1. Ecological Nowcasting in Chesapeake Bay
Christopher Brown
NOAA Satellite Climate Studies Branch
CICS - ESSIC
University of Maryland, College Park

2. Importance of Coastal Ocean Monitoring & Prediction
National Goal
Congress initiated efforts to establish a coastal monitoring system and develop coastal hydrodynamic models
NOAA Goal
VADM Lautenbacher stated that an ecosystem assessment and prediction capability was a critical NOAA to provide information on coastal and marine ecosystems
He also wrote that by 2011 NOAA “should be able to forecast routinely the extent and impact of critical ecosystem events, such as harmful algal blooms”
Biological Oceanography Goal
Develop the understanding and the means to detect and predict distribution pattern of organisms

3. Motivation for Study
Detect and predict distribution pattern of organisms that affect society, both beneficial and harmful
Few existing methods work well and in near-real time
Bloom of the coccolithophorid Emiliania huxleyi in the Barents Sea in July 2003 in SeaWiFS imagery. Image courtesy of NASA SeaWiFS Project and OrbImage.

4. Approaches for Predicting Organisms
Process-Oriented or Mechanistic Modeling
Empirical or Statistical Modeling

5. Mechanistic Modeling

6. Statistical Modeling Develop multi-variate empirical habitat models
Quantitatively define the preferred environmental conditions of the organism
Based on Concept of Ecological Niche
Identify the geographic locations where ambient conditions coincide with the preferred habitat of target organism

7. Hybrid Statistical – Mechanistic Approach
Develop multi-variate empirical habitat models
Drive habitat models using real-time data acquired from a variety of sources
Habitat Model

8. Hybrid Statistical – Mechanistic Ecological Approach
Old technique employed in new way
GAP Analysis: retrospective analysis
Ecological Nowcasting: near-real time

9. Ecological Nowcasting In Chesapeake Bay
Currently generate nowcasts of two species in Chesapeake Bay
Sea Nettles, Chrysaora quinquecirrha
Dinoflagellate Karlodinium micrum
Chance of encountering sea nettle, C. quinquecirrha, on August 15, 2004
Relative abundance of the harmful algal bloom K. micrum on May 27, 2004

10. Nowcasting Sea Nettle Distributions in Chesapeake Bay: An Overview
C. W. Brown1, R. R. Hood2, T. Gross3, Z. Li3, M.-B. Decker2, J. Purcell2 and H. Wang4
1NOAA/NESDIS Office of Research & Applications
2Horn Point Laboratory, UMCES
3NOAA/NOS Coast Survey Development Laboratory
4VIMS, College of William and Mary
Funded by NORS Grant, Maryland SeaGrant, NCCOS EcoFore 04
Chrysaora quinquecirrha
(Photo by Rob Condon)

11. Introduction: Sea Nettles
Chrysaora ephyra and medusa seasonally populate Chesapeake Bay
Chrysaora is biologically important and impacts recreational activities
Knowing the distribution of Chrysaora would provide valuable information
strobila
polyp
ephyra
scyphistoma
larva
egg
juvenile
medusa (adult)
From: T.L. Bryant and J.R. Pennock (eds) The Delaware Estuary: Rediscovering a Forgotten Resource. University of Delaware Sea Grant College Program. Newark, DE.
Life Cycle of Chrysaora

12. Sea Nettle Nowcasting Procedure
Estimate current surface salinity and temperature fields
Georeference salinity and SST fields
Apply habitat model
Generate image illustrating the likelihood of encounter of Chrysaora
Salinity
SST
Likelihood of Chrysaora
Habitat Model

13. Surface Salinity 35 30 25 20 15 10 5
Generated using hydrodynamic model developed for the Chesapeake Bay
Model forced using near-real time input
Model attributes:
Horizontal Resolution: 1-5 kilometers
Vertical Resolution: 1.52 meters
Error: ppt
Model generated surface salinity in Chesapeake Bay for April 20, 2005

14. Sea-Surface Temperature35 30 25 20 15 10 5
Two Sources:
Generated by hydrodynamic model
Error: °C
Derived from NOAA AVHRR satellite imagery
Resolution: 1 km
Weekly composite
Bias: 0.5 °C; STD: 1.0°C
Sea-surface Temperature (ºC)
Model generated sea-surface temperature in Chesapeake Bay for April 20, 2005

15. Sea Nettle Habitat Model
Models developed to predict:
Probability of encountering Chrysaora
Density of Chrysaora
Analyzed relationship between Chrysaora, salinity and sea-surface temperature
Samples collected in surface waters (0 –10 m) of Chesapeake Bay (n = 1064)
2/3 model training
1/3 model testing

16. Sea Nettle Habitat
Nettle medusa occupy narrow temperature (26-31 °C) and salinity (10-16 PSU) range. Salinity optimum = 13.5 PSU.

