Forecasting conditional climate-change using a hybrid approach
Environmental Modelling and Software
Links
- More information: Publisher Index Page (via DOI)
- Download citation as: RIS | Dublin Core
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.
Study Area
| 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 |
| Country | United States |
| State | Arizona;California;Colorado;Nevada;New Mexico;Utah |
| Google Analytic Metrics | Metrics page |