1. Towards a Hydrodynamic and Optical Modeling System with Remote Sensing Feedback Yan Li Dr. Anthony Vodacek Digital Imaging and Remote Sensing Laboratory Center for Imaging Science Rochester Institute of Technology April 5, 2006

2. Outline Objective Methods –Modeling (ALGE and Hydrolight 4.1) –Remote sensing feedback Experimental Design & Data Results Summary

3. Objective High resolution plume simulations at the mouth of Niagara River and Genesee River to study the transport and the 3D distribution of CDOM and suspended sediments Spectral remote-sensing reflectance at various locations in the mouth of Genesee River was calculated Simulated remote-sensing reflectance compared to remote imagery to provide a feedback mechanism to the hydrodynamic model

4. 3D finite differencing hydrodynamic model solving momentum, mass and energy conservation equations Realistic predictions of movement and dissipation of plumes, sediments, and passive tracers discharged into lakes High resolution simulations for node-to-node matching with satellite thermal imagery or airborne imagery ALGE Model output Spatial data Satellite image Geo-referenced site specific Bathymetry Weather data Inflow and outflow

5. Basic Hydrolight World air/water interface bottom reflectance CHL TSS CDOM solar and atmospheric radiance

6. Radiative transfer numerical model Input  IOPs (absorption and scattering coefficients, scattering phase function)  state of the wind-blown air/water interface (wind speed)  sky spectral radiance distribution (built-in model/MODTRAN)  nature of the bottom boundary AOPs (remote sensing reflectance Rrs) Hydrolight L w : water leaving radiance E d : evaluated just above the water surface

7. Physical Forcing Inputs ALGE 3D Distribution of CDOM and TSS Algal Growth Model IOPs (a, b, bb) Hydrolight 4.1 Spectral Rrs or Radiance Remote Imagery (Plume) Remote Imagery or Lab Analysis

8. Study Area – Niagara River and Genesee River Niagara River Niagara River Genesee River Genesee River

9. Plume Simulation Forcing Factors Horizontal resolution (m) Vertical resolution (m) TimePrevailing wind direction Average wind speed (m/s) Discharge flow rate (m^3/s) Niagara River Plume 325.03.0June 6 ~ 15, 2004 west6.17000.0 Genesee River Plume 135.03.0June 6 ~ 15, 2004 west4.72500.0 Meteorological data was from Buffalo weather station Discharge flow rate was from US Army Corp. of Eng. Detroit District The high resolution, limited area simulations of the plume were nudged from large scale whole lake simulation TSS modeled as particles and CDOM modeled as passive tracers

10. a(760 nm) = 2.55 (where water absorbs maximally) a(430 nm) = 0.0144 (where water absorbs minimally) Absorption of red light is 177 times stronger than absorption of blue light Absorption coefficients: Pope and Fry (1997) Scattering coefficients: Smith and Baker (1981)

11. DIRS capabilities for field sampling and in-water measurements (Dr. Tony Vodacek) HydroRad-4 spectroradiometer HydroScat-2 backscatter meter normalized to a(350)=1.0 CDOM no scattering

12. Chlorophyll has maximal absorption coefficients at 430 and 670 nm Assuming chlorophyll scattering goes to zero soon after 700 nm

13. Specific absorption and scattering coefficients are determined by Dr. Vodacek from the May 20, 1999 Lake Ontario water samples Maximal absorption occurs at the lowest wavelengths (~ 350 nm) Absorption falls off rapidly as wavelength increasing Absorption is negligible beyond 500 nm

14. LANDSAT-7 visible image showing the Genesee River plume on June, 14 2004 (spatial resolution 30 m) Genesee River Plume

15. MODIS calibrated and geo-located radiance (L1B) image showing the Genesee River plume on June, 15 2004 (spatial resolution 250 m) Genesee River Plume Blue circle: plume water Green circle: lake water

16. visiblethermal plume Lake Ontario Airborne line scanner 70 VNIR channels 5 thermal channels nominal 2 milliradian FOV (20ft GSD at 10,000ft) sharpening bands in VIS and LWIR Modular Imaging Spectrometer Instrument (MISI) LWIR thermal band detecting the upwelling track caused by boat traffic Plume traveling northward because of calm wind conditions on June 7, 2004 Westward track of the plume shown in MODIS image due to prevailing wind from the east

17. Murthy, C.R., and K.C. Miners. 1989. Mixing characteristics of the Niagara River plume in Lake Ontario. Water Pollution Research Journal of Canada 24(1):143-162. Niagara River Plume shown by simulated surface flow currents and passive tracer

18. Simulated Genesee River Plume Suspended sediment concentration profile from ALGE (g/m^3) plume water

19. CHL profile (Chl0 = 4.2, Zmax = 100, h = 7.5, = 3.0) CDOM absorption as an exponential function of both wavelength and depth Genesee River Plume

20. Water Quality Conditions Concentrations (Hydrolight variables) estimated from laboratory analysis on water samples CHL (mg/m^3)TSS (g/m^3)CDOM (absorption at 350 nm) Lake Ontario0.760.57 Genesee River Plume4.2810.002.75

21. compare Rrs The shaded bars at the bottom show the nominal SeaWiFs sensor bands Optical Identification of the Plume Lake Ontario Genesee River Plume

22. Summary Future work High resolution hydrodynamic simulations showing the spread of plumes Simulated vertical profile of suspended sediment from ALGE Spectral Rrs simulated from lab analysis showing the optical identification of plume Study of remote satellite/airborne imagery (LANDSAT-7, MODIS, MISI) Modify ALGE to be spectral on shortwave range (CDOM) More optical property data for Niagara River Plume Retrieve more spectral information from remote satellite/airborne imagery (LANDSAT-7, MODIS, MISI)