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Inland waters

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  • 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

  • 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

  • Data were collected in 2015 and 2016 to provide information about spatial variations in water depth and river bed morphology (including bedform height) on the South Saskatchewan River, Canada. Water depth measurements were obtained with a Navisound NS 215 system and a Reson TC 2024 200kHz high-resolution dual frequency single beam echo sounder (SBES) operating at a sampling frequency of 10hz. Data were geolocated via a Leica 1230 Real-Time Kinematic (RTK) dGPS system. Data were collected in 2015 (between 7th and 9th September) and 2016 (between 2nd and 14th September) as part of NERC project NE/L00738X/1. Full details about this dataset can be found at https://doi.org/10.5285/14c80b71-6eb6-4dba-a298-b95a37059f55

  • This dataset contains water flow velocity, discharge, and suspended sediment compositions of the Irrawaddy (Ayeyarwady) River at Pyay, Myanmar and the Salween (Thanlwin) River at Hpa-An, Myanmar. The suspended sediment samples and the hydrological data were collected both during peak monsoon conditions (August 2017 and August 2018) and peak dry season conditions (February 2018 and May 2019). Water velocity was measured using Acoustic Doppler Current Profiler (ADCP) while collecting suspended sediment samples at various depths in the river. Additional flow velocity data was collected while laterally crossing the river channel from bank to bank, and was used to calculate total river discharge at these sites. The dataset includes suspended sediment concentrations, particulate organic carbon concentrations, and particle size distributions of sediment samples collected at various depths and locations in the two river channels. Full details about this dataset can be found at https://doi.org/10.5285/86f17d61-141f-4500-9aa5-26a82aef0b33

  • Data on peat depth from >250 locations in the Pastaza-Marañón Basin, Amazonian Peru. The data were collected during a series of field campaigns in 2019 and 2020. These data, along with similar data collected under other projects, were used to train a predictive model of peat distribution. Locations of a small number of other sites are given without peat depth measurements (i.e. with NA in the column Peat_depth_cm); these sites relate to data reported elsewhere in the ‘Carbon Storage in Amazonian Peatlands’ data collection. Full details about this dataset can be found at https://doi.org/10.5285/ab13a06f-392f-4bc6-b1bf-06dd8b020307

  • This is a set of six ASCII grids describing the peak flood event for six return periods (2-100 years) at each point of the river network in the state of Kerala, India. Estimates were derived in a similar way to the Flood Estimation Handbook* approach. The data is measured in cubic metres per second, and is given on an unprojected resolution of 15 arc-seconds per grid cell. This work was supported by the Natural Environment Research Council as part of the LAWIS programme delivering National Capability. * (Flood Estimation Handbook. Centre for Ecology & Hydrology, 1999, ISBN: 9781906698003) Full details about this dataset can be found at https://doi.org/10.5285/cba9f9db-4706-4f1e-aaf4-fd7769e00db0

  • 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

  • These data are input files for CAESAR-Lisflood (CL), a numerical hydrodynamic-landscape evolution model. These files were created to support coupled hydrodynamic-landscape evolution modelling to evaluate the geomorphological response of river channels affected by the 7th February 2021 ice-rock avalanche and debris flow in Chamoli District, Uttarakhand, India. They include 10 m digital elevation models (DEMs) of bed rock and land surface topography in a gridded (raster) format. They also include reanalysis-derived river discharge data generated by the GEOGloWS project at the following locations: Rontigad, Rishiganga, Dhauliganga, and Alaknanda. The configuration settings and parameters for CL modelling are also included. Full details about this dataset can be found at https://doi.org/10.5285/4cdd86b3-bf58-457d-b8cf-b57aed2d56d0

  • The data set contains grain size distributions, organic matter (OM) content and trace metal distribution (including Fe, Zn, Cu, Cr and Pb) of 37 shallow cores of sediments sampled from dams across the Limpopo River Basin. The dams include: Gaborone, Lotsane and Shashe dams in Botswana; Houtrivier, Nwanedi and Mutshedzi dams in South Africa; Ripple Creek and Zhovhe dams in Zimbabwe; and Massingir Dam in Mozambique. Data from two cores sampled from an oxbow lake in Mozambique are also included. The cores were collected with a gravity corer using PVC pipes of 5 cm diameter by a team from Botswana International University of Science and Technology (BIUST) led by Dr. Franchi between July 2018 and April 2021. Full details about this dataset can be found at https://doi.org/10.5285/b8db8239-3bde-454a-aa75-d1cec24c8763

  • Data comprise reservoir inflows and release data (including spills), evaporation loss and optimised monthly rule curve ordinates (upper, lower and critical) for Pong and Bhakra reservoirs in Northern India. Also included in the rule curve data are associated reservoir rationing ratios that can be applied to gross demand when rationing is also indicated. Data contain monthly Inflows, net-evaporation loss and release (all in million cubic metres, i.e. x 10^6 m^3) as simulated by WEAP for the Pong and Bhakra reservoir for the baseline (1989 - 2008); mid-century (2032-2050) and end-century (2082-2100) periods. The future inflows were based on forcing the WEAP model of the basin with climate projections of the GFDL-CM3 CMIP model The data were collected by Heriot-Watt University under the Sustaining Himalayan Water Resources in a Changing Climate (SusHi-Wat) project funded by NERC. Full details about this dataset can be found at https://doi.org/10.5285/46135938-cc6c-44a0-b35b-f6e5f5dd1221