High-Speed Limnology: A Sensor Platform for Investigating Processes and Spatial Variability in Hydrology and Biogeochemistry

Monday, 15 December 2014
John T Crawford1,2, Luke C Loken1, Nora J Casson3, Emily H Stanley4, Robert G Striegl5 and Luke Winslow4, (1)University of Wisconsin Madison, Center for Limnology, Madison, WI, United States, (2)USGS, National Research Program, Boulder, CO, United States, (3)University of Winnipeg, Winnipeg, MB, Canada, (4)Univ Wisconsin, Madison, WI, United States, (5)USGS WRD, Boulder, CO, United States
Inland water sensor data acquisition is dominated by a fixed station reference frame, where water passes over a stationary sensor. These single point measurements are useful in understanding temporal patterns, but the assumptions needed to apply them to entire ecosystems are rarely tested. No single location within a stream reach or on a lake’s surface appropriately represents the inherent spatial variability, and extrapolations from these single points should be used with caution. In this context, we illustrate the approach of “taking the sensor to the water,” in either a quasi-Lagrangian or spatially explicit view. We present a sensor platform capable of rapid and dynamic (0-60 km hr-1) spatial sampling in navigable waters using multiple flow-through systems and a unique intake manifold connected to a small boat. We highlight data collected on medium-sized lakes (1.6 to 39 km2), and a heterogeneous reach of the upper Mississippi River (35 km). We use these datasets to infer hypolimnetic upwelling sensed from temperature and dissolved oxygen in a eutrophic lake and estimate the relative contributions of riverine and littoral methane production and epilimnetic transport. In addition to mapping the mixing dynamics of surface waters, we can estimate biogeochemical transformations by comparing multiple sensor outputs and applying conservative mixing models. We present apparent loss of riverine fDOM entering a eutrophic lake, effects of a constructed fringing wetland on solutes and nitrate cycling in a large river reach. This relatively simple platform uses sensors designed for the traditional fixed reference framework and will contribute to a new understanding of the spatial heterogeneity inherent in aquatic ecosystems.