{"type": "FeatureCollection", "features": [{"id": "10.3390/agronomy11040652", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:54Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.3390/agronomy11040652"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy11040652", "name": "item", "description": "10.3390/agronomy11040652", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy11040652"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.1002/essoar.10507003.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:14:22Z", "type": "Journal Article", "created": "2021-12-10", "title": "Embracing Data Incompleteness for Better Earthquake Forecasting", "description": "Abstract<p>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.</p>", "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": "10.1002/qj.3497", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:14:29Z", "type": "Journal Article", "created": "2019-02-05", "title": "Current state of the global operational aerosol multi\u2010model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)", "description": "<p>Since the first International Cooperative for Aerosol Prediction (ICAP) multi\uffe2\uff80\uff90model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP\uffe2\uff80\uff90MME over 2012\uffe2\uff80\uff932017, with a focus on June 2016\uffe2\uff80\uff93May 2017. Evaluated with ground\uffe2\uff80\uff90based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate\uffe2\uff80\uff90resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP\uffe2\uff80\uff90MME AOD consensus remains the overall top\uffe2\uff80\uff90scoring and most consistent performer among all models in terms of root\uffe2\uff80\uff90mean\uffe2\uff80\uff90square error (RMSE), bias and correlation for total, fine\uffe2\uff80\uff90 and coarse\uffe2\uff80\uff90mode AODs as well as dust AOD; this is similar to the first ICAP\uffe2\uff80\uff90MME study. Further, over the years, the performance of ICAP\uffe2\uff80\uff90MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP\uffe2\uff80\uff90MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP\uffe2\uff80\uff90MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012\uffe2\uff80\uff932017 suggests a general tendency for model improvements in fine\uffe2\uff80\uff90mode AOD, especially over Asia. No significant improvement in coarse\uffe2\uff80\uff90mode AOD is found overall for this time period.</p>", "keywords": ["[SDU] Sciences of the Universe [physics]", "Special Supplement on 25 Years of Ensemble Forecasting", "01 natural sciences", "[PHYS.PHYS.PHYS-AO-PH] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3497"}, {"href": "https://doi.org/10.1002/qj.3497"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Quarterly%20Journal%20of%20the%20Royal%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/qj.3497", "name": "item", "description": "10.1002/qj.3497", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/qj.3497"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-02T00:00:00Z"}}, {"id": "10.1016/j.crm.2021.100375", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:03Z", "type": "Journal Article", "created": "2021-11-09", "title": "Playing the long game: Anticipatory action based on seasonal forecasts", "description": "Acting in advance of floods, drought and cyclones often requires decision-makers to work with weather forecasts. The inherently probabilistic nature of these forecasts can be problematic when deciding whether to act or not. Cost-loss analysis has previously been employed to support forecast based decision-making such as Forecast-based Financing (FbF), providing insight to when an FbF system has \u2018potential economic value\u2019 relative to a no-forecast alternative. One well-known limitation of cost-loss analysis is the difficulty of estimating losses (which vary with hazard magnitude and extent, and with the dynamics of population vulnerability and exposure). A less-explored limitation is ignorance of the temporal dynamics (sequencing) of costs and losses. That is, even if the potential economic value of a forecast system is high, the stochastic nature of the atmosphere and the probabilistic nature of forecasts could conspire over the first few forecasts to increase the expense of using the system over the no-forecast alternative. Thus, for a forecast-based action system to demonstrate value, it often needs to be used over a prolonged length of time. However, knowing exactly how long it must be used to guarantee value is unquantified. This presents difficulties to institutions mandated to protect those at risk, who must justify the use of limited funds to act in advance of a potential, but not definite disaster, whilst planning multi-year strategies. Here we show how to determine the period over which decision makers must use forecasts in order to be confident of achieving \u2018value\u2019 over a no-forecast alternative. Results show that in the context of seasonal forecasting it is plausible that more than a decade may pass before a FbF system will have some certainty of showing value, and that if a particular user requires an almost-certain guarantee that using a forecast will be better than a no-forecast strategy, they must hold out until a near-perfect forecast system is available. The implication: there is potential value in seasonal forecasts, but to exploit it one must be prepared to play the long game.", "keywords": ["Early warning", "Forecast-based financing", "13. Climate action", "Climate adaptation", "Seasonal climate forecasting", "Meteorology. Climatology", "0207 environmental engineering", "Anticipatory action", "Disaster risk reduction", "02 engineering and technology", "QC851-999", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://sro.sussex.ac.uk/id/eprint/103124/1/macleod_2021_CRM.pdf"}, {"href": "https://doi.org/10.1016/j.crm.2021.100375"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Climate%20Risk%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.crm.2021.100375", "name": "item", "description": "10.1016/j.crm.2021.100375", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.crm.2021.100375"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.1016/j.jenvman.2016.07.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:41Z", "type": "Journal Article", "created": "2016-08-05", "title": "The PRECOS framework: Measuring the impacts of the global changes on soils, water, agriculture on territories to better anticipate the future", "description": "In a context of increased land and natural resources scarcity, the possibilities for local authorities and stakeholders of anticipating evolutions or testing the impact of envisaged developments through scenario simulation are new challenges. PRECOS's approach integrates data pertaining to the fields of water and soil resources, agronomy, urbanization, land use and infrastructure etc. It is complemented by a socio-economic and regulatory analysis of the territory illustrating its constraints and stakes. A modular architecture articulates modeling software and spatial and temporal representations tools. It produces indicators in three core domains: soil degradation, water and soil resources and agricultural production. As a territory representative of numerous situations of the Mediterranean Basin (urban pressures, overconsumption of spaces, degradation of the milieus), a demonstration in the Crau's area (Southeast of France) has allowed to validate a prototype of the approach and to test its feasibility in a real life situation. Results on the Crau area have shown that, since the beginning of the 16th century, irrigated grasslands are the cornerstones of the anthropic-system, illustrating how successfully men's multi-secular efforts have maintained a balance between environment and local development. But today the ecosystem services are jeopardized firstly by urban sprawl and secondly by climate change. Pre-diagnosis in regions of Emilia-Romagna (Italy) and Valencia (Spain) show that local end-users and policy-makers are interested by this approach. The modularity of indicator calculations and the availability of geo-databases indicate that PRECOS may be up scaled in other socio-economic contexts.", "keywords": ["DYNAMICS", "Conservation of Natural Resources", "550", "330", "[SDE.MCG]Environmental Sciences/Global Changes", "CITY", "Climate Change", "0211 other engineering and technologies", "Environmental Sciences & Ecology", "CELLULAR-AUTOMATA", "02 engineering and technology", "URBAN", "01 natural sciences", "CROP MODEL", "Urban sprawl", "SUSTAINABILITY", "Soil", "Theoretical", "Models", "Water Supply", "MD Multidisciplinary", "11. Sustainability", "Climate change", "Humans", "Territory", "0105 earth and related environmental sciences", "2. Zero hunger", "Science & Technology", "STICS", "Water", "Agriculture", "REGIONAL-SCALE", "Models", " Theoretical", "15. Life on land", "LAND-USE PATTERNS", "Resources", "Europe", "[SDE.MCG] Environmental Sciences/Global Changes", "LIFE-CYCLE ANALYSIS", "13. Climate action", "Life Sciences & Biomedicine", "Environmental