Open Collections

Eugeni, Sophia

University of British Columbia

Climate change is modifying the behaviour of natural systems, with variation in precipitation patterns making effective watershed management increasingly critical. These issues are especially relevant in mountainous areas of southern British Columbia such as around the Village of Lions Bay. The two Lions Bay streams with small catchments experience low streamflows during the dry summer season, when they rely on snowmelt and subsurface water. Changed precipitation and snowmelt patterns in southern British Columbia have translated into significant seasonal streamflow fluctuations and droughts. For prediction of these dynamics, hydrologic modelling is essential to guide effective water management planning. In small watersheds, modeling under limited data availability is a key challenge. Recent research investigates the relative contributions of data and process knowledge, and the role of model complexity for predictive accuracy. This research explores the relationship between model complexity, data availability, and predictive performance in modeling hydrologic behaviour leading to low flows in a data-sparse case study. This method involves the use of linear (Area Scaling model), data-driven (Regional Linear Regression model, Machine Learning model) and conceptual modeling (Bucket model) frameworks to represent catchment parameters and collected data to establish predictive relationships between streamflow and meteorological data. Through construction of increasingly complex hydrologic models to represent the Lions Bay watersheds’ characteristics, the goal was to quantify the value of added information (precipitation, flow, catchment parameters) regarding model accuracy in predicting low flow rates. Preliminary field data collection was completed in the Harvey and Magnesia Creek catchments to provide a basis for hydrologic modelling. Four hydrologic models were constructed with the use of topography, static, and dynamic watershed parameters, increasing complexity as more local data were incorporated. It was established that the relevancy and information content of the input data plays a significant role in the model performance, especially under limited data availability, where relevancy indicates the usefulness of the information to the application. The lower complexity models using information with higher relevancy to the model had a higher performance than the other developed models using more input data with less explicit utility to the model.

Text

2022-10-31

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80093

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/