Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3537-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3537-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
08 Jul 2022
Research article | Highlight paper |
| 08 Jul 2022
| 08 Jul 2022
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
Hongren Shen
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
Bryan A. Tolson
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
Étienne Gaborit
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Richard Arsenault
Department of Construction Engineering, École de technologie supérieure, Montreal, QC, Canada
James R. Craig
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON, Canada
Vincent Fortin
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Lauren M. Fry
Great Lakes Environmental Research Laboratory, National Oceanic and Atmospheric Administration, Ann Arbor, MI, USA
Martin Gauch
Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Daniel Klotz
Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Frederik Kratzert
Institute for Machine Learning, Johannes Kepler University, Linz, Austria
Google Research, Vienna, Austria
Nicole O'Brien
National Hydrological Service, Environment and Climate Change Canada, Burlington, ON, Canada
Daniel G. Princz
National Hydrological Service, Environment and Climate Change Canada, Saskatoon, SK, Canada
Sinan Rasiya Koya
Department of Civil and Environmental Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Tirthankar Roy
Department of Civil and Environmental Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA
Frank Seglenieks
National Hydrological Service, Environment and Climate Change Canada, Burlington, ON, Canada
Narayan K. Shrestha
National Hydrological Service, Environment and Climate Change Canada, Burlington, ON, Canada
André G. T. Temgoua
National Hydrological Service, Environment and Climate Change Canada, Burlington, ON, Canada
Vincent Vionnet
Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada
Jonathan W. Waddell
Great Lakes Hydraulics and Hydrology Office, U.S. Army Corps of Engineers, Detroit, MI, USA
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- Benchmarking hydrological models for an uncertain future K. Beven 10.1002/hyp.14882
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- Applicability of a flood forecasting system for Nebraska watersheds S. Rasiya Koya et al. 10.1016/j.envsoft.2023.105693
- Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. 10.3389/frwa.2023.1108108
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- On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization H. Maier et al. 10.1016/j.envsoft.2023.105779
- Projected seasonal flooding in Canada under climate change with statistical and machine learning M. Grenier et al. 10.1016/j.ejrh.2024.101754
- Ten strategies towards successful calibration of environmental models J. Mai 10.1016/j.jhydrol.2023.129414
- Assessing the impact of distributed snow water equivalent calibration and assimilation of Copernicus snow water equivalent on modelled snow and streamflow performance A. Beaton et al. 10.1002/hyp.15075
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
- Comparing quantile regression forest and mixture density long short-term memory models for probabilistic post-processing of satellite precipitation-driven streamflow simulations Y. Zhang et al. 10.5194/hess-27-4529-2023
- Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction F. Gurbuz et al. 10.1016/j.jhydrol.2023.130504
- A multi-model evaluation of probabilistic streamflow predictions via residual error modelling J. Romero-Cuellar et al. 10.1016/j.jhydrol.2024.131152
- Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
- Reconstructing 42 Years (1979–2020) of Great Lakes Surface Temperature through a Deep Learning Approach M. Kayastha et al. 10.3390/rs15174253
- Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins R. Mantilla et al. 10.5194/hess-28-1373-2024
3 citations as recorded by crossref.
- The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin N. Shrestha et al. 10.3389/frwa.2022.801134
- On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration S. Wi & S. Steinschneider 10.5194/hess-28-479-2024
- The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) J. Mai et al. 10.5194/hess-26-3537-2022
Latest update: 01 May 2024
Executive editor
The study provides a unique effort undertaken by several research groups in the context of a remarkably instrumented large scale system. The latter is subject to a variety of measurement approaches and techniques and a wide range of models are considered in a remarkable inter-comparison study. Key methodological elements stemming from this very comprehensive work are highly relevant to the broad hydrological community.
The study provides a unique effort undertaken by several research groups in the context of a...
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Model intercomparison studies are carried out to test various models and compare the quality of...
