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The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL)
| Description: | Abstract: Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1 million square kilometer study domain. The study comprises 13 models covering a wide range of model types from Machine Learning based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways: either by aggregating model outputs and the reference to basin-level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. Citation (Journal Publication): Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL) Hydrol. Earth Syst. Sci. https://doi.org/10.5194/hess-2022-113 Citation (Data Publication): Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL) FRDR. https://doi.org/10.20383/103.0598 Datasets: There are two categories of GRIP-GL datasets available. Part A includes all the data that original study participants were provided with in order to train/calibrate their model. Part B are request-only data associated with performing blind model validation. Users can access Part A at any time but, as described below, users can only be granted access to Part B of the dataset after they have accessed Part A. More information is provided in the README.pdf attached. |
| Notes: | More information about the individual files provided can be found in README.pdf |
| Authors: | Mai, Juliane; University of Waterloo; https://orcid.org/0000-0002-1132-2342  Shen, Hongren; University of Waterloo; https://orcid.org/0000-0002-5979-2159 Tolson, Bryan A.; University of Waterloo; https://orcid.org/0000-0002-3092-5536 Gaborit, Étienne; Environment and Climate Change Canada; https://orcid.org/0000-0002-9787-9124 Arsenault, Richard; École de Technologie Supérieure; https://orcid.org/0000-0003-2834-2750 Craig, James R.; University of Waterloo; https://orcid.org/0000-0003-2715-7166 Fortin, Vincent; Environment and Climate Change Canada; https://orcid.org/0000-0002-2145-4592 Fry, Lauren M.; National Oceanic and Atmospheric Administration; https://orcid.org/0000-0002-5480-5408 Gauch, Martin; Johannes Kepler University of Linz; https://orcid.org/0000-0002-4587-898X Klotz, Daniel; Johannes Kepler University of Linz; https://orcid.org/0000-0002-9843-6798 Kratzert, Frederik; Google Research; https://orcid.org/0000-0002-8897-7689 O'Brien, Nicole; Environment and Climate Change Canada Princz, Daniel G.; Environment and Climate Change Canada; https://orcid.org/0000-0001-9605-1184 Rasiya Koya, Sinan; University of Nebraska–Lincoln Roy, Tirthankar; University of Nebraska–Lincoln; https://orcid.org/0000-0002-6279-8447 Seglenieks, Frank; Environment and Climate Change Canada Shrestha, Narayan K.; Environment and Climate Change Canada; https://orcid.org/0000-0001-6292-7379 Temgoua, André G. T.; Environment and Climate Change Canada Vionnet, Vincent; Environment and Climate Change Canada; https://orcid.org/0000-0002-9142-9739 Waddell, Jonathan W.; U.S. Army Corps of Engineers |
| Keywords: | Great Lakes Streamflow Actual Evapotranspiration Surface Soil Moisture Snow Water Equivalent Hydrology |
| Field of Research: | Earth and related environmental sciences > Hydrology > Surface water hydrology
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| Publication Date: | 2022-06-24 |
| Related Identifiers: |
This dataset is cited by
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| Geographic Coverage: | Place Name Great Lakes watershed |
| Geolocation Box: | West Longitude -95 East Longitude -70 North Latitude 55 South Latitude 40 |
| Appears in Collections: | Water Institute |
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Access to this dataset is subject to the following terms:
This dataset is made available under a custom license. Most of the data files are available under the Creative Commons Attribution 4.0 (CC BY 4.0) License, however a subset of data files are available under the Creative Commons Attribution-NonCommercial 4.0 (CC BY NC 4.0) License. All code is available under the GNU General Public Licence, v3. Please see the appended LICENSE.txt file for the full terms of use. https://doi.org/10.20383/103.0598
Citation
Mai, J., Shen, H., Tolson, B., Gaborit, É., Arsenault, R., Craig, J., Fortin, V., Fry, L., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N., Temgoua, A., Vionnet, V., Waddell, J. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL). Federated Research Data Repository. https://doi.org/10.20383/103.0598
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