Articles | Volume 14, issue 12
https://doi.org/10.5194/gmd-14-7309-2021
© Author(s) 2021. 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-14-7309-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
30 Nov 2021
Development and technical paper |
| 30 Nov 2021
| 30 Nov 2021
Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations
Shannon de Roos
CORRESPONDING AUTHOR
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, 3001, Belgium
Gabriëlle J. M. De Lannoy
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, 3001, Belgium
Dirk Raes
Department of Earth and Environmental Sciences, KU Leuven, Heverlee, 3001, Belgium
Related authors
Louise Busschaert, Shannon de Roos, Wim Thiery, Dirk Raes, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 3731–3752, https://doi.org/10.5194/hess-26-3731-2022, https://doi.org/10.5194/hess-26-3731-2022, 2022
Short summary
Short summary
Increasing amounts of water are used for agriculture. Therefore, we looked into how irrigation requirements will evolve under a changing climate over Europe. Our results show that, by the end of the century and under high emissions, irrigation water will increase by 30 % on average compared to the year 2000. Also, the irrigation requirement is likely to vary more from 1 year to another. However, if emissions are mitigated, these effects are reduced.
Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, https://doi.org/10.5194/nhess-24-445-2024, 2024
Short summary
Short summary
With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Matzler, and Hans Lievens
EGUsphere, https://doi.org/10.5194/egusphere-2023-2927, https://doi.org/10.5194/egusphere-2023-2927, 2023
Short summary
Short summary
To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation, but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
Short summary
Short summary
The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Short summary
We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
Short summary
Short summary
Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
Short summary
Short summary
In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
Louise Busschaert, Shannon de Roos, Wim Thiery, Dirk Raes, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 3731–3752, https://doi.org/10.5194/hess-26-3731-2022, https://doi.org/10.5194/hess-26-3731-2022, 2022
Short summary
Short summary
Increasing amounts of water are used for agriculture. Therefore, we looked into how irrigation requirements will evolve under a changing climate over Europe. Our results show that, by the end of the century and under high emissions, irrigation water will increase by 30 % on average compared to the year 2000. Also, the irrigation requirement is likely to vary more from 1 year to another. However, if emissions are mitigated, these effects are reduced.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
Short summary
Short summary
Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
Short summary
Short summary
Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
Short summary
Short summary
In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
Short summary
Short summary
The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Gabriëlle J. M. De Lannoy and Rolf H. Reichle
Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, https://doi.org/10.5194/hess-20-4895-2016, 2016
Short summary
Short summary
The SMOS mission provides various various products to estimate soil moisture. This paper evaluates the performance of assimilating either Level-1-based multi-angle brightness temperature (Tb) observations, Level-1-based single-angle Tb observations, or Level 2 soil moisture retrievals, into the NASA Catchment land surface model.
Related subject area
Biogeosciences
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes
biospheremetrics v1.0.1: An R package to calculate two complementary terrestrial biosphere integrity indicators: human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Optimal enzyme allocation leads to the constrained enzyme hypothesis: The Soil Enzyme Steady Allocation Model (SESAM v3.1)
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Preprint under review for GMD
Short summary
Short summary
We adapt a fire-behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
Short summary
Short summary
Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
EGUsphere, https://doi.org/10.5194/egusphere-2023-2503, https://doi.org/10.5194/egusphere-2023-2503, 2023
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract >25 % of the potential pre-industrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of land, water, and fertilizer use, as well as climate change.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Elin Ristorp Aas, Heleen A. de Wit, and Terje Koren Berntsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2069, https://doi.org/10.5194/egusphere-2023-2069, 2023
Short summary
Short summary
By including microbial processes in soil models, we learn about how the soil system interacts with its environment, and responds to climate change. We present a soil process model, MIMICS+, able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also found that when adding nitrogen, the relationship between soil microbes changed notably. Coupling the model to a vegetation model, will allow further investigation of these mechanisms.
