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Modeling and Analysis","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# Rprebasso - R library for PRELES + CROBAS + YASSO model\n\n\u003c!-- badges: start --\u003e\n\u003c!-- badges: end --\u003e\n\nRprebasso is a forest growth and ecosystem carbon balance model. In PREBASSO three different models PRELES (PREdict Light-use efficiency, Evapotranspiration and Soil water), CROBAS (Tree growth and CROwn BASe from carbon balance), and  YASSO (Yet Another Simulator of Soil Organic matter) are coupled together in order to predict the growth and carbon balance of the forest ecosystem. CROBAS provides estimates of LAI (Leaf Area Index) that is used in PRELES to compute gross primary production. GPP is then used by CROBAS to estimate forest growth. The stand structural variables and the biomass components of the forest are updated. Meanwhile, CROBAS estimates the litter production that is the input of YASSO.\n\n## Installation\n\nYou can install the the development version from [GitHub](https://github.com/ForModLabUHel/Rprebasso) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"ForModLabUHel/Rprebasso\")\n```\n## References\n\nMäkelä, Annikki. 1997. “A Carbon Balance Model of Growth and Self-Pruning in Trees Based on Structural Relationships.” Forest Science 43 (1): 7–24. https://doi.org/10.1093/forestscience/43.1.7.\n\nMäkelä, Annikki, Minna Pulkkinen, Pasi Kolari, Fredrik Lagergren, Paul Berbigier, Anders Lindroth, Denis Loustau, Eero Nikinmaa, Timo Vesala, and Pertti Hari. 2008. “Developing an Empirical Model of Stand GPP with the LUE Approach: Analysis of Eddy Covariance Data at Five Contrasting Conifer Sites in Europe.” Global Change Biology 14 (1): 92–108. https://doi.org/10.1111/j.1365-2486.2007.01463.x.\n\nMinunno, F., M. Peltoniemi, S. Launiainen, M. Aurela, A. Lindroth, A. Lohila, I. Mammarella, K. Minkkinen, and A. Mäkelä. 2016. “Calibration and Validation of a Semi-Empirical Flux Ecosystem Model for Coniferous Forests in the Boreal Region.” Ecological Modelling 341. https://doi.org/10.1016/j.ecolmodel.2016.09.020.\n\nMinunno, Francesco, Mikko Peltoniemi, Sanna Härkönen, Tuomo Kalliokoski, Harri Makinen, and Annikki Mäkelä. 2019. “Bayesian Calibration of a Carbon Balance Model PREBAS Using Data from Permanent Growth Experiments and National Forest Inventory.” Forest Ecology and Management 440 (May): 208–57. https://doi.org/10.1016/J.FORECO.2019.02.041.\n\nPeltoniemi, Mikko, Minna Pulkkinen, Mika Aurela, Jukka Pumpanen, Pasi Kolari, and Annikki Mäkelä. 2015. “A Semi-Empirical Model of Boreal-Forest Gross Primary Production, Evapotranspiration, and Soil Water — Calibration and Sensitivity Analysis.” Boreal Environment Research 20 (2): 151–71. https://helda.helsinki.fi/handle/10138/228031.\n\nTian, X., Minunno, F., Cao, T., Kalliokoski, T., Mäkelä, A. 2020. “Extending the range of applicability of the semi‐empirical ecosystem flux model PRELES for varying forest types and climate.” Global Change Biology 26: 2923–2943.https://doi.org/10.1111/gcb.14992\n\n## Acknowledgements: \n\nThe model and package developments were supported by: \n\nThe Horizon 2020 Research and innovation framework program (Forest Carbon Flux and Storage Mapping Service, proposal #821860) \n\nThe Strategic Research Council at the Academy of Finland (IBC-CARBON, decision. #312635) \n\n… \n\nPREBAS has been initially developed, calibrated and tested for the boreal forest ecosystems, while in the project [Forest Carbon Flux and Storage Mapping Service](https://www.forestflux.eu/) we are extending the model application to temperate, Mediterranean and sub-tropical ecosystems.\n\nThis work used eddy covariance data acquired and shared by the FLUXNET community, including these networks: AmeriFlux, AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, Fluxnet-Canada, GreenGrass, ICOS, KoFlux, LBA, NECC, OzFlux-TERN, TCOS-Siberia and USCCC. The ERA-Interim reanalysis data were provided by the ECMWF and processed by the LSCE. The FLUXNET eddy covariance data processing and harmonization were carried out by the European Fluxes Database Cluster, AmeriFlux Management Project and Fluxdata project of FLUXNET, with the support of the CDIAC and ICOS Ecosystem Thematic Centre, and the OzFlux, ChinaFlux and AsiaFlux offices. \n\nModel performance has been evaluated for stands in Baden-Württemberg of Germany. Results produced using map and other data types kindly provided by the Department for Forest Management and Forest Geoinformation of the State Forest Administration of the German Federal State Baden-Württemberg in 2021 (https://rp.baden-wuerttemberg.de/rpf/abt8/ref85/). We further thank the communities located in the German Federal State Baden-Württemberg that kindly allowed the utilization of their forest management data that were provided via the Department for Forest Management and Forest Geoinformation: Ettenheim, Zell a.H., Bad Peterstal-Griesbach, Friesenheim, Kappel-Grafenhausen, Lauf, Neuried, Oberharmersbach, Sasbach. \n\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1093/forestscience/43.1.7","https://doi.org/10.1111/j.1365-2486.2007.01463.x","https://doi.org/10.1016/j.ecolmodel.2016.09.020","https://doi.org/10.1016/J.FORECO.2019.02.041","https://doi.org/10.1111/gcb.14992"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/179975","html_url":"https://ost.ecosyste.ms/projects/179975"}