Identifying Hydrologic Regimes and Drivers in Nova Scotia, Canada: Catchment Classification Efforts for a Data-Limited Region
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 11
Abstract
Nova Scotia is a small maritime province with limited capacity to gauge its abundant water resources, but it possesses a diverse geologic setting and surface water distribution. The common engineering practice of using nearest neighbor catchments as hydrologic surrogates may be unreliable in regions such as this. Catchment classification provides a tool to identify and explain variability in hydrologic regimes and inform data transfer across catchments. Here we develop a catchment classification framework using hydrometric, climatic, and landscape data from Nova Scotia, Canada. An inductive classification approach was first used to identify five generalized hydrologic metaclasses based on streamflow signatures derived from 47 long-term streamflow records. We then attempted to replicate this classification using deductive approaches, and identified key physiographic and meteorological variables that could be useful in classifying ungauged catchments. Due to the limited number of gauged catchments, two supervised deductive classification methodologies were applied for comparison: (1) an automated approach often used in more data-rich scenarios (random forests and classification and regression trees); and (2) a nonautomated approach, which involved manual construction and testing of decision trees. The products of the automated approach (random forests), although more robust, may be challenging to apply, while the manually constructed decision tree, which was guided by a combination of local knowledge and theoretical reasoning, could be a useful tool for practitioners. Climate did not emerge as a particularly strong controlling factor in hydrologic variability in this region, but surface water storage had an important role in flow regime across the province. Results demonstrate that this type of hybrid approach can be effective for understanding hydrologic variability and identifying surrogate watersheds in data-limited regions.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Natural Science and Engineering Research Council (NSERC) of Canada and Nova Scotia Environment and Climate Change.
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Journal of Hydrologic Engineering
Volume 27 • Issue 11 • November 2022
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Received: Nov 19, 2021
Accepted: May 17, 2022
Published online: Sep 13, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 13, 2023
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