First Advisor

Hamid Moradkhani

Date of Publication

Spring 6-4-2013

Document Type


Degree Name

Doctor of Philosophy (Ph.D.) in Civil & Environmental Engineering


Civil and Environmental Engineering




Climatic changes -- Environmental aspects -- Research -- Pacific Northwest, Floods -- Environmental aspects -- Research -- Pacific Northwest, Climatic extremes -- Environmental aspects -- Research -- Pacific Northwest, Bayesian statistical decision theory



Physical Description

1 online resource (xvii, 252 pages)


The rising temperature of the earth due to climate change has shown to alter the variations of hydro-climate variables, including their intensities, frequencies and durations. Extreme events such as floods are, in particular, susceptible to any disturbances in climate cycles. As such it is important to provide policymakers with sufficient knowledge about the probable impacts of climate change on hydrologic extremes and most importantly on floods, which have the highest impacts on the societies. For this reason analysis of hydro-climate extremes is commonly performed using data at each site (or grid cell), however due to the limited number of extreme events, these analyses are not robust. Current methods, such as the regional frequency analysis, which combine data from different locations are incapable of incorporating the spatial structure of the data as well as other explanatory variables, and do not explicitly, assess the uncertainties. In this thesis the spatial hierarchical Bayesian model is proposed for hydro-climate extreme analyses using data recorded at each site or grid. This method combines limited number of data from different locations, estimates the uncertainties in different stages of the hierarchy, incorporates additional explanatory variables (covariates), and can be used to estimate extreme events at un-gaged sites. The first project develops a spatial hierarchical Bayesian method to model the extreme runoffs over two spatial domains in the Columbia River Basin, U.S. The model is also employed to estimate floods with different return levels within time slices of fifteen years in order to detect possible trends in runoff extremes.

Continuing on the extreme analysis, the impact of climate change on runoff extremes is investigated over the whole Pacific Northwest (PNW). This study aims to address the question of how the runoff extremes will change in the future compared to the historical time period, investigate the different behaviors of the regional climate models (RCMs) regarding the runoff extremes, and assess the seasonal variations of runoff extremes.

Given the increasing number of climate model simulations the goal of the third project is to provide a multi-model ensemble average of hydro-climate extremes and characterize the inherent uncertainties. Outputs from several regional climate models provided by NARCCAP are considered for the analysis in all seasons. Three combination scenarios are defined and compared for multi-modeling of extreme runoffs. The biases of each scenario are calculated and the scenario with the least bias is selected for projecting seasonal runoff extremes.

The aim of the fourth project is to quantify and compare the uncertainties regarding global climate models to the ones from the hydrologic model structures in climate change impact studies.

Various methods have been proposed to downscale the coarse resolution General Circulation Model (GCM) climatological variables to the fine scale regional variables; however fewer studies have been focused on the selection of GCM predictors. Additionally, the results obtained from one downscaling technique may not be robust and the uncertainties related to the downscaling scheme are not realized. To address these issues, in the fifth study we employed Independent Component Analysis (ICA) for predictor selection which determines spatially independent GCM variables (as discussed in Appendix A). Cross validation of the independent components is employed to find the predictor combination that describes the regional precipitation over the upper Willamette basin with minimum error. These climate variables along with the observed precipitation are used to calibrate three downscaling models: Multi Linear Regression (MLR), Support Vector Machine (SVM) and Adaptive-Network-Based Fuzzy Inference System (ANFIS).


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