{"count":5502,"next":"https://catalogue.ceda.ac.uk/api/v3/migrationproperties/?format=json&limit=100&offset=5500","previous":"https://catalogue.ceda.ac.uk/api/v3/migrationproperties/?format=json&limit=100&offset=5300","results":[{"id":11198,"key":"project.moles2_activity_subtype","value":"dgActivityDataCollection","modified":"2015-01-08","ob_ref":12039},{"id":11199,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12040},{"id":11200,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12041},{"id":11201,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12042},{"id":11202,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12043},{"id":11203,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12044},{"id":11204,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12045},{"id":11206,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12047},{"id":11207,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12048},{"id":11208,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12049},{"id":11209,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12050},{"id":11210,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12051},{"id":11211,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12052},{"id":11212,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12053},{"id":11213,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12054},{"id":11214,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12055},{"id":11215,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12056},{"id":11216,"key":"project.moles2_activity_subtype","value":"dgActivityDataProject","modified":"2015-01-08","ob_ref":12057},{"id":11217,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12058},{"id":11218,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12059},{"id":11219,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12060},{"id":11220,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12061},{"id":11221,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12062},{"id":11222,"key":"project.moles2_activity_subtype","value":"dgFundingProgram","modified":"2015-01-08","ob_ref":12063},{"id":11223,"key":"project.moles2_activity_subtype","value":"dgFundingProgram","modified":"2015-01-08","ob_ref":12064},{"id":11224,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12065},{"id":11225,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12066},{"id":11226,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12067},{"id":11227,"key":"project.moles2_activity_subtype","value":"dgFundingProgram","modified":"2015-01-08","ob_ref":12068},{"id":11231,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12072},{"id":11232,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12073},{"id":11233,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12074},{"id":11235,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12076},{"id":11237,"key":"project.moles2_activity_subtype","value":"dgFundingProgram","modified":"2015-01-08","ob_ref":12078},{"id":11239,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12080},{"id":11240,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12081},{"id":11241,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12082},{"id":11242,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12083},{"id":11243,"key":"project.moles2_activity_subtype","valu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This project will employ LiDAR and CASI image analysis to determine the structural properties of woody vegetation on floodplains, in order to develop physical parameterizations of flow resistance by vegetation. This approach will offer improved spatial characterization of floodplain friction for the new generation of distributed hydraulic models, allowing enhanced estimation of inundation area, velocity and depth of floodwater. \n\nLiDAR is an important source of information for floodplain studies and has recently been applied to map floodplain roughness using simple object height models. This project aims to progress this, using dual-pulse altimetry and morphological filtering to identify individual trees, determine their height, canopy diameter and stand density. Allometric and fractal models will then be used estimate trunk diameter and vertical physiology, enabling the identification of transfer functions to predict frontal area and spatial drag coefficients. CASI data analysis will be used to classify plant species, allowing different transfer functions to be developed for different functional physiologies. Sensitivity analysis using 2d finite element and raster hydraulic models will then be used to evaluate different model structures. \n\nThe project will develop an existing collaboration with experts at Toulouse and Clermont and will study sites on the Garonne and Allier rivers that have an unrivalled data context.","modified":"2015-01-08","ob_ref":12104},{"id":11264,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12104},{"id":11265,"key":"project.content.extra","value":"
