WHOI-2005-03

Woods Hole Oceanographic Institution

Estimating the Economic Benefits of 

Regional Ocean Observing Systems 

by

Hauke L. Kite-Powell

Charles S. Colgan

Katharine F. Wellman

Thomas Pelsoci

Kenneth Wieand

Linwood Pendleton

Mark J. Kaiser

Allan G. Pulsipher

Michael Luger 

April 2005

Technical Report

Funding was provided by the Office of Naval Research under Grant No. N00014-02-1-1037.

Approved for public release; distribution unlimited.

Estimating the Economic Benefits of 

Regional Ocean Observing Systems

Report to the National Oceanographic Partnership Program

Marine Policy Center

Woods Hole Oceanographic Institution

May 2005

Hauke L. Kite-Powell,

Woods Hole Oceanographic Institution

Charles S. Colgan

University of Southern Maine

Principal Investigators

Katharine F. Wellman 

Seattle, Washington

Thomas Pelsoci

Delta Research Company

Kenneth Wieand

University of South Florida

Linwood Pendleton

University of California at Los Angeles

Mark J. Kaiser and Allan G. Pulsipher

Louisiana State University

Michael Luger

University of North Carolina at Chapel Hill

Associate Investigators
2

Abstract

We develop a methodology to estimate the potential economic benefits from new investments in

regional coastal ocean observing systems in US waters, and apply this methodology to generate

preliminary estimates of such benefits. The approach focuses on potential economic benefits

from coastal ocean observing information within ten geographic regions encompassing all coastal 

waters of the United States, and within a wide range of industrial and recreational activities

including recreational fishing and boating, beach recreation, maritime transportation, search and

rescue operations, spill response, marine hazards prediction, offshore energy, power generation,

and commercial fishing. 

Our findings suggest that annual benefits to users from the deployment of ocean observing

systems are likely to run in the multiple $100s of millions of dollars per year. 

The project results should be considered first-order estimates that are subject to considerable

refinement as the parameters of regional observing systems are better defined, and as our

understanding of user sectors improves.

Key Words: economic benefits, ocean observing systems, benefit estimation
3

Table of Contents

EXECUTIVE SUMMARY........................................................................................................................... 5

INTRODUCTION AND SUMMARY ..................................................................................................... 15

THE ECONOMIC VALUE OF OCEAN OBSERVING INFORMATION ........................................ 19

AN ILLUSTRATIVE EXAMPLE: BEACH CLOSURES AND BEACH USE DECISIONS .......................................... 19

THE ECONOMIC VALUE OF INFORMATION ............................................................................................... 21

QUANTIFYING ECONOMIC VALUE............................................................................................................ 23

COMPONENTS OF THE REGIONAL OBSERVING SYSTEMS ............................................................................ 25

ESTIMATES OF POTENTIAL ECONOMIC VALUES OF OBSERVING SYSTEMS..................... 27

CONCLUSIONS AND RECOMMENDATIONS .................................................................................. 35

CONTRIBUTORS ..................................................................................................................................... 38

ACKNOWLEDGEMENTS ....................................................................................................................... 39

APPENDIX A: BEACH MANAGEMENT IN CALIFORNIA ............................................................. 41

APPENDIX B: OFFSHORE ENERGY PRODUCTION IN THE GULF OF MEXICO.................. 61

APPENDIX C: COMMERCIAL FISHERIES IN ALASKA AND THE PACIFIC NORTHWEST .. 89

APPENDIX D: RECREATIONAL BOATING ON THE GREAT LAKES....................................... 119
4
5

Executive Summary

This report summarizes the findings of a preliminary investigation of the magnitude 

of potential economic benefits that can be realized by deploying a network of ocean 

observing systems throughout the coastal waters of the United States. Such a network is 

currently being developed through collaborative efforts of federal, state, and local 

governments, universities, and organizations in both the non-profit and for-profit sectors. 

Estimating the economic benefits from ocean observing systems is inherently 

difficult. Not only are the systems themselves only partially deployed around the country at 

present; the technology and information products comprising the inputs and outputs of such 

systems are undergoing such rapid evolution that any estimates can only represent a partial 

snapshot. Moreover, the economic information needed to compile estimates of both the 

users of the information generated by such systems and the value they place on such 

information is only sporadically available and usually incomplete. 

Therefore this report provides what may be considered “order of magnitude” 

estimates only, along with recommendations on developing more accurate and useful 

estimates of economic benefits. Furthermore, there are many possible uses of improved 

ocean observing systems that have no readily quantifiable economic value but may lead to 

significant benefits in the future. Prominent among these are the uses of better ocean 

observing data in a wide range of basic and applied scientific research endeavors and in 

education programs.

The economic benefits of ocean observing systems derive from the value of the 

information generated by such systems and the effects that information has on the behavior 

of individuals and organizations. The ideal measure of these economic benefits is the value 

that users of the information place on it, based on their willingness to pay for such 

information to either enhance their uses of ocean resources or to avoid harms that may 

come from oceanic or atmospheric phenomena affecting individuals and organizations. The 

willingness to pay for such information is a measure of what economists call “social 

surplus:” the value of the information in excess of the costs of acquiring it. When such 

value accrues to businesses, it is referred to as “producer surplus;” when it accrues to 

individual users, it is “consumer surplus.”

The number of users and types of uses to which the information from ocean 

observing systems can be put is large and expanding. A number of uses of ocean observing 

information have been identified to date.1 Some of these make use of ocean observing data 

directly, obtaining near-real-time measurements from buoys or other platforms via a 

telephone or internet interface. Most use the data indirectly, via the output of various 

models that produce “nowcasts” or forecasts.

 

1

 See R. Adams, M. Brown, C. Colgan, N. Flemming, H. Kite-Powell, B. McCarl, J. Mjelde, A. Solow, T. 

Teisberg, and R. Weiher, 2000, The economics of ISOOS: benefits and the rationale for public funding, 

Washington DC: US Department of Commerce, NOAA Office of Policy and Strategic Planning.
6

A “nowcast” provides information about oceanographic/atmospheric conditions on 

a real time or near-real time basis to users, who adjust their behavior accordingly. Nowcasts 

may use models of various degrees of sophistication to interpolate between or extrapolate 

from the actual observations. In a forecast, the observation data are used as input to some 

form of model that predicts the future state of ocean or atmospheric conditions or the 

conditions of marine resources such as fish stocks. In both cases, the information is

collected and distributed using a variety of information and communication technologies. 

Forecasts and nowcasts are an input to decisions about economically significant activities. 

These decisions currently are made with less information than will be available with 

improved observing systems; and economic value is created by both the availability of 

observing information not previously available and by its timely distribution to users. 

Table 1 Types of Information from Observing Systems

Type of Information Type of Data Type of Information Change

new parameters

improved accuracy

improved resolution

finer temporal scale

(raw) data

present and historical 

conditions

finer geographic scale

improved accuracy/resolution (fewer 

false positives/negatives)

finer temporal/geographic scale

products

model output 

(interpolation/nowcasts, 

forecasts, etc.)

longer forecast horizon

Nowcasts may include both modeled data and the actual observations themselves. 

Nowcasts can be used to affect trips for transportation or recreation by causing a route to be 

changed, cancelling a potentially unsafe trip, or permitting a trip that was perceived to be 

potentially unsafe to proceed with confidence. Nowcasts can also be used to increase 

commercial or recreational fishing success by accurately pointing to favorable conditions. 

In forecasts, the data from observing systems is incorporated into some form of model that 

permits future values of the variables to be estimated. The data from observing systems may 

improve the forecasts in a number of ways, including reduced statistical errors, increased the 

spatial and temporal scale scale, or increased resolution of forecasted data. Forecast 

improvements can also include reducing false negatives (e.g., forecasting an oil spill will not 

hit an area when it actually does) and false positives (e.g. forecasting an oil spill will hit an 

area when it does not).

This report does not assume, nor do its findings imply, any specific observing 

technologies or technology systems. The underlying assumption is that measurement of 

parameters of interest to users will be undertaken with a high degree of accuracy, that those 

measurements and resulting nowcasts/forecasts are communicated to users in a timely and 

accurate way, and that users will make full use of the information available to them. The 

resulting benefits depend on the amount, timeliness, and quality of information, and not on 

a specific technology or organizational arrangement. 
7

The research reported here examines five general types of activities, including 

recreational activities, transportation, health and safety, energy, and commercial fishing. 

Nine more specific activities are examined (Table 2). 

Table 2 Activities affected by ocean observing information

Boating

Recreational Activities Beach Going

Fishing

Transportation Freight

Passenger (Cruise Ships)

Search and Rescue

Health and Safety Oil Spill & Hazard Cleanup

Property Damage

Energy OCS Development

Electric Generation Management

Commercial Fishing

The project proceeded in two phases. In the first phase, a survey of these activities 

was conducted for each of nine regions (Pacific Northwest, California, Gulf of Mexico, 

Florida, Southern Atlantic coast, Mid Atlantic coast, New England/Gulf of Maine, Alaska, 

Hawaii, and the Great Lakes) with the objective of identifying the levels of economic values 

that might be affected by the availability of improved and expanded information from ocean 

observing systems. This analysis may be said to identify the “footprint” of economic values. 

Rough estimates of the potential value of ocean observing system information were made by 

assuming that small increments of the total value (typically on the order of 1%) could be 

realized as possible benefits. 

Table 3 shows a summary of these estimated possible economic effects. The bases 

for these estimates are shown in the final column. Because of limitations on the data 

available in the different regions regarding each of the activities, these figures are not 

estimated from a consistent base and cannot be summed. Data were not available in each of 

the regions for each of the activities; for example, data on beach utilization and values are 

more thoroughly estimated for Southern California than for other regions. Some data 

permit direct estimation of benefits using willingness to pay measures, which are the 

appropriate measures of economic benefits. But these data are not available for all uses in all 

regions. Substitutes or proxies, such as consumer expenditures for recreation activities, are 

used in these cases. True willingness to pay benefits generally will be some fraction of the 

resulting estimates. The data in Table 3 thus suggests highly approximate levels of benefits 

that are possible from ocean observing systems across a partial array of users of the 

information from such systems in parts of the country.

The data generated in this first phase of the project are very rough estimates that do

not take explicit account of the many variables involved in the realization of benefits. These 

variables include the probabilities of events such as oil spills or high impact weather events 
8

such as hurricanes, the issue of how information is distributed to users, and how the 

information is actually used to make decisions. In order to investigate the influence of these 

and other factors, the second phase of the project undertook to develop more detailed, but 

still preliminary, models of economic effects for a subset of activities in specific regions.

Table 3 Preliminary Estimates of Benefits & Impacts

User Sector Users Region

Estimated 

Economic Effects 

($Millions/Year) Benefit Definition

Pacific NW $3.5 Willingness to pay

G.o.Mexico* $11.0

Mid Atlantic $30.0

South Atlantic $2.0

Increased expenditures

Florida $7.6 Willingness to pay

G.o.Mexico* $6.7-34.0 Willingness to pay

California $2.0

Hawaii $6.0

Recreational 

Fishing

Great Lakes $19.5

Increased expenditures

California $2.0

G.o.Mexico* $4.0

G.o.Maine $1.0

Mid Atlantic $2.0

South Atlantic $1.0

Alaska $6.0

Hawaii $9.0

Florida See Rec Fishing

Great Lakes $18.0

Recreational 

Boating

California $16.0

Increased expenditures

$7.7 Beach-related consumer 

expenditures 

$65.6 Increased economic impact

$1.6 Operating cost savings

Florida

$30.7 Increased business sales

Great Lakes $16.5 Increased visitor daily 

values

California $94.6 Increased expenditures

Recreational 

Activities

Beaches

California $58.0 Increased consumer 

surplus

Pacific NW $1.2

G.o.Maine $1.0

Mid Atlantic $2.0

South Atlantic $1.0

Alaska $1.0

Transportation

Freight

Florida $55.2

Daily cost savings
9

User Sector Users Region

Estimated 

Economic Effects 

($Millions/Year) Benefit Definition

Great Lakes $0.6

California $34.0

G.o.Mexico* $30.7

Cruise Ships Pacific NW $0.1

Pacific NW $10.0

G.o.Maine $24.0

Mid Atlantic $16.0

South Atlantic $32.0

California $19.0

Alaska $12.0

Hawaii $6.0

Value of life-$4m

Florida $11.3 Costs saved to USCG plus 

value of lost lives saved

Florida $22.0 Cost saved to local rescue 

squads plus value of lost 

lives saved.

Great Lakes $18.9

Search & 

Rescue

Search & 

Rescue

G.o.Mexico* $28.0

Value of life-$4m

Pacific NW $0.4

California $0.1

Oil Spills

G.o.Mexico* $0.8

Reductions in clean up and 

compensation costs

Tropical 

Storm 

Prediction

South Atlantic $15.6 Reduced loss of life, 

evacuation cost, and lost 

tourism revenue

Residential 

Property

Florida

South Atlantic

$32.9

$24.0

Avoided costs from earlier 

preparation for storms

Health and 

Safety

Health and 

Safety

Beach 

Restoration

California $1.8 Reduced expenditures on 

beach restoration

Electric Load 

Planning

Great Lakes $55.8-111.6 Avoided use of most 

expensive peak generators

Oil and Gas 

Development

G.o.Mexico* $5.1-11.3 Operating cost savings

Energy

$9-15 Increased accuracy of 

oceanographic risks in 

design

Pacific NW $2.7

G.o.Maine $4.0

Mid Atlantic $3.0

South Atlantic $3.0

Alaska $10.0

Florida $2.0

Increased Landed Values

Great Lakes $0.2 Total regional economic 

impact

California $1.2 Reduced operating costs

Commercial 

Fishing

G.o.Mexico* $2.1 Increased Landed Values

*Note that the Gulf of Mexico region in this study excludes the west (Gulf) coast of Florida.
10

Phase 2 estimates for the selected uses and regions are shown in Table 4. Low and 

high ranges of estimates based on the models and assumptions are shown. (Where only one 

estimate is available, it is shown in both the high and low columns.) Measures that can be 

counted as economic benefits in the sense of contribution to social surplus are shown in 

bold. Where an estimate is based on total production values and not on contribution to 

social surplus, it is shown in plain type. The two categories of estimates are summed 

separately.

Table 4 Estimates of Economic Benefits for Selected Activities and Regions

User Group IOOS Information Benefit Source

Low 

Estimate*

High 

Estimate* Region Measure

Energy Improved Hurricane 

Forecasts

Avoided False 

Positives

$3.8 $7.5 Gulf of 

Mexico

Cost savings

Health and Safety Oil Spill Dispersion 

Models

Improved 

spatial/temporal 

accuracy

$0.6 $1.0 Gulf of 

Mexico

Social cost 

savings

$35.6 $35.6 Southeast 

US Atlantic 

coast

Storm Prediction Improved Tropical Cost savings

Storm Track and 

Intensity Forecasts

Improved 

spatial/temporal 

accuracy

$4.0 $4.0 Southeast 

US Atlantic 

coast

Tourism 

revenue

Transportation Seastate & Visibility 

Forecasts and 

Nowcasts

Improved 

spatial/temporal 

accuracy

$0.5 $1.0 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

$10.0 $15.0 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

Search and Rescue Surface currents & 

winds

Improved 

spatial/temporal 

accuracy

$2.3 $4.7 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

Salmon run 

forecasts

Improved 

capital/labor 

investments

$77.0 $77.0 Bristol Bay, 

Alaska

Producer 

Surplus

Reduced risk in 

management 

decisions

Increased 

groundfish catch

$504.0 $504.0 Alaska Wholesale 

value

Commercial Fishing

Reduced risk in 

management 

decisions

Increased crab 

catch

$62.5 $62.5 Alaska Wholesale 

value

Beach closure 

forecasts

Decrease in false 

negatives

$2.3 $3.5 Southern 

California

Consumer 

Surplus

Beach closure 

forecasts

Decrease in false 

negatives

$4.2 $9.3 Southern 

California

Total 

Expenditures

Recreation

Beach closure 

forecasts

Decrease in false 

positives

$1.1 $1.1 Southern 

California

Total 

Expenditures
11

Recreational boating 

conditions forecasts

Improved 

spatial/temporal 

accuracy

$20.7 $103.5 Great Lakes Total 

Expenditures

Recreational fishing 

conditions forecast

Improved 

spatial/temporal 

accuracy

$91.2 $91.2 Florida Consumer 

Surplus

TOTAL Social Surplus Estimates $223.3 $236.5

TOTAL Other Value Estimates $596.5 $684.4

*millions of dollars per year

Based on the analyses in both phases of the project, the order of magnitude for 

benefits that may be derived for major uses of ocean information systems on an annual basis 

were estimated. These are shown in Table 5.

Table 5 Orders of Magnitude of Possible Economic Benefits from Ocean Observing Systems

Order of magnitude of 

possible annual benefits

(millions of dollars)

Regions with 

greatest benefits

Recreational Fishing 100s Great Lakes, Gulf 

of Mexico

Recreational Boating 100s Great Lakes, Gulf 

of Mexico, Atlantic

Recreational Activities

Beaches/Shore Recreation 100s Florida, California

Transportation-Freight 10s Florida, Mid 

Atlantic

Transportation

Transportation-Cruise Ships 10s Florida

Health and Safety SAR All

Oil Spills

Tropical Storm Prediction

10s

10s

10s

All

Atlantic, Gulf of 

Mexico

Electricity Load Planning 10s to 100s Great Lakes, 

California, Atlantic

Energy

Ocean Structures 10s Gulf of Mexico

Commercial Fishing Commercial Fishing 100s Alaska, New 

England

The numbers in Table 5 suggest that annual benefits to users from the deployment 

of ocean observing systems are likely to run in the multiple $100s of millions of dollars per 

year. 

These data should not be compared directly with the projected costs of ocean 

observing systems without further analysis. In a benefit-cost analysis, the basis for 

estimating costs and benefits must be consistent, and the time frames must be appropriately 

defined. While various estimates of the costs of ocean observing systems have been 

produced, the basis for those estimates may not be consistent with that of the figures shown 
12

in this report. At this stage, our findings are also not precise enough to be used to conduct 

benefit-cost analyses of specific technologies or specific regions. 

However, we can conclude from these data that on a discounted present value basis 

over time2, the benefits from a national investment in such systems are likely to exceed the 

costs. This finding is consistent with previous conclusions on the economic benefits of such 

systems (see, for example, the report cited in Footnote 1 above). 

The data also indicate that ocean observing systems will have the largest benefits 

where the information from such systems is used by the largest possible groups. 

Recreational activities are consistently the highest generators of benefits because of the very 

large number of people who use beaches, boat on the Great Lakes or in the coastal ocean, or 

engage in marine recreational fishing. Although the per-user benefits are smaller than those 

realized by other activities, the large number of users drive the overall magnitude of potential 

benefits to substantial sums.

Several important caveats are required in interpreting the results presented above:

• The fact that the benefits from the systems as a whole will likely exceed the costs 

does not mean that the benefits will exceed the costs in every individual case. The 

configuration of observing systems in each region should take into account the 

priorities of local and regional user groups.

The estimates presented assume:

• Full and successful deployment of existing, near-deployment, or reasonably 

forseeable technologies.

• Cost efficient and effective means of communicating the information derived from 

the ocean observations to users in a timely manner.

• Users are aware of, and effectively incorporate, the information into decisions 

regarding their activities.

• In the case of commercial fisheries, additional information concerning the state of 

the marine environment is relevant to decisions about managed fisheries and (at 

some point) will permit increases in allowable catches.

Violation of any of these assumptions may reduce the potential or actual benefits to levels 

below those estimated here.

The analysis of economic benefits from ocean observing systems in this study is by 

no means exhaustive. For example, this study does not address benefits that may arise for 

the hotel and resort industry or for certain aspects of emergency management. One source 

 

2

 “Discounted present value” is a means of adding benefits (or costs) that accrue at different points in time 

to obtain a meaningful single value. A discount rate is applied to convert benefits (or costs) that arise in 

future years into “present” dollars. This is necessary because (a) the value of a dollar next year is (usually) 

less than the value of a dollar today, and (b) there is (generally) greater uncertainty about benefits (or costs) 

expected to arise in future years.
13

of complementary information about benefits in these and other areas is a series of reports 

prepared for NOAA by SAIC Inc. (http://www.saic.com/weather/papers.html).

Based on these findings, we believe additional research is needed to develop more 

precise estimates of benefits for specific observing systems, instruments, technologies, and 

applications. Specifically:

1. Operators of regional observing systems should incorporate in their operational 

plans strategies and activities to measure the economic benefits of their products and 

services. 

2. Investments should be made by federal, state, and local governments in more 

precisely estimating economic benefits and in sharing data and methods for benefits 

estimation among operators of observing systems. To build on the work presented 

in this report, a series of coordinated pilot projects should be funded at the regional 

level to develop, apply, and share with other regions detailed guidelines for benefit 

tracking and estimation. These pilot projects should focus on one or two prominent 

user sectors in each region, and cover the major sectors identified in this report.

3. Consumer surplus benefits should be estimated for all categories of recreation users 

in various regions of the country. Current estimates of such benefits do not fully 

account for the possibility of substitution among different recreation resources in 

different regions and remain subject to considerable methodological variability.
14
15

Introduction and Summary 

The United States and other countries are designing and building a large network of 

instrumentation and data links to continuously monitor biological, physical, and chemical 

conditions in the ocean and in the ocean-atmosphere interface. This network will extend

from the near shore areas of America’s ocean and Great Lakes coasts (and those of other 

countries) to the deep ocean areas. 

The expansion of ocean observing systems is made possible by innovations in 

sensor, computer, and communication technologies that have lowered the cost of 

instrumentation and made it possible to measure more parameters than ever before. The 

presence of data distribution technologies such as the Internet have also enhanced the value 

and cost-effectiveness of such systems, because data can now be delivered directly to 

potential users at very low or no cost.

While the costs of collecting and distributing data from ocean observing 

technologies have come down on a per unit basis, the creation of the systems of observing 

technologies still requires significant investments. Those investments will be made by 

federal, state, and local governments and by other organization in both the private profit and 

non-profit sectors. The magnitude of investments required raises questions about what the 

benefits from such systems will be, and whether these will be sufficient to warrant the 

required investments.

This report presents preliminary information about the magnitude of likely benefits 

that may accrue from current and expected regional observing systems. The focus is on 

observing systems to be deployed in the coastal waters of the United States, including both 

the oceans and the Great Lakes. These observing systems are being formed as a series of 

regional systems in areas such as the Gulf of Maine, South Atlantic, California, Gulf of 

Mexico, Pacific Northwest, and Gulf of Alaska. It is anticipated that all of the coastal waters 

of the United States eventually will be covered by such systems, which will provide nationally 

consistent measurement of certain parameters (the “national backbone”) and also meet 

particular needs in each region. Output from the network of regional systems will merge to

provide an integrated ocean observing system for the United States, which in turn will be a 

component of the Global Ocean Observing System (GOOS) and the Global Earth 

Observing System (GEOS). For more information about the network of regional observing 

systems see www.ocean.us.

The information needed to develop detailed estimates of the economic benefits of 

ocean observing systems is, for the most part, unavailable at this time. Both the 

development of the observing systems themselves and the economic information needed to 

estimate benefits are presently incomplete. The analysis developed here therefore attempts 

to identify likely magnitude of benefits based on the levels of economic activity potentially 

affected by the information derived from ocean observations, and to explore the methods 

that can be used to develop detailed estimates for specific applications in selected regions.
16

The project proceeded in two phases. In the first phase, a survey of ocean industries 

and activities (Table 6) was conducted for each of nine regions (Pacific Northwest, 

California, Gulf of Mexico, Florida, Southern Atlantic coast, Mid Atlantic coast, New 

England/Gulf of Maine, Alaska, Hawaii, and the Great Lakes) to identify the levels of 

economic values that might be affected by the availability of improved and expanded 

information from ocean observing systems. This analysis may be said to identify the 

“footprint” of economic values. Rough estimates of the potential value of ocean observing 

system information were made by assuming that small increments of the total value (typically 

on the order of 1%) could be realized as possible benefits. The second phase of the project 

developed more formal models of the relationship between ocean observing information 

and decision making in a selected set of user sectors and regions.

Table 6 Activities affected by observing system information

Boating

Recreational Activities Beach Going

Fishing

Transportation Freight

Passenger (Cruise Ships)

Search and Rescue

Health and Safety Oil Spill & Hazard Cleanup

Property Damage

Energy OCS Development

Electric Generation Management

Commercial Fishing

The results of the analysis permit the identification of the order of magnitude of 

benefits that may be expected on an annual basis from a fully implemented network of 

regional ocean observing systems (see Table 5 in the Executive Summary or Table 11 

below). Our findings suggest that annual benefits to users from the deployment of ocean 

observing systems are likely to run in the multiple $100s of millions of dollars per year. On a 

discounted present value basis over time, the benefits from a national investment in such 

systems are likely to exceed the costs (although these costs, along with the specific design of 

the system, remain to be determined in detail). This finding is consistent with previous 

conclusions on the economic benefits of such systems. 

The data also indicate that ocean observing systems will have the largest benefits 

where the information from such systems is used by the largest possible groups. Recreation 

activities are consistently the highest generators of benefits because of the very large number 

of people who use beaches, boat on the Great Lakes or in the coastal ocean, or engage in 

marine recreational fishing. Although the per-user benefits are smaller than those realized by 

others, the large number of users drive the overall magnitude of potential benefits to 

substantial sums.

Several important caveats are required in interpreting these results:
17

• The fact that the benefits from the systems as a whole will exceed the costs does not 

mean that the benefits will exceed the costs in every individual case. The 

configuration of observing systems in each region should take into account the 

priorities of local and regional user groups.

The estimates presented assume:

• Full and successful deployment of existing or near-deployment technologies.

• Cost efficient and effective means of communicating the information derived from 

the ocean observations to users in a timely manner.

• Users are aware of, and effectively incorporate, the information into their decisions 

regarding their activities

• In the case of commercial fisheries, additional information concerning the state of 

the marine environment is relevant to decisions about managed fisheries and (at 

some point) will permit increases in allowable catches.

Violation of any of these assumptions may reduce the possible or actual benefits to levels 

below those estimated here.

The following sections of the report discuss the derivation of these estimates. In the 

next section, the general theory of economic benefits is discussed, along with a general 

introduction to ocean observing technologies and their information products. The following 

two sections discuss the details of estimating procedures in the two phases of the project. 

The final section provides conclusions and recommendations.

Selected individual reports from Phase 2 of the project are included in the 

Appendices. 
18
19

The Economic Value of Ocean Observing Information

The information derived from ocean observing systems creates economic value 

primarily by leading to improved decision making. “Improvement” in this context means 

reducing the uncertainty associated with actions taken to use marine resources in some way. 

A large degree of uncertainty surrounds such decisions; and much of this uncertainty exists 

because the person facing a decision does not have complete information about the relevant 

state of the ocean at the relevant time. Ocean observing data and the information derived 

from them reduce this uncertainty, and that reduction in uncertainty is economically 

valuable. What a decision maker should be willing to pay for this information (the market 

value of the information) is related to the extent to which it reduces uncertainty, and to the 

economic resources at stake in the decision.

An Illustrative Example: Beach Closures and Beach Use Decisions

This definition of the value of information provides the elements necessary to 

estimate the value of ocean observing (or other) information. Consider the following 

example:

A surfer in Southern California wants to go to the beach for a day’s surfing, but her 

decision to actually go depends on knowing whether the beach is open for swimming and 

what is the current state of the surf. General weather forecasts are available, as is 

information about whether the beach is closed or not. (Beach closures usually follow from

sewage overflows that may increase the presence of pathogenic bacteria in the water.)

The decision about whether to travel to the beach can be depicted as the interaction 

between two factors, each of which has two possible outcomes. One is whether the beach is 

open or closed, and the other, which applies only if the beach is open, is whether the surf 

conditions are good or bad. 

