Evaluation of wireless sensor networks (WSNs) for remote wetland monitoring: design and initial results
- 523 Downloads
Here, we describe and evaluate two low-power wireless sensor networks (WSNs) designed to remotely monitor wetland hydrochemical dynamics over time scales ranging from minutes to decades. Each WSN (one student-built and one commercial) has multiple nodes to monitor water level, precipitation, evapotranspiration, temperature, and major solutes at user-defined time intervals. Both WSNs can be configured to report data in near real time via the internet. Based on deployments in two isolated wetlands, we report highly resolved water budgets, transient reversals of flow path, rates of transpiration from peatlands and the dynamics of chromophoric-dissolved organic matter and bulk ionic solutes (specific conductivity)—all on daily or subdaily time scales. Initial results indicate that direct precipitation and evapotranspiration dominate the hydrologic budget of both study wetlands, despite their relatively flat geomorphology and proximity to elevated uplands. Rates of transpiration from peatland sites were typically greater than evaporation from open waters but were more challenging to integrate spatially. Due to the high specific yield of peat, the hydrologic gradient between peatland and open water varied with precipitation events and intervening periods of dry out. The resultant flow path reversals implied that the flux of solutes across the riparian boundary varied over daily time scales. We conclude that WSNs can be deployed in remote wetland-dominated ecosystems at relatively low cost to assess the hydrochemical impacts of weather, climate, and other perturbations.
KeywordsWireless sensor networks Ecosystem observatories Wetlands Dissolved organic carbon Climate change
Funding was provided by the Wisconsin Focus on Energy-EERD Program (www.focusonenergy.com/Enviro-Econ-Research/) and the Wisconsin Department of Natural Resources. Logistical support was provided by the Global Lake Ecological Observatory Network (www.gleon.org) and by the North Temperate Lakes Long Term Ecological Research Project (www.lter.limnology.wisc.edu/). We thank JR Rubsam for technical assistance in the field and laboratory, and we thank Harry Hemond for helpful discussions of wetland processes. The prototype nodes in Crystal Bog were built by Sean Scannell and Steve Yazicioglu, undergraduates in Electrical and Computer Engineering at UW-Madison, under the direct supervision of ECE instructor Mike Morrow. This is a contribution from the Trout Lake Research Station, University of Wisconsin-Madison.
- Heinemeyer, A., Croft, S., Garnett, M. H., Gloor, E., Holden, J., Lomas, M. R., et al. (2010). The MILLENNIA peat cohort model: predicting past, present and future soil carbon budgets and fluxes under changing climates in peatlands. Climate Research, 45, 207–226. doi: 10.3354/cr00928.CrossRefGoogle Scholar
- Kido, M. H., Mundt, C. W., Montgomery, K. N., Asquith, A., Goodale, D. W., & Kaneshiro, K. Y. (2008). Integration of wireless sensor networks into cyberinfrastructure for monitoring Hawaiian “Mountain-to-Sea” environments. Environmental Management, 42(4), 658–666. doi: 10.1007/s00267-008-9164-9.CrossRefGoogle Scholar
- Kratz, T. K., Webster, K. E., Riera, J. L., Lewis, D. B., & Pollard, A. I. (2006). Making sense of the landscape: geomorphic legacies and the landscape position of lakes. In J. J. Magnuson, T. K. Kratz, & B. J. Benson (Eds.), Long-term dynamics of lakes in the landscape (pp. 49–66). New York: Oxford.Google Scholar
- Magnuson, J. J., Kratz, T. K., & Benson, B. J. (Eds.). (2006). Long-term dynamics of lakes in the landscape (Long-term Ecological Research Network Series). Oxford: Oxford University Press.Google Scholar
- Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., & Anderson, J. (2002). Wireless sensor networks for habitat monitoring. In: Proceedings of the1st ACM International Workshop on Wireless Sensor Networks and Applications (Atlanta, Sept.). New York: ACM Press. pp. 88–97.Google Scholar
- Mitra, S., Wassmann, R., & Vlek, P. l. G. (2005). An appraisal of global wetland area and its organic carbon stack. Current Science, 88(1), 25–35.Google Scholar
- Watras, C. J., Hanson, P. C., Stacy, T. L., Morrison, K. M., Mather, J., Hu, Y.-H., et al. (2011). A temperature compensation method for CDOM fluorescence sensors in freshwater. Limnology and Oceanography: Methods, 9, 296–301.Google Scholar
- Webster, K. E., Bowser, C. J., Anderson, M. P., & Lenters, J. D. (2006). Understanding the lake-groundwater system: just follow the water. In J. J. Magnuson, T. K. Kratz, & B. J. Benson (Eds.), Long-term dynamics of lakes in the landscape (Long-Term Ecological Research Network Series) (pp. 19–48). Oxford: Oxford University Press.Google Scholar
- White, W. N. (1932). A method of estimating ground-water supplies based on the discharge by plants and evaporation from soils: results of investigations in Escalante Valley, Utah. U.S. Geol. Surv. Water Supply Paper.Google Scholar