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New dataset of water level monitoring stations covering Trafford and environs. The CSV dataset is in a tidy and technical format whereas the data in the GeoJSON dataset is in a more presentational style suitable for viewing within mapping applications such as our Explore app.
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‎water_level_monitoring/README.md‎

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<table>
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<tr>
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<td>Dataset name</td>
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<td>Water level monitoring stations</td>
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</tr>
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<tr>
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<td>Dataset description</td>
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<td>Location and related information of river/water level monitoring stations in Trafford and the surrounding areas, including typical water level range, and historic low/high levels where available. Obtained via API <a href="https://environment.data.gov.uk/flood-monitoring/doc/reference">https://environment.data.gov.uk/flood-monitoring/doc/reference</a></td>
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</tr>
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<tr>
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<td>Source</td>
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<td>Environment Agency</td>
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</tr>
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<tr>
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<td>Publisher</td>
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<td>Environment Agency</td>
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</tr>
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<tr>
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<td>Publisher URL</td>
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<td><a href="https://environment.data.gov.uk/dataset/ae80cf81-f3aa-4703-88e7-41fbe80c67b2">https://environment.data.gov.uk/dataset/ae80cf81-f3aa-4703-88e7-41fbe80c67b2</a></td>
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</tr>
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<tr>
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<td>Geography</td>
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<td>Local Authority</td>
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</tr>
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<tr>
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<td>Geographic coverage</td>
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<td>Trafford and environs</td>
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</tr>
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<tr>
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<td>Temporal coverage</td>
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<td>2025-01-31</td>
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</tr>
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<tr>
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<td>Update frequency</td>
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<td>Real-time/near-real-time</td>
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</tr>
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<tr>
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<td>Licence</td>
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<td><a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence</a></td>
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</tr>
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<tr>
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<td>Attribution</td>
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<td>© Environment Agency copyright and/or database right 2025. All rights reserved.</td>
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</tr>
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<tr>
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<td>Format</td>
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<td>CSV, GeoJSON</td>
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</tr>
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<tr>
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<td>Openness rating</td>
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<td>&#9733&#9733&#9733&#9734&#9734&nbsp; Structured data in open format (e.g. CSV)</td>
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</tr>
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<tr>
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<td>Last updated</td>
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<td>2025-01-31</td>
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</tr>
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<tr>
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<td>Notes</td>
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<td>Typical water level, historic low/high information is not available for all monitoring stations. However, a URL is provided within the GeoJSON dataset to view the latest available water level readings for each station.</td>
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</tr>
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<tr>
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<td>Lab visualisation</td>
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<td>View data within the Lab's <a href="https://www.trafforddatalab.io/maps/explore/index.html?dataset=water_level_monitoring">Explore application</a>.</td>
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</tr>
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</table>

