Responses of alpine tree line ecosystems to increasing atmospheric CO2 concentrations and global warming are poorly understood. We used an experiment at the Swiss tree line to investigate changes in vegetation biomass after 9\uffc2\uffa0years of free air CO2 enrichment (+200\uffc2\uffa0ppm; 2001\uffe2\uff80\uff932009) and 6\uffc2\uffa0years of soil warming (+4\uffc2\uffa0\uffc2\uffb0C; 2007\uffe2\uff80\uff932012). The study contained two key tree line species, Larix decidua and Pinus uncinata, both approximately 40\uffc2\uffa0years old, growing in heath vegetation dominated by dwarf shrubs. In 2012, we harvested and measured biomass of all trees (including root systems), above\uffe2\uff80\uff90ground understorey vegetation and fine roots. Overall, soil warming had clearer effects on plant biomass than CO2 enrichment, and there were no interactive effects between treatments. Total plant biomass increased in warmed plots containing Pinus but not in those with Larix. This response was driven by changes in tree mass (+50%), which contributed an average of 84% (5.7\uffc2\uffa0kg\uffc2\uffa0m\uffe2\uff88\uff922) of total plant mass. Pinus coarse root mass was especially enhanced by warming (+100%), yielding an increased root mass fraction. Elevated CO2 led to an increased relative growth rate of Larix stem basal area but no change in the final biomass of either tree species. Total understorey above\uffe2\uff80\uff90ground mass was not altered by soil warming or elevated CO2. However, Vaccinium myrtillus mass increased with both treatments, graminoid mass declined with warming, and forb and nonvascular plant (moss and lichen) mass decreased with both treatments. Fine roots showed a substantial reduction under soil warming (\uffe2\uff88\uff9240% for all roots <2\uffc2\uffa0mm in diameter at 0\uffe2\uff80\uff9320\uffc2\uffa0cm soil depth) but no change with CO2 enrichment. Our findings suggest that enhanced overall productivity and shifts in biomass allocation will occur at the tree line, particularly with global warming. However, individual species and functional groups will respond differently to these environmental changes, with consequences for ecosystem structure and functioning.
", "keywords": ["0106 biological sciences", "2. Zero hunger", "Models", " Statistical", "Temperature", "Larix", "Carbon Dioxide", "15. Life on land", "Pinus", "Global Warming", "01 natural sciences", "Soil", "Species Specificity", "13. Climate action", "Biomass", "Tundra", "Switzerland"]}, "links": [{"href": "https://doi.org/10.1111/gcb.12819"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/gcb.12819", "name": "item", "description": "10.1111/gcb.12819", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.12819"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-01-30T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:02Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers. The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions. For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes. The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables. The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location. The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions. Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "1854/LU-8743335", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:26:33Z", "type": "Report", "title": "Global maps of soil temperature", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km(2) resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km(2) pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10 degrees C (mean = 3.0 +/- 2.1 degrees C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 +/- 2.3 degrees C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 +/- 2.3 degrees C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Technology and Engineering", "soil temperature", "Biology and Life Sciences", "soil-dwelling organisms", "SNOW-COVER", "MITIGATION", "MOISTURE", "FOREST", "weather stations", "LITTER DECOMPOSITION", "PERMAFROST", "near-surface temperatures", "PLANT-RESPONSES", "bioclimatic variables", "CLIMATIC CONTROLS", "Earth and Environmental Sciences", "temperature offset", "SUITABILITY", "global maps", "MICROCLIMATE", "CBCE", "microclimate"]}, "links": [{"href": "https://doi.org/1854/LU-8743335"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8743335", "name": "item", "description": "1854/LU-8743335", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8743335"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1016/j.agrformet.2018.04.