\nThis proposal is designed to use an integrated suite of advanced remote sensing techniques and field based data collection to generate a model of landslides associated with glacio-lacustrine deposits in the French Alps. The scale and extent of the slope instability associated with these deposits warrants a significant research effort, as the landslides pose a significant threat to local population centres and infrastructure. Airborne remote sensing can provide an important contribution to this effort, allowing the study of the geomorphology, evolution and engineering geological state of the landslides using LiDAR, multispectral and hyperspectral imagery; combined with the existing time series analysis, this study will lead to a better understanding of alpine landslide terrains and the development of improved methods of hazard mapping of slope instability associated with glacio-lacustrine deposits.","modified":"2015-01-08","ob_ref":12105},{"id":11266,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12105},{"id":11267,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12106},{"id":11268,"key":"project.content.extra","value":"
\nHyperspectral sensors offer an alternative to time-consuming wet chemistry techniques for measuring humification (degree of decomposition). SHAC results for the Dark Peak showed strong correlations between spectral indices developed for HyMap and the degree of humification of exposed peat, measured colorimetrically as percent transmission (McMorrow et al., 2002; Cutler et al., 2002; McMorrow et al,. 2004a, b). This application for the new hyperspectral sensor, with supporting LiDAR and aerial photos, aims to test the transferability of the relationships to another date and sensor at the Dark Peak site. A related bid by Cutler (Dundee) will test spatial transferability to a Scottish site. We will use data collected for SHAC and CASI-SWIR flights, plus additional sites within resources available. Humification indices developed for HyMap, and moisture indices developed with the ASD (McMorrow et al., 2003), will be calculated for the new sensor. Correlation, stepwise regression and artificial neural networks will be used to investigate relationships with humification and gravimetric moisture content. The analysis will be repeated with ASD spectra in contact probe and field mode to investigate sensitivity to spatial resolution. The best models will be used to produce images of exposed peat humification and surface moisture content. Results will be compared with those for the Scottish site.","modified":"2015-01-08","ob_ref":12107},{"id":11269,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12107},{"id":11271,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12108},{"id":11273,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12109},{"id":11274,"key":"project.content.extra","value":"

This research forms part of a multi-scale study on habitat selection by steppe birds in Portugal. Our aim is to assess the environmental features that are important in habitat selection for two cereal steppe bird species - Little Bustard (Tetrax tetrax) and Calandra Lark (Melanocorypha calandra) - at a fine spatial scale (patch level), in the Special Protection Area of Castro Verde (Birds Directive 79/409/CEE), in South Portugal. Bird sampling is focusing on the breeding habitat selection. Bird occurrence and abundance data will be recorded using the point count method on a systematic sampling grid of 300 x 300m with the aim of assigning observations to 10 x 10 m pixels. All locations are being recorded using GPS and DGPS. We propose to use co-registered CASI, ATM and LiDAR data to study local scale habitat use within fallow fields (a key habitat for cereal-steppe birds) and within cereal fields (where patchiness could be a strong predictor of use). Relevant aspects that could influence bird populations are topography, vegetation height, density and percentage cover, presence of shrubs, floristic composition, soil type and moisture. LiDAR will provide topographic and vegetation height data, ATM will collect data on soil, while vegetation type, phenology and vigour will be measured using CASI. After ground-truthing and validation, the remotely sensed data will be used to predict the species��� distributions and to identify key parameters in habitat selection. The results at all spatial scales will be used for conservation planning and management.

","modified":"2015-01-08","ob_ref":12110},{"id":11275,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12110},{"id":11276,"key":"project.content.extra","value":"
