{"type": "FeatureCollection", "features": [{"id": "10.1016/j.earscirev.2022.104055", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:21Z", "type": "Journal Article", "created": "2022-05-12", "title": "The uncertain role of rising atmospheric CO2 on global plant transpiration", "description": "As CO2 concentration in the atmosphere rises, there is a need for improved physical understanding of its impact on global plant transpiration. This knowledge gap poses a major hurdle in robustly projecting changes in the global hydrologic cycle. For this reason, here we review the different processes by which atmospheric CO2 concentration affects plant transpiration, the several uncertainties related to the complex physiological and radiative processes involved, and the knowledge gaps which need to be filled in order to improve predictions of plant transpiration. Although there is a high degree of certainty that rising CO2 will impact plant transpiration, the exact nature of this impact remains unclear due to complex interactions between CO2 and climate, and key aspects of plant morphology and physiology. The interplay between these factors has substantial consequences not only for future climate and global vegetation, but also for water availability needed for sustaining the productivity of terrestrial ecosystems. Future changes in global plant transpiration in response to enhanced CO2 are expected to be driven by water availability, atmospheric evaporative demand, plant physiological processes, emergent plant disturbances related to increasing temperatures, and the modification of plant physiology and coverage. Considering the universal sensitivity of natural and agricultural systems to terrestrial water availability we argue that reliable future projections of transpiration is an issue of the highest priority, which can only be achieved by integrating monitoring and modeling efforts to improve the representation of CO2 effects on plant transpiration in the next generation of earth system models. \u00a9 2022 The Authors", "keywords": ["0301 basic medicine", "2. Zero hunger", "VAPOR-PRESSURE DEFICIT", "COMMUNITY LAND MODEL", "DECIDUOUS FOREST TREES", "TROPICAL RAIN-FOREST", "EARTH SYSTEM MODELS", "STOMATAL CONDUCTANCE", "Earth system models", "15. Life on land", "01 natural sciences", "6. Clean water", "Transpiration", "03 medical and health sciences", "DYNAMIC VEGETATION MODELS", "13. Climate action", "Earth and Environmental Sciences", "MOJAVE DESERT SHRUBS", "Climate change", "CO2", "ELEVATED CO2", "Atmospheric water demand", "WATER-USE EFFICIENCY", "Projections", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.earscirev.2022.104055"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth-Science%20Reviews", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.earscirev.2022.104055", "name": "item", "description": "10.1016/j.earscirev.2022.104055", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.earscirev.2022.104055"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-01T00:00:00Z"}}, {"id": "10.1088/1748-9326/aaeae7", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:17:13Z", "type": "Journal Article", "created": "2018-10-24", "title": "Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling", "description": "Open AccessA wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.", "keywords": ["Global vegetation models", "550", "manipulation experiments", "Terrestrial-Aquatic Linkages", "Kolefni", "01 natural sciences", "Nutrient cycle", "Agricultural and Biological Sciences", "Terrestrial ecosystem", "SDG 13 - Climate Action", "Climate change", "Jar\u00f0vegur", "Environmental resource management", "Global change", "General Environmental Science", "SDG 15 - Life on Land", "Carbon-nutrient cycle interactions", "2. Zero hunger", "Data syntheses", "Global and Planetary Change", "Ecology", "Geography", "Physics", "Life Sciences", "Application of Stable Isotopes in Trophic Ecology", "Cycling", "Carbon cycle", "04 agricultural and veterinary sciences", "Chemistry", "ORGANIC-MATTER", "Archaeology", "Physical Sciences", "Nutrient availability", "NET PRIMARY PRODUCTIVITY", "Ecosystem Functioning", "570", "LAND", "TROPICAL RAIN-FOREST", "carbon-nutrient cycle interactions", "data syntheses", "Soil Science", "Environmental science", "[SDU] Sciences of the Universe [physics]", "SOIL-PHOSPHORUS AVAILABILITY", "global vegetation models", "SDG 3 - Good Health and Well-being", "nutrients", "USE EFFICIENCY", "SDG 7 - Affordable and Clean Energy", "GLOBAL CHANGE", "Key (lock)", "Biology", "Ecosystem", "Manipulation experiments", "0105 earth and related environmental sciences", "Renewable Energy", " Sustainability and the Environment", "Ecosystem Structure", "Public Health", " Environmental and Occupational Health", "Nutrients", "15. Life on land", "Computer science", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "ECOSYSTEM RESPONSES", "FOS: Biological sciences", "Global Methane Emissions and Impacts", "Environmental Science", "0401 agriculture", " forestry", " and fisheries", "NITROGEN-FIXATION", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Nutrient Limitation", "ELEVATED CO2", "Nutrient"]}, "links": [{"href": "https://doi.org/10.1088/1748-9326/aaeae7"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1748-9326/aaeae7", "name": "item", "description": "10.1088/1748-9326/aaeae7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1748-9326/aaeae7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-12-07T00:00:00Z"}}, {"id": "10.3390/rs10101601", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:10Z", "type": "Journal Article", "created": "2018-10-09", "title": "Sensitivity of Evapotranspiration Components in Remote Sensing-Based Models", "description": "<p>Accurately estimating evapotranspiration (ET) at large spatial scales is essential to our