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:15:44Z", "type": "Journal Article", "created": "2018-04-19", "title": "A phenomenological model of soil evaporative efficiency using surface soil moisture and temperature data", "description": "Abstract Modeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. In this context, a parsimonious model is developed to estimate soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. It uses a soil resistance driven by surface (0\u20135\u202fcm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. The new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. Three calibration strategies are adopted based on SEE time series derived from (1) eddy-covariance measurements, (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. The correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) SEE are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. Moreover, the correlation coefficient (and slope of the linear regression) between simulated and observed SEE is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. The reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes SEE decrease at the hourly time scale. The soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spaceborne microwave and thermal data.", "keywords": ["550", "0207 environmental engineering", "Soil resistance", "02 engineering and technology", "Remote sensing", "15. Life on land", "calibration", "surface temperature", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "Surface temperature", "remote sensing", "Calibration", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "soil resistance", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil moisture", "environment", "Soil evaporation"]}, "links": [{"href": "https://doi.org/10.1016/j.agrformet.2018.04.010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Forest%20Meteorology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agrformet.2018.04.010", "name": "item", "description": "10.1016/j.agrformet.2018.04.010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agrformet.2018.04.010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-06-01T00:00:00Z"}}, {"id": "00fa75f3-0640-4096-b1ea-1287004ceadf", "type": "Feature", "geometry": null, "properties": {"updated": "2025-06-16T06:24:30.797077", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Temperatura del suolo (C) - (2025-06-15 ore 12 UTC).", "description": "Temperatura del suolo (C). Corsa del 2025-06-15 ore 12 UTC - Valido dalle ore 12 UTC del 2025-06-15 alle ore 00 UTC del 2025-06-19. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["120000", "2025-06-15", "20250615t120000000z", "3km", "arw", "below", "between", "depths", "it", "lamma", "layer", "soil", "surface", "temperature", "two"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN12/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-temperatura-del-suolo-c-2025-06-15-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250615T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/00fa75f3-0640-4096-b1ea-1287004ceadf"}, {"rel": "self", "type": "application/geo+json", "title": "00fa75f3-0640-4096-b1ea-1287004ceadf", "name": "item", "description": "00fa75f3-0640-4096-b1ea-1287004ceadf", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/00fa75f3-0640-4096-b1ea-1287004ceadf"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "oai:serval.unil.ch:BIB_38E93A02220B", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:35:39Z", "type": "Report", "title": "Global maps of soil temperature.", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km <sup>2</sup> resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km <sup>2</sup> pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations"], "contacts": [{"organization": "Lembrechts, J.J., van den Hoogen, J., Aalto, J., Ashcroft, M.B., De Frenne, P., Kemppinen, J., Kopeck\u00fd, M., Luoto, M., Maclean, IMD, Crowther, T.W., Bailey, J.J., Haesen, S., Klinges, D.H., Niittynen, P., Scheffers, B.R., Van