Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands
Bellocchi, G.; Barcza, Z.; Hollós, R.et al.
2023 • In Agricultural and Forest Meteorology, 343, p. 109778
Biomass; Ecosystem manipulation; Evapotranspiration; Global change experiments; Grassland models; Sensitivity; Forestry; Global and Planetary Change; Agronomy and Crop Science; Atmospheric Science
Abstract :
[en] Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with single-factor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1–2 °C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning.
Disciplines :
Agriculture & agronomy
Author, co-author :
Bellocchi, G.; UCA, INRAE, VetAgro Sup, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), Clermont-Ferrand, France
Barcza, Z. ; ELTE Eötvös Loránd University, Department of Meteorology, Budapest, Hungary ; Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, Prague, Czech Republic
Hollós, R. ; ELTE Eötvös Loránd University, Department of Meteorology, Budapest, Hungary ; HUN-REN Centre for Agricultural Research, Agricultural Institute, Martonvásár, Hungary
Acutis, M. ; University of Milan, Department of Agricultural and Environmental Sciences - Production, Milan, Italy
Bottyán, E. ; ELTE Eötvös Loránd University, Department of Meteorology, Budapest, Hungary
Doro, L. ; University of Sassari, Desertification Research Centre, Sassari, Italy ; Texas A&M AgriLife Research, Blackland Research Center, Temple, United States
Hidy, D.; Hungarian University of Agriculture and Life Sciences, MTA-MATE Agroecological Research Group, Gödöllő, Hungary
Lellei-Kovács, E.; HUN-REN Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary
Ma, S. ; Tottori University, International Platform for Dryland Research and Education, Tottori, Japan
Minet, Julien ; Université de Liège - ULiège > Département des sciences et gestion de l'environnement (Arlon Campus Environnement)
Pacskó, V.; Lechner Knowledge Center, Space Remote Sensing Unit, Hungary ; ELTE Eötvös Loránd University, Doctoral School of Earth Sciences, Budapest, Hungary
Perego, A.; University of Milan, Department of Agricultural and Environmental Sciences - Production, Milan, Italy
Ruget, F.; French National Institute for Agricultural Research, Modelling Agricultural and Hydrological Systems in the Mediterranean Environment, Avignon, France
Seddaiu, G.; University of Sassari, Desertification Research Centre, Sassari, Italy
Wu, L. ; Rothamsted Research, North Wyke, Okehampton, United Kingdom
Sándor, R.; UCA, INRAE, VetAgro Sup, Unité Mixte de Recherche sur l'Écosystème Prairial (UREP), Clermont-Ferrand, France ; HUN-REN Centre for Agricultural Research, Agricultural Institute, Martonvásár, Hungary
Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands
The present article was published under the auspices of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) Science-Policy Knowledge Forum (MACSUR SciPol Pilot), June 2021-December 2022, with the support of the INRAE metaprogramme “Climate change in agriculture and forests: Adaptation and mitigation” (CLIMAE). The results of this research were obtained within the MACSUR pilot, which received funding in 2012 by a multi-partner call of the Joint Programming Initiative ‘FACCE JPI’ through national financing bodies. We also acknowledge the Hungarian Scientific Research Fund ( OTKA K104816 , OTKA PD115637 ), the Széchenyi 2020 programme, the European Regional Development Fund and the Hungarian Government ( GINOP-2.3.2–15–2016–00028 ), the BioVeL project (Biodiversity Virtual e-Laboratory Project, FP7-INFRASTRUCTURES-2011–2, project number 283359 ), the National Multidisciplinary Laboratory for Climate Change ( RRF-2.3.1–21–2022–00014 ) project, the Italian Ministry of Agricultural, Food and Forestry Policies, the Cabinet of the French Community of Belgium, and the metaprogramme “Adaptation of Agriculture and Forests to Climate Change” (AAFCC) of the former French National Institute for Agricultural Research (INRA). The research has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2021 funding scheme. This study was also supported by the TKP2021-NKTA-06 project that has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund , financed under the [ TKP2021-NKTA ] funding scheme. Renáta Sándor and Gianni Bellocchi received mobility funding from the French-Hungarian bilateral partnership through the BALATON (N° 44703TF)/TéT (2019–2.1.11-TÉT-2019–00031) programme. Zoltán Barcza was supported by grant "Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution", No. CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE". We thank the individual site PIs (Katja Klumpp, Christof Ammann, Damiano Gianelle, Christian Bernhofer) and the technical staff for sharing their eddy covariance data. We also thank Katharina Braunmiller (Thünen Institute of Market Analysis, Braunschweig, Germany) for facilitating contacts with the Partner Institutions which provided grassland data, and acknowledge technical support from the European Fluxes Database Cluster ( http://www.europe-fluxdata.eu ). We thank Haythem Ben Touhami for helping in the calibration of PaSim during his PhD at INRA (2011–2014). We also thank Francesca Piseddu, INRAE PhD at the Unité Mixte de Recherche sur l’Écosystème Prairial (UREP) from 2019 to 2022, who provided access to the core dataset of her meta-analyses ( Piseddu et al., 2021 ), which helped to support the discussion of this study. We thank Mattia Sanna for supporting the project and the modelling work. Biome-BGC version 4.1.1 (the predecessor of Biome-BGCMuSo used here) was provided by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana, Missoula MT (USA), which assumes no responsibility for the proper use by others. We thank the three Anonymous Reviewers for the valuable comments that helped us to improve the quality of the manuscript.The present article was published under the auspices of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) Science-Policy Knowledge Forum (MACSUR SciPol Pilot), June 2021-December 2022, with the support of the INRAE metaprogramme “Climate change in agriculture and forests: Adaptation and mitigation” (CLIMAE). The results of this research were obtained within the MACSUR pilot, which received funding in 2012 by a multi-partner call of the Joint Programming Initiative ‘FACCE JPI’ through national financing bodies. We also acknowledge the Hungarian Scientific Research Fund (OTKA K104816, OTKA PD115637), the Széchenyi 2020 programme, the European Regional Development Fund and the Hungarian Government (GINOP-2.3.2–15–2016–00028), the BioVeL project (Biodiversity Virtual e-Laboratory Project, FP7-INFRASTRUCTURES-2011–2, project number 283359), the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1–21–2022–00014) project, the Italian Ministry of Agricultural, Food and Forestry Policies, the Cabinet of the French Community of Belgium, and the metaprogramme “Adaptation of Agriculture and Forests to Climate Change” (AAFCC) of the former French National Institute for Agricultural Research (INRA). The research has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the KDP-2021 funding scheme. This study was also supported by the TKP2021-NKTA-06 project that has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the [TKP2021-NKTA] funding scheme. Renáta Sándor and Gianni Bellocchi received mobility funding from the French-Hungarian bilateral partnership through the BALATON (N° 44703TF)/TéT (2019–2.1.11-TÉT-2019–00031) programme. Zoltán Barcza was supported by grant "Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution", No. CZ.02.1.01/0.0/0.0/16_019/0000803 financed by OP RDE". We thank the individual site PIs (Katja Klumpp, Christof Ammann, Damiano Gianelle, Christian Bernhofer) and the technical staff for sharing their eddy covariance data. We also thank Katharina Braunmiller (Thünen Institute of Market Analysis, Braunschweig, Germany) for facilitating contacts with the Partner Institutions which provided grassland data, and acknowledge technical support from the European Fluxes Database Cluster (http://www.europe-fluxdata.eu). We thank Haythem Ben Touhami for helping in the calibration of PaSim during his PhD at INRA (2011–2014). We also thank Francesca Piseddu, INRAE PhD at the Unité Mixte de Recherche sur l’Écosystème Prairial (UREP) from 2019 to 2022, who provided access to the core dataset of her meta-analyses (Piseddu et al. 2021), which helped to support the discussion of this study. We thank Mattia Sanna for supporting the project and the modelling work. Biome-BGC version 4.1.1 (the predecessor of Biome-BGCMuSo used here) was provided by the Numerical Terradynamic Simulation Group (NTSG) at the University of Montana, Missoula MT (USA), which assumes no responsibility for the proper use by others. We thank the three Anonymous Reviewers for the valuable comments that helped us to improve the quality of the manuscript.
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