Search results for "CSH"

showing 10 items of 20174 documents

A space-time rainfall generator for highly convective Mediterranean rainstorms

2003

Distributed hydrological models require fine resolution rainfall inputs, enhancing the practical interest of space-time rainfall models, capable of generating through numerical simulation realistic space-time rainfall intensity fields. Among different mathematical approaches, those based on point processes and built upon a convenient analytical description of the raincell as the fundamental unit, have shown to be particularly suitable and well adapted when extreme rainfall events of convective nature are considered. Starting from previous formulations, some analytical refinements have been considered, allowing practical generation of space-time rainfall intensity fields for that type of rai…

010504 meteorology & atmospheric sciencesMeteorology0207 environmental engineering[SDU.STU]Sciences of the Universe [physics]/Earth Sciences02 engineering and technologyMethod of moments (statistics)01 natural sciencesPoint processlcsh:TD1-1066lcsh:Environmental technology. Sanitary engineering020701 environmental engineering[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environmentlcsh:Environmental sciences0105 earth and related environmental scienceslcsh:GE1-350[SDU.OCEAN]Sciences of the Universe [physics]/Ocean AtmosphereComputer simulationRain gauge[SDU.OCEAN] Sciences of the Universe [physics]/Ocean AtmosphereSpace timelcsh:QE1-996.5lcsh:Geography. Anthropology. Recreation[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces environment6. Clean waterRunoff modellcsh:Geologylcsh:G13. Climate actionClimatology[SDU.STU] Sciences of the Universe [physics]/Earth SciencesGeneral Earth and Planetary SciencesEnvironmental scienceIntensity (heat transfer)Generator (mathematics)
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2016

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) gran…

010504 meteorology & atmospheric sciencesMeteorologyFLUXNET0208 environmental biotechnology0207 environmental engineeringlcsh:Life02 engineering and technologySensible heatAtmospheric sciences7. Clean energy01 natural sciencesFlux (metallurgy)FluxNetMachine learning; Carbon fluxes; Energy fluxes; FLUXNET; Remote sensing; FLUXCOMlcsh:QH540-549.5Latent heatMachine learningCarbon fluxes020701 environmental engineeringEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEarth-Surface ProcessesFLUXCOMMultivariate adaptive regression splineslcsh:QE1-996.5Empirical modellingPrimary production15. Life on landRemote sensingEnergy fluxes020801 environmental engineeringlcsh:Geologylcsh:QH501-531Kernel method13. Climate actionEnvironmental sciencelcsh:EcologyBiogeosciences
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Towards a long-term dataset of ELBARA-II measurements assisting SMOS level-3 land product and algorithm validation at the Valencia Anchor Station

2015

[EN] The Soil Moisture and Ocean Salinity (SMOS) mission was launched on 2nd November 2009 with the objective of providing global estimations of soil moisture and sea salinity. The main activity of the Valencia Anchor Station (VAS) is currently to assist in a long-term validation of SMOS land products. This study focus on a level 3 SMOS data validation with in situ measurements carried out in the period 2010-2012 over the VAS. ELBARA-II radiometer is placed in the VAS area, observing a vineyard field considered as representative of a major proportion of an area of 50×50 km, enough to cover a SMOS footprint. Brightness temperatures (TB) acquired by ELBARA-II have been compared to those obser…

010504 meteorology & atmospheric sciencesMeteorologyGeography Planning and Development0211 other engineering and technologiesData validationlcsh:G1-92202 engineering and technology01 natural sciencesVineyardSoil roughnessFootprintEarth and Planetary Sciences (miscellaneous)Vegetation optical depth14. Life underwaterPrecipitationWater content021101 geological & geomatics engineering0105 earth and related environmental sciencesRadiometerHumedad del suelobrightness temperature ELBARA-II L-MEB SMOS SMOS level 3 data soil moisture soil roughness Valencia Anchor Station vegetation optical depth15. Life on landEspesor óptico de la vegetaciónTerm (time)GeographyL-MEB13. Climate actionBrightness temperatureRugosidad del sueloTemperatura de brilloSoil moistureBrightness temperaturelcsh:Geography (General)
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The making of the New European Wind Atlas - Part 1: Model sensitivity

