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What drives Great Lakes water levels?
A lake’s water level reflects the balance of water entering and leaving it. Net basin supply (NBS) combines precipitation directly onto the lake and runoff flowing into it, minus evaporation. Inflow and outflow through connecting channels also affect levels. The balance can shift with weather and with conditions in upstream and downstream lakes.
That makes prediction challenging: drivers interact, their effects may take time to appear, and their relative influence differs among lakes. Monitoring provides the observations needed to track changes. NOAA describes a binational monitoring network that includes 53 real-time water-level gauges in U.S. Great Lakes waters and over 500 river-level monitoring locations across the region. NOAA’s overview of Great Lakes water levels and monitoring explains the network.
How could AI predict water levels?
Machine-learning models can learn patterns from historical lake levels and related inputs, such as precipitation, evaporation, runoff, air temperature, and inflow and outflow. Rather than assuming each driver has a simple, fixed effect, a model can represent nonlinear relationships and delayed responses—for example, a weather-related change that affects a lake level over subsequent months.
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One approach predicts parts of the water balance, such as NBS, and then uses those estimates to forecast levels. NOAA-affiliated work describes developing this kind of subseasonal-to-annual framework with operational climate-model inputs; it presents a developing framework and early results, not a complete operational validation record. The NOAA GLERL and CIGLR presentation describes the approach.
Other research models lake levels more directly. A 2026 study tested tree-based ensemble methods using monthly records from 1982 through 2022. A 2025 study explored a dual-transformer deep-learning framework for seasonal forecasting. Their methods and evaluation setups differ, so they should not be treated as directly comparable demonstrations of forecast skill. The 2026 ensemble study and the 2025 dual-transformer study describe those research approaches.
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How can AI explain a forecast?
Some machine-learning methods are difficult to interpret from their output alone. Explanation techniques can estimate how much each input feature contributed to a particular prediction or to the model’s behavior overall. The 2026 ensemble study used SHAP to examine feature contributions and a lag-aware sensitivity method, VARS, to investigate the timing of drivers’ effects.
The study reports that inflow and outflow dominate overall, while secondary drivers and lag effects vary by lake. It identifies Ontario’s regulation and Erie’s upstream inflow as notable contrasts. These are findings from that study’s model and evaluation, not a universal ranking for every season or forecast horizon.
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Feature attribution is a description of how a model produced an estimate; it is not, by itself, proof that a feature physically caused a level change. A useful explanation should be checked against the water-budget processes, historical observations and established hydrologic understanding. It should also be presented with forecast uncertainty, not as a definitive account of what will happen.
How do AI methods fit with current forecasts?
Great Lakes water-level forecasts already cover multiple time horizons. NOAA GLERL’s Great Lakes Operational Forecast System (GLOFS) provides nowcast and forecast guidance up to 120 hours, updated four times per day. NOAA describes GLOFS as model-based and operated by CO-OPS; the NOAA GLERL water-level page provides information on levels and forecasting. NOAA says lake-wide averages are used to monitor and forecast the water budget; the listed record begins in 1918 because earlier gauge coverage was insufficient for a reasonable lake-wide mean.
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| Product | Horizon and schedule | What it provides |
|---|---|---|
| NOAA GLOFS | Nowcast and guidance up to 120 hours, issued four times daily | Operational model-based guidance. NOAA’s water-level forecasting overview describes the system. |
| USACE and Environment and Climate Change Canada coordinated forecast | Six months, issued monthly | Lake-wide still-water elevations relative to International Great Lakes Datum 1985 (IGLD 1985), with a range associated with weather variation and comparisons to long-term averages and historical extremes. The USACE bulletin has been published since 1952. The monthly coordinated forecast bulletin explains its contents. |
| USACE one-month forecast | One month, updated weekly | A shorter-term forecast product. USACE’s one-month forecast page provides the current product. |
USACE also publishes future-scenario charts, but those are not forecasts: the agency says, “This product is not an official forecast of Great Lakes water levels.” The charts illustrate possible outcomes using historical NBS sequences and current scenario conditions. USACE’s future-scenarios page explains the distinction.
The official product descriptions identify these operational systems, but do not identify the cited AI approaches as replacements. Research models should therefore be understood as developing methods, not as evidence that an official forecast has already been improved or superseded.
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What would show that an AI forecast is ready to use?
A convincing assessment needs to compare the AI model with relevant operational forecasts and established hydrologic or hydrodynamic models, using the same target, lead time and evaluation period. Aggregate performance alone is not enough: a model may perform differently across lakes, seasons and unusual high-variation periods.
- Forecast skill: Evaluate accuracy separately by lake, season and lead time, including periods of unusually large changes.
- Uncertainty: Check whether forecast ranges communicate the plausible spread of outcomes rather than implying false precision.
- Explanations: Test whether the identified drivers and timing are physically plausible, and make clear that feature attribution is not proof of causation.
- Operational readiness: Verify that input data arrive reliably and on time, forecasts update at useful intervals, and the system has validation and oversight appropriate to operational use.
Until those comparisons establish performance for a specific use, the defensible claim is that AI could add forecasting and explanation capabilities—not that it universally beats existing systems or prevents flooding.
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