Acoustic
Environment of the Haro Strait:
Preliminary
propagation modeling and data analysis
Christopher D. Jones,
Michael A. Wolfson
Applied Physics Laboratory
University of Washington
Seattle, WA 98105
Date: April 2006
Abstract: This report presents a preliminary analysis of the acoustic environments of the southern resident killer whales in the Haro Strait of the Puget Sound, combining analysis of field measurements and acoustic propagation modeling for the frequency range 1-10 kHz. The Haro Strait is a highly variable acoustic environment with active commercial shipping, whale watching, and Naval activity. Southern resident killer whales are of unique public concern in this area because of increasing anthropogenic noise levels that may interfere with the animalÕs foraging strategies and behavior. Predictive acoustic modeling in combination with field measurements can be used as a tool for understanding the mechanisms of impact and assessment of the risk, providing a quantitative evaluation of sound source levels in the context of complicated acoustic environments, changing background sound levels, and emerging management issues. Of principle concern in this report is background sound levels created by commercial shipping traffic or other persistent sound sources that propagate from the main shipping channel. The scope of the modeling effort encompasses numerical modeling of transmission loss and propagation at ranges of less than 10 km. Preliminary modeling results are analyzed and compared with recordings of ship noise collected in the spring/summer of 2004.
Contents
1. Introduction
2. Environmental Characterization
2.1. Bathymetry
2.3. Sound Speed Profiles
3. Ship Traffic
3.1. Vessel Tracking Operations Support System (VTOSS)
4. Measurements of Underwater Sound
4.1. Passive Aquatic Listeners (PAL)
4.2. PAL Deployment in the Haro Strait
4.3. Ship Signatures
5. Acoustic Propagation Modeling
5.1. Model Description
5.2. Model Inputs
5.2.1. Geo-acoustic Parameters of the Sea Floor
5.2.2. Sound Speed Profile
5.2.3. Rough Sea Surface and Sea Floor
5.2.4. Monte-Carlo Simulations
5.3. Model Outputs
5.4. Comparison of Model Results with Field Measurements
5.4.1. Single Ship Comparisons
5.4.2. Shipping Lanes
6.2. Estimation of Total Shipping Noise
1. Introduction
The Haro Strait is a complex, shallow water acoustic environment with steep bathymetric relief combined with an active shipping channel, frequent small boat activity, and Naval operations. The western side of San Juan Island is also a primary foraging area for the Southern Resident killer whales[1]. Consequently, these animals are of unique public concern in this area because of the potentially high impact of human activity on their environment. Questions regarding the acoustic environment of these animals have arisen as recreational whale watching, commercial shipping, and Naval activity[2] have grown in this area. A reasonable question to ask in this context is whether increasing underwater noise levels affect the killer whaleÕs ability to forage for prey by echo-location. For example, the analysis of the echo-location signals from killer whales[3] indicate that backscattered signal levels from salmon can be very low and comparable in level to natural background noise levels.
This report will address specific aspects of modeling the propagation of sound sources in the Haro Strait, focusing on the numerical estimation of transmission loss in the open channel. In particular, we will investigate the propagation of sound generated by large commercial ship traffic in the Strait and the estimation of sound source levels of individual ships. We will illustrate the role of modeling as a tool for model/data comparisons and the interpretation of field measurements of underwater sound. In this process we will employ a variety of compiled databases of the environment, information on ship traffic and vessel tracking, and field measurements of underwater noise collected recently in the Haro Strait in an area frequented by killer whales.
For purposes of this report, acoustic modeling is used to complement field measurements, as the shallow water environment of the Haro Strait is far too complex, and the geo-acoustic parameters of the area are not characterized well enough to rely on modeling alone. When modeling is constrained by measurements it can provide a useful tool to fill the gaps in measurements in both space and time. For example, measured data will be shown for a specific receiver location and time, and modeling results will be compared with this data to infer source levels of individual large commercial ships. If model results compare favorably and confidence is developed in the modeling strategy for this particular area, then the model may be used to estimate sound pressure levels at other locations in the region where measurements are not available.
