Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance
Section snippets
List of the most important symbols and abbreviations used in the paper
Symbol/abbreviation Long version Explanation b Aridity index Agro-climatic metric hw Heat wave days frequency Agro-climatic metric GRI Grillenburg Grassland site OEN Oensingen Grassland site LAQ Laqueuille Grassland site MBO Monte Bondone Grassland site KEM Kempten Grassland site LEL Lelystad Grassland site MAT Matta Grassland site ROT Rothamsted Grassland site SAS Sassari Grassland site AnnuGrow Process-based model of the growth of annual plants in drylands Grassland model ARMOSA Monitoring and modelling nitrogen cycle and crop
Study sites
The nine long-term grassland sites used for the modelling exercise (Table 1) cover a broad range of geographic and climatic conditions (Fig. 1; see also Fig. A and Table A1 in the Supplementary material, Section 1) as well as a variety of management practices (Table A2 in the Supplementary material, Section 1). The sites represent typical grassland cultivation conditions in Europe, which include both intensive and extensive management practices for livestock production (Chang et al., 2015a).
Evaluation of soil temperature (ST) estimates (flux sites)
Fig. 2 shows the range of model results (represented by the shaded area) and the multi-model median (MMM hereinafter) together with the measured values at weekly resolution (see also Figs. B and C of Supplementary material, Section 3, with daily and monthly time resolutions, respectively).
The figure suggests that the range of model results decreased drastically after calibration. However, it is worth noting that the upper bound in Fig. 2 (left) (almost constant ST around 28 °C) is caused by
Soil temperature (ST)
All the models simulated ST relatively well, and their performance for representing ST generally improved after calibration. However, modelling efficiency (ME, at times <0) indicated problems with the quality of the results. It means that the information content of the simulations is questionable in spite of the level of explained variance, which appears high. Therefore, developments are still needed in terms of ST representation of the models to improve the quality of the simulations. Error
Conclusions and future directions
In this study, we presented a framework for proper interpretation of model performances and uncertainties obtained with a set of biophysical models (individually and in an ensemble) simulating grasslands systems at a variety of sites.
There are multiple foci when designing multi-model studies of complex ecosystems (such as grasslands) depending on the questions to be answered. We have not identified the best model for grasslands and we have not assigned probability of success to prove the
Acknowledgements
The results of this research were obtained within an international research project named “FACCE MACSUR – Modelling European Agriculture with Climate Change for Food Security, a FACCE JPI knowledge hub”, with the support of the Hungarian Scientific Research Fund (OTKA K104816) and the EU-FP7 INFRASTRUCTURES-2011-2, BioVel - Biodiversity Virtual e-Laboratory Project (project number 283359), the German Ministry of Education and Research (031A103A), the Italian Ministry of Agricultural, Food and
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Cited by (0)
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University of New South Wales, Climate Change Research Center, Sydney, Australia.
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Catholic University of the Sacred Heart, Department of Sustainable Food Production, Piacenza, Italy.