Yannek Käber, Florian Hartig, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2114, https://doi.org/10.5194/egusphere-2023-2114, 2023
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed with forest inventory data, and climatic effects are challenging to capture.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
EGUsphere, https://doi.org/10.5194/egusphere-2023-2003, https://doi.org/10.5194/egusphere-2023-2003, 2023
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that based on satellite records represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1492, https://doi.org/10.5194/egusphere-2023-1492, 2023
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behavior at low microbial biomass led us formulate the constrained enzyme hypothesis. We now can better describe how microbial mediated linkages of elemental fluxes adapt across decades.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
Short summary
Short summary
Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Cited articles
Abedinpour, M., Sarangi, A., Rajput, T. B. S., Singh, M., Pathak, H., and Ahmad, T.: Performance evaluation of AquaCrop model for maize crop in a semi-arid environment, Agr. Water Manage., 110, 55–66, https://doi.org/10.1016/j.agwat.2012.04.001, 2012.
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements, FAO Irrigation and drainage paper 56, FAO, Rome, Italy, ISBN 92-5-104219-5, 1998.
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P. J., Rötter, R. P., Cammarano, D., Brisson, N., Basso, B., Martre, P., Aggarwal, P. K., Angulo, C., Bertuzzi, P., Biernath, C., Challinor, A. J., Doltra, J., Gayler, S., Goldberg, R., Grant, R., Heng, L., Hooker, J., Hunt, L. A., Ingwersen, J., Izaurralde, R. C., Kersebaum, K. C., Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G., Olesen, J. E., Osborne, T. M., Palosuo, T., Priesack, E., Ripoche, D., Semenov, M. A., Shcherbak, I., Steduto, P., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Travasso, M., Waha, K., Wallach, D., White, J. W., Williams, J. R., and Wolf, J.: Uncertainty in simulating wheat yields under climate change, Nat. Clim. Change, 3, 827–832, https://doi.org/10.1038/nclimate1916, 2013.
Aznar-Sánchez, J. A., Piquer-Rodríguez, M., Velasco-Muñoz, J. F., and Manzano-Agugliaro, F.: Worldwide research trends on sustainable land use in agriculture, Land Use Policy, 87, 104069, https://doi.org/10.1016/j.landusepol.2019.104069, 2019.
Balkovic, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov, N., Obersteiner, M., Stürmer, B., and Xiong, W.: Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation, Agr. Syst., 120, 61–75, https://doi.org/10.1016/j.agsy.2013.05.008, 2013.
Bauer-Marschallinger, B., Freeman, V., Cao, S., Paulik, C., Schaufler, S., Stachl, T., Modanesi, S., Massari, C., Ciabatta L., Brocca L., and Wagner, W.: Toward global soil moisture monitoring with Sentinel-1: Harnessing assets and overcoming obstacles, IEEE T. Geosci. Remote, 57, 520–539, https://doi.org/10.1109/TGRS.2018.2858004, 2018.
Boogaard, H., Wolf, J., Supit, I., Niemeyer, S., and van Ittersum, M.: A
regional implementation of WOFOST for calculating yield gaps of autumn-sown
wheat across the European Union, Field Crop. Res., 143, 130–142,
https://doi.org/10.1016/j.fcr.2012.11.005, 2013.
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but significant improvements for Earth-gridded data sets, ISPRS Int. Geo-Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012.
Büttner, G.: CORINE land cover and land cover change products, Land use and land cover mapping in Europe, Springer, Dordrecht, the Netherlands, 55–74, https://doi.org/10.1007/978-94-007-7969-3, 2014.
Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., and Piguet, B.: In situ soil moisture observations for the CAL/VAL of SMOS: the SMOSMANIA network, International Geoscience and Remote Sensing Symposium, IGARSS, Barcelona, Spain, 23–28 July 2007, 1196–1199, https://doi.org/10.1109/IGARSS.2007.4423019, 2007.
