Problem Statement
Cold regions, defined by the seasonal or perennial presence of snow, ice, and frozen ground, cover more than half of the Earth's land surface and supply freshwater to billions of people downstream. Yet cold-region hydrology remains among the most difficult domains in operational forecasting. Processes such as glacier melt, snowpack accumulation and release, ice-jam flooding, and glacial lake outburst floods (GLOFs) operate through physical mechanisms that are fundamentally different from the rainfall-runoff dynamics that underlie most standard hydrologic models. As a result, forecast systems calibrated on temperate basins often fail without warning when applied in cold regions, not because of data limitations alone, but because the dominant controls on streamflow are not represented in the model at all.
This gap has direct consequences for communities. In Southeast Alaska, the rapid retreat of Mendenhall Glacier has destabilized a glacier-dammed lake called Suicide Basin (Kʼóox Ḵaadí Basin), which has released catastrophically into the Mendenhall River every summer since 2023. These GLOFs have broken historical flood records, damaged hundreds of homes in Juneau's Mendenhall Valley, and repeatedly caught operational forecasters without adequate warning. The Tlingit & Haida Indian Tribes of Alaska and the City and Borough of Juneau have both called for improved understanding of these events and better tools to anticipate them. The Mendenhall case is not unique. Glacier-dammed lakes exist across Alaska, Yukon, British Columbia, and Greenland, most with no dedicated monitoring infrastructure.
The broader challenge is one of transferability: how do we build hydrologic understanding and forecast skill in basins where the physics are different, the record is short, and the stakes are high? Addressing this requires both conceptual fluency in cold-region processes and practical experience diagnosing where and why standard approaches break down. This module is designed to build both.
Module Overview
This module introduces the hydrology of cold regions through a combination of conceptual content and hands-on data analysis. Students move from foundational landscape and climate classification through hydrologic regime characterization, cold-region hazard diagnosis, and ultimately a capstone authentic task in which they take the role of an Alaska-Pacific River Forecast Center (APRFC) hydrologist diagnosing the 2023–2025 Mendenhall River GLOF events. The module uses real observational data and scaffolded Python notebooks to ground every concept in a specific, consequential place.
Topics Covered
Cold-region landscape categories (arctic tundra, boreal forest, alpine, maritime, glacierized); cold-region climate classification and its relationship to streamflow; hydrologic regime classification using Pardé coefficients, seasonal flow fractions, seasonal snow fraction, and center of timing; cold-region hazards including GLOFs, ice-jam flooding, rain-on-snow events, thermokarst, and permafrost thaw; multiple linear regression and feature importance for streamflow prediction; operational forecast limitations in cold regions; and community impacts of extreme cold-region flood events.
Prerequisites
Students should have a basic working knowledge of hydrology at the introductory graduate level, including familiarity with the water balance, streamflow measurement, and watershed concepts. Some exposure to Python is expected; students do not need to be proficient programmers, but should be comfortable reading and modifying code in a Jupyter notebook environment. Basic statistics through linear regression is assumed. No prior knowledge of cold-region hydrology or Alaska is required; the module is designed to be self-contained from a cold-region perspective.
Learning Objectives
At the end of this module, students will be able to: (1) classify cold-region climatology and explain how climate drives seasonal streamflow response; (2) characterize the hydrologic regime of a cold-region basin using quantitative metrics including Pardé coefficients, seasonal flow fractions, and center of timing; (3) diagnose cold-region hazards from streamflow records and static basin attributes and explain their effects on operational modeling; and (4) reflect on forecast limitations for cold-region stakeholders and communicate findings in writing to a mixed technical and community audience.
These objectives are assessed through activities within each section and a capstone authentic task. The authentic task integrates all four objectives through a single diagnostic scenario grounded in the August 2023 Mendenhall River GLOF.
Course Authors
Kaitlin Meyer
Kaitlin Meyer is a PhD student in Civil and Environmental Engineering at the University of Utah. Her dissertation research spans two areas: developing machine learning-based large-sample models to address blind spots in operational streamflow forecasting for Alaska, and improving process representation in snow models with a focus on the melt process. Her broader research background includes cold-region hydrology, snow microstructure, and large-sample hydrologic prediction. This module was developed as part of her CIROH-funded doctoral work.
kaitlinhope.meyer@gmail.com
Liza McLatchy
Liza McLatchy is a PhD student in Civil and Environmental Engineering at the University of Utah. Her dissertation research focuses on stream temperature modeling across Alaska and the contiguous United States (CONUS), comparing machine learning and physically based numerical approaches and investigating how groundwater–surface water exchange shapes stream thermal regimes. Her broader research background includes environmental engineering, computational modeling, and cold-region hydrology. This module was developed as part of her CIROH-funded doctoral work.
lizamclatchy@gmail.com
Solution Keys
Completed results templates for each learning activity are available and can be requested from the course authors.
Target Audience
This module is designed for graduate students in hydrology, civil and environmental engineering, water resources, or related earth science fields. It is appropriate for any student who has completed introductory hydrology coursework and wants to develop applied fluency in cold-region processes and operational forecast contexts. The module is relevant for students pursuing careers in federal and state water agencies, river forecasting, climate adaptation, or academic research in cold or data-sparse regions.
Expected Effort
The module developers estimate that this module will take between 6 to 8 hours to complete, including reading, interactive notebook activities, and the capstone authentic task. The authentic task alone is estimated at 3 to 5 hours depending on familiarity with Python and data analysis workflows.
Course Sharing and Adaptation
This course is available for export by clicking the "Export Link" at the top right of this page. You will need a HydroLearn instructor studio account to do this. You will first need to sign up for a hydrolearn.org account, then you should register as an instructor by clicking 'studio.hydrolearn' and requesting course creation permissions.
Recommended Citation
McLatchy, L and Meyer, K. (2026). Foundations of Cold Region Hydrology. CIROH. https://edx.hydrolearn.org/courses/course-v1:HydroLearn_CIROH+999+Fall_2026/about
Adapted From
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Acknowledgement
Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research to Operations in Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003.
