We propose two methods to calibrate the parameters of the epidemic\uffe2\uff80\uff90type aftershock sequence (ETAS) model based on expectation maximization (EM) while accounting for temporal variation of catalog completeness. The first method allows for model calibration on long\uffe2\uff80\uff90term earthquake catalogs with temporal variation of the completeness magnitude,mc. This calibration technique is beneficial for long\uffe2\uff80\uff90term probabilistic seismic hazard assessment (PSHA), which is often based on a mixture of instrumental and historical catalogs. The second method generalizes the concept ofmc, considering rate\uffe2\uff80\uff90 and magnitude\uffe2\uff80\uff90dependent detection probability, and allows for self\uffe2\uff80\uff90consistent estimation of ETAS parameters and high\uffe2\uff80\uff90frequency detection incompleteness. With this approach, we aim to address the potential biases in parameter calibration due to short\uffe2\uff80\uff90term aftershock incompleteness, embracing incompleteness instead of avoiding it. Using synthetic tests, we show that both methods can accurately invert the parameters of simulated catalogs. We then use them to estimate ETAS parameters for California using the earthquake catalog since 1932. To explore how model calibration, inclusion of small events, and accounting for short\uffe2\uff80\uff90term incompleteness affect earthquakes' predictability, we systematically compare variants of ETAS models based on the second approach in pseudo\uffe2\uff80\uff90prospective forecasting experiments for California. Our proposed model significantly outperforms the ETAS null model, with decreasing information gain for increasing target magnitude threshold. We find that the ability to include small earthquakes for simulation of future scenarios is the primary driver of the improvement and that accounting for incompleteness is necessary. Our results have significant implications for our understanding of earthquake interaction mechanisms and the future of seismicity forecasting.
", "keywords": ["Physics - Geophysics", "13. Climate action", "0103 physical sciences", "earthquake forecasting", "ETAS", "FOS: Physical sciences", "short-term aftershock incompleteness", "data incompleteness", "01 natural sciences", "model inversion", "data incompleteness; model inversion; ETAS; earthquake forecasting", "Geophysics (physics.geo-ph)", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1002/essoar.10507003.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Solid%20Earth", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/essoar.10507003.1", "name": "item", "description": "10.1002/essoar.10507003.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/essoar.10507003.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-11T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2014.07.016", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:17:33Z", "type": "Journal Article", "created": "2014-08-02", "title": "Effect Of Nutrients Availability And Long-Term Tillage On Priming Effect And Soil C Mineralization", "description": "Abstract Agricultural management practices including soil tillage exert strong control on soil organic matter (SOM) turnover and its interactions with global C cycle through different mechanisms. One control mechanism is the priming effect (PE) which consists in stimulating SOM mineralization with the addition of fresh, energetic plant material. In this study, we quantified C mineralization and PE in soils sampled in two contrasted long-term (40 years) tillage treatments which deeply modified soil properties (e.g. organic C concentration, microbial biomass, pH). We hypothesized that soil tillage might affect these processes through changes in C addition rates, nutrient availability, and long-term variations in SOM content and microbial communities. We investigated the relationship between PE intensity, tillage and nutrients availability in soil samples taken in no till (NT) and full inversion tillage (FIT) in two layers (0\u20135 and 15\u201320\u00a0cm). Soils were incubated with or without addition of 13 C labeled cellulose and mineral nutrients. Potential C mineralization and primed C were measured during 262 days. Unlabeled soil microbial biomass C was determined at the end of the experiment to separate apparent and real priming effect. Basal cumulative C mineralization in the control soil ranged from 363 to 1490\u00a0mg\u00a0kg \u22121 soil at day 262. It was strongly correlated with soil organic carbon (SOC) concentration. Specific mineralization