{"type": "FeatureCollection", "features": [{"id": "1887/4246123", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:28Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["Inland waters", "N2O cycling", " long-term temporal changes", "long-term temporal changes", "Nitrous oxide", "Asia", " Southern", "Nitrous Oxide", "Integrated process-based modeling", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "Greenhouse gas emission", "13. Climate action", "Environmental Chemistry", "14. Life underwater", "Spatial distributions", "closed N2O budget"]}, "links": [{"href": "https://doi.org/1887/4246123"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1887/4246123", "name": "item", "description": "1887/4246123", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1887/4246123"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "10.1016/j.eja.2022.126569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:33Z", "type": "Journal Article", "created": "2022-07-08", "title": "Mixing process-based and data-driven approaches in yield prediction", "description": "Yield prediction models can be divided between data-driven and process-based models (crop growth models). The first category contains many different types of models with parameters learned from the data themselves and where domain knowledge is only used to select the predictors and engineer features. In the second category, models are based upon biophysical principles, whose structure and parameters are derived primarily from domain knowledge. Here we investigate if the integration of the two approaches can be beneficial as it allows to overcome the limitations of the two approaches taken individually - lack of sufficiently large, reliable and orthogonal datasets for data-driven approaches and the need of many inputs for process-based models. The applications of the two categories of models have been reviewed, paying special attention to the cases where the two approaches have been mixed. By analysing the literature we identified three major cases of integration between the two approaches: (1) using crop growth models to engineer features and expand the predictors space, (2) use data-driven approaches to estimate missing inputs for process-based models (3) using data-driven approaches to produce meta-models to reduce computation burden. Finally we propose a methodology based on metamodels and transfer learning to integrate data-driven and process-based approaches.", "keywords": ["Process-based", "0106 biological sciences", "2. Zero hunger", "Artificial intelligence", "Crop growth models", "04 agricultural and veterinary sciences", "Data-driven", "01 natural sciences", "Yield prediction", "Dynamic crop growth models", "Surrogate models", "0401 agriculture", " forestry", " and fisheries", "Crop models", "Metamodels", "Neural networks"]}, "links": [{"href": "https://doi.org/10.1016/j.eja.2022.126569"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.eja.2022.126569", "name": "item", "description": "10.1016/j.eja.2022.126569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.eja.2022.126569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1021/acs.est.1c04605", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:32Z", "type": "Journal Article", "created": "2021-12-02", "title": "Exploring Spatially Explicit Changes in Carbon Budgets of Global River Basins during the 20th Century", "description": "Rivers play an important role in the global carbon (C) cycle. However, it remains unknown how long-term river C fluxes change because of climate, land-use, and other environmental changes. Here, we investigated the spatiotemporal variations in global freshwater C cycling in the 20th century using the mechanistic IMAGE-Dynamic Global Nutrient Model extended with the Dynamic In-Stream Chemistry Carbon module (DISC-CARBON) that couples river basin hydrology, environmental conditions, and C delivery with C flows from headwaters to mouths. The results show heterogeneous spatial distribution of dissolved inorganic carbon (DIC) concentrations in global inland waters with the lowest concentrations in the tropics and highest concentrations in the Arctic and semiarid and arid regions. Dissolved organic carbon (DOC) concentrations are less than 10 mg C/L in most global inland waters and are generally high in high-latitude basins. Increasing global C inputs, burial, and CO2 emissions reported in the literature are confirmed by DISC-CARBON. Global river C export to oceans has been stable around 0.9 Pg yr-1. The long-term changes and spatial patterns of concentrations and fluxes of different C forms in the global river network unfold the combined influence of the lithology, climate, and hydrology of river basins, terrestrial and biological C sources, in-stream C transformations, and