‎water_level_monitoring/pre-processing.R‎

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# River/water level data from monitoring stations
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# Source: DEFRA, published by Environment Agency
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# URL: https://environment.data.gov.uk/dataset/ae80cf81-f3aa-4703-88e7-41fbe80c67b2
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# API: https://environment.data.gov.uk/flood-monitoring/doc/reference
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# Example API calls:
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# - All stations recording water levels within 9km of the centre of Trafford: https://environment.data.gov.uk/flood-monitoring/id/stations?lat=53.41671&long=-2.36572&dist=9&parameter=level
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# - Get the data from a specific station (which includes historic highs, lows etc.: https://environment.data.gov.uk/flood-monitoring/id/stations/692727 (.json)
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# - Get the latest reading from a specific station: https://environment.data.gov.uk/flood-monitoring/id/measures/692727-level-stage-i-15_min-m/readings?latest
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# - Get the latest 7 days worth of data from a specific station: https://environment.data.gov.uk/flood-monitoring/id/stations/692727/readings?_sorted&_limit=672
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# NOTE: 692727 is Flixton Bridge. Readings are taken every 15 minutes, so 4 readings per hour for 7 days = 672 stated as the _limit parameter
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# Load libraries ---------------------------
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library(tidyverse) ; library(sf) ; library(httr) ; library(jsonlite) ; library(lubridate)
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# Monitoring stations covering the Trafford area and environs ---------------------------
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# Call the API and attempt to get all stations recording water levels within 9km of the centre of Trafford
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api_response <- GET(
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url = 'https://environment.data.gov.uk/flood-monitoring/id/stations?lat=53.41671&long=-2.36572&dist=9&parameter=level',
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timeout(50)
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)
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# After examining the API response, pull out the data we're interested in ("items") into a tibble
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monitoring_stations_raw <- fromJSON(content(api_response, as = "text"), flatten = TRUE) %>%
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pluck("items") %>%
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as_tibble()
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# Tidy up the variables
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monitoring_stations <- monitoring_stations_raw %>%
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rename(lon = long,
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station_name = label,
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station_reference = stationReference,
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river_name = riverName,
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catchment_name = catchmentName) %>%
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mutate(station_reference = as.numeric(station_reference)) %>%
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select(station_name, station_reference, river_name, catchment_name, town, lon, lat)
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# Historic data from the chosen monitoring stations ---------------------------
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# Call the API again based on all the monitoring station IDs we've obtained and retrieve the record highs, lows and typical range
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# Function to use within purrr:map_df() to iterate through the monitoring station IDs
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getHistoricData <- function(.station_ref) {
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api_response <- GET(
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url = paste0("https://environment.data.gov.uk/flood-monitoring/id/stations/", .station_ref, "/stageScale"),
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timeout(50)
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)
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if (api_response$status_code == 200) {
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# Get the node containing the data of interest from the API response - we will then successively pull what we need from this
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historic_levels_raw <- fromJSON(content(api_response, as = "text"), flatten = TRUE) %>%
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pluck("items")
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# Typical range low and high values
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df_tmp <- historic_levels_raw %>%
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as_tibble() %>% # turn list into a tibble so we can work with the data like a dataframe
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rename(typical_range_low = typicalRangeLow,
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typical_range_high = typicalRangeHigh) %>%
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mutate(station_reference = .station_ref) %>%
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select(station_reference, typical_range_low, typical_range_high) %>%
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head(1) # due to the "items" list containing 3 sub lists (recentHigh, highOnRecord, minOnRecord), the dataframe/tibble contains 3 identical rows, so here we just pick the first row
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# Lowest level on record
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df_tmp <- df_tmp %>%
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left_join(
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historic_levels_raw %>%
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pluck("minOnRecord") %>%
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as_tibble() %>% # turn list into a tibble so we can work with the data like a dataframe
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rename(record_low_date = dateTime,
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record_low = value) %>%
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mutate(station_reference = .station_ref,
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record_low_date = as.character(date(record_low_date))) %>%
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select(station_reference, record_low, record_low_date)
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)
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# Highest level on record
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df_tmp <- df_tmp %>%
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left_join(
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historic_levels_raw %>%
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pluck("maxOnRecord") %>%
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as_tibble() %>% # turn list into a tibble so we can work with the data like a dataframe
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rename(record_high_date = dateTime,
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record_high = value) %>%
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mutate(station_reference = .station_ref,
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record_high_date = as.character(date(record_high_date))) %>%
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select(station_reference, record_high, record_high_date)
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)
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}
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else {
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df_tmp <- tibble(station_reference = .station_ref)
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}
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return(df_tmp)
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}
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# Call the GetHistoricData function passing in every `station_reference` from the dataframe monitoring_stations and then join the returned data to it
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monitoring_stations <- monitoring_stations %>%
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left_join(map_df(monitoring_stations$station_reference, getHistoricData)) %>%
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mutate(measure = "Water level",
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unit = "Metres",
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across(ends_with(c("_low", "_high")), round, 2)) %>% # Convert all water levels to 2 decimal places
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select(station_name, station_reference, lon, lat, river_name, catchment_name, town, measure, unit, typical_range_low, typical_range_high, record_low, record_high, record_low_date, record_high_date)
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# write data ---------------------------
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file.remove("water_level_monitoring_stations_trafford_and_environs.geojson") # first remove the previous file
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monitoring_stations %>%
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write_csv("water_level_monitoring_stations_trafford_and_environs.csv") %>% # write out the CSV as-is from above, then make changes for the GeoJSON output below to make it more presentable
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mutate(measure = "Water level in metres (m)",
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typical_range = if_else(is.na(typical_range_low), "Not available", paste0(typical_range_low, "m - ", typical_range_high, "m")),
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record_low = if_else(is.na(record_low), "Not available", paste0(record_low, "m (", record_low_date, ")")),
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record_high = if_else(is.na(record_high), "Not available", paste0(record_high, "m (", record_high_date, ")")),
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latest_levels_url = paste0("https://environment.data.gov.uk/flood-monitoring/id/measures/", station_reference, "-level-stage-i-15_min-m/readings.html?__htmlView=table&_sorted")) %>%
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mutate(across(c(river_name, catchment_name, town), ~replace_na(., "Not available"))) %>%
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st_as_sf(coords = c("lon", "lat")) %>%
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st_set_crs(4326) %>%
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select(station_name, station_reference, river_name, catchment_name, town, measure, typical_range, record_low, record_high, latest_levels_url) %>%
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st_write("water_level_monitoring_stations_trafford_and_environs.geojson", driver = "GeoJSON")
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‎water_level_monitoring/water_level_monitoring_stations_trafford_and_environs.csv‎

