ArticleScientific data2025
Hydrologic outputs generated over the Great Lakes with a calibrated version of the GEM-Hydro model.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine Learning-Based Reconstructions of Historical Daily and Monthly Runoff for the Laurentian Great Lakes.Scientific data · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
This dataset contains outputs from a calibrated version of the GEM-Hydro model developed at Environment and Climate Change Canada (ECCC) and is available on the Federated Research Data Repository. The dataset covers the basins of the Laurentian Great Lakes and the Ottawa River and extends over the period 2001-2018. The data consist of all variables (hourly fluxes and state variables) related to the water balance of GEM-Hydro's land-surface scheme (including precipitation, surface and sub-surface runoff, drainage, evaporation, snow water equivalent, soil moisture…) and mean daily streamflow at 212 gauge locations. These outputs were simulated with a calibrated version of the GEM-Hydro model that was run in open-loop mode (no assimilation) and driven with atmospheric forcings coming from ECCC's Canadian Surface Reanalysis version 2.1. GEM-Hydro achieves satisfactory simulations of various hydrologic variables when compared to reference datasets. This dataset can be used for example to drive any routing model, compute climatologies or statistics for different hydrologic variables and study their variability as a function of the local geo-morphology, etc.
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Registered trials
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