Abstract. Classification and clustering approaches provide a means to group watersheds
according to similar attributes, functions, or behaviours, and can aid in
managing natural resources. Although they are widely used, approaches based
on hydrological response parameters restrict analyses to regions where
well-developed hydrological records exist, and overlook factors contributing
to other management concerns, including biogeochemistry and ecology. In the
Canadian Prairie, hydrometric gauging is sparse and often seasonal.
Moreover, large areas are endorheic and the landscape is highly modified by
human activity, complicating classification based solely on hydrological
parameters. We compiled climate, geological, topographical, and land-cover
data from the Prairie and conducted a classification of watersheds using a
hierarchical clustering of principal components. Seven classes were
identified based on the clustering of watersheds, including those
distinguishing southern Manitoba, the pothole region, river valleys, and
grasslands. Important defining variables were climate, elevation, surficial
geology, wetland distribution, and land cover. In particular, three classes
occur almost exclusively within regions that tend not to contribute to major
river systems, and collectively encompass the majority of the study area.
The gross difference in key characteristics across the classes suggests that
future water management and climate change may carry with them heterogeneous
sets of implications for water security across the Prairie. This emphasizes
the importance of developing management strategies that target sub-regions
expected to behave coherently as current human-induced changes to the
landscape will affect how watersheds react to change. The study provides the
first classification of watersheds within the Prairie based on climatic and
biophysical attributes, with the framework used being applicable to other
regions where hydrometric data are sparse. Our findings provide a foundation
for addressing questions related to hydrological, biogeochemical, and
ecological behaviours at a regional level, enhancing the capacity to address
issues of water security.
A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies
