C1W Team Meetings (Jan 2023 update)

To provide technical exchange within the project team, a monthly science talk is held to provide ongoing context for project participants and to provide critical commentary to support integration and data conformity across sub disciplines within the project.  We’ve held many science talks in the first 15-months of this project, covering a spectrum of subjects related to data assembly layers (soils, DEM, bathymetry), land surface and climate modelling approaches, the integration of GRACE (Gravity Recovery and Climate Experiment) and the HydroGeoSphere environment.

Here’s a list of science talks so far:    

• November 2022 - Mani Mahdinia, University of Toronto - New contionental-scale WRF climate simulations: improved lake models & ERAS/CMIPS6 forcing

• October 2022 - Aruna Nayak, University of Toronto - Quantifying the linkages between satellite soil moisture and high-resolution integrated hydrologica simulations across-multiple spatial scales

• September 2022 - Shusen Wang, Canadian Centre Remote for Sensing - Estimating surface water area using LandSat data

• June 2022 - All collaborators - Biannual progress meeting, with the following progress updates (CLICK HERE TO REVIEW MEETINGS SLIDES):

  • Introduction - Hazen Russell

  • Year One in Summary - Steve Frey

  • Climate/Land Surface Modelling and Data Update - Andre Erler

  • WRF Lake Modelling to Support Regional Climate Modelling - Mani Mahdinia

  • Surficial and Bedrock Hydrostatigraphic Model Development - Amanda Taylor

  • 3D HydroGeoSphere (HGS) Model Development - Eric Kessel

  • Cold Region HGS Model Development for the Carcajou Watershed - Melissa Bunn

  • Connecting Groundwater and HGS to Ecosystem Services - Tariq Aziz

  • Project Communication and Outreach Update - Hazen Russell

  • Year 2 Project Objectives - Steve Frey

• May 2022 - Dr. Chris Fletcher, University of Waterloo - Applying machine-learning to support climate modelling and monitoring

  • Abstract: We live in a Golden Age for weather and climate data, with a multitude of satellites, reanalysis systems and Earth system models providing outputs for an ever-increasing number of variables. These data streams almost all depend on theoretical and/or dynamical models of some kind, and such models depend on assumptions and physical parameterizations that contribute to biases and uncertainty in the output data. In this talk, I will review several recent projects conducted in my research group at the University of Waterloo that apply machine learning (Artificial Intelligence) methods in different ways to quantify some of the uncertainties and reduce biases. I will describe some of our key contributions related to monitoring snowfall and snow accumulation across Canada, constraining uncertain parameters in Earth system models, and some of our ongoing work and potential future applications.

• March 2022 - Tyler Herrington, University of Waterloo/Aquanty - Influence of Resolution and Snow Cover on Reanalysis of Soil Temperatures

• January 2022 - Dr. David Rudolph, University of Waterloo - Predictive Soil Mapping and Applications in Canada

• November 2021 - Xioayong Xu, University of Toronto - Satellite soil moisture and linkages to hydrological modelling

• October 2021 - Eric Kessel, Aquanty - DEMs and lake bathymetric modelling

• September 2021 - John Crowley, CGS - Canada’s changing water: the gravity of the situation

• July 2021 - Andre Erler, Aquanty - Land surface and climate modelling strategies

• June 2021 - Steven Frey, Aquanty - HydroGeoSphere and cold region process representations

• May 2021 - Xiaoyuan Geng, AAFC - Predictive soil mapping and applications in Canada