' sensitivity analysis for an environmental model: application of an appropriate estimate for the square of the expectation value and the total variance, International Congress on Envir onmental Modelling and Software, 14, https://scholarsarchive.byu.edu/iemssconference/2012/Stream-B/14 (last access: 20 March 2022), 2012. a
Ostle, N. J., Smith, P., Fisher, R., Woodward, F. I., Fisher, J. B., Smith, J. U., Galbraith, D., Levy, P., Meir, P., McNamara, N. P., and Bardgett, R. D.: Integrating plant–soil interactions into global carbon cycle models, J. Ecol., 97, 851–863, https://doi.org/10.1111/j.1365-2745.2009.01547.x, 2009. a, b
Pan, N., Wang, S., Wei, F., Shen, M., and Fu, B.: Inconsistent changes in NPP and LAI determined from the parabolic LAI versus NPP relationship, Ecol. Indic., 131, 108134, https://doi.org/10.1016/j.ecolind.2021.108134, 2021. a
Pan, Y., Birdsey, R., Fang, J., Houghton, R., Kauppi, P., Kurz, W., Phillips, O., Shvidenko, A., Lewis, S., Canadell, J., Ciais, P., Jackson, R., Pacala, S., McGuire, A., Piao, S., Rautiainen, A., Sitch, S., and Hayes, D.: A Large and Persistent Carbon Sink in the World's Forests, Science, 333, 988–993, https://doi.org/10.1126/science.1201609, 2011. a, b
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Ribeca, A., Ingen, C., Zhang, L., Amiro, B., Ammann, C., Arain, M., Ardö, J., and Papale, D.: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020. a, b
Perez, G. R.: On the use of satellite data to calibrate a parsimonious ecohydrological model in ungauged basins, PhD thesis, Departamento de Ingenieria Hidraulica y Medio Ambiente, Universitat Politecnica de Valencia, https://doi.org/10.4995/Thesis/10251/72639, 2016. a, b
Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochem. Cy., 7, 811–841, https://doi.org/10.1029/93GB02725, 1993. a, b, c, d, e, f
Rahman, A., Zhang, X., Houser, P., Sauer, T., and Maggioni, V.: Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model, Frontiers in Water, 3, 789352, https://doi.org/10.3389/frwa.2021.789352, 2022. a, b
Rakovec, O., Hill, M. C., Clark, M. P., Weerts, A. H., Teuling, A. J., and Uijlenhoet, R.: Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrologic models, Water Resour. Res., 50, 409–426, https://doi.org/10.1002/2013WR014063, 2014. a
Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M., Seneviratne, S., Zscheischler, J., Beer, C., Buchmann, N., Frank, D., Papale, D., Rammig, A., Smith, P., Thonicke, K., Velde, M., Vicca, S., Walz, A., and Wattenbach, M.: Climate extremes and the carbon cycle, Nature, 500, 287–295, https://doi.org/10.1038/nature12350, 2013. a, b
Reinmann, A. B. and Hutyra, L. R.: Edge effects enhance carbon uptake and its vulnerability to climate change in temperate broadleaf forests, P. Natl. Acad. Sci. USA, 114, 107–112, https://doi.org/10.1073/pnas.1612369114, 2017. a, b
Ruimy, A., Saugier, B., and Dedieu, G.: Methodology for the estimation of terrestrial net primary production from remotly sensed data, J. Geophys. Res.-Atmos., 99, 5263–5283, 1994. a
Ruimy, A., Kergoat, L., Bondeau, A., and The Participants of the Potsdam NPP Model Intercomparison: Comparing global models of terrestrial net primary productivity (NPP): analysis of differences in light absorption and light-use efficiency, Glob. Change Biol., 5, 56–64, https://doi.org/10.1046/j.1365-2486.1999.00007.x, 1999. a, b
Ruiz-Pérez, G., Koch, J., Manfreda, S., Caylor, K., and Francés, F.: Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI, Hydrol. Earth Syst. Sci., 21, 6235–6251, https://doi.org/10.5194/hess-21-6235-2017, 2017. a
