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AI could help forecast Great Lakes water levels by learning how precipitation, evaporation, runoff and flows through connecting channels interact over time—and help explain a forecast by showing which inputs influenced it. Researchers are testing machine-learning methods for these tasks, but the official forecast products described by NOAA and the U.S. Army Corps of Engineers (USACE) are not identified as having been replaced by those approaches. For now, AI is best understood as a developing complement to established forecasting systems, not a proven operational upgrade.
Why Great Lakes water levels are difficult to predict
A lake’s level reflects more than recent rainfall. Water enters through precipitation on the lake and runoff from its watershed, while evaporation removes water. Water also moves between the lakes and onward through their connecting channels. The combined water-balance inputs and outputs are often discussed as net basin supply; the direction and amount of connecting-channel flow matter too. NOAA outlines the monitoring network and water-level context on its Great Lakes water levels and NOAA page.
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These influences do not necessarily show up in a lake’s level at the same time they occur. A weather pattern can affect precipitation, evaporation or runoff, and its effects can carry forward. The balance also differs from lake to lake: upstream inflow is important to Lake Erie, while regulation is a notable factor for Lake Ontario. An AI model may be useful here because it can learn nonlinear relationships and delayed associations in historical data—but learning an association does not, by itself, establish a physical cause.
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Combine past levels with water-balance drivers
A model can be trained on historical lake levels alongside variables such as precipitation, evaporation, runoff, air temperature and inflow or outflow. It looks for patterns that have preceded level changes and uses the available inputs to estimate future levels. The target and forecast horizon matter: predicting monthly lake levels is not the same task as predicting conditions several days ahead.
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A NOAA-affiliated presentation describes work on machine-learning and AI predictions for components of net basin supply and subsequent lake levels, using operational climate-model inputs. It presents a developing framework and early results rather than a complete operational validation record. See Subseasonal-to-Annual Net Basin Supply and Water Level Forecasting for the Great Lakes.
Learn patterns that simpler relationships may miss
In a 2026 study, researchers tested tree-based ensemble methods against monthly Great Lakes level records covering 1982–2022. They used SHAP explanations and VARS sensitivity analysis to examine the influence of model inputs and their timing. The study reports that inflow and outflow were dominant overall, with other important drivers varying by lake and with effects that can involve lags. Its findings are specific to that study’s methods, data and evaluation; they do not establish that the same model will perform better for every lake, season or forecast horizon. The paper is Explaining Great Lakes water level variability through interpretable ensemble machine learning.
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- Clear reading: The water gauge scale uses bold numbers, and the measuring stick is protected by an anti-glare coating. It is 4 feet long and 4 inches wide, so the readings can be clearly seen at any distance or angle.
- Durable Materials: The water level gauge is made of high-quality fiberglass and digitally processed. It is corrosion-resistant and rust-proof, and is frost- and sun-resistant, ensuring continued performance.
- Easy Installation: The depth gauge tool comes with pre-drilled holes and four mounting screws. These pre-drilled holes allow for easy installation behind a support pole.
- Versatile Applications:: Ideal for lakes, rivers, pools, marinas, dams, and educational projects. Track tides, measure rainfall runoff, or demonstrate hydrology principles with confidence.
- Use Individually or in Combinations: Available in different sizes, it can be combined.
A separate 2025 paper describes a dual-transformer deep-learning framework for seasonal Great Lakes water-level forecasting and examines feature contributions. Its method and evaluation are distinct from the 2026 ensemble study, so their results should not be treated as a direct head-to-head comparison. See Dual-Transformer Deep Learning Framework for Seasonal Forecasting of Great Lakes Water Levels.
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How AI could explain its forecast
Show which inputs influenced an estimate
Methods such as SHAP can attribute portions of a model’s prediction to its input features. In practical terms, an explanation might indicate that a particular estimate was influenced by recent inflow, precipitation or evaporation inputs. This can help forecasters inspect what the model is responding to instead of receiving a number with no account of its inputs.
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Show when a driver may matter
Lag-aware sensitivity analysis can help identify whether a modeled driver is associated with an immediate change or an effect that appears in later months. That distinction matters for interpreting a forecast: an input can be important without having its strongest modeled influence at the same time it was observed.