Sciences", "Software", "Forecasting"]}, "links": [{"href": "https://doi.org/10.1016/j.jenvman.2016.07.002"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jenvman.2016.07.002", "name": "item", "description": "10.1016/j.jenvman.2016.07.002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jenvman.2016.07.002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-10-01T00:00:00Z"}}, {"id": "10.1016/j.foodres.2017.03.045", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:16:20Z", "type": "Journal Article", "created": "2018-08-22", "title": "Past, present and future: The strength of plant-based dairy substitutes based on gluten-free raw materials", "description": "As the pace of vaccine uptake accelerates globally, there is a need to document low-income country experiences with vaccine introductions. Over the course of five years, the government of Rwanda rolled out vaccines against pneumococcus, human papillomavirus, rotavirus, and measles & rubella, achieving over 90% coverage for each. To carry out these rollouts, Rwanda's Ministry of Health engaged in careful review of disease burden information and extensive, cross-sectoral planning at least one year before introducing each vaccine. Rwanda's local leaders, development partners, civil society organizations and widespread community health worker network were mobilized to support communication efforts. Community health workers were also used to confirm target population size. Support from Gavi, UNICEF and WHO was used in combination with government funds to promote country ownership and collaboration. Vaccination was also combined with additional community-based health interventions. Other countries considering rapid consecutive or simultaneous rollouts of new vaccines may consider lessons from Rwanda's experience while tailoring the strategies used to local context.", "keywords": ["Plant-based milk substitutes", "Measles Vaccine", "Plant Proteins", " Dietary", "Pneumococcal Vaccines", "Diet", " Gluten-Free", "03 medical and health sciences", "0404 agricultural biotechnology", "0302 clinical medicine", "Protein-high foods", "Food Quality", "Humans", "Rubella Vaccine", "Protein requirement", "Papillomavirus Vaccines", "Vaccines", " Combined", "Milk alternatives", "Plant-based dairy substitutes", "Food", " Formulated", "Community Health Workers", "Population Density", "2. Zero hunger", "Vaccines", "Immunization Programs", "Protein", "Diet", " Vegetarian", "Communication", "Vaccination", "Rotavirus Vaccines", "Rwanda", "1. No poverty", "04 agricultural and veterinary sciences", "Diets", "plant-based milk substitutes; protein requirement; milk alternatives; protein-high foods; protein; diets; plant-based dairy substitutes", "3. Good health", "Food Technology", "Dairy Products", "0405 other agricultural sciences", "Forecasting"], "contacts": [{"organization": "Gatera, Maurice, Bhatt, Sunil, Ngabo, Fidele, Utamuliza, Mathilde, Sibomana, Hassan, Karema, Corine, Mugeni, Cathy, Nutt, Cameron T., Nsanzimana, Sabin, Wagner, Claire M., Binagwaho, Agnes,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.foodres.2017.03.045"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Food%2C%20Nutrition%20and%20Population%20Health", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.foodres.2017.03.045", "name": "item", "description": "10.1016/j.foodres.2017.03.045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.foodres.2017.03.045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-06-01T00:00:00Z"}}, {"id": "10.1017/s0021859618000084", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:28Z", "type": "Journal Article", "created": "2018-02-28", "title": "Forecasting potential evapotranspiration by combining numerical weather predictions and visible and near-infrared satellite images: an application in southern Italy", "description": "Abstract<p>Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014\uffe2\uff80\uff932015). The mean absolute bias of the forecasted ETp was &lt;0.3 mm/day and the RMSE was &lt;0.6 mm/day, both for lead times up to 5 days.</p>", "keywords": ["2. Zero hunger", "Earth observation", "Crop water requirements", "0207 environmental engineering", "forecasting", "02 engineering and technology", "15. Life on land", "01 natural sciences", "numerical weather predictions", "13. Climate action", "potential evapotranspiration", "11. Sustainability", "Genetics", "Animal Science and Zoology", "Agronomy and Crop Science", "Crop water requirements; Earth observation; forecasting; numerical weather predictions; potential evapotranspiration; Animal Science and Zoology; Agronomy and Crop Science; Genetics", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1017/s0021859618000084"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Journal%20of%20Agricultural%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1017/s0021859618000084", "name": "item", "description": "10.1017/s0021859618000084", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1017/s0021859618000084"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-28T00:00:00Z"}}, {"id": "10.1038/s41598-019-56868-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:17:52Z", "type": "Journal Article", "created": "2020-01-09", "title": "Modelling photovoltaic soiling losses through optical characterization", "description": "Abstract<p>The accumulation of soiling on photovoltaic (PV) modules affects PV systems worldwide. Soiling consists of mineral dust, soot particles, aerosols, pollen, fungi and/or other contaminants that deposit on the surface of PV modules. Soiling absorbs, scatters, and reflects a fraction of the incoming sunlight, reducing the intensity that reaches the active part of the solar cell. Here, we report on the comparison of naturally accumulated soiling on coupons of PV glass soiled at seven locations worldwide. The spectral hemispherical transmittance was measured. It was found that natural soiling disproportionately impacts the blue and ultraviolet (UV) portions of the spectrum compared to the visible and infrared (IR). Also, the general shape of the transmittance spectra was similar at all the studied sites and could adequately be described by a modified form of the \uffc3\uff85ngstr\uffc3\uffb6m turbidity equation. In addition, the distribution of particles sizes was found to follow the IEST-STD-CC 1246E cleanliness standard. The fractional coverage of the glass surface by particles could be determined directly or indirectly and, as expected, has a linear correlation with the transmittance. It thus becomes feasible to estimate the optical consequences of the soiling of PV modules from the particle size distribution and the cleanliness value.</p>", "keywords": ["Photovoltaic Arrays", "Cleanliness", "Particle", "PV", "02 engineering and technology", "Oceanography", "7. Clean energy", "soiling; experimental; transmittance; spectrum", "Turbidity", "Size", "Materials Science and Engineering", "\u00c5ngstr\u00f6m turbidity equation", "Transmittance", "0202 electrical engineering", " electronic engineering", " information engineering", "Photovoltaic system", "Ultraviolet", "Microscopy", "Soiling", "Energy", "Ecology", "Physics", "Q", "R", "Imaging and sensing", "Geology", "Particle size", "6. Clean water", "Photovoltaic Efficiency", "Chemistry", "Physical chemistry", "Particle (ecology)", "Physical Sciences", "Sunlight", "Medicine", "Infrared", "570", "Particle-size distribution", "PV System", "Energy science and technology", "Science", "Optical spectroscopy", "Partial Shading", "530", "Modelling", "Article", "Environmental science", "Techniques and instrumentation", "Optical physics", "Meteorology", "Artificial Intelligence", "Machine Learning Methods for Solar Radiation Forecasting", "Optical techniques", "Optoelectronics", "Aerosol", "Biology", "Renewable Energy", " Sustainability and the Environment", "Electronics", " photonics and device physics", "Building Integrated Photovoltaics", "Optics", "Photovoltaic Maximum Power Point Tracking Techniques", "FOS: Earth and related environmental sciences", "Materials science", "Photovoltaics", "Optics and photonics", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Solar Thermal Energy Technologies"]}, "links": [{"href": "https://iris.uniroma1.it/bitstream/11573/1625670/2/Smestad_Modelling_2020.pdf"}, {"href": "https://www.nature.com/articles/s41598-019-56868-z.pdf"}, {"href": "https://doi.org/10.1038/s41598-019-56868-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41598-019-56868-z", "name": "item", "description": "10.1038/s41598-019-56868-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41598-019-56868-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.1088/1367-2630/abeb46", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:22Z", "type": "Journal Article", "created": "2021-03-02", "title": "Improved earthquake aftershocks forecasting model based on long-term memory", "description": "A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.", "keywords": ["0301 basic medicine", "550", "Science", "Physics", "QC1-999", "Q", "forecasting", "earthquake memory", "530", "01 natural sciences", "ETAS model", "03 medical and health sciences", "13. Climate action", "0103 physical sciences", "earthquake memory; ETAS model; forecasting"]}, "links": [{"href": "https://doi.org/10.1088/1367-2630/abeb46"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/New%20Journal%20of%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1088/1367-2630/abeb46", "name": "item", "description": "10.1088/1367-2630/abeb46", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1088/1367-2630/abeb46"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.1101/2021.03.18.435447", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:32Z", "type": "Journal Article", "created": "2021-03-19", "title": "Predicting tomato field-yield using continuous monitoring of young tomato water status", "description": "Abstract<p>To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zetaz2083and tangerinet3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82, and multiple physiological characteristics were measured continuously, before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY:r2= 0.9 and 0.77, respectively) and plant vegetative weight (PW:r2= 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience.