In this webinar, Dr. Mai discussed efforts to build a framework for model intercomparison amongst project participants, along with results from the several phases of the Great Lakes Runoff Inter-comparison Project (GRIP) for Lake Erie (GRIP-E; finished) and its extension to the entire Great Lakes (GRIP-GL; ongoing). These projects compare a wide range of lumped, distributed, and Machine Learning models that are used operationally and/or for research purposes across Canada, the United States, and Europe. These models to-date include GR4J, LBRM, HMETS, HYMOD2, GEM-Hydro, MESH-SVS, MESH-CLASS, VIC, SWAT, WATFLOOD, Noah-MP, WRF-Hydro, HYPE, mHM, and Machine Learning models. While GRIP-E focused only on streamflow, GRIP-GL is comparing model performance for streamflow and additional types of observations. The presentation focused on what was learned during the GRIP-E project and how the GRIP-GL project is setup and its current status.

GWF has made significant progress towards developing and improving modelling technologies for prediction and forecasting of water-related risks across Canada. From the fundamental modeling capacity delivered by the Core Forecasting and Modelling Team, to application-specific modelling advances pursued in projects such as the Integrated Modelling Program for Canada, there are many examples of modelling contributions to improved management, policy and practice outcomes across GWF. This session highlighted these examples in a series of case studies co-presented by researchers and collaborators from Canadian businesses, governments, and organizations. Case studies reviewed major modelling advances since the inception of GWF and highlighted how these have contributed to new management regimes, planning exercises and operations policy to reduce water-related threats in Canada’s major river basins.

This session was convened by IMPC as part of the Global Water Futures Open Annual Science Meeting (May 17-19) 2021. To view recordings of all other sessions of the GWF Open Annual Science Meeting 2021, please follow this link.