Extensive online documentation is available at the MESH wiki page project web pages: https://wiki.usask.ca/pages/viewpage.action?pageId=1331527756
Assimilation of remote sensing observations into the MESH model in Canada. https://www.researchgate.net/project/Assimilation-of-remote-sensing-observations-into-the-MESH-model-in-Canada
Primary modeler: Ala Bahrami
MESH training and support: Daniel Princz (ECCC) and Mohamed Elshamy (GWF)
Advisors: Kalifa Goïta (University of Sherbrooke), Ramata Magagi (University of Sherbrooke), Bruce Davison (ECCC & GWF) and Saman Razavi (GWF)
The project was started in 2016 and was finished in 2020.
During this project, the data assimilation (DA) framework has been developed into the Environment and Climate Change Canada’s (ECCC) community environmental modeling open source code software. The purpose of this work focuses on the integration of satellite-based terrestrial water storage anomaly (TWSA) observations derived from the Gravity Recovery and Climate Experiment (GRACE) into ECCC’s modeling system. The semi-distributed hydrology land surface model called MESH (Modélisation Environmentale Communautaire (MEC) – Surface et Hydrologie; Pietroniro et al., 2007) has been used for the developement of this project.
List the objectives of the modeling, as well as a summary of the project structure and tasks completed
The basin meta-data is accessible via the MESH wiki page [https://wiki.usask.ca/display/MESH/Mackenzie+River+Basin]
The general objective of this thesis focuses on developing a framework to improve snow water equivalent (SWE) and streamflow simulations in the MESH model over Canada through the assimilation of Terrestrial Water Storage Anomaly (TWSA) retrievals provided by the GRACE satellites. The ensemble Kalman smoother (EnKS) approach has been implemented for the integration of GRACE observations within the MESH model.
Bahrami, A. (2020). Assimilation of GRACE data into the MESH model to improve the estimation of snow water equivalent [Doctoral Thesis, Université de Sherbrooke]. https://savoirs.usherbrooke.ca/handle/11143/17283?show=full
Bahrami, A., Goïta, K., Magagi, R., Davison, B., Razavi, S., Elshamy, M., Princz, D., 2020. Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model. J. Hydrol. 125744. https://doi.org/10.1016/j.jhydrol.2020.125744
The MESH-Data-Assimilation is distributed under the Open Government Licence - Canada version 2.0 or any later version