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inlandWaters

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  • Stream water discharge data from the UK Environmental Change Network (ECN) terrestrial sites. The data (stage and discharge) are collected by loggers at ECN's terrestrial sites (where a stream is present) using a standard protocol. They represent continuous 15-minute records from 1993 to 2015. The sites at which these data are collected are: Glensaugh, Moor House - Upper Teesdale, Sourhope, Wytham and Y Wyddfa (Snowdon). ECN is the UK's long-term environmental monitoring programme. It is a multi-agency programme sponsored by a consortium of fourteen government departments and agencies. These organisations contribute to the programme through funding either site monitoring and/or network co-ordination activities. These organisations are: Agri-Food and Biosciences Institute, Biotechnology and Biological Sciences Research Council, Cyfoeth Naturiol Cymru - Natural Resources Wales, Defence Science & Technology Laboratory, Department for Environment, Food and Rural Affairs, Environment Agency, Forestry Commission, Llywodraeth Cymru - Welsh Government, Natural England, Natural Environment Research Council, Northern Ireland Environment Agency, Scottish Environment Protection Agency, Scottish Government and Scottish Natural Heritage. Full details about this dataset can be found at https://doi.org/10.5285/8b58c86b-0c2a-4d48-b25a-7a0141859004

  • [This dataset is embargoed until August 31, 2024]. This dataset contains high-resolution (5-minute) raw, atmospheric corrected and mean sea level adjusted water level data for 9 flood storage areas (FSAs) in the Littlestock Brook catchment (a tributary of the River Evenlode, Thames Basin) from 2018 to 2022. The dataset also includes the estimated 9 x FSA stored volume time-series, estimated using a depth-stored volume lookup table for each FSA, produced from a digital elevation model of each feature and a depth-area-volume toolset. The annual barometric pressure time-series used to correct water level is also provided. This dataset was collected by UKCEH as part of a hydrological monitoring programme for the Littlestock Brook Natural Flood Management scheme. This work was supported by the SPITFIRE NERC DTP (NE/L002531/1) and the SCENARIO NERC DTP (NE/L002566/1). Full details about this dataset can be found at https://doi.org/10.5285/cf70f798-442a-4775-963c-b6600023830f

  • [This dataset is embargoed until January 1, 2024]. This dataset comprises multiple baseline and future ensembles of hydrological model estimates of monthly mean and annual maximum river flows (m3s-1) on a 0. 0.008333° × 0. 0.008333° grid (approximate grid of 1 km × 1 km) across Peninsular Malaysia. Specifically, these are provided for historical (1971 to 2005) and projected future (2006 to 2099) periods, for 3 Representative Concentration Pathways (RCPs). This dataset is the output from the Hydrological Modelling Framework for Malaysia, or “HMF-Malaysia” model. The projected future hydrology simulations are provided for CORDEX-SEA (Coordinated Regional Downscaling Experiment – South East Asia) three RCPs (RCP2.6, RCP4.5 and RCP8.5) assuming (i) current artificial influences (CAI) such as water transfers and diversions and (ii) planned future artificial influences (FAI). This dataset is an output from the hydrological modelling study from the Malaysia - Flood Impacts Across Scales (FIAS) project. Full details about this dataset can be found at https://doi.org/10.5285/9b70bebe-189c-4ae8-9aee-1bb1db7b1ad5

  • Data from two small streams, two rivers and rainfall fractions in the Western Amazonian basin at Tambopata National Reserve in Madre de Dios region, Peru. Data presented are nutrients (calcium, magnesium, potassium, sodium, total soluble phosphorus and silica) and fluvial carbon - dissolved inorganic carbon (DIC) and its isotopic composition δ13C-DIC, dissolved organic carbon (DOC) and particulate organic carbon (POC). Samples were collected during the period from February 2011 to May 2012 targeting both wet and dry seasons. Samples for DIC samples were collected using pre-acidified evacuated Exetainers. Established standard methods were used to take samples for DOC and nutrients. Established standard methods were used to analyse samples for DIC, DOC and nutrients These methods are outlined in the lineage. The samples were taken to understand the hydrological controls on the carbon concentrations and fluxes during different flow conditions. The data collection was carried out as part of the Natural Environment Research Council funded Amazonica project. Full details about this dataset can be found at https://doi.org/10.5285/ee1b9eb7-6fbd-4dd5-8f8f-e07d32c057e4

