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": "10.1016/j.agrformet.2018.04.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:15:35Z", "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": "10.1016/j.agwat.2021.107290", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:15:37Z", "type": "Journal Article", "created": "2021-11-22", "title": "Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions", "description": "Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ET c act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET c act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK c. Surface SM observations were assimilated into the soil evaporation (E s) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T c act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K s) as a proxy of LST (LST proxy). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K s estimates from FAO-56 model and thermal-derived K s. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET c act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK c using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET c act measurements.", "keywords": ["2. Zero hunger", "0106 biological sciences", "Evapotranspiration", "550", "Evapotranspiration Data assimilation FAO-dualK c Soil moisture Land surface temperature", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "FAO-dualK(c)", "13. Climate action", "Data assimilation", "[SDU.STU.HY] Sciences of the Universe [physics]/Earth Sciences/Hydrology", "Soil moisture", "[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "Land surface temperature"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.107290"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2021.107290", "name": "item", "description": "10.1016/j.agwat.2021.107290", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.107290"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "10.1016/j.agwat.2021.106827", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:15:37Z", "type": "Journal Article", "created": "2021-02-27", "title": "Implementing a new texture-based soil evaporation reduction coefficient in the FAO dual crop coefficient method", "description": "Abstract Crop evapotranspiration (ET) is a fundamental component of the hydrological cycle, especially in arid/semi-arid regions. The FAO-56 offers an operational method for deriving ET from the reduction (dual crop coefficient Kc) of the atmospheric evaporative demand (ET0). The dual coefficient approach (FAO-2Kc) is intended to improve the daily estimation of ET by separating the contribution of bare soil evaporation (E) and crop transpiration components. The FAO-2Kc has been a well-known reference for the operational monitoring of crop water needs. However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning. This paper aims at improving the soil module of the FAO-2Kc by modifying the E reduction coefficient (Kr) according to soil texture information and state-of-the-art formulations, hence, to amend the mismatch between FAO-2Kc and field-measured data beyond standard conditions. In practice this work evaluates the performance of two evaporation models, using the classical Kr (Kr,FAO) and a new texture-based Kr (Kr,text) over 33 bare soil sites under different evaporative demand and soil conditions. An offline validation is investigated by forcing both models with observed soil moisture ( \u03b8 s ) data as input. The Kr,text methodology provides more accurate E estimations compared to the Kr,FAO method and systematically reduces biases. Using Kr,text allows reaching the lowest root means square error (RMSE) of 0.16\u2009mm/day compared to the Kr,FAO where the lowest RMSE reached is 0.88\u2009mm/day. As a step further in the assessment of the proposed methodology, ET was estimated in three wheat fields across the entire agricultural season. Both approaches were thus inter-compared in terms of ET estimates forced by SM estimated as a residual of the water balance model (online validation). Compared to ET measurements, the new formulation provided more accurate results. The RMSE was 0.66\u2009mm/day (0.71\u2009mm/day) and the R2 was 0.83 (0.78) for the texture-based (classical) Kr.", "keywords": ["0106 biological sciences", "2. Zero hunger", "570", "Evapotranspiration", "Soil texture", "FAO-2Kc", "0207 environmental engineering", "Soil moisture", "02 engineering and technology", "15. Life on land", "Soil evaporation", "01 natural sciences", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.106827"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2021.106827", "name": "item", "description": "10.1016/j.agwat.2021.106827", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.106827"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-01T00:00:00Z"}}, {"id": "/publisher", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:14:21Z", "type": "Dataset", "title": "Soil moisture and temperature datasets in LIDO orchard", "description": "Soil moisture and temperature timeserie datasets of the apple orchard located in the Laimburg Integrated Digital Orchard in Bolzano province. 