Meerbeek, K., Aartsma, P., Abdalaze, O., Abedi, M., Aerts, R., Ahmadian, N., Ahrends, A., Alatalo, J.M., Alexander, J.M., Allonsius, C.N., Altman, J., Ammann, C., Andres, C., Andrews, C., Ard\u00f6, J., Arriga, N., Arzac, A., Aschero, V., Assis, R.L., Assmann, J.J., Bader, M.Y., Bahalkeh, K., Baran\u010dok, P., Barrio, I.C., Barros, A., Barthel, M., Basham, E.W., Bauters, M., Bazzichetto, M., Marchesini, L.B., Bell, M.C., Benavides, J.C., Benito Alonso, J.L., Berauer, B.J., Bjerke, J.W., Bj\u00f6rk, R.G., Bj\u00f6rkman, M.P., Bj\u00f6rnsd\u00f3ttir, K., Blonder, B., Boeckx, P., Boike, J., Bokhorst, S., Brum, BNS, Br\u016fna, J., Buchmann, N., Buysse, P., Camargo, J.L., Campoe, O.C., Candan, O., Canessa, R., Cannone, N., Carbognani, M., Carnicer, J., Casanova-Katny, A., Cesarz, S., Chojnicki, B., Choler, P., Chown, S.L., Cifuentes, E.F., \u010ciliak, M., Contador, T., Convey, P., Cooper, E.J., Cremonese, E., Curasi, S.R., Curtis, R., Cutini, M., Dahlberg, C.J., Daskalova, G.N., de Pablo, M.A., Della Chiesa, S., Dengler, J., Deronde, B., Descombes, P., Di Cecco, V., Di Musciano, M., Dick, J., Dimarco, R.D., Dolezal, J., Dorrepaal, E., Du\u0161ek, J., Eisenhauer, N., Eklundh, L., Erickson, T.E., Erschbamer, B., Eugster, W., Ewers, R.M., Exton, D.A., Fanin, N., Fazlioglu, F., Feigenwinter, I., Fenu, G., Ferlian, O., Fern\u00e1ndez Calzado, M.R., Fern\u00e1ndez-Pascual, E., Finckh, M., Higgens, R.F., Forte, TGW, Freeman, E.C., Frei, E.R., Fuentes-Lillo, E., Garc\u00eda, R.A., Garc\u00eda, M.B., G\u00e9ron, C., Gharun, M., Ghosn, D., Gigauri, K., Gobin, A., Goded, I., Goeckede, M., Gottschall, F., Goulding, K., Govaert, S., Graae, B.J., Greenwood, S., Greiser, C., Grelle, A., Gu\u00e9nard, B., Guglielmin, M., Guillemot, J., Haase, P., Haider, S., Halbritter, A.H., Hamid, M., Hammerle, A., Hampe, A., Haugum, S.V., Hederov\u00e1, L., Heinesch, B., Helfter, C., Hepenstrick, D., Herberich, M., Herbst, M., Hermanutz, L., Hik, D.S., Hoffr\u00e9n, R., Homeier, J., H\u00f6rtnagl, L., H\u00f8ye, T.T., Hrbacek, F., Hylander, K., Iwata, H., Jackowicz-Korczynski, M.A., Jactel, H., J\u00e4rveoja, J., Jastrz\u0119bowski, S., Jentsch, A., Jim\u00e9nez, J.J., J\u00f3nsd\u00f3ttir, I.S., Jucker, T., Jump, A.S., Juszczak, R., Kanka, R., Ka\u0161par, V., Kazakis, G., Kelly, J., Khuroo, A.A., Klemedtsson, L., Klisz, M., Kljun, N., Knohl, A., Kobler, J., Koll\u00e1r, J., Kotowska, M.M., Kov\u00e1cs, B., Kreyling, J., Lamprecht, A., Lang, S.I., Larson, C., Larson, K., Laska, K., le Maire, G., Leihy, R.I., Lens, L., Liljebladh, B., Lohila, A., Lorite, J., Loubet, B., Lynn, J., Macek, M., Mackenzie, R., Magliulo, E., Maier, R., Malfasi, F., M\u00e1li\u0161, F.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/oai:serval.unil.ch:BIB_38E93A02220B"}, {"rel": "self", "type": "application/geo+json", "title": "oai:serval.unil.ch:BIB_38E93A02220B", "name": "item", "description": "oai:serval.unil.ch:BIB_38E93A02220B", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/oai:serval.unil.ch:BIB_38E93A02220B"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "0233df11-412c-4c84-a9b2-7eb9dc3409d4", "type": "Feature", "geometry": null, "properties": {"updated": "2025-04-30T11:21:35.673675Z", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Temperatura del suolo (C) - (2025-04-30 ore 00 UTC).", "description": "Temperatura del suolo (C). 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Combining the compensation effect with our model, we simulate 6 different experimental studies covering the two experimental configurations, a wide range of sample sizes (heights ranging from 5\u202fmm to 126\u202fmm), and various coal origins (6 countries). The model accurately predicts critical ignition temperature (Tig) for all 24 experiments with an error below 7\u202f\u00b0C. This computational model unifies for the first time the two most used self-heating ignition experiments and provides theoretical insights to understand self-ignition for different fuels under different conditions.", "keywords": ["Technology", "Engineering", " Chemical", "Energy & Fuels", "LOW-TEMPERATURE OXIDATION", "0306 Physical Chemistry (Incl. Structural)", "0904 Chemical Engineering", "Chemical", "7. 