The Great Lakes Runoff Intercomparison Project (GRIP-GL)
- 1University of Waterloo, Engineering, Civil & Environmental Engineering, Waterloo, ON, Canada (juliane.mai@uwaterloo.ca)
- 2ScaDS.AI - Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig, Leipzig, Germany
- 3Department Computational Hydrosystems, UFZ - Helmholtz Centre for Environmental Research, Leipzig, Germany
- 4Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
- 5Department of Construction Engineering, École de technologie supérieure, Montreal, QC, Canada
- 6Great Lakes Environmental Research Laboratory, National Oceanic and Atmospheric Administration, Ann Arbor, MI, USA
- 7Institute for Machine Learning, Johannes Kepler University, Linz, Austria
- 8Google Research, Vienna, Austria
- 9National Hydrological Service, Environment and Climate Change Canada, Burlington, ON, Canada
- 10National Hydrological Service, Environment and Climate Change Canada, Saskatoon, SK, Canada
- 11Department of Civil and Environmental Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
- 12Great Lakes Hydraulics and Hydrology Office, U.S. Army Corps of Engineers, Detroit, MI, USA
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 1x106 km2 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. 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 main results of this study are:
- The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performed.
- While the locally calibrated models lead to good performance in calibration and temporal, they lose performance when they are transferred to locations the model has not been calibrated on.
- The regionally calibrated models exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US.
- Comparisons of additional model outputs against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale.
- A multi-objective-based analysis of the model performances across all variables reveals overall excellent performing locally calibrated models as well as regionally calibrated models.
- Model outputs and observations produced and used in this study are available on an interactive website (www.hydrohub.org/mips_introduction.html#grip-gl) and on FRDR (http://www.frdr-dfdr.ca).
How to cite: 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., Shretha, N., Temgoua, A. G., Vionnet, V., and Waddell, J.: The Great Lakes Runoff Intercomparison Project (GRIP-GL), EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-968, https://doi.org/10.5194/egusphere-egu23-968, 2023.
