A water-monitoring report - what it should contain

Knowledge base · July 18, 2026

Monitoring ends where the report begins - because it is the report that turns tables of figures into understandable conclusions. Whether you are writing an account for an institution, a client or your own team, a well-arranged report saves time and builds trust in the data. Here is what it should contain.

The skeleton of a report

  1. Aim and scope - why the monitoring was run, what it concerns, over what period.
  2. Area and measurement points - a description of the sites (ideally with a map and coordinates).
  3. Methodology - what, how and with what was measured; references to standards/methods; the way of sampling and preservation.
  4. Results - tables and charts; series over time, not just single values.
  5. Assessment - relating the results to standards / classes / reference values (see Classes and status of water quality).
  6. Conclusions and recommendations - what the data shows and what next.
  7. Sources and appendices - raw data, metadata, references.

What sets a good report apart

  • Context - results set against weather, season, events (see interpreting results).
  • Readable charts - a time series says more than a column of figures.
  • Explicit limitations - gaps in the data, uncertainty, values below the limit of quantification.
  • Repeatability - a methodology described so that someone else could reproduce the monitoring.

Common shortcomings

  • no reference to standards (figures alone without assessment),
  • hidden gaps in the data,
  • charts with a “categorical” time axis instead of one proportional to the dates,
  • no metadata (units, the method of measurement).

In practice

The better organised the data, the faster the report comes together. LimnoLog keeps measurements in a structure of stations and sessions, generates charts, lets you mark guidelines (for assessment) and export the data to Excel - ready to paste into the report. On the data reliability on which such a report stands, we write in the article QA/QC and data quality.

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