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": "2026-10-06T16:14:20Z", "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. The data from the sensor were collected by two networks: a Zigbee-based and one LoraWAN-based network.", "keywords": ["capacitive", "environmental-monitoring-facilities", "eu", "humidity", "irrigation", "lorawan", "mais", "sensor", "soil", "soil-moisture", "tdr", "temperature", "water", "zigbee"]}, "links": [{"href": "http://data.europa.eu/88u/dataset/17af841d-1329-4c5a-a8b8-c4326f0614f9"}, {"rel": "self", "type": "application/geo+json", "title": "/", "name": "item", "description": "/", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items//"}, {"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": "00370eef-d266-4f5d-9c79-b93488983b54", "type": "Feature", "geometry": null, "properties": {"updated": "2025-09-16T06:24:05.968528", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-09-15 ore 12 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). Corsa del 2025-09-15 ore 12 UTC - Valido dalle ore 12 UTC del 2025-09-15 alle ore 00 UTC del 2025-09-19. 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", "2025-09-15", "20250915t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "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-umidita-del-suolo-volumetrica-2025-09-15-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250915T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/00370eef-d266-4f5d-9c79-b93488983b54"}, {"rel": "self", "type": "application/geo+json", "title": "00370eef-d266-4f5d-9c79-b93488983b54", "name": "item", "description": "00370eef-d266-4f5d-9c79-b93488983b54", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/00370eef-d266-4f5d-9c79-b93488983b54"}, {"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": "00682004-c6b9-4c1d-8b40-3afff8bbec69", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[11.16, 47.52], [11.16, 47.52], [11.16, 47.52], [11.16, 47.52], [11.16, 47.52]]]}, "properties": {"themes": [{"concepts": [{"id": "climatologyMeteorologyAtmosphere"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "environmental factors"}, {"id": "water"}, {"id": "Soil analysis"}, {"id": "Soil"}, {"id": "soil amendments"}, {"id": "Soil biology"}, {"id": "Temperature profile"}, {"id": "moisture content"}, {"id": "Temperature"}, {"id": "Soil temperature"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "soil profile"}, {"id": "soil moisture"}, {"id": "temperature"}], "scheme": "GEMET - Concepts, version 2.4"}, {"concepts": [{"id": "farming systems"}, {"id": "Grassland management"}, {"id": "Grassland soils"}, {"id": "grasslands"}, {"id": "permanent grasslands"}, {"id": "agriculture"}, {"id": "agricultural practices"}, {"id": "Climatic change"}], "scheme": "AGROVOC Multilingual agricultural thesaurus"}, {"concepts": [{"id": "Boden"}], "scheme": "GEMET - INSPIRE themes, version 1.0"}, {"concepts": [{"id": "opendata"}], "scheme": "Individual"}], "rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. (e.g. 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: \u201cData re-used from the BonaRes Data Centre www.bonares.de. This data were created as part of BonaRes Module A-Project - SUSALPS's research activities.\u201d Although every care has been taken in preparing and testing the data, BonaRes Module A-Project- SUSALPS and BonaRes Data Centre cannot guarantee that the data are correct; neither does BonaRes Module A-Project-SUSALPS and 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. The BonaRes Module A-Project-SUSALPS and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data. The access to this data is restricted during embargo time. If prior access is requested, contact the data owner/author.)", "updated": "2020-02-14", "type": "Dataset", "created": "2018-12-05", "language": "eng", "title": "SUSALPS temperature and volumetric soil water content Esterberg Subplot 3 in Esterberg intensiv", "description": "Grassland is a precious good. Grassland contributes to food security by providing fodder for dairy and beef farming, storing nutrients and increasing biodiversity. These functions that secure the fertility and yields of soil are jeopardized by climate change, especially in monane and alpine areas.\nIn SUSALPS, scientists, authorities and farmers work together to investigate the influence of climate change on i) plant biodiversity, ii) C and N storage, iii) greenhouse gas exchange, iv) socio economic conditions that influence decision making of farmers.\nA central experimental aspect is the translocation of soil mesocosms from higher elevation to lower elevation (Esterberg site at 1200m, Graswang site at 860m, Fendt at 600m, Bayreuth at 300m). To reflect the spatial heterogeneity of soils, mesocosms from three different subplots approx. 100-300m apart from each other are translocated. 