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Inland waters

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  • Data comprise modelled flood extents for the Kampala district produced by simulating rainfall events over a 5m Digital Elevation Model (DEM) using a 2D finite-volume hydrodynamic model. The DEM was obtained from Makerere University and rainfall events were sampled across a range of depths and durations (for 20, 40, 60, 80 and 100 mm of rainfall over 1, 3 and 6 hours using flood depth thresholds of 0.1, 0.2 and 0.3 mm). The effects of infiltration were included within green areas based on spatial data obtained from Makerere University. Maximum depths were converted into extents using various thresholds. Full details about this dataset can be found at https://doi.org/10.5285/e53dea2e-cb25-4f0f-b5f9-937eecf15aff

  • This dataset contains maximum water depth and maximum water velocity for 12 different Glacial Lake outburst floods (GLOFs) scenarios of the Tsho Rolpa Lake, Nepal. Also included is the water depth of dam breach flow and discharge of dam breach flow under each scenario. The GLOFs scenarios were created using a simple dam breach model. A high-performance hydrodynamic model was then used to simulate the resulting flood hydrodynamics. Full details about this dataset can be found at https://doi.org/10.5285/f4292d99-de93-4a28-a821-b2a6a826df4c

  • This dataset reports the responses of annual river flow to forestation in 43 catchments and contains 770 data points. Data shows the change in river flow following forestation at annual time scales, along control river flow measurements and associated metadata from primary and secondary sources. Data collection, processing and interpretation were performed by Laura Bentley and David A. Coomes between January 2018 and October 2019. Forestation was defined as a change in land cover from a stable, non-forested state to a forested one, independent of the long-term history of forest cover. Paired measurements of annual river flow following forestation (mm) and river flow under control land cover conditions (mm) are provided for each year that the catchment dataset satisfied our inclusion criteria. River flow response is provided as both an absolute difference (mm) and as a percentage of control flow in the same year. Estimates of catchment annual precipitation, annual potential evapotranspiration, forest age, forest area, and the year of study are provided for each river flow response data point. Metadata are provided concerning catchment land cover history, land use history, catchment area, forest type, average climate and the method of forest establishment. The dataset contains catchments that were planted with trees and catchments in which forest cover regenerated without planting. Historical forest cover was reported in some catchments, and not reported in others. The 43 catchments a distributed unevenly across the globe, in 13 countries. The length of time series for each catchment varies from 2 years to 57 years, with and average duration of 19 years. Full details about this dataset can be found at https://doi.org/10.5285/5baa5d91-d552-4fc6-8a8c-29ae45192d77

  • This dataset includes radiocarbon (14C) content and d13C for river water samples collected across the UK. Samples were concentrated to four major catchments - the Ribble, Conwy, Hampshire Avon and Scottish Dee. Samples were collected at high flow. The dataset also includes suspended particulate matter concentration and % organic carbon content. Full details about this dataset can be found at https://doi.org/10.5285/4962468f-54c4-49ff-adb8-03e9e88cffdd

  • [This dataset is embargoed until November 1, 2021]. This dataset contains time series observations of land surface-atmosphere exchanges of net ecosystem carbon dioxide exchange (NEE), sensible heat (H) and latent heat (LE), and momentum (τ) measured at the Wicken Sedge Fen, a conservation managed lowland fen (site code: EF-LN) in the Cambridgeshire Fens, UK. Turbulent flux densities (fluxes) were monitored using the micrometeorological eddy covariance (EC) technique between 2009-03-20 to 2009-12-31, and 2010-04-09 to 2011-01-16. The dataset includes ancillary weather and soil physics observations, as well as variables that characterise atmospheric turbulence. Full details about this dataset can be found at https://doi.org/10.5285/a70ebc3a-4a11-4e7d-bae0-c9808e0cb585

  • The data consist of stable water isotope composition in the rivers , lakes, soils and flooded areas in the Western Siberia Lowlands (WSL). Sampling area encompassed a 1700 km south-north transect spanning from approx. 56°N to 68°N in latitude and 74°E to 84°E in longitude. Samples were collected during multiple field campaigns between February 2014 and November 2016. The dataset in produced as a part of the JPI/NERC funded SIWA project "Climate impact on the carbon emission and export from Siberian inland waters". The dataset has resulted in two publications submitted to peer-review: (i) Ala-aho et al. (2018). Using stable isotopes to assess surface water source dynamics and hydrological connectivity in a high-latitude wetland and permafrost influenced landscape. Journal of Hydrology, 556, 279-293. (ii) Ala-aho et al. (2018). Permafrost and lakes control river isotope composition across a boreal Arctic transect in the Western Siberian lowlands. Environmental Research Letters, 13(3), 34028. Full details about this dataset can be found at https://doi.org/10.5285/ca17e364-638d-4949-befb-b18b3770aec6

  • [This dataset is embargoed until July 1, 2020]. Datasets consists of the results of Computational Fluid Dynamics (CFD) flow simulations for a section of the South Saskatchewan River, Canada. The aim of these CFD simulations was to investigate the effect of dunes on the depth-averaged and near-bed flow fields. Modelling was carried out using the open source CFD package OpenFOAM to solve the three-dimensional Navier-Stokes equations. The dataset consists of two files, one with simulation results for a river bed characterised by alluvial bedforms (dunes) and one for a smooth river bed without dunes. This work was part of NERC project NE/L00738X/1. Digital Surface Models (DSMs) were constructed using imagery obtained on four occasions (13th May 2015; 2nd Sept 2016; 8th June 2017; and 12th June 2017). Full details about this dataset can be found at https://doi.org/10.5285/7db04405-2f5e-4543-aa94-948ddbcd588a

  • Data were generated to investigate the influence of bed roughness on the dynamics of large sand-bed rivers like the South Saskatchewan, Canada. The influence of roughness was investigated by using a numerical model to simulate the evolution of the river bed for a hypothetical sand-bed river modelled on the South Saskatchewan. The model generated information on the evolving river bed topography, water depth, flow velocities and sediment transport rates, over a period of 28 years as part of NERC project NE/L00738X/1 Full details about this dataset can be found at https://doi.org/10.5285/790e507c-ce99-47ca-99b4-c97a684ee8c6

  • This dataset contains instream dissolved oxygen data collected continuously at one minute intervals for five sites in the Hampshire Avon catchment in the United Kingdom. Data were collected between August 2014 and August 2015 using miniDOT loggers. Full details about this dataset can be found at https://doi.org/10.5285/840228a7-40a1-4db4-aef0-a9fea2079987

  • Data were collected in 2017, to provide information on spatial patterns of dune migration rates and associated water flow characteristics, at locations on the South Saskatchewan River, Canada. Dune migration rates were measured using repeat aerial imagery. Bedform crests were digitised in individual images, and average dune migration rates were calculated from the mean migration distance between image pairs, divided by the time between image collection. Water depth and velocity data were collected using a Sontek M9 acoustic Doppler current profiler (aDcp) mounted on a small zodiac boat. The position of the aDcp was recorded using a RTK dGPS system. Data were collected on 12th June 2017 as part of NERC project NE/L00738X/1 Full details about this dataset can be found at https://doi.org/10.5285/864434b7-2102-4edc-802d-ebdbfe9ff766