Carbon

Land use changes and the intensification of agricultural production have dramatically altered the flow of nutrients resulting in unprecedented transcontinental inter-watershed and intra-watershed transfers of nitrogen (N), phosphorus (P), and other nutrients with fertilizer, harvest product, and pollutant flow (Galloway et al., 2008). Nutrient cycling in soils plays a major role in the control of these flows. Carbon (C), N, and P cycling are intimately linked through soil, plant and microbial processes. These processes affect the level of inorganic N and P and the C:N and C:P ratios of SOM. To realistically represent these C, N, and P transfers in river-basin scale models such as the Soil Water Assessment Tool (SWAT+, Arnold et al., 1998). a comprehensive integration of the cycling of these nutrients through soil organic matter (SOM) is required.

Most conceptual and quantitative SOM cycling models compartmentalize soil C and N in pools with different, yet stable, turnover rates and C:N ratios (Paul et al., 2006, McGill et al., 1981; Parton et al., 1988; Verberne et al., 1990). Incubation experiments also suggest the existence of pools with varying turnover rates (e.g. Collins et al, 2000); however, Six et al. (2002) indicated after an extensive literature review that the success at matching measurable and modelable SOM pools has been minimal. Furthermore, the division of SOM in pools has been criticized on mathematical grounds, as continuous turnover rates distributions can be artificially represented by discrete pools (Bruun and Luxhoi, 2006). These criticisms do not deny the existence of pools but rather emphasize the difficulty in establishing generalized methods to measure or predict their size and turnover rate. This unpredictability can limit the applicability of multi-pool SOM sub-models if the parameterization for different agricultural soils, pasturelands, forestlands and organic soils is uncertain or requires intensive calibration.

In this chapter, we describe the one-pool SOM sub-model implemented in SWAT+. This sub-model is conceptually based on the model described in Kemanian and Stockle (2010), and was adapted to the SWAT+ algorithms and integrated to the cycling of N and P.