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The timeseries collects data about soil moisture and temperature at 20 cm, 30 cm and 40 cm under the soil surface in 3 different position of the orchard. Acquisition frequency is 30 minutes for all the parameters.", "keywords": ["agricultural-and-aquaculture-facilities", "cct", "eu", "sensor", "soil", "soil-moisture", "temperature", "water"]}, "links": [{"href": "https://doi.org/10.48784/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"href": "http://data.europa.eu/88u/dataset/d41997c7-d9ea-4f10-bd2e-baf1d96bb8f8"}, {"rel": "self", "type": "application/geo+json", "title": "/publisher", "name": "item", "description": "/publisher", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//publisher"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "/", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:13:53Z", "type": "Dataset", "title": "DrSch\u00e4r mais field monitoring timeseries", "description": "Timeseries of the DrSch\u00e4r field monitoring in Este (PD) during summer 2022. Dataset of the soil temperature and soil humidity collected from different type of sensors distributed among the mais field. In the field were installed different types of sensors as many TDR sensors and one capacitive sensor; they collect temperature and humidity of the soil. 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Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the ZALF Datenerfassung's research activities.\" Although every care has been taken in preparing and testing the data, the ZALF Datenerfassung and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the ZALF Datenerfassung and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. 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Workgroup Research Data Management", "roles": ["publisher"], "phones": [{"value": "+49 33432 82 300"}], "emails": [{"value": "dataservice@zalf.de"}], "addresses": [{"deliveryPoint": ["Eberswalder Strasse 84"], "city": "M\u00fcncheberg", "administrativeArea": "Brandenburg", "postalCode": "15374", "country": "Germany"}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "https://ror.org/01ygyzs83", "name_url": "", "description": "ROR", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Hubert Jochheim", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "Hubert.jochheim@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-8047-4553", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Stephan Wirth", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "swirth@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-8047-4553", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Hubert Jochheim", "organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "position": null, "roles": ["projectLeader"], "phones": [{"value": null}], "emails": [{"value": "Hubert.jochheim@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-8047-4553", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Valentin Gartiser", "organization": "Georg-August-Universit\u00e4t G\u00f6ttingen", "position": null, "roles": ["researcher"], "phones": [{"value": null}], "emails": [{"value": "valentin.gartiser@stud.uni-goettingen.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0001-5320-374X", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Martin Maier", "organization": "Georg-August-Universit\u00e4t G\u00f6ttingen", "position": null, "roles": ["researcher"], "phones": [{"value": null}], "emails": [{"value": "martin.maier@uni-goettingen.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": {"url": null, "protocol": null, "protocol_url": "", "name": "0000-0002-7959-0108", "name_url": "", "description": "ORCID", "description_url": "", "applicationprofile": null, "applicationprofile_url": "", "function": null}}]}, {"name": "Dieter Sowa", "organization": "Leibniz Centre for Agricultural Landscape Research", "position": null, "roles": ["dataCollector"], "phones": [{"value": null}], "emails": [{"value": "Annett.Stange@zalf.de"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"organization": "Leibniz Centre for Agricultural Landscape Research (ZALF)", "roles": ["contributor"]}], "themes": [{"concepts": [{"id": "infoMapAccessService"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "Soil"}, {"id": "soil respiration"}, {"id": "soil air"}, {"id": "soil temperature"}, {"id": "soil water content"}, {"id": "Fagus sylvatica"}, {"id": "Pinus sylvestris"}, {"id": "measurement"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "Soil"}, {"id": "soil respiration"}, {"id": "soil air"}, {"id": "soil temperature"}, {"id": "soil water content"}, {"id": "Fagus sylvatica"}, {"id": "Pinus