Objectives and Scope:
The objective of the this report is to determine the feasibility of modeling the sound propagation environment of the southern resident killer whales in the Haro Strait and to compare initial model results with acoustic measurements taken in June and July 2004. Model/data analysis is limited to data provided by Jeff Nystuen recorded on the PAL system.[4] A significant portion of this effort involves collecting and compiling a database of environmental parameters required for acoustic modeling. The longer-term objectives are to extend these methodologies for model/data analysis by incorporating new acoustic data, more detailed environmental data, and new information on sound sources (e.g. shipping data) as they become available.
The scope of the modeling effort encompasses propagation modeling using readily available methods and codes[5] and the interpretation of existing acoustic data sets. Modeling and data analysis are focused at the frequency of 3.6 kHz, which is representative of the frequency range of 1-10 kHz (within killer whale auditory response). The modeling can be extended to lower frequencies. However, extending the models to higher frequencies (>10 kHz) is problematic due to the sensitivity of the model to uncertainties in the geo-acoustic environment at high spatial scales. Modeling high frequency propagation (>10 kHz) and reverberation is beyond the scope of this study.
The area of interest will be limited to the Haro Strait with propagation ranges less than 10 kilometers. However, the methods can be applied to larger scale studies such as in the coastal ocean or different regions (e.g. beaked whale habitat). We will investigate the effects of canyons and steep walls on forward propagation combined with randomness in the sea surface and the seafloor. We will include the effects of temporal and spatial variability in the environment to model and gain insight on how sound propagation may change as a function of time and location.
Technical Approach:
For the purpose of this report, the acoustic environment is characterized by the propagation loss only. Defined in terms of the standard sonar equation,[6] propagation loss is the amount of signal intensity lost as it propagates from a source to a receiver location. The numerical simulations will provide an estimate of the mean propagation loss between two positions and the variability of the estimate as a function of randomness and uncertainty in the environment. Both the mean and the associated variability (uncertainty bounds) of the estimate are necessary when comparing simulation results with field measurements.
In general, propagation between two locations in the ocean includes both the direct propagation path between a source and a receiver and reverberation. Reverberation is the reflection and scattering of an acoustic signal as a result of its interaction with inhomogeneities and boundaries in the ocean. In this report acoustic propagation modeling is performed using two-dimensional parabolic equation (PE) numerical methods.[7] This type of propagation modeling includes only that component of reverberation in the forward direction, such as forward scattering from the sea surface and bottom. No backscattering is included, which includes the echoes back from a canyon wall, for example.
The application of PE simulations, as typically used in lower frequency, open-ocean modeling, requires special attention when used in shallow water environments. Improper application will likely produce results that will be difficult to compare with field measurements. Modeling issues that are given special attention include:
1) Effects of random roughness at the sea surface and sea bottom that will impact propagation at the frequencies of interest in this study.
2) Analysis of acoustic variability due to such randomness and the definition of uncertainty bounds for the model predictions.
3) High spatial resolution characterization of the geo-acoustic parameters (i.e. sediment properties, high resolution bathymetry, sound speed profiles).
2. Environmental Characterization
2.1 Bathymetry
The bathymetry of Haro Strait is characterized by a relatively deep canyon with a very steep wall at the western coast of San Juan Island. The channel rises to a relatively shallow region on the west side of the channel (Figure 2.1 and 2.2). Since bathymetry is a critical component of understanding acoustic propagation, a high-resolution bathymetry database of Haro Strait has been compiled for the Haro Strait region comprised of data from several sources. The highest resolution bathymetry (to our knowledge) is a recent multi-beam survey conducted by MBML[8] providing partial coverage of the area of interest with a 5 meter grid spacing resolution. The other primary source of lower resolution bathymetric data include NOAA and USGS.[9] These data where combined to provide a continuous bathymetric grid of the region at grid scales up to 5 meters. In practice, 20 meter grid spacing was adequate for modeling.