</p>", "keywords": ["Crops", " Agricultural", "0301 basic medicine", "2. Zero hunger", "Dehydration", "Genotype", "Genetic Variation", "15. Life on land", "Genes", " Plant", "Adaptation", " Physiological", "6. Clean water", "Droughts", "03 medical and health sciences", "Phenotype", "Solanum lycopersicum", "Gene Expression Regulation", " Plant", "Mutation", "Plant Physiological Phenomena", "Forecasting"]}, "links": [{"href": "https://doi.org/10.1101/2021.03.18.435447"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1101/2021.03.18.435447", "name": "item", "description": "10.1101/2021.03.18.435447", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1101/2021.03.18.435447"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-19T00:00:00Z"}}, {"id": "10.5194/gmd-14-6403-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:51Z", "type": "Journal Article", "created": "2021-10-25", "title": "Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust\u2013radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20\u2009\u00b5m, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000\u2009Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about \u22120.90 and \u22120.63\u2009W\u2009m\u22122 and at the top of the atmosphere between \u22120.20 and \u22120.28\u2009W\u2009m\u22122. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.                     </p></article>", "keywords": ["Mineral dusts", "Previsi\u00f3 del temps", "QE1-996.5", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "Geology", "15. Life on land", "01 natural sciences", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Weather forecasting", "Climate Action", "[SDU] Sciences of the Universe [physics]", "Earth sciences", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth Sciences", "Pols", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/6403/2021/gmd-14-6403-2021.pdf"}, {"href": "https://escholarship.org/content/qt2r39x8b5/qt2r39x8b5.pdf"}, {"href": "https://doi.org/10.5194/gmd-14-6403-2021"}, {"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-14-6403-2021", "name": "item", "description": "10.5194/gmd-14-6403-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gmd-14-6403-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.1111/gcb.13902", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:18:45Z", "type": "Journal Article", "created": "2017-09-11", "title": "CO2 evasion from boreal lakes: Revised estimate, drivers of spatial variability, and future projections", "description": "Abstract<p>Lakes (including reservoirs) are an important component of the global carbon (C) cycle, as acknowledged by the fifth assessment report of the IPCC. In the context of lakes, the boreal region is disproportionately important contributing to 27% of the worldwide lake area, despite representing just 14% of global land surface area. In this study, we used a statistical approach to derive a prediction equation\uffc2\uffa0for the partial pressure of CO2 (pCO2) in lakes as a function of lake area, terrestrial net primary productivity (NPP), and precipitation (r2\uffc2\uffa0=\uffc2\uffa0.56), and to create the first high\uffe2\uff80\uff90resolution, circumboreal map (0.5\uffc2\uffb0) of lake pCO2. The map of\uffc2\uffa0pCO2 was combined with lake area from the recently published GLOWABO database and three different estimates of the gas transfer velocity k to produce a resulting map of CO2 evasion (FCO2). For the boreal region, we estimate an average, lake area weighted, pCO2 of 966 (678\uffe2\uff80\uff931,325) \uffce\uffbcatm and a total\uffc2\uffa0FCO2 of 189 (74\uffe2\uff80\uff93347) Tg\uffc2\uffa0C\uffc2\uffa0year\uffe2\uff88\uff921, and evaluate the corresponding uncertainties based on Monte Carlo simulation. Our estimate of FCO2 is approximately twofold greater than previous estimates, as a result of methodological and data source differences. We use our results along with published estimates of the other C fluxes through inland waters to derive a C budget for the boreal region, and find that FCO2 from lakes is the most significant flux of the land\uffe2\uff80\uff90ocean aquatic continuum, and of a similar magnitude as emissions from forest fires. Using the model and applying it to spatially resolved projections of terrestrial NPP and precipitation while keeping everything else constant, we predict a 107% increase in boreal lake FCO2 under emission scenario RCP8.5 by 2100. Our projections are largely driven by increases in terrestrial NPP over the same period, showing the very close connection between the terrestrial and aquatic C cycle.</p", "keywords": ["0106 biological sciences", "Precipitation", "precipitation", "01 natural sciences", "Lake", "Environnement et pollution", "carbon budget", "Carbon budget", "Geovetenskap och relaterad milj\u00f6vetenskap", "terrestrial NPP", "boreal", "Climate change", "Boreal", "lake", "Ecosystem", "Future projections", "0105 earth and related environmental sciences", "Ecologie", "Arctic Regions", "Terrestrial NPP", "Carbon Dioxide", "Models", " Theoretical", "15. Life on land", "6. Clean water", "Carbon", "Lakes", "climate change", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "contr\u00f4le de la pollution", "CO2", "Technologie de l'environnement", "Earth and Related Environmental Sciences", "future projections", "Forecasting"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.13902"}, {"href": "https://doi.org/10.1111/gcb.13902"}, {"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.13902", "name": "item", "description": "10.1111/gcb.13902", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/gcb.13902"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-16T00:00:00Z"}}, {"id": "2914742561", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:01Z", "type": "Journal Article", "created": "2019-02-05", "title": "Current state of the global operational aerosol multi\u2010model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Since the first International Cooperative for Aerosol Prediction (ICAP) multi\u2010model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP\u2010MME over 2012\u20132017, with a focus on June 2016\u2013May 2017. Evaluated with ground\u2010based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate\u2010resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP\u2010MME AOD consensus remains the overall top\u2010scoring and most consistent performer among all models in terms of root\u2010mean\u2010square error (RMSE), bias and correlation for total, fine\u2010 and coarse\u2010mode AODs as well as dust AOD; this is similar to the first ICAP\u2010MME study. Further, over the years, the performance of ICAP\u2010MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP\u2010MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP\u2010MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012\u20132017 suggests a general tendency for model improvements in fine\u2010mode AOD, especially over Asia. No significant improvement in coarse\u2010mode AOD is found overall for this time period.</p></article>", "keywords": ["[SDU] Sciences of the Universe [physics]", "Special Supplement on 25 Years of Ensemble Forecasting", "01 natural sciences", "[PHYS.PHYS.PHYS-AO-PH] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3497"}, {"href": "https://doi.org/2914742561"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Quarterly%20Journal%20of%20the%20Royal%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2914742561", "name": "item", "description": "2914742561", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2914742561"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-02T00:00:00Z"}}, {"id": "10.1126/science.1155359", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:10Z", "type": "Journal Article", "created": "2008-06-12", "title": "Predictive Models Of Forest Dynamics", "description": "<p>Dynamic global vegetation models (DGVMs) have shown that forest dynamics could dramatically alter the response of the global climate system to increased atmospheric carbon dioxide over the next century. But there is little agreement between different DGVMs, making forest dynamics one of the greatest sources of uncertainty in predicting future climate. DGVM predictions could be strengthened by integrating the ecological realities of biodiversity and height-structured competition for light, facilitated by recent advances in the mathematics of forest modeling, ecological understanding of diverse forest communities, and the availability of forest inventory data.