sylvestris"}, {"id": "measurement"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "Germany"}, {"id": "Brandenburg"}, {"id": "Barnim"}, {"id": "Ostprignitz-Ruppin"}], "scheme": "individual"}]}, "links": [{"href": "https://maps.bonares.de/mapapps/resources/apps/bonares/index.html?lang=en&mid=0d41a483-52b2-464f-b526-3ae29e6ad089", "rel": "information"}, {"href": "https://maps.bonares.de/wss/service/ags-relay/ags/guest/arcgis/rest/services/Zalf/ID_4731_both_sites/MapServer/WMSServer?request=GetCapabilities&service=WMS"}, {"rel": "self", "type": "application/geo+json", "title": "0d41a483-52b2-464f-b526-3ae29e6ad089", "name": "item", "description": "0d41a483-52b2-464f-b526-3ae29e6ad089", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0d41a483-52b2-464f-b526-3ae29e6ad089"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-05T00:00:00Z"}}, {"id": "10.1002/2015gb005239", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:04Z", "type": "Journal Article", "created": "2015-12-19", "title": "Toward More Realistic Projections Of Soil Carbon Dynamics By Earth System Models", "description": "AbstractSoil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real\uffe2\uff80\uff90world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first\uffe2\uff80\uff90order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth\uffe2\uff80\uff90dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool\uffe2\uff80\uff90 and flux\uffe2\uff80\uff90based data sets through data assimilation is among the highest priorities for near\uffe2\uff80\uff90term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.
", "keywords": ["550", "LAND MODELS", "Oceanography", "HETEROTROPHIC RESPIRATION", "01 natural sciences", "Atmospheric Sciences", "LITTER DECOMPOSITION", "ORGANIC-CARBON", "Geoinformatics", "GLOBAL CLIMATE-CHANGE", "DATA-ASSIMILATION", "Meteorology & Atmospheric Sciences", "TEMPERATURE SENSITIVITY", "CMIP5", "MICROBIAL MODELS", "0105 earth and related environmental sciences", "2. Zero hunger", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "500", "Earth system models", "04 agricultural and veterinary sciences", "15. Life on land", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "TERRESTRIAL ECOSYSTEMS", "Climate Action", "Geochemistry", "Climate change impacts and adaptation", "realistic projections", "13. Climate action", "recommendations", "Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "soil carbon dynamics", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Climate Change Impacts and Adaptation", "Environmental Sciences", "PARAMETER-ESTIMATION"]}, "links": [{"href": "https://escholarship.org/content/qt1pw7g2r2/qt1pw7g2r2.pdf"}, {"href": "https://doi.org/10.1002/2015gb005239"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Biogeochemical%20Cycles", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2015gb005239", "name": "item", "description": "10.1002/2015gb005239", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2015gb005239"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10.1002/2016rg000543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:04Z", "type": "Journal Article", "created": "2017-03-23", "title": "A review of spatial downscaling of satellite remotely sensed soil moisture", "description": "AbstractSatellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.The land surface controls the partitioning of water and energy fluxes and therefore plays a crucial role in the climate system. The coupling between soil moisture and air temperature, in particular, has been shown to affect the severity and occurrence of temperature extremes and heat waves. Here we study soil moisture\uffe2\uff80\uff90temperature coupling in five land surface models, focusing on the terrestrial segment of the coupling in the warm season. All models are run off\uffe2\uff80\uff90line over a common period with identical atmospheric forcing data, in order to allow differences in the results to be attributed to the models' partitioning of energy and water fluxes. Coupling is calculated according to two semiempirical metrics, and results are compared to observational flux tower data. Results show that the locations of the global hot spots of soil moisture\uffe2\uff80\uff90temperature coupling are similar across all models and for both metrics. In agreement with previous studies, these areas are