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
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
Related authors
No articles found.
Gökben Demir, Andrew J. Guswa, Janett Filipzik, Johanna Clara Metzger, Christine Römermann, and Anke Hildebrandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-91, https://doi.org/10.5194/hess-2023-91, 2023
Preprint under review for HESS
Short summary
Short summary
Precipitation passing through the canopy in forest ecosystems creates recurring wetter and drier patches on the soil surface. The wetter locations may provide readily accessible water inputs to root structures. Here, we investigated the drivers of root water uptake patterns. We found that not below-canopy precipitation patterns, but tree species interactions and soil moisture variability, control root water uptake patterns in a natural forest.
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
Short summary
Short summary
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.
Christine Fischer, Johanna Clara Metzger, Goekben Demir, Thomas Wutzler, and Anke Hildebrandt
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-418, https://doi.org/10.5194/hess-2022-418, 2023
Revised manuscript under review for HESS
Short summary
Short summary
Canopies change how rain reaches the soil, with some spots receiving 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 high macroporosity, suggesting that canopy drip enhances bypassflow.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-222, https://doi.org/10.5194/hess-2022-222, 2022
Preprint under review for HESS
Short summary
Short summary
We analyzed the uncertainty of water transit time distribution (TTD) resulting from model input (interpolated tracer data) and structure (StorAge Selection (SAS) functions). We found that uncertainty was mainly associated with tracer data time interpolation, time-variant SAS function and low flow conditions. We convey the importance to characterize the specific uncertainty sources, and their combined effects on predicted TTDs, as it has relevant implications for both water quantity and quality.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Jan Pisek, Angela Erb, Lauri Korhonen, Tobias Biermann, Arnaud Carrara, Edoardo Cremonese, Matthias Cuntz, Silvano Fares, Giacomo Gerosa, Thomas Grünwald, Niklas Hase, Michal Heliasz, Andreas Ibrom, Alexander Knohl, Johannes Kobler, Bart Kruijt, Holger Lange, Leena Leppänen, Jean-Marc Limousin, Francisco Ramon Lopez Serrano, Denis Loustau, Petr Lukeš, Lars Lundin, Riccardo Marzuoli, Meelis Mölder, Leonardo Montagnani, Johan Neirynck, Matthias Peichl, Corinna Rebmann, Eva Rubio, Margarida Santos-Reis, Crystal Schaaf, Marius Schmidt, Guillaume Simioni, Kamel Soudani, and Caroline Vincke
Biogeosciences, 18, 621–635, https://doi.org/10.5194/bg-18-621-2021, https://doi.org/10.5194/bg-18-621-2021, 2021
Short summary
Short summary
Understory vegetation is the most diverse, least understood component of forests worldwide. Understory communities are important drivers of overstory succession and nutrient cycling. Multi-angle remote sensing enables us to describe surface properties by means that are not possible when using mono-angle data. Evaluated over an extensive set of forest ecosystem experimental sites in Europe, our reported method can deliver good retrievals, especially over different forest types with open canopies.
Manuela I. Brunner, Lieke A. Melsen, Andrew W. Wood, Oldrich Rakovec, Naoki Mizukami, Wouter J. M. Knoben, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 105–119, https://doi.org/10.5194/hess-25-105-2021, https://doi.org/10.5194/hess-25-105-2021, 2021
Short summary
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Ulrike Hiltner, Andreas Huth, and Rico Fischer
Biogeosciences Discuss., https://doi.org/10.5194/bg-2020-264, https://doi.org/10.5194/bg-2020-264, 2020
Revised manuscript not accepted
Short summary
Short summary
The aim of our study is to to analyze the consequences of elevated tree mortality on tropical forest dynamics and to provide a methodology that can reduce uncertainties in estimating biomass loss due to tree mortality at local and country levels.
Miao Jing, Rohini Kumar, Falk Heße, Stephan Thober, Oldrich Rakovec, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 24, 1511–1526, https://doi.org/10.5194/hess-24-1511-2020, https://doi.org/10.5194/hess-24-1511-2020, 2020
Short summary
Short summary
This study investigates the response of regional groundwater system to the climate change under three global warming levels (1.5, 2, and 3 °C) in a central German basin. A comprehensive uncertainty analysis is also presented. This study indicates that the variability of responses increases with the amount of global warming, which might affect the cost of managing the groundwater system.
