Steven Weijs

Associate Professor

Research Classification

Research Interests

Hydrological Cycle and Reservoirs
Drinking Water
Fresh Water
Ice and Snow
control of water systems
experimental hydrology
Hydrological Prediction
information theory
mountain hydrology
water resources management

Relevant Thesis-Based Degree Programs


Research Methodology

wireless sensor networks
salt dilution gauging
information theory
conceptual hydrological prediction models


Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round

Projects regarding new measurement techniques for catchment hydrology
Projects working on uncertainty analysis / quantification for hydrological prediction
Projects on optimal monitoring network layout / optimal expermental design
Projects on applications of information theory in hydrology and water resources

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).

Complete these steps before you reach out to a faculty member!

Check requirements
  • Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
  • Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.
Focus your search
  • Identify specific faculty members who are conducting research in your specific area of interest.
  • Establish that your research interests align with the faculty member’s research interests.
    • Read up on the faculty members in the program and the research being conducted in the department.
    • Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.
Make a good impression
  • Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
    • Do not send non-specific, mass emails to everyone in the department hoping for a match.
    • Address the faculty members by name. Your contact should be genuine rather than generic.
  • Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
  • Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
  • Demonstrate that you are familiar with their research:
    • Convey the specific ways you are a good fit for the program.
    • Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
  • Be enthusiastic, but don’t overdo it.
Attend an information session

G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.



These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.

Graduate Student Supervision

Doctoral Student Supervision

Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.

Application of machine learning and information theory to monitor and predict environmental signals (2021)

Environmental signal forecasting is the process of making predictions of the future based on past and present data. In general, forecasting has built on the process of science to uncover knowledge and interpret the meaning of those discoveries. In the last decades, data availability revolutionized the process of investigation into the natural world and the knowledge generated through that process. Recent progress in environmental signal predictions has been driven by 1) methodological improvement in prediction models; and 2) emergence of new data acquisition techniques and resulting data sets. This dissertation is divided into two main parts to focus on both aspects of recent progress (i.e., striving for better models and better data).Machine learning is the fast-growing branch of data-driven models and is one of the most influential contributing factors to model improvement. There are many ways to improve model predictions in this field, and Bootstrap AGGregatING (Bagging), which uses a large collection of models (called an ensemble) instead of a single one, is one of the widely applied methods. The training of those models can be computationally expensive. In this research, we propose a method to pick only the most informative samples for model training, to achieve equally good performance with a smaller ensemble. For problems where computational effort is a limitation, this could lead to better predictions. The pursuit of better data is partly relying on optimally designing the monitoring network. Monitoring network optimization using information theory measures, like other statistical approaches, faces multiple problems regarding assumptions made in the choices of objective function and data discretization. The research undertaken in the second part of dissertation is mainly focused on investigating how assumptions would affect the optimal network layouts. We propose a single objective optimization of joint entropy (network's information content) to maximize information collection. The first application of the K-means quantization method is proposed to improve data representativeness in monitoring network design. We introduce information partitioning techniques to improve network selection process once it reaches its saturation point from achievable information content perspective; we address a novel framework in a case of high-density raingauge network design.

View record

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Streamflow monitoring in a time of change : using image velocimetry methods on citizen videos of the November 2021 flooding in Merritt, British Columbia (2023)

The atmospheric river event in November 2021 is one of the costliest natural disasters in Canadian history. Among others, the Coldwater River in Merritt, British Columbia breached its banks on November 15th, 2021, resulting in extensive damage to the infrastructure and total evacuation of the residents. Estimating the magnitude of this flood is difficult, as it damaged the local flow monitoring station and altered the surrounding landscape. Parts of this flooding event, including the flow close to its peak, were filmed by local residents using mobile devices or drones. Though with significant perspective distortion and imprecision, they still provide valuable information on this extreme event, which would have otherwise been neglected. This study aims to apply image velocimetry techniques to some of these videos, with limited resources and outdated geodata, for reconstructing surface velocities and discharges during the flood. The analysis method consists of using Large Scale Particle Image Velocimetry and Farneback optical flow on the original clips where possible. The extreme and post-event nature of the flood requires changes to many aspects of the conventional image velocimetry workflow. Ground Control Points are identified in the videos, then geolocated or surveyed after the flood, for rectification of raw velocities from image to real-world coordinates. This conservative measure allows unlimited iterations in orthorectification. Discharges are then calculated using surveyed transects, with water surface elevations estimated from the video frames. Results from both methods show a maximum of 20% difference against estimates from from the Water Survey of Canada, proving the versatility of image velocimetry under adverse conditions. Uncertainties in one standard deviation of all four transect discharges, at a maximum of 47%, are higher than expected but still reasonable, likely due to deviations in estimating the stage directly from the videos of poor quality. Extensive testing on the Farneback method show a different response on velocity estimation, especially when surface features are not as rich as those from flooding. Edge pixels are tested and proven to be a promising metric for quantifying natural surface features, without the need for image binarization which does not work well with dense optical flow.

View record

Subsurface storage in small coastal BC watersheds and uncertainty in salt dilution gauging (2023)

Many small municipalities are reliant on surface water supply for their municipal water needs. Studies of water resources are seen as cost prohibitive and rarely implemented, even under development pressures and climate insecurities. Natural tracers can provide an economic alternative to extensive monitoring networks. This study used electrical conductivity (EC) and stable isotopes of water to characterize the subsurface water storage with regards to streamflow contribution, transit times and dominant flow processes in two small, mountainous watersheds of coastal southwestern BC. Long-term EC and periodic discharge measurements were used to establish an EC-discharge relationship. Transit times were evaluated by comparing the diel cycles in EC and temperature during snowmelt, and responses of EC to fall rain events. The relative contribution of snowmelt, shallow subsurface water and deeper subsurface water was calculated by hydrograph separation using EC and stable isotopes as tracers. Discharge measurements were made using the salt dilution gauging method and the second part of this study identified the appropriate measurement model, significant uncertainty sources, and how to combine these to a discharge uncertainty using standardized methods. Snowmelt was identified as a significant contributor to streamflow during the spring and summer months. The summer was characterized by more higher elevation groundwater storage, fed by snowmelt, than lower elevation or deeper groundwater storage. Using both tracers, the Harvey Creek stream consisted of 12% of reacted pre-event water attributed to deeper subsurface flows, 32% of unreacted pre-event water representative of high elevation headwater streams, and 55% of snowmelt. Discharge and EC were related by a power law relationship that appears distinct between spring to summer and fall to winter. Transit times differed with season and were 5 to 24 hours, although unquantified processes likely influenced this. The economic tracer usage had other limitations, most notably the requirement for sufficient spatial and temporal coverage for a good understanding of hydrological processes. The uncertainty evaluation noted a preferred discharge and calibration model and found that calibration prediction represented the largest uncertainty source for a well performed test. The major effects influencing discharge measurements were coarse integration resolution and non-linearly varying background EC.

View record

Streamflow modelling over a range of complexities and inputs for two steep coastal mountainous catchments in Canada (2021)

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.

View record

Green Infrastructure in the City of Vancouver: Performance Monitoring of Stormwater Tree Trenches and Bioswales (2019)

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.

View record

Model Predictive Control for real-time operation of a stormwater drainage system (2019)

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.

View record

Probabilistic Dynamic Rating Curves using Auxiliary Information (2018)

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.

View record


If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.


Follow these steps to apply to UBC Graduate School!