More project information.
Shared data and results available are linked.
Great Lakes Runoff Intercomparison Project for Lake Erie (GRIP-E)
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.
The work has been published in:
Mai, Tolson, Shen, Gaborit, Fortin, Gasset, et al. (2021).
Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).
Journal of Hydrologic Engineering, 26(9), 05021020.
Great Lakes Runoff Intercomparison Project for the entire Great Lakes watershed (GRIP-GL)
is the fourth in a sequence of the Great Lakes Runoff
Intercomparison Projects covering the entire Great Lakes
watershed after the predecessors focussed on in
individual lakes (Michigan, Ontario, and Erie). 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.
The main results of this study are: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models - while losing less performance in spatial and spatio-temporal validation than locally calibrated models - exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) 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. The latter is deemed preferable; especially for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The Machine Learning based model was not included here as is not setup to simulate AET, SSM, and SWE. (6) All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download data and model outputs.
The work is published in:
Mai, J., Shen, H., Tolson, B. A., Gaborit, E., 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., 26, 3537–3572. Highlight paper. Accepted Jun 10, 2022.
The data and codes are deposited in the Federated Research Data Repository. A description of these deposited data is described in a README. The thumbnail images for GRIP-GL are available for download as well (grip-gl_thumbnail_images.zip). The following interactive maps show the model simulations and performances of streamflow, actual evapotranspiration, surface soil moisture, and snow water equivalent presented in the study. Data can be downloaded for each station on those websites.
The following map will display the results of streamflow simulations of the 13 participating models. The results can be displayed as hydrographs for each of the 212 streamflow locations (141 calibration locations and 71 validation locations) for either the calibration period (Jan 2001 to Dec 2010) or the validation period (Jan 2011 to Dec 2017).
The following map displays the simulated actual evapotranspiration of twelve of the 13 participating models; the Machine Learning based ML-LSTM-lumped model did not simulate this variable. The simulations (Jan 2001 to Dec 2017) are compared to the GLEAM v3.5b evapotranspiration (variable 'E' in dataset). Only the basin-wise simulations and comparisons are available on the webpage.
Surface soil moisture
The following map displays the simulated (standardized) surface soil moisture of twelve of the 13 participating models; the Machine Learning based ML-LSTM-lumped model did not simulate this variable. The simulations (Jan 2001 to Dec 2017) are compared to the GLEAM v3.5b surface soil mositure (variable 'SMsurf' in dataset). Only the basin-wise simulations and comparisons are available on the webpage.
Snow water equivalent
The following map displays the simulated snow water equivalent of twelve of the 13 participating models; the Machine Learning based ML-LSTM-lumped model did not simulate these variable. The simulations (Jan 2001 to Dec 2017) are compared to the ERA5-Land snow water equivalent (variable 'sd' in dataset). Only the basin-wise simulations and comparisons are available on the webpage.