The decision to open or close the beach rests not with the surfer but with public 

health officials who monitor the presence and location of pathogenic bacteria3 that could 

pose a threat to health. The presence of pathogens generally results from overflow of 

sewage systems from storm events. The location and concentration of the bacteria depends 

on the location of the sewage outfalls and local tidal and other currents. Based on sampling 

data and information on currents, the public health official must decide whether to close a 

beach, post it as potentially hazardous4, or take no action (leave the beach open). This 

decision depends on the information from the sampling regimen and predictions of currents,

both of which have elements of uncertainty in them. Because of those elements, the public 

official faces the probability that the decision to close a beach will be in error. The beach 

may be safe for swimming, but the official closes it (a false positive outcome, since the data 

 

3

 Actually, current technologies require monitoring of indicator bacteria species rather than the pathogens 

themselves.

4

 For simplicity, we ignore the effects of “posting” rather than closing a beach.
20

indicates a positive result for pathogenic exposure, leading to a closure decision). Or the 

beach may actually be unsafe for swimming and kept open in error (a false negative

outcome). Since the official is likely to be risk averse, more beaches are likely to be closed 

when they could be open if uncertainty were reduced.

Decision

to Surf

Beach

Open

Beach

Closed

Good

Conditions

Bad

Conditions

Figure 1 Decision to Surf

The decision to open or close the beach is influenced strongly by knowledge of local 

conditions in the vicinity of sewage outfalls and storm drains. Ocean observing system can 

provide fine scale (both temporal and spatial) information on physical, chemical, and 

biological conditions, and thereby significantly alter the public health official’s decision 

problem. By reducing uncertainty, the length of beaches that must be closed can be reduced, 

as can the risk of false positives or false negatives. A reduction in false positives increases 

the amount of time beaches are open for recreation, while a reduction in false negatives 

decreases the risks to swimmers’ and surfers’ health and safety. 

For the surfer, the question of conditions is a subjective one that depends on wind 

and wave conditions, which may be unique to the particular destination beach. Again, finer 

temporal and spatial scale oceanographic and meteorological information provides the 

information the surfer needs to decide whether to make the trip to the beach.

The economic value at stake in these decisions is the value received from safely 

enjoying the recreational activity. That value is the amount the surfer would be willing to 

pay for the opportunity to go surfing less the amount that is actually paid (usually 
21

transportation costs). If the surfer makes the trip only to find the beach closed or to find 

surf conditions too large or too small for enjoyable surfing, then there is a loss of value. It 

is thus the value to the surfer (or other recreationist) that is at stake in this use of the ocean 

observing system information. The reduction in uncertainty for the public health official 

creates value to the extent that it increases the value of recreation to those who use the 

beach.

This example illustrates the two most fundamental ways that ocean observing 

systems information is used: to create forecasts of future information on which decisions 

depend, and to create nowcasts of conditions in real or near-real time. Forecasts are 

generated when data from observing systems are fed into models of ocean and atmospheric 

processes to generate the required information. Nowcast information may be direct 

observation data (wind speed, wave height) or may also be produced by models. These 

effects are summarized in Table 7.

Table 7 Ocean observing information uses

Type of 

Information

Type of Data Type of Information 

Change

Forecast Accuracy

Spatial Scale

Temporal Scale

Avoiding False Negatives

Forecasts Modeled data

Avoiding False Positives

Nowcasts Observation data Transportation routing

Modeled data Fishing success

The Economic Value of Information

As the surfer example illustrates, a nowcast or forecast based on ocean observing 

system data represents information about conditions or phenomena in the ocean. This 

information has value when it can be used by an individual or an organization to make a 

better decision – that is, a decision that results in an outcome that is economically superior. 

The standard economic approach to valuing information requires:

• A description of the information being valued (in this case, typically an improved 

forecast or nowcast) and of the uncertainty in the phenomena is describes.

• A model of how this information is used to make decisions. Most decisions are 

made in the face of imperfect information, or uncertainty about how conditions will 

in fact develop and what the exact outcome will be. Therefore, a basic principle of 

economic valuation of information is that of “expected values.” Expected values are 

defined as values adjusted for the probability that they will be realized. In the 

absence of a specific model of decision behavior, it makes sense to assume the 

decision maker is rational and seeks to maximize benefits. This is described in 

greater detail below.
22

• A model of how these decisions affect physical outcomes.

• A model of how physical outcomes can be translated into economic outcomes. The 

value of a forecast is the difference between the expected value of the outcome of 

decisions using the forecast, and the expected value of the outcome without the 

forecast.

A standard Bayesian approach can be used to estimate the value of information 

contained in a forecast (see Berger 1995).5 In this model, a decision maker (user of 

forecasts) must choose among a range of actions represented by A. The outcome of each 

action depends on a state of nature, S, which is not known at the time of the decision but 

becomes manifest later. The manifestation of S is modeled as a random variable with 

probability density function f(s). This probability density function (pdf) describes the 

probability that the condition (for example, the height of waves at a surfing beach) will lie 

within a particular range considering only what is known from past observation, and 

disregarding the new forecast.

Let B(a,s) be the consequence (net benefit) to the decision maker of pursuing action a

if it turns out that S=s. The expected net benefit of pursuing action a is then the integral of 

the product of B(a,s) and f(s) (see Raiffa 1970):6

E B a s f s ds o( , ) ( )

∫

=

The optimal choice of action without the new forecast (a0*) is that which produces the 

maximum expected net benefit (E0*). If we now provide a useful new forecast to the 

decision maker, the optimal choice of action and the associated expected net benefit will 

change. To determine the value of this new forecast, we need to know something about the 

accuracy of this forecast and something about the frequency with which different conditions 

arise on average. (For instance, a storm forecast may be more valuable if storms are more 

frequent than if they only happen once a decade.) How a decision maker revises her 

estimate of the likelihood of s is described by Bayes’ Theorem: 

f (s | x) = l(x | s) f (s) / p(x)

where X is the information in the forecast, 

l(x|s) is the probability that X =x given S=s, and

p(x) is the probability that X=x :

∫

p(x) = l(x | s) f (s)ds

 

5

 Berger, J.O. 1985. Statistical decision theory and Bayesian analysis. New York, Springer Verlag.

6

 Raiffa, Howard. 1970. Decision Analysis: Introductory Lectures on Choices under Uncertainty. 

Boston: Addison Wesley.
23

In simple terms, Bayes’ Theorem describes how the decision maker should adjust her prior 

expectation of the occurrence of event s when the forecast says x, taking into account how 

“good” this forecast tends to be.

The new optimal action given forecast X is found be maximizing

E (a | x) B(a,s)f (s | x)ds ∫=

The outcome of the optimal choice, E*(x), now depends on x, and the expected value of net 

benefit is

E E x p x dx X* * ( ) ( )

∫

=

Since the decision maker could realize expected benefit E0* without the forecast and EX*

with the forecast, the value of this forecast to the decision maker is EX* - E0*.

In this description of the theoretical underpinning of the value of information, we 

have not addressed the question of how the net benefit (E) is quantified in each case. We 

turn to this question in the following section.

Quantifying Economic Value

The information uses outlined in the example of the surfer can be extended to many 

different types of users. Recreational boaters and those who fish in marine waters have 

similar needs for fine scale oceanographic and meteorological data to decide when and 

where to go. Cargo and cruise ships, both sensitive to fuel costs, need real time current 

information to optimize their routes to and from harbors; and tug/barges and pilot boats are 

interested in wave height information to avoid hazardous operating conditions. Commercial 

fishermen have similar needs; and both recreational and commercial fishing success can be 

improved by knowledge of such parameters as water temperature. Electric generators can 

optimize fuel generation to minimize costs depending on when the sea breeze sets up on a 

high demand summer afternoon, while offshore oil and gas operators need information on 

high velocity loop currents that develop in the Gulf of Mexico and can affect the safety of 

operations. With accurate surface current information, oil spill response teams can more 

effectively deploy equipment to where the oil will be and avoid where the oil will not be.

The appropriate measure of economic value in all of these cases is the change in 

what economists refer to as “social surplus.” Social surplus has two components: producer 

surplus and consumer surplus. Producer surplus in this case is generally the difference 

between the costs incurred by businesses (including opportunity costs, or reasonable rates of 

return on inputs to production) and the revenues they realize. Consumer surplus, as in the 

case of the surfer, is the difference between what one would be willing to pay and what one 

actually pays for, for example, a recreational experience. “Social surplus” is the sum of 

producer and consumer surplus. Social surplus is the best single measure of economic 
24

benefits because it assures that only value in excess of costs is included, and avoids the 

artificial inflation of values caused by double counting.

The problem with social surplus and both of its compoents is that they can only be 

measured using exacting, time-consuming, and costly techniques. Other measures of 

economic activity (broadly termed “economic impacts”), such as the value of sales at the 

wholesale or retail level, or value added (the most common example of which is the Gross 

Domestic Product), are widely available, but measure social surplus only in an imperfect 

manner.

Studies of economic values from investments such as ocean observing systems thus 

face a dilemma. The most appropriate measure is the least readily available, while the most 

available measures are the least appropriate. This is a major reason why estimates of 

economic benefits from ocean observing activities at this stage of analysis must be 

considered preliminary and approximate. In this study, most of the estimates have been 

developed using an indirect and somewhat restrictive approach.

The first step is to identify some of the activities that could be affected by ocean 

observing systems, and to obtain data from public statistical sources that indicate 

approximate levels of economic activity in these areas. Simple assumptions are then made 

about the possible level of benefits from improved information. In most cases, social 

surplus benefits are assumed to be no more than 1% of total activity values; this is a 

conservative assumption which reflects the reality that changes in producer and consumer 

surplus are likely to small relative to aggregate expenditure or sales data.7

Table 8 Phase 2 case studies

User Group IOOS Information Region

Energy Improved Hurricane 

Forecasts

Gulf of Mexico

Oil Spill Dispersion 

Models

Gulf of Mexico

Search and Rescue North Atlantic

Health and Safety

Tropical Storm Prediction Southeast Atlantic coast

Salmon run forecasts

Reduced risk in 

management decisions

Commercial Fishing

Reduced risk in 

management decisions

Alaska

Transportation Sea state and weather North Atlantic

Beach closure forecasts Southern California

Recreational boating 

conditions forecasts

Great Lakes

Recreation

Recreational fishing 

conditions forecast

Florida

 

7

 See W.D. Nordhaus (1986), The Value of Information, in R. Krasnow, Ed., Policy Aspects of Climate 

Change: Proceedings of a Seminar held in Washington, D.C., March 4, 1986. Resources for the Future, 

Washington D.C.
25

In order to test methods for more precise estimates of social surplus, case studies 

were undertaken as a second step in this study. These case studies are shown in Table 8 and 

discussed in a subsequent section of this report.

Components of the regional observing systems

Before proceeding to a discussion of economic values, it is necessary to discuss 

briefly the assumptions used in this study about ocean observing systems. Team members 

consulted widely with organizations and scientists currently designing and operating 

observing systems in the coastal waters of the U.S., including the Great Lakes. The team 

also received valuable guidance from the U.S. Global Ocean Observing Systems Steering 

Committee.

The technologies comprising ocean observing systems include a wide array of 

instruments and platforms. They include moored and unmoored buoys, radar, satellite 

imagery, fixed platforms such as light stations, and platforms of opportunity. Satellites, 

moored buoys, and radar make up much of the current generation of improvements in 

ocean observing systems. The data output from these instruments consist of a wide array of 

parameters, including:

• Wind speed and direction

• Current speed and direction

• Wave height and periodicity

• Air and water temperature at varying heights/depths

• Chemical composition such as salinity

• Biological composition, particularly the density of chlorophyll-A

• Visibility

• Ice (Great Lakes)

The data derived from these observations are distributed directly and indirectly 

through a variety of means. The data may be fed to forecasting centers operated by the 

federal government, universities, or private organizations for incorporation into forecast 

products that are distributed widely through public and private channels including television, 

newspapers, radio, or the Internet. The data may also be delivered directly to subscribers.

 

In conducting the analysis for this project, no attempt was made to evaluate the 

benefits of specific technologies, instruments, platforms, or communication channels. The 

assumption was made that economically-relevant data would be available in an integrated 

form and timely manner to users irrespective of observation technology or data 

dissemination means. 

In general, we assumed the sort of data and information streams that are already 

being delivered by one or more of the ocean observing system organizations. We made no 

specific assumptions about improvements in data other than that development of the 
26

observing technologies and systems would permit a substantial increase in the amount, 

quality, and usability of information delivered to users. In some cases, such as the 

development of methods for more rapid and direct measurement of pathogenic bacteria, we 

have assumed that such developments will take place and be implemented (we include the 

use of rapid microbial indicators as one type of instrumentation that could produce data on 

biological pathogens).

In short, we have assumed that when users need information, it will be available and 

fully utilized (incorporated into relevant decisions). The benefits shown are thus potential 

benefits from systems being established and expanded, not from the array of observing 

technologies, platforms, and data distribution in place at the time of this study.
27

Estimates of Potential Economic Values of Observing Systems

The first phase of analysis of possible economic benefits followed the general 

methodology set out in Kite-Powell and Colgan (2001)8. In that study, estimates of possible 

benefit levels were made based on activity in the Gulf of Maine and informed by the thenbeing-established Gulf of Maine Ocean Observing System. Each of the regional studies 

undertaken in Phase 1 of this project used assumptions similar to those of the Gulf of Maine 

study. 

Data sources for Phase 1 work include those used in the Gulf of Maine study, where

available, e.g. national data from the Coast Guard on search and rescue missions and on oil 

spills. In other cases, new data had become available: for example, the National Survey on 

Recreation and the Environment (NSRE; http://marineeconomics.noaa.gov/NSRE/) 

provides national data on coastal and ocean recreational activities for 2000. Phase 1 results 

also used a number of region-specific studies of economic activity and economic value.

Table 9 shows the summary of findings from Phase 1, grouped by user sectors, 

users, and region. The table shows the assumed effect of the ocean observing system on the 

users and the resulting change in economic value. The classification of benefits by value 

type is also shown.

