ArticleJournal of hydrologic engineering2021
Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).
Article in Journal of hydrologic engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- More efficient reproducible research in hydrology: moving research down the academic career scale (MRDTACS).Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Challenges and opportunities of ML and explainable AI in large-sample hydrology.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- Hydrologic outputs generated over the Great Lakes with a calibrated version of the GEM-Hydro model.Scientific data · 2025Article
- Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).Journal of hydrologic engineering · 2021Article
Corrections and comments
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Authors and funding
34 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.