The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA.Ĭomparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. This is likely to be improved with more advanced strategies to transfer these models in space. 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 that the model has not been calibrated on. ![]() The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. The main results of this study are as follows: 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 latter three outputs are compared against gridded reference datasets. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). 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 the six predefined regions of the watershed. 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 1×106 km2 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 transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. 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. 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. ![]() Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. 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. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture.
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