understanding of land-atmosphere coupling and the surface balance of water and energy. Comparisons between remote sensing-based ET models are difficult due to diversity in model formulation, parametrization and data requirements. The constituent components of ET have been shown to deviate substantially among models as well as between models and field estimates. This study analyses the sensitivity of three global ET remote sensing models in an attempt to isolate the error associated with forcing uncertainty and reveal the underlying variables driving the model components. We examine the transpiration, soil evaporation, interception and total ET estimates of the Penman-Monteith model from the Moderate Resolution Imaging Spectroradiometer (PM-MOD), the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL) and the Global Land Evaporation Amsterdam Model (GLEAM) at 42 sites where ET components have been measured using field techniques. We analyse the sensitivity of the models based on the uncertainty of the input variables and as a function of the raw value of the variables themselves. We find that, at 10% added uncertainty levels, the total ET estimates from PT-JPL, PM-MOD and GLEAM are most sensitive to Normalized Difference Vegetation Index (NDVI) (%RMSD = 100.0), relative humidity (%RMSD = 122.3) and net radiation (%RMSD = 7.49), respectively. Consistently, systemic bias introduced by forcing uncertainty in the component estimates is mitigated when components are aggregated to a total ET estimate. These results suggest that slight changes to forcing may result in outsized variation in ET partitioning and relatively smaller changes to the total ET estimates. Our results help to explain why model estimates of total ET perform relatively well despite large inter-model divergence in the individual ET component estimates.</p>", "keywords": ["550", "Science", "TROPICAL RAIN-FOREST", "0208 environmental biotechnology", "evapotranspiration", "0207 environmental engineering", "02 engineering and technology", "interception", "SOIL-MOISTURE", "transpiration", "modelling", "partitioning", "soil evaporation", "uncertainty", "DROUGHT", "evapotranspiration; modelling; sensitivity; uncertainty; transpiration; soil evaporation; interception; partitioning", "CLIMATE-CHANGE", "Q", "Biology and Life Sciences", "PLANT TRANSPIRATION", "sensitivity", "6. Clean water", "CHIHUAHUAN DESERT", "GLOBAL TERRESTRIAL EVAPOTRANSPIRATION", "13. Climate action", "Earth and Environmental Sciences", "LAND EVAPORATION", "WATER-BALANCE", "FEEDBACKS", "[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]", "[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/10/1601/pdf"}, {"href": "https://doi.org/10.3390/rs10101601"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10101601", "name": "item", "description": "10.3390/rs10101601", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10101601"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-09T00:00:00Z"}}, {"id": "20.500.11815/1261", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:13Z", "type": "Journal Article", "created": "2018-10-24", "title": "Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling", "description": "Open AccessA wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.", "keywords": ["Global vegetation models", "550", "manipulation experiments", "Terrestrial-Aquatic Linkages", "Kolefni", "01 natural sciences", "Nutrient cycle", "Agricultural and Biological Sciences", "Terrestrial ecosystem", "SDG 13 - Climate Action", "Climate change", "Jar\u00f0vegur", "Environmental resource management", "Global change", "General Environmental Science", "SDG 15 - Life on Land", "Carbon-nutrient cycle interactions", "2. Zero hunger", "Data syntheses", "Global and Planetary Change", "Ecology", "Geography", "Physics", "Life Sciences", "Application of Stable Isotopes in Trophic Ecology", "Cycling", "Carbon cycle", "04 agricultural and veterinary sciences", "Chemistry", "ORGANIC-MATTER", "Archaeology", "Physical Sciences", "Nutrient availability", "NET PRIMARY PRODUCTIVITY", "Ecosystem Functioning", "570", "LAND", "TROPICAL RAIN-FOREST", "carbon-nutrient cycle interactions", "data syntheses", "Soil Science", "Environmental science", "[SDU] Sciences of the Universe [physics]", "SOIL-PHOSPHORUS AVAILABILITY", "global vegetation models", "SDG 3 - Good Health and Well-being", "nutrients", "USE EFFICIENCY", "SDG 7 - Affordable and Clean Energy", "GLOBAL CHANGE", "Key (lock)", "Biology", "Ecosystem", "Manipulation experiments", "0105 earth and related environmental sciences", "Renewable Energy", " Sustainability and the Environment", "Ecosystem Structure", "Public Health", " Environmental and Occupational Health", "Nutrients", "15. Life on land", "Computer science", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "ECOSYSTEM RESPONSES", "FOS: Biological sciences", "Global Methane Emissions and Impacts", "Environmental Science", "0401 agriculture", " forestry", " and fisheries", "NITROGEN-FIXATION", "Soil Carbon Dynamics and Nutrient Cycling in Ecosystems", "Nutrient Limitation", "ELEVATED CO2", "Nutrient"]}, "links": [{"href": "https://doi.org/20.500.11815/1261"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11815/1261", "name": "item", "description": "20.500.11815/1261", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11815/1261"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-12-07T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=TROPICAL+RAIN-FOREST&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=TROPICAL+RAIN-FOREST&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=TROPICAL+RAIN-FOREST&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=TROPICAL+RAIN-FOREST&offset=4", "hreflang": "en-US"}], "numberMatched": 4, "numberReturned": 4, "distributedFeatures": [], "timeStamp": "2026-09-20T23:02:14.411205Z"}