Jessica Hetzer, Andreas Huth, Thorsten Wiegand, Hans Jürgen Dobner, and Rico Fischer
Biogeosciences, 17, 1673–1683, https://doi.org/10.5194/bg-17-1673-2020, https://doi.org/10.5194/bg-17-1673-2020, 2020
Short summary
Short summary
Due to limited accessibility in tropical regions, only small parts of the forest landscape can be surveyed in forest plots. Since there is an ongoing debate about how representative estimations based on samples are at larger scales, this study analyzes how many plots are needed to quantify the biomass of the entire South American tropical forest. Through novel computational and statistical investigations we show that the spatial plot positioning is crucial for continent-wide biomass estimations.
Johanna C. Metzger, Jens Schumacher, Markus Lange, and Anke Hildebrandt
Hydrol. Earth Syst. Sci., 23, 4433–4452, https://doi.org/10.5194/hess-23-4433-2019, https://doi.org/10.5194/hess-23-4433-2019, 2019
Short summary
Short summary
Variation in stemflow (rain water running down the stem) enhances the formation of flow hot spots at the forest floor. Investigating drivers based on detailed measurements, we find that forest structure affects stemflow, both for individual trees and small communities. Densely packed forest patches received more stemflow, due to a higher proportion of woody structure and canopy morphology adjustments, which increase the potential for flow path generation connecting crowns and soil.
Sophie Ehrhardt, Rohini Kumar, Jan H. Fleckenstein, Sabine Attinger, and Andreas Musolff
Hydrol. Earth Syst. Sci., 23, 3503–3524, https://doi.org/10.5194/hess-23-3503-2019, https://doi.org/10.5194/hess-23-3503-2019, 2019
Short summary
Short summary
This study shows quantitative and temporal offsets between nitrogen input and riverine output, using time series of three nested catchments in central Germany. The riverine concentrations show lagged reactions to the input, but at the same time exhibit strong inter-annual changes in the relationship between riverine discharge and concentration. The study found a strong retention of nitrogen that is dominantly assigned to a hydrological N legacy, which will affect future stream concentrations.
Stephan Thober, Matthias Cuntz, Matthias Kelbling, Rohini Kumar, Juliane Mai, and Luis Samaniego
Geosci. Model Dev., 12, 2501–2521, https://doi.org/10.5194/gmd-12-2501-2019, https://doi.org/10.5194/gmd-12-2501-2019, 2019
Short summary
Short summary
We present a model that aggregates simulated runoff along a river
(i.e. a routing model). The unique feature of the model is that it
can be run at multiple resolutions without any modifications to the
input data. The model internally (dis-)aggregates all input data to
the resolution given by the user. The model performance does not
depend on the chosen resolution. This allows efficient model
calibration at coarse resolution and subsequent model application at
fine resolution.
Naoki Mizukami, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar
Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, https://doi.org/10.5194/hess-23-2601-2019, 2019
Short summary
Short summary
We find that Nash–Sutcliffe (NSE)-based model calibrations result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. The use of Kling–Gupta efficiency (KGE) results in annual peak flow estimates that are better than from NSE, with only a slight degradation in performance with respect to other related metrics.
Anne Klosterhalfen, Alexander Graf, Nicolas Brüggemann, Clemens Drüe, Odilia Esser, María P. González-Dugo, Günther Heinemann, Cor M. J. Jacobs, Matthias Mauder, Arnold F. Moene, Patrizia Ney, Thomas Pütz, Corinna Rebmann, Mario Ramos Rodríguez, Todd M. Scanlon, Marius Schmidt, Rainer Steinbrecher, Christoph K. Thomas, Veronika Valler, Matthias J. Zeeman, and Harry Vereecken
Biogeosciences, 16, 1111–1132, https://doi.org/10.5194/bg-16-1111-2019, https://doi.org/10.5194/bg-16-1111-2019, 2019
Short summary
Short summary
To obtain magnitudes of flux components of H2O and CO2 (e.g., transpiration, soil respiration), we applied source partitioning approaches after Scanlon and Kustas (2010) and after Thomas et al. (2008) to high-frequency eddy covariance measurements of 12 study sites covering various ecosystems (croplands, grasslands, and forests) in different climatic regions. We analyzed the interrelations among turbulence, site characteristics, and the performance of both partitioning methods.