human interferences such as damming.", "keywords": ["global budget", "Arctic Regions", "Fresh Water", "General Chemistry", "15. Life on land", "carbon biogeochemistry", "Dissolved Organic Matter", "01 natural sciences", "river fluxes", "6. Clean water", "process-based hydrology-biogeochemistry model", "Rivers", "13. Climate action", "SDG 13 - Climate Action", "Environmental Chemistry", "Humans", "SDG 14 - Life Below Water", "14. Life underwater", "Hydrology", "spatiotemporal variations", "SDG 15 - Life on Land", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://pubs.acs.org/doi/pdf/10.1021/acs.est.1c04605"}, {"href": "https://doi.org/10.1021/acs.est.1c04605"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1021/acs.est.1c04605", "name": "item", "description": "10.1021/acs.est.1c04605", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1021/acs.est.1c04605"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "10.1021/acs.est.3c04230", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:32Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["inland waters", "Inland waters", "Asia", " Southern", "NO cycling", "Nitrous Oxide", "Integrated process-based modeling", "Greenhouse gas emission", "greenhouse gas emission", "Environmental Chemistry", "14. Life underwater", "closed N2O budget", "integrated process-based modeling", "N2O cycling", " long-term temporal changes", "Nitrous oxide", "long-term temporal changes", "nitrous oxide", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "closed NO budget", "13. Climate action", "spatial distributions", "Spatial distributions"]}, "links": [{"href": "https://doi.org/10.1021/acs.est.3c04230"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1021/acs.est.3c04230", "name": "item", "description": "10.1021/acs.est.3c04230", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1021/acs.est.3c04230"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "10.1111/gcb.15441", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:17:34Z", "type": "Journal Article", "created": "2020-11-07", "title": "Ensemble modelling, uncertainty and robust predictions of organic carbon in long\u2010term bare\u2010fallow soils", "description": "Abstract<p>Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate\uffe2\uff80\uff90change studies. It is imperative to increase confidence in long\uffe2\uff80\uff90term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process\uffe2\uff80\uff90based C models by comparing simulations to experimental data from seven long\uffe2\uff80\uff90term bare\uffe2\uff80\uff90fallow (vegetation\uffe2\uff80\uff90free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi\uffe2\uff80\uff90year simulation periods (from 28 to 80\uffc2\uffa0years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge\uffe2\uff80\uff90based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin\uffe2\uff80\uff90up initialization of SOC. Changes in the multi\uffe2\uff80\uff90model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (\uffc2\uffb115.5)\uffc2\uffa0Mg\uffc2\uffa0C/ha compared to the observed mean of 36.0 (\uffc2\uffb119.7)\uffc2\uffa0Mg\uffc2\uffa0C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5\uffc2\uffa0\uffc2\uffb1\uffc2\uffa016.7\uffc2\uffa0Mg\uffc2\uffa0C/ha) and Spe (36.8\uffc2\uffa0\uffc2\uffb1\uffc2\uffa019.8\uffc2\uffa0Mg\uffc2\uffa0C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.</p", "keywords": ["[SDE] Environmental Sciences", "330", "550", "Supplementary Data", "soil organic carbon dynamics", "QH301 Biology", "[SDE.MCG]Environmental Sciences/Global Changes", "Soil organic carbon dynamics", "bare\u2010fallow soils", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "630", "protocol for model comparison", "Russia", "QH301", "Soil", "NE/M021327/1", "SDG 13 - Climate Action", "Environmental Chemistry", "774378", "process based models", "European Commission", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "General Environmental Science", "Sweden", "Global and Planetary Change", "Ecology", "Natural Environment Research Council (NERC)", "NE/P019455/1", "bare-fallow soils", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Carbon", "United Kingdom", "process-based models", "[SDE.MCG] Environmental Sciences/Global Changes", "13. Climate action", "[SDE]Environmental Sciences", "bare-fallow soils; model parametrization; process-based models; protocol for model comparison; soil organic carbon dynamics", "0401 agriculture", " forestry", " and fisheries", "774124", "France", "bare fallow soils", "model parametrization"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/809186/2/GCB-20-1834_Proof_fl.