</p>", "keywords": ["0106 biological sciences", "Light", "Nonlinear Dynamics", "13. Climate action", "Climate", "Biodiversity", "Models", " Theoretical", "15. Life on land", "01 natural sciences", "Ecosystem", "Mathematics", "Forecasting", "Trees"], "contacts": [{"organization": "Drew W. Purves, Stephen W. Pacala,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1126/science.1155359"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1126/science.1155359", "name": "item", "description": "10.1126/science.1155359", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1126/science.1155359"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-06-13T00:00:00Z"}}, {"id": "10.1175/bams-d-21-0145.1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:20Z", "type": "Journal Article", "created": "2021-11-15", "title": "MSWX: Global 3-Hourly 0.1\u00b0 Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles", "description": "Abstract <p>We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1\uffc2\uffb0 resolution, near-real-time updates (\uffe2\uff88\uffbc3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within \uffe2\uff88\uffbc3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx.</p>", "keywords": ["Climate services", "Reanalysis data", "Operational forecasting", "Climate records", "13. Climate action", "Downscaling", "01 natural sciences", "Data science", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://journals.ametsoc.org/downloadpdf/journals/bams/103/3/BAMS-D-21-0145.1.xml"}, {"href": "https://doi.org/10.1175/bams-d-21-0145.1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bulletin%20of%20the%20American%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1175/bams-d-21-0145.1", "name": "item", "description": "10.1175/bams-d-21-0145.1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1175/bams-d-21-0145.1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.1785/0220200368", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:57Z", "type": "Journal Article", "created": "2021-03-17", "title": "Comparing Short-Term Seismic and COVID-19 Fatality Risks in Italy", "description": "Abstract                <p>Risks assessment and risks comparison are basic concepts for emergency management. In the fields of earthquake engineering and engineering seismology, the operational earthquake loss forecasting (OELF) is the research frontier for the assessment of short-term seismic risk. It combines seismicity models, continuously updated based on ground-motion monitoring (i.e., operational earthquake forecasting), with large-scale vulnerability models for the built environment and exposure data. With the aim of contributing to the discussion about capabilities and limitations of OELF, the study presented aims at comparing the OELF results and the fatality risk (based on fatality data) related to coronavirus 2019 (COVID-19) that, at the time of writing, is perceived as very relevant and required unprecedented risk reduction measures in several countries, most notably Italy. Results show that, at a national scale in Italy, the COVID-19 risk has been higher than the seismic risk during the two pandemic waves even if, at the end of the so-called lockdown, the evolution of the pandemic suggested the possibility (not realized) of reaching a situation of comparable seismic and COVID-19 risks in a few weeks. Because the two risks vary at a local scale, risks comparison was also carried out on a regional basis, showing that, before the beginning of the second wave, in some cases, the seismic risk, as assessed by means of OELF, was larger than the pandemic one.</p>", "keywords": ["lockdown", "emergency management.", "SARS\u2010Cov\u20102 pandemic", "13. Climate action", "Operational earthquake loss forecasting", "01 natural sciences", "3. Good health", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://pubs.geoscienceworld.org/ssa/srl/article-pdf/92/4/2382/5336536/srl-2020368.1.pdf"}, {"href": "https://doi.org/10.1785/0220200368"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Seismological%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1785/0220200368", "name": "item", "description": "10.1785/0220200368", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1785/0220200368"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-17T00:00:00Z"}}, {"id": "10.1785/0220200428", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:19:58Z", "type": "Journal Article", "created": "2021-07-28", "title": "Comment on \u201cTwo Foreshock Sequences Post Gulia and Wiemer (2019)\u201d by Kelian Dascher-Cousineau, Thorne Lay, and Emily E. Brodsky", "description": "Abstract                <p>Dascher-Cousineau et\uffc2\uffa0al. (2020) apply the so-called foreshock traffic-light system (FTLS) model proposed by Gulia and Wiemer (2019) to two earthquake sequences that occurred after the submission of the model: the 2019 Ridgecrest (Mw\uffc2\uffa07.1) and the 2020 Mw\uffc2\uffa06.4 Puerto Rico earthquakes. We show in this comment that the method applied by Kelian Dascher-Cousineau et\uffc2\uffa0al. (2020) deviates in at least six substantial and not well-documented aspects from the original FTLS method. As a consequence, they used for example in the Ridgecrest case only 1% of the data available to estimate b-values and from a small subvolume of the relevant mainshock fault. In the Puerto Rico case, we document here substantial issues with the homogeneity of the magnitude scale that in our assessment make a meaningful analysis of b-values impossible. We conclude that the evaluation by Dascher-Cousineau et\uffc2\uffa0al. (2020) is misrepresentative and a not a fair test of the FTLS hypothesis.</p>", "keywords": ["b-value", " foreshock traffic-light system", " FTLS", " forecasting models", " aftershocks", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Gulia, Wiemer,", "roles": ["creator"]}]}, "links": [{"href": "http://pubs.geoscienceworld.org/ssa/srl/article-pdf/92/5/3251/5390782/srl-2020428.1.pdf"}, {"href": "https://doi.org/10.1785/0220200428"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Seismological%20Research%20Letters", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1785/0220200428", "name": "item", "description": "10.1785/0220200428", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1785/0220200428"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-28T00:00:00Z"}}, {"id": "10.3390/app12126194", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:56Z", "type": "Journal Article", "created": "2022-06-17", "title": "Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.</p></article>", "keywords": ["Technology", "330", "QH301-705.5", "univariate", "T", "Physics", "QC1-999", "forecasting", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "forecasting; univariate; time series; Python; PSF", "Chemistry", "0203 mechanical engineering", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "time series", "TA1-2040", "Biology (General)", "QD1-999", "PSF", "Python"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://research.usq.edu.au/download/a41f7e6afaf72d3aab08e4fbf5850ce9baed364db9cd274b284e7956b4aa1a6e/1339682/applsci-12-06194-v3.pdf"}, {"href": "https://doi.org/10.3390/app12126194"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/app12126194", "name": "item", "description": "10.3390/app12126194", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/app12126194"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-17T00:00:00Z"}}, {"id": "10.2134/jeq2005.0259", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:13Z", "type": "Journal Article", "created": "2006-07-07", "description": "ABSTRACT<p>Soil carbon sequestration (SCS) has the potential to attenuate increasing atmospheric CO2 and mitigate greenhouse warming. Understanding of this potential can be assisted by the use of simulation models. We evaluated the ability of the EPIC model to simulate corn (Zea mays L.) yields and soil organic carbon (SOC) at Arlington, WI, during 1958\uffe2\uff80\uff931991. Corn was grown continuously on a Typic Argiudoll with three N levels: LTN1 (control), LTN2 (medium), and LTN3 (high). The LTN2 N rate started at 56 kg ha\uffe2\uff88\uff921 (1958), increased to 92 kg ha\uffe2\uff88\uff921 (1963), and reached 140 kg ha\uffe2\uff88\uff921 (1973). The LTN3 N rate was maintained at twice the LTN2 level. In 1984, each plot was divided into four subplots receiving N at 0, 84, 168, and 252 kg ha\uffe2\uff88\uff921 Five treatments were used for model evaluation. Percent errors of mean yield predictions during 1958\uffe2\uff80\uff931983 decreased as N rate increased (LTN1 = \uffe2\uff88\uff925.0%, LTN2 = 3.5%, and LTN3 = 1.0%). Percent errors of mean yield predictions during 1985\uffe2\uff80\uff931991 were larger than during the first period. Simulated and observed mean yields during 1958\uffe2\uff80\uff931991 were highly correlated (R2 = 0.961, p &lt; 0.01). Simulated SOC agreed well with observed values with percent errors from \uffe2\uff88\uff925.8 to 0.5% in 1984 and from \uffe2\uff88\uff925.1 to 0.7% in 1990. EPIC captured the dynamics of SOC, SCS, and microbial biomass. Simulated net N mineralization rates were lower than those from laboratory incubations. Improvements in EPIC's ability to predict annual variability of crop yields may lead to improved estimates of SCS.