Development of an Integrated Hydro-Climatic Systems Analysis Framework and its Application to the Athabasca River Basin, Canada

Date

2016-09

Journal Title

Journal ISSN

Volume Title

Publisher

Faculty of Graduate Studies and Research, University of Regina

Abstract

Climate change has profound impacts on regional hydrological characteristics in

large unregulated continental river basins (LUCRiBs) such as the Athabaasca River

Basin (ARB), Canada. A systematic analysis of these impacts is confronted with many

challenges. For instance, the performances of general circulation models (GCMs) vary

with many factors, e.g. climate variables, geographic locations, temporal scales, and

evaluation measures. Mesoscale atmospheric features can barely be provided by

coarse-resolution GCMs. Filling this gap by statistical downscaling is further

challenged by redundant computations, resulting from spatial climatic similarities, and

the complexities of data uncertainties, nonlinear correspondences, normality

prerequisites, and multivariate dependencies. Climatic projection may lack a solid

GCM-evaluation foundation and a high spatial resolution. These complexities in

downscaling may also exist and be coupled with massive computations in integer

optimization in hydrological simulation. Furthermore, an integration of these

challenges would decrease the reliability of long-term streamflow forecastings for

guiding socio-economic development and eco-environmental conservation over

LUCRiBs such as the ARB under climate change.

To fill the gap of few effective techniques, an integrated hydro-climatic systems

analysis framework is developed and applied to the ARB. This framework includes six

modules. (a) The multi-dimensional performances of CMIP5 GCMs and their

ensemble are evaluated. (b) The climate over the ARB is classified by recursive

dissimilarity and similarity inferences. (c) The spatial resolution of GCM is enhanced

by recursive multivariate principal-monotonicity inferential downscaling based on (a)

and (b). (d) High-resolution climatic projection under four representative

concentration pathways (RCPs) are generated by coupling (a) to (c). (e) The correspondence between climate and streamflow is reproduced by Bayesian principalmonotonicity

inference based on (b). (f) Modules (d) and (e) are integrated for

streamflow forecasting under climate change.

A series of findings are revealed while methodological reliability is verified. For

instance, the multi-model ensemble has a relatively high modeling accuracy. The

climatic conditions over the ARB are classified into 20 classes based on their

dissimilarity and similarity. The overall downscaling accuracies are relatively high for

temperature and acceptable for precipitation although varying with multiple factors.

At the scale of octo-decades, daily minimum temperature would increase by 1.7, 2.3,

2.1 and 3.0 􀔨, daily maximum temperature by 1.4, 1.8, 1.6 and 2.2 􀔨, and daily total

precipitation by 0.03, 0.07, 0.08 and 0.16 mm under RCPs 2.6, 4.5, 6.0 and 8.5,

respectively. The approach in module (e) is effective at capturing the temporal

variability and the multi-year averages of streamflow and the uncertainties of climatestreamflow

correspondences. Streamflow tends to increase at the upper and middle

reaches and decline at the lower one. The increments of streamflow would be the

highest in March and the decrements would be dominated by less flow in July or

Summer. Either RCP scenarios or modeling biases are significant for the temporal

variability and trends and are insignificant for the overall magnitudes of streamflow.

The methods and findings in this study would be helpful for gaining insights into

coupled climatic and hydrological systems over the ARB, evaluating the impacts of

climate change, guiding regional socio-economic development and eco-environmental

conservation, and promoting developations of more advanced climatic and hydrometeorological

systems analysis methods.

Description

A Thesis Submitted to the Faculty of Graduate Studies and Research in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Environmental Systems Engineering at University of Regina. xvii, 311 p.

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