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, https://doi.org/10.5194/hess-23-171-2019, 2019
Short summary
Short summary
We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007, https://doi.org/10.5194/gmd-11-1989-2018, https://doi.org/10.5194/gmd-11-1989-2018, 2018
Vimal Mishra, Reepal Shah, Syed Azhar, Harsh Shah, Parth Modi, and Rohini Kumar
Hydrol. Earth Syst. Sci., 22, 2269–2284, https://doi.org/10.5194/hess-22-2269-2018, https://doi.org/10.5194/hess-22-2269-2018, 2018
Michael Peichl, Stephan Thober, Volker Meyer, and Luis Samaniego
Nat. Hazards Earth Syst. Sci., 18, 889–906, https://doi.org/10.5194/nhess-18-889-2018, https://doi.org/10.5194/nhess-18-889-2018, 2018
Short summary
Short summary
Crop yields are routinely derived from meteorological variables, especially temperature. However, the primary water source for plant growth (soil moisture) is neglected. In this study, the predictability of maize yield is investigated using soil moisture or meteorological variables in Germany. The effects of soil moisture dominate those of temperature and are time-dependent. For example, comparatively moist soil conditions in June reduce crop yields, while in August they increase yields.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
Short summary
Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Andreas Marx, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Niko Wanders, Matthias Zink, Eric F. Wood, Ming Pan, Justin Sheffield, and Luis Samaniego
Hydrol. Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, https://doi.org/10.5194/hess-22-1017-2018, 2018
Short summary
Short summary
Hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K). The multi-model ensemble results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean, while they increase in the Alpine and Northern regions. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Adaptation should make use of change and uncertainty information.
Chunjing Qiu, Dan Zhu, Philippe Ciais, Bertrand Guenet, Gerhard Krinner, Shushi Peng, Mika Aurela, Christian Bernhofer, Christian Brümmer, Syndonia Bret-Harte, Housen Chu, Jiquan Chen, Ankur R. Desai, Jiří Dušek, Eugénie S. Euskirchen, Krzysztof Fortuniak, Lawrence B. Flanagan, Thomas Friborg, Mateusz Grygoruk, Sébastien Gogo, Thomas Grünwald, Birger U. Hansen, David Holl, Elyn Humphreys, Miriam Hurkuck, Gerard Kiely, Janina Klatt, Lars Kutzbach, Chloé Largeron, Fatima Laggoun-Défarge, Magnus Lund, Peter M. Lafleur, Xuefei Li, Ivan Mammarella, Lutz Merbold, Mats B. Nilsson, Janusz Olejnik, Mikaell Ottosson-Löfvenius, Walter Oechel, Frans-Jan W. Parmentier, Matthias Peichl, Norbert Pirk, Olli Peltola, Włodzimierz Pawlak, Daniel Rasse, Janne Rinne, Gaius Shaver, Hans Peter Schmid, Matteo Sottocornola, Rainer Steinbrecher, Torsten Sachs, Marek Urbaniak, Donatella Zona, and Klaudia Ziemblinska
Geosci. Model Dev., 11, 497–519, https://doi.org/10.5194/gmd-11-497-2018, https://doi.org/10.5194/gmd-11-497-2018, 2018
Short summary
Short summary
Northern peatlands store large amount of soil carbon and are vulnerable to climate change. We implemented peatland hydrological and carbon accumulation processes into the ORCHIDEE land surface model. The model was evaluated against EC measurements from 30 northern peatland sites. The model generally well reproduced the spatial gradient and temporal variations in GPP and NEE at these sites. Water table depth was not well predicted but had only small influence on simulated NEE.
Simon Höllering, Jan Wienhöfer, Jürgen Ihringer, Luis Samaniego, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 203–220, https://doi.org/10.5194/hess-22-203-2018, https://doi.org/10.5194/hess-22-203-2018, 2018
Short summary
Short summary
Hydrological fingerprints are introduced as response targets for sensitivity analysis and combined with a conventional approach using streamflow data for a temporally resolved sensitivity analysis. The joint benefit of both approaches is presented for several headwater catchments. The approach allows discerning a clarified pattern for parameter influences pinpointed to diverse response characteristics and detecting even slight regional differences.