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station_name,station_reference,lon,lat,river_name,catchment_name,town,measure,unit,typical_range_low,typical_range_high,record_low,record_high,record_low_date,record_high_date
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Northern Moor,693115,-2.294665,53.407591,Baguley Brook,Upper Mersey,Northern Moor,Water level,Metres,0.1,1.22,0.06,1.45,2010-07-10,2019-07-31
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Bollington Mill,693515,-2.406589,53.380045,River Bollin,Upper Mersey,Little Bollington,Water level,Metres,0.26,1,0.2,1.49,2010-07-05,2019-08-01
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Ashton Weir,692726,-2.344072,53.438308,River Mersey,Upper Mersey,Urmston,Water level,Metres,0.29,4.15,0.2,3.91,1984-08-26,2021-01-21
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Flixton Bridge,692727,-2.388645,53.439969,River Mersey,Upper Mersey,Flixton,Water level,Metres,0.23,4.3,0.17,4.92,2010-07-12,2002-02-20
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Chestnut Road,690350,-2.375559,53.494067,Worsley Brook,Northern Manchester,Alder Forest,Water level,Metres,0.07,1.79,0.06,1.96,2011-01-27,2019-07-31
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Stanneylands,693333,-2.232417,53.343712,Dean,Upper Mersey,Styal,Water level,Metres,0.07,0.51,0.02,1.72,1996-07-21,2019-07-31
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Northenden,692710,-2.252674,53.408784,River Mersey,Upper Mersey,Northenden,Water level,Metres,0.27,1.8,0.15,3.32,2005-09-05,2022-02-21
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Little Woolden Hall,693032,-2.475817,53.440813,Glaze Brook,Lower Mersey,Glazebrook,Water level,Metres,0.38,1.9,0.24,3.58,1996-05-12,2021-01-21
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Adelphi Weir,690511,-2.26639,53.485246,River Irwell,Northern Manchester,Salford,Water level,Metres,0.18,2,0.12,3.86,1976-09-08,2015-12-26
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Partington,693132,-2.413046,53.410314,Sinderland Brook,Upper Mersey,Partington,Water level,Metres,0.12,0.8,0.06,1.42,1982-06-17,1981-08-06
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Athol Road,692810,-2.246761,53.439852,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Sale Ees,692717,-2.288164,53.429377,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Mauldeth Road,692800,-2.255783,53.438035,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Bedford FW,693023,-2.501157,53.496087,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Didsbury River,692706,-2.235618,53.40126,River Mersey,Upper Mersey,Didsbury,Water level,Metres,3.2,6.2,2.52,7.23,2020-05-20,2022-02-20
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Sale Waterpark,692722,-2.297206,53.431153,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Didsbury Basin Stenner Lane,692707,-2.233923,53.410225,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Jennets Lane PS,693015,-2.49657,53.478947,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA
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Didsbury,692705,-2.23534,53.402528,NA,NA,NA,Water level,Metres,NA,NA,NA,NA,NA,NA

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