\nComparison of CASI-2 and VIS-SWIR hyperspectral sensor capabilities for detection and characterisation of vegetation anomalies associated with buried gas pipelines\n\nAirborne hyperspectral imaging offers a potential solution for operational monitoring of soil disturbance associated with the instatement and remediation of land adjacent to a buried gas pipeline. However, suitable methodologies have yet to be developed to reliably detect vegetation stress associated with buried pipeline soil disturbance, due to constraints imposed by the available sensors on spatial resolution, signal to noise ratio, bandwidth, and spectral range. This project will compare the performance of the new hyperspectral sensor with CASI-2 for detection and characterisation of vegetation stress associated with a buried gas pipeline in Aberdeenshire, for which CASI & ATM data were acquired in 2004.\n\nLaboratory spectroscopy experiments from previous studies have established that VIS-NIR techniques based on the red-edge cannot reliably distinguish stress caused by gas from waterlogging effects. SWIR sensors have the potential to detect vegetation stress on the basis of absorption and reflectance features, but airborne experiments have established that the spatial resolution achievable with HyMAP may not be sufficient.\n\nA generic methodology for detection of vegetation stress associated with buried pipeline soil disturbance will be developed, utilising the improved capabilities of the new sensor, integrating identified stress indicator absorption and reflectance features from the full VIS-SWIR range. Data processing will be carried out by an EPSRC CASE PhD student, supported by Shell International Exploration and Production (SIEP).","modified":"2015-01-08","ob_ref":12111},{"id":11277,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12111},{"id":11278,"key":"project.content.extra","value":"
It is proposed that LiDAR and CASI/ATM will be used to create three-dimensional models of canopy vertical structure, height, density and gaps in representative vegetation communities that were burned in the western Algarve, Portugal in August 2003. The objectives are two-fold: a shorter term ecological aim to develop an understanding of the area's vegetation regeneration in terms of biomass recovery and vegetation structure, and a longer-term aim to use the results of this to develop and validate individual-based models of vegetation recovery with the eventual modelling of future fire potential. Detailed pre-fire vegetation survey data already exist for the field sites, which therefore provide a baseline from which vegetation regeneration can be analysed. The standard vegetation bandset of CASI will enable examination of the vigour of the vegetation response. Together with the LiDAR data, this will provide a basis for 'scaling up' of the data by integration with enhanced thematic mapper satellite imagery available from pre-fire and post-fire periods for the survey area and elsewhere in southern Portugal.","modified":"2015-01-08","ob_ref":12112},{"id":11279,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12112},{"id":11280,"key":"project.content.extra","value":"
The aim is to develop a quantitative biogeochemical budget for heavy metal fluxes in the highly-polluted Rio Tinto catchment by applying hyperspectral remote sensing data to establish a) the nature and distribution of the Fe speciation in water bodies; and b) the Fe-speciation, and hence, mineralogy of the Fe-soluble salt minerals. We will develop and test a methodology for remotely sensing metal storage and transport associated with acid mine waste. The application of hyperspectral remote sensing techniques to liquid phase samples and its potential to infer iron speciation, (and hence microbial activity) will be a major scientific contribution from this project. This will allow us, for the first time, to produce a catchment-wide speciation, mineralogical and water chemistry model of the Rio Tinto system which can be used to mitigate large-scale mining impacts.","modified":"2015-01-08","ob_ref":12113},{"id":11281,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12113},{"id":11282,"key":"project.content.extra","value":"
This project examines slope instability features (debris flows, gullies, soil piping and erosion) in regolith derived from phyllite and graphitic schist in semi-arid SE Spain. Within the study areas there are areas with flash-flood hazard and potential neotectonic activity (Almeria-Palomares fault zone): these features will also be examined, though the focus is on slope instability.\n\nTwo study areas have been selected: a coastal zone south of Mojacar and an inland zone in the Tabernas Basin. Recent land cover changes and the impacts of climate change look set to increase slope instability and flash flood hazards: there is therefore an urgent need for regional geohazard risk maps - we wish to see if these could be produced more effectively by utilising various remote sensing approaches.