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WOM21 Carbon Sequestration
Resource ID
b77b7d7f-1348-43d5-bf08-6c496ee9dbbb
Title
WOM21 Carbon Sequestration
Date
Aug. 4, 2021, midnight, Creation Date
Abstract
This layer shows, from modelling at a spatial resolution of 250m2, the maximum possible tonnes of carbon sequestered per hectare per year from planting trees. Forest Research (FR) modelled a range of species (broadleaved and conifer) using their Ecological Site Classification (ESC) tool, taking into account accumulated temperature, moisture deficit, windiness, continentality, soil moisture and soil nutrient regimes. The highest yielding species of each woodland type were then taken as the most suitable species choices for a given location and modelled in CARBINE (FR’s carbon modelling tool). The CARBINE model estimates the change in carbon stocks for forests (including the biomass in standing trees and deadwood, and in the soil) and any associated harvested wood products, as well as the greenhouse gas emissions avoided through the use of wood products that displace fossil fuels and fossil-fuel intensive materials. The UK Centre for Ecology and Hydrology (UKCEH) took these modelled outcomes and selected the woodland type that sequestered the most carbon on that area of land, whether coniferous (around 90% of area) or broadleaved (around 10% of area). Broadleaf management assumed to be LISS – low impact silvicultural systems. Conifer management assumed to be thinning and felling. Scoring is based on tonnes of carbon sequestered per hectare per year, classified based on 0 being worst (net emissions), and 5 being best (most carbon sequestration).
Edition
--
Responsible
superuser
Point of Contact
User
superuser@email.com
Purpose
--
Maintenance Frequency
None
Type
not filled
Restrictions
None
License
Open Government Licence for Public Sector Information
Language
en
Temporal Extent
Start
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End
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Supplemental Information
Data Quality
--
Extent
  • x0: 146611.8011
  • x1: 355308.0008
  • y0: 164586.2969
  • y1: 395984.399900001
Spatial Reference System Identifier
EPSG:27700
Keywords
no keywords
Category
None
Regions
Global