There are a number of different bases for estimating economic values affected by 

ocean observing information. In some cases, data are available on willingness to pay (social 

or producer surplus). These include changes in operating costs for businesses or 

government agencies, and consumer values for recreational activities. Other data represent 

changes in gross value such as fisheries landed values, sales of businesses, or regional 

economic impact (including both direct and indirect regional economic output). In these 

cases, the economic benefits will be some fraction of these larger values. The result of this 

mix of value bases is that the results cannot be meaningfully summed.

The use of 1% of economic activity as a short-hand estimate of value of forecast 

information is based on experience with economic assessments of weather and climate 

forecasts generally (Nordhaus 1986).9 In our study, we use this approach because, in most 

cases, no specific data about user values are available. Studies have not been done to 

specifically identify how users will actually value the information available to them from 

ocean observations. Indeed, in many of the regions and for many of the users, the observing 

system information is only now becoming available, and many users are not yet widely aware 

 

8

 Kite-Powell, H.L. and C.S. Colgan 2001 The Potential Economic Benefits of Coastal Ocean Observing 

Systems: The Gulf of Maine. Marine Policy Center Woods Hole Oceanographic Institution. 

9

 Nordhaus, W.D. 1986. The value of information. In: R. Krasnow, ed., Policy aspects of climate 

forecasting. Proceedings of a Seminar held in Washington, D.C., March 4, 1986. Resources for the Future, 

Washington.
28

of the availability of such information, nor have users (and economists) gained the 

experience with the data to develop meaningful “willingness to pay” values. 

Table 9 Potential Economic Values of Observing Systems

User Sector Users Region Estimated 

Economic 

Effects 

($Millions/Year)

Assumed Effects of 

Observing Systems

Benefit Definition

PNW $3.5 Willingness to pay

GoME $11.0

Mid Atl $30.0

So Atl $2.0

1% increase in 

activity/expenditures Increased 

expenditures

FL $7.6 5% of boaters/fishers 

on water avoiding 10% 

of 45 days of bad 

weather

GoMex* $6.7-34.0 1% increase in 

willingness to pay for 

an additional day of 

fishing

Willingness to pay

CA $2.0

HI $6.0

Recreational 

Fishing

GL $19.5

1% increase in 

activity/expenditures

Increased 

expenditures

CA $2.0

GoMex* $4.0

GoME $1.0

Mid Atl $2.0

So Atl $1.0

AK $6.0

HI $9.0

FL See Rec Fishing

GL $18.0

Recreational 

Boating

CA $16.0

1% increase in 

activity/expenditures

Increased 

expenditures

$7.7 1% increase Beach-related 

consumer 

expenditures 

$65.6 1% increase Increased economic 

impact

$1.6 Employee costs 

avoided in recreation 

businesses from false 

negatives

Operating cost 

savings

FL

$30.7 Estimated avoided lost 

sales from false 

positive storm forecasts

Increased business 

sales

Recreational 

Activities

Beaches

GL $16.5 1% increase in beach 

day values assuming 

21.4% of national 

beach days are in Great 

Lakes

Increased visitor daily 

values
29

User Sector Users Region Estimated 

Economic 

Effects 

($Millions/Year)

Assumed Effects of 

Observing Systems

Benefit Definition

CA $94.6 1% increase Increased 

expenditures

CA $58.0 1% increase Increased consumer 

surplus

PNW $1.2

GoME $1.0

Mid Atl $2.0

So Atl $1.0

AK $1.0

FL $55.2

GL $0.6

CA $34.0

1% of annual variable 

costs

Freight

GoMex* $30.7 1% of ships save 1 

hour of daily operating 

costs

Transportation

Cruise Ships PNW $0.1 1% of annual variable 

costs

Daily cost savings

PNW $10.0

GoME $24.0

Mid Atl $16.0

So Atl $32.0

CA $19.0

AK $12.0

HI $6.0

1% increase in number 

of lives saved

Value of life-$4m

$11.3 5% reduction in search 

costs to USCG plus 

5% reducion in value 

of life

Costs saved to USCG 

plus value of lost lives 

saved

FL

$22.0 25% reduction in lives 

lost due to rip currents 

and other unsafe 

swimming conditions 

+ avoided costs in surf 

rescues

Cost saved to local 

rescue squads plus 

value of lost lives 

saved.

GL $18.9

Search & 

Rescue

GoMex* $28.0

1% increase in number 

of lives saved

Value of life-$4m

PNW $0.4

CA $0.1 1% decline in costs

Oil Spills

GoMex* $0.8 1 % increase in oil spill 

response effectiveness

Reductions in clean 

up and compensation 

costs

Residential 

Property

FL $32.9 Avoided costs from 

earlier preparation for 

storms

Health and 

Safety

Beach 

Restoration

CA $1.8 1% of annual 

expenditures on beach 

restoration resulting 

from improved design 

of projects

Reduced expenditures 

on beach restoration
30

User Sector Users Region Estimated 

Economic 

Effects 

($Millions/Year)

Assumed Effects of 

Observing Systems

Benefit Definition

Electric Load 

Planning

GL $55.8-111.6 Avoided use of most 

expensive peak 

generators

$5.1-11.3 Operator savings from 

avoiding un-necessary 

evacuations

Operating cost 

savings

Energy

Oil and Gas 

Development

GoMex*

$9-15 Deep water structure 

engineering

Increased accuracy of 

oceanographic risks 

in design

PNW $2.7

GoME $4.0

Mid Atl $3.0

So Atl $3.0

AK $10.0

FL $2.0

Increased Landed 

Values

GL $0.2

1% increase in landed 

values

Total regional

economic impact

CA $1.2 1% reduction in 

operating costs

Reduced operating 

costs

Commercial 

Fishing

GoMex* $2.1 1 additional fishing day 

in all fisheries

Increased Landed 

Values

*Gulf of Mexico region excludes the west coast of Florida.

The total magnitude of ocean observing information benefits cannot be accurately 

estimated without more detailed studies of the specific connections between information 

and users. The “scoping” of benefits in Table , while useful, is still very rough. To check on

these estimates, Phase 2 of this study began to develop more explicit estimates based on 

models of specific uses in specific regions. These are shown in Table .

The estimates in Phase 2 are based in most cases on more explicit Bayesian models 

of the type discussed above. Although they follow a similar general structure, each model 

and its parameters is specific to one activity and regional parameters. To illustrate, we use an 

example from the estimation of benefits to the oil industry from avoiding false positive 

hurricane forecasts and unnecessary evacuations of oil rigs, from the work of Kaiser and 

Pulsipher (see Appendix). In this case the model used is:

Ei = ( ph)(P(h))(Δr)C

where:

• Ei is the expected value of the information from the observing system.
31

• ph describes the probability of a hurricane in the Gulf of Mexico, the path of which 

could be better predicted with ocean observing system information. In this 

calculation the frequency is assumed to be one hurricane per year.

• P(h) is the number of platforms in the Gulf (here assumed to be 750) multiplied by 

(h) the proportion of rigs potentially affected by the hurricane. h is assumed to be 

.13, for a total of 100 potentially affected rigs.

• Δris the change in forecasting accuracy due to the observing system. In this case, 

there is an assumed 10-20% reduction in the probability of a false positive forecast 

that a hurricane will hit a specific set of rigs.

• C is the cost of evacuating a rig, estimated at $10,000 per platform and $50,000 per 

rig.

The resulting calculation is:

(750)(0.1-0.2)($10,000-$50,000)=$1.25-2.5M

Details on the specific forms for each model and/or supporting information are 

discussed in the Appendices. 

The Phase 2 results in Table 10 are generally of the same order of magnitude as the 

Phase 1 results in Table 9, which suggests that the range of estimates is reasonable. The 

available social surplus estimates (the correct measure of economic benefits) range from 

$187 million to $200 million per year. For a limited array of regions and users, these figures 

suggest benefits that will likely easily exceed investment and operating costs from the 

observing systems when scaled up to national levels and when the non-social surplus 

estimates can be appropriately disaggregated. 

There are still weaknesses in the data, however, as gross values are still required for 

some of the estimates, particularly in the fisheries. One exception is the study by Wellman 

and Hartley on Alaskan fisheries, which develops estimates of producer surplus changes 

from more accurate forecasts of fish stock levels in the Bristol Bay salmon fishery. There, 

the short time of the open fishery and its remoteness from larger settlements places severe 

risks on companies, which must precisely estimate the amount of labor and capital they will

employ each year or risk either cost penalties from overstocking supplies and labor or severe 

profit penalties from under-stocking them. This case provides a good example of possible 

benefits where the ocean observing system information could be highly useful to businesscritical decision making, although even here the probability of benefits for now must rest on 

the judgment of those engaged in the fishery. At the same time, this is one of the few 

examples in the fishery where cost data needed for estimates of changes in producer surplus 

are available.
32

Table 10 Phase 2 Estimates of Benefits (social surplus estimates are shown in bold)

User Group IOOS Information Benefit Source

Low 

Estimate*

High 

Estimate* Region Measure

Energy Improved Hurricane 

Forecasts

Avoided False 

Positives

$3.8 $7.5 Gulf of 

Mexico

Cost savings

Health and Safety Oil Spill Dispersion 

Models

Improved 

spatial/temporal 

accuracy

$0.6 $1.0 Gulf of 

Mexico

Social cost 

savings

$35.6 $35.6 Southeast 

US Atlantic 

coast

Storm Prediction Improved Tropical Cost savings

Storm Track and 

Intensity Forecasts

Improved 

spatial/temporal 

accuracy

$4.0 $4.0 Southeast 

US Atlantic 

coast

Tourism 

revenue

Transportation Seastate & Visibility 

Forecasts and 

Nowcasts

Improved 

spatial/temporal 

accuracy

$0.5 $1.0 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

$10.0 $15.0 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

Search and Rescue Surface currents & 

winds

Improved 

spatial/temporal 

accuracy

$2.3 $4.7 Gulf of 

Maine/Mid 

Atlantic

Producer 

surplus

Salmon run 

forecasts

Improved 

capital/labor 

investments

$77.0 $77.0 Bristol Bay, 

Alaska

Producer 

Surplus

Reduced risk in 

management 

decisions

Increased 

groundfish catch

$504.0 $504.0 Alaska Wholesale 

value

Commercial Fishing

Reduced risk in 

management 

decisions

Increased crab 

catch

$62.5 $62.5 Alaska Wholesale 

value

Beach closure 

forecasts

Decrease in false 

negatives

$2.3 $3.5 Southern 

California

Consumer 

Surplus

Beach closure 

forecasts

Decrease in false 

negatives

$4.2 $9.3 Southern 

California

Total 

Expenditures

Beach closure 

forecasts

Decrease in false 

positives

$1.1 $1.1 Southern 

California

Total

Expenditures

Recreational boating 

conditions forecasts

Improved 

spatial/temporal 

accuracy

$20.7 $103.5 Great Lakes Total 

Expenditures

Recreation

Recreational fishing 

conditions forecast

Improved 

spatial/temporal 

accuracy

$91.2 $91.2 Florida Consumer 

Surplus

TOTAL Social Surplus Estimates $223.3 $236.5 

TOTAL Other Value Estimates $596.5 $684.4 

*millions of dollars per year
33

Estimates of consumer surplus also have potential weaknesses. There are dozens of 

studies of the economic value of beach recreation, but there is also large variation in the 

results of what a beach day is worth. Estimates cited in studies used here range from about 

$7.00 per day to $28.00 per day. The variances in values arise from a number of sources, 

most importantly the sampling and estimating methodologies used. Some studies use a 

version of contingent valuation, a method based on surveys of beach users. Others used the 

travel cost method, in which the costs of travel to beaches are used as a proxy for willingness 

to pay. 

Whatever methods are used, there are difficult issues of substitution, particularly 

among recreation users. To return to the surfer example: when a beach is closed or is open 

with unfavorable conditions, the surfer may simply choose another beach. Some benefits 

from the observing system may be realized if its information is used to make this selection, 

and so it is unclear whether the benefits are increased from the ability to substitute, or 

reduced somewhat because the substitute requires higher travel costs (and thus lower 

potential net benefits). A similar problem of substitution arises for recreational boating and 

fishing activities.

As a result of these data issues, the estimates presented here are best used to suggest 

an order of magnitude for potential benefits of ocean observing systems. These are shown 

in Table 11. The data in this table are for estimated annual potential benefits. For those 

designated as “10s” of thousands, benefits likely range from $10,000 to $90,000 per year, 

while those designated in the “100s” benefits likely range from $100,000 to $900,000 per 

year. 

Table 11 Order of Magnitude Estimates of Benefits by Major Users

Order of magnitude of 

possible annual benefits

(millions of dollars)

Regions with 

greatest benefits

Recreational Fishing 100s Great Lakes, Gulf 

of Mexico

Recreational Boating 100s Great Lakes, Gulf 

of Mexico, Atlantic

Recreational Activities

Beaches/Shore Recreation 100s Florida, California

Transportation-Freight 10s Florida, Mid 

Atlantic

Transportation

Transportation-Cruise Ships 10s Florida

Health and Safety SAR All

Oil Spills

Tropical Storm Prediction

10s

10s

10s

All

Atlantic, Gulf of 

Mexico

Electricity Load Planning 10s to 100s Great Lakes, 

California, Atlantic

Energy

Ocean Structures 10s Gulf of Mexico

Commercial Fishing Commercial Fishing 100s Alaska, New 

England
34
35

Conclusions and Recommendations

The data in Table 11 suggest that annual benefits to users in the United States from 

the deployment of coastal ocean observing systems are likely to run in the multiple $100s of 

millions of dollars per year. On a discounted present value basis over time, the benefits 

from a national investment in such systems are likely to exceed the costs (though these 

remain to be quantified carefully). This finding is consistent with previous conclusions on 

the economic benefits of such systems, such as that of Kite-Powell and Colgan (2001)10 on 

the Gulf of Maine.

