2020
DOI: 10.5194/essd-12-1295-2020
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The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests

Abstract: Abstract. Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide c… Show more

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Cited by 44 publications
(52 citation statements)
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“…The site topography is gently sloping and the soil is humic alisol with a variable depth (40-100 cm), developed on calcareous bedrock. The climate is of Mediterranean mountain type, during the period 1989-2014 the mean annual temperature was 7.2 C, and the mean annual precipitation was 1,178 mm, of which $10% falls in summer (Collalti et al, 2016;Guidolotti et al, 2013;Reyer et al, 2020).…”
Section: Study Sitementioning
confidence: 99%
See 1 more Smart Citation
“…The site topography is gently sloping and the soil is humic alisol with a variable depth (40-100 cm), developed on calcareous bedrock. The climate is of Mediterranean mountain type, during the period 1989-2014 the mean annual temperature was 7.2 C, and the mean annual precipitation was 1,178 mm, of which $10% falls in summer (Collalti et al, 2016;Guidolotti et al, 2013;Reyer et al, 2020).…”
Section: Study Sitementioning
confidence: 99%
“…For the period 1989-2014, FLUXNET2015 data release was used for half-hourly air temperature and precipitation (Pastorello et al, 2020;Reyer et al, 2020). For the study period (2015-2017), measured data were gap filled using downloaded data by the ERA5 database of the (Zhang et al, 2003).…”
Section: Meteorological and Phenological Datamentioning
confidence: 99%
“…This can identify model structural uncertainties, which have been highlighted as a major source of model uncertainties (Famiglietti et al, 2020;Lovenduski & Bonan, 2017;Raiho et al, 2020), and thus foster new model developments as well as novel empirical investigations. Although the increasing complexity of models makes the interpretation of model inter-comparison results challenging (Fisher & Koven, 2020; Appendix B), model benchmarking is facilitated by new tools of code and data sharing (e.g., Ram, 2013) as well as the availability of detailed standardized databases (Collier et al, 2018;Reyer et al, 2020). Additionally, simulation experiments where different versions of a model are compared allow for insights into the effects of specific process representation in addition to comparisons among models.…”
Section: Strength In Unity: Insights From Model Intercomparison and Couplingmentioning
confidence: 99%
“…In other words, bringing models to data, rather than the other way around, may eventually reduce artificial inconsistencies between datasets that stem from additional manipulations for making data and models match. Concomitantly, community cyberinfrastructure would facilitate [R23] interaction with a compilation of standard datasets that models need to be able to reproduce repeatedly (Anderson‐Teixeira et al., 2018; Kraemer et al., 2020; Reyer et al., 2020).…”
Section: Model Intercomparison and Benchmarkingmentioning
confidence: 99%