Improving forest soil carbon models using spatial data and geostatistical approaches

Forest soils store large amounts of carbon (C), and stock changes in this C pool may significantly increase the CO2 concentration in the atmosphere. However, estimation of soil organic carbon (SOC) stocks and stock changes following land use transition to forestry is subject to large uncertainty. Many currently used geochemical modelling approaches, such as YASSO, are used to estimate regional changes in forest SOC stocks, but these are difficult to calibrate to reflect regional conditions because of limited availability of sufficient SOC data. In addition, most model frameworks give little consideration regarding the appropriate use of geospatial climatic and topographical data, as dependent variables in the model. As a result, many regional models may exhibit spatial autocorrelation (SAC) of residuals, which contributes to overall model error. In this paper, we develop a method for assessing SOC stock changes in Irish forests by compiling a spatial SOC database and using these data to calibrate and improve on an existing YASSO model. Careful consideration was given to the use of available climatic and digital elevation GIS data in YASSO with the aim of reducing SAC of model residuals and to more precisely predict soil- and site-specific variations in SOC stock changes following transition to forestry.Analysis of the complied national SOC database shows that stock changes in afforested mineral soils may increase or decrease depending on previous land use and soil type. During refinement of the YASSO model, conventional statistical approaches confirmed that model performance can be improved by using climatic GIS data at the appropriate scale (resolution), together with additional use of novel topographical spatial data. The current YASSO model does not use these topographical factors as dependent variables, nor is there any consideration given to the spatial or temporal resolution of GIS datasets used. Use of GIS geo-statistical approaches to determine if SAC was reduced, as the YASSO model accuracy was improved on, produced conflicting results. We suggest that the use of Anselin Local Moran's I outlier analysis may not be suitable for this purpose because it may falsely detect spatial outliers due to the presence of neighbouring points with very high or low residual values. In contrast, semi-variogram analysis appeared to be the most useful geo-statistical measure of the spatial dependency, distribution and scale at which residual SAC occurs. Use of fine resolution (50m) slope and topographical position index (TPI) raster datasets to predict forest SOC stocks significantly improved the final YASSO model accuracy and precision. In addition, semi-variogram analysis confirmed that the final YASSO model residuals exhibited no spatial dependency and residual error was uniformly distributed over the entire sample area, from which the SOC database was derived. However, the final YASSO model we describe requires considerable refinement using more intensive sampling studies and independent validation before it can be applied at a national level. In the future, particular emphasis should be directed to sampling forest brown earth soils, which are suggested to result in a net emission of C following transition from grassland to forest land.