The data also indicate that ocean observing systems will have the largest benefits 

where the information from such systems is used by the largest possible groups. Benefits 

from recreational activities consistently generate the greatest values because of the very large 

number of people who use beaches, boat on the Great Lakes or in the coastal ocean, or 

engage in marine recreational fishing. Although the per-user benefits are smaller than those 

realized in other user sectors, the large number of users drives the overall magnitude of 

potential benefits to substantial sums.

Several important caveats are required in interpreting the results of this study:

• The fact that the benefits from the systems as a whole will exceed the costs does not 

mean that the benefits will exceed the costs in every individual case. The 

configuration of observing systems in each region should take into account the 

priorities of user local and regional user groups.

The estimates presented assume:

• Full and successful deployment of existing, near-deployment, or reasonably 

forseeable technologies.

• Cost efficient and effective means of communicating the information derived from 

the ocean observations to users in a timely manner.

• Users are aware of, and effectively incorporate, the information into their decisions 

regarding their activities.

• In the case of commercial fisheries, additional information concerning the state of 

the marine environment is relevant to decisions about managed fisheries and (at 

some point) will permit increases in allowable catches.

Violation of any of these assumptions may reduce the potential or actual benefits to levels 

below those estimated here.

 

10 Kite-Powell, H.L. and C.S. Colgan 2001 The Potential Economic Benefits of Coastal Ocean Observing 

Systems: The Gulf of Maine. Marine Policy Center Woods Hole Oceanographic Institution.
36

Based on these findings, we believe additional research is needed to develop more 

precise estimates of benefits for specific observing systems, instruments, technologies, and 

applications. Specifically:

• Operators of regional observing systems should incorporate in their operational 

plans strategies and activities to measure the economic benefits of their products and 

services. 

Each of the regional observing organizations is expected to undertake some form of 

regular estimates of benefits.11 This will require two pieces of information: the number of 

users and the value placed on the use. Regular evaluation of regional observing system 

information will require close monitoring of the number of users of different information 

products, and should include verification not only of the number of users but how they 

utilize the information. 

Valuation of information uses can be accomplished in several ways. For example, 

user surveys incorporated into websites that distribute observing system information could 

be an effective tool to measure both the number of users and the values they put on the 

information. As was done in this study, benefit studies performed for other purposes may 

be used to infer values associated with observing system information, although careful 

review of such studies is required to assure both theoretical and empirical appropriateness. 

• Investments should be made by federal, state and local governments in more 

precisely estimating economic benefits and in sharing data and methods for benefits 

estimation among operators of observing systems. To build on the work presented 

in this report, a series of coordinated pilot projects should be funded at the regional 

level to develop, apply, and share with other regions detailed guidelines for benefit 

tracking and estimation. These pilot projects should focus on one or two prominent 

user sectors in each region, and cover the major sectors identified in this report.

Those who operate regional observing systems are likely to be experts in the ocean 

sciences, technologies, and data management, and are unlikely to have substantial expertise 

in economic benefit evaluation. If the expectation of consistent economic benefits 

assessment is to be met, regional associations will need access to resources, including 

personnel, easily implementable methods, standard instruments for surveys, etc. Developing 

such methodologies on a pilot basis with different regional associations and with the

intention that methods, data, etc. thus developed could be transferred nationally to the 

maximum extent possible, could save a great deal of potential wheel reinvention and greatly 

improve the quality and quantity of economic benefits data available for future evaluations.

• Consumer surplus benefits should be estimated for all categories of recreational

users in various regions of the country. Current estimates of such benefits do not 

 

11

 See “Guidance for the Establishment of Regional Associations and the National Federation of Regional 

Associations”, produced by Ocean.US (www.ocean.us). [The business plan] should describe expected benefits 

for users and how products and services will be evaluated periodically (e.g., annually) in terms of the timely 

provision of data, data quality, user satisfaction, system integration, and the achievement of the RA’s objectives.
37

fully account for the possibility of substitution among different recreation resources 

in different regions and remain subject to considerable methodological variability.

The estimation of consumer and producer surplus presents two different challenges. 

In general, any estimates of cost savings by public or private organizations that result from 

observing information can be counted as producer surplus. The case of the Bristol Bay 

salmon is a good example. While cost data are far from universally available, they are more 

available than data on consumer surplus, which is the key to measuring benefits in the 

recreation area. As noted above, these are likely to be the largest generators of total benefits 

simply because of the number of users. 

Because of the variance in consumer surplus methodologies and results, operators of 

observing systems will need both guidance on the estimation of such benefits and access to 

existing and new studies. A library of accessible and relevant consumer benefits studies is 

one way to meet needs. There is also substantial variability in the availability of such studies. 

Marine recreational fishing tends to be the most intensively studied recreational activity 

relevant to ocean observing systems in most of the country. Beaches are the next most 

common, although there is great variability across regions. California and Florida tend to 

have the most studies, New England the least. Recreational boating value studies are sparse 

in all regions. Examples of relevant resources include:

• National Survey on Recreation and the Environment (NSRE): 

http://marineeconomics.noaa.gov/NSRE/

• National Ocean Economics Project (NOEP) Non-Market Value Portal: 

http://noepdata.csumb.edu/nonmarket/NMmain.html
38

Contributors

Hauke L. Kite-Powell PhD

Mail Stop 41, WHOI

Woods Hole, MA 02543-1138

Phone: (508) 289-2938 FAX: (508) 457-2184 E-mail: hauke@whoi.edu

Charles S. Colgan PhD

Muskie School of Public Service

University of Southern Maine

PO Box 9300 Portland, Maine 04104

Phone: (207) 780-4008 FAX: (207) 780-4417 E-mail: csc@usm.maine.edu

Michael Luger PhD

UNC Chapel Hill, Campus Box 3440, The Kenan Center

Chapel Hill, NC 27599-3440

Phone: (919) 962-8201 FAX: (919) 962-8202 E-mail: mluger@email.unc.edu

Ken Wieand PhD

College of Business Administration, University of South Florida

Tampa, FL 33620

Phone: (813) 974-3629 FAX: (813) 905-5856 E-mail: kwieand@coba.usf.edu

Allan G. Pulsipher PhD Mark J. Kaiser PhD

Center for Energy Studies, Louisiana State University

Baton Rouge, LA 80893

Phone: (225) 578-4550 FAX:(225) 578-4541 E-mail: agpul@lsu.edu

Linwood Pendleton PhD

School of Public Health

University of California at Los Angeles

Los Angeles, CA

Phone: (805) 794-8206 E-mail: linwoodp@ucla.edu

Katharine F. Wellman PhD

2611 42nd Ave. W.

Seattle, WA 98199

Phone: (206) 284-2413 E-mail: kfwellman@attbi.com

Thomas M. Pelsoci PhD

Delta Research Co.

Two First National Plaza, 20 S. Clark Street, Suite 620, Chicago, IL 60603

Phone: (312) 332-5739 FAX: (312) 372-3874 E-mail: 

tpelsoci@deltaresearchco.com
39

Acknowledgements

We thank the member agencies of the National Oceanographic Partnership Program for 

their support in the funding of this study. Agencies contributing to the study included the 

National Oceanic and Atmospheric Administration, Office of Naval Research, National 

Science Foundation, National Aeronautics and Space Administration, United States Coast 

Guard, Environmental Protection Agency, and Minerals Management Service.

Dr. Rodney Weiher of NOAA served as Project Administrator for NOPP. Dr. Melbourne

Briscoe of the Office of Naval Research served as Project Coordinator.

Our appreciation also goes to the staff of Dr. Tom Malone, Dr. Larry Atkinson, Muriel Cole, 

and the staff of Ocean.US who provided valuable technical assistance and facilities for the 

project team to meet. We also thank Margaret Davidson of the NOAA Coastal Services 

Center for providing additional funding support.

The Global Ocean Observing System Steering Committee, and its chair, Dr. Worth Nowlin 

of Texas A&M University provided input and assistance through the project.
40
41

Appendix A: Beach Management in California

Linwood Pendleton

University of California, Los Angeles
42

Harnessing Ocean Observing Technologies to Improve Beach Management:

Examining the Potential Economic Benefits of an Improvement in the Southern 

California Coastal Ocean Observing System

July 19, 2004

Linwood Pendleton

Associate Professor

Environmental Science and Engineering Program

School of Public Health

University of California, Los Angeles

Acknowledgments

Special thanks are owed to: Steve Weisberg for his help in identifying ways in which 

improved coastal ocean observing technologies could be applied to coastal water quality 

management, Stan Grant and Eric Terrill for explanations of the way in which an 

improved COOS could improve a geographic understanding of the fate and dispersion of 

human pathogens in the coastal zone, Jessica Morton for research assistance, and 

Alexandria Boehm, Sharyl Rabinovici, and Michael Hanemann for their comments 

during a presentation of the paper at Stanford University. 
43

1. Introduction

Despite efforts to reduce coastal water pollution, bacterial contamination continues to 

affect beaches in Southern California; often these contamination events result in the 

closure of beaches to swimming. Between 1999 and 200212, Los Angeles and Orange 

Counties suffered an average of 147 beach closure days annually (where one beach 

closure day represents a closure of access to the water at one beach for one day). These 

closures represent 0.7 percent of all possible beach days. In addition, during 2000, 2001, 

and 2002, there were an average of 2456 days of beach postings in Los Angeles and 

Orange Counties (NRDC 2003)13 representing more than 12% of all possible beach days. 

The economic impacts of such closures, postings, and the contamination that leads to 

these closures could be substantial. Beach closures reduce recreational opportunities for 

beach goers and deprive coastal businesses of revenues. Further, the methods for 

determining when beaches should be closed are imperfect; failures to quickly detect the 

bacterial contamination that leads to closures may result in serious exposure of beach 

goers to viruses and other human pathogens.

Improved coastal monitoring, especially through improvements in the Coastal Ocean 

Observing System, could significantly improve the way in which coastal managers 

monitor beach water quality and mitigate the exposure of beach goers to potentially 

hazardous water quality conditions. In this paper, we briefly examine the most serious 

shortcomings of current beach water quality monitoring in Southern California and 

explore ways in which improved coastal water monitoring technologies could be used to 

improve coastal water management.

1.1 The Economic Value of Beach Recreation in California

Beach recreation is a cornerstone of the California coastal economy and even California 

culture. For at least four decades, Hollywood has carefully documented the California 

beach life. A more complete and accurate assessment of the number of actual beach 

users and the economic value of beach use, however, has only just begun. Nevertheless, 

the emerging picture of beach visitation and the potential value of market and non-market 

economic impacts of beach use in California corroborate the obvious importance of beach 

visitation for the California coastal economy.

The California Coastal Act protects access to public beaches throughout California. As a 

result, beaches are an important source of recreational open space for Californians with as 

many as 63.4% of all Californians making at least one visit to a California beach each 

year – 2.5 times the national average (California Department of Boating and Waterways 

2002). Philip King of the San Francisco State University conservatively estimates that as 

many as 378.5 million day trips were made to California beaches by Californians in 2001 

 

12 Data on closures are taken from Morton and Pendleton and the State Water Resource Control Board 

updated database on beach closures.

13 The State Water Board reports an average of 6819 days of beach postings for the period, including many 

Orange County beaches that were posted continuously through the period.
44

(California Department of Boating and Waterways, Chapter 3). The United States Life 

Saving Association estimates that as many as 146 million visitor days were made to 

southern California beaches alone (USLA 2002). In another study Morton and Pendleton 

(2001) estimate that total beach attendance in Los Angeles and Orange County in 2000 

exceeded 79 million visits. Morton and Pendleton’s estimates, detailed in a report to the 

State Water Resources Control Board, are taken directly from lifeguard records.

Day trips to beaches generate two distinct sources of economic value for the coastal and 

ocean economy: market expenditures and non-market consumer surplus values. First, 

day visitors to beaches spend money locally on food, beverages, parking, and beach 

related activities and rentals (e.g. body boards, umbrellas, etc.). These expenditures 

partially represent a transfer of expenditures that may have been made elsewhere in the 

state (e.g. gas and auto), but are largely expenditures that would not have been made in 

the absence of the beach trip. We use two previous studies to estimate the average 

expenditures per person per day trip ($/trip/person) for visits to California beaches. A 

survey of beach goers in southern California (Hanemann et al. 2002) found that per 

person per trip expenditures on beach related items and services were $23.19 for beach 

goers that took at least one trip in the summer of 2000. In another study by King 

(California Department of Boating and Waterways 2002), average beach related 

expenditures (excluding gas and automobile costs) were $29.66. Based on these two 

studies, we conservatively estimate the average per trip per person beach related 

expenditure for California beach visits to be $25 and the total annual beach related 

expenditures to be $9.46 billion. We estimate the total annual expenditures for beach 

goers in Los Angeles and Orange Counties to be $1.8 billion.

Visitors to beaches also place a value on beach visits above and beyond what they spend 

at the beach – the consumer surplus of beach visits. Unlike many marketed goods, access 

to the beach is largely free (aside from parking fees) in California. Because of the low 

cost of beach access and the importance of beach recreation to Californians, numerous 

studies have estimated the consumer surplus of beach going in California to better 

measure the true value of beaches and beach management in the state. Two primary 

methods were used to value consumer surplus estimates: the travel cost method and the 

contingent valuation method. Chapman and Hanemann (2001) argue that to date 

contingent valuation estimates of California beach visits have been flawed and generate 

unreliable estimates of beach values, largely because the contingent valuation surveys 

often are not site specific and fail to account for varying travel costs to beaches around 

the state. 

Travel cost estimates of consumer surplus for beach visits have been employed to 

estimate the value of visits to beaches, largely along the central and southern California 

coast. Table 1 provides estimates of consumer surplus values for visits to beaches in 

California. Consumer surplus estimates range from a low of $10.98 (in 2001 dollars) for 

visits to Cabrillo Beach in Los Angeles County (Leeworthy and Wiley 1993) to a high of 

greater than $70 (in 2001 dollars) per person per trip for visits to San Diego beaches 

(Lew 2002). In 1997, Michael Hanemann estimated the value of the consumer surplus of 

beach visits to Huntington Beach at $15/visit (Hanemann 1997). Hanemann’s estimate of 
45

beach related consumer surplus was later discounted by ten percent and used as the basis 

for a jury award regarding lost beach recreation due to the American Trader oil spill 

(Chapman and Hanemann 2001). More recent results, still under review, by the Southern 

California Beach Valuation project indicate that in Southern California alone, the nonmarket value of swimming at beaches may exceed $1 billion14 and the value of visits to 

beaches for swimming and non-swimming activities may exceed $2 billion.

