Akbari Esfahani, AFriedel, MJ2018-05-172013-11-132014-022013-10-08Esfahani, A. A., & Friedel, M. J. (2014). Forecasting conditional climate-change using a hybrid approach. Environmental Modelling & Software, 52, 83-97.1364-8152AX2CY (isidoc)https://hdl.handle.net/10182/9381A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009-2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.pp.83-97enclimate-changedroughtforecastfractal modelingPalmer Drought Severity IndexPDSIprecipitationtemperatureSouthwestern United StatesJournal Article10.1016/j.envsoft.2013.10.0091873-6726