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15441"}, {"href": "https://doi.org/10.1111/gcb.15441"}, {"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.15441", "name": "item", "description": "10.1111/gcb.15441", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.15441"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.14243689", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:36Z", "type": "Report", "title": "A digital twin for arable crops and for grass", "description": "There is an opportunity to use process-based cropping systems models (CSMs) to support tactical farm management decisions, by monitoring the status of the farm, by predicting what will happen in the next few weeks, and by exploring scenarios. In practice, the responses of a CSM will deviate more and more from reality as time progresses because the model is an abstraction of the real system and only approximates the responses of the real system. This limitation may be overcome by using the CSM as a digital twin. A digital twin (DT) is a model of a specific physical object, that is kept synchronized by using real-time observations on that object. In this paper we present the Digital Future Farm (DFF), a digital twin for arable and dairy farming. The DFF comprises access to data sources (e.g. weather, soils, farm management, remote sensing), a suite of models, and utilities for data assimilation and visualization of simulation results. The working of the DFF is demonstrated with examples from a multi-year experiment and from a commercial potato farm. In addition to a CSM, the DFF is also demonstrated to work with a summary model for potato growth. Initial experiences\u00a0indicate that the DFF produces information that is helpful to farmers but it is difficult to evaluate\u00a0the performance of the DFF in quantitative terms because of variability between years, fields,\u00a0and the lack of availability of on-farm data. The most immediate contribution of the DFF is to\u00a0provide farmers with a ranking of their fields according to how urgently they need an\u00a0intervention. Experiences with the DFF have helped to formulate further research questions.", "keywords": ["machine learning", "fertilization", "Life Science", "process-based model", "recursive neural network", "Kalman filter", "data assimilation", "nitrogen", "irrigation"], "contacts": [{"organization": "van Evert, F.K., Boersma, S., van Oort, P.A.J., Maestrini, B., Kopanja, M., Mimic, Gordan, Pronk, A.A.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14243689"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14243689", "name": "item", "description": "10.5281/zenodo.14243689", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14243689"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.2166/wst.2021.611", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:19:03Z", "type": "Journal Article", "created": "2021-12-04", "title": "Determining the impact of flow velocities on reactive processes associated with Enterococcus faecalis JH2-2", "description": "Abstract                <p>This study focuses on the impact of infiltration rates on colloidal transport and reactive processes associated with Enterococcus faecalis JH2-2 using water-saturated sediment columns. The infiltration rates influence the physical transport of bacteria by controlling the mean flow velocity. This, in turn, impacts biological processes in pore water owing to the higher or lower residence time of the bacteria in the column. In the present study, continuous injection of E. faecalis (suspended in saline water with varying conditions of dissolved oxygen and nutrient concentrations) into a lab-scale sediment column was performed at flow velocities of 0.02 cm min\uffe2\uff88\uff921 and 0.078 cm min\uffe2\uff88\uff921, i.e., at residence times of 1\uffe2\uff80\uff935 hours. The impact of residence times on reactive processes is significant for field scale setups. A process-based model with a first-order rate coefficient for each biological process was fitted for each obtained condition-specific dataset from the experimental observations (breakthrough curves). The coefficients were converted to a dimensionless form to facilitate the comparison of biological processes. These results indicate that the processes of attachment and growth were flow-dependent. The growth process in the absence of dissolved oxygen was the most dominant process, with a Damkoehler number of approximately 48.