NOAA Great Lakes Environmental Research Laboratory Blog

During recent fieldwork, Dr. Philip Chu, scientist at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and Professor Chin Wu, from the University of Wisconsin Madison, retrieved six water level sensors and one Acoustic Doppler Current Profiler (ADCP) from the Manistique River—a 71.2 mile long river in the Upper Peninsula of Michigan that drains into Lake Michigan.

An ADCP measures water currents with sound by using the Doppler effect— sound wave has a higher frequency, or pitch, when it moves toward you than it does when it moves away. Think of the Doppler effect in action the next time you hear a speeding train pass you by. As the train moves toward you, the pitch of its whistle will be higher. As it moves away, it will be lower. The same effect happens as sound moves through water. The ADCP emits pulses of sounds that bounce off of particles moving through the water. Particles that are moving toward the sensor will produce a higher frequency than those moving away from the sensor. This effect allows the profiler to record data about sediment transport in the river.

After quality control and assurance procedures back in the lab, currents and water level data collected during this deployment, scientists will use the information to research the impacts of meteotsunamis, seiches, and flooding events on sediment transport through the river. The outcomes of this research will then will be used by organizations, such as the U.S. Army Corps of Engineers, for dredging operations on the river with the ultimate goal of improving water quality. (See the Great Lakes Water Quality Agreement for more on why the Manistique River is considered an “Area of Concern.”)

In addition, researchers will use this valuable field data while validating the NOAA next generation Lake Michigan-Huron Operational Forecasting System, one of the forecast systems within the Great Lakes Operational Forecasting System, or GLOFS. GLOFS is a prediction system that provides timely information to lake carriers, mariners, port and beach managers, emergency response teams, and recreational boaters, surfers, and anglers through both nowcast and forecast guidance.

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Nowcast vs. Forecast: What’s the difference?

A nowcast is a description of the present lake conditions based on model simulations using observed meteorology. Nowcasts are generated every 6 hours and you can step backward in hourly increments to view conditions over the previous 48 hours, or view animations over this time period.

A forecast is a prediction of what will happen in the future. Our models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through these predictions in hourly increments, or view animations over this time period.

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Professor Wu, along with Dr. Eric Anderson from GLERL, deployed these sensors earlier this summer. As with the majority of GLERL’s projects, this is a collaborative effort. Through the Cooperative Institute for Limnology and Ecosystems Research (CILER), this work is supported by NOAA National Marine and Fishery Service and funded by EPA Great Lakes Restoration Initiative. The University of Wisconsin is one of ten CILER Consortium partners.

An early online release of GLERL researcher Brent Lofgren’s paper entitled “Physically Plausible Methods for Projecting Changes in Great Lakes Water Levels Under Climate Change Scenarios” can be found on the American Meteorological Society’s Journal of Hydrometeorology website.

In the paper, Dr. Lofgren and his co-author, Jonathan Rouhana, explore two different ways to model the effects of climate change on evapotranspiration (the movement of water from the land to the atmosphere as the combined result of evaporation and transpiration), and, subsequently, on the water levels of the Great Lakes.

Predicting how climate change will affect the water levels of the Great Lakes is a tricky business. To answer questions like this, it is often best to use models. Modeling is central to what scientists do, both in their research as well as when communicating their explanations. Within their models, scientists study relationships between variables in nature and then apply those relationships to possible future scenarios with one or more tweaked variables.

However, earth systems are so complex and have so many moving parts, that it’s almost impossible to capture them completely in an equation or series of equations. The beauty of modeling, is that it allows scientists to start with a small amount of data and, as time goes on, to build up a better and better representation of the phenomenon they are explaining or using for prediction.

Sometimes, particularly when modeling climate change, problems arise with so-called empirically-based models. Empirically-based models are created by making observations about two or more variables over a certain time period and under certain conditions, and inferring relationships from those observations. Often, those models don’t hold up when conditions change.

An alternative is physically-based models, which use the laws of physics (like conservation of mass, energy, etc.) to make predictions. Complexity is still a hurdle, but the laws of physics hold up no matter what—even when the climate changes.

Dr. Lofgren’s paper details issues with an empirically-based model widely used in Great Lakes research, the Large Basin Runoff Model (LBRM). From the abstract:

This model uses near-surface air temperature as a primary predictor of evapotranspiration (ET); as in previous published work, we show here that its very high sensitivity to temperature makes it overestimate ET in a way that is greatly at variance with the fundamental principle of conservation of energy at the land surface. The traditional formulation is characterized here as being equivalent to having several suns in the virtual sky created by LBRM.

Several suns in the sky – wow! In the most extreme case, this method of calculating evapotranspiration behaves as though there were 565 suns. 

In the context of climate modeling, “The LBRM oversimplifies the physics of the interaction between the earth and the atmosphere,” says Dr. Lofgren.

This doesn’t mean the LBRM isn’t useful in specific instances (e.g. short-term forecasting), or that you shouldn’t ever trust empirically-based models. It just means that different types of models have their place in different circumstances, and that the LBRM probably isn’t the best choice for modeling hydrologic response under climate change conditions.

Scientists often argue about the rightness of their model, and in the process, the model can evolve or even be rejected. Consequently, models are central to the process of knowledge-building.

Scientists who dare to create models know that their models will be scrutinized and tested. Research like Dr. Lofgren’s ensures not only that models are used appropriately with an acknowledgment of their limitations, but that they are continually improved upon.

Last week, the International Joint Commission featured an article on GLANSIS in their monthly newsletter.

Here’s a snippet:

A poster showing images of aquatic nonindigenous species established in the Great Lakes. Credit: NOAA