Abstract
A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.
Highlights
Process-based models for agricultural systems provide a widely used and efficient tool for understanding the complex interactions between soil water, carbon (C), nitrogen (N), phosphorus (P), and plant growth [1,2], where both production under various environmental conditions [3] and nutrient cycling [4] can be simulated
Our analysis suggests that simulations were sensitive to changes in minimum roughness length (MRL), where it is known to strongly affect water and heat fluxes on the soil surface [42,43]
Our study showed that soil water dynamics were sensitive to m hydraulic conductivity (MHC), drainpipe level (DPL), distance between drainpipes (DBD), and MRL (Figures 2 and A1)
Summary
Process-based models for agricultural systems provide a widely used and efficient tool for understanding the complex interactions between soil water, carbon (C), nitrogen (N), phosphorus (P), and plant growth [1,2], where both production under various environmental conditions [3] and nutrient cycling [4] can be simulated. Parameters that might vary with environmental conditions and have varying, possibly complex distributions themselves [5] are often difficult to precisely characterise, especially if they are costly to measure, requiring long-term monitoring [6] This parameter variability, uncertainty, or quality directly affects the reliability of the model simulations [1,7]. Given this study is the first to identify parameter input sensitivity for the SPACSYS model, we chose to present results in two stages: first, an LSA as presented here, and second, a GSA (which is prep.) for presentation elsewhere This two-stage reporting approach is followed for computational reasons, together with differences in associated interpretations, visualisations, and comparisons of the LSA and GSA outputs. DDaaiillyy rraaiinnffaallll aanndd mmeeaann aaiirr tteemmppeerraattuurree oovveerr tthhee oobbsseerrvvaattiioonnaall ppeerriioodd ((22001111 ttoo 22001144))
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