\n \nThe project aim is to determine the geomorphological, geotechnical, spatial and spectral properties of the study area geohazards, with findings scaled-up and applied to relevant space data (notably ASTER, SRTM and simulated Hyper-X). The airborne survey will use the new hyperspectral sensor, ATM, LiDAR and stereoscopic aerial photography, with additional data from fieldwork, spectrometry and geotechnical analyses.","modified":"2015-01-08","ob_ref":12114},{"id":11283,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12114},{"id":11284,"key":"project.content.extra","value":" \nThe Ria de Vigo is a coastal inlet controlled by the processes of coastal upwelling and downwelling on the adjacent continental shelf. The introduction of nutrient-rich waters far into the Ria by summer upwelling circulation (surface outflow, deep inflow) makes it highly productive and allows the economically important cultivation of mussels. Downwelling (surface inflow, deep outflow) in autumn and winter can introduce damaging 'red tides' that damage and force temporary but expensive closure of the culture. An ongoing project will elucidate the three-dimensional circulation in the Ria de Vigo and the accompanying patterns of distribution of temperature, salt, nutrients, as well as nano-, pico- and bacterio-plankton at various stages of the annual cycle. An important aspect is the role of lateral circulations and horizontal re-circulations, which may favour retention of red tides or enhance upwelling blooms. We here request complementary airborne observations during one or both of the field experiments to provide a completely synoptic sampling of the near surface distributions in the visible and near infra-red bands. These, in combination with sea truth data of phytoplankton and primary production, will provide a highly detailed mapping of the near-instantaneous situation in the Ria. The circulation is governed by the wind forcing on the continental shelf outside since the Ria itself is relatively sheltered. Airborne wind mapping would allow an unprecedented knowledge of the variation of wind forcing over the entire Ria and significantly enhance efforts to model numerically the relationships between the flow field, the hydrography and the biogeochemistry.","modified":"2015-01-08","ob_ref":12115},{"id":11285,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12115},{"id":11286,"key":"project.content.extra","value":"
\nIn April 1998 the Aznalcollar tailings dam in southwest Spain failed flooding the Agrio and Guadiamar rivers with an estimated 7.5 million m3 of acidic water and heavy metal bearing tailings. Following the spill, intensive clean-up operations were conducted which removed more than 7 million m3 of soils and sediment. The entire Agrio-Guadiamar valley floor was transformed as a result of the spill and clean-up operations. The principal aim of this project is to assess the geomorphological-geochemical recovery of the Agrio-Guadiamar River system eight years after the 1998 tailings dam failure. The range of remote sensing dataset requested for this project has the potential to be an invaluable methodology for monitoring and modelling of the environmental impact of mine waste from past and present mining on river systems. This project will investigate the use of hyperspectral remote sensing as an operational methodology for identifying the distribution and relative concentrations of (i) mine waste, (ii) secondary iron minerals, (iii) alteration clay minerals and (iv) mine-waste induced vegetation stress. The utility of an integrated LiDAR and aerial photography dataset in resolving the geomorphological controls on the dispersal, storage and remobilisation of mine waste will also be studied.","modified":"2015-01-08","ob_ref":12116},{"id":11287,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12116},{"id":11288,"key":"project.content.extra","value":"
\nBiological soil crusts, composed of primitive plants growing in and on the soil surface, cover large areas of the ground in semi arid regions where taller plant cover is scarce. They play important roles in the ecology, hydrology and soil stability of such regions. In SE Spain the crusts vary with climate and also in the extent to which they protect soils from erosion. A research project (PECOS) is currently investigating the distribution and dynamics of these crusts in SE Spain using a variety of survey, monitoring and experimental approaches. Project work is concentrated in the El Cautivo badlands near to Tabernas in Almeria and is mainly carried out in small plots of 1x1 to 5x2m. If we can extrapolate the information we gain from plots to the scale of slopes and catchments, then we will be in a much better position to understand the roles that these custs play in the broader landscape and also to predict the likely impact on these functions of changes in land use and climate. Spectral signatures of crusts suggest that the main types can potentially be distinguished from one another and from bare soils using hyperspectral data and thus, in order inestigate extrapolation between scales, we require high resolution airborne imagery, photography and digital elevation model data.","modified":"2015-01-08","ob_ref":12117},{"id":11289,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12117},{"id":11290,"key":"project.content.extra","value":"
