Towards a Hydrodynamic and Optical Modeling Towards a Hydrodynamic and Optical Modeling System with Remote Sensing FeedbackSystem with Remote Sensing Feedback

Yan LiDr. Anthony Vodacek

Digital Imaging and Remote Sensing LaboratoryCenter for Imaging Science

Rochester Institute of Technology

April 5, 2006

OutlineOutline

• Objective• Methods

– Modeling (ALGE and Hydrolight 4.1)

– Remote sensing feedback

• Experimental Design & Data• Results• Summary

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

• 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

ALGE

Model outputSpatial data

Satellite imageGeo-referenced site specific

• Bathymetry

• Weather data

• Inflow and outflow

Basic Hydrolight World

air/water interface

bottom reflectance

CHLTSSCDOM

solar andatmosphericradiance

• 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

dwrs ELR /Lw: water leaving radiance

Ed: evaluated just above the water surface

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 Imageryor Lab Analysis

Study Area – Niagara River and Genesee River

Niagara RiverNiagara River Genesee RiverGenesee River

Plume Simulation Forcing Factors

Horizontal resolution (m)

Vertical resolution (m)

Time Prevailing wind direction

Average wind speed (m/s)

Discharge flow rate (m^3/s)

Niagara River Plume

325.0 3.0 June 6 ~ 15, 2004

west 6.1 7000.0

Genesee River Plume

135.0 3.0 June 6 ~ 15, 2004

west 4.7 2500.0

• 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

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)

DIRS capabilitiesfor field samplingand in-watermeasurements(Dr. Tony Vodacek)

HydroRad-4 spectroradiometer

HydroScat-2 backscatter meter

normalized to a(350)=1.0

CDOM no scattering

Chlorophyll has maximal absorption coefficients at 430 and 670 nm

Assuming chlorophyll scattering goes to zero soon after 700 nm

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

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

Genesee River Plume

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

visible thermal

plumeLake 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

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

Simulated Genesee River Plume

Suspended sediment concentration profile from ALGE (g/m^3)

0

5

10

15

20

25

30

35

10 11 12 13 14

TSS cnc. (g/m^3))

z (m

)

plume water

]2/)(exp[])2(/[)( 22max

5.00 zzhChlzChl

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

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 Ontario 0.76 0.57 0.57

Genesee River Plume 4.28 10.00 2.75

compare Rrs

The shaded bars at the bottom show the nominal

SeaWiFs sensor bands

Optical Identification of the PlumeLake Ontario

Genesee River Plume

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)