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 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 provides a coherent, quality-controlled collection of 15-minute river flow observations from across the United Kingdom. It brings together more than 1,300 gauging stations and over 50,000 station-years of data collected by the main UK measuring authorities - the Environment Agency (England), Scottish Environment Protection Agency, Natural Resources Wales, the Department for Infrastructure (Northern Ireland), and the UK Centre for Ecology & Hydrology. The records span from 1948 to the present day and represent the first national-scale compilation of sub-daily flow data in a consistent format. The dataset was created by assembling raw hydrometric records from open APIs and data requests to measuring authorities, then standardising them to a uniform 15-minute time step. A structured quality control framework was applied to identify and flag potential issues such as missing or duplicated values, irregular time steps, and implausible flow events. Each record includes a detailed quality code indicating the outcome of these checks, and a suite of accompanying metadata files provides full traceability of data provenance, quality control results, and any adjustments made during processing. The resource is designed to support large-sample and national-scale hydrological research, particularly for applications requiring high-resolution data such as flood analysis, catchment response studies, and model calibration. Full details about this dataset can be found at https://doi.org/10.5285/211710ac-f01b-4b52-807f-373babb1c368

River water alkalinity, chloride-ion, conductivity, pH and nutrients data from the Frome Piddle; Pang Lambourn and Tern catchments, recorded between 2003 and 2006. River water samples were collected fortnightly at twenty three sites within these catchments and analysed for Alkalinity pH 4.5, Ammonia, Chloride-ion, Conductivity 20 deg C, Nitrate, pH, Phosphorus soluble reactive (SRP), Silicate reactive dissolved (SRD) and Sulphate. The samples were collected as part of the NERC funded Lowland Catchment Research (LOCAR) Programme to provide comparable baseline river water chemistry data across the LOCAR catchments. Full details about this dataset can be found at https://doi.org/10.5285/bb600a56-c9cf-4fca-b9be-be3de0039a79

Data on the carbon and nitrogen cycling in sediments from rivers within the Hampshire Avon catchment, UK. The dataset includes functional gene data, anion and cation concentrations, methane production and oxidation potential, carbohydrates concentrations, pigment data, and particle size data. Data were collected between February 2013 and November 2014. Data were collected to address the hypotheses of how the functional microbial community involved in carbon and nitrogen cycling changed seasonally and with geology. Data were collected as part of the project "The role of lateral exchange in modulating the seaward flux of C, N, P", funded under NERC's Macronutrients Cycles research programme. Full details about this dataset can be found at https://doi.org/10.5285/976602b3-a58d-460c-a52d-088d0bb09989