Wind Erosion in Western Queensland Australia
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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 –Modelling Soil Erodibility Dynamicsthis process can be used to determine the starting position of a simulation on the logisticcurve, and whether the soil erodibility is in a minimum (i), transition (ii) or maximum (iii)phase.For subsequent time-steps in a simulation, t new can be computed as a function of t init (thatbecomes t prev ) and short term (e.g. weekly) antecedent rainfall (∑r). This can be achievedusing a conditional statement of the form:t new = (t prev + 1) for ∑ r = 0 (4.14)= (t prev - α) for ∑ r > 0where α represents an adjustment factor for t when antecedent rainfall leading up to asimulation day is > 0. The effect of the adjustment factor (α) can be defined by:α = α 1 for ∑ r < w r (4.15)= α 2 for w r < ∑ r < x r= … …= α n for ∑ r > y rwhere w r , x r ,…, y r are antecedent rainfall ranges determining values for α , α 1 , α 2 ,…, α n basedon ∑r. The effect of α is on increasing (t prev +1) or decreasing (t prev – α) soil erodibility inresponse to the rainfall effect on the soil surface condition. Under this scheme, a period withno rainfall that is longer than the period over which ∑r is computed (e.g. 10 days) willinstigate forward movement up the erodibility continuum toward Q max . Conversely, smallrainfall events may temporarily decrease erodibility through a shift back down thecontinuum. A series of small rainfall events or a single large rainfall event may be sufficientto reset the soil surface to a position at the bottom of the continuum (Q min ). While the logisticcurve has lower and upper asymptotes, the limits b min and b max (Equation 4.12) aremaintained to prevent erodibility predictions being affected by circumstances of sustainedrainfall events (giving a large α), or prolonged drought that may result in a large t value andno effect on erodibility with following small rainfall events.116

Chapter 4 –Modelling Soil Erodibility Dynamics4.4.3 Sensitivity TestingThe model framework has two components that control temporal changes in soil erodibility.These are: 1) the logistic model parameters that control rates of increases in erodibility, and2) the model rainfall thresholds (Equation 4.15) that control the soil sensitivity to rainfall anddecreases in erodibility. Figure 4.5 provides some example logistic soil erodibility ‘growth’curves to illustrate a range of model parameterisations affecting increases in erodibility:(i) r = 0.5, M = 0.5, T = 0.5(ii) r = 0.09, M = 0.5, T = 0.5(iii) r = 0.04, M = 0.5, T = 0.5(iv) r = 0.1, M = 30, T = 0.25(v) r = 0.1, M = 50, T = 0.5(vi) r = 0.15, M = 0.5, T = 0.5(vii) r → dynamic, M = 0.5, T = 0.5Figure 4.5 Graph illustrating the model sensitivity to changes in growth rate and growth timingparameters (i to vi), and model response to variable growth rates that can be expected under dynamicclimate and management conditions (vii).The parameterisations from (i) to (iii) demonstrate the model sensitivity to changes in thegrowth rate, r (Equation 4.11). Small variations in r are sufficient to induce significant117

Chapter 4 –Modell<strong>in</strong>g Soil Erodibility Dynamics4.4.3 Sensitivity Test<strong>in</strong>gThe model framework has two components that control temporal changes <strong>in</strong> soil erodibility.These are: 1) the logistic model parameters that control rates of <strong>in</strong>creases <strong>in</strong> erodibility, and2) the model ra<strong>in</strong>fall thresholds (Equation 4.15) that control the soil sensitivity to ra<strong>in</strong>fall anddecreases <strong>in</strong> erodibility. Figure 4.5 provides some example logistic soil erodibility ‘growth’curves to illustrate a range of model parameterisations affect<strong>in</strong>g <strong>in</strong>creases <strong>in</strong> erodibility:(i) r = 0.5, M = 0.5, T = 0.5(ii) r = 0.09, M = 0.5, T = 0.5(iii) r = 0.04, M = 0.5, T = 0.5(iv) r = 0.1, M = 30, T = 0.25(v) r = 0.1, M = 50, T = 0.5(vi) r = 0.15, M = 0.5, T = 0.5(vii) r → dynamic, M = 0.5, T = 0.5Figure 4.5 Graph illustrat<strong>in</strong>g the model sensitivity to changes <strong>in</strong> growth rate and growth tim<strong>in</strong>gparameters (i to vi), and model response to variable growth rates that can be expected under dynamicclimate and management conditions (vii).The parameterisations from (i) to (iii) demonstrate the model sensitivity to changes <strong>in</strong> thegrowth rate, r (Equation 4.11). Small variations <strong>in</strong> r are sufficient to <strong>in</strong>duce significant117

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