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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", "2025-06-21", "20250621t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "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-umidita-del-suolo-volumetrica-2025-06-21-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250621T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0c78250a-cf71-47f0-9f23-7cf6c8da13d4"}, {"rel": "self", "type": "application/geo+json", "title": "0c78250a-cf71-47f0-9f23-7cf6c8da13d4", "name": "item", "description": "0c78250a-cf71-47f0-9f23-7cf6c8da13d4", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0c78250a-cf71-47f0-9f23-7cf6c8da13d4"}, {"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": "0d2617d0-9ab4-496d-8895-6ef414b07bea", "type": "Feature", "geometry": null, "properties": {"updated": "2025-08-15T06:23:07.037813", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Umidit\u00e0 del suolo (volumetrica) - (2025-08-14 ore 12 UTC).", "description": "Umidit\u00e0 del suolo (volumetrica). Corsa del 2025-08-14 ore 12 UTC - Valido dalle ore 12 UTC del 2025-08-14 alle ore 00 UTC del 2025-08-18. 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", "2025-08-14", "20250814t120000000z", "3km", "arw", "below", "between", "content", "depths", "it", "lamma", "layer", "moisture", "soil", "surface", "two", "volumetric"], "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-umidita-del-suolo-volumetrica-2025-08-14-ore-12-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run12/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z/arw_3km_Volumetric_soil_moisture_content_layer_between_two_depths_below_surface_layer_20250814T120000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/0d2617d0-9ab4-496d-8895-6ef414b07bea"}, {"rel": "self", "type": "application/geo+json", "title": "0d2617d0-9ab4-496d-8895-6ef414b07bea", "name": "item", "description": "0d2617d0-9ab4-496d-8895-6ef414b07bea", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0d2617d0-9ab4-496d-8895-6ef414b07bea"}, {"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": "10.1002/2015wr018233", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:14:28Z", "type": "Journal Article", "created": "2016-04-20", "title": "Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta\u2010analysis approach", "description": "AbstractA meta\uffe2\uff80\uff90analysis data\uffe2\uff80\uff90driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02\uffe2\uff80\uff930.56, a sand fraction range of 0.05\uffe2\uff80\uff930.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (\uffce\uffb8) and two resistance parameters and \uffce\uffb8efolding. The data\uffe2\uff80\uff90driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut\uffe2\uff80\uff90off soil moisture value at which SEE=0.5, and first derivative of SEE at , named . An analytical relationship between and is first built by running a soil energy balance model for two extreme conditions with rss\uffe2\uff80\uff89=\uffe2\uff80\uff890 and using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair either from the time series of SEE and \uffce\uffb8 observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear relationships and potentially large random deviations of observed SEE from the mean observed . The second method parameterizes as a multi\uffe2\uff80\uff90linear regression of clay and sand percentages, and sets to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol\uffe2\uff80\uff90Biosph\uffc3\uffa8re\uffe2\uff80\uff90Atmosph\uffc3\uffa8re), H\uffe2\uff80\uff90TESSEL (Hydrology\uffe2\uff80\uff90Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land\uffe2\uff80\uff90surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.A new diagnostic metric based on soil moisture bimodality is developed in order to examine and compare soil moisture from satellite observations and Earth System Models. The methodology to derive this diagnostic is based on maximum likelihood estimator encoded into an iterative algorithm, which is applied to the soil moisture probability density function. This metric is applied to satellite data from the Advanced Microwave Scanning Radiometer for the Earth Observing System and global climate models data from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Results show high soil moisture bimodality in transitional climate areas and high latitudes, potentially associated with land\uffe2\uff80\uff90atmosphere feedback processes. When comparing satellite versus climate models, a clear difference in their soil moisture bimodality is observed, with systematically higher values in the case of CMIP5 models. These differences appear related to areas where land\uffe2\uff80\uff90atmospheric feedback may be overestimated in current climate models.
", "keywords": ["PREFERENTIAL STATES", "IMPACT", "MIXTURE", "SCHEME", "0207 environmental engineering", "NORMAL-DISTRIBUTIONS", "02 engineering and technology", "15. Life on land", "01 natural sciences", "PART I", "satellite soil moisture", "climate models", "13. Climate action", "Earth and Environmental Sciences", "LAND-SURFACE MODEL", "PRECIPITATION", "SDG 13 - Climate Action", "CMIP5", "ATMOSPHERE COUPLING EXPERIMENT", "land-atmosphere interactions", "soil moisture", "bimodality", "SYSTEM", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026099"}, {"href": "https://doi.org/10.1002/2016JD026099"}, {"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": "10.1002/2016JD026099", "name": "item", "description": "10.1002/2016JD026099", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2016JD026099"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-04-27T00:00:00Z"}}, {"id": "10.1002/2016rg000543", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:14:28Z", "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.Catchment