Hydrologic outputs generated over the Great Lakes with a calibrated version of the GEM-Hydro model.

This dataset contains outputs from a calibrated version of the GEM-Hydro model developed at Environment and Climate Change Canada (ECCC) and is available on the Federated Research Data Repository. The dataset covers the basins of the Laurentian Great Lakes and the Ottawa River and extends over the period 2001-2018. The data consist of all variables (hourly fluxes and state variables) related to the water balance of GEM-Hydro's land-surface scheme (including precipitation, surface and sub-surface runoff, drainage, evaporation, snow water equivalent, soil moisture…) and mean daily streamflow at 212 gauge locations.

In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance.

Building accurate rainfall-runoff models is an integral part of hydrological science and practice. The variety of modeling goals and applications have led to a large suite of evaluation metrics for these models. Yet, hydrologists still put considerable trust into visual judgment, although it is unclear whether such judgment agrees or disagrees with existing quantitative metrics.

The sensitivity of simulated streamflow to individual hydrologic processes across North America.

Streamflow sensitivity to different hydrologic processes varies in both space and time. This sensitivity is traditionally evaluated for the parameters specific to a given hydrologic model simulating streamflow. In this study, we apply a novel analysis over more than 3000 basins across North America considering a blended hydrologic model structure, which includes not only parametric, but also structural uncertainties.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

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.

A new formulation for feedforward neural networks.

Feedforward neural network is one of the most commonly used function approximation techniques and has been applied to a wide variety of problems arising from various disciplines. However, neural networks are black-box models having multiple challenges/difficulties associated with training and generalization. This paper initially looks into the internal behavior of neural networks and develops a detailed interpretation of the neural network functional geometry.