Figure 2.1 Perspective view of the bathymetry of the Haro Strait.
Figure 2.2 Bathymetry of the Haro Strait, 20 meter contours.
2.2 Geo-Acoustic parameters
The Haro Strait was glacially formed. The steep walls about the west side of San Juan Island are exposed rock. Silt and sand material lie on the bottom of the channel, but because of the strong and variable currents, the thickness of this silty sediment layer will vary temporally and spatially. More precise details of the bottom properties are known for very small sections of Haro Strait as a result of recent geo-acoustic inverse studies[10]. Here the bottom was surveyed using echo sounders, and sediment samples taken with grab samples and cores.
Since the bottom is likely variable and little data is available, we modeled the geo-acoustic environment with three different parameterizations, each within a bound we felt reasonable from the limited amount of geology known and measurements taken. Crudely speaking, the bottom acts as a sink of acoustic energy, with silt and sand absorbing much more sound energy than hard rock. The limiting cases are a thick layer of silt (thick compared to the wavelength of the sound waves) and exposed hard rock. In the case of hard exposed rock with large slopes, such as off the west coast of San Juan Island, backscattering and reverberation can be an important concern, but is beyond the scope of the present study. We also do not include erratic blocks in the geo-acoustic modeling as their population density is expected to be too low.
For the acoustic propagation model the relevant geo-acoustic parameters are 1) the sound speed profile, 2) the density profile of the bottom, and 3) bottom attenuation profile. Since we are mainly concerned with the deep channel of Haro Strait, we assumed a nominal sediment thickness of twenty-five meters and used a critical slope of fourteen degrees to set the bottom type to either a sand-mud-gravel composition or exposed rock. In other words, if the slope of the bottom (determined from the bathymetric data described in Section 2.1) is greater than this critical slope, we assumed the bottom would be scoured by the strong tidal currents so that it would remain as exposed rock. Otherwise the material is assumed to be a layer of sand, gravel, and mud. Tables in Section 5.2 summarize the geo-acoustic parameterization used for the modeling, as discussed further in Section 5.
2.3 Sound Speed profiles
Conductivity, temperature and depth (CTD) data were collected over many years in the Haro Strait region. We obtained this data for the last twenty years from DFO-Canada.[11] There were seventy CTD locations for the months of May and June, the time during which the acoustic data was collected; see Figure 2.3. Of these CTD locations, only locations that were within fifteen kilometers of the receiver location were used to produce sound speed profiles according to the equation of state given by Del Grosso.[12] The resulting sound speed profiles from May and June data taken over a twelve year span in the Haro Strait are shown in Figure 2.4. As seen in Figure 2.4, the sound speed profiles reveal a nearly invariant nature with both geographic location and detph. Seasonal variations exist (not shown), and may be of interest for future studies. Considering the short ranges of propagation (< 10 km) along with the very weak variation in the sound speed environment (< 0.5 %), we feel justified in using a single sound speed profile for our modeling. We chose 1480 m/s for our sound speed in our propagation model (see Section 5.).
Figure 2.3. Map of Haro Strait showing positions where temperature and conductivity data (CTD) were collected.
Figure 2.4. Sound speed profiles derived from CTD casts collected in May and June between the years spanning 1990 to 2002. Only profiles that spanned the full water column are show. The average of these profiles is shown in green.