2020

This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They…

010504 meteorology & atmospheric sciencesMeteorologyPlanetary boundary layer010505 oceanography020209 energylcsh:QE1-996.5Mesoscale meteorologyFísica atmosférica02 engineering and technology01 natural sciences7. Clean energyWind speedlcsh:GeologyBoundary layerRoughness length/dk/atira/pure/sustainabledevelopmentgoals/climate_action13. Climate actionWeather Research and Forecasting ModelWind resource assessmentWind atlasSDG 13 - Climate Action0202 electrical engineering electronic engineering information engineeringEnvironmental science0105 earth and related environmental sciences
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Modelling nitrous oxide emissions from cropland at the regional scale

2006

Arable soils are a large source of nitrous oxide (N2O) emissions, making up half of the biogenic emissions worldwide. Estimating their source strength requires methods capable of capturing the spatial and temporal variability of N2O emissions, along with the effects of crop management. Here, we applied a process-based model, CERES, with geo-referenced input data on soils, weather, and land use to map N2O emissions from wheat-cropped soils in three agriculturally intensive regions in France. Emissions were mostly controlled by soil type and local climate conditions, and only to a minor extent by the doses of fertilizer nitrogen applied. As a result, the direct emission factors calculated at …

010504 meteorology & atmospheric sciencesNITROUS OXIDElcsh:TP670-699Atmospheric sciences01 natural sciencesBiochemistryREGIONAL SCALE[SDV.IDA]Life Sciences [q-bio]/Food engineeringAGRONOMIENitrogen cycleComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences2. Zero hungerLand useIntensive farmingARABLE CROPSMODELLING04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering15. Life on landSoil type13. Climate actionGreenhouse gasSoil water040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceSpatial variabilitylcsh:Oils fats and waxesArable landFood Science
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Influence of basement heterogeneity on the architecture of low subsidence rate Paleozoic intracratonic basins (Reggane, Ahnet, Mouydir and Illizi bas…

2018

The Paleozoic intracratonic North African Platform is characterized by an association of arches (ridges, domes, swells, or paleo-highs) and low subsidence rate syncline basins of different wavelengths (75–620 km). The Reggane, Ahnet, Mouydir and Illizi basins are successively delimited from east to west by the Amguid El Biod, Arak-Foum Belrem, and Azzel Matti arches. Through the analysis of new unpublished geological data (i.e., satellite images, well logs, seismic lines), the deposits associated with these arches and syncline basins exhibit thickness variations and facies changes ranging from continental to marine environments. The arches are characterized by thin amalgamated deposi…

010504 meteorology & atmospheric sciencesPaleozoicStratigraphyInversion (geology)Soil Science[SDU.STU]Sciences of the Universe [physics]/Earth Sciences010502 geochemistry & geophysics01 natural sciencesDevonianHorst and grabenPaleontologylcsh:StratigraphyGeochemistry and PetrologyCarboniferous[SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geologylcsh:QE640-6990105 earth and related environmental sciencesEarth-Surface Processes[SDU.STU.TE]Sciences of the Universe [physics]/Earth Sciences/TectonicsProterozoiclcsh:QE1-996.5PaleontologyGeologyOrogeny15. Life on landlcsh:GeologyGeophysicsSyncline[SDU.STU.PG]Sciences of the Universe [physics]/Earth Sciences/PaleontologyGeology
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Steroid Biomarkers Revisited - Improved Source Identification of Faecal Remains in Archaeological Soil Material.

2017

Steroids are used as faecal markers in environmental and in archaeological studies, because they provide insights into ancient agricultural practices and the former presence of animals. Up to now, steroid analyses could only identify and distinguish between herbivore, pig, and human faecal matter and their residues in soils and sediments. We hypothesized that a finer differentiation between faeces of different livestock animals could be achieved when the analyses of several steroids is combined (Δ5-sterols, 5α-stanols, 5β-stanols, epi-5β-stanols, stanones, and bile acids). We therefore reviewed the existing literature on various faecal steroids from livestock and humans and analysed faeces …