</p>", "keywords": ["enterococcus", "Bacteria", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "Environmental technology. Sanitary engineering", "6. Clean water", "bacteria transport", "colloidal transport", "damkoehler numbers", "Enterococcus faecalis", "Water Movements", "process-based model", "TD1-1066"]}, "links": [{"href": "https://iwaponline.com/wst/article-pdf/85/1/485/985586/wst085010485.pdf"}, {"href": "https://doi.org/10.2166/wst.2021.611"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water%20Science%20and%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2166/wst.2021.611", "name": "item", "description": "10.2166/wst.2021.611", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2166/wst.2021.611"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-04T00:00:00Z"}}, {"id": "10.5281/zenodo.14391888", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:20:37Z", "type": "Other", "title": "The effects of peat thickness and water table depth on the CO2 emissions of an agricultural peatland - a process-based modelling approach", "keywords": ["peatland", "process-based modelling", "CO2 emissions"], "contacts": [{"organization": "Kajasilta, Henri", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14391888"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14391888", "name": "item", "description": "10.5281/zenodo.14391888", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14391888"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-08T00:00:00Z"}}, {"id": "10.5281/zenodo.5040380", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:21:10Z", "type": "Dataset", "title": "Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping", "description": "Open AccessThis work was supported by the National Key Research and Development Program of China (2017YFA0603002).", "keywords": ["2. Zero hunger", "Digital soil mapping", "Soil organic carbon", "Process-based SOC model", "15. Life on land", "Long-time series", "Space-for-time substitution"], "contacts": [{"organization": "Zhao, Yongcun, Xie, Enze, Zhang, Xiu, Peng, Yuxuan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5040380"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5040380", "name": "item", "description": "10.5281/zenodo.5040380", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5040380"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-29T00:00:00Z"}}, {"id": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:28Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["inland waters", "Inland waters", "Asia", " Southern", "NO cycling", "Nitrous Oxide", "Integrated process-based modeling", "Greenhouse gas emission", "greenhouse gas emission", "Environmental Chemistry", "14. Life underwater", "closed N2O budget", "integrated process-based modeling", "N2O cycling", " long-term temporal changes", "Nitrous oxide", "long-term temporal changes", "nitrous oxide", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "closed NO budget", "13. Climate action", "spatial distributions", "Spatial distributions"]}, "links": [{"href": "https://doi.org/1871.1/c6f52add-8521-4556-b94a-6153906d8366"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "name": "item", "description": "1871.1/c6f52add-8521-4556-b94a-6153906d8366", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/c6f52add-8521-4556-b94a-6153906d8366"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "1871.1/d6f80066-698b-4a74-a44e-d83364279614", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:28Z", "type": "Journal Article", "created": "2021-12-02", "title": "Exploring Spatially Explicit Changes in Carbon Budgets of Global River Basins during the 20th Century", "description": "Rivers play an important role in the global carbon (C) cycle. However, it remains unknown how long-term river C fluxes change because of climate, land-use, and other environmental changes. Here, we investigated the spatiotemporal variations in global freshwater C cycling in the 20th century using the mechanistic IMAGE-Dynamic Global Nutrient Model extended with the Dynamic In-Stream Chemistry Carbon module (DISC-CARBON) that couples river basin hydrology, environmental conditions, and C delivery with C flows from headwaters to mouths. The results show heterogeneous spatial distribution of dissolved inorganic carbon (DIC) concentrations in global inland waters with the lowest concentrations in the tropics and highest concentrations in the Arctic and semiarid and arid regions. Dissolved organic carbon (DOC) concentrations are less than 10 mg C/L in most global inland waters and are generally high in high-latitude basins. Increasing global C inputs, burial, and CO2 emissions reported in the literature are confirmed by DISC-CARBON. Global river C export to oceans has been stable around 0.9 Pg yr-1. The long-term changes and spatial patterns of concentrations and fluxes of different C forms in the global river network unfold the combined influence of the lithology, climate, and hydrology of river basins, terrestrial and biological C sources, in-stream C transformations, and human interferences such as damming.", "keywords": ["global budget", "Arctic Regions", "Fresh Water", "General Chemistry", "15. Life on land", "carbon biogeochemistry", "Dissolved Organic Matter", "01 