Chaubell, M. J., Yueh, S. H., Dunbar, R. S., Colliander, A., Chen, F., Chan, S. K., Entekhabi, D., Bindlish, R., O'Neill, P. E., Asanuma, J., Berg, A. A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H., Martinez-Fernandez, J., Seyfried, M., Starks, P. J., Su, Z., Thibeault, M., and Walker, J.: Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture, IEEE T. Geosci. Remote, 58, 3894–3905, https://doi.org/10.1109/TGRS.2019.2959239, 2020.
Copernicus Land Monitoring Service: CORINE land cover (CLC) 2012, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=mapview (last access: 5 September 2019), 2018.
Copernicus Global Land Service: Dry matter productivity 1km product version 2, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/global/products/dmp (last access: 2 February 2020), 2019a.
Copernicus Global Land Service: Surface soil moisture, European Environment Agency (EEA) [data set], available at: https://land.copernicus.eu/global/products/ssm (last access: 2 June 2020), 2019b.
Dale, A., Fant, C., Strzepek, K., Lickley, M., and Solomon, S.: Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa, Earths Future, 5, 337–353, https://doi.org/10.1002/2017EF000539, 2017.
De Lannoy, G. J. M. and Reichle, R. H.: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model, Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, 2016.
De Lannoy, G. J., Koster, R. D., Reichle, R. H., Mahanama, S. P., and Liu, Q.: An updated treatment of soil texture and associated hydraulic properties in a global land modeling system, J. Adv. Model. Earth Sy., 6, 957–979, https://doi.org/10.1002/2014MS000330, 2014.
De Lannoy, G. J. M., de Rosnay, P., and Reichle, R. H.: Soil Moisture Data
Assimilation, in: Handbook of Hydrometeorological Ensemble Forecasting, edited
by: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H., and Schaake, J. C., Springer Verlag, New York, USA, 43 pp., https://doi.org/10.1007/978-3-642-40457-3_32-1, 2015.
de Roos, S., De Lannoy, G., and Raes, D.: source code and datasets for gmd-2021-98, Version 1, Zenodo [data set], https://doi.org/10.5281/zenodo.4770738, 2021.
De Wit, A. D. and Van Diepen, C. A.: Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts, Agr. Forest Meteorol., 146, 38–56, https://doi.org/10.1016/j.agrformet.2007.05.004, 2007.
Di Paola, A., Valentini, R., and Santini, M.: An overview of available crop growth and yield models for studies and assessments in agriculture, J. Sci. Food Agr., 96, 709–714, https://doi.org/10.1002/jsfa.7359, 2016.
Dirmeyer, P. and Oki, T.: The Second Global Soil Wetness project (GSWP-2)
Science 2 and Implementation Plan, International GEWEX Project Office Publication (IGPO), Columbia, Md, IGPO Publication Series No. 37, 64 pp.,
2002.
Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T.: The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675–1698, https://doi.org/10.5194/hess-15-1675-2011, 2011.
Elliott, J., Müller, C., Deryng, D., Chryssanthacopoulos, J., Boote, K. J., Büchner, M., Foster, I., Glotter, M., Heinke, J., Iizumi, T., Izaurralde, R. C., Mueller, N. D., Ray, D. K., Rosenzweig, C., Ruane, A. C., and Sheffield, J.: The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0), Geosci. Model Dev., 8, 261–277, https://doi.org/10.5194/gmd-8-261-2015, 2015.
Entekhabi, D., Yueh, S., O'Neill, P., Kellogg, K. H., Allen, A., Bindlish, R., Brown, M., Chan, S., Colliander, A., Crow, W. T, Das, N., De Lannoy, G., Dunbar, R. S., Edelstein, W. N., Entin, J. K., Escobar, V., Goodman, S. D., Jackson, T. J., Jai, B., Johnson, J., Kim, E., Kim, S., Kimball, J., Koster, R. D., Leon, A., McDonald, K. C., Moghaddam, M., Mohammed, P., Moran, S., Njoku, E. G., Piepmeier, J. R., Reichle, R., Rogez, F., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., Van Zyl, J., Weiss, B., and West, R.: SMAP Handbook–soil moisture active passive: Mapping soil moisture and freeze/thaw from space, JPL publication, Pasadena, California USA, 192 pp., JPL 400-1567, 2014.
