A decade in the making, version 3 of Raven, an open-source hydrologic modelling platform used by a number of flood and reservoir forecasting agencies across Canada, has seen its release.
The version update coincides with the publication of a new paper in Environmental Modeling and Software describing Raven’s object-oriented code’s unique ability to emulate, modify and extend existing models.
Raven automates some of the tedious work often required to calibrate and run hydrological models.
“We have spent over 10 years developing this tool in support of both research and practical application to water management problems, and are immensely proud of its success,” said Professor James Craig, primary author of Raven, and an associate professor in the Department of Civil and Environmental Engineering at the University of Waterloo.
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The latest environmental engineering news direct to your inbox. You can unsubscribe at any time.Craig, who is also a Canada Research Chair in Hydrologic Modelling and Analysis, intended Raven to have tremendous flexibility in terms of hydrologic model structure, process representations, spatial discretization approaches, interpolation methods and numerical algorithm. The software allows for a vast array of individual model choices to be individually or collectively assessed, opening up the ability to address a number of interesting research questions about how various parts of the water cycle are represented.
Raven also includes a number of features that support practitioner needs, such as simulating water management practices, reservoir operations and land use modifications.
According to the University of Waterloo, the software has become quite popular. Some of Raven’s users include BC Hydro; TransAlta; New Brunswick Power; Ontario Power Generation; the BC River Forecasting Centre; New Brunswick Water Sciences; and the City of Calgary.
The University of Waterloo’s computational hydrology research group is currently working on a number of research projects that use Raven to answer questions about model identifiability, model structural optimization, regional sensitivity analysis, upscaling of hydrological processes, and model choice.
