Steven Weijs

Assistant 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 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).

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Graduate Student Supervision

Master's Student Supervision (2010 - 2018)
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.

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