Keywords
grassland, meta-modelling, soil organic matter, steady-state
Start Date
1-7-2012 12:00 AM
Abstract
Spin-up runs usually used to initialize mechanistic biogeochemical models highly increase the time required to make simulations. The aim of this paper is to evaluate the use of linear and quadratic regression models, as an alternative way to initialize such models. This option is illustrated with the grassland ecosystem Pasture Simulation model (PaSim) under a range of climate, soil and management conditions. Coupled to the CENTURY model for the soil processes, PaSim simulates fluxes of C, N, water and energy at the soil-plant-animalatmosphere interface for managed grasslands at the plot scale. This study demonstrates the feasibility of approximating steady state SOM (Soil Organic Matter) by a quadratic regression. For instance, PaSim initialization using a quadratic regression with P-ET0 (Climatic Water Balance indicator) is about 500 times faster than using spin-up runs. However, quadratic SOM regression provides a 10-15% gap, due to the existing variability in SOM response to climate (e.g. ~7% standard deviation for one P-ET0 value of the climate year). Anyway, these quadratic regressions could be used in future vulnerability assessments that require a prohibitive number of simulations for complex models.
Steady-state soil organic matter approximation model: application to the Pasture Simulation Model
Spin-up runs usually used to initialize mechanistic biogeochemical models highly increase the time required to make simulations. The aim of this paper is to evaluate the use of linear and quadratic regression models, as an alternative way to initialize such models. This option is illustrated with the grassland ecosystem Pasture Simulation model (PaSim) under a range of climate, soil and management conditions. Coupled to the CENTURY model for the soil processes, PaSim simulates fluxes of C, N, water and energy at the soil-plant-animalatmosphere interface for managed grasslands at the plot scale. This study demonstrates the feasibility of approximating steady state SOM (Soil Organic Matter) by a quadratic regression. For instance, PaSim initialization using a quadratic regression with P-ET0 (Climatic Water Balance indicator) is about 500 times faster than using spin-up runs. However, quadratic SOM regression provides a 10-15% gap, due to the existing variability in SOM response to climate (e.g. ~7% standard deviation for one P-ET0 value of the climate year). Anyway, these quadratic regressions could be used in future vulnerability assessments that require a prohibitive number of simulations for complex models.