Martin Schrön, Markus Köhli, Lena Scheiffele, Joost Iwema, Heye R. Bogena, Ling Lv, Edoardo Martini, Gabriele Baroni, Rafael Rosolem, Jannis Weimar, Juliane Mai, Matthias Cuntz, Corinna Rebmann, Sascha E. Oswald, Peter Dietrich, Ulrich Schmidt, and Steffen Zacharias
Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, https://doi.org/10.5194/hess-21-5009-2017, 2017
Short summary
Short summary
A field-scale average of near-surface water content can be sensed by cosmic-ray neutron detectors. To interpret, calibrate, and validate the integral signal, it is important to account for its sensitivity to heterogeneous patterns like dry or wet spots. We show how point samples contribute to the neutron signal based on their depth and distance from the detector. This approach robustly improves the sensor performance and data consistency, and even reveals otherwise hidden hydrological features.
Luis Samaniego, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Matthias Zink, Niko Wanders, Stephanie Eisner, Hannes Müller Schmied, Edwin H. Sutanudjaja, Kirsten Warrach-Sagi, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 4323–4346, https://doi.org/10.5194/hess-21-4323-2017, https://doi.org/10.5194/hess-21-4323-2017, 2017
Short summary
Short summary
We inspect the state-of-the-art of several land surface (LSMs) and hydrologic models (HMs) and show that most do not have consistent and realistic parameter fields for land surface geophysical properties. We propose to use the multiscale parameter regionalization (MPR) technique to solve, at least partly, the scaling problem in LSMs/HMs. A general model protocol is presented to describe how MPR can be applied to a specific model.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
Short summary
Short summary
In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
Short summary
Short summary
The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Nicolas Dalla Valle, Karin Potthast, Stefanie Meyer, Beate Michalzik, Anke Hildebrandt, and Thomas Wutzler
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-336, https://doi.org/10.5194/hess-2017-336, 2017
Manuscript not accepted for further review
Short summary
Short summary
Dual permeability models are an important tool to simulate water movement in soils and can be used to assess the risk of groundwater contamination by pesticides or the risk of flooding after strong precipitation events. However, their application is often hampered by the large amount of data they require. We developed a method to run this kind of models based on mostly just soil water content measurements, which will hopefully increase their usage and improve environmental risk assessment.
Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
Short summary
Short summary
Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.
Anne F. Van Loon, Rohini Kumar, and Vimal Mishra
Hydrol. Earth Syst. Sci., 21, 1947–1971, https://doi.org/10.5194/hess-21-1947-2017, https://doi.org/10.5194/hess-21-1947-2017, 2017
Short summary
Short summary
Summer 2015 was extremely dry in Europe, hampering groundwater supply to irrigation and drinking water. For effective management, the groundwater situation should be monitored in real time, but data are not available. We tested two methods to estimate groundwater in near-real time, based on satellite data and using the relationship between rainfall and historic groundwater levels. The second method gave a good spatially variable representation of the 2015 groundwater drought in Europe.
Matthias Zink, Rohini Kumar, Matthias Cuntz, and Luis Samaniego
Hydrol. Earth Syst. Sci., 21, 1769–1790, https://doi.org/10.5194/hess-21-1769-2017, https://doi.org/10.5194/hess-21-1769-2017, 2017
Short summary
Short summary
We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of hydro-meteorological variables for Germany. Here we describe the derivation of national-scale parameter sets and analyze the uncertainty of the estimated hydrologic variables (focusing on the parametric uncertainty). Our study highlights the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
Falk Heße, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 549–570, https://doi.org/10.5194/hess-21-549-2017, https://doi.org/10.5194/hess-21-549-2017, 2017
Short summary
Short summary
Travel-time distributions are a comprehensive tool for the characterization of hydrological systems. In our study, we used data that were simulated by virtue of a well-established hydrological model. This gave us a very large yet realistic dataset, both in time and space, from which we could infer the relative impact of different factors on travel-time behavior. These were, in particular, meteorological (precipitation), land surface (land cover, leaf-area index) and subsurface (soil) properties.
Anke Hildebrandt, Axel Kleidon, and Marcel Bechmann
Hydrol. Earth Syst. Sci., 20, 3441–3454, https://doi.org/10.5194/hess-20-3441-2016, https://doi.org/10.5194/hess-20-3441-2016, 2016
Short summary
Short summary
This theoretical paper describes the energy fluxes and dissipation along the flow paths involved in root water uptake, an approach that is rarely taken. We show that this provides useful additional insights for understanding the biotic and abiotic impediments to root water uptake. This approach shall be applied to explore efficient water uptake strategies and help locate the limiting processes in the complex soil–plant–atmosphere system.
