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The modeller then tests the sensitivity of the agro-ecosystem-scale model to variations in the simple sub-models to see how much difference it makes to the agro-ecosystem-scale model's output.
The sensitivity of the model to variations in thermophysical properties and design parameters has been studied.
First, a set of simulations was carried out to better understand the response of the model to variations in latitude, altitude, and amount of sulfur dioxide injections.
The simulations reveal a high sensitivity of the SDCDD model to variations in the course of the sorption isotherm.
Lastly, we evaluated the sensitivities of our LAI retrieval model to variations in canopy/ground reflectance ratio and to waveform noise such as induced by topographic slopes.
The results from numerical simulation are compared with data collected for validation, and the sensitivity of the model to variations in key parameters, and to the use of Clausius Clapeyron and generalised Clapeyron equations, is investigated.
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MAIN OUTCOMES AND MEASURES: Sensitivity of the decision tree model to variation in complication rate.
Because many of the parameters of the model are not tightly constrained by experiment, it is important to assess the sensitivity of the model to variation in those values.
The inclusion of higher order effects in the sensitivity index accounts for nonlinear response of the model to variation in model parameters.
Sensitivity of the model simulation to variation in parameter values and environmental conditions was examined.
This makes it possible to subject the modeling to variation of different parameters, such as confinement, Re-number or flame power, or adiabatic or nonadiabatic conditions.
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