Example - Use reaches to extract water quality metrics from data encoded in the river network
Scenario
You are researching water quality, specifically the impact of lead concentration on biota. Your river network has been encoded with lead concentration (µg/L) sampled from the water course and the dominant riparian land use. Your site data are 1km reaches that trace along the river network (so not point data).
The following guide is using a fictitious set of reaches and data encoded into a river network and is in no way representing real data.
Workflow
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Step |
Processing Task |
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1 |
You have prepared your river network and ensured your reach data is in the same coordinate system as the river network and there is a numeric field (integer) that identifies each reach uniquely. Reaches must trace along the river network and must not weave on or off the river, see tool help for examples of valid and invalid data.
Valid reach data labelled with ID value. |
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2 |
The information encoded into the river network should ideally have no NULL values. Any NULL values found will be ignored in the calculation. Some times its worth symbolising your encoded data so you get a feel of the range of values and an understanding of the location of the values.
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3 |
With the network and dataset prepared you can run the Summarize network metrics for reach/link polyline tool. Below we are extracting out the minimum, maximum and mean lead concentration and grouping the results by Landuse
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4 |
The output table is as shown below, we see the various metrics on lead concentration for each reach grouped by the riparian land use. Note the field names are tagged with the metric they are reporting
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5 |
From the table above we can see that reach 5 has the greatest range in lead concentration, but it is reach 6 that had the largest mean concentration at 50 µg\L. Something is happening at reaches 5 and 6; with data symbolised we get a fuller picture. Reach 5 starts in moorland and ends in coniferous woodland at its downstream end. It takes a tributary where the dominant riparian land use was recorded as including mine waste, the waste from a lead mine. This explains the large jump in lead concentration. Reach 6, although its riparian land use was coniferous woodland it intersects a small reservoir. The slow moving flow is concentrating lead in the water column before it is captured by sediment.
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In this worked exampled you saw how to use the tool and outputs for a fictitious scenario looking at lead concentration in the water. The important thing to appreciate is that the information encoded into the river network could be anything! Examples could be sediment scores, species number, counts of in-channel structures, slope, elevation, temperature, pollution, land ownership,accessibility scores, confinement ratios, beaver habitat, quality indices to name but a few. |
