On the benefit of the Canadian Small Lake Model to better represent the impact of small natural lakes on GEM-Hydro streamflow simulations.
Bibliographic record
Abstract
This study aims at assessing the impact of a new lake model on streamflow simulations performed with the GEM-Hydro hydrologic model developed at ECCC. GEM-Hydro is at the heart of the National Surface and River Prediction System (NSRPS) which ECCC uses to forecast river flows over most of Canada. The GEM-Hydro model mainly consists of the GEM-Surf component to represent surface processes, and of the Watroute model to represent river and lake routing, in order to perform streamflow simulations and forecasts. The surface component of GEM-Hydro can simulate 5 different types of surfaces. Currently, the water tile consists of a very simple algorithm which, in terms of water balance, consists of producing runoff fluxes simply equal to precipitation minus evaporation. This runoff over water surfaces is then provided as input, along with runoff and drainage generated over other surface tiles, to the Watroute model. The Watroute version used in GEM-Hydro currently only represents major lakes (area greater than 100km2) along the river networks, and does not represent the impact that small lakes can have on streamflow, which mainly consists in slowing down runoff before it reaches the main streams of the network. Recently, the Canadian Small Lake Model (CSLM) was implemented in the surface component of GEM-Hydro to represent the energy and water balance over water tiles more accurately. So far, CSLM simulations have been shown promising in terms of evaporation, ice cover, absolute and dew point temperature simulations, compared with the former algorithm used over water. However, the impact of CSLM on the resulting streamflow simulations performed with GEM-Hydro has not been evaluated yet. This study aims first at evaluating the impact of CSLM on streamflow simulations, and secondly at testing different CSLM configurations as well as different coupling strategies with Watroute, with the objective of finding the best set up for the prediction of streamflow in Canada. For example, overland runoff generated by the land tile can be provided to the water tile of the same grid point in different ways, and the outflow computed at the outlet of the water tile can be computed with different parameters. Moreover, different outflow computations have to be taken into account depending on if the water tile of a grid point represents subgrid-scale lakes, or if on the contrary it belongs to a lake spanning over multiple model grid points. To do so, different GEM-Hydro open-loop simulations have been performed on the Lake of the Woods watershed, located in Canada, with and without CSLM to represent water tiles. The CSLM configurations leading to the best results are presented here. CSLM simulations are also evaluated in terms of surface fluxes, to ensure that the main purpose of the model, which is to improve surface fluxes to ultimately improve atmospheric forecasts, is preserved, compared to the default configuration of the model. Ideas for further improving the coupling between the GEM-Hydro surface and routing components, in terms of lake processes, are also presented and will be tested in future work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".