Soil Emissions and Removals Reporting in Portugal 2014 JRC LULUCCF Workshop 06/05/2014 Paulo Canaveira

(Our) Objective for Soil Reporting (for now) Characterise emissions and removals in the soil pool associated with land-use changes – [changes in land remaining land are more compicated to estimate] General Approach – Characterise average C Stock in each land-use – Emission Factor = difference in C stocks / 20 years Land remaining land = 0

Soil Data Lots of soil data but... – Mostly on Soil Organic Matter Almost no data on C Stocks – Not treated / consolidated into usable databases 4

(almost usable) Data Sources for Soil Data ICP Forests Level 2 Grid – 1995 (159 plots, forests and shrubland only) – 2005 (103 plots, forests and shrubland only) BioSoil – Based on ICP Forests Level 2 Grid – 1999 (102 plots, agriculture and grasslands only) LUCAS soil survey – 2009 (463 plots, almost all land uses) Total 828 plots

Challenges & “Solutions” Depth of soil sampling not constant – 0-20cm exist for all samples (828) – 20-40cm only exist in some (208) No data in LUCAS – IPCC recommends 30cm (!) “Solution” – Data 0-40 cm used – “real data” for all plots where it exists – “gap filling” for missing data based on ratio / 0-20 calculated on plots where both measurements exist (average 58%)

Challenges & “Solutions” Bulk density missing in many samples – Important to convert soil organic matter (%) into tonC/ha – No data in LUCAS samples – No data in Biosoil samples – Limited data in ICP Forests Level 2 samples “Solution” – “real data” for all plots where it exists – “gap filling” for missing data based on reference BD in JRC maps

Challenges & “Solutions” Different sampling dates – 1995 (159 plots, forests and shrubland only) – 2005 (103 plots, forests and shrubland only) – 1999 (102 plots, agriculture and grasslands only) – 2009 (463 plots, almost all land uses) “Solution” – Use combination of all data Assumed to be OK for characterising average C Stock per land-use type

Challenges & “Solutions” Land-use data is missing for some plots – No data in LUCAS samples “Solution” – “gap filling” for missing data Use georeferenced location of the plot combined with land use map (2010)

Challenges & “Solutions” Coverage per land-use reporting category is not uniform – Major land-uses all well represented – Some categories have very limited data or are not represented at all “Solution” – Aggregate some categories Pinus pinea + other coniferous Rice + irrigated crops – Assume zero C Stock for some categories Wetlands; Settlements

The Resulting Dataset

Used Soil Dataset

Calculated Emission Factors C Stock old land-use – C Stock new land-use divided by 20 (years)

Calculated Emission Factors – Uncertainty 95% confidence interval divided emission factor

Are all these emission factors significant? Compare all possible pairs of means – T-test (for comparing averages of unequal sizes and unequal variances) – Consider only differences that are significant at 95% confidence level Non-significant differences  ZERO emission factor

From these... (calculated emission factors) C Stock old land-use – C Stock new land-use divided by 20 (years)

...to these (calculated AND significant emission factors) C Stock old land-use – C Stock new land-use divided by 20 (years)

Used Emission Factors – Uncertainty 95% confidence interval divided emission factor

Impact of Soil EF in Accounted Numbers