Simon Höllering, Jürgen Ihringer, Luis Samaniego, and Erwin Zehe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-249, https://doi.org/10.5194/hess-2016-249, 2016
Preprint withdrawn
Remko C. Nijzink, Luis Samaniego, Juliane Mai, Rohini Kumar, Stephan Thober, Matthias Zink, David Schäfer, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, https://doi.org/10.5194/hess-20-1151-2016, 2016
Short summary
Short summary
The heterogeneity of landscapes in river basins strongly affects the hydrological response. In this study, the distributed mesoscale Hydrologic Model (mHM) was equipped with additional processes identified by landscapes within one modelling cell. Seven study catchments across Europe were selected to test the value of this additional sub-grid heterogeneity. In addition, the models were constrained based on expert knowledge. Generally, the modifications improved the representation of low flows.
Rohini Kumar, Jude L. Musuuza, Anne F. Van Loon, Adriaan J. Teuling, Roland Barthel, Jurriaan Ten Broek, Juliane Mai, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 20, 1117–1131, https://doi.org/10.5194/hess-20-1117-2016, https://doi.org/10.5194/hess-20-1117-2016, 2016
Short summary
Short summary
In a maiden attempt, we performed a multiscale evaluation of the widely used SPI to characterize local- and regional-scale groundwater (GW) droughts using observations at 2040 groundwater wells in Germany and the Netherlands. From this data-based exploratory analysis, we provide sufficient evidence regarding the inability of the SPI to characterize GW drought events, and stress the need for more GW observations and accounting for regional hydrogeological characteristics in GW drought monitoring.
O. Rakovec, A. H. Weerts, J. Sumihar, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 2911–2924, https://doi.org/10.5194/hess-19-2911-2015, https://doi.org/10.5194/hess-19-2911-2015, 2015
Short summary
Short summary
This is the first analysis of the asynchronous ensemble Kalman filter in hydrological forecasting. The results of discharge assimilation into a hydrological model for the catchment show that including past predictions and observations in the filter improves model forecasts. Additionally, we show that elimination of the strongly non-linear relation between soil moisture and assimilated discharge observations from the model update becomes beneficial for improved operational forecasting.
M. Guderle and A. Hildebrandt
Hydrol. Earth Syst. Sci., 19, 409–425, https://doi.org/10.5194/hess-19-409-2015, https://doi.org/10.5194/hess-19-409-2015, 2015
Short summary
Short summary
This paper is the result of a numerical study to test the application of water balance methods for estimating evapotranspiration and water extraction profiles based on measured soil water content data. The advantage of the tested methods is that they do not rely on a priori information of any root distribution parameters. Our research shows the potential of water balance methods for derivation of water extraction profiles, but their application may be challenging in realistic conditions.
A. Piayda, M. Dubbert, C. Rebmann, O. Kolle, F. Costa e Silva, A. Correia, J. S. Pereira, C. Werner, and M. Cuntz
Biogeosciences, 11, 7159–7178, https://doi.org/10.5194/bg-11-7159-2014, https://doi.org/10.5194/bg-11-7159-2014, 2014
S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 18, 4861–4870, https://doi.org/10.5194/hess-18-4861-2014, https://doi.org/10.5194/hess-18-4861-2014, 2014
M. Bechmann, C. Schneider, A. Carminati, D. Vetterlein, S. Attinger, and A. Hildebrandt
Hydrol. Earth Syst. Sci., 18, 4189–4206, https://doi.org/10.5194/hess-18-4189-2014, https://doi.org/10.5194/hess-18-4189-2014, 2014
H. V. Gupta, C. Perrin, G. Blöschl, A. Montanari, R. Kumar, M. Clark, and V. Andréassian
Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, https://doi.org/10.5194/hess-18-463-2014, 2014
Related subject area
Hydrology
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Simulation of crop yield using the global hydrological model H08 (crp.v1)
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3
Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs
Computation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)
Inishell 2.0: semantically driven automatic GUI generation for scientific models
Irrigation quality and management determine salinization in Israeli olive orchards
Implementing the Water, HEat and Transport model in GEOframe (WHETGEO-1D v.1.0): algorithms, informatics, design patterns, open science features, and 1D deployment
HydroPy (v1.0): a new global hydrology model written in Python
How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model
GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models
SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes
POET (v0.1): speedup of many-core parallel reactive transport simulations with fast DHT lookups
Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States
Cosmic-Ray neutron Sensor PYthon tool (crspy 1.2.1): an open-source tool for the processing of cosmic-ray neutron and soil moisture data
SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models
DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
Short summary
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-285, https://doi.org/10.5194/gmd-2022-285, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Simultaneously simulating food production and the requirements and availability of water resources in a spatial explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water–energy-land nexus studies in the future.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Short summary
