Using artificial neural networks to predict future dryland responses to human and climate disturbances

Land degradation and sediment remobilisation in dryland environments is considered to be a significant global environmental problem. Given the potential for currently stabilised dune systems to reactivate under climate change and increased anthropogenic pressures, identifying the role of external disturbances in driving geomorphic response is vitally important. We developed a novel approach, using artificial neural networks (ANNs) applied to time series of historical reactivation-deposition events from the Nebraska Sandhills, to determine the relationship between historic periods of sand deposition in semi-arid grasslands and external climatic conditions, land use pressures and wildfire occurrence. We show that both vegetation growth and sediment re-deposition episodes can be accurately estimated. Sensitivity testing of individual factors shows that localised forcings (overgrazing and wildfire) have a statistically significant impact when the climate is held at present-day conditions. However, the dominant effect is climate-induced drought. Our approach has great potential for estimating future landscape sensitivity to climate and land use scenarios across a wide range of potentially fragile dryland environments.