\nThis research will investigate radiative transfer-based methods to interpret hyperspectral imagery and to estimate leaf biochemical and canopy biophysical variables. These are leaf chlorophyll a+b (Ca+b), dry matter (Cm), water (Cw), leaf area index (LAI), canopy fractional cover, crown volume and tree dimensions. AThese variables are required for modelling land surface / atmosphere interactions, and are indicators of stress and growth. The data will be used to develop and test methofd for estimation by inverse radiative transfer modelling from hyperspectral data in the 400-2500 nm spectral region. In addition, LiDAR data, if available, will provide information regarding the canopy structure that is required for input into the physical models for estimation of canopy biophysical variables. \n\nA field sampling campaign in collaboration with IAS, Cordoba, will be conducted for biochemical analysis of leaf chlorophyll content, measuring reflectance and transmittance using a Li-Cor 1800-12. LAI will be measured using a PCA LAI-2000 instrument. Atmospheric measurements will be collected at the time of over-flights for atmospheric correction of images. The research will study i) the simulation of crown and canopy reflectance with the FLIGHT 3-D model; ii) the estimation of crown and canopy structural variables with a LiDAR instrument, if available, such as crown dimensions, tree height and architecture; and iii) validate the link of the PROSPECT leaf model with FLIGHT for estimation of Ca+b and LAI, and canopy fractional cover.","modified":"2015-01-08","ob_ref":12118},{"id":11291,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12118},{"id":11292,"key":"project.content.extra","value":"
UK river catchments are experiencing increasing development which, in association with climate change, is placing many urban areas at an increased risk from flooding. In order to understand the likely damage and cost of urban flooding, as well as the influence of urban areas on channel discharge, fluvial flood models need to explicitly represent the complex topography and hydraulically important features of urban areas; information difficult to obtain by traditional survey methods. This research will employ an integrated remote sensing approach to derive urban topographic features and hydraulic parameters required for an explicit urban fluvial flood model. LIDAR, digital aerial photography and multispectral image data will be employed in an integrated framework to derive information on surface topography, 3-D features, surface type and surface texture/roughness for model parameterisation. These will be used to model known historical urban flood inundation for the river Ouseburn; for which data on previous flood events is available. The model will also be compared to models that do not include urban topography and features. The output of this research will be a better understanding of how urban areas should be managed in flood models and the utility of remote sensing for the parameterisation of these.","modified":"2015-01-08","ob_ref":12119},{"id":11293,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12119},{"id":11294,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12120},{"id":11295,"key":"project.content.extra","value":"
\nThe white ribbon zone is an area marked as 'no data' on geological and marine maps. This zone is never mapped from either land or sea and yet important processes occur in this zone. With a possible rise in sea level due to climate change and an increase in the risk of coastal flooding it is important to characterise the sediments within the integrated coastal zone (ICZ) for coastal management activities. So far only aerial photography has been used to map the coastal zone. Combined analysis of hyperspectral airborne data and field spectrometry data will provide spectral information to determine the composition of deposits within the coastal zone and give more detailed information for costal zone mapping. Grain size will be determined using integrated Lidar data and ground truthing techniques. The study site chosen is a section of coast around the Isle of Wight, chosen because of the large amount of field data that exists for the coastal zone around the Isle of Wight and for the diverse range of sediment types exposed. Techniques will be developed to characterise these coastal sediments remotely both in an out of the shallow marine zone.","modified":"2015-01-08","ob_ref":12121},{"id":11296,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12121},{"id":11297,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12122},{"id":11298,"key":"project.content.extra","value":"
