The Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)
Bibliographic record
Abstract
Model inter-comparison studies help to evaluate the agility of models to simulate variables of interest such as streamflow, evaporation and soil moisture. The study presented here is the third in a sequence of Great Lakes Runoff Intercomparison Projects (GRIP). The densely populated Lake Erie watershed studied here (GRIP-E) is facing major environmental issues such as eutrophication caused by urban and agricultural runoff. Seventeen hydrologic and land-surface models of different complexity are setup over the same domain using the same meteorological forcings and are compared regarding streamflow at 46 calibration and seven independent validation stations. The results show that 1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data, 2) models calibrated at individual stations perform surprisingly well in validation, and 3) most distributed models calibrated over the entire domain have problems to simulate urban areas but outperform Machine Learning and locally calibrated models in validation. This is the project documentation of the Great Lakes Runoff Inter-comparison Project for Lake Erie GRIP-E funded under IMPC project of Global Water Futures program. Aim of the project The main scopes of the GRIP-E project are: Develop strategies to handle cross-border issues of available data and develop unifying approaches Test operational applicability of different models Identify respective strengths of models, i.e., learning which models perform best under certain conditions Generating multi-model ensembles to quantify uncertainty of model outputs We contacted some model users to get a better feeling of their needs and determine model end-use and thus inform participants about end-goals of model development. Models and Partners There are several models participating in the model inter comparison. The setup is made such that models can be added easily as long as they are setup with the below mentioned input data over the modelling domain. Details on the models can be found here. Objectives Model setups depend on the modelling objective. Not every model is appropriate for every objective. We therefore have defined several objectives the partners can choose from. Models with the same objective will be compared at the end. The objectives can be found here. Datasets The modelling domain is set as specified by the Great Lakes Aquatic Habitat Framework (GLAHF). The intention of the GRIP-E project is to setup the models with as many common datasets as possible. Shared inputs and setups between the models are the digital elevation model (DEM), the soil data and the land use data. Details can be found here. Results The results of the individual models in different phases and objectives are presented. Details can be found here. Citation Journal Publication Mai, J. , B. A. Tolson, H. Shen, É. Gaborit, V. Fortin, N. Gasset, H. Awoye, T. A. Stadnyk, L. M. Fry, E. A. Bradley, F. Seglenieks, A. G. Temgoua, D. G. Princz, S. Gharari, A. Haghnegahdar, M. E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan, M. McLeod, N. B. Basu, R. Kumar, O. Rakovec, L. Samaniego, S. Attinger, N. K. Shrestha, P. Daggupati, T. Roy, S. Wi, T. Hunter, J. R. Craig, and A. Pietroniro (2021). The Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) Journal of Hydrologic Engineering. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002097 Code and Data Publication Code and data that can be found in this GitHub are published in this Zenodo dataset. Zenodo. https://doi.org/10.5281/zenodo.4301003 Gridded model outputs of mHM-UFZ are published under: Rakovec, O., Kumar, R., McLeod, M., Mai, J., and Samaniego, L. (2020). mHM_UFZ gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie Zenodo. https://doi.org/10.5281/zenodo.3886551 Gridded model outputs of GEM-Hydro are published under: Gaborit, É., Princz, D.G., Fortin, V., Durnford, D., Mai, J. (2020). GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) Zenodo. https://doi.org/10.5281/zenodo.3890487 Basin outlines and shapefiles are published under: Shen, H., Mai, J., Tolson, B. A., and Han, M. (2020). Watershed shapes for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) Zenodo. https://doi.org/10.5281/zenodo.3888690
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".