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Forecasting conditional climate-change using a hybrid approach

Environmental Modelling and Software

By:
and
DOI: 10.1016/j.envsoft.2013.10.009

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Abstract

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.

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Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Forecasting conditional climate-change using a hybrid approach
Series title:
Environmental Modelling and Software
DOI:
10.1016/j.envsoft.2013.10.009
Volume
52
Year Published:
2014
Language:
English
Publisher:
Elsevier
Contributing office(s):
Crustal Geophysics and Geochemistry Science Center
Description:
15 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Environmental Modelling and Software
First page:
83
Last page:
97
Number of Pages:
15
Country:
United States
State:
Arizona;California;Colorado;Nevada;New Mexico;Utah