</p>", "keywords": ["Greenhouse Effect", "2. Zero hunger", "Time Factors", "Nitrogen", "04 agricultural and veterinary sciences", "Carbon Dioxide", "15. Life on land", "Zea mays", "Carbon", "Soil", "0401 agriculture", " forestry", " and fisheries", "Biomass", "Fertilizers", "Ecosystem", "Environmental Monitoring", "Forecasting"]}, "links": [{"href": "https://doi.org/10.2134/jeq2005.0259"}, {"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": "10.2134/jeq2005.0259", "name": "item", "description": "10.2134/jeq2005.0259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2134/jeq2005.0259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-07-01T00:00:00Z"}}, {"id": "10.3389/feart.2021.729282", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:20:46Z", "type": "Journal Article", "created": "2021-09-07", "title": "An Operational Earthquake Forecasting Experiment for Israel: Preliminary Results", "description": "<p>Operational Earthquake Forecasting (OEF) aims to deliver timely and reliable forecasts that may help to mitigate seismic risk during earthquake sequences. In this paper, we build the first OEF system for the State of Israel, and we evaluate its reliability. This first version of the OEF system is composed of one forecasting model, which is based on a stochastic clustering Epidemic Type Earthquake Sequence (ETES) model. For every day of the forecasting time period, January 1, 2016 - November 15, 2020, the OEF-Israel system produces a weekly forecast for target earthquakes with local magnitudes greater than 4.0 and 5.5 in the entire State of Israel. Specifically, it provides space-time-dependent seismic maps of the weekly probabilities, obtained by using a fixed set of the model\uffe2\uff80\uff99s parameters, which are estimated through the maximum likelihood technique based on a learning period of about 32\uffc2\uffa0years (1983\uffe2\uff80\uff932015). According to the guidance proposed by the Collaboratory for the Study of Earthquake Predictability (CSEP), we also perform the N- and S-statistical tests to verify the reliability of the forecasts. Results show that the OEF system forecasts a number of events comparable to the observed one, and also captures quite well the spatial distribution of the real catalog with the exception of two target events that occurred in low seismicity regions.</p>", "keywords": ["seismic predictability in the short-term", "Science", "Q", "statistical tests", "reliable forecasts", "operational earthquake forecasting", "01 natural sciences", "ETES model; operational earthquake forecasting; reliable forecasts; seismic predictability in the short-term; statistical tests", "ETES model", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.3389/feart.2021.729282"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Earth%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/feart.2021.729282", "name": "item", "description": "10.3389/feart.2021.729282", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/feart.2021.729282"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-07T00:00:00Z"}}, {"id": "10.3390/rs13163101", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:08Z", "type": "Journal Article", "created": "2021-08-06", "title": "Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in Morocco.", "description": "<p>Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000\uffe2\uff80\uff932017 (i.e., 15 \uffc3\uff97 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha\uffe2\uff88\uff921. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.</p>", "keywords": ["[SDE] Environmental Sciences", "330", "Science", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "[INFO] Computer Science [cs]", "crop yield forecasting", "01 natural sciences", "630", "indices", "[INFO]Computer Science [cs]", "Climate indices", "remote sensing drought indices", "weather data", "0105 earth and related environmental sciences", "2. Zero hunger", "Remote sensing drought indices", "climate indices", "remote sensing drought", "Q", "Crop yield forecasting", "04 agricultural and veterinary sciences", "semiarid region", "15. Life on land", "6. Clean water", "machine learning", "13. Climate action", "[SDE]Environmental Sciences", "crop yield forecasting; machine learning; remote sensing drought indices; climate indices; weather data; semiarid region", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "Semiarid region"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3101/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/16/3101/pdf"}, {"href": "https://doi.org/10.3390/rs13163101"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13163101", "name": "item", "description": "10.3390/rs13163101", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13163101"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-06T00:00:00Z"}}, {"id": "10.3390/s22051851", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:10Z", "type": "Journal Article", "created": "2022-02-28", "title": "Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting", "description": "<p>Forecasting the values of essential climate variables like land surface temperature and soil moisture can play a paramount role in understanding and predicting the impact of climate change. This work concerns the development of a deep learning model for analyzing and predicting spatial time series, considering both satellite derived and model-based data assimilation processes. To that end, we propose the Embedded Temporal Convolutional Network (E-TCN) architecture, which integrates three different networks, namely an encoder network, a temporal convolutional network, and a decoder network. The model accepts as input satellite or assimilation model derived values, such as land surface temperature and soil moisture, with monthly periodicity, going back more than fifteen years. We use our model and compare its results with the state-of-the-art model for spatiotemporal data, the ConvLSTM model. To quantify performance, we explore different cases of spatial resolution, spatial region extension, number of training examples and prediction windows, among others. The proposed approach achieves better performance in terms of prediction accuracy, while using a smaller number of parameters compared to the ConvLSTM model. Although we focus on two specific environmental variables, the method can be readily applied to other variables of interest.</p>", "keywords": ["deep learning; time-series forecasting; remote sensing; climate variables; surface temperature; soil moisture", "Chemical technology", "Temperature", "0211 other engineering and technologies", "deep learning", "climate variables", "TP1-1185", "02 engineering and technology", "surface temperature", "time-series forecasting", "Article", "remote sensing", "Soil", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "soil moisture"]}, "links": [{"href": "http://www.mdpi.com/1424-8220/22/5/1851/pdf"}, {"href": "https://www.mdpi.com/1424-8220/22/5/1851/pdf"}, {"href": "https://doi.org/10.3390/s22051851"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/s22051851", "name": "item", "description": "10.3390/s22051851", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/s22051851"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-26T00:00:00Z"}}, {"id": "10.3390/w11112245", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:13Z", "type": "Journal Article", "created": "2019-10-28", "title": "Irrigation-Advisor\u2014A Decision Support System for Irrigation of Vegetable Crops", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%\u201320%) were detected when comparing IA against ETcFAO, although the index of agreement proved reasonable adjustments. The IA recommendations led to water savings up to 13% when compared to F, except for lettuce, with a 31% surplus in irrigation when using IA. Crop yield was not compromised and water productivity was increased by IA. Therefore, IA mimicked the farmers\u2032 irrigation strategies fairly well without deploying sensors on-site. Nevertheless, improvements are needed for increasing the accuracy of IA estimations.