rates were 44.8 and 68.8\u00a0g\u00a0kg \u22121 SOC in the 0\u20135\u00a0cm layer for the FIT and NT treatments, respectively and were strongly linked with the particulate organic matter content ( r \u00a0=\u00a00.99***). These results suggest that SOC was more active in the upper layer of the NT treatment due to the high concentration of readily-decomposable, particulate organic matter. The cellulose was entirely metabolized after 60 days and its kinetics of mineralization was affected neither by tillage, depth nor nutrients. The percentage of cellulose C released as CO 2 represented 55\u201361% of the added cellulose-C at day 262. A positive PE was found in all treatments and its kinetics was parallel to that of cellulose mineralization. The cumulative PE significantly varied with nutrients level but not tillage, ranging from 73 to 78\u00a0mg\u00a0kg \u22121 under high nutrients level and from 116 to 136\u00a0mg\u00a0kg \u22121 in low nutrients level. No significant differences were found in unlabeled microbial biomass C between control and amended soil, suggesting no apparent priming effect. We conclude that the priming was mainly controlled by nutrient availability but not tillage, in spite of strong tillage-induced changes in SOC concentration and microbial biomass. Since PE is known to depend on C addition rate, tillage is expected to affect in situ PE through variations in the ratio of fresh carbon to nutrient concentration along the soil profile.", "keywords": ["priming effect", "2. Zero hunger", "microbial biomass", "no till", "nutrient mining", "04 agricultural and veterinary sciences", "15. Life on land", "soil organic carbon mineralization", "630", "6. Clean water", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "full inversion tillage", "0401 agriculture", " forestry", " and fisheries", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology"], "contacts": [{"organization": "Dimassi, Bassem, Mary, Bruno, Fontaine, S\u00e9bastien, Perveen, Nazia, Revaillot, Sandrine, Cohan, Jean-Pierre,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2014.07.016"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2014.07.016", "name": "item", "description": "10.1016/j.soilbio.2014.07.016", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2014.07.016"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-11-01T00:00:00Z"}}, {"id": "10.1029/2019gb006393", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:18:11Z", "type": "Journal Article", "created": "2020-02-07", "title": "Sources of Uncertainty in Regional and Global Terrestrial CO 2 Exchange Estimates", "description": "The Global Carbon Budget 2018 (GCB2018) estimated by the atmospheric CO growth rate, fossil fuel emissions, and modeled (bottom\uffe2\uff80\uff90up) land and ocean fluxes cannot be fully closed, leading to a \uffe2\uff80\uff9cbudget imbalance,\uffe2\uff80\uff9d highlighting uncertainties in GCB components. However, no systematic analysis has been performed on which regions or processes contribute to this term. To obtain deeper insight on the sources of uncertainty in global and regional carbon budgets, we analyzed differences in Net Biome Productivity (NBP) for all possible combinations of bottom\uffe2\uff80\uff90up and top\uffe2\uff80\uff90down data sets in GCB2018: (i) 16 dynamic global vegetation models (DGVMs), and (ii) 5 atmospheric inversions that match the atmospheric CO growth rate. We find that the global mismatch between the two ensembles matches well the GCB2018 budget imbalance, with Brazil, Southeast Asia, and Oceania as the largest contributors. Differences between DGVMs dominate global mismatches, while at regional scale differences between inversions contribute the most to uncertainty. At both global and regional scales, disagreement on NBP interannual variability between the two approaches explains a large fraction of differences. We attribute this mismatch to distinct responses to El\uffc2\uffa0Ni\uffc3\uffb1o\uffe2\uff80\uff93Southern Oscillation variability between DGVMs and inversions and to uncertainties in land use change emissions, especially in South America and Southeast Asia. We identify key needs to reduce uncertainty in carbon budgets: reducing uncertainty in atmospheric inversions (e.g., through more observations in the tropics) and in land use change fluxes, including more land use processes and evaluating land use transitions (e.g., using high\uffe2\uff80\uff90resolution remote\uffe2\uff80\uff90sensing), and, finally, improving tropical hydroecological processes and fire representation within DGVMs.
