A Linking Test that investigates the feasibility of inverse modelling: application to a simple rainfall interception model for Mt Gambier, southeast South Australia

Abstract

International audienceInterception loss has an important influence on the water yield of forested areas. Nevertheless, in most studies stemflow is not measured, therefore the question of how to determine the feasibility of optimizing interception and stemflow parameters simultaneously by matching daily simulated throughfall to fortnightly measurements of cumulative throughfall is an important one. By applying a daily empirical interception model, a goodness fit of 2·2 mm/day is obtained between observed and simulated cumulative throughfall. However, by applying the simple but robust Linking Test, it was shown that the parameters are non-unique and falsely linked, i.e. inter-relationships between different vegetation parameter sets give similar throughfall but non-unique net precipitation. The Linking Test investigates the causes of obtaining falsely linked parameters and shows that objective equifinality is not the source of the problem. Objective equifinality occurs when an inappropriate objective function is used. The Linking Test also shows that falsely linked parameters are not caused by measuring throughfall on a non-daily basis (termed frequency sampling equifinality). By expanding the interception model to the second degree, it was found that the non-uniqueness is due to the inherent nature of interception and stemflow functions that behave similarly and therefore can easily compensate each other (termed similarity equifinality). It is also shown that a simple daily empirical exponential interception model developed for conifers in the uplands of the United Kingdom is suitable to model interception in Pinus radiata plantations in the Mediterranean climate of southern Australia by using only daily gross precipitation data and two parameters

Derivation of physically based soil hydraulic parameters in New Zealand by combining soil physics and hydropedology

“…However, a severe drawback of deriving the hydraulic parameters by inverse modelling is that they suffer from equifinality or nonuniqueness (Fern andez-G alvez et al, 2021;. Equifinality occurs when more than one set of parameters gives acceptable simulations relative to a given measure of goodness-of-fit between simulated and measured values Pollacco & Angulo-Jaramilo, 2009). found that to eliminate the equifinality and obtain a unique set of hydraulic parameters, it is necessary to invert the hydraulic parameters simultaneously from observations of both θ(ψ) and K(ψ) and that the measurements cover the full range of θ, from fully saturated to oven dry.…”

Instrumental method for reducing error in compression-derived measurements of rainfall interception for individual trees

“…However, estimates of canopy water storage and evaporation are primarily based on indirect methods and model estimates which are capable of producing >30% error (Muzylo et al 2009). Error is compounded further if these indirectly-derived interception components are falsely linked to net precipitation measurements by simply treating interception as a "black-box" process (Pollacco and Angulo-Jaramilo 2009), or when splash droplet evaporation is neglected (Murakami 2006, 2007, Dunkerley 2009). The identification of uniquely linked interception mechanisms (Pollacco and Angulo-Jaramilo 2009) and investigation of splash droplet evaporation requires direct canopy interception measurements at the intra-storm scale (Dunkerley 2009).…”

Inverse modeling of cloud-aerosol interactions – Part 1: Detailed response surface analysis

“…In essence, improved interpretation of parameter uncertainty can yield valuable information to enable a better judgement of the limits of our theoretical understanding of droplet activation. When applying an inverse approach to a new problem or model, it is prudent to first investigate the posedness and identifiability of the model parameters (Pollacco and Angulo-Jaramilo, 2009;Cressie et al, 2009). One method to achieve this is via response surface analysis (Toorman et al, 1992;Šimůnek et al, 1998;Vrugt et al, 2001).…”