located in transitional climate regimes. The magnitude and local patterns of model coupling, however, can vary considerably. Model coupling fields are compared to tower data, bearing in mind the limitations in the geographical distribution of flux towers and the differences in representative area of models and in situ data. Nevertheless, model coupling correlates in space with the tower\uffe2\uff80\uff90based results (r = 0.5\uffe2\uff80\uff930.7), with the multimodel mean performing similarly to the best\uffe2\uff80\uff90performing model. Intermodel differences are also found in the evaporative fractions and may relate to errors in model parameterizations and ancillary data of soil and vegetation characteristics.
", "keywords": ["ENVIRONMENT SIMULATOR JULES", "FLUXES", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "CO2 EXCHANGE", "models", "WATER", "SCALE", "Research Articles", "0105 earth and related environmental sciences", "land surface", "CARBON-DIOXIDE EXCHANGE", "eartH2Observe", "temperature", "15. Life on land", "DECIDUOUS FOREST", "CLIMATE", "EVAPORATION", "VARIABILITY", "13. Climate action", "Earth and Environmental Sciences", "BALANCE", "land surface models", "SENSIBLE HEAT", "land-atmosphere interactions", "soil moisture"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JD027346"}, {"href": "https://doi.org/10.1002/2017JD027346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2017JD027346", "name": "item", "description": "10.1002/2017JD027346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2017JD027346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-01T00:00:00Z"}}, {"id": "10.1002/ece3.1867", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:07Z", "type": "Journal Article", "created": "2016-01-11", "title": "Grazing Exclusion Reduced Soil Respiration But Increased Its Temperature Sensitivity In A Meadow Grassland On The Tibetan Plateau", "description": "AbstractUnderstanding anthropogenic influences on soil respiration (Rs) is critical for accurate predictions of soil carbon fluxes, but it is not known how Rs responds to grazing exclusion (GE). Here, we conducted a manipulative experiment in a meadow grassland on the Tibetan Plateau to investigate the effects of GE on Rs. The exclusion of livestock significantly increased soil moisture and above\uffe2\uff80\uff90ground biomass, but it decreased soil temperature, microbial biomass carbon (MBC), and Rs. Regression analysis indicated that the effects of GE on Rs were mainly due to changes in soil temperature, soil moisture, and MBC. Compared with the grazed blocks, GE significantly decreased soil carbon release by 23.6% over the growing season and 21.4% annually, but it increased the temperature sensitivity (Q10) of Rs by 6.5% and 14.2% for the growing season and annually respectively. Therefore, GE may reduce the release of soil carbon from the Tibetan Plateau, but under future climate warming scenarios, the increases in Q10 induced by GE could lead to increased carbon emissions.
", "keywords": ["570", "MICROBIAL RESPIRATION", "Environmental Sciences & Ecology", "Plant Productivity", "Temperature Sensitivity", "ALPINE GRASSLAND", "630", "Microbial Biomass Carbon", "NORTHERN CHINA", "SEASONAL PATTERNS", "MOUNTAIN GRASSLANDS", "Grazing Exclusion", "Tibetan Plateau", "PLANT-COMMUNITIES", "Original Research", "2. Zero hunger", "Science & Technology", "CLIMATE-CHANGE", "CO2 EFFLUX", "Ecology", "04 agricultural and veterinary sciences", "15. Life on land", "INNER-MONGOLIA", "BELOW-GROUND BIOMASS", "Soil Respiration", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Life Sciences & Biomedicine"]}, "links": [{"href": "https://doi.org/10.1002/ece3.1867"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology%20and%20Evolution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ece3.1867", "name": "item", "description": "10.1002/ece3.1867", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ece3.1867"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-11T00:00:00Z"}}, {"id": "10.1002/ecy.2199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:14:08Z", "type": "Journal Article", "created": "2018-02-27", "title": "Temperature and aridity regulate spatial variability of soil multifunctionality in drylands across the globe", "description": "AbstractThe relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.