3. Ship Traffic
3.1 Vessel Tracking System (VTOSS)
The Marine Communications and Traffic Services of the Canadian Coast Guard operate the proprietary Vessel Traffic Operations Support System (VTOSS). VTOSS collects radar signatures of vessels greater than twenty meters length, providing position, course, and speed. The system also collects Ôelectronic handoffÕ data from vessels that are approaching within one hour of an exchange line or upon departure for vessels that are berthed one hour from an exchange line. The handoff data include the vessel name, call sign, type, number and type of barges (loaded or empty), port of origin and destination, speed, exchange line time of arrival estimate, and possibly additional information that might be useful to MCTS in regards to safe-guarding vessel traffic. Brian Bain, the Officer in Charge of MCTS Victoria, gave us permission to use data from radar signatures of ships that pass in the vicinity of Haro Strait. Ian Wade (DFO, A/OIC Victoria MCTS Centre) provided us a Ôxbase databaseÕ file of vessel tracking data that we parced for the data fields we desired. The database file contained a maximum of twenty-eight fields of data for ships operating in the Haro Strait region over the period of May 27 to June 30, 2004 (note May 26, June 9,19, and 20 were not supplied due to software errors).
Typically, only 20 fields held data. Table 3.1 shows the field labels and two examples from two different ships. The first field signifies the date and time the radar signature was recorded; it is stored in the format Ôyyyy mm dd hhmmÕ (year/month/hour/minute), and the signatures are recorded in six minute intervals. Other fields we used were the ship name (field 3. in Table 3.1), the ship latitude and longitude (fields 17. and 18. respectively in Table 3.1), and course and speed (fields 27. and 28. respectively in Table 3.1). The original data file was quite large of order 100 MB, so we separated it into smaller files according to the day. These were then filtered to obtain ship tracks, which were used for our data model comparisons described in Section 5.
The VTOSS database comprises a relatively complete record of large ship traffic in the region. Figure 3.1 illustrates categorization of traffic by ship type during the period of interest (of May 27 to June 30, 2004). Figures 3.2 illustrate the geographic sorting of vessel tracks by type during this same period. Figure 3.3 illustrates an estimate of the mean north and south shipping lanes for commercial cargo ships (not including Tugs).
|
field # |
Field label |
example #1 |
example #2 |
|
1. |
LAST_UDDTG |
"200405170005", |
"200405170005", |
|
2. |
VSL_ID |
"CSTL19931231000495", |
"CSTL19931231000494", |
|
3. |
NAME |
"JACQUES CARTIER", |
"CAPTAIN COOK", |
|
4. |
CALLSIGN |
"CY6103", |
"CY7903", |
|
5. |
LLOYDS_ID |
"0314837", |
"6613483", |
|
6. |
FLAG |
"CA", |
"CA", |
|
7. |
SATCOMNUM |
"A", |
"A", |
|
8. |
TYPE_ENC |
, |
, |
|
9. |
TYPE_DEC |
"TUG", |
ÒTUG", |
|
10. |
LOA |
19.40, |
22.90, |
|
11. |
GRT |
72.00, |
124.00 |
|
12. |
TOW_ENC |
"1HE", |
"1 EMPTY BULK BARGE", |
|
13. |
TOW_DEC |
, |
|
|
14. |
IS_DC |
, |
, |
|
15. |
IS_DD |
, |
, |
|
16. |
IS_SPI |
, |
, |
|
17. |
POS_LAT |
49.42, |
49.36, |
|
18. |
POS_LON |
123.96, |
123.90, |
|
19. |
POS_RDRDTG |
"200405170005", |
"200405170005", |
|
20. |
POS_CIP |
, |
, |
|
21. |
POS_CIPDTG |
, |
, |
|
22. |
POS_SRC |
, |
, |
|
23. |
CVTOSS_ZONE |
"RDR", |
ÒRDR", |
|
24. |
FROM_AT |
"VIC", |
ÒVIC", |
|
25. |
NEXT_TO |
"LAF", |
ÒLAF", |
|
26. |
SERVICE |
"JER", |
ÒMID", |
|
27. |
COURSE |
251.00, |
298.00, |
|
28. |
SPEED |
15.6 |
8.3 |
Table 3.1.
Examples from two lines of the VTOSS data file.
|
SHIP TYPE |
COUNT |
PERCENTAGE |
|
BULK CARRIER |
5090 |
23.6 |
|
TUG |
3195 |
14.8 |
|
CONTAINER SHIP |
2901 |
13.4 |