Multi-resolution analysis of aggregated spatial data to simulate yield and irrigation water demand at regional scales

Input data aggregation influences crop model estimates at the regional level (Eyshi Rezaei et al., 2015). Previous studies have focused on the impact of aggregating the climate data used to compute crop yields (Hoffmann et al., 2015; van Bussel et al., 2011; Zhao et al., 2015). Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolution input data on model outputs (irrigated and rainfed yield and irrigation water demand [IWD]) in Tasmania, Australia by (i) separately assessing the DAEc and DAEs of model input data and (ii) assessing the combined impact of DAEc and DAEs. We provide a framework to quantifying the input uncertainty introduced by using aggregated data to meet the objectives of modelling exercises.