  • This is part of an ongoing long-term monitoring dataset of surface temperature, surface oxygen, water clarity, water chemistry and phytoplankton chlorophyll a from fortnightly sampling by the UK Centre for Ecology & Hydrology (UKCEH) at Derwent Water in Cumbria, England. The data available to download comprise surface temperature (TEMP) in degree Celsius, surface oxygen saturation (OXYG) in % air-saturation, Secchi depth (SECC) in metres, alkalinity (ALKA) in µg per litre as CaCO3 and pH. Ammonium (NH4N), nitrate (NO3N), soluble reactive phosphate (PO4P), total phosphorus (TOTP), dissolved reactive silicon expressed as SiO2 (SIO2) and phytoplankton chlorophyll a (TOCA) are all given in µg per litre. Measurements are made from a boat at a marked location (buoy) at the deepest part of the lake. When it was not possible to visit the buoy, samples were taken from the shore, thus water samples were not integrated on these occasions, marked as Flag 2. All data are from January 2014 until the end of 2018. Unfortunately, due to funding shortages, the long-term monitoring of Derwent Water ended early 2019. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this dataset can be found at https://doi.org/10.5285/7e7d722c-bdd7-4900-a443-e26370d72438

  • Gridded hydrological model river flow estimates on a 1km grid over Northern Ireland for the period Dec 1980 - Nov 2080. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept), along with the date of occurrence, and annual minima of 7-day mean river flow (years spanning Dec-Nov), along with the date of occurrence (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable and ensemble member. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid and c) a 1km x 1km grid identifying majority lake cells. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award. Full details about this dataset can be found at https://doi.org/10.5285/76057b0a-b18f-496f-891c-d5b22bd0b291

  • This dataset contains modelled outputs of the European river network modelled as 33,668 cells (5° longitude by 5° latitude). For each cell, modelled monthly flows were generated for an ensemble of tenscenarios for the 2050s and for the study baseline (naturalized flows for 1961 to 1990). Score classes are categorisation of flow alteration scenarios. Full details about this dataset can be found at https://doi.org/10.5285/d8ef71eb-3d22-4f98-af15-9d8e046ccb63

  • Data were collected in 2015, 2016 and 2017 to provide high resolution imagery for two sections of the South Saskatchewan River, Canada. Photographs were acquired using conventional aerial plane images with a 0.06m ground resolution, captured at a height of approximately 1500m from a fixed-wing aeroplane with an UltraCamXp sensor. Imagery was obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). The dataset consists of eight orthomosaics; one for each of the two river sections on each of the four dates. Images were collected as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/7473d4f9-c9a7-40ad-9f58-e58e25997fc5

  • [THIS DATASET HAS BEEN WITHDRAWN]. Gridded hydrological model river flow estimates on a 1km grid over Great Britain for the period Dec 1980 - Nov 2080. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept), along with the date of occurrence, and annual minima of 7-day mean river flow (years spanning Dec-Nov), along with the date of occurrence (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable and ensemble member. To aid interpretation, two additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment; www.ceh.ac.uk/ukscape, Work Package 2: Case Study – Water) programme, a NERC-funded National Capability Science Single Centre award number NE/R016429/1. Full details about this dataset can be found at https://doi.org/10.5285/b7a98440-8742-40d5-a518-46dc6420416e

  • Gridded hydrological model river flow estimates on a 1km grid over Northern Ireland for the period Dec 1980 - Nov 2011. The dataset includes monthly mean river flow, annual maxima of daily mean river flow (water years Oct - Sept) and annual minima of 7-day mean river flow (years spanning Dec-Nov) (units: m3/s). The data are provided in gridded netCDF files. There is one file for each variable. To aid interpretation, three additional spatial datasets are provided: a) digitally-derived catchment areas and b) estimated locations of flow gauging stations both on the 1km x 1km grid and c) a 1km x 1km grid identifying majority lake cells. The data were produced as part of UK-SCAPE (UK Status, Change And Projections of the Environment, Work Package 2: Case Study - Water) a NERC-funded National Capability Science Single Centre award. Full details about this dataset can be found at https://doi.org/10.5285/f5fc1041-e284-4763-b8b7-8643c319b2d0