1.2 Current Procedures for Determining Beach Closures

An improved and substantially modified in the Coastal Ocean Observing System could 

improve the accuracy of the system used to determine when and where beaches are 

closed in Southern California. The state of California, through State Bill AB 411, 

mandates the closure of beaches that are thought to be contaminated by sewage and 

requires the “posting” of beaches that exceed specific levels of bacteriological 

concentrations. Currently, the procedures used to close beaches to swimming rely on a 

notification of sewage spills by sanitation authorities or in situ measures of 

bacteriological water quality. Only rarely are sewage spills detected immediately and 

sometimes even known spills are not reported to the public.15 Heal the Bay reports that 

between April 2002 and March 2003 there were 222 sewage spills in Los Angeles 

county, none of which led to beach closures (including one spill that emitted 745 gallons 

of untreated sewage into the waters of Will Rogers State Beach)16. More commonly, 

water quality impairment is discovered by the daily water sampling that is conducted at 

stations along California’s beaches; sewage spills are often reported by beach goers and 

residents. These samples are collected in the surf zone and sent to labs for analysis. 

Three serious shortcomings exist in the methods used to monitor water quality and 

inform the public about water quality contamination. First, weekly and even daily water 

sampling is known to be an extremely imprecise means of detecting water quality 

contamination by bacteria (see for instance Leecaster and Wiesberg 2001 or Kim and 

Grant 2004.) The effects of tides, lunar cycles, and other vagaries in near shore 

oceanographic conditions can seriously impair the effectiveness of surf zone and “point 

zero” storm drain monitoring. Temporarily high bacteria readings may not indicate 

continued serious coastal water contamination while at other times a false negative 

reading may mask serious contamination problems. Leecaster and Weisberg (2001) 

found that only thirty percent of positive first day readings were associated with 

significant water quality contamination during the following day. The findings of 

Leecaster and Weisberg indicate that by the time water quality contamination is detected, 

water quality has returned to normal in seventy percent of the cases. Kim and Grant 

(2004) estimate that at Huntington State Beach, current water quality methods result in 

errors of public posting that can reach forty percent at times. 

 

14 The estimates of Hanemann et al are not weighted to reflect sampling bias and so should be considered as 

an order of magnitude estimate for non-market swimming values.

15 For instance, on March 3, 2004 the Hyperion Treatment plant released more than 150,000 gallons of 

partially treated effluent into Santa Monica Bay, but the release was not reported publicly until March 5, 

2004.

16 See www.healthebay.org
46

A second serious problem with current water quality monitoring efforts is that a 

significant time lag exists between water sample collection and the reporting of 

laboratory results; the time between sampling and the determination of water quality can 

take up to three days. This lag suggests that potentially harmful water quality conditions 

may exist for as long as three days before the public is notified. (The time lag is even 

longer for beaches that have only weekly sampling.) Further, water quality conditions 

may have improved substantially by the time that results are reported. The practical 

result of these time lags is that many beaches remain open when they should be closed 

and are closed after they should have been re-opened. The economic impact of this 

dissonance between monitoring and closures is that a) beach goers may get sick from 

swimming at beaches where water quality has been shown to be impaired and b) 

recreational beach goers may be prevented from visiting beaches that are no longer 

contaminated and therefore need not be closed.

Finally, the indicator bacteria currently monitored by water quality agencies provides an 

inexact determination of the source of water quality contamination. Indicator bacteria are 

not specific to human beings and may result from natural sources. The reliance on these 

indicator bacteria as proxies results in two types of monitoring errors. First, positive 

indications of water quality contamination may be inappropriately linked to human 

sources. This may have been the case at Huntington Beach during the closures of 1999. 

Second, without primary data indicating the presence of human pathogens, some 

contaminated beaches may remain open because the actual link between human sewage 

and bacteria levels remains undetected. Without knowledge of an active sewage spill or 

other source of contamination by human wastes, beaches may only be posted and not 

closed; the result is that bathers may continue to use posted waters believing that the 

contamination risk is less serious than during a closure17.

Once a high level of bacteria has been detected, managers must determine the extent of 

beaches that may be impacted. If a sewage spill is known to be associated with high 

levels of water borne bacteria, managers often close large sections of beach. If two or 

more sampling stations indicate high-levels of potentially sewage-related bacteria, then 

beach areas adjacent to and between stations will be closed. Such extensive closures are 

intended to provide closures that err on the side of precaution. Nevertheless it is known 

that bacteria from point sources (e.g. stormdrains or breaks in sewer lines) are not 

dispersed uniformly throughout the surf zone. Instead, bacteria and pathogens follow 

local near shore currents. The results are hotspots of bacterial contamination that may 

affect only very small sections of the shoreline. Steve Weisberg, Director of the Southern 

California Coastal Water Research Project, estimates that the geographical imprecision of 

current beach closure protocols may result in the closure of as much as two times more 

shoreline than is needed to protect beach goers from coastal bacterial water pollution. 

The economic result of this imprecision is that beach goers are unnecessarily displaced 

from beaches; non-market values and expenditures are diminished unnecessarily.

 

17 There are no empirical data that indicate the effect of postings on swimmer behavior.
47

For the purposes of analysis, we group the policy errors associated with the shortcomings 

of the current protocol for monitoring and reporting water quality data into two types of 

errors in the execution of beach closure policy (we follow the example of Rabinovici et 

al. 2004). First, in some the current protocol causes beaches to be closed when these 

beaches are, in fact, in compliance with water quality standards; we refer to these types of 

errors as Type I errors and note that these errors largely impact the recreational value of 

beaches. Second, time lags in reporting and a failure to adequately identify human 

pathogens in coastal water quality leads to a Type II error in which beaches are not in 

compliance with water quality standards for safe swimming, but beaches are not closed to 

swimming (in many cases these beaches may be posted with advisories). Type II errors 

result primarily in public health costs. Of course, it is unlikely that Type I and II errors 

could ever be eliminated completely. Nevertheless, in the discussion that follows we 

begin to estimate an upper bound for the value of the elimination of these errors in the 

management of beaches in Los Angeles and Orange Counties.

1.3 Using the SCCOOS To Better Manage Beach Postings and Closures

We use a basic framework of “models” to explore and evaluate the potential policy 

impacts of improvements in the SCCOOS that could improve the monitoring of coastal 

water quality. The first model in the framework begins with a description of the 

proposed technologies and the resulting data and analyses that would be part of the 

improved SCCOOS. We call this model “NOWCASTS AND FORECASTS.” New raw 

data and analyses will become part of the portfolio of information that coastal managers 

use to make decisions regarding coastal water quality and beach closures. In the 

DECISION MODEL we describe these basic decisions and how they are affected by the 

information that could be generated by improvements in the SCCOOS. The decisions 

made by coastal managers in turn result in real changes in the behavior, health, and well 

being of beach goers. In the PHYSICAL MODEL, we describe the policy and behavioral 

outcomes of that result from better decision making. Finally, in the ECONOMIC 

MODEL we begin to put the physical outcomes in the context of potential economic 

changes that might result from this potential, but hypothetical, use of improved coastal 

ocean observing technology.

2. NOWCAST AND FORECASTS

The Southern California Coastal Ocean Observing System could be modified to better 

detect, track, and monitor coastal ocean contamination; two primary technologies could 

be employed towards this end. First, several technologies, broadly known as Rapid 

Microbial Indicator Methods, now exist that permit the immediate detection and 

identification, and thus reporting, of bacteriological pathogens in coastal waters (ACT 

2003). In June 2004, the Southern California Coastal Water Research Project began a 

test of three principal types of rapid microbial indicator methods: Immunoassays, 

Chromogenic Substrate Analyzers, and Polymerase Chain Reaction Methods. These 

methods could improve beach water quality monitoring by a) providing continuous 

monitoring of water quality (thus providing more data to help overcome the temporal 
48

vagaries that affect water quality testing), b) more accurate detection of human 

pathogens, and c) real time (or near real time) notification of health risks (i.e. reduced 

time lag between sampling and reporting). Secondly, oceanographic buoy data (e.g. 

wind, waves, current, temperature) can be combined with satellite data to more accurately 

model, and thus predict, the fate of water borne pathogens near the coast. Systems like 

the CODAR (personal communication, Eric Tyrell, Scripps Institute of Oceanography), 

already part of the Southern California Coastal Ocean Observing System, have 

demonstrated the potential for more accurately tracking the source and dispersion of 

contaminants in coastal areas of San Diego County including the Tijuana River outfall. 

Stanley Grant of the University of California, Irvine proposes a similar system in which 

HF Radar data, NEOCO data, mooring data, and ocean current modeling efforts will be 

linked to existing water quality monitoring programs, to create a water quality forecasting 

algorithm suitable for deployment at coastal sites (personal communication). Better 

prediction of the fate of contaminants will allow managers to more narrowly target 

beaches for closure and also will allow authorities to close beaches that are likely to be 

affected by contaminant flows in advance of actual detection at those beaches.

3. DECISION MODEL

The data from the potential improvements in the SCCOOS, described above, will be used 

to inform three types of policy actions:

1) when pathogens levels are sufficiently high to warrant beach closures, which 

beaches should be closed,

2) the provision of accurate information to the public regarding the geographical 

extent and duration of water quality contamination events, and

3) the determination of the existence of human pathogens in coastal waters.

By better informing these three areas of policy decision making, the improved SCCOOS 

could substantially reduce the kinds of errors in identification and reporting of water 

quality contamination problems.

4. PHYSICAL OUTCOMES MODEL

4.1 General Outcomes

The provision of more timely and accurate data about the extent and duration of coastal 

water quality contamination would result in a number of tangible policy outcomes. We 

review these outcomes here and in the next section describe and begin to place a value 

upon the economic impacts of these outcomes.

4.2 Type I and Type II Errors in Closing Beaches

As described above, an improved SCCOOS could potentially improve the accuracy of 

water quality monitoring and in turn would improve the economic and public health 

efficiency of the current system of beach monitoring and closures. Following Rabinovici 

et al. (2004), we examine the ways in which an improved SCCOOS could reduce the two 

primary errors made by beach managers: Type I errors in which beaches are in 
49

compliance with water quality standards, but are inappropriately closed to swimming and 

Type II errors in which beaches are not in compliance with water quality standards, but 

are not closed to swimming.

4.2.1 Type I Errors

Specifically, the improved SCCOOS could potentially

• reduce false positive pathogen indications that are caused by temporally 

varying water conditions and sampling regimes,

• reduce false positives that are caused by non-human bacteria, and

• improve the geographic accuracy of closures by using models and real 

time data collection to determine areas most likely to be contaminated 

following a detection.

We begin our exploration of the economic benefit of reducing Type I errors by focusing 

on the gains from a better geographic understanding of the fate of waterborne pathogens 

and assume that the economic value of such improvements would come from reducing by 

half the spatial extent, and thus the number, of unnecessary closures. 

4.2.2 Type II Errors:

Improvements in the SCCOOS could reduce the number of days in which beaches are 

contaminated and should be closed, but are not closed. These Type II errors occur for 

two primary reasons: 1) the time lag between sampling and monitoring means that severe 

water quality impairment is not reported to the public until two or more days after the 

water quality event and 2) some contaminated beaches are not closed if authorities cannot 

determine a link between human sewage and high levels of bacteria. Type II errors 

primarily have public health impacts; swimmers on these days are likely to get sick more 

frequently than on uncontaminated days. 

Turbow, et al. (2003) show that most illnesses occur even when beaches remain open, but 

are posted. The deployment of rapid microbial indicators methods could lead to an 

improvement in the accuracy of detection of human pathogens and the ability to 

accurately differentiate between high levels of non-human fecal bacteria and the more 

virulent human pathogens. As noted at the beginning of this report, the NRDC reports 

that, on average, more than 2450 beach postings are made Los Angeles and Orange 

County. In fact, the State Water Board’s beach posting database indicates as many as 

6000 postings on average over the period 1999-2002. Many of these postings are likely 

to represent serious human health hazards; with better pathogen identification many of 

these posting might become closures. (Postings are likely to be an imperfect means of 

eliminating swimming at contaminated beaches. Therefore, the conversion of postings to 

closures when appropriate would further reduce exposure to pathogens and represent a 

further decline in the number of Type II errors that are made by beach managers.)
50

5. ECONOMIC OUTCOME MODEL

5.1 Recreational Impacts

We begin by examining an upper bound for the value of reducing Type I errors that 

create inappropriate closures and thus diminish recreational values associated with beach 

use. Beach closures represent a loss of recreational opportunities for beach goers in 

Southern California. Rabinovici et al (2004) show that for Great Lakes beaches, a beach 

closure represents a net economic loss to society even when the beach should have been 

closed under public health guidelines. In our analysis, we focus only on the value of 

beach closures during which visitors may have been prevented from swimming on days 

when water quality might have fallen within the range of bacteriological levels deemed 

safe by public health standards. As stated above, we limit our analysis here to the value of 

reducing by half the number of unnecessary beach closures in Los Angeles and Southern 

California. Under this scenario, there would be an additional 73.5 beach days available 

to beach goers in Southern California.

Hanemann et al. (2004) show that the exact value of a beach closure in Southern 

California depends on the beach in question and the season of the closure. Predicting 

which beaches will close in the future, when, and how long those beaches might remain 

closed or open more often with an improved COOS is not possible. Nevertheless, we 

examine an upper bound for the potential value of reduced unnecessary beach closures by 

assuming that future unnecessary beach closures would be random. 

On average, in Los Angeles and Orange Counties there were 147 beach days lost to 

closures each year from 1999 to 2002. Based on 51 primary public beaches in Los 

Angeles County, these closures represent approximately 0.07% of all possible beach “day 

trip” recreational possibilities. In fact, the entire beach is rarely closed to visitors; instead 

isolated stretches of beach are closed and often these closures apply only to swimmers. 