\nThe artificial breaching of existing seawalls - managed realignment - has been implemented to counteract habitat losses by re-creating saltmarsh on formerly reclaimed land. Changes in shoreline position and the re-establishment of tidal exchange are likely to have implications for salt marshes and mudflats in front of, and adjacent to, such newly created intertidal areas. However, the way in which such schemes should be designed for maximum benefit but minimal environmental impact on adjacent coastal ecosystems is still poorly known. Optical remote sensing, and particularly the recording of multi-spectral imagery from aircraft-mounted instruments flown over the coastal zone, offers a rapid, repeatable, non-intrusive and relatively large scale monitoring system for assessing these external impacts. The Wash Banks Flood Defence Scheme is being studied with ATM imagery collected once before breaching (pre-9/2002) and twice afterwards. It has identified the impact of inundation on formerly reclaimed land, the relative stability of the existing saltmarsh surface and the major changes to the depth and distribution of creeks. It is crucial that this data collection is continued to demonstrate the full impacts over a realistic time interval.","modified":"2015-01-08","ob_ref":12123},{"id":11299,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12123},{"id":11301,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12124},{"id":11302,"key":"project.content.extra","value":"
Previous studies by the applicants have quantified the applicability of LiDAR data for reach-scale topographic mapping of gravel-bed channel systems. To model interactions between the macro-scale system drivers (hydrology, sediment movement) and the biotic communities on the meso-scale (geomorphological unit, response to individual events) the requirement is for an integrated mapping approach by augmenting LiDAR using appropriately georeferenced ground survey. The Coquet research has as its central focus better determination of the 'properties of patchiness' and how these influence river corridor processes and habitat availability and biodiversity. Earlier approaches have focused on attributes such as patch geometry and sediment accrual, but have ignored the influence of this on habitat change. Research objectives include elucidating temporal aspects of patch formation, persistence and function, determining feedback mechanisms between patchiness, instream processes and biotic diversity and linking hydrological processes with 'ex-channel habitat' including the wider floodplain. A detailed baseline exists for the study area as a result of acquisition of the 1998 LiDAR dataset. Updating the elevation models by a new LiDAR survey, along with ground-truth validation will allow spatial up-scaling from the site to an extended (5.5 km) reach scale, an exercise that will also benefit management approaches for these systems.","modified":"2015-01-08","ob_ref":12125},{"id":11303,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12125},{"id":11304,"key":"project.moles2_activity_subtype","value":"dgFundingProgram","modified":"2015-01-08","ob_ref":12126},{"id":11306,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12127},{"id":11308,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12128},{"id":11310,"key":"project.moles2_activity_subtype","value":"dgActivityDataCampaign","modified":"2015-01-08","ob_ref":12129},{"id":11315,"key":"Computation.content.introduction","value":"

MITgcm:\r\n

\r\n\r\nTo render atmosphere and ocean models from one dynamical core MITGCM exploit `isomorphisms' between equation sets that govern the evolution of the respective fluids. One system of hydrodynamical equations is written down and encoded. The model variables have different interpretations depending on whether the atmosphere or ocean is being studied. Thus, for example, the vertical coordinate `r' is interpreted as pressure, `p', in the atmosphere and height, `z', in the ocean. From a numerical implementation point of view there is no fundamental difference between atmosphere and ocean in the MITGCM model.\r\n\r\nTo know more about the way MITGCM exploit `isomorphisms' between atmosphere and ocean governing equations see : <a href=\"../../../paoc/papers/atmosphere_ocean_modeling.pdf\" target=\"_blank\">\r\n\tMarshall, J. A. Adcroft, J-M Campin and C. Hill (2004) Atmosphere-ocean \r\n\tmodeling exploiting fluid isomorphisms.</a>&#160; Mon. Wea. Rev., 132 (12), \r\n\t2882-2894</p>\r\n\r\n<p> The horizontal and vertical representation, resolution and other important characteristics of the <a href=\"http://mitgcm.org/public/docs.html\">\" MITgcm \"</a> \r\n hydrodynamical kernel used for the study of the circulation of atmosphere and ocean are as follows (2011):</p>\r\n\r\n\r\n<p> </p><h2> A. Atmosphere </h2>\r\n<ol>\r\n<li> resolution\r\n\r\n<p> The model has run using different grids. The coarsest resolution grid used was C32 (the cubic grid of Rancic and Purser\r\nwith 32 points across a tile) which is equivalent to G64 (128&#215;64 points in spherical polar coordinates) in equatorial resolution. Other grid resolutions are C46, C64 and C96 all using the conformal cubic grid of Rancic et al. (1996).\r\n<br />More on model grid resolution: <a href=\"http://paoc.mit.edu//paoc/papers/adcroft_et_al_MWR_2004.pdf\" target=\"_blank\">Adcroft,\r\n A., J-M Campin, C. Hill and J. Marshall (2004) Implementation of\r\n an atmosphere-ocean general circulation model on the expanded\r\n spherical cube.