Remotely Sensed Estimates of Surface Salinity and Environmental Vibrio in the Chesapeake Bay

1. Remotely Sensed Estimates of Surface Salinity and Environmental Vibrio in the Chesapeake Bay Erin Urquhart1, Matt Hoffman2, Ben Zaitchik1, Seth Guikema1 1Johns Hopkins University, 2Rochester Institute of Technology

2. Chesapeake Bay • Salinity gradient • Oligohalic (0-6 ppt) • Mesohalic (6-18 ppt) • Polyhalic (18-30+ ppt) • Major inputs • Atlantic Ocean • Susquehanna River • 2-Layer gravitational circulation scheme Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

3. Vibrio in the Chesapeake Bay • V. cholerae • V. vulnificus • V. parahaemolyticus Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

4. Vibrio in the Chesapeake Bay * V. cholerae *V. vulnificus *V. parahaemolyticus Maryland Department of Health and Mental Hygiene Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

5. Pre-existing Empirical Vibrio Models z(V.v)= -7.867 + (0.316 * Temp) + (-0.342 * (|Saln- 11.5|) Jacobs et al., (2010) z(V.c)= -1.1939 + (0.1233 * Temp) – (0.1997 * Saln) – (0.0324 * (Temp * Saln) Louis et al., (2003) f(z)= ez/(1 + ez ) • Probability of occurrence Vibrio spp. models • In situ and modeled temperature and salinity inputs • Need for continuous spatial and temporal data • Use of satellite remote sensing • RS temperature • Satellite-derived salinity Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

6. Neural Network Surface Salinity • Statistically Derived from MODIS-Aqua Ocean Color • Additional RS band ratios • Trained on Mid-Atlantic region • Historical cruise data Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

7. Satellite-derived Salinity Algorithms • - MODIS-Aqua Ocean Color Standard Products • 10 Remote sensing reflectances (visible) • 2003-2010 • - In situ – remote sensed measurement matchups • 68 CBay Program in situ stations • Single pass RS ocean color data • 1km radius RS averaging • 2003-2010 - Salinity Prediction Models • GLM • GAM • ANN • MARS • CART • BCART • RF • BART - Cross- validation study Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

8. Satellite-derived Salinity Algorithms • Top performing prediction models: GAM andANN • All models outperform MEAN • GLM and GAM are fairly generalizable in a • cross-validation study Urquhart et al. (2012). Remotely Sensed Estimates of surface salinity in the Chesapeake Bay: A Statistical Approach. Remote Sensing of Environment. 123: 522-531 Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

9. Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

10. Geostatistical Spatial Interpolation • Prediction of values at unsampled locations based on measured values at locations within the area of interest • Spatial autocorrelation • Observations closer together tend to be more similar than observations farther apart. • Variogram • Quantifies the notion of spatial autocorrelation • Ordinary Kriging • Assumes mean of data is constant • . • Universal Kriging • Uses a general linear regression trend to estimate the mean • . Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

11. Interpolation Results Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

12. Interpolation Results Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

13. Model Comparison Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

14. Model Comparison Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

15. ModelComparison Summary Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

16. Remote Sensing of Vibrio in the Chesapeake Bay z(V.c)= -1.1939 + (0.1233 * Temp) – (0.1997 * Saln) – (0.0324 * (Temp * Saln) Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

17. Remote Sensing of Vibrio in the Chesapeake Bay Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

18. In situ Vibrio Sampling Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

19. Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

20. Vibrio Modeling in the Chesapeake Bay Qualitative - Likelihood of Presence *z(V.spp.)= β0 + β1(Temp) + β2(Saln) + βn(Xn) f(z)= ez/(1 + ez ) Quantitative - Estimation of Cell Concentrations * Zero-Inflated Poisson * Logistic/Tree- GAM Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes

21. Acknowledgments Johns Hopkins Applied Physics Lab, Carlos del Castillo Johns Hopkins University, Rebecca Murphy Cornell University, Dr. Bruce Monger University of Delaware, Erick Geiger University of Maryland, Bradd Haley, Elisa Taviani NASA Goddard, Molly Brown, Vanessa Escobar Funding Sources Johns Hopkins University

22. Interpolation Results Urquhart et al. Chesapeake Modeling Symposium 2012 May 21, 2012 Observations and Physical-Biogeochemical Modeling at the Fringes