</p></article>", "keywords": ["Water-use efficiency", "0106 biological sciences", "2. Zero hunger", "Evapotranspiration", "2508.02 Evaporaci\u00f3n", "evapotranspiration", "Modeling", "Water use efficiency", "modeling", "15. Life on land", "F06 Irrigation", "3103.02 Hibridaci\u00f3n de Cultivos", "01 natural sciences", "Modelling", "Ingenier\u00eda del Terreno", "6. Clean water", "Weather forecasting", "weather forecasts", "Edafolog\u00eda y Qu\u00edmica Agr\u00edcola", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Weather forecasts", "13. Climate action", "soil water balance", "water-use efficiency", "Soil water balance"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/11/2245/pdf"}, {"href": "https://www.iris.unict.it/bitstream/20.500.11769/552480/2/Water%202019.pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/11/2245/pdf"}, {"href": "https://doi.org/10.3390/w11112245"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/w11112245", "name": "item", "description": "10.3390/w11112245", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/w11112245"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-26T00:00:00Z"}}, {"id": "10.5194/isprs-archives-xlii-3-w6-9-2019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:21:53Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/isprs-archives-xlii-3-w6-9-2019", "name": "item", "description": "10.5194/isprs-archives-xlii-3-w6-9-2019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/isprs-archives-xlii-3-w6-9-2019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "20.500.11769/552480", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:27Z", "type": "Journal Article", "created": "2019-10-28", "title": "Irrigation-Advisor\u2014A Decision Support System for Irrigation of Vegetable Crops", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%\u201320%) were detected when comparing IA against ETcFAO, although the index of agreement proved reasonable adjustments. The IA recommendations led to water savings up to 13% when compared to F, except for lettuce, with a 31% surplus in irrigation when using IA. Crop yield was not compromised and water productivity was increased by IA. Therefore, IA mimicked the farmers\u2032 irrigation strategies fairly well without deploying sensors on-site. Nevertheless, improvements are needed for increasing the accuracy of IA estimations.</p></article>", "keywords": ["Water-use efficiency", "0106 biological sciences", "evapotranspiration", "F06 Irrigation", "01 natural sciences", "Modelling", "Weather forecasting", "weather forecasts", "water-use efficiency", "Soil water balance", "2. Zero hunger", "Evapotranspiration", "2508.02 Evaporaci\u00f3n", "Modeling", "Water use efficiency", "modeling", "15. Life on land", "3103.02 Hibridaci\u00f3n de Cultivos", "Ingenier\u00eda del Terreno", "6. Clean water", "Edafolog\u00eda y Qu\u00edmica Agr\u00edcola", "2511 Ciencias del Suelo (Edafolog\u00eda)", "Weather forecasts", "13. Climate action", "soil water balance"]}, "links": [{"href": "http://www.mdpi.com/2073-4441/11/11/2245/pdf"}, {"href": "https://www.iris.unict.it/bitstream/20.500.11769/552480/2/Water%202019.pdf"}, {"href": "https://www.mdpi.com/2073-4441/11/11/2245/pdf"}, {"href": "https://doi.org/20.500.11769/552480"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11769/552480", "name": "item", "description": "20.500.11769/552480", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11769/552480"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-10-26T00:00:00Z"}}, {"id": "3154587330", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:20Z", "type": "Journal Article", "created": "2021-10-25", "title": "Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust\u2013radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20\u2009\u00b5m, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000\u2009Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about \u22120.90 and \u22120.63\u2009W\u2009m\u22122 and at the top of the atmosphere between \u22120.20 and \u22120.28\u2009W\u2009m\u22122. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.                     </p></article>", "keywords": ["Mineral dusts", "Previsi\u00f3 del temps", "QE1-996.5", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "Geology", "15. Life on land", "01 natural sciences", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Weather forecasting", "Climate Action", "[SDU] Sciences of the Universe [physics]", "Earth sciences", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth Sciences", "Pols", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/6403/2021/gmd-14-6403-2021.pdf"}, {"href": "https://escholarship.org/content/qt2r39x8b5/qt2r39x8b5.pdf"}, {"href": "https://doi.org/3154587330"}, {"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": "3154587330", "name": "item", "description": "3154587330", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3154587330"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.5281/zenodo.16842801", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:16Z", "type": "Journal Article", "created": "2022-06-17", "title": "Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.</p></article>", "keywords": ["Technology", "330", "QH301-705.5", "univariate", "T", "Physics", "QC1-999", "forecasting", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "forecasting; univariate; time series; Python; PSF", "Chemistry", "0203 mechanical engineering", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "time series", "TA1-2040", "Biology (General)", "QD1-999", "PSF", "Python"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://research.usq.edu.au/download/a41f7e6afaf72d3aab08e4fbf5850ce9baed364db9cd274b284e7956b4aa1a6e/1339682/applsci-12-06194-v3.pdf"}, {"href": "https://doi.org/10.5281/zenodo.16842801"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16842801", "name": "item", "description": "10.5281/zenodo.16842801", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16842801"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16895104", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:23:16Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/10.5281/zenodo.16895104"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16895104", "name": "item", "description": "10.5281/zenodo.16895104", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16895104"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "2117/354852", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:40Z", "type": "Journal Article", "created": "2021-10-25", "title": "Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. We present the dust module in the Multiscale Online Non-hydrostatic AtmospheRe CHemistry model (MONARCH) version 2.0, a chemical weather prediction system that can be used for regional and global modeling at a range of resolutions. The representations of dust processes in MONARCH were upgraded with a focus on dust emission (emission parameterizations, entrainment thresholds, considerations of soil moisture and surface cover), lower boundary conditions (roughness, potential dust sources), and dust\u2013radiation interactions. MONARCH now allows modeling of global and regional mineral dust cycles using fundamentally different paradigms, ranging from strongly simplified to physics-based parameterizations. We present a detailed description of these updates along with four global benchmark simulations, which use conceptually different dust emission parameterizations, and we evaluate the simulations against observations of dust optical depth. We determine key dust parameters, such as global annual emission/deposition flux, dust loading, dust optical depth, mass-extinction efficiency, single-scattering albedo, and direct radiative effects. For dust-particle diameters up to 20\u2009\u00b5m, the total annual dust emission and deposition fluxes obtained with our four experiments range between about 3500 and 6000\u2009Tg, which largely depend upon differences in the emitted size distribution. Considering ellipsoidal particle shapes and dust refractive indices that account for size-resolved mineralogy, we estimate the global total (longwave and shortwave) dust direct radiative effect (DRE) at the surface to range between about \u22120.90 and \u22120.63\u2009W\u2009m\u22122 and at the top of the atmosphere between \u22120.20 and \u22120.28\u2009W\u2009m\u22122. Our evaluation demonstrates that MONARCH is able to reproduce key features of the spatiotemporal variability of the global dust cycle with important and insightful differences between the different configurations.</p></article>", "keywords": ["Mineral dusts", "Previsi\u00f3 del temps", "QE1-996.5", "info:eu-repo/classification/ddc/550", "550", "ddc:550", "Geology", "15. Life on land", "01 natural sciences", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "Weather forecasting", "Climate Action", "[SDU] Sciences of the Universe [physics]", "Earth sciences", "[SDU]Sciences of the Universe [physics]", "13. Climate action", "Earth Sciences", "Pols", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://gmd.copernicus.org/articles/14/6403/2021/gmd-14-6403-2021.pdf"}, {"href": "https://escholarship.org/content/qt2r39x8b5/qt2r39x8b5.pdf"}, {"href": "https://doi.org/2117/354852"}, {"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": "2117/354852", "name": "item", "description": "2117/354852", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/354852"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-01T00:00:00Z"}}, {"id": "10.60692/t1jsz-vm842", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:24:18Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/10.60692/t1jsz-vm842"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.60692/t1jsz-vm842", "name": "item", "description": "10.60692/t1jsz-vm842", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.60692/t1jsz-vm842"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "2966009560", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:05Z", "type": "Journal Article", "created": "2019-07-29", "title": "EVAPOTRANSPIRATION AND EVAPORATION/TRANSPIRATION RETRIEVAL USING DUAL-SOURCE SURFACE ENERGY BALANCE MODELS INTEGRATING VIS/NIR/TIR DATA WITH SATELLITE SURFACE SOIL MOISTURE