{"type": "FeatureCollection", "features": [{"id": "10.1111/gcb.13902", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:49Z", "type": "Journal Article", "created": "2017-09-11", "title": "CO2 evasion from boreal lakes: Revised estimate, drivers of spatial variability, and future projections", "description": "Abstract<p>Lakes (including reservoirs) are an important component of the global carbon (C) cycle, as acknowledged by the fifth assessment report of the IPCC. In the context of lakes, the boreal region is disproportionately important contributing to 27% of the worldwide lake area, despite representing just 14% of global land surface area. In this study, we used a statistical approach to derive a prediction equation\uffc2\uffa0for the partial pressure of CO2 (pCO2) in lakes as a function of lake area, terrestrial net primary productivity (NPP), and precipitation (r2\uffc2\uffa0=\uffc2\uffa0.56), and to create the first high\uffe2\uff80\uff90resolution, circumboreal map (0.5\uffc2\uffb0) of lake pCO2. The map of\uffc2\uffa0pCO2 was combined with lake area from the recently published GLOWABO database and three different estimates of the gas transfer velocity k to produce a resulting map of CO2 evasion (FCO2). For the boreal region, we estimate an average, lake area weighted, pCO2 of 966 (678\uffe2\uff80\uff931,325) \uffce\uffbcatm and a total\uffc2\uffa0FCO2 of 189 (74\uffe2\uff80\uff93347) Tg\uffc2\uffa0C\uffc2\uffa0year\uffe2\uff88\uff921, and evaluate the corresponding uncertainties based on Monte Carlo simulation. Our estimate of FCO2 is approximately twofold greater than previous estimates, as a result of methodological and data source differences. We use our results along with published estimates of the other C fluxes through inland waters to derive a C budget for the boreal region, and find that FCO2 from lakes is the most significant flux of the land\uffe2\uff80\uff90ocean aquatic continuum, and of a similar magnitude as emissions from forest fires. Using the model and applying it to spatially resolved projections of terrestrial NPP and precipitation while keeping everything else constant, we predict a 107% increase in boreal lake FCO2 under emission scenario RCP8.5 by 2100. Our projections are largely driven by increases in terrestrial NPP over the same period, showing the very close connection between the terrestrial and aquatic C cycle.</p", "keywords": ["0106 biological sciences", "Precipitation", "precipitation", "01 natural sciences", "Lake", "Environnement et pollution", "carbon budget", "Carbon budget", "Geovetenskap och relaterad milj\u00f6vetenskap", "terrestrial NPP", "boreal", "Climate change", "Boreal", "lake", "Ecosystem", "Future projections", "0105 earth and related environmental sciences", "Ecologie", "Arctic Regions", "Terrestrial NPP", "Carbon Dioxide", "Models", " Theoretical", "15. Life on land", "6. Clean water", "Carbon", "Lakes", "climate change", "13. 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The release here is the base code and information used in the 'State of Wildfire's report 2023/24'. https://doi.org/10.5194/essd-2024-218  Key Features:    ConFire fire model now implemented with zero-inflated logistic link distribution  Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon.  Utilizes various environmental and climatic data for isimip and Copernicus data store  Robust statistical analysis now uses PyMC at version 5 and ArviZ.   Installation and Usage:  For detailed installation and usage instructions, please refer to the README, also in this repository archive.  Acknowledgments:  Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa,  Douglas Kelley, Chantelle Burton  Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1", "keywords": ["Canada", "Attribution", "Greece", "Amazonia", "Wildfire", "Climatic changes", "Fire", "Bayesian statistics", "Future projections"], "contacts": [{"organization": "Barbosa, Maria Lucia Ferreira, Kelley, Douglas, Burton, Chantelle, Anderson, Liana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11421746"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11421746", "name": "item", "description": "10.5281/zenodo.11421746", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11421746"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-03T00:00:00Z"}}, {"id": "10.5281/zenodo.11460232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:22:24Z", "type": "Software", "title": "ConFire: State of Wildfires 2023/24", "description": "Project Overview:  This is the first release of our Bayesian-based fire models, designed for fire prediction and analysis using Bayesian inference and simple fire models. The release here is the base code and information used in the 'State of Wildfire's report 2023/24'. https://doi.org/10.5194/essd-2024-218  Key Features:    ConFire fire model now implemented with zero-inflated logistic link distribution  Configuration files for near real-time, attribution and future projections for Greece, Canada, and NW Amazon.  Utilizes various environmental and climatic data for isimip and Copernicus data store  Robust statistical analysis now uses PyMC at version 5 and ArviZ.   Installation and Usage:  For detailed installation and usage instructions, please refer to the README, also in this repository archive.  Acknowledgments:  Special thanks to all contributors and the developers of the dependencies used in this project. Particularly Maria Lucia Ferreira Barbosa,  Douglas Kelley, Chantelle Burton  Full Changelog: https://github.com/douglask3/Bayesian_fire_models/compare/v0.1...SoW23_v0.1", "keywords": ["Canada", "Attribution", "Greece", "Amazonia", "Wildfire", "Climatic changes", "Fire", "Bayesian statistics", "Future projections"], "contacts": [{"organization": "Barbosa, Maria Lucia Ferreira, Kelley, Douglas, Burton, Chantelle, Anderson, Liana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.11460232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.11460232", "name": "item", "description": "10.5281/zenodo.11460232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.11460232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-03T00:00:00Z"}}, {"id": "10.7941/D1432P", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:24:40Z", "type": "Dataset", "title": "Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100", "description": "unspecifiedAims: High-resolution information on soils\u2019 vulnerability to  climate-induced soil organic carbon (SOC) loss can enable environmental  scientists, land managers, and policy makers to develop targeted  mitigation strategies. This study aims to estimate baseline and decadal  changes in continental US surface SOC stocks under future emission  scenarios. \u00a0 Location: Continental United States \u00a0 Time  Period: 2014-2100 \u00a0 Results: Baseline SOC projections from ML  approaches captured more than 50% of variability in SOC observations,  whereas ESMs represented only 6-16% of observed SOC variability. ML  estimates showed a mean total loss of 1.8 Pg C from US surface soils under  the high-emission scenario by 2100, whereas ESMs showed no significant  change in SOC stocks with wide variation among ESMs. Both ML and ESM  predictions agree on the direction of SOC change (net emissions or  sequestration) across 46%\u201351% of continental US land area. These  differences are attributable to the high-resolution site-specific data  used in ML model compared to the relatively coarse grid represented in  CMIP6 ESMs. \u00a0 Main conclusions: Our high-resolution estimates of  baseline SOC stocks, identification of key environmental controllers, and  projection of SOC changes from US land cover types under future climate  scenarios suggest the need for high-resolution simulations of SOC in ESMs  to represent the heterogeneity of SOC. We found that the SOC change is  sensitive to key soil related factors (e.g. soil drainage and soil order)  that have not been historically considered as input parameters in ESMs,  because currently more than 95% variability in the SOC of CMIP6 ESMs are  controlled by net primary productivity, temperature, and precipitation.  Using additional environmental factors to estimate the baseline SOC stocks  and predict the future trajectory of SOC change can provide more accurate  results.", "keywords": ["soil organic carbon", "earth system model", "13. Climate action", "environmental factors", "future projection", "FOS: Earth and related environmental sciences", "15. 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