Non-linearity and error in modelling soil processes

Error in models and their inputs can be propagated to outputs. This is impo rtant for modelling soil processes because soil properties used as paramete rs commonly contain error in the statistical sense, that is, variation. Mod el error can be assessed by validation procedures, but tests are needed for the propagation of (statistical) error from input to output. Input error i nteracts with non-linearity in the model such that it contributes to the me an of the output as well as its error. This can lead to seriously incorrect results if input error is ignored when a non-linear model is used, as is d emonstrated for the Arrhenius equation. Tests for non-linearity and error p ropagation are suggested. The simplest test for non-linearity is a graph of the output against the input. This can be supplemented if necessary by tes ting whether the mean of the output changes as the standard deviation of th e input increases. The tests for error propagation examine whether error is suppressed or exaggerated as it is propagated through the model and whethe r changes in the error in one input influence the propagation of another. A pplying these tests to a leaching model with rate and capacity parameters s howed differences between the parameters, which emphasized that statements about non-linearity must be for specific inputs and outputs. In particular, simulations of mean annual concentrations of solute in drainage and concen trations on individual days differed greatly in the amount of non-linearity revealed and in the way error was propagated. This result is interpreted i n terms of decoherence.