National Science Foundation
12/01/2006 – 11/30/2009
Graham, Wendy Dimbero
Cohen, Matthew J
Delfino, Joseph J
Martin, Jonathan Bowman
Slatton, Kenneth Clint
Goals and Objectives
Watershed characterization requires well-planned sampling to track simultaneous time-variable fluxes and flowpaths of water, nutrients, sediments, and energy. In this research project legacy hydrologic, meteorologic and water quality data from the Santa Fe basin in the Suwannee river watershed will be assembled into a web-accessible digital watershed. These data, together with predictions from physically-based hydrologic models, will be used to develop a probabilistic algorithm to predict surface water stage and flux throughout the Santa Fe river basin and evaluate the accuracy of these predictions. Information on prediction uncertainty will be used to design a spatial network of new conductivity-temperature-depth (CTD) sensors to improve the predictions. The adequacy of the assembled data and the utility of the optimal estimation algorithm will be evaluated by comparing resulting predictions with observations of surface water stage and flux from the newly deployed CTD sondes.
In addition, off-the-shelf optical and cadmium reduction continuous nitrate sensors will be deployed at selected locations in the Santa Fe watershed to develop improved local relationships among flow, stage, conductivity and nitrate. This new knowledge will lay the groundwork for developing a general methodology to augment continuous measurement of nitrate with correlated surrogates (i.e., in this case flow and conductivity) to decrease the density of sensors needed to accurately predict the nitrate in the system over space and time. Furthermore, the data will also lay the groundwork for developing improved understanding of the chemical and physical controls of nitrate and water fluxes through watersheds required to address science questions that cannot be answered with current data sampling and monitoring programs.
Title: A Method for Measuring the Incremental Information Contributed from Non-Stationary Spatio-Temporal Data to Be Fused, doi: 10.1109/IGARSS.2008.4778977, vol. 2, pp. II-261 – II-264, Proc. IEEE 2008 IGARRS.
Authors: Carolyn Krekeler, Karthik Nagarajan, K. Clint Slatton
Title: A Scalable Approach to Fusing Spatiotemporal Data to Estimate Streamflow via a Bayesian Network
Authors: Nagarajan, K.; Krekeler, C.; Slatton, K.C.; Graham, W.D.
Title: Direct and indirect coupling of primary production and diel nitrate dynamics in a subtropical spring-fed river
Authors: James B. Heffernana,b and Matthew J. Cohen
Title: Environmentally-mediated consumer control of algal proliferation in Florida springs
Authors: Dina M. Liebowitz,Matthew J. Cohen, James B. Heffernan, Lawrence V. Korhnak and Thomas K. Frazer
Title: Hydrologic and biotic influences on nitrate removal in a subtropical spring-fed river. Limnol. Oceonogr. 55(1), 2010, 249-263.
Authors: Heffernan, J.B., M.J. Cohen, T.K. Frazer, R.G. Thomas, T.J. Rayfield, J. Gulley, J.B. Martin, J.J. Delfino, W.D. Graham
Title: Inferring nitrogen removal in large rivers from high-resolution longitudinal profiling
Authors: Robert T. Hensley, Matthew J. Cohen, and Larry V. Korhnak
Title: Probabilistic Fusion of spatio-temporal data to estimate stream flow via Bayesian belief networks,pp.4870 – 4873, doi: 10.1109/IGARSS.2007.4423952. Proc. IEEE 2007 IGARRS.
Authors: K. Nagarajan, C. Krekeler, K. C. Slatton