17. Probability of Encountering Sea Nettles
Combination of salinity and SST is a good predictor of Chrysaora presence
If SST < 34°C:
p = elogit / (elogit + 1),
where,
logit = (0.351*SST) - (0.572* |SAL |)
Hosmer-Lemeshow Goodness of Fit P = 0.493

20. Nowcasting the Relative Abundance of Karlodinium micrum in Chesapeake Bay
Christopher W. Brown1, Douglas L. Ramers2, Thomas F. Gross3, Raleigh R. Hood4, Peter J. Tango5 and Bruce D. Michael5
1NOAA, 2University Of Evansville, 3NOAA & Chesapeake Research Consortium, 4University of Maryland Center for Environmental Science – Horn Point Laboratory, 5Maryland Department of Natural Resources
Project Funded by NOS MERHAB Program

21. Photomicrograph of the dinoflagellate Karlodinium micrum.
A common estuarine dinoflagellate found along the U.S. East Coast
Seasonally abundant in Chesapeake Bay
Contributed to several fish kills in Chesapeake Bay
Significant blooms confined to a relatively narrow range of salinity and temperature
Photomicrograph of the dinoflagellate Karlodinium micrum.

22. K. micrum Nowcasting Procedure
Estimate current surface salinity and temperature fields
Georeference salinity and SST fields
Apply habitat model
Generate image illustrating the relative abundance of K. micrum
Salinity
SST
Relative Abundance of
K. micrum
Habitat Model

23. Habitat Model
Neural Network (NN) employs sea surface temperature, salinity and month to predict the relative abundance of K. micrum at low, medium and high or “bloom” concentrations
NN trained with samples (n = 151) of in-situ K. micrum abundance and various environmental variables
A test data set (n = 81) was extracted from the available data to assess the model’s performance

24. Schematic Representation of Neural Network
Input Layer
Hidden Layer
Output Layer X1 X2 X3 Xn I N P U T S X
wij
Xi * wij
PE1
f (W h X + b)
PE2
PEm h
PE out
Classify a= 1 -1
aPE
= foutput(Woutputhfhidden (Whidden h X + bhidden) + boutput)

25. Issues and Advantages of Neural Networks
“Black Box”
Advantages & Uses
Useful for representing and processing inexact and sparse data and for performing approximate reasoning over uncertain knowledge and ill-defined problems
Useful in discerning patterns and relationships
No a-priori distribution assumed

26. K. micrum Neural Network Performance

27. Nowcast vs. In-Situ Comparison
May 27, Nowcast
May 23-26, In-situ
0-10 cells/ml
cells/ml
>2000 cells/ml

28. Nowcast WWW Sites
Sea Nettle and K. micrum nowcasts are generated daily and are available on the World Wide Web.

29. Future Directions and Work
Continue nowcast validation and refine habitat models of Chrysaora and Karlodinium
Develop habitat models for additional HAB species in Chesapeake Bay
Incorporate additional environmental variables into habitat models and nowcast system to enhance HAB prediction capability
Generate historical distribution patterns of occurrence and relative abundance from retrospective salinity and temperature to document interannual variability

30. Issues With Empirical Approach
Empirical models are specific for each location and population
Development of empirical models require sufficient number of samples
Species acclimate to environment, i.e. habitat model may change

31. Regional Ecosystem Modeling
Objective: Develop a fully integrated, bio-physical model of Chesapeake Bay and its watershed that assimilates in-situ and satellite-derived data.
Purpose:
Near-Real Time Applications: Nowcasting and forecasting of marine organisms, ocean health, and coastal conditions
Climate Research: Estimating effect of climate change on the health of coastal marine ecosystems
Partners: NOAA, CICS-ESSIC, other UMD departments, Meteorology, and programs, e.g. UMCES.
SeaWiFS True-Color Image of Mid-Atlantic Region
from April 12, 1998.
Image provided by the SeaWiFS Project, NASA/Goddard Space Flight Center and ORBIMAGE

35. Regional Ecosystem Model Plans & Objectives
Develop transportable modeling system that can be modified for other regions
Chesapeake Bay used as “test bed” site due to extensive in-situ data for verification
Employ satellite imagery in system for monitoring, model forcing and data assimilation to permit use in locations where in-situ assets are limited

36. Advanced Study Institute for Environmental Prediction
Institute dedicated to research on environmental prediction and monitoring
Perform research and provide core support to determine what present and future observations need to be sustained beyond numerical weather prediction in support of Earth system predictive models, crops models, and predictive disease models
Staffed by personnel from NOAA, NASA Goddard, and the University of Maryland
$1.5M budgeted for Institute in FY Science, State, Justice and Commerce Appropriations conference report

37. Thank You!

38. Relationships between observed and model-predicted medusae (a), sea-surface temperature (b) and salinity (c) at the Horn Point Laboratory (closed circles) and Chesapeake Bay Laboratory (open circles) piers during the period May 1 – Oct 31, Lines were determined by linear regression.

39. Interannual Variability
Probability of Encountering C. quinquecirrha
July 25, 1996
July 29, 1999
Likelihood of Encountering C. quinquecirrha
in July 1996 and 1999

40. Vibrio cholerae
Presence predicted as function of water temperature and salinity (Louis et al., 2003)
Association with plankton
Electron photomicrograph of Vibrio cholerae: curved rods with polar flagellum.