Carbon Accounting Tools for Moorland Restoration: An Explainer for Landowners

Rewetting a drained peatland is one of the few management changes that can flip a holding from carbon source to carbon sink. Proving it is another matter. A funder, a carbon standard or an accountant will eventually ask for a figure in tonnes of carbon dioxide equivalent — and that figure has to come from somewhere defensible.

Carbon accounting tools do the arithmetic. They combine what is known about your peat with what is known about how drained and rewetted peat behaves. The output is an estimate, not a measurement, and treating it as an estimate is what makes it useful.

What the numbers are actually counting

Almost every peatland carbon tool answers one of three questions, and it helps to know which one you are asking.

• Stock. How much carbon is held in the peat body right now, usually expressed as tonnes of carbon per hectare. This is a static figure — useful for baseline reporting, less useful for judging progress.
• Emissions. How much carbon leaves the site each year, as carbon dioxide from peat oxidation, as dissolved and particulate carbon in water, and as methane and nitrous oxide. Expressed as tonnes CO2e per hectare per year.
• Outcome. The difference between emissions under the current drainage state and emissions under the restored state. This is the number that funders and carbon standards care about, and it is always modelled rather than measured directly.

A ditch blocking scheme does not remove carbon from the atmosphere on the day the digger leaves. It reduces the rate at which carbon is being lost, and over years it may tip the site towards accumulation. Good accounting reflects that trajectory instead of pretending the change is instant.

How an estimate gets built

Most tools work on the same principle: activity data multiplied by an emission factor. Activity data describes the site — peat extent, peat depth, drainage density, typical water table depth, vegetation type, climate. The emission factor describes how much carbon that condition releases per hectare per year.

Emission factors come from three broad tiers. Tier 1 uses international default values, such as those in the IPCC guidance for drained organic soils. Tier 2 uses country-specific figures, usually developed from national survey work and more appropriate to local conditions. Tier 3 uses direct measurement or process modelling at or near the site. Each step up costs more and buys more confidence.

Stock estimates are built differently. A common approach multiplies peat depth by bulk density by carbon content; peat is often assumed to be around half carbon by dry mass. Depth is usually the weakest input, because peat thickness varies dramatically over a few metres. A single probe in the middle of a bog tells you very little.

The main families of tool

Standard-based calculators. Carbon codes and grant schemes publish their own calculation methods, often as spreadsheets or online forms. If you intend to sell carbon units or claim against a public scheme, you will probably have to use the tool they specify — there is little point building your own model first.

GIS and remote sensing. Satellite imagery, LiDAR and aerial photography can map peatland extent, drainage lines, bare peat and vegetation change across a whole holding. These layers are excellent for prioritising where to work, and increasingly used as inputs to emissions models. They are poor at telling you what is happening a metre below the surface.

Process models. These simulate water table depth, decomposition and greenhouse gas flux over time. They are useful for testing scenarios — what happens if you block every ditch versus every second one — but they need decent input data and someone who understands their assumptions.

Direct measurement. Peat probes and augers for depth, dipwells for water table, and flux chambers or towers for gas exchange. Expensive and slow, but they are the only way to check whether a model is behaving plausibly on your ground.

Where uncertainty creeps in

Peat depth variability is the usual culprit, followed by drainage mapping: a shallow hand-cut grip and a two-metre agricultural drain behave very differently but may look similar on a map. Emission factors are annual averages and are sensitive to water table depth, soil temperature and vegetation, so a dry summer shifts the real figure without shifting the model.

Restored sites also produce more methane in the early years while the water table stabilises, which can offset some of the carbon dioxide savings in the short term. Any tool that ignores this is overselling.

An estimate with an honest uncertainty range is worth more than a precise-looking number that hides its assumptions.

Matching the tool to the job

1. Decide what the number is for. A grant application, a carbon unit sale and internal monitoring have different accuracy requirements and different acceptable costs.
2. Fix the boundary before you start. Which parcels, which ditches, which years. Boundaries chosen after the results are known are not credible.
3. Baseline before the works. Once a ditch is blocked, the pre-restoration condition is gone. Photograph, probe and record first.
4. Pick the lowest tier that satisfies the end use. Tier 1 is often enough for an internal decision; a carbon standard will usually demand more.
5. Plan the monitoring. Fixed dipwells, repeat photographs from the same points, and a field notebook will serve you better than a sophisticated model with no ground truth.
6. Get the paperwork checked. Carbon contracts, land registration and tax treatment of any income are areas where professional advice is worth paying for.

A sensible first season on the ground

Walk the site with an Ordnance Survey map or a handheld GPS and mark every drain, hag, gully and track. Probe peat depth on a rough grid — say every fifty paces across the areas you plan to restore — and write the figures down as you go. Note the vegetation in each zone and photograph the same viewpoints each visit.

That notebook, transferred to a simple spreadsheet, gives you a baseline that can be fed into whichever calculator the funder or standard requires. It also gives you something no model can supply: a record of what the site looked like before you changed it. Start there, and the software becomes a way of reporting what you already understand, rather than a substitute for understanding it.

Photo: eloneo / Pixabay