Modelling soil organic Carbon in space and time

In recent times there is an increasing interest in the quantification of the variation in soil organic carbon (SOC) in space and time. Quantification of this variation is important since SOC is important for many soil physical, chemical and biological properties and soil processes which lead to sustainable crop production in agricultural soil. In addition, SOC also helps to reduce the impacts of climatic change if it can be stored in soil for the long term in what is called “soil carbon sequestration”. The focus of the work included in this thesis is to model the space and time variation using both statistical as well as process/mechanistic models of SOC. In process modelling of SOC, the Rothamsted carbon model (RothC model) was used to assess the spatial and temporal changes in SOC. The RothC model can be used to simulate the variation of SOC over the time using readily available spatial data. Therefore, this research has (a) tested the application of mid infra red / partial least-square regression models (MIR/PLSR models) in predicting SOC in archived soil data in combination with newly collected SOC data; (b) assessed changes in SOC using legacy soil data as the baseline survey; (c) mapped the measurable SOC fractions related to RothC model at the catchment scale; (d) simulated SOC across a catchment with the RothC model using readily available spatial data; (e) calibrated the rate constants of the RothC model at the catchment scale using Bayesian inverse modelling. The first research chapter (chapter 3) concentrates on the development of MIR/PLSR models to predict total SOC in archived soil datasets in relation to legacy soil datasets. The legacy soil information can be used to assess the temporal changes of SOC if they are considered to be the baseline survey. However, the use of legacy soil data directly for comparison will not be possible due to differences in the laboratory method used to measure SOC (analytical) and in the sampling support (see chapter 4 for more details). Therefore, an attempt was made to predict total SOC for archived soil data which corresponds to a legacy soil dataset collected in year 2000 in combination with newly collected data in year 2010. A total of eight (8) different MIR/PLSR calibration models were developed to predict SOC in archived soils. In development of these models an attempt was made to select samples (n = 24) from archived soil data using different sampling strategies which were used in combination (spiking) with the newly collected dataset for year 2010. It was found that all developed calibration models performed well based on internal cross validation. However, the independent validation results revealed sample selection through the Kernnard Stone algorithm performed best compared to other approaches, e.g. conditional latin hypercube sampling. In practical terms, it is not possible to analyse a large number of soil samples in archives with traditional lab based methods. Therefore, development of effective and practical oriented MIR/PLSR models will be cost effective and save laboratory processing time in relation to the determination of total SOC in archived soil properties. Chapter 4 is focused on the assessment of the change in SOC at the catchment scale using legacy soil data as the baseline survey. In this chapter two main approaches were used to assess the change in SOC namely; design-based inference methods and model-based inference methods. It also demonstrated “how to get design-based estimates when the sampling design is non-probabilistic” which is common for most legacy datasets. Design-based inference was carried out to see the change in SOC after calculating the 95 % confidence interval around the mean for the considered soil-land use complexes (SLU). If the 95 % confidence intervals for a considered SLU complex overlap each other, then it was concluded that the change is statistically not significant at the 0.05 probability level. In the model-based approach digital soil mapping (DSM) techniques were utilized where linear mixed models (LMM) were used to map the changes in SOC across the catchment. This chapter also reported issues with legacy soil data when they are used as the baseline survey and some of the ways to overcome those issues. Both statistical inference methods revealed that there is a drop in SOC between the two surveys (year 2000 and year 2010). However, that drop was not reported as statistically significant at the 0.05 probability level for both inference methods. Chapter 5 is focused on mapping measurable fractions of the RothC model at the catchment scale. The measurable fractions of the RothC model were predicted based on MIR spectra acquired for the 2010 dataset using newly developed MIR/PLSR models from the Australian carbon research programme (SCaRP) lead by CSIRO (2009 – 2012). Even though there are many papers related to mapping SOC there are only very few papers that are available related to mapping of SOC fractions. According to the reviewed literature this is the first time that measurable fractions of SOC related to the RothC model have been mapped. For the mapping purposes three separate LMMs were used and developed models were validated with leave-one-out-cross validation. In addition, conditional sequential Gaussian simulations were carried out to assess the uncertainties related to predicted maps. Throughout this chapter it is discussed how these DSM outputs can be used as inputs to the RothC model in order to run it spatially. Finally chapter 6 and 7 are focused on process modelling of SOC with RothC model. Chapter 6 highlights different ways of running RothC model spatially across a catchment. As the first step, the RothC model was initialized across the landscape using different initialization methods. A novel approach was tested where temporal C inputs were predicted from MODIS derived NPP data. Once data is prepared simulations across landscapes were carried out with 50 model combinations. These different model combinations consisting of different rate constants (2 levels), methods of initialization (5 levels) and sources of C inputs (5 levels) were compared (2 × 5 × 5 = 50 model combinations). It was found that different methods of initialization resulted in statistically significant initial SOC pools that are used as part of the RothC model. Further, it revealed that at the end of the simulations, (after 10 years) total SOC was statistically different at the 0.05 probability level based on different combinations. Results highlighted that there is great potential to use satellite derived products as drivers for future modelling of SOC. In chapter 7 Bayesian inverse modelling was utilized to estimate the uncertainty of the rate constants of the RothC model. The RothC model was re-programmed and calibrated in a Bayesian context using the “DREAM” algorithm. Once the posterior probability density functions (PDF) for the four (4) rate constants were obtained, they were used to carry out simulations using the entire PDF. Simulated results show the uncertainty created due to uncertainty about the model rate constants. This is an important step since process models such as RothC are widely applied to assess the impact of future climatic scenarios in relation to SOC without any calibration or assessment of uncertainties of the simulations. According to reviewed literature this is the first application of DREAM algorithm in calibration of RothC model rate constants for a catchment scale dataset