Wind Erosion in Western Queensland Australia
Modelling Land Susceptibility to Wind Erosion in Western ... - Ninti One
Modelling Land Susceptibility to Wind Erosion in Western ... - Ninti One
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Chapter 4 –Modell<strong>in</strong>g Soil Erodibility Dynamicschanges <strong>in</strong> the time it takes for a soil to reach a condition of maximum erodibility def<strong>in</strong>ed byb max . Parameterisations from (iv) to (vi) demonstrate the effects of vary<strong>in</strong>g the modelcomponents M and T that def<strong>in</strong>e the time of maximum growth.In reality, temporal changes <strong>in</strong> soil erodibility are driven by dynamic variations <strong>in</strong> r, M and T.Therefore, a soil will not move up and down a s<strong>in</strong>gle growth curve <strong>in</strong> response to ra<strong>in</strong>fall anddisturbance conditions. Rather, variations <strong>in</strong> the growth rate and disturbance <strong>in</strong>tensities will<strong>in</strong>duce an irregular/fluctuat<strong>in</strong>g growth pattern, for example (vii). Temporary <strong>in</strong>creases <strong>in</strong> soilmoisture and changes <strong>in</strong> aggregation and crust strength due to small ra<strong>in</strong>fall events will<strong>in</strong>duce additional variations <strong>in</strong> the growth curve. These effects will be further moderated bysoil properties like organic matter content and climatic factors such as solar radiation<strong>in</strong>tensity and evaporation rates. Soil erodibility dynamics could be modelled us<strong>in</strong>g staticgrowth rates based on soil type dependence on drought to erode (e.g. after Gillette, 1978).However, the model output would not display realistic temporal patterns unless the effects ofall dom<strong>in</strong>ant controls can be <strong>in</strong>cluded <strong>in</strong> the growth rate formulation. The strength of theframework lies <strong>in</strong> this potential for <strong>in</strong>corporat<strong>in</strong>g a dynamic growth rate model to account forvariations <strong>in</strong> soil responses to ra<strong>in</strong>fall, drought and disturbance mechanisms.Figure 4.6 demonstrates the model sensitivity to changes <strong>in</strong> the ra<strong>in</strong>fall thresholds that drivedecreases <strong>in</strong> erodibility. A hypothetical simulation was run with model parameters set for r =-0.15, M = 0.5, T = 0.5 (Equation 4.11). Ra<strong>in</strong>fall thresholds (Equation 4.15) were set for:(a) ∑r < 3, α = 1; 3 < ∑r < 10, α = -20; 10 < ∑r < 30, α = -40; ∑r > 30, α = -60(b) ∑r < 5, α = 1; 5 < ∑r < 10, α = -20; 10 < ∑r < 30, α = -40; ∑r > 30, α = -60(c) ∑r < 10, α = 1; 10 < ∑r < 20, α = -20; 10 < ∑r < 40, α = -40; ∑r > 40, α = -60Decreas<strong>in</strong>g the model sensitivity to ra<strong>in</strong>fall, from (a) through to (c), results <strong>in</strong> <strong>in</strong>creases <strong>in</strong> themagnitude of output erodibility values and <strong>in</strong> the time for which a soil may have an elevatederodibility. Accurately def<strong>in</strong><strong>in</strong>g both the model growth rates and ra<strong>in</strong>fall sensitivitythresholds will therefore be essential if the conceptual framework is to be applied to modelsoil erodibility dynamics.118