Multi-model ensemble analysis of runoff extremes for climate change impact assessments

JOURNAL OF HYDROLOGY

卷:525

Article

Najafi, Mohammad Reza¹  Moradkhani, Hamid¹ 

[1] Portland State Univ, Dept Civil & Environm Engn, Portland, OR 97207 USA

关键词: Climate change;    Extreme events;    Bayesian hierarchical;    Bayesian model averaging;    Multi-modeling;   

DOI  :  10.1016/j.jhydrol.2015.03.045

来源: Elsevier

【 摘 要 】

In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free