As an upper bound, we assume that the entire beach is closed due to a beach closure, but 

we limit this closure to that proportion of visitors that would swim at the beach. 

Pendleton et al. (2001) found that 38.4 % of beach goers in Los Angeles planned to swim 

during their trip to the beach. In a more recent study, Hanemann et al. (2004) found that 

28% of all trips made to the beach by a panel of beach goers in four southern California 

counties include a water based activity. In this study, we assume that 28% of all beach 

day trips in Southern California include a water-based activity.

We explore two methods for calculating the economic value of reduced unnecessary 

closures: 1) an estimation of the increase in total beach visitation and thus an increase in 

per trip non-market values and expenditures and 2) an estimation of a proportional 

increase in total non-market and expenditure values for Southern California beach 

visitation. 

METHOD 1: VALUING AN INCREASE IN TOTAL BEACH VISITATION

In the first method, we use an average daily attendance figure for beaches closed in 1999 

and 2000 to estimate the total number of beach visits that could be recovered. Average 
51

daily beach attendance, at all reporting beaches in Los Angeles and Orange Counties 

during 1999 and 2000 was 8,142 visitors/beach day. (The average daily attendance, two 

days before closure, at closed beaches in 1999 was 8,606 visitors/beach day6. This 

indicates that from an attendance perspective, the beaches that were closed in these years 

were slightly less more heavily visited than beaches on average; in other words, popular 

beaches were being closed.) Based on the assumptions outlined above, approximately 

167,500 new beach visits could have been made were there better geographic resolution 

of beach closures. As described earlier, we value a beach visit at $13.50/visit/beach day 

and the per person expenditures are estimated to be $25/visit/beach day.

Method 1: Non-market valuation -

(Average visits/day) x (proportion of visitors that swim) x (additional beach 

days) x (value of a beach day) = recreational value of reducing unnecessary 

closures.

(8142 visits/beach day) x 28% (swimmers/total visitors) x 73.5 beach days x 

$13.50/visit = $2,262,000

Method 1: Market Valuation

(8142 visits/beach day) x 28% x 73.5 beach days x $25/visit/beach day= 

$4,189,000

METHOD 2: VALUING A PROPORTIONAL INCREASE IN TOTAL VALUES

As a check on our estimates from Method 1, we consider the change in value that would 

have resulted had there been a proportional increase in the total non-market value and 

total expenditures associated with beach visits in Los Angeles and Orange Counties. In 

reality, a proportional increase assumes that the provision of more beach recreational 

opportunities would lead to a linear increase in value. Such a proportional increase 

should be considered an upper bound on the potential impact of additional beach days. 

First, a proportional increase assumes that the non-market value of additional beach days 

is equal to the average value of all other beach days. In fact, the value of these additional 

beach days depends on whether or not these additional beach days represent better than 

average or worse than average beach recreational opportunities. Second, it may be the 

case that a proportional increase in beach opportunities will not lead to a proportional 

increase in beach visits of the same size. Therefore, our estimates of the value of a 

proportional change in beach expenditures also should be considered an upper bound.

Method 2: Non-market Valuation

Current total non-market value of water related activities x (additional beach 

days/ total beach days) = change in non-market value

$1 billion18 x (0.0035) = $3,500,000

 

18 The estimates of Hanemann et al are not weighted to reflect sampling bias and so should be considered 

an order of magnitude estimate for non-market swimming values.
52

Method 2: Market Valuation

Current total expenditures x (swimmers/total visitors) x (additional beach days/ 

total beach days) = change in market value

$9.46 billion x .28 x .0035 =$9,270,000

Using the more conservative estimates from the two methods, we find that better 

geographic accuracy in closures could yield potential economic benefits of: $2,262,000 

for non-market values and $4,189,00 for expenditures for a total of $6, 451,000/year. 

(We remind the reader that the above analysis focuses exclusively on beach closures. To 

date, the impact of beach postings on beach visitation has not been quantified. If beach 

postings effectively eliminated beach visitation by swimmers, then our approach here 

could be applied to beach postings as well. The NRDC reports an average annual number 

of beach posting days in Los Angeles and Orange County of 2456. This represents 

sixteen times more days than in the closure analysis above.) 

5.2 Public Health Impacts

In the short run, public health benefits of an improved SCCOOS could come from the 

reduction in the time lag between sampling and reporting water quality impairments that 

would be possible with the deployment of telemetric rapid microbial indicators (i.e. 

microbial indicators that could transmit data in real time or near real time). Reducing this 

time lag, in turn, could reduce the number of days in which beaches are contaminated, but 

not closed (Type II errors in compliance). Further public health benefits would result 

from the deployment of rapid microbial indicators that could differentiate between human 

pathogens and non-human pathogens. This more precise species identification of 

pathogens could potentially reduce closures that are mistakenly linked to sewage spills, 

but also could increase the overall number of closures by leading authorities to close 

beaches that might otherwise have been posted. (These increased closures would result in 

a loss of recreational values. Rabinovici et al., 2004, argue that even appropriate closures 

result in recreational value losses that may exceed gains in public health values.) We 

focus only on the gains in public health values that could result from a reduction in Type 

II compliance errors.

5.2.1 Cost of water related illnesses

Recreational contact with marine bathing water has been shown to result in an increased 

likelihood of a suite of human illnesses including upper respiratory infections, 

gastrointestinal infection, ear and eye ailments, and fever. Prüss (1998) reviews the 

literature prior to 1998, while a number of more recent studies further explore and model 

these links (including Henrickson et al. 2001, Wymer and Dufour 2002). Even in the 

absence of known contamination by human sewage, coastal swimmers can be subject to 

elevated risk levels for disease (see Cabelli et al. 1982, Calderon et al. 1991, and Haile et 

al. 1999). Pathogens in bathing water can come from marshes (see Grant et al. 2004) and 

surface water run-off (see for instance Haile et al. 1999 and Jiang et al 2001). Known 

contamination of coastal waters by human sewage has been shown to increase the relative 

rates of illness even more (see Fleisher 1996 and 1998). The literature does not indicate 
53

whether the rates of illness are additive or whether these symptoms appear in clusters 

(with swimmers getting one or more illness simultaneously). 

In this study, we focus exclusively on the public health impacts of bathing waters that 

should be closed to swimming due to contamination by human sewage (the principle 

criterion for beach closures in Los Angeles and Orange County). Specifically, we 

examine illnesses that may have resulted from a lag between sampling and the closure to 

swimming of beaches in Los Angeles and Orange Counties. To begin, we consider only 

excess illnesses that may have resulted exposure to sewage contamination. In Table 2, 

we provide adjusted relative risk rates for the most common categories of illness 

associated with swimming in coastal waters contaminated by sewage. Column 1 gives 

the proportion of swimmers that came down with illnesses after swimming in sewage 

contaminated waters in the United Kingdom (Fleisher et al. 1998). For our purposes, the 

appropriate risk measure is that for the additional risk that comes from swimming in 

marine water contaminated by sewage compared to swimming in marine water generally. 

Haile et al. (1999) provide estimates for the risk of illness associated with swimming in 

the marine waters of Santa Monica Bay, California having very low concentrations of 

fecal bacteria. Column 3 gives the excess risk of swimming in sewage contaminated 

waters (based on Fleisher et al.) compared to non-contaminated waters (based on Haile et 

al.). Of course, it is likely that the populations considered in the studies by Fleisher et al. 

and Haile et al. have different background levels of illness, even for non-swimmers. In 

fact, the rates of illness for swimmers in “clean” areas of Santa Monica Bay is generally 

equal to or lower than the background levels for non-swimmers in the study by Fleisher 

et al. Because the background levels of sickness differ between the two studies, we use 

assume the net excess risk associated with swimming vs. non-swimming in the sewage 

contaminated waters as a more conservative estimate of the potential excess illness that 

could result from swimming in sewage contaminated marine waters in Southern 

California.

Gastrointestinal illness and ear ailments are the most common illness associated with 

recreational water contact when sewage contamination is present with excess rates of 

illness of 8.4 and 4.6 illnesses per 100 swimmers, respectively. Eye ailments are also 

common more common when sewage is present, but at lower rates (2.5).

The economic impact of swimming related illnesses has not been estimated directly. 

Rabinovici et al (2004) use the estimated willingness to pay of $280 (in real terms 

adjusted to year 2000 dollars) to avoid a mild case of food-related gastrointestinal illness 

(estimated originally by Mauskopf and French 1991.) Bloomquist et al. (2001) value 

illnesses associated with coastal bathing water by using the estimated costs of a case of 

influenza, $380 (including the willingness to pay for illness avoidance, cost of treatment, 

and lost wages) originally estimated by Nichol (2001). Because gastroenteritis is 2.5 

times more likely in beach goers than flu-like symptoms, we follow Rabinovici et al and 

use a figure of $280 for each case of excess illness.

We estimate the cost of Type II errors in compliance by assuming that bathers are 

exposed to contaminated water for up to two days before beaches are closed. The 
54

assumption of a two day exposure is conservative; in many cases the time delay is three 

days and sometimes as long as a week. Leecaster and Weisberg (2003) and Kim and 

Grant (2004) show that indications of water quality impairments on one day do not 

necessarily result in impairments on following days. Given the unknown duration of 

water contamination during the two-day period between sampling and reporting, we 

assume that bathers would be exposed to contaminated water: a) for only the initial day 

of sampling when a beach closure lasts for one day and b) for the two days preceding a 

closure when a beach closure lasts for two days or more. To estimate the number of 

bathers exposed, we combine beach attendance data and beach closure data for 1999, the 

only year for which both sets of data are available19. As before, we assume that 28% of 

beach goers went swimming and that water quality along the entire beach was impaired. 

We also assume that the swimmer may have gone swimming elsewhere had the beach in 

question been closed and thus are exposed only to the additional risk associated with 

swimming in sewage contaminated marine water as compared to background levels of 

risk from swimming in the same water. Following these assumptions, we find the cost of 

Type II errors that could be corrected by an improved SCCOOS at just over $1.25 

million, using the following formula:

(

i, t-1 i, t-2 i, t-2

i, closed >1 day j, closed 1 day

∑ ∑ Visits + + Visits Visits ) x (proportion of swimmers) x 

(adjusted excess risk x cost of illness) = cost of excess illness

(313,760 Visits) x .28 x (5.1/100) x $280 = $1,254,538

While the previous analysis focuses entirely on the public health benefits of reducing 

Type II errors associated with beach closures, identical technology and analysis could be 

applied to beach postings. Turbow et al. (2003) show that most illnesses occur when 

beaches are not closed. Bacteria levels during postings are comparable to those during 

closures and potentially could lead to substantial exposure to gastrointestinal illness of a 

similar magnitude (see Cabelli et al 1982). As stated earlier, there are approximately 16 

times more beach posting days than beach closure days in Los Angeles and Orange 

Counties. As a result, if the analysis were to be extended to beach postings and closures, 

the public health value would rise substantially (provided that beach postings lead to 

substantial declines in swimming at posted beaches).

6. Conclusion

The current protocol and method of monitoring recreational water quality in the United 

States is known to be imperfect. On site sampling, off site laboratory analysis, and a 

reliance on fecal indicator bacteria instead of human pathogens result in two principle 

types of errors associated with water quality monitoring (Rabinovici et al 2004): 1) Type 

I errors in which beaches are closed even though water quality parameters are within a 

 

19 For 9 closure days, we did not have attendance data. In these cases we used figures from the prior year 

or a nearby beach.
55

“compliance” range thought to be safe for swimming and 2) Type II errors in which 

water quality parameters exceed safe “compliance” levels yet beaches are not closed. 

The causes of these errors include a) precautionary beach closures when a source of 

contaminants are known, but the exact fate of contaminants in near shore waters is not 

known and b) lag times of two or more days between sampling and notification of water 

quality impairment. Type I errors lead to a loss of recreational value when beachgoers 

are prevented from swimming at safe beaches. Type II errors result in public health costs 

when swimmers are not adequately warned about water quality contamination.

Water quality engineers in Southern California are now testing a number of technologies, 

including the use of rapid microbial indicator methods and oceanographic methods, that 

could potentially reduce the incidence of Type I and II errors in beach closure policy in 

Southern California. These new technologies would require modifications of the current 

California Ocean Observation System including the deployment of rapid microbial 

indicator devices on nearshore buoys and better integration and analysis of oceanographic 

data from buoys and satellites. 

This study conservatively estimates the potential benefits of improving water quality 

monitoring methods in Southern California. A complete elimination of the most basic 

types of errors in water quality monitoring Los Angeles and Orange County could result 

in an annual economic savings of between $7.7million and $14.0million. These savings 

are conservative because they only consider the value of public health impacts and the 

non-market values of current beach users. In addition to current beach users, many 

residents and tourists are likely to avoid swimming at Southern California beaches 

because of concerns about water pollution (Pendleton 2001). Better water quality 

monitoring could have significant impacts on the public’s perception of beach water 

quality. Better monitoring could improve the public’s confidence that beaches that are 

open for swimming are, in fact, safe. This, in turn, could increase the number of people 

visiting Southern California beaches. The potential value of these changes in perceptions 

has, to date, remained undocumented, but could significantly add to the overall value of 

improvements in water quality monitoring.

Finally, in Southern California, beach closures are limited to those days in which fecal 

indicator bacteria levels exceed safe standards and the source of bacteria is believed to be 

associated with human sewage. Beach closures represent less than six percent of the total 

number of incidences during which water quality contamination exceeds recommended 

“safe” levels for swimming, but beaches are only “posted” with warning signs. The 

improvements in the coastal ocean observing system described here could be extended to 

the monitoring and posting of beaches, even in the absence of sewage contamination. A 

better understanding of the impacts of these “postings” on public health and recreational 

values is required before we can estimate the potential impacts of a reduction in Type I 

and II errors in the context of beach postings.
56

7. Literature Cited

Alliance for Coastal Technologies. 2003. Rapid Microbial Indicators Methods. An ACT 

2003 Workshop Report. Moss Landing, CA. May 14-16, 2003.

Blomquist, W., Collins, H., and D. Friedman. 2001 Science, Risk, and the Public: 

Controlling Coastal Runoff in Southern California. Workshop in Political Theory 

and Policy Analysis, Indiana University.