</a>&#160; Mon. Wea. Rev., 132 (12), 2845-2863\r\n</p></li>\r\n\r\n<li> numerical scheme/grid \r\n\r\n<p> o Grid - Arakawa C grid. The basic algorithm employed for stepping forward the momentum equations is based on retaining non-divergence of the flow at all times. This is most naturally done if the components of flow are staggered in space in the form of an Arakawa C grid. \r\nThe finite volume method is used to discretize the equations in space.\r\n <br align=\"left\" />More on the finite volume implementation: Adcroft, A.J., Hill, C.N. and J. Marshall, (1997) Representation of topography by shaved cells in a height coordinate ocean model&#160; <em>Mon Wea Rev</em>, vol 125, 2293-2315 \r\n</p><p> o Time-stepping - The algorithm for each of the 5 basic formulations in which the model comes is:\r\n </p><ol>\r\n <li> the semi-implicit pressure method for hydrostatic equations with a rigid-lid, variables co-located in time and with Adams-Bashforth time-stepping,</li> \r\n <li> as 1 but with an implicit linear free-surface,</li> \r\n <li> as 1 or 2 but with variables staggered in time,</li> \r\n <li> as 1 or 2 but with non-hydrostatic terms included,</li> \r\n <li> as 1 or 3 but with non-linear free-surface.</li> \r\n </ol> \r\n<p><br align=\"left\" />More on discretization and time-stepping: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node30.html\"> \" here \"</a>\r\n\r\n\r\n</p></li><li> list of prognostic variables :\r\n<p> The equations of motion integrated by the model involve four prognostic equations for flow: the two horizontal components of velocity, temperature, potential temperature and salt/moisture, and three diagnostic equations for vertical flow, density/buoyancy, and pressure/geo-potential. \r\n<br /> In addition, the surface pressure or height may by described by either a prognostic or diagnostic equation and if non-hydrostatics terms are included then a diagnostic equation for non-hydrostatic pressure is also solved. </p></li>\r\n\r\n<li> Major atmospheric parameterizations.\r\nThe atmospheric parameterizations are based on the Atmospheric Intermediate Physics aim_v23 package that is based on the version v23 of the SPEEDY code described in: Molteni, F., Atmospheric simulations using a GCM with simplified physical parametrization, I: Model climatology and variability in multidecadal experiments, Clim. Dynamics, 20, 175-191, 2003. The parameters are:\r\n\r\n<p>\r\n</p><pre>------------------------------------------------------------------------\r\n&lt;-Name-&gt;|Levs|&lt;-parsing code-&gt;|&lt;-- Units --&gt;|&lt;- Tile (max=80c) \r\n------------------------------------------------------------------------\r\nDIABT | 5 |SM ML |K/s |Pot. Temp. Tendency (Mass-Weighted) from Diabatic Processes\r\nDIABQ | 5 |SM ML |g/kg/s |Spec.Humid. Tendency (Mass-Weighted) from Diabatic Processes\r\nRADSW | 5 |SM ML |K/s |Temperature Tendency due to Shortwave Radiation (TT_RSW)\r\nRADLW | 5 |SM ML |K/s |Temperature Tendency due to Longwave Radiation (TT_RLW)\r\nDTCONV | 5 |SM MR |K/s |Temperature Tendency due to Convection (TT_CNV)\r\nTURBT | 5 |SM ML |K/s |Temperature Tendency due to Turbulence in PBL (TT_PBL)\r\nDTLS | 5 |SM ML |K/s |Temperature Tendency due to Large-scale condens. (TT_LSC)\r\nDQCONV | 5 |SM MR |g/kg/s |Spec. Humidity Tendency due to Convection (QT_CNV)\r\nTURBQ | 5 |SM ML |g/kg/s |Spec. Humidity Tendency due to Turbulence in PBL (QT_PBL)\r\nDQLS | 5 |SM ML |g/kg/s |Spec. Humidity Tendency due to Large-Scale Condens. (QT_LSC)\r\nTSR | 1 |SM P U1 |W/m^2 |Top-of-atm. net Shortwave Radiation (+=dw)\r\nOLR | 1 |SM P U1 |W/m^2 |Outgoing Longwave Radiation (+=up)\r\nRADSWG | 1 |SM P L1 |W/m^2 |Net Shortwave Radiation at the Ground (+=dw)\r\nRADLWG | 1 |SM L1 |W/m^2 |Net Longwave Radiation at the Ground (+=up)\r\nHFLUX | 1 |SM L1 |W/m^2 |Sensible Heat Flux (+=up)\r\nEVAP | 1 |SM L1 |g/m^2/s |Surface Evaporation (g/m2/s)\r\nPRECON | 1 |SM P L1 |g/m^2/s |Convective Precipitation (g/m2/s)\r\nPRECLS | 1 |SM M1 |g/m^2/s |Large Scale Precipitation (g/m2/s)\r\nCLDFRC | 1 |SM P M1 |0-1 |Total Cloud Fraction (0-1)\r\nCLDPRS | 1 |SM PC167M1 |0-1 |Cloud Top Pressure (normalized)\r\nCLDMAS | 5 |SM P LL |kg/m^2/s |Cloud-base Mass Flux (kg/m^2/s)\r\nDRAG | 5 |SM P LL |kg/m^2/s |Surface Drag Coefficient (kg/m^2/s)\r\nWINDS | 1 |SM P L1 |m/s |Surface Wind Speed (m/s)\r\nTS | 1 |SM L1 |K |near Surface Air Temperature (K)\r\nQS | 1 |SM P L1 |g/kg |near Surface Specific Humidity (g/kg)\r\nENPREC | 1 |SM M1 |W/m^2 |Energy flux associated with precip. (snow, rain Temp)\r\nALBVISDF| 1 |SM P L1 |0-1 |Surface Albedo (Visible band) (0-1)\r\nDWNLWG | 1 |SM P L1 |W/m^2 |Downward Component of Longwave Flux at the Ground (+=dw)\r\nSWCLR | 5 |SM ML |K/s |Clear Sky Temp. Tendency due to Shortwave Radiation\r\nLWCLR | 5 |SM ML |K/s |Clear Sky Temp. Tendency due to Longwave Radiation\r\nTSRCLR | 1 |SM P U1 |W/m^2 |Clear Sky Top-of-atm. net Shortwave Radiation (+=dw)\r\nOLRCLR | 1 |SM P U1 |W/m^2 |Clear Sky Outgoing Longwave Radiation (+=up)\r\nSWGCLR | 1 |SM P L1 |W/m^2 |Clear Sky Net Shortwave Radiation at the Ground (+=dw)\r\nLWGCLR | 1 |SM L1 |W/m^2 |Clear Sky Net Longwave Radiation at the Ground (+=up)\r\nUFLUX | 1 |UM 184L1 |N/m^2 |Zonal Wind Surface Stress (N/m^2)\r\nVFLUX | 1 |VM 183L1 |N/m^2 |Meridional Wind Surface Stress (N/m^2)\r\nDTSIMPL | 1 |SM P L1 |K |Surf. Temp Change after 1 implicit time step\r\n\r\n</pre>\r\n</li>\r\n\r\n</ol>\r\n<p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n</p><h2> B. Ocean </h2>\r\n\r\n<ol>\r\n <li> List of prognostic variables and tracers: \r\n\r\n <p> Velocities U and V, Temperature and Salinity. </p></li>\r\n\r\n <li> Main parametrisations. \r\n\r\n <p> Gent/McWiliams/Redi SGS Eddy Parameterization scheme and Nonlocal K-Profile Parameterization for Vertical Mixing of Large et al. [1994] describe in Large, W., J. McWilliams, and S. Doney, Oceanic vertical mixing: A review and a model with nonlocal boundary layer parameterization, Rev. Geophys., 32, 363-403, 1994. </p></li>\r\n\r\n<p> <br align=\"left\" />More on Ocean Parametrization packages in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node239.html\"> \" here \"</a>\r\n\r\n</p><li> Model top \r\n <p>The upper surface of the ocean is a free surface which is driven by the divergence of volume flux (Boussinesq) in the interior. There are three treatments of the upper boundary available in MITgcm:\r\n</p><p> a. Rigid-lid approximation in which the upper surface is imagined to be an impermeable boundary\r\nwhich exerts a pressure on the fluid. \r\n</p><p> b. The linear free-surface which ignores some small terms in the depth integrated continuity equation,\r\nthat permits surface gravity waves to propagate with finite phase speed and introduces a Helmholtz term in the surface pressure equation when treated implicitly in time.\r\nThis is a very good approximation in deep water for whic.\r\n</p><p> c. The non-linear free-surface an un-approximated treatment of the upper surface </p></li>\r\n</ol>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n<p> </p><h2> C. sea ice </h2>\r\n<p>The MITgcm sea ice model (MITgcm/sim) is based on a variant of the viscous-plastic (VP) dynamic-thermodynamic sea ice model [Zhang and Hibler, 1997] first introduced by Hibler [1980,1979]. In order to adapt this model to the requirements of coupled ice-ocean state estimation, many important aspects of the original code have been modified and improved: </p>\r\n<p>\r\n</p><pre> * the code has been rewritten for an Arakawa C-grid, both B- and C-grid variants are available; the C-grid code allows for no-slip and free-slip lateral boundary conditions;\r\n * two different solution methods for solving the nonlinear momentum equations have been adopted: LSOR [Zhang and Hibler, 1997], and EVP [Hunke and Dukowicz, 1997];\r\n * ice-ocean stress can be formulated as in Hibler and Bryan [1987] or as in Campin et al. [2008];\r\n * ice variables are advected by sophisticated, conservative advection schemes with flux limiting;\r\n * growth and melt parameterizations have been refined and extended in order to allow for more stable automatic differentiation of the code.\r\n</pre>\r\n\r\n<p>The sea ice model requires the following input fields: 10-m winds, 2-m air temperature and specific humidity, downward longwave and shortwave radiations, precipitation, evaporation, and river and glacier runoff. The sea ice model also requires surface temperature from the ocean model and the top level horizontal velocity. Output fields are surface wind stress, evaporation minus precipitation minus runoff, net surface heat flux, and net shortwave flux. The sea-ice model is global: in ice-free regions bulk formulae are used to estimate oceanic forcing from the atmospheric fields. </p>\r\n\r\n<p> <br align=\"left\" />More on Sea Ice packages in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node251.html\"> \" here \"</a>\r\n \r\n\r\n\r\n\r\n </p><h2> D. Land / ice sheets </h2>\r\n\r\n<p> The land model is a simple two-layer model with prognostics temperature, liquid groundwater and snow height. There is no continental ice.\r\n</p><p> <br align=\"left\" />More on the land model package in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node249.html\"> \" here \"</a>\r\n\r\n</p><h2> E. coupling details </h2>\r\n\r\n<p>1. frequency of coupling\r\n\r\n</p><p> Every ocean time step. \r\n\r\n</p><p>2. Are heat and water conserved by coupling scheme?\r\n\r\n</p><p>Yes.\r\n</p><p>3. list of variables passed between components:\r\n</p><p>\r\n<br align=\"left\" />More on the coupling package in MITgcm: <a href=\"http://mitgcm.org/public/r2_manual/latest/online_documents/node256.html\"> \" here \"</a>#\r\n\r\n </p><p>\r\n\r\n</p></div>","modified":"2015-09-03","ob_ref":2870},{"id":11319,"key":"moles2.provider","value":"badc.nerc.ac.uk","modified":"2022-06-16","ob_ref":37049},{"id":11320,"key":"moles2.provider","value":"badc.nerc.ac.uk","modified":"2022-07-08","ob_ref":37313}]}