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Forecasting conditional climate-change using a hybrid approach

January 8, 2014

A 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.

Citation Information

Publication Year 2014
Title Forecasting conditional climate-change using a hybrid approach
DOI 10.1016/j.envsoft.2013.10.009
Authors Akbar Akbari Esfahani, Michael J. Friedel
Publication Type Article
Publication Subtype Journal Article
Series Title Environmental Modelling and Software
Series Number
Index ID 70048449
Record Source USGS Publications Warehouse
USGS Organization Crustal Geophysics and Geochemistry Science Center

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