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