INFORMATION", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Evapotranspiration is an important component of the water cycle. For the agronomic management and ecosystem health monitoring, it is also important to provide an estimate of evapotranspiration components, i.e. transpiration and soil evaporation. To do so, Thermal InfraRed data can be used with dual-source surface energy balance models, because they solve separate energy budgets for the soil and the vegetation. But those models rely on specific assumptions on raw levels of plant water stress to get both components (evaporation and transpiration) out of a single source of information, namely the surface temperature. Additional information from remote sensing data are thus required. This works evaluates the ability of the SPARSE dual-source energy balance model to compute not only total evapotranspiration, but also water stress and transpiration/evaporation components, using either the sole surface temperature as a remote sensing driver, or a combination of surface temperature and soil moisture level derived from microwave data. Flux data at an experimental plot in semi-arid Morocco is used to assess this potentiality and shows the increased robustness of both the total evapotranspiration and partitioning retrieval performances. This work is realized within the frame of the Phase A activities for the TRISHNA CNES/ISRO Thermal Infra-Red satellite mission.                     </p></article>", "keywords": ["Technology", "Environmental Engineering", "550", "Ecosystem Resilience", "Soil Moisture", "Evaporation", "Energy balance", "Biochemistry", "Environmental science", "Transpiration", "Meteorology", "Artificial Intelligence", "Soil water", "Thermal Infrared", "Applied optics. Photonics", "Machine Learning Methods for Solar Radiation Forecasting", "Photosynthesis", "TRISHNA", "Water balance", "Biology", "Soil science", "Global and Planetary Change", "Water content", "Evapotranspiration", "Geography", "Ecology", "Global Forest Drought Response and Climate Change", "T", "FOS: Environmental engineering", "Geology", "FOS: Earth and related environmental sciences", "Remote sensing", "15. Life on land", "Engineering (General). Civil engineering (General)", "Remote Sensing of Soil Moisture", "6. Clean water", "TA1501-1820", "[SDE.MCG] Environmental Sciences/Global Changes", "Chemistry", "Geotechnical engineering", "13. Climate action", "FOS: Biological sciences", "Environmental Science", "Physical Sciences", "Computer Science", "TA1-2040", "Water cycle"]}, "links": [{"href": "https://doi.org/2966009560"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20International%20Archives%20of%20the%20Photogrammetry%2C%20Remote%20Sensing%20and%20Spatial%20Information%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2966009560", "name": "item", "description": "2966009560", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2966009560"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-26T00:00:00Z"}}, {"id": "2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/273667", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:25:35Z", "type": "Journal Article", "created": "2017-09-11", "title": "CO2 evasion from boreal lakes: Revised estimate, drivers of spatial variability, and future projections", "description": "Abstract<p>Lakes (including reservoirs) are an important component of the global carbon (C) cycle, as acknowledged by the fifth assessment report of the IPCC. In the context of lakes, the boreal region is disproportionately important contributing to 27% of the worldwide lake area, despite representing just 14% of global land surface area. In this study, we used a statistical approach to derive a prediction equation\uffc2\uffa0for the partial pressure of CO2 (pCO2) in lakes as a function of lake area, terrestrial net primary productivity (NPP), and precipitation (r2\uffc2\uffa0=\uffc2\uffa0.56), and to create the first high\uffe2\uff80\uff90resolution, circumboreal map (0.5\uffc2\uffb0) of lake pCO2. The map of\uffc2\uffa0pCO2 was combined with lake area from the recently published GLOWABO database and three different estimates of the gas transfer velocity k to produce a resulting map of CO2 evasion (FCO2). For the boreal region, we estimate an average, lake area weighted, pCO2 of 966 (678\uffe2\uff80\uff931,325) \uffce\uffbcatm and a total\uffc2\uffa0FCO2 of 189 (74\uffe2\uff80\uff93347) Tg\uffc2\uffa0C\uffc2\uffa0year\uffe2\uff88\uff921, and evaluate the corresponding uncertainties based on Monte Carlo simulation. Our estimate of FCO2 is approximately twofold greater than previous estimates, as a result of methodological and data source differences. We use our results along with published estimates of the other C fluxes through inland waters to derive a C budget for the boreal region, and find that FCO2 from lakes is the most significant flux of the land\uffe2\uff80\uff90ocean aquatic continuum, and of a similar magnitude as emissions from forest fires. Using the model and applying it to spatially resolved projections of terrestrial NPP and precipitation while keeping everything else constant, we predict a 107% increase in boreal lake FCO2 under emission scenario RCP8.5 by 2100. Our projections are largely driven by increases in terrestrial NPP over the same period, showing the very close connection between the terrestrial and aquatic C cycle.</p", "keywords": ["0106 biological sciences", "Precipitation", "precipitation", "01 natural sciences", "Lake", "Environnement et pollution", "carbon budget", "Carbon budget", "Geovetenskap och relaterad milj\u00f6vetenskap", "terrestrial NPP", "boreal", "Climate change", "Boreal", "lake", "Ecosystem", "Future projections", "0105 earth and related environmental sciences", "Ecologie", "Arctic Regions", "Terrestrial NPP", "Carbon Dioxide", "Models", " Theoretical", "15. Life on land", "6. Clean water", "Carbon", "Lakes", "climate change", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "contr\u00f4le de la pollution", "CO2", "Technologie de l'environnement", "Earth and Related Environmental Sciences", "future projections", "Forecasting"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/gcb.13902"}, {"href": "https://doi.org/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/273667"}, {"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": "2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/273667", "name": "item", "description": "2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/273667", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/273667"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-16T00:00:00Z"}}, {"id": "3146201181", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:20Z", "type": "Journal Article", "created": "2021-03-29", "title": "Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013\u20132018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018\u20132019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41\u201371) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.</p></article>", "keywords": ["Landsat 8", "2. Zero hunger", "SAVI", "NDVI", "S", "13. Climate action", "wheat", "yield forecasting", "Agriculture", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/4/652/pdf"}, {"href": "https://doi.org/3146201181"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3146201181", "name": "item", "description": "3146201181", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3146201181"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "57bab07f06c51e313fbf59cabe032bb2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:26:59Z", "type": "Report", "title": "Lietuvos investicini\u0173 fond\u0173 rodikli\u0173 ir makroveiksni\u0173 vertinimas", "description": "Open AccessStraipsnyje pristatomi investicini\u0173 fond\u0173, pirmiausia mi\u0161ri\u0173 (akcij\u0173 ir obligacij\u0173) fond\u0173, pagrindini\u0173 rodikli\u0173 ir jiems \u012ftakos turin\u010di\u0173 makroveiksni\u0173 vertinimo teoriniai pagrindai. Pastaruoju metu ypa\u010d akcentuojamas daugiaaspek\u010dio (daugiakriterio) vertinimo metod\u0173 perspektyvumas, tod\u0117l aktualus j\u0173 taikymas ir vertinant investicinius fondus. Si\u016bloma vertinant apimti esmini\u0173 makroveiksni\u0173, turin\u010di\u0173 reik\u0161ming\u0105 \u012ftak\u0105 investiciniams fondams (daugiausia tai socialiniai, ekonominiai bei tarptautinio pob\u016bd\u017eio (globali\u0173 poky\u010di\u0173 finans\u0173 rinkose \u012ftakos) indikatoriai), identifikacij\u0105, kokybin\u0119 analiz\u0119 bei j\u0173 kiekybin\u012f vertinim\u0105 (taikant kriterij\u0173 reik\u0161mi\u0173 ir j\u0173 reik\u0161mingum\u0173 sandaug\u0173 sumavimo metod\u0105), pajamingumo rodikli\u0173 vertinim\u0105 atsi\u017evelgiant \u012f rizikos lyg\u012f (pagal Sharpe rodikl\u012f) ir fondo perspektyv\u0173 (sukaupto kapitalo apimties, investicinio fondo vieneto vert\u0117s poky\u010di\u0173) prognozavim\u0105 (daugianar\u0117s regresijos arba dalini\u0173 naudingumo funkcij\u0173 pagrindu). Tai aktualu kompleksi\u0161kai pagrind\u017eiant investavimo per investicinius fondus sprendimus. Reik\u0161miniai \u017eod\u017eiai: Daugiakriterio vertinimo metodai; Investicinis fondas; Kapitalo apim\u010di\u0173 prognozavimas; Makroveiksniai; Rodikli\u0173 