Monitored flow and water quality data from upper river taw observatory in 2018 and 2019 water years

N - Datasets

Granger, S. J., Zhang, Y., Sint, H. M. and Collins, A. L. 2022. Monitored flow and water quality data from upper river taw observatory in 2018 and 2019 water years. Rothamsted Research. https://doi.org/10.23637/rothamsted.9882v

AuthorsGranger, S. J., Zhang, Y., Sint, H. M. and Collins, A. L.
Abstract

As part of the delivery for a strategic research programme, Soil to Nutrition, a multi-scale landscape observatory - the upper River Taw observatory (URTO, https://www.rothamsted.ac.uk/projects/upper-river-taw-observatory-ur...) has been established in a landscape with mixed land use to support the integration of science, stakeholder engagement and policy support. 15-minute resolution data have been collected from 3 nested catchments, namely: Upper Ratcombe, Lower Ratcombe and Pecketsford, using multi-parameter sensors. These sensors cover river flow (water level, flow velocity, discharge) and physiochemical parameters (temperature, pH, turbidity, pH, conductivity, ammonium and nitrate). The recorded data have been visually inspected and assessed based on expert judgement with built-in instrument logs. Data considered to be erroneous were removed and each data point has been assigned a quality assurance code to assist the appropriate use of the data. Ultimately, the user of the data must assess the data they are using according to the context in which they wish to use it. Separate image files in British National Grid Reference (NGR) are also provided for mapping the catchment boundaries.

Year of Publication2022
PublisherRothamsted Research
Digital Object Identifier (DOI)https://doi.org/10.23637/rothamsted.9882v
Keywordswater quality
monitoring
watersheds
England
FunderBiotechnology and Biological Sciences Research Council
Related Output
Is cited byhttps://doi.org/10.1016/j.jclepro.2022.130633
Funder project or codeS2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Data files
Copyright license
CC BY 4.0
Data type
Spreadsheet
Contents
Data
File Access Level
Open
Data files
Copyright license
CC BY 4.0
Data type
Text
Contents
Additional metadata
File Access Level
Open
Data collection period08 Oct 2018 to end of 31 Mar 2020
Geographic location
-3.979,50.655,-3.875,50.783
Data collection method

At 3 catchment monitoring sites a stilling well was installed to house a multi-parameter sonde (YSI, Xylem Inc Rye Brook, New York, U.S). The sondes hold five sensors which measure multiple water quality parameters; temperature, specific conductivity, turbidity, and ion selective electrodes for nitrate-N (NO3-), ammonium-N (NH4+) and pH. Instruments were returned to the laboratory every month for cleaning, assessment and recalibration. Performance logs were made of the equipment to assess the quality of the data collected, and whether sonde instruments needed replacing.

Instrument measurements are controlled, and data recalled via Adcon (ADCON, Austria) remote telemetry units (RTUs) using UHF radio every 15-minutes. A base-station (A850 Gateway) manages the RTUs which collect in-field data every 15-minutes. The central base-station ensures that measurements across the 3 sites are conducted at the same time and time stamped accordingly. Software (AddVantage Pro) collects, stores and displays the data via an integrated web server and an extension running within the software automatically creates and exports weekly CSV files for each parameter for archiving and further data processing.

Data preparation and processing activities

A log of all sensor downtime issues is maintained in MS Access where input forms and restricted fields are used to ensure that the correct and required data is recorded. Data exported in CSV files from the AddVantage software is visually inspected for obvious errors/discrepancies and the data is ‘flagged’ accordingly or removed from the post quality control (QC) output dataset. Information on the sensor type, location, the start and end times the sensor was not functioning correctly, and information about the problem were combined with regular lab-based analysis on sensor performance to generate relevant quality assurance codes for each data point. The codes used include: 'Acceptable', 'Suspicious' and 'High sensor drift'.

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