Running, S., Nemani, R., Heinsch, F., Zhao, M., Reeves, M., and Hashimoto, H.: A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production, BioScience, 54, 547–560, https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2, 2004. a
Saltelli, A.: Making best use of model evaluations to compute sensitivity indices, Comput. Phys. Commun., 145, 280–297, https://doi.org/10.1016/S0010-4655(02)00280-1, 2002. a
Saltelli, A., Tarantola, S., and Chan, K. P.-S.: A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output, Technometrics, 41, 39–56, https://doi.org/10.1080/00401706.1999.10485594, 1999. a
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010. a, b, c
Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J. M., Davis, K. J., Dimitrov, D., Hilton, T. W., Hollinger, D. Y., Humphreys, E., Poulter, B., Raczka, B. M., Richardson, A. D., Sahoo, A., Thornton, P., Vargas, R., Verbeeck, H., Anderson, R., Baker, I., Black, T. A., Bolstad, P., Chen, J., Curtis, P. S., Desai, A. R., Dietze, M., Dragoni, D., Gough, C., Grant, R. F., Gu, L., Jain, A., Kucharik, C., Law, B., Liu, S., Lokipitiya, E., Margolis, H. A., Matamala, R., McCaughey, J. H., Monson, R., Munger, J. W., Oechel, W., Peng, C., Price, D. T., Ricciuto, D., Riley, W. J., Roulet, N., Tian, H., Tonitto, C., Torn, M., Weng, E., and Zhou, X.: A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis, J. Geophys. Res.-Biogeo., 117, G03010, https://doi.org/10.1029/2012JG001960, 2012. a, b, c, d
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018. a
Schnabel, F., Purrucker, S., Schmitt, L., Engelmann, R. A., Kahl, A., Richter, R., Seele-Dilbat, C., Skiadaresis, G., and Wirth, C.: Cumulative growth and stress responses to the 2018–2019 drought in a European floodplain forest, bioRxiv, https://doi.org/10.1101/2021.03.05.434090, 2021. a
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A., Gharun, M., Grams, T. E., Hauck, M., Hajek, P., Hartmann, H., Hiltbrunner, E., Hoch, G., Holloway-Phillips, M., Körner, C., Larysch, E., Lübbe, T., Nelson, D. B., Rammig, A., Rigling, A., Rose, L., Ruehr, N. K., Schumann, K., Weiser, F., Werner, C., Wohlgemuth, T., Zang, C. S., and Kahmen, A.: A first assessment of the impact of the extreme 2018 summer drought on Central European forests, Basic Appl. Ecol., 45, 86–103, https://doi.org/10.1016/j.baae.2020.04.003, 2020. a
Schweppe, R., Thober, S., Müller, S., Kelbling, M., Kumar, R., Attinger, S., and Samaniego, L.: MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models, Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, 2022. a
Senf, C., Pflugmacher, D., Zhiqiang, Y., Sebald, J., Knorn, J., Neumann, M., Hostert, P., and Seidl, R.: Canopy mortality has doubled in Europe's temperate forests over the last three decades, Nat. Commun., 9, 4978, https://doi.org/10.1038/s41467-018-07539-6, 2018. a
Seo, H. and Kim, Y.: Role of remotely sensed leaf area index assimilation in eco-hydrologic processes in different ecosystems over East Asia with Community Land Model version 4.5 – Biogeochemistry, J. Hydrol., 594, 125957, https://doi.org/10.1016/j.jhydrol.2021.125957, 2021. 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, b, c, d, e, f, g, h, i, j, k
Spielmann, F. M., Wohlfahrt, G., Hammerle, A., Kitz, F., Migliavacca, M., Alberti, G., Ibrom, A., El-Madany, T. S., Gerdel, K., Moreno, G., Kolle, O., Karl, T., Peressotti, A., and Delle Vedove, G.: Gross Primary Productivity of Four European Ecosystems Constrained by Joint CO2 and COS Flux Measurements, Geophys. Res. Lett., 46, 5284–5293, https://doi.org/10.1029/2019GL082006, 2019. a
Springer, K., Wang, R., and Gamon, J.: Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees, Remote Sens.-Basel, 9, 691, https://doi.org/10.3390/rs9070691, 2017. a
