Steven Weijs

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.

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Green infrastructure (GI) is an approach that aims to reduce the amount of stormwater that reaches the combined or stormwater sewer networks and protect receiving waterbodies in urban watersheds. Cities across North America and the world are devoting resources to implement different types of GI to showcase their use. As it is a new approach, the field of GI research is emerging. The main objective of this thesis is to contribute to the GI literature by assessing the water quantity and water quality performances of three green infrastructure practices constructed in 2018 by the City of Vancouver. A stormwater tree trench and two bioswales were monitored. The soil moisture levels in the structural soil stormwater tree trench and one of the bioswales were monitored to assess the drought resistance of these practices and to evaluate the salt migration. This research introduced low cost monitoring options that can simplify the monitoring of stormwater tree trenches and bioswale practices. This research concluded that structural soil stormwater tree trenches and bioswale practices are effective in treating heavy metals, suspended solids, and other pollutants harmful street pollutants. These practices are also effective tools in removing stormwater from the stormwater/sewer networks by promoting infiltration to native soils.

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Stormwater drainage system operators in lowland areas use weather forecast information, tide tables, a hydraulic model and heuristic experiences to balance the water table in the region close to the desired water level setpoints. Water can be discharged using pumps and gravity outflow flap gates, or can be stored in the system if the discharge capacity is limited. In the lower mainland of British Columbia (BC), climate change projections are showing an increasing trend in high-intensity, short-duration rainfall events, and sea level is expected to rise up to 1.0 m by the end of 2100. Given the uncertainties in climate change projections, the challenge is to build more resilient stormwater drainage system whilst reducing the cost of pumping operation or other capacity expansions. Experiences in the Netherlands have shown that algorithmic control of drainage system using model predictive control (MPC) can be a way to link water and energy objectives more cohesively. To maintain water level at the desired water level setpoint, MPC calculates water levels that need to be controlled using rainfall and sea tides forecasts, and computes optimal control actions for pump stations. This thesis aims to gain operational insights into algorithmic control of urban stormwater drainage system in Richmond BC, using a simplified drainage system model. Smart control strategies serving different objectives are explored to reduce pump energy costs while avoiding flood. Furthermore, an application of bottom-up vulnerability assessment under different control strategies aiming to maximize the operational flexibilities, illuminates the vulnerabilities and adaptation capacity of the (modeled) existing stormwater drainage system to plausible scenarios of climate change induced sea level rise, heavy rainstorms, and land-use changes. This provides necessary information to water systems operators and engineers about the smart, real-time control of such a system, and finding ways to combine engineering designs with operational flexibilities for better adapting to future conditions.

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Rating curves play a vital part in hydrology for producing streamflow time-series. The derivedstreamflow is an integral component to any hydrological study and therefore requires proper quantification of not only a discharge point value, but also an uncertainty measure. Using multivariate Gaussian distributions as kernels, a probabilistic rating curve was developed from the conditional distribution as an alternative model for the standard deterministic rating curve. Auxiliary information from a run-of-river hydroelectric project, as well as the temporal variability from the gauging measurements, were used to study the possible reduction in the uncertainty of the developed rating curve. The temporal information was modeled using an exponential function that updated upon receiving new gaugings and the sluicing model was a continuously updated kernel distribution that assigned more weight to gaugings taken after a sluicing event. Four models of varying complexity were created and their performance was evaluated using information theory measures such as surprise and the Kullback-Leibler divergence measure. The results indicate that probabilistic rating curves are useful tools for modeling and evaluating the dynamic uncertainty of the curves. The uncertainty was shown to be reduced by up to 19% by including the temporal information of the gaugings and sluicing information. Auxiliary information can be beneficial to rating curve development and an argument is made for why probabilistic rating curves should become a norm in the hydrology field.

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