Authors
Juliane Mai, University of Waterloo
Bryan A. Tolson, University of Waterloo
Hongren Shen, University of Waterloo
Etienne Gaborit, Environment and Climate Change Canada
Vincent Fortin, Environment and Climate Change Canada
Nicolas Gasset, Environment and Climate Change Canada
Herve Awoye, University of Calgary
Tricia A. Stadnyk, University of Calgary
Lauren M. Fry, National Oceanic and Atmospheric
Emily A. Bradley, Great Lakes Hydraulics and Hydrology Office
Frank Seglenieks, Environment and Climate Change Canada
Andre G.T. Temgoua, Environment and Climate Change Canada
Daniel G. Princz, Environment and Climate Change Canada
Shervan Gharari, University of Saskatchewan
Amin Haghnegahdar, University of Saskatchewan
Mohamed E. Elshamy, University of Saskatchewan
Saman Razavi, University of Saskatchewan
Martin Gauch, University of Waterloo
Jimmy Lin, University of Waterloo
Xiaojing Ni, US Environmental Protection Agency, Research Triangle Park
Yongping Yuan, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park
Meghan McLeod, University of Waterloo
Nandita B. Basu, University of Waterloo
Rohini Kumar, Helmholtz Centre for Environmental Research–UFZ
Oldrich Rakovec, Czech Univ. of Life Sciences
Luis Samaniego, Helmholtz Centre for Environmental Research–UFZ
Sabine Attinger, Helmholtz Centre for Environmental Research–UFZ
Narayan K. Shrestha, University of Guelph
Prasad Daggupati, University of Guelph
Tirthankar Roy, University of Nebraska - LincolnFollow
Sungwook Wi, University of Massachusetts Amherst
Tim Hunter, , Great Lakes Environmental Research Laboratory, National Oceanic and Atmospheric Administration
James R. Craig, University of Waterloo
Alain Pietroniro, Environment and Climate Change Canada
Date of this Version
2021
Citation
J. Hydrol. Eng., 2021, 26(9): 05021020 : 10.1061/(ASCE)HE.1943-5584.0002097
Abstract
Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. 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 equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.
Comments
OPEN ACCESS