FAO: The future of food and agriculture–Trends and challenges, Annual Report, FAO, Rome, Italy, ISBN 978-92-5-109551-5, 2017.
FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database v 1.2, Food and Agricultural Organization [data set], Rome, available at: http://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/ (last accessed: 30 August 2019), 2012.
Feddes, R. A.: Simulation of field water use and crop yield, in: Simulation
of plant growth and crop production, edited by: Penning de Vries, F. W. T. and van Laar H. H., Pudoc, Wageningen, the Netherlands, 194–209, ISBN 9789022008096, 1982.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., and Zaks, D. P. M.: Solutions for a cultivated planet, Nature, 478, 337–342, https://doi.org/10.1038/nature10452, 2011.
Folberth, C., Elliott, J., Müller, C., Balkovič, J., Chryssanthacopoulos,
J., Izaurralde, R. C., Jones, C. D., Khabarov, N., Liu, W., Reddy, A., Schmid, E., Skalský, R., Yang, H., Arneth, A., Ciais, P., Deryng, D., Lawrence P. J., Olin, S., Pugh, T. A. M., Ruance, A.C., and Wang, X.: Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble, PLoS ONE, 14, e0221862, https://doi.org/10.1371/journal.pone.0221862, 2019.
Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J. A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., and Mamani, J.: Simulating yield response of quinoa to water availability with AquaCrop, Agron. J., 101, 499–508, https://doi.org/10.2134/agronj2008.0137s, 2009.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gonzalez-Zamora, A., Sanchez, N., Pablos, M., and Martinez-Fernandez, J.: CCI soil moisture assessment with smos soil moisture and in situ data under different environmental conditions and spatial scales in Spain, Remote Sens. Environ., 225, 469–482, https://doi.org/10.1016/j.rse.2018.02.010, 2018.
GEWEX/CEOS/GCOS-TOPC/GEO/GTN-H: International Soil Moisture Network, GEWEX/CEOS/GCOS-TOPC/GEO/GTN-H [data sets], available at: https://ismn.geo.tuwien.ac.at/en/ (last access: 29 June 2020), 2011.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/VJAFPLI1CSIV (data available at: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, last access: 24 May 2019), 2015a.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavg1_2d_lnd_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Land Surface Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, USA, https://doi.org/10.5067/RKPHT8KC1Y1T, 2015b (data available at: https://disc.gsfc.nasa.gov/datasets?project=MERRA-2, last access: 24 May 2019).
Gruber, A., De Lannoy, G., Albergel, C., Al-Yaari, A., Brocca, L., Calvet, J.-C., Colliander, A., Cosh, M., Crow, W., Dorigo, W., Draper, C., Hirschi, M., Kerr, Y., Konings, A., Lahoz, W., McColl, K., Montzka, C., Muñoz-Sabater, J., Peng, J., Reichle, R. M., Richaume, P., Rüdiger C., Scanlon T., van der Schalie R., Wigneron J.-P., and Wagner, W.: Validation practices for satellite soil moisture retrievals: What are (the) errors?, Remote Sens. Environ., 244, 111806, https://doi.org/10.1016/j.rse.2020.111806, 2020.
Han, C., Zhang, B., Chen, H., Liu, Y., and Wei, Z.: Novel approach of upscaling the FAO AquaCrop model into regional scale by using distributed crop parameters derived from remote sensing data, Agr. Water Manage., 240, 106288, https://doi.org/10.1016/j.agwat.2020.106288, 2020.
Hsiao, T. C., Heng, L., Steduto, P., Rojas-Lara, B., Raes, D., and Fereres, E.: AquaCrop – The FAO Crop Model to Simulate Yield Response to Water: III. Parameterization and Testing for Maize, Agron. J., 101, 448–59, https://doi.org/10.2134/agronj2008.0218s, 2009.