natural sciences", "river fluxes", "6. Clean water", "process-based hydrology-biogeochemistry model", "Rivers", "13. Climate action", "SDG 13 - Climate Action", "Environmental Chemistry", "Humans", "SDG 14 - Life Below Water", "14. Life underwater", "Hydrology", "spatiotemporal variations", "SDG 15 - Life on Land", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://pubs.acs.org/doi/pdf/10.1021/acs.est.1c04605"}, {"href": "https://doi.org/1871.1/d6f80066-698b-4a74-a44e-d83364279614"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/d6f80066-698b-4a74-a44e-d83364279614", "name": "item", "description": "1871.1/d6f80066-698b-4a74-a44e-d83364279614", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/d6f80066-698b-4a74-a44e-d83364279614"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "20.500.11850/688246", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:37Z", "type": "Journal Article", "created": "2024-07-29", "title": "Hydro-pedotransfer functions: a roadmap for future development", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Hydro-pedotransfer functions\u00a0(PTFs) relate easy-to-measure and readily available soil information to soil hydraulic properties\u00a0(SHPs) for applications in a wide range of process-based and empirical models, thereby enabling the assessment of soil hydraulic effects on hydrological, biogeochemical, and ecological processes. At least more than 4 decades of research have been invested to derive such relationships. However, while models, methods, data storage capacity, and computational efficiency have advanced, there are fundamental concerns related to the scope and adequacy of current PTFs, particularly when applied to parameterise models used at the field scale and beyond. Most of the PTF development process has focused on refining and advancing the regression methods, while fundamental aspects have remained largely unconsidered. Most soil systems are not represented in PTFs, which have been built mostly for agricultural soils in temperate climates. Thus, existing PTFs largely ignore how parent material, vegetation, land use, and climate affect processes that shape SHPs. The PTFs used to parameterise the Richards\u2013Richardson equation are mostly limited to predicting parameters of the van\u00a0Genuchten\u2013Mualem soil hydraulic functions, despite sufficient evidence demonstrating their shortcomings. Another fundamental issue relates to the diverging scales of derivation and application, whereby PTFs are derived based on laboratory measurements while often being applied at the field to regional scales. Scaling, modulation, and constraining strategies exist to alleviate some of these shortcomings in the mismatch between scales. These aspects are addressed here in a joint effort by the members of the International Soil Modelling Consortium\u00a0(ISMC) Pedotransfer Functions Working Group with the aim of systematising PTF research and providing a roadmap guiding both PTF development and use. We close with a 10-point catalogue for funders and researchers to guide review processes and research.</p></article>", "keywords": ["Technology", "550", "Bodenanalyse", "Modell", "SPHAGNUM MOSS", "Environmental technology. Sanitary engineering", "630", "Ing\u00e9nierie", " informatique & technologie", "Biogeochemical process", "Earth and Planetary Sciences (miscellaneous)", "Geography. Anthropology. Recreation", "GE1-350", "SATURATED HYDRAULIC CONDUCTIVITY", "Geosciences", " Multidisciplinary", "TD1-1066", "Water Science and Technology", "2. Zero hunger", "T", "Geology", "Hydraulics effects", "Agriculture & agronomy", "Life sciences", "Daten", "Pedo-transfer functions", "6. Clean water", "Soil hydraulics", "REFLECTANCE SPECTROSCOPY", "Roadmap", "Physical Sciences", "Sciences du vivant", "Water Resources", "SOIL-WATER-RETENTION", "0406 Physical Geography and Environmental Geoscience", "3709 Physical geography and environmental geoscience", "Process-based modeling", "Environmental Engineering", "Physique", " chimie", " math\u00e9matiques & sciences de la terre", "PHYSICAL-PROPERTIES", "SENSITIVITY-ANALYSIS", "Soil hydraulic properties", "0905 Civil Engineering", "333", "G", "Physical", " chemical", " mathematical & earth Sciences", "Empirical model", "Agriculture & agronomie", "Life Science", "UNSATURATED CONDUCTIVITY", "SEASONAL-CHANGES", "Pedotransfer functions", "HYSTERETIC MOISTURE PROPERTIES", "info:eu-repo/classification/ddc/550", "Science & Technology", "3707 Hydrology", "Physikochemische Bodeneigenschaft", "500", "15. Life on land", "Engineering", " computing & technology", "Sciences de la terre & g\u00e9ographie physique", "Environmental sciences", "0907 Environmental Engineering", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "Earth sciences & physical