010504 meteorology & atmospheric sciencesPhysiologySwineSocial Scienceslcsh:MedicinePlant ScienceBreeding01 natural sciencesFecesSoilchemistry.chemical_compoundChenodeoxycholic acidMedicine and Health SciencesBilelcsh:ScienceMammalsMultidisciplinaryEcologyOrganic CompoundsGoatsAgricultureRuminantsBreedBody FluidsTrophic InteractionsCoprostanolChemistrySterolsArchaeologyCommunity EcologyPhysical SciencesVertebratesSteroidsLivestockDonkeyAnatomyResearch Article010506 paleontologyLivestockEquinesBiologyGas Chromatography-Mass SpectrometryBile Acids and SaltsGoosePlant-Animal Interactionsbiology.animalAnimalsHumansHerbivoryHorsesFeces0105 earth and related environmental sciencesHerbivorebusiness.industryPlant EcologyOrganic ChemistryEcology and Environmental Scienceslcsh:RChemical CompoundsOrganismsBiology and Life SciencesArchaeologychemistryAmnioteslcsh:QbusinessBiomarkersPLoS ONE
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Active tectonics along the south east offshore margin of Mt. Etna: New insights from high-resolution seismic profiles

2018

The offshore margin of Mt. Etna has been shaped by Middle Pleistocene to Holocene shortening and extension and, more recently, by gravity-related sliding of the volcanic edifice. These processes have acted contemporaneously although the gravitational component largely prevails over the tectonic one. In order to investigate this issue, we focused on the main role of active tectonics along the south-eastern offshore of Mt. Etna by means of marine high-resolution seismic data. Seismic profiles revealed post-220 ka sedimentary deposits unconformably overlaying the Lower-Middle Pleistocene Etnean clayey substratum and volcanics of the Basal Tholeiitic phase and the Timpe phase. Offshore Aci Trez…

010504 meteorology & atmospheric sciencesPleistoceneSettore GEO/02 - Geologia Stratigrafica E SedimentologicaSettore GEO/03 - Geologia Strutturaleactive tectonics010502 geochemistry & geophysics01 natural sciencesSicilian thrust-beltPaleontologyActive tectonicActive tectonics; Ionian Sea; Mt. Etna; Seismic investigation; Sicilian thrust-belt; Earth and Planetary Sciences (all)Seismic investigationHolocene0105 earth and related environmental sciencesgeographygeography.geographical_feature_categoryContinental shelfMt. Etnalcsh:QE1-996.5Fold (geology)Ionian SeaMt. Etna; seismic investigation; active tectonics; Sicilian thrust-belt; Ionian SeaSeafloor spreadingVolcanic rocklcsh:GeologyTectonicsGeneral Earth and Planetary SciencesSedimentary rockEarth and Planetary Sciences (all)Geology
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Plio-Pleistocene Dust Traps on Paleokarst Surfaces: A Case Study From the Carpathian Basin

2020

Plio-Pleistocene silt/clay-rich deposits and paleo-karst fissure sediments from sites of the northern and southern parts of the Carpathian Basin were investigated. These materials were supposed to be mixed during transport before being captured in karstified fissures. Evidence that the eolian fissure sediments of Plio-Pleistocene age in the older Triassic–Cretaceous limestones are derived from eolian silt and clay includes compositional and textural matches, especially decreasing grain-size trends observed downwards from the paleo-surface of the former landscape. Various environmental factors could be recognized by the statistical evaluation of grain-size distribution curves of fissure fill…

010504 meteorology & atmospheric sciencesPlioceneGeochemistryWeatheringclaySiltkarst010502 geochemistry & geophysics01 natural sciencesPaleosolPleistocenepaleosolLoessAeolian processesGeneral Earth and Planetary Scienceslcsh:QParent rockSiltstoneeolian sedimentationlcsh:ScienceSediment transportCarpathian basinGeology0105 earth and related environmental sciencesFrontiers in Earth Science
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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

2021

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…

010504 meteorology & atmospheric sciencesReceiver operating characteristicbusiness.industryDeep learningSpatial databaselcsh:QE1-996.5Deep learningLandslideIranLandslide susceptibility010502 geochemistry & geophysicsRNN01 natural sciencesConvolutional neural networklcsh:GeologyLandslideRecurrent neural networkGeneral Earth and Planetary SciencesArtificial intelligenceScale (map)businessAlgorithmCNNGeology0105 earth and related environmental sciencesGeoscience Frontiers
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