Street, L. E., Shaver, G. R., Williams, M., and Van Wijk, M. T.: What is the relationship between changes in canopy leaf area and changes in photosynthetic CO2 flux in arctic ecosystems?, J. Ecol., 95, 139–150, https://doi.org/10.1111/j.1365-2745.2006.01187.x, 2007. a
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007. a
Turner, D., Ritts, W., Styles, J., Yang, Z., Cohen, W., Law, B., and Thornton, P.: A diagnostic carbon flux model to monitor the effects of disturbance and interannual variation in climate on regional NEP, Tellus B, 58, 476–490, https://doi.org/10.1111/j.1600-0889.2006.00221.x, 2006. a
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U., Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J., Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer Functions in Earth System Science: Challenges and Perspectives, Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017. a
Vargas, R., Sonnentag, O., Abramowitz, G., Carrara, A., Chen, J., Ciais, P., Correia, A., Keenan, T., Kobayashi, H., Ourcival, J., Papale, D., Pearson, D., Pereira, J., Piao, S., Rambal, S., and Baldocchi, D.: Drought Influences the Accuracy of Simulated Ecosystem Fluxes: A Model-Data Meta-analysis for Mediterranean Oak Woodlands, Ecosystems, 16, 749–764, https://doi.org/10.1007/s10021-013-9648-1, 2013. a
Vicca, S., Balzarolo, M., Filella, I., Granier, A., Herbst, M., Knohl, A., Bernard, L., Mund, M., Nagy, Z., Pintér, K., Rambal, S., Verbesselt, J., Verger, A., Zeileis, A., Zhang, C., and Penuelas, J.: Remotely-sensed detection of effects of extreme droughts on gross primary production, Sci. Rep.-UK, 6, 28269, https://doi.org/10.1038/srep28269, 2016. a
Wang, H., Jia, G., Fu, C., Feng, J., Zhao, T., and Ma, Z.: Deriving maximal LUE from coordinated flux measurements and satellite data for regional gross primary production modeling, Remote Sens. Environ., 114, 2248–2258, https://doi.org/10.1016/j.rse.2010.05.001, 2010. a, b, c
Wang, L., Zhu, H., Lin, A., Zou, L., Qin, W., and Du, Q.: Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data, Remote Sens.-Basel, 9, 418, https://doi.org/10.3390/rs9050418, 2017. a
Wegehenkel, M.: Modeling of vegetation dynamics in hydrological models for the assessment of the effects of climate change on evapotranspiration and groundwater recharge, Adv. Geosci., 21, 109–115, https://doi.org/10.5194/adgeo-21-109-2009, 2009. a
Wei, S., Yi, C., Fang, W., and Hendrey, G.: A global study of GPP focusing on light-use efficiency in a random forest regression model, Ecosphere, 8, e01724, https://doi.org/10.1002/ecs2.1724, 2017. a, b, c, d
White, M., Thornton, P., Running, S., and Nemani, R.: Parameterization and Sensitivity Analysis of the BIOME–BGC Terrestrial Ecosystem Model: Net Primary Production Controls, Earth Interact., 4, https://doi.org/10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2, 2000. a, b
Xia, J., Yuan, W., Wang, Y.-P., and Zhang, Q.: Adaptive Carbon Allocation by Plants Enhances the Terrestrial Carbon Sink, Sci. Rep.-UK, 7, 3341, https://doi.org/10.1038/s41598-017-03574-3, 2017. a
Xiao, X., Zhang, Q., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore, B., and Ojima, D.: Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data, Remote Sens. Environ., 91, 256–270, https://doi.org/10.1016/j.rse.2004.03.010, 2004. a, b, c, d, e
Xin, Q., Dai, Y., and Liu, X.: A simple time-stepping scheme to simulate leaf area index, phenology, and gross primary production across deciduous broadleaf forests in the eastern United States, Biogeosciences, 16, 467–484, https://doi.org/10.5194/bg-16-467-2019, 2019. a, b, c
Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A. M., Dormann, C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf, R. F., Gregr, E. J., Halpin, P. N., Heikkinen, R. K., Heinänen, S., Jones, A. R., Krishnakumar, P. K., Lauria, V., Lozano-Montes, H., Mannocci, L., Mellin, C., Mesgaran, M. B., Moreno-Amat, E., Mormede, S., Novaczek, E., Oppel, S., Ortuño Crespo, G., Peterson, A. T., Rapacciuolo, G., Roberts, J. J., Ross, R. E., Scales, K. L., Schoeman, D., Snelgrove, P., Sundblad, G., Thuiller, W., Torres, L. G., Verbruggen, H., Wang, L., Wenger, S., Whittingham, M. J., Zharikov, Y., Zurell, D., and Sequeira, A. M.: Outstanding Challenges in the Transferability of Ecological Models, Trends Ecol. Evol., 33, 790–802, https://doi.org/10.1016/j.tree.2018.08.001, 2018. a
Yuan, W., Liu, S., Zhou, G., Zhou, G., Tieszen, L., Baldocchi, D., Bernhofer, C., Gholz, H., Goldstein, A., Goulden, M., Hollinger, D., Hu, Y., Law, B., Stoy, P., Vesala, T., and Wofsy, S.: Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes, Agr. Forest Meteorol., 143, 189–207, https://doi.org/10.1016/j.agrformet.2006.12.001, 2007. a, b, c, d, e, f, g, h
Yuan, W., Liu, S., Yu, G., Bonnefond, J.-M., Chen, J., Davis, K., Desai, A. R., Goldstein, A. H., Gianelle, D., Rossi, F., Suyker, A. E., and Verma, S. B.: Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data, Remote Sens. Environ., 114, 1416–1431, https://doi.org/10.1016/j.rse.2010.01.022, 2010. a
Yuan, W., Cai, W., Xia, J., Chen, J., Liu, S., Dong, W., Merbold, L., Law, B., Arain, A., Beringer, J., Bernhofer, C., Black, A., Blanken, P. D., Cescatti, A., Chen, Y., Francois, L., Gianelle, D., Janssens, I. A., Jung, M., Kato, T., Kiely, G., Liu, D., Marcolla, B., Montagnani, L., Raschi, A., Roupsard, O., Varlagin, A., and Wohlfahrt, G.: Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database, Agr. Forest Meteorol., 192–193, 108–120, https://doi.org/10.1016/j.agrformet.2014.03.007, 2014. a, b
Yuan, W., Zheng, Y., Piao, S., Ciais, P., Lombardozzi, D., Wang, Y., Ryu, Y., Chen, G., Dong, W., Hu, Z., Jain, A. K., Jiang, C., Kato, E., Li, S., Lienert, S., Liu, S., Nabel, J. E., Qin, Z., Quine, T., Sitch, S., Smith, W. K., Wang, F., Wu, C., Xiao, Z., and Yang, S.: Increased atmospheric vapor pressure deficit reduces global vegetation growth, Sci. Adv., 5, eaax1396, https://doi.org/10.1126/sciadv.aax1396, 2019. a
Zacharias, S. and Wessolek, G.: Excluding Organic Matter Content from Pedotransfer Predictors of Soil Water Retention, Soil Sci. Soc. Am. J., 71, 43–50, https://doi.org/10.2136/sssaj2006.0098, 2007.
a
Zaehle, S., Sitch, S., Smith, B., and Hatterman, F.: Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics, Global Biogeochem. Cy., 19, GB3020, https://doi.org/10.1029/2004GB002395, 2005. a, b
Zhang, L. and Han, J.: Improving water retention capacity of an aeolian sandy soil with feldspathic sandstone, Sci. Rep.-UK, 9, 1–8, https://doi.org/10.1038/s41598-019-51257-y, 2019. a
Zhang, L., Zhou, D., Fan, J.-W., and Hu, Z.: Comparison of four light use efficiency models for estimating terrestrial gross primary production, Ecol. Model., 300, 30–39, https://doi.org/10.1016/j.ecolmodel.2015.01.001, 2015. a, b, c
Zhou, H., Yue, X., Lei, Y., Tian, C., Ma, Y., and Cao, Y.: Large Contributions of Diffuse Radiation to Global Gross Primary Productivity During 1981–2015, Global Biogeochem. Cy., 35, e06957, https://doi.org/10.1029/2021GB006957, 2021. a, b
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.: A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty, Hydrol. Earth Syst. Sci., 21, 1769–1790, https://doi.org/10.5194/hess-21-1769-2017, 2017. a
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon...