geography", "HETEROGENEOUS SOILS", "4013 Geomatic engineering", "ITC-GOLD", "Hydrological process"]}, "links": [{"href": "https://orbi.uliege.be/bitstream/2268/321088/1/hess-28-3391-2024.pdf"}, {"href": "https://hess.copernicus.org/articles/28/3391/2024/hess-28-3391-2024.pdf"}, {"href": "https://doi.org/20.500.11850/688246"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/688246", "name": "item", "description": "20.500.11850/688246", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/688246"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-29T00:00:00Z"}}, {"id": "3215981337", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:23:15Z", "type": "Journal Article", "created": "2021-12-02", "title": "Exploring Spatially Explicit Changes in Carbon Budgets of Global River Basins during the 20th Century", "description": "Rivers play an important role in the global carbon (C) cycle. However, it remains unknown how long-term river C fluxes change because of climate, land-use, and other environmental changes. Here, we investigated the spatiotemporal variations in global freshwater C cycling in the 20th century using the mechanistic IMAGE-Dynamic Global Nutrient Model extended with the Dynamic In-Stream Chemistry Carbon module (DISC-CARBON) that couples river basin hydrology, environmental conditions, and C delivery with C flows from headwaters to mouths. The results show heterogeneous spatial distribution of dissolved inorganic carbon (DIC) concentrations in global inland waters with the lowest concentrations in the tropics and highest concentrations in the Arctic and semiarid and arid regions. Dissolved organic carbon (DOC) concentrations are less than 10 mg C/L in most global inland waters and are generally high in high-latitude basins. Increasing global C inputs, burial, and CO2 emissions reported in the literature are confirmed by DISC-CARBON. Global river C export to oceans has been stable around 0.9 Pg yr-1. The long-term changes and spatial patterns of concentrations and fluxes of different C forms in the global river network unfold the combined influence of the lithology, climate, and hydrology of river basins, terrestrial and biological C sources, in-stream C transformations, and human interferences such as damming.", "keywords": ["global budget", "Arctic Regions", "Fresh Water", "General Chemistry", "15. Life on land", "carbon biogeochemistry", "Dissolved Organic Matter", "01 natural sciences", "river fluxes", "6. Clean water", "process-based hydrology-biogeochemistry model", "Rivers", "13. Climate action", "SDG 13 - Climate Action", "Environmental Chemistry", "Humans", "SDG 14 - Life Below Water", "14. Life underwater", "Hydrology", "spatiotemporal variations", "SDG 15 - Life on Land", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://pubs.acs.org/doi/pdf/10.1021/acs.est.1c04605"}, {"href": "https://doi.org/3215981337"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3215981337", "name": "item", "description": "3215981337", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3215981337"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-02T00:00:00Z"}}, {"id": "2164/15915", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:45Z", "type": "Journal Article", "created": "2020-07-01", "title": "Global Research Alliance N2O chamber methodology guidelines: Summary of modeling approaches", "description": "Abstract<p>Measurements of nitrous oxide (N2O) emissions from agriculture are essential for understanding the complex soil\uffe2\uff80\uff93crop\uffe2\uff80\uff93climate processes, but there are practical and economic limits to the spatial and temporal extent over which measurements can be made. Therefore, N2O models have an important role to play. As models are comparatively cheap to run, they can be used to extrapolate field measurements to regional or national scales, to simulate emissions over long time periods, or to run scenarios to compare mitigation practices. Process\uffe2\uff80\uff90based models can also be used as an aid to understanding the underlying processes, as they can simulate feedbacks and interactions that can be difficult to distinguish in the field. However, when applying models, it is important to understand the conceptual process differences in models, how conceptual understanding changed over time in various models, and the model requirements and limitations to ensure that the model is well suited to the purpose of the investigation and the type of system being simulated. The aim of this paper is to give the reader a high\uffe2\uff80\uff90level overview of some of the important issues that should be considered when modeling. This includes conceptual understanding of widely used models, common modeling techniques such as calibration and validation, assessing model fit, sensitivity analysis, and uncertainty assessment. We also review examples of N2O modeling for different purposes and describe three commonly used process\uffe2\uff80\uff90based N2O models (APSIM, DayCent, and DNDC).