A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
Short summary
Short summary
We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
Short summary
Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
Short summary
Short summary
This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
Short summary
Short summary
We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
Short summary
Short summary
We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
Short summary
Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
Short summary
Short summary
A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
Short summary
Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
Short summary
Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
Short summary
Short summary
Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
Short summary
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
Short summary
Short summary
This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
Short summary
Short summary
The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary
Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
Short summary
Short summary
Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
Short summary
Short summary
Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Sandra Hellmers and Peter Fröhle
Geosci. Model Dev., 15, 1061–1077, https://doi.org/10.5194/gmd-15-1061-2022, https://doi.org/10.5194/gmd-15-1061-2022, 2022
Short summary
Short summary
A hydrological method to compute backwater effects in surface water streams and on adjacent lowlands caused by mostly complex flow control systems is presented. It enables transfer of discharges to water levels and calculation of backwater volume routing along streams and lowland areas by balancing water level slopes. The developed, implemented and evaluated method extends the application range of hydrological models significantly for flood-routing simulation in backwater-affected catchments.
Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378, https://doi.org/10.5194/gmd-15-365-2022, https://doi.org/10.5194/gmd-15-365-2022, 2022
Short summary
Short summary
Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Vladimir Mirlas, Yaakov Anker, Asher Aizenkod, and Naftali Goldshleger
Geosci. Model Dev., 15, 129–143, https://doi.org/10.5194/gmd-15-129-2022, https://doi.org/10.5194/gmd-15-129-2022, 2022
Short summary
Short summary
Salinization owing to irrigation water quality causes soil degradation and soil fertility reduction that with poor drainage conditions impede plant development and manifest in economic damage. This study provided a soil salting process evaluation procedure through a combination of soil salinity monitoring, field experiments, remote sensing, and unsaturated soil profile saline water movement modeling. The modeling results validated the soil salinization danger from using brackish irrigation.
Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, https://doi.org/10.5194/gmd-15-75-2022, 2022
Short summary
Short summary
This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
Tobias Stacke and Stefan Hagemann
Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, https://doi.org/10.5194/gmd-14-7795-2021, 2021
Short summary
Short summary
HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-425, https://doi.org/10.5194/gmd-2021-425, 2021
Revised manuscript accepted for GMD
Short summary
Short summary
Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows selecting the most influential input parameters but it has to be adapted to spatial modelling. This study will (i) identify relevant methods that can be transposed to any hydrological and water quality models, (ii) improve the fate of pesticides knowledge.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Marco Toffolon, Luca Cortese, and Damien Bouffard
Geosci. Model Dev., 14, 7527–7543, https://doi.org/10.5194/gmd-14-7527-2021, https://doi.org/10.5194/gmd-14-7527-2021, 2021
Short summary
Short summary
The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0 °C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.
Marco De Lucia, Michael Kühn, Alexander Lindemann, Max Lübke, and Bettina Schnor
Geosci. Model Dev., 14, 7391–7409, https://doi.org/10.5194/gmd-14-7391-2021, https://doi.org/10.5194/gmd-14-7391-2021, 2021
Short summary
Short summary
POET is a parallel reactive transport simulator which implements a mechanism to store and reuse previous results of geochemical simulations through distributed hash tables. POET parallelizes chemistry using a master/worker design with noncontiguous grid partitions to maximize its efficiency and load balance on shared-memory machines and compute clusters.
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, https://doi.org/10.5194/gmd-14-7223-2021, 2021
Short summary
Short summary
Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem
Geosci. Model Dev., 14, 7287–7307, https://doi.org/10.5194/gmd-14-7287-2021, https://doi.org/10.5194/gmd-14-7287-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
Marco Dal Molin, Dmitri Kavetski, and Fabrizio Fenicia
Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, https://doi.org/10.5194/gmd-14-7047-2021, 2021
Short summary
Short summary
This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, https://doi.org/10.5194/gmd-14-6893-2021, 2021
Short summary
Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
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...
