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 comprises field sensor physicochemical and optical/fluorescence measurements, as well as laboratory microbiological and chemical analysis, for urban surface water samples. Samples were collected at different locations throughout the urban area of Kolkata, with the latitude and longitude of all sample location provided within the spreadsheets. Samples/data were collected across three separate field surveys undertaken in June 2018 (file 1), March 2019 (file 2) and December 2019 (file 3). This dataset forms a case study of the water quality of three different types of urban surface freshwaters within the city of Kolkata, India. This case study was created to deploy a prototype multichannel fluorimeter and assess its ability to identify waters with a high bacterial load and biological contamination events through the use of Peak T fluorescence. Full details about this dataset can be found at https://doi.org/10.5285/9bc3dce7-7c2b-49dd-9b76-819267d7a352

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 data on geomorphological characteristics and flow-related variables along the Beas River (Punjab, India) between Pong dam and Harike barrage in January 2020. The variables provided include cross-sectional area, water depth, river channel width, river flow velocity and dry-season discharge measured at ten reference sites with stable banks and straight, linear channels without islands or other mid-channel structures. Full details about this dataset can be found at https://doi.org/10.5285/f899fbc5-7034-45c0-a15c-9ee1d92a693f

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 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 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 dataset contains river (fluvial) and surface water (pluvial) flooding maps for the central highlands of Vietnam and surrounding provinces. Flood depth is estimated at 30m horizontal grid spacing for 10 return periods, ranging from the 1 in 5 year to the 1 in 1000 year return period flood. These maps are of relevance to planners and policy makers to estimate which areas of most at risk of flooding and can contribute towards policy such as the sustainable development goals. Full details about this dataset can be found at https://doi.org/10.5285/74e4e6ec-a119-4dc7-8ada-9513252b1b60

The dataset provides raster gridded estimates of open water and inundated vegetation for the Barotseland Region in Western Zambia. There are a total of 55 images covering the period 2016-2019 at a spatial resolution of 10m. The images were generated using an automatic classification routine applied to Sentinel-1 radar imagery, with classification refinements made using ancillary datasets such as the Global Urban Footprint, and the Height Above Nearest Drainage terrain derivative generated using SRTM digital elevation data. These data are valuable for a range of applications including public health and water resources. Full details about this dataset can be found at https://doi.org/10.5285/4ef558d2-05d4-4ae2-988e-a5c2450b95dd

The dataset contains concentrations of Total Organic Carbon, Chloride, Fluoride, Bromine, Sulfate, Potassium, Aluminium, Calcium, Iron, Magnesium, Sodium, Phosphorus, Chromium, Manganese, Cobalt, Nickel, Copper, Zinc, Arsenic, Selenium, Molybdenum, Cadmium, Lead and stable water isotopes (δD and δ18O) for 25 groundwater and surface water sampling locations, surveyed over the period February 2017 to May 2018 immediately following Dineo floods. The data were collected as part of the PULA project, which aimed at understanding the immediate effect of heavy rainfall and floods on water resources in arid Botswana and their transitional hydrologic readjustment towards the dry period, and the role of these events in supporting either or both resources replenishment and contamination. The project was co-ordinated by the University of Aberdeen, with partners at the Botswana International University of Science and Technology, the Government of Botswana Department of Water Affairs, and the International Water Management Institute. The project was funded by the Natural Environment Research Council as part of its Urgency grants scheme. Full details about this dataset can be found at https://doi.org/10.5285/c7793128-1961-45d5-aa18-5f023116784b