H53L-04:
A sensor-based energy balance method for the distributed estimation of evaporation over the North American Great Lakes
Friday, 19 December 2014: 2:25 PM
Kevin John Fries1, Branko Kerkez1, Andrew Gronewold2 and John D Lenters3, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)NOAA Ann Arbor, Ann Arbor, MI, United States, (3)LimnoTech, Ann Arbor, MI, United States
Abstract:
We introduce a novel energy balance method to estimate evaporation across large lakes using real-time data from moored buoys and mobile, satellite-tracked drifters. Our work is motivated by the need to improve our understanding of the water balance of the Laurentian Great Lakes basin, a complex hydrologic system that comprises 90% of the United States’ and 20% of the world’s fresh surface water. Recently, the lakes experienced record-setting water level drops despite above-average precipitation, and given that lake surface area comprises nearly one third of the entire basin, evaporation is suspected to be the primary driver behind the decrease in water levels. There has historically been a need to measure evaporation over the Great Lakes, and recent hydrological phenomena (including not only record low levels, but also extreme changes in ice cover and surface water temperatures) underscore the urgency of addressing that need. Our method tracks the energy fluxes of the lake system – namely net radiation, heat storage and advection, and Bowen ratio. By measuring each of these energy budget terms and combining the results with mass-transfer based estimates, we can calculate real-time evaporation rates on sub-hourly timescales. To mitigate the cost prohibitive nature of large-scale, distributed energy flux measurements, we present a novel approach in which we leverage existing investments in seasonal buoys (which, while providing intensive, high quality data, are costly and sparsely distributed across the surface of the Great Lakes) and then integrate data from less costly satellite-tracked drifter data. The result is an unprecedented, hierarchical sensor and modeling architecture that can be used to derive estimates of evaporation in real-time through cloud-based computing. We discuss recent deployments of sensor-equipped buoys and drifters, which are beginning to provide us with some of the first in situ measurements of overlake evaporation from Earth’s largest lake system, opening up the potential for improved and integrated monitoring and modeling of the Great Lakes water budget.