", "keywords": ["[SDE] Environmental Sciences", "FLUXES", "550", "BURNED AREA PRODUCT", "atmospheric inversions", "01 natural sciences", "Environnement et pollution", "DATA ASSIMILATION", "Ph\u00e9nom\u00e8nes atmosph\u00e9riques", "PLANT FUNCTIONAL TYPES", "global carbon budget", "carbon cycle", "ATMOSPHERIC CO2", "0105 earth and related environmental sciences", "LAND-COVER CHANGE", "FOSSIL-FUEL", "VEGETATION MODEL ORCHIDEE", "15. Life on land", "ddc:910", "CARBON-DIOXIDE EMISSIONS", "13. Climate action", "[SDE]Environmental Sciences", "dynamic global vegetation models", "contr\u00f4le de la pollution", "Technologie de l'environnement", "INCORPORATING SPITFIRE"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GB006393"}, {"href": "https://doi.org/10.1029/2019gb006393"}, {"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": "10.1029/2019gb006393", "name": "item", "description": "10.1029/2019gb006393", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1029/2019gb006393"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-01T00:00:00Z"}}, {"id": "10.1098/rstb.2017.0302", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:19:07Z", "type": "Journal Article", "created": "2018-10-08", "title": "Tropical land carbon cycle responses to 2015/16 El Ni\u00f1o as recorded by atmospheric greenhouse gas and remote sensing data", "description": "The outstanding tropical land climate characteristic over the past decades is rapid warming, with no significant large-scale precipitation trends. This warming is expected to continue but the effects on tropical vegetation are unknown. El Ni\uffc3\uffb1o-related heat peaks may provide a test bed for a future hotter world. Here we analyse tropical land carbon cycle responses to the 2015/16 El Ni\uffc3\uffb1o heat and drought anomalies using an atmospheric transport inversion. Based on the global atmospheric CO 2 and fossil fuel emission records, we find no obvious signs of anomalously large carbon release compared with earlier El Ni\uffc3\uffb1o events, suggesting resilience of tropical vegetation. We find roughly equal net carbon release anomalies from Amazonia and tropical Africa, approximately 0.5 PgC each, and smaller carbon release anomalies from tropical East Asia and southern Africa. Atmospheric CO anomalies reveal substantial fire carbon release from tropical East Asia peaking in October 2015 while fires contribute only a minor amount to the Amazonian carbon flux anomaly. Anomalously large Amazonian carbon flux release is consistent with downregulation of primary productivity during peak negative near-surface water anomaly (October 2015 to March 2016) as diagnosed by solar-induced fluorescence. Finally, we find an unexpected anomalous positive flux to the atmosphere from tropical Africa early in 2016, coincident with substantial CO release.
This article is part of a discussion meeting issue \uffe2\uff80\uff98The impact of the 2015/2016 El Ni\uffc3\uffb1o on the terrestrial tropical carbon cycle: patterns, mechanisms and implications\uffe2\uff80\uff99.
", "keywords": ["Life Sciences & Biomedicine - Other Topics", "FLUX", "0301 basic medicine", "Hot Temperature", "550", "551", "global warming", "01 natural sciences", "Carbon Cycle", "Greenhouse Gases", "03 medical and health sciences", "[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/Climatology", "CHEMICAL-TRANSPORT MODEL", "carbon cycle", "INVERSION", "Biology", "TEMPERATURE", "11 Medical and Health Sciences", "0105 earth and related environmental sciences", "tropical forests", "El Nino-Southern Oscillation", "Evolutionary Biology", "Tropical Climate", "Science & Technology", "Atmosphere", "PHOTOSYNTHESIS", "EQUATORIAL PACIFIC", "Articles", "06 Biological Sciences", "15. Life on land", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "6. Clean water", "Droughts", "[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology", "13. Climate action", "PRECIPITATION", "Remote Sensing Technology", "INDUCED CHLOROPHYLL FLUORESCENCE", "CO2", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "SENSITIVITY", "environment", "Life Sciences & Biomedicine", "fire"]}, "links": [{"href": "https://eprints.whiterose.ac.uk/135234/8/Tropical%20land%20carbon%20cycle%20responses%20to%202015/16%20El%20Ni%C3%B1o%20as%20recorded%20by%20atmospheric%20greenhouse%20gas%20and%20remote%20sensing%20data.pdf"}, {"href": "https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2017.0302"}, {"href": "https://doi.org/10.1098/rstb.2017.0302"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Philosophical%20Transactions%20of%20the%20Royal%20Society%20B%3A%20Biological%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rstb.2017.0302", "name": "item", "description": "10.1098/rstb.2017.0302", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rstb.2017.0302"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-08T00:00:00Z"}}, {"id": "10.3389/frwa.2021.767910", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:21:41Z", "type": "Journal Article", "created": "2021-12-14", "title": "Combining Models of Root-Zone Hydrology and Geoelectrical Measurements: Recent Advances and Future Prospects", "description": "Recent advances in measuring and modeling root water uptake along with refined electrical petrophysical models may help fill the existing gap in hydrological root model parametrization. In this paper, we discuss the choices to be made to combine root-zone hydrology and geoelectrical data with the aim of characterizing the active root zone. For each model and observation type we discuss sources of uncertainty and how they are commonly addressed in a stochastic inversion framework. We point out different degrees of integration in the existing hydrogeophysical approaches to parametrize models of root-zone hydrology. This paper aims at giving emphasis to stochastic approaches, in particular to Data Assimilation (DA) schemes, that are generally identified as the best way to combine geoelectrical data with Root Water Uptake (RWU) models. In addition, the study points out a more suitable objective function taken from the optimal transport theory that better captures complex geometry of root systems. Another pathway for improvement of geoelectrical data integration into RWU models using DA relies on the use of stem based methods as a leverage to introduce more extensive root knowledge into RWU macroscopic hydrological models.
", "keywords": ["hydrogeophysics", "0208 environmental biotechnology", "0207 environmental engineering", "02 engineering and technology", "data assimilation; geoelectrical imaging; hydrogeophysics; inversion; root water uptake; soil-plant modeling; Wasserstein distance", "Environmental technology. Sanitary engineering", "root water uptake", "Hydrogeophysics", "inversion", "geoelectrical imaging", "soil-plant modeling", "Wasserstein distance", "data assimilation", "TD1-1066"]}, "links": [{"href": "https://www.research.unipd.it/bitstream/11577/3410667/1/frwa-03-767910.pdf"}, {"href": "https://doi.org/10.3389/frwa.2021.767910"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/frwa.2021.767910", "name": "item", "description": "10.3389/frwa.2021.767910", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/frwa.2021.767910"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-14T00:00:00Z"}}, {"id": "10.5194/gmd-2017-222", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:22:49Z", "type": "Journal Article", "created": "2018-08-30", "title": "CTDAS-Lagrange v1.0: A high-resolution data assimilation system for regional carbon dioxide observations", "description": "Abstract. We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10\uffc2\uffa0days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a\uffc2\uffa0priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model\uffe2\uff80\uff93data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year\uffc2\uffa02010 in a range from \uffe2\uff88\uff920.92 to \uffe2\uff88\uff921.26\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91\uffc2\uffb11.10\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921) and CarbonTracker Europe (version CTE2016, -0.91\uffc2\uffb10.31\uffe2\uff80\uff89PgC\uffe2\uff80\uff89yr\uffe2\uff88\uff921). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.