hydrological responses to precipitation inputs, particularly during exceptionally large storms, are complex and variable, and our understanding of the associated runoff generation processes during those events is limited. Hydrological monitoring of climatically and hydrologically distinct catchments can help to improve this understanding by shedding light on the interplay between antecedent soil moisture conditions, hydrological connectivity, and rainfall event characteristics. This knowledge is urgently needed considering that both the frequency and magnitude of extreme precipitation events are increasing worldwide as a consequence of climate change. In autumn 2018, we installed water level sensors to monitor stream water and near\uffe2\uff80\uff90stream groundwater levels at two Mediterranean forest headwater catchments with contrasting hydrological regimes: Font del Reg\uffc3\uffa0s (sub\uffe2\uff80\uff90humid climate, perennial flow regime) and Fuirosos (semi\uffe2\uff80\uff90arid climate, intermittent flow regime). Both catchments are located in northeastern Spain, where the extratropical cyclone Gloria hit in January 2020 and left in ca. 65\uffe2\uff80\uff89h outstanding accumulated rainfalls of 424\uffe2\uff80\uff89mm in Font del Reg\uffc3\uffa0s and 230\uffe2\uff80\uff89mm in Fuirosos. During rainfall events of low mean intensity, hydrological responses to precipitation inputs at the semi\uffe2\uff80\uff90arid Fuirosos were more delayed and more variable than at the sub\uffe2\uff80\uff90humid Font del Reg\uffc3\uffa0s. We explain these divergences by differences in antecedent soil moisture conditions and associated differences in catchment hydrological connectivity between the two catchments, which in this case are likely driven by differences in local climate rather than by differences in local topography. In contrast, during events of moderate and high mean rainfall intensities, including the storm Gloria, precipitation inputs and hydrological responses correlated similarly in the two catchments. We explain this convergence by rapid development of hydrological connectivity independently of antecedent soil moisture conditions. The data set presented here is unique and contributes to our mechanistic understanding on how streams respond to rainfall events and exceptionally large storms in catchments with contrasting flow regimes.
", "keywords": ["info:eu-repo/classification/ddc/550", "550", "ddc:550", "rainfall intensity", "climate extreme", "15. Life on land", "551", "extreme hydrological event", "01 natural sciences", "6. Clean water", "antecedent soil moisture conditions", "Earth sciences", "13. Climate action", "heavy rainfall", "Mediterranean climate", "catchment hydrological connectivity", "environmental monitoring", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14451"}, {"href": "https://doi.org/10.1002/hyp.14451"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrological%20Processes", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/hyp.14451", "name": "item", "description": "10.1002/hyp.14451", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/hyp.14451"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-01T00:00:00Z"}}, {"id": "10.1002/hyp.14667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-10-06T16:14:31Z", "type": "Journal Article", "created": "2022-08-09", "title": "Non\u2010linearity in event runoff generation in a small agricultural catchment", "description": "AbstractUnderstanding the role of soil moisture and other controls in runoff generation is important for predicting runoff across scales. This paper aims to identify the degree of non\uffe2\uff80\uff90linearity of the relationship between event peak runoff and potential controls for different runoff generation mechanisms in a small agricultural catchment. The study is set in the 66\uffe2\uff80\uff89ha Hydrological Open Air Laboratory, Austria, where discharge was measured at the catchment outlet and for 11 sub\uffe2\uff80\uff90catchments or hillslopes with different runoff generation mechanisms. Peak runoff of 73 events was related to three potential controls: event precipitation, soil moisture and groundwater levels. The results suggest that the hillslopes dominated by ephemeral overland flow exhibit the most non\uffe2\uff80\uff90linear runoff generation behaviour for its controls; runoff is only generated above a threshold of 95% of the maximum soil moisture. Runoff generation through tile drains and in wetlands is more linear. The largest winter and spring events at the catchment outlet are caused by runoff from hillslopes with shallow flow paths (ephemeral overland flow and tile drainage mechanisms), while the largest summer events are caused by other hillslopes, those with deeper flow paths or with saturation areas throughout the year. Therefore, the response of the entire catchment is a mix of the various mechanisms, and the groundwater contribution makes the response more linear. The implications for hydrological modelling are discussed.