We will discuss alternative approaches to process-based hydrological modeling and summarize common uses of numerical models in hydrology. Participants will introduce their interests in hydrological modeling and explain what they hope to gain from the class.

In this theme we will explain how hydrologic models are constructed; how our understanding of dominant hydrological processes is represented in time-stepping mechanistic models. The theme will summarize the key ingredients of hydrological models, including state equations, flux parameterizations, and time stepping schemes. Participants will build a simple model "from scratch" to gain first-hand experience in model construction.

In this theme we will discuss algorithmic descriptions of dominant processes across different landscapes, including glacier dynamics, snow energetics, frozen ground, forest-snow interactions, hillslope-riparian interactions, surface water - groundwater interactions, infiltration, evapotranspiration, runoff generation, surface water storage, and lakes and wetlands. A key focus will be on representing spatial heterogeneity in models, especially modelling how large-scale fluxes are shaped by small-scale heterogeneity. Participants will run models using alternative algorithms of dominant process and gain understanding of the differences among competing modeling approaches.

In this theme we will discuss methods to develop the spatial information required for model simulations (e.g., new geospatial data; hierarchal spatial organization of the landscape into GRUs/HRUs to represent spatial variability in topography, vegetation and soils). Participants will use terrain analysis methods to gain understanding of the importance of uncertainties in landscape structure.

This theme will discuss methods to generate spatial fields of meteorological inputs (e.g., spatial interpolation methods, empirical methods to estimate radiation/humidity from standard precipitation/temperature measurements, use of data from atmospheric model reanalyses, temporal disaggregation methods, and ensemble methods to explicitly characterize uncertainties in forcing inputs). Moreover, this theme will discuss methods to represent changes in time, specifically, use of information from Earth System models to produce scenarios of the impacts of climate change on water resources.

In this theme we will introduce strategies used for computationally intensive model simulations, including parallelization of models and parallelization of analysis methods. This theme will also cover the common case where the problem is "embarrassingly parallel" and parallelization can be achieved using simple scripting procedures. Participants will run a suite of simulations on super-computers to gain experience with computationally-intensive modeling problems.

In this theme we will discuss methods for code sharing and code review (e.g., GitHub), fully documented and sharable model workflows (e.g., jupyter notebooks), and the use of containers to simplify porting of models (e.g., docker images). We will discuss coding conventions, modularity, approaches for intra-component and inter-component coupling, and workflow management. Participants will undertake exercises in collaborative model development, where different participants are working on individual model components. Participants will also develop prototypical model workflows for specific modeling applications.

In this theme we will confront the model with data. We will consider global parameter screening and sensitivity analysis methods to understand how model parameters affect model behavior. We will consider alternative single-objective and multiple-objective parameter estimation strategies, including regularization strategies necessary for high-dimensional modeling problems. Participants will run a suite of exercises to understand model behavior and refine model simulations.

In this theme we evaluate the fidelity of the model simulations. This theme includes (a) synthetic test cases to ensure that the equations are implemented correctly, (b) process-based model evaluation, using multivariate data on spatial scales from hillslopes to continents, and (c) model benchmarking, to compare model simulations to a-priori expectations of model performance and to estimates of system-scale predictability. Participants will run a suite of exercises to gain experience with model evaluation.

In this theme we will consider a suite of uncertainty quantification methods, including methods to quantify uncertainty in individual model components (e.g., model inputs; model states), methods to infer model uncertainty from observations, and ad-hoc methods to quantify uncertainty through ensembles of opportunity. We will consider ensemble data assimilation strategies, focusing on the ensemble Kalman filter and the particle filter to use snow data and streamflow data to update time stepping simulation models. We will consider ensemble streamflow forecasting methods to produce probabilistic depictions of risk. Participants will run a suite of predictability experiments to understand how forecast skill in different basins is shaped by both basin initial conditions and meteorological forecasts, taking advantage of uncertainty quantification methods and data assimilation methods to improve the statistical reliability of probabilistic streamflow predictions.

In this final theme we will consider alternative typologies of hydrological models and explore the relative merits of models with varying levels of spatial complexity and process complexity. We will discuss some of the great historical debates on the "correct" approach to process-based hydrological modelling. We will explore how the hydrological sciences community is evolving to more unified hydrological modelling paradigms. Participants will discuss what they have learned in this course and how they will use the course material in their own work.

The "shell" is the command-line interface of Linux and other UNIX operating systems. In this theme, you will learn how to connect to a remote Linux system, use basic commands, navigate and work with files and directories, use wildcards and pipes to build powerful commands, and write simple shell scripts to automate your work. These basic skills will make you autonomous with Linux and allow you to continue learning on your own.

High-performance computing (HPC) clusters allow researchers to perform work that would be impossible with ordinary computers. In this theme, you will learn the basics of supercomputers and how to use them: finding and loading scientific software, performing calculations in batch jobs or interactive sessions, storing and managing data, and using resources efficiently and responsibly.

• Students request permission to register in the course, GEOG 825, by emailing Dr. Martyn Clark (martyn.clark@usask.ca) and outlining how they meet the course prerequisites listed in the course synopsis.
• Students should then contact the department of Geography and Planning (geography.planning@usask.ca) and provide their student number so that a permission to register can be entered.
• After getting email confirmation from the department, students taking the course for credit can register themselves on PAWS. Students wishing to audit the course can contact Student Central at askus@usask.ca using their PAWS email account and request to be registered as an audit student in the course.

• Students request permission to register in the course by emailing Dr. Martyn Clark (martyn.clark@usask.ca) and outlining how they meet the course prerequisites listed in the course synopsis.
• Students submit either a Canadian Universities Graduate Transfer Agreement (CUGTA) form or a Western Dean's Agreement (WDA) form, depending on the home institution.

• Students will have their home institution sign the form and send it to the department of Geography and Planning (geography.planning@usask.ca) at the University of Saskatchewan. They will be automatically registered in the course, but must pay student fees and tuition directly to the University of Saskatchewan.
  • CUGTA-based students will pay tuition and student fees
  • WDA-based students should expect to only pay student fees

Please note: You must complete all 5 steps below in order to apply for admission.

• Request permission to register in the course by emailing Dr. Martyn Clark (martyn.clark@usask.ca) and outlining how they meet the course prerequisites listed in the course synopsis.
• Apply for admissions here, choose Graduate program, Non-degree and term.
  Note: International students and professionals will pay $120 CDN non-refundable application fee, but DO NOT submit transcripts, or submit proof of English equivalency and should ignore these requests if prompted automatically by the form.
• International students and professionals MUST submit a letter of support from their home institution or employer on official letterhead, listing the course, the term it will be taken, and the dates that the course will be held.  This letter should be submitted to grad.studies@usask.ca 
• Once an application has been received by Graduate Studies, students can log back into the application created to monitor their application status.  Upon receiving a letter of admission, students should then notify the department of Geography and Planning (geography.planning@usask.ca) that they have been admitted and provide their student number so that a permission to register can be entered.
• After getting email confirmation from the department, students taking the course for credit can register themselves and pay their tuition on PAWS.  Students wishing to audit the course can contact Student Central at askus@usask.ca using their PAWS email account and request to be registered as an audit student in the course.  Tuition will be paid on PAWS.