", "keywords": ["QE1-996.5", "SATELLITE-OBSERVATIONS", "TECHNICAL NOTE", "Geology", "NORTH-AMERICA", "15. Life on land", "AIR-SAMPLING-NETWORK", "01 natural sciences", "MODEL", "13. Climate action", "ATMOSPHERIC CO2 INVERSIONS", "Life Science", "ANTHROPOGENIC EMISSIONS", "GREENHOUSE-GAS MEASUREMENTS", "INTERANNUAL VARIABILITY", "EXCHANGE", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.geosci-model-dev-discuss.net/gmd-2017-222/gmd-2017-222.pdf"}, {"href": "https://doi.org/10.5194/gmd-2017-222"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gmd-2017-222", "name": "item", "description": "10.5194/gmd-2017-222", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-2017-222"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-12-05T00:00:00Z"}}, {"id": "10.5445/IR/1000073025", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:24:56Z", "type": "Journal Article", "created": "2017-07-18", "title": "The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001\u20132015", "description": "Abstract. Data assimilation systems are used increasingly to constrain the budgets of reactive and long-lived gases measured in the atmosphere. Each trace gas has its own lifetime, dominant sources and sinks, and observational network (from flask sampling and in situ measurements to space-based remote sensing) and therefore comes with its own optimal configuration of the data assimilation. The CarbonTracker Europe data assimilation system for CO2 estimates global carbon sources and sinks, and updates are released annually and used in carbon cycle studies. CarbonTracker Europe simulations are performed using the new modular implementation of the data assimilation system: the CarbonTracker Data Assimilation Shell (CTDAS). Here, we present and document this redesign of the data assimilation code that forms the heart of CarbonTracker, specifically meant to enable easy extension and modification of the data assimilation system. This paper also presents the setup of the latest version of CarbonTracker Europe (CTE2016), including the use of the gridded state vector, and shows the resulting carbon flux estimates. We present the distribution of the carbon sinks over the hemispheres and between the land biosphere and the oceans. We show that with equal fossil fuel emissions, 2015 has a\uffc2\uffa0higher atmospheric CO2 growth rate compared to 2014, due to reduced net land carbon uptake in later year. The European carbon sink is especially present in the forests, and the average net uptake over 2001\uffe2\uff80\uff932015 was 0.\uffe2\uff80\uff8917\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff890.\uffe2\uff80\uff8911\uffe2\uff80\uffafPgC\uffe2\uff80\uff86yr\uffe2\uff88\uff921 with reductions to zero during drought years. Finally, we also demonstrate the versatility of CTDAS by presenting an overview of the wide range of applications for which it has been used so far.
", "keywords": ["FLUXES", "QE1-996.5", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "ENSEMBLE", "Geology", "BUDGET", "15. Life on land", "01 natural sciences", "7. Clean energy", "Earth sciences", "DIOXIDE EXCHANGE", "INVERSIONS", "13. Climate action", "MODEL TM5", "Life Science", "CO2", "EMISSIONS", "DROUGHT", "SYSTEM", "SDG 15 - Life on Land", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5445/IR/1000073025"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5445/IR/1000073025", "name": "item", "description": "10.5445/IR/1000073025", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5445/IR/1000073025"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-21T00:00:00Z"}}, {"id": "103f081c-cd35-4e15-a508-7df47ed4483d", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:25:56Z", "type": "Dataset", "title": "City of Gelsenkirchen: Environmental Issues (WFS)", "description": "The \u201cEnvironmental Issues\u201d service provides data on the following topics: Synthetic climate function map, climate bonuses, city climate planning information, night temperature distribution, noise mapping road traffic, noise mapping industry and commerce as well as the urban heat islands. The service is supplemented with spatial data on the topics of water