Huang, J., Scherer, L., Lan, K., Chen, F., and Thorp, K. R.: Advancing the application of a model-independent open-source geospatial tool for national-scale spatiotemporal simulations, Environ. Modell. Softw., 119, 374–378, https://doi.org/10.1016/j.envsoft.2019.07.003, 2019.
Iizumi, T., Shin, Y., Kim, W., Kim, M., and Choi, J.: Global crop yield forecasting using seasonal climate information from a multi-model ensemble, Climate Services, 11, 13–23, https://doi.org/10.1016/j.cliser.2018.06.003, 2018.
Jensen, K. H. and Refsgaard, J. C.: HOBE: The Danish hydrological observatory. Vadose Zone J., 17, 1–24, https://doi.org/10.2136/vzj2018.03.0059, 2018.
Kopittke, P. M., Menzies, N. W., Wang, P., McKenna, B. A., and Lombi, E.: Soil and the intensification of agriculture for global food security, Environ. Int., 132, 105078, https://doi.org/10.1016/j.envint.2019.105078, 2019.
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma, M. J.: On the nature of soil moisture in land surface models, J. Climate, 22, 4322–4335, https://doi.org/10.1175/2009JCLI2832.1, 2009.
Li, B., Rodell, M., Kumar, S., Beaudoing, H. K., Getirana, A., Zaitchik, B. F., de Goncalves, L.G., Cossetin, C., Bhanja, S., Mukherjee, A. and Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I. B., Daira, D., Bila, M., De Lannoy, G., Mocko, D., Steele-Dunne, S. C., Save, H., and Bettadpur, S.: Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges, Water Resour. Res., 55, 7564–7586, https://doi.org/10.1029/2018WR024618, 2019.
Liu, J., Williams, J. R., Zehnder, A. J., and Yang, H.: GEPIC–modelling wheat yield and crop water productivity with high resolution on a global scale, Agr. Syst., 94, 478–493, https://doi.org/10.1016/j.agsy.2006.11.019, 2007.
Lorite, I. J., García-Vila, M., Santos, C., Ruiz-Ramos, M., and Fereres, E.: AquaData and AquaGIS: two computer utilities for temporal and spatial simulations of water-limited yield with AquaCrop, Comput. Electron. Agr., 96, 227–237, https://doi.org/10.1016/j.compag.2013.05.010, 2013.
Mahanama, S. P., Koster, R. D., Walker, G. K., Tackacs, L., Reichle, R. H., De
Lannoy, G., Liu, Q., Zhao, B., and Suarez, M.: Land Boundary Conditions for
the Goddard Earth Observing System Model Version 5 (GEOS-5) Climate Modeling
System – Recent Updates and Data File Descriptions, NASA Technical Report
Series on Global Modeling and Data Assimilation 104606, Vol. 39, NASA Goddard Space Flight Center, MD, USA, 51 pp., 2015.
Maniruzzaman, M., Talukder, M. S. U., Khan, M. H., Biswas, J. C., and Nemes, A.: Validation of the AquaCrop model for irrigated rice production under varied water regimes in Bangladesh, Agr. Water Manage., 159, 331–340, https://doi.org/10.1016/j.agwat.2015.06.022, 2015.
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017.
Mladenova, I. E., Bolten, J. D., Crow, W. T., Sazib, N., Cosh, M. H.,
Tucker, C. J., and Reynolds, C.: Evaluating the operational application of
SMAP for global agricultural drought monitoring. IEEE J. Sel. Top. Appl., 12, 3387–3397, https://doi.org/10.1109/JSTARS.2019.2923555, 2019.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cy., 22, https://doi.org/10.1029/2007GB002947, 2008.
Monteith, J. L.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, https://doi.org/10.2307/2401901, 1972.
Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., Iizumi, T., Izaurralde, R. C., Jones, C., Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Ray, D. K., Reddy, A., Rosenzweig, C., Ruane, A. C., Sakurai, G., Schmid, E., Skalsky, R., Song, C. X., Wang, X., de Wit, A., and Yang, H.: Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017, 2017.
Nichols, J., Kang, S., Post, W., Wang, D., Bandaru, V., Manowitz, D., Zhang, X., and Izaurralde, R.: HPC-EPIC for high resolution simulations of environmental and sustainability assessment, Comput. Electron. Agr., 79, 112–115, https://doi.org/10.1016/j.compag.2011.08.012, 2011.
O'Neill, P., Bindlish, R., Chan, S., Njoku, E., and Jackson, T.: Algorithm Theoretical Basis Document. Level 2 & 3 Soil Moisture (Passive) Data Products, NASA Jet Propulsion Laboratory, California, USA, 2018.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and Chaubell, J.: SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 4, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado, USA, https://doi.org/10.5067/Q8J8E3A89923, 2020 (data available at: https://nsidc.org/data/SPL2SMP_E/versions/4, last access: 14 November 2020).
Pingali, P. L.: Green revolution: impacts, limits, and the path ahead, P. Natl. Acad. Sci. USA, 109, 12302–12308, https://doi.org/10.1073/pnas.0912953109, 2012.
Raes, D. and Vanuytrecht, E.: Food production and water: constraints and solutions for the future, Meded. Zitt. K. Acad. Overzeese Wet., 63, 265–288, https://doi.org/10.5281/zenodo.3894493, 2017.
Raes, D., Geerts, S., Kipkorir, E., Wellens, J., and Sahli, A: Simulation of yield decline as a result of water stress with a robust soil water balance model, Agr. Water Manage., 81, 335–357, https://doi.org/10.1016/j.agwat.2005.04.006, 2006.
Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop –
the FAO crop model to simulate yield response to water: II. Main algorithms and software description, Agron. J., 101, 438–447, https://doi.org/10.2134/agronj2008.0140s, 2009.
Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop version 6.0-6.1 – Chapt. 3: calculation procedures, FAO of the UN, Rome, Italy, 25 pp., 2018.
Razzaghi, F., Zhou, Z., Andersen, M. N., and Plauborg, F.: Simulation of potato yield in temperate condition by the AquaCrop model, Agr. Water Manage., 191, 113–12, https://doi.org/10.1016/j.agwat.2017.06.008, 2017.
Reichle, R. H. and Koster, R. D.: Bias reduction in short records of satellite
soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938,
2004.
Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J.,
Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M.,
Kolassa, J., Mahanama, S. P., Prueger, J., Starks, P., and Walker, J. P.: Version 4 of the SMAP Level-4 Soil Moisture algorithm and data product, J. Adv. Model. Earth Sy., 11, 3106–3130, https://doi.org/10.1029/2019MS001729, 2019.
Resop, J. P., Fleisher, D. H., Wang, Q., Timlin, D. J., and Reddy, V. R.: Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units, Comput. Electron. Agr., 89, 51–61, https://doi.org/10.1016/j.compag.2012.08.001, 2012.
Ritchie, J. T.: Model for predicting evaporation from a row crop with incomplete cover, Water Resour. Res., 8, 1204–1213, https://doi.org/10.1029/WR008i005p01204, 1972.
Roerink, G. J., Bojanowski, J. S., De Wit, A. J. W., Eerens, H., Supit, I., Leo, O., and Boogaard, H. L.: Evaluation of MSG-derived global radiation estimates for application in a regional crop model, Agr. Forest Meteorol., 160, 36–47, https://doi.org/10.1016/j.agrformet.2012.02.006, 2012.
Sallah, A. H. M., Tychon, B., Piccard, I., Gobin, A., Van Hoolst, R., Djaby, B., and Wellens, J.: Batch-processing of AquaCrop plug-in for rainfed maize using satellite derived Fractional Vegetation Cover data, Agr. Water Manage., 217, 346–355, https://doi.org/10.1016/j.agwat.2019.03.016, 2019.
Sandhu, R. and Irmak, S.: Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation, Agr. Water Manage., 223, 105687, https://doi.org/10.1016/j.agwat.2019.105687, 2019.
Shangguan, W., Hengl, T., de Jesus, J. M., Yuan, H., and Dai, Y.: Mapping the global depth to bedrock for land surface modeling. J. Adv. Model. Earth Sy., 9, 65–88, https://doi.org/10.1002/2016MS000686, 2017.
Siebert, S., Henrich, V., Frenken, K., and Burke, J.: Global Map of Irrigation Areas version 5, Rheinische Friedrich-Wilhelms-University, Bonn, Germany/Food and Agriculture Organization of the United Nations, Rome, Italy, [data set], available at https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/, (last access: 8 June 2020), 2013.
Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B. R.: A global data set of the extent of irrigated land from 1900 to 2005, Hydrol. Earth Syst. Sci., 19, 1521–1545, https://doi.org/10.5194/hess-19-1521-2015, 2015.
Smets B., Swinnen E. and Van Hoolst R.: Copernicus Global Land Operations “Vegetation and Energy” “CGLOPS-1” – product user manual: Dry Matter Productivity(DMP) – Gross Dry Matter Productivity (GDMP) – Collection 1 km – Version 2, CGLOPS-1 consortium, Brussels, Belgium, 47 pp., I322, 2019.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO crop model to simulate yield response to water: I. Concepts and underlying principles, Agron. J., 101, 426–437, https://doi.org/10.2134/agronj2008.0139s, 2009.
Still, C. J., Berry, J. A., Collatz, G. J., and DeFries, R. S.: Global distribution of C3 and C4 vegetation: carbon cycle implications, Global Biogeochem. Cy., 17, 6–1, https://doi.org/10.1029/2001GB001807, 2003.
Stöckle, C. O., Kemanian, A. R., Nelson, R. L., Adam, J. C., Sommer, R., and Carlson, B.: CropSyst model evolution: From field to regional to global scales and from research to decision support systems, Environ. Modell. Softw., 62, 361–369, https://doi.org/10.1016/j.envsoft.2014.09.006, 2014.
USDA: Estimation of direct runoff from storm rainfall, Section 4 Hydrology,
Chapter 4, in: National Engineering Handbook, USDA, Washington DC, USA, 1-241964, 1964.
Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture
from ERS scatterometer and soil data, P. SPIE, 70, 191–207,
https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
Zacharias, S., Bogena, H., Samaniego, L., Mauder, M., Fuß, R., Pütz, T., Frenzel, M., Schwank, M., Baessler, C., Butterbach-Bahl, K., Bens, O., Borg, E., Brauer, A., Dietrich, P., Hajnsek, I., Helle, G., Kiese, R., Kunstmann, H., Klotz, S., Munch, J. C., Papen, H., Priesack, E., Schmid, H. P., Steinbrecher, R., Rosenbaum, U., Teutsch, G., and Vereecken, H.: A Network of Terrestrial Environmental Observatories in Germany, Vadose Zone J., 10, 955–973, https://doi.org/10.2136/vzj2010.0139, 2011.
Zhuo, W., Huang, J., Li, L., Zhang, X., Ma, H., Gao, X., Huang H, Xu, B., and Xiao, X.: Assimilating soil moisture retrieved from Sentinel-1 and Sentinel-2 data into WOFOST model to improve winter wheat yield estimation, Remote Sens.-Basel, 11, 1618, https://doi.org/10.3390/rs11131618, 2019.
Short summary
A spatially distributed version of the field-scale crop model AquaCrop v6.1 was developed for applications at various spatial scales. Multi-year 1 km simulations over central Europe were evaluated against biomass and surface soil moisture products derived from optical and microwave satellite missions, as well as in situ observations of soil moisture. The regional version of the AquaCrop model provides a suitable setup for subsequent satellite-based data assimilation.
A spatially distributed version of the field-scale crop model AquaCrop v6.1 was developed for...