vertinimas; Forecasting; Forecasting the volume of capital; Integrated evaluation; Multicriteria methods; Mutual fund; Surrounding factors; The volume of capital", "keywords": ["05 social sciences", "650", "Makroveiksniai", "Investicinis fondas", "Lietuva (Lithuania)", "0504 sociology", "Ekonomikos ir finans\u0173 valdymo fakultetas", "Surrounding factors", "04S - Ekonomika", "Investicijos / Investments", "Integrated evaluation", "Rodikli\u0173 vertinimas", "Ekonomin\u0117 analiz\u0117. Prognozavimas / Economic analysis. Forecasting", "Mutual funds", "0503 education"], "contacts": [{"organization": "\u017dvirblis, Algis, Rimkevi\u010di\u016bt\u0117, Vilma,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/57bab07f06c51e313fbf59cabe032bb2"}, {"rel": "self", "type": "application/geo+json", "title": "57bab07f06c51e313fbf59cabe032bb2", "name": "item", "description": "57bab07f06c51e313fbf59cabe032bb2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/57bab07f06c51e313fbf59cabe032bb2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "PMC6876662", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:28:11Z", "type": "Journal Article", "created": "2019-02-05", "title": "Current state of the global operational aerosol multi\u2010model ensemble: An update from the International Cooperative for Aerosol Prediction (ICAP)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Since the first International Cooperative for Aerosol Prediction (ICAP) multi\u2010model ensemble (MME) study, the number of ICAP global operational aerosol models has increased from five to nine. An update of the current ICAP status is provided, along with an evaluation of the performance of ICAP\u2010MME over 2012\u20132017, with a focus on June 2016\u2013May 2017. Evaluated with ground\u2010based Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and data assimilation quality MODerate\u2010resolution Imaging Spectroradiometer (MODIS) retrieval products, the ICAP\u2010MME AOD consensus remains the overall top\u2010scoring and most consistent performer among all models in terms of root\u2010mean\u2010square error (RMSE), bias and correlation for total, fine\u2010 and coarse\u2010mode AODs as well as dust AOD; this is similar to the first ICAP\u2010MME study. Further, over the years, the performance of ICAP\u2010MME is relatively stable and reliable compared to more variability in the individual models. The extent to which the AOD forecast error of ICAP\u2010MME can be predicted is also examined. Leading predictors are found to be the consensus mean and spread. Regression models of absolute forecast errors were built for AOD forecasts of different lengths for potential applications. ICAP\u2010MME performance in terms of modal AOD RMSEs of the 21 regionally representative sites over 2012\u20132017 suggests a general tendency for model improvements in fine\u2010mode AOD, especially over Asia. No significant improvement in coarse\u2010mode AOD is found overall for this time period.</p></article>", "keywords": ["[SDU] Sciences of the Universe [physics]", "Special Supplement on 25 Years of Ensemble Forecasting", "01 natural sciences", "[PHYS.PHYS.PHYS-AO-PH] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3497"}, {"href": "https://doi.org/PMC6876662"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Quarterly%20Journal%20of%20the%20Royal%20Meteorological%20Society", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC6876662", "name": "item", "description": "PMC6876662", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC6876662"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-04-02T00:00:00Z"}}, {"id": "19464892eaaf9440fdedf0723f641dad", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-25T16:30:20Z", "type": "Dataset", "language": "en", "title": "SINFO: ESDB Data adapted for the MARS Crop Yield Forecasting System", "description": "A database (2007) that is the result of adapting the European Soil Database for the provision of new and specific soil information for the CGMS (Crop Growth Monitoring System) for use in the MARS Crop Yield Forecast Sing System.", "formats": [{"name": "HTML"}], "keywords": ["cgms", "crop-mars-growth-monitoring-system", "eu", "mars-crop-yield-forecasting-system", "mcyfs", "soil"], "contacts": [{"organization": "http://publications.europa.eu/resource/authority/corporate-body/JRC", "roles": ["publisher"]}]}, "links": [{"href": "https://esdac.jrc.ec.europa.eu/content/sinfo-esdb-data-adapted-mars-crop-yield-forecasting-system"}, {"href": "http://data.europa.eu/88u/dataset/jrc-esdac-12"}, {"href": "http://data.europa.eu/89h/jrc-esdac-12"}, {"rel": "self", "type": "application/geo+json", "title": "19464892eaaf9440fdedf0723f641dad", "name": "item", "description": "19464892eaaf9440fdedf0723f641dad", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/19464892eaaf9440fdedf0723f641dad"}, {"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": "4a8344c0058856bab03b751feaaae78e", "type": "Feature", "geometry": null, "properties": {"updated": "2024-04-11T08:26:16.206371Z", "type": "Dataset", "language": "en", "title": "Modelling best management practices for reducing nutrient losses from agricultural catchments under different climate trajectories.", "description": "This dataset contains all the geospatial information and HYPE inputs and outputs related to the publication of \"How to achieve a 50% reduction in nutrient losses from agricultural catchments under different climate trajectories?\".   In this study, we build high-resolution geospatial data to build a semi-distributed water quantity and water quality model for two Swedish Agricultural Catchments in Hydrological Predictions of the Environment (HYPE). We calibrated and validated the model using discharge and water quality monitoring data from the streams in our study sites.  We subsequently used the calibrated model to forecast the impacts of climate change on nutrient (Inorganic Nitrogen and Total Phosphorus) loads under three relative concentration pathways (RCP2.6, RCP4.5, and RCP 8.5) and three periods (2022-2035, 2050-2065, and 2085-2100). Finally, we backcasted a 50% reduction in nutrient loads using  catchment mitigation scenarios (20% reduction in fertilisation, increasing in floodplain area, implementation of cover crops). This dataset contains all the monitoring data, model inputs (including parameterisation), and the model outputs. Moreover, it contains the R scripts with summary statistics and plotting and the summarised outputs of all model runs in csv files.  The dataset contains three folders.  1. The Geopatial Information folder contains all the geospatial data for both study catchments. These include land cover, soil, DEM, and finally the Soil Land Cover maps, which were used to build the HYPE models. The coding of the geospatial shapefiles and raster files can be found in the Readme document.  2. The HYPE_model folder contains all of the HYPE model building blocks necessary to run the calibrated model for Hestadb\u00e4cken and Tullstorp\u00e5n in seperate folders. It also contains the goodness-of-fit outcomes for both the calibrated model and the validation period. This folder also contains the future climate forecasts and the different mitigation scenario testing outcomes.  3. The outputs_and_data_analysis folder contains csv files with all of the model outcomes for IN, TP, and Q in both catchments for all combinations of RCP, period, and climate models. It also contains R scripts used to calculate trends, summary statistics, t-tests, and plot the figures. Moreover, it contains the outcomes of the percentages of change, correlation tests, and t-tests.", "keywords": ["anla\u0308ggningar-fo\u0308r-miljo\u0308o\u0308vervakning", "backcasting", "catchment-mitigation", "den-europeiska-gro\u0308na-given", "diffusa-na\u0308ringsfo\u0308roreningar", "diffuse-nutrient-pollution", "environmental-monitoring-facilities", "european-green-deal", "forecasting", "fo\u0308rba\u0308ttring-av-upptagningsomra\u030adet", "https:-hypeweb.smhi.se-model-water-", "hydrografi", "hydrography", "hydrological-predictions-of-the-environment", "kvalitet-pa\u030a-vatten", "land-cover", "land-use", "landta\u0308cke", "mark", "markanva\u0308ndning", "modelling", "se", "soil", "water-quality"], "contacts": [{"organization": "Maarten Wynants", "roles": ["creator"]}, {"organization": "http://dataportal.se/organisation/SE2021002817", "roles": ["publisher"]}]}, "links": [{"href": "http://data.europa.eu/88u/dataset/https-doi-org-10-5878-3j5c-yh37"}, {"href": "https://doi.org/10.5878/3j5c-yh37"}, {"href": "https-doi-org-10-5878-3j5c-yh37"}, {"rel": "self", "type": "application/geo+json", "title": "4a8344c0058856bab03b751feaaae78e", "name": "item", "description": "4a8344c0058856bab03b751feaaae78e", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/4a8344c0058856bab03b751feaaae78e"}, {"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=Forecasting&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=Forecasting&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=Forecasting&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Forecasting&offset=37", "hreflang": "en-US"}], "numberMatched": 37, "numberReturned": 37, "distributedFeatures": [], "timeStamp": "2026-07-26T16:04:04.615343Z"}