and groundwater", "formats": [{"name": "WFS_SRVC"}], "keywords": ["ags", "ausgleichsraum", "baum", "baumbestand", "ba\u0308ume", "bergbau", "boden", "de", "grundwasser", "immissionsindex", "industrie-und-gewerbe", "innenstadt", "inversionsgefahr", "isoanomalenkarte", "isophonen", "kaltluftflu\u0308sse", "kaltluftsammelgebiete", "klimabonita\u0308t", "klimabonnita\u0308ten", "klimaindikator", "klimaqualita\u0308t", "klimatope", "lastraum", "la\u0308rm", "la\u0308rmbelastung", "la\u0308rmkartierung", "la\u0308rmkartierung-industrie-und-gewerbe-sowie-die-sta\u0308dtischen-hitzeinseln", "la\u0308rmkartierung-stra\u00dfenverkehr", "luftleitbahn", "lufttemperatur", "lufttemperaturverteilung", "natur-und-umwelt", "na\u0308chtliche-temperaturverteilung", "ogcdienst", "opendata", "planungshinweise", "scha\u0308chte", "schutzwu\u0308rdige-bo\u0308den", "stadt-gelsenkirchen", "stadtklima", "stadtklimatische-planungshinweise", "sta\u0308dtische-hitzeinsel", "stra\u00dfenverkehr", "synthetische-klimafunktionskarte", "ungunstraum", "verdichtete-bebauung", "wasser", "wa\u0308rmeinsel", "wms"], "contacts": [{"organization": "Herr Amberge, P.", "roles": ["creator"]}]}, "links": [{"href": "https://gdi.gelsenkirchen.de/wss/service/WFSUmwelt/guest?"}, {"href": "http://data.europa.eu/88u/dataset/103f081c-cd35-4e15-a508-7df47ed4483d"}, {"rel": "self", "type": "application/geo+json", "title": "103f081c-cd35-4e15-a508-7df47ed4483d", "name": "item", "description": "103f081c-cd35-4e15-a508-7df47ed4483d", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/103f081c-cd35-4e15-a508-7df47ed4483d"}, {"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": "b82e4180-5aff-4c8a-b4fe-6b564e59cdf6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-30T16:31:15Z", "type": "Dataset", "title": "City of Gelsenkirchen: Environmental Issues (WMS)", "description": "The \u201cEnvironmental Issues\u201d service provides data on the following topics: Synthetic climate function map, climate bonuses, city climate planning information, night temperature distribution, noise mapping road traffic, noise mapping industry and commerce as well as the urban heat islands. The service is supplemented with spatial data on the topics of water and groundwater", "formats": [{"name": "WMS_SRVC"}], "keywords": ["ags", "ausgleichsraum", "bergbau", "boden", "de", "grundwasser", "immissionsindex", "industrie-und-gewerbe", "innenstadt", "inversionsgefahr", "isoanomalenkarte", "isophonen", "kaltluftflu\u0308sse", "kaltluftsammelgebiete", "klimabonita\u0308t", "klimabonnita\u0308ten", "klimaindikator", "klimaqualita\u0308t", "klimatope", "lastraum", "la\u0308rm", "la\u0308rmbelastung", "la\u0308rmkartierung", "la\u0308rmkartierung-industrie-und-gewerbe-sowie-die-sta\u0308dtischen-hitzeinseln", "la\u0308rmkartierung-stra\u00dfenverkehr", "luftleitbahn", "lufttemperatur", "lufttemperaturverteilung", "natur-und-umwelt", "na\u0308chtliche-temperaturverteilung", "ogcdienst", "planungshinweise", "scha\u0308chte", "schutzwu\u0308rdige-bo\u0308den", "stadt-gelsenkirchen", "stadtklima", "stadtklimatische-planungshinweise", "sta\u0308dtische-hitzeinsel", "stra\u00dfenverkehr", "synthetische-klimafunktionskarte", "ungunstraum", "verdichtete-bebauung", "wasser", "wa\u0308rmeinsel", "wms"], "contacts": [{"organization": "Herr Ullisch-Nelken, C.", "roles": ["creator"]}]}, "links": [{"href": "https://gdi.gelsenkirchen.de/wss/service/WMSUmwelt/guest?"}, {"href": "http://data.europa.eu/88u/dataset/b82e4180-5aff-4c8a-b4fe-6b564e59cdf6"}, {"rel": "self", "type": "application/geo+json", "title": "b82e4180-5aff-4c8a-b4fe-6b564e59cdf6", "name": "item", "description": "b82e4180-5aff-4c8a-b4fe-6b564e59cdf6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/b82e4180-5aff-4c8a-b4fe-6b564e59cdf6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=INVERSION&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=INVERSION&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=INVERSION&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=INVERSION&offset=10", "hreflang": "en-US"}], "numberMatched": 10, "numberReturned": 10, "distributedFeatures": [], "timeStamp": "2026-05-31T03:11:41.746243Z"}