</p", "keywords": ["Process-based", "Environmental Engineering", "Monitoring", "330", "Supplementary Data", "QH301 Biology", "Nitrous Oxide", "Goodness-of-fit", "01 natural sciences", "Empirical", "QH301", "Soil", "NE/M021327/1", "SDG 13 - Climate Action", "774378", "Nitrous Oxide/analysis", "European Commission", "Waste Management and Disposal", "Water Science and Technology", "0105 earth and related environmental sciences", "Policy and Law", "Natural Environment Research Council (NERC)", "NE/P019455/1", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Pollution", "Management", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sensitivity analysis"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/jeq2.20119"}, {"href": "https://doi.org/2164/15915"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Quality", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/15915", "name": "item", "description": "2164/15915", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/15915"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-27T00:00:00Z"}}, {"id": "2164/17550", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:45Z", "type": "Journal Article", "created": "2020-11-07", "title": "Ensemble modelling, uncertainty and robust predictions of organic carbon in long\u2010term bare\u2010fallow soils", "description": "Abstract<p>Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate\uffe2\uff80\uff90change studies. It is imperative to increase confidence in long\uffe2\uff80\uff90term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process\uffe2\uff80\uff90based C models by comparing simulations to experimental data from seven long\uffe2\uff80\uff90term bare\uffe2\uff80\uff90fallow (vegetation\uffe2\uff80\uff90free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi\uffe2\uff80\uff90year simulation periods (from 28 to 80\uffc2\uffa0years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge\uffe2\uff80\uff90based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin\uffe2\uff80\uff90up initialization of SOC. Changes in the multi\uffe2\uff80\uff90model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (\uffc2\uffb115.5)\uffc2\uffa0Mg\uffc2\uffa0C/ha compared to the observed mean of 36.0 (\uffc2\uffb119.7)\uffc2\uffa0Mg\uffc2\uffa0C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5\uffc2\uffa0\uffc2\uffb1\uffc2\uffa016.7\uffc2\uffa0Mg\uffc2\uffa0C/ha) and Spe (36.8\uffc2\uffa0\uffc2\uffb1\uffc2\uffa019.8\uffc2\uffa0Mg\uffc2\uffa0C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.</p", "keywords": ["[SDE] Environmental Sciences", "330", "550", "Supplementary Data", "soil organic carbon dynamics", "QH301 Biology", "[SDE.MCG]Environmental Sciences/Global Changes", "Soil organic carbon dynamics", "bare\u2010fallow soils", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "630", "protocol for model comparison", "Russia", "QH301", "Soil", "NE/M021327/1", "SDG 13 - Climate Action", "Environmental Chemistry", "774378", "process based models", "European Commission", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "Institut f\u00fcr Biochemie und Biologie", "General Environmental Science", "Sweden", "Global and Planetary Change", "Ecology", "Natural Environment Research Council (NERC)", "NE/P019455/1", "bare-fallow soils", "Uncertainty", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Carbon", "United Kingdom", "process-based models", "[SDE.MCG] Environmental Sciences/Global Changes", "13. Climate action", "[SDE]Environmental Sciences", "bare-fallow soils; model parametrization; process-based models; protocol for model comparison; soil organic carbon dynamics", "0401 agriculture", " forestry", " and fisheries", "774124", "ddc:570", "France", "bare fallow soils", "model parametrization"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/809186/2/GCB-20-1834_Proof_fl.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.15441"}, {"href": "https://doi.org/2164/17550"}, {"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": "2164/17550", "name": "item", "description": "2164/17550", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/17550"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-24T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Process-based&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=Process-based&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=Process-based&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Process-based&offset=15", "hreflang": "en-US"}], "numberMatched": 15, "numberReturned": 15, "distributedFeatures": [], "timeStamp": "2026-09-22T08:42:53.605805Z"}