These explanations describe how a model produced an estimate; they are not automatic proof that a variable physically caused a level change. A useful explanation should be checked against water-budget processes and established hydrologic understanding. It should also be accompanied by uncertainty information and tested to see whether it remains plausible across lakes, seasons and high-variance periods.
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What forecast information is already available
AI would enter a landscape that already includes products for different time horizons. NOAA GLERL says lake-wide averages are used in monitoring and forecasting the water budget; its listed record begins in 1918 because earlier gauge coverage was insufficient for a reasonable lake-wide mean. Its Great Lakes Operational Forecast System (GLOFS) provides nowcast and forecast guidance out to 120 hours, issued four times per day. USACE and Environment and Climate Change Canada (ECCC) also issue a coordinated six-month forecast monthly. Details are available from NOAA GLERL’s Great Lakes water levels page.
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| Product or work | Horizon and schedule | What it represents |
|---|---|---|
| NOAA GLOFS | Nowcast and guidance up to 120 hours, four times per day (NOAA GLERL) | Operational, model-based forecast guidance; NOAA describes GLOFS as operated by CO-OPS. See NOAA Water Level Forecasting. |
| USACE–ECCC coordinated forecast | Six months, issued monthly | Lake-wide still-water elevations relative to International Great Lakes Datum 1985 (IGLD 1985), with a weather-related range and comparisons to long-term averages and historical extremes. USACE says its monthly bulletin has been published since 1952. See the six-month coordinated forecast. |
| USACE one-month forecast | One month, issued weekly | A shorter-horizon USACE forecast product. See the one-month forecast page. |
| AI approaches in published or developing work | Varies by study; includes monthly-level analysis and seasonal forecasting | Methods under evaluation, not identified in the cited operational descriptions as replacements for official products. See the 2026 ensemble study, the NOAA-affiliated forecasting presentation and the 2025 dual-transformer study. |
USACE also publishes future-scenario charts, but those are not the coordinated six-month forecast. The agency explicitly says, “This product is not an official forecast of Great Lakes water levels.” The charts illustrate possible outcomes using historical net-basin-supply sequences and current scenario conditions. See USACE’s Great Lakes water-level future scenarios.
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The monitoring network provides a broad observational basis for water-level work: NOAA reports 53 water-level gauges in U.S. Great Lakes waters and more than 500 river-level monitoring locations in the region. Those counts describe the network NOAA reports, not a claim that every measurement is an input to every forecast model. See NOAA’s overview.
What would show that an AI forecast is useful?
A convincing evaluation would compare an AI model with the existing operational products on the same lakes, forecast horizons and evaluation periods. A single accuracy figure is not enough: performance may differ by lake, season and whether levels are changing rapidly. Comparisons should also examine high-variance or extreme periods, which can be especially consequential for users.
- Forecast skill: Does it improve on an appropriate baseline at the same lead time, and does any improvement hold across lakes and seasons?
- Uncertainty: Does it communicate a useful range of possible levels rather than implying greater certainty than the inputs support?
- Explanation quality: Do feature attributions and timing analyses make sense alongside known water-balance processes?
- Operational readiness: Are inputs available promptly and consistently, and have accuracy, latency, oversight and failure handling been evaluated?
- Decision relevance: Does the forecast answer a real user need at a useful horizon, rather than simply performing well on a study’s historical data?
NOAA and USACE describe existing operational systems, while the cited AI work is presented as studies or developing approaches. The available descriptions therefore do not establish that these AI methods have replaced official systems, improved their operational accuracy, or prevent flooding. Claims of superiority would need to be supported by a specific, comparable evaluation.
What this could mean for people using Great Lakes forecasts
For now, readers seeking current water-level guidance should use the relevant official NOAA or USACE product and note its horizon, update schedule, reference datum and uncertainty presentation. AI may eventually help forecast teams extract more information from complex and lagged relationships, or make model behavior easier to inspect. Its practical value will depend on whether it adds reliable, validated skill and explanations that withstand comparison with established water-balance knowledge—not simply on using a more elaborate algorithm.
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