Cabelli, V.J., Dufour, A.P., McCabe, L.J., and M.A. Levin. 1982. Swimming-Associated 

Gastroenteritis and Water Quality. American Journal of Epidemiology. 4(115): 

606-611.

Calderon, R.L., Mood, E.W., and A.P. Dufour. 1991. Health Effects of Swimmers and 

Nonpoint Sources of Contaminated Water. International Journal of 

Environmental Health Research. 1:21-31

California Department of Boating and Waterways and State Coastal Conservancy, 2002, 

California Beach Restoration Study. Sacramento California (references in the text 

as CDBW 2002)

Chapman, David and Michael Hanemann. 2001. Environmental damages in court: the 

American Trader case, in The Law and Economics of the Environment, Anthony 

Heyes, Editor, pp. 319-367

Fliesher, J.M., Kay, D. K., Wyer, M. D. and A.F. Godfree. 1998. Estimates of the 

severity of illnesses associated with bathing in marine recreational waters 

contaminated with domestic sewage. International Journal of Epidemiology, 

27:722-726.

Grant, S.B., Sanders, B.F., Boehm, A.B., Redman, J.A., Kim, J.A., Mrše, R.D., Chu, 

A.K., Gouldin, M., McGee, C.D., Gardiner, N.A., Jones, B.H., Svejkovsky, J., 

Leipzig, G.V., and A. Brown. 2004. Generation of Enterocci Bacteria in a 

Coastal Saltwater Marsh and Its Impact on Surf Zone Water Quality. 

Environmental Science and Technology. GET VOLUME AND PAGES

Hanemann, W. Michael. 1997. Final conclusions of Professor Michael Hanemann 

regarding lost recreational damages resulting from the American Trader Oil Spill. 

Report submitted to the State of California Attorney General’s Office. August 15, 

1997.

Hanemann, M., Pendleton, L., Hilger, J., and D. Layton. 2002. Expenditure Report for 

the Southern California Beach Valuation Project. Prepared for the National 

Ocean and Atmospheric Administration, Minerals Management Service 

(Department of the Interior), The California State Water Resources Control 

Board, and The California Department of Fish and Game.

Hanemann, M., Pendleton, L., Mohn, C., Hilger, J., Kurisawa, K., Layton, D. and Felipe 

Vasquez. 2003. Interim Report on the Southern California Beach Valuation 

Project. Prepared for the National Ocean and Atmospheric Administration, 

Minerals Management Service (Department of the Interior), The California State 

Water Resources Control Board, and The California Department of Fish and 

Game.

Hanemann, M., Pendleton, L., Mohn, C., Hilger, J., Kurisawa, K., Layton, D. and Felipe 

Vasquez. 2004. Using revealed preference models to estimate the affect of 

coastal water quality on beach choice in Southern California. Prepared for the 
57

National Ocean and Atmospheric Administration, Minerals Management Service 

(Department of the Interior), The California State Water Resources Control 

Board, and The California Department of Fish and Game.

Henrickson, S.E., Wong, W., Allen, P. Ford, T. and P.R. Epstein. 2001. Marine 

Swimming-Related Illness: Implications for Monitoring and Environmental 

Policy.

Jiang, S., Noble, R. and W. Chu. 2001. Human Adenoviruses and Coliphages in Urban 

Runoff-Impacted Coastal Waters of Southern California. Applied and 

Environmental Microbiology. 67(1):179-184.

Kim, J.H. and S.B. Grant. 2004. Public Mis-notification of Coastal Water Quality: A 

Probabilistic Evaluation of Posting Errors at Huntington Beach, California. 

Environmental Science and Technology. Forthcoming

King, Philip. 2001. The Economic Analysis of Beach Spending and the Recreational 

Benefits of Beaches in the City of San Clemente, mimeo, San Francisco State 

University.

Leecaster, M.K. and S.B. Weisberg. 2003. Effect of Sampling Frequency on Shoreline 

Microbiology Assessments. Marine Pollution Bulletin. 42(11): 1150-1154.

Leeworthy, Vernon R. 1995. Transferability of Bell and Leeworthy Beach study to 

Southern California Beaches” Memo to David Chapman, June 22 (Exhibited 939) 

reported in Chapman, David and Michael Hanemann 2001. Environmental 

damages in court: the American Trader case, in The Law and Economics of the 

Environment, Anthony Heyes, Editor, pp. 319-367.

Leeworthy, Vernon R. and Peter C. Wiley. 1993. Recreational use value for three 

southern California beaches. Rockville, MD. NOAA Strategic Environmental 

Assessments Division. Office of Ocean Resources and Conservation, March.

Lew, Daniel Kevin. 2002. Valuing recreation, time, and water quality improvements 

using non-market valuation: an application to San Diego beaches. Doctoral 

Dissertation. University of California Davis. 

Mauskopf, J.A. and M.T. French. 1991. Estimating the Value of Avoiding Morbidity and 

Mortality of Food Borne Illnesses. Risk Analysis. 4:619.

Morton, J. and L. Pendleton. 2001. A Database of Beach Closures and Historical Water 

Quality. Prepared for the State Water Resources Control Board. Sacramento, 

California.

Nichol, K. L. (2001). Cost-Benefit Analysis of a Strategy to Vaccinate Healthy Working 

Adults Against Influenza. Arch Intern Med 161: 749-759 (in Blomquist et al. 

2001) 

Pendleton, L., Martin, N. and D.G. Webster. 2001. “Public Perceptions of Environmental 

Quality: A Survey Study of Beach Use and Perceptions in Los Angeles County.” 

Marine Pollution Bulletin 42: 1155-1160.

Prüss, A. 1998. Review Of Epidemiological Studies On Health Effects From Exposure 

To Recreational Water. International Journal of Epidemiology. 27:1-9

Rabinovici, S.J.M., Bernknopf, R.L., Coursey, D.L., Wein, A.M., and Whitman, R.L. 

Economic and Health Risk Trade-Offs of Swim Closures at a Lake Michigan 

Beach, Environ. Sci. Technol. 2004, 38 (10), 2737-2745. 

United States Lifesaving Association, 2002, (USLA) http://www.usla.org/PublicInfo/
58

Wymer, L.J. and A.P. Dufour. 2002. A Model for Estimating the Incidence of 

Swimming-related Gastrointestinal illness as a function of water quality 

indicators. Environmetrics. 13:669-678.
59

Table 1: Estimates of the Consumer Surplus Value of Beach Visits in California

Consumer 

Surplus/Trip US$(1990) US$ (2000)

Cabrillo-Long Beach1$8.16 $10.98 

Santa Monica1$18.36 $24.71 

Pismo State Beach2$26.20 $35.26 

Leo Carillo State 

Beach1$51.94 $69.91 

San Onofre State 

Beach2$57.31 $77.14 

San Diego2$60.79 $81.82 

Source: Environmental Damages in Court: The American Trader Case, published in 

The Law and Economics of the Environment, 2001, Anthony Heyes, Editor, pp. 319-367. 

The data are extracted from 1) Leeworthy and Wiley (1993) and 2) Leeworthy (1995).

Consumer 

Surplus/Day US$ (2001)

Individual Surplus/Day Carpinteria Encinitas San Clemente Solana Beach

Method 1 $20.48 $18.84 $25.70 $14.58

Method 2 $24.43 $22.17 $30.58 $17.35

Source: Philip King, The Economic Analysis of Beach Spending and the Recreational Benefits of 

Beaches in the City of San Clemente, 2001. Note: Method 1 - dependent variable is a discrete random 

variable, CS calculated as the sum of a series of rectangles, each one day wide, touching the demand 

curve at its upper right corner. Method 2 - CS calculated as the sum of a rectangle for the area under 

the curve between zero and one, and the definite integral for the area between one and the average 

number of trips.

Total Value of Beach Trip (San Diego) US$(2002)

Statistic Two-step HeckmanTwo-step HFS Joint Heckman Joint HFS

Mean $71.43 $74.86 $43.97 $33.70

Median $74.03 $77.33 $46.31 $36.13

Standard Deviation $10.57 $10.79 $9.70 $9.77

Source: Dissertation by Daniel Kevin Lew, University of California Davis. Valuing Recreation, Time, and Water Quality 

Improvements Using Non-Market Valuation: An Application to San Diego Beaches. 
60

Table 2: Excess Rate of Illness Associated With Bathing Waters

Illness Adjusted Rate of 

Illness (x/100) when 

water contaminated 

by sewage a

Excess Adjusted 

Rate of Illness 

(x/100) when 

water 

contaminated by 

sewage (compared 

to nonswimmers)a

Excess Adjusted Rate 

of Illness (x/100) when 

water contaminated 

by sewage (compared 

to swimmers in Santa 

Monica Bay where 

TC<1000cfu/100ml)a

Excess Duration of 

Illnessa

Gastroenteritis 14.8 5.1 8.4 .2

Acute Febrile 

Respiratory Illness

5.0 2.0 0.1 0.6

Ear ailments 8.2 5.4 4.6 2.7

Eye Ailments 4.5 2.4 2.5 -1.3

a

 From Fleisher et al. (1998). b From Haile et al. (1999). Bold indicates significantly different from 

background at < 0.10 level.
61

Appendix B: Offshore Energy Production in the Gulf of 

Mexico

Mark J. Kaiser and Allan G. Pulsipher

Louisiana State University
62

The Impact of Weather and Ocean Forecasting on Hydrocarbon 

Production and Pollution Management in the Gulf of Mexico

Mark J. Kaiser and Allan G. Pulsipher 

Center for Energy Studies, Louisiana State University 

Energy Coast & Environment Building

Nicholson Extension Drive, Baton Rouge, LA 70803

 

 

Abstract. Weather delay is a common risk in offshore energy production, and the 

occurrence of tropical cyclones regularly force operators to shut-down production, cease 

drilling and construction activities, and evacuate personnel. Loop currents and eddies can 

also have a serious impact on offshore operations and may delay installation and drilling 

activities and reduce the effectiveness of oil spill response strategies. The purpose of this 

paper is to describe the manner in which weather and ocean currents impact hydrocarbon 

production and pollution management in the Gulf of Mexico. Physical outcome and 

decision models in support of production and development activities and oil spill 

response management are presented, and the expected economic benefits that may result 

from the implementation of an integrated ocean observation network in the region are 

summarized. For effective planning and decision-making, reliable forecasts of weather 

and ocean current conditions are required. Improved ocean observation systems are 

expected to reduce the uncertainty of forecasting and to enhance the value of 

ocean/weather information throughout the Gulf region. The source of benefits and the 

size of activity from which improved ocean observation benefits may be derived are 

estimated for energy development and production activities and oil spill response 

management.
63

1. Introduction

The Outer Continental Shelf(OCS)20 of the Gulf of Mexico (GOM) is the most 

extensively developed and mature offshore petroleum province in the world. More than 

40,000 wells have been drilled in the OCS since offshore production began in 1947, and 

there are currently over 4,000 active structures in water depths ranging up to 7,000 ft. 

About 25 percent of the United States domestic oil and gas supply comes from the OCS, 

and in 2002, OCS lands averaged daily production of about 1.6 million barrels (MMbbl) 

of oil and 14.5 billion cubic feet (Bcf/d) of natural gas. The Minerals Management 

Service estimates that oil production levels will rise to at least 2 MMbbl/d and perhaps as 

high as 2.5 MMbbl/d by 2006, while gas projections through 2006 offer contrasting 

scenarios21, with production estimated to range between 11 Bcf/d – 16.4 Bcf/d [1]. The 

deepwater GOM is America’s newest production frontier and now accounts for more than 

half of the Gulf’s total oil production [2]. 

Weather plays a major factor in human activities in the GOM, and extreme weather in 

particular, can have an enormous impact on the cost of “doing business.” Storms and 

hurricanes regularly challenge and endanger the coastal community and energy 

infrastructure throughout the Gulf region. Every year about 10 storms form over the 

tropical portions of the Atlantic Ocean, the Caribbean Sea, and the Gulf of Mexico, and 

about half of these storms will grow into 75 mph hurricanes (www.nws.gov). Of these 

five hurricanes, two-three are likely to strike the coast of the United States (Table 1, 

Figure 1). Tropical storms cause damage to physical, economic, biological, and social 

systems, but the severest effects tend to be highly localized.

When a hurricane enters the GOM, oil production and transportation pipelines shut down, 

crews are evacuated, and refineries along the Gulf coast close. Drilling rigs pull pipe and 

move out of the projected path of the storm, if possible, or anchor down, and supply 

vessels, commercial ships, and barges may be moved into one of Louisiana’s many 

bayous where they have more protection from the storm. Ocean-going vessels transiting 

into or out of the GOM near the time of the event use hurricane forecasts to plot course to 

avoid the storm. The Louisiana Offshore Oil Port (LOOP), the biggest and only 

deepwater oil port in the country, closes to shipping and flows through on-shore pipelines 

are halted. Crude oil from the Gulf to the Midwest via the Capline pipeline, and the 

gasoline and distillate fuel conduit the Colonial Pipeline, also shut down ahead of the 

storm. 

Hurricanes are not the only extreme weather event that impacts offshore oil and gas 

production activities. As operators have pushed into deeper waters in the GOM in search 

for oil and gas, the impact of loop currents on operations have become increasingly 

problematic. The Loop Current is an offshoot of the Gulf Stream, a major North Atlantic 

Ocean boundary current located off the east coast of the United States. The loop is 

 

20 The OCS of each coastal state generally begins 3 nautical miles from shore for all but two states – Texas 

and Florida – which are 3 marine leagues (9 nautical miles), and extends at least 200 miles through the

Exclusive Economic Zone.

21 Although oil and gas production in the heavily leased shallow waters of the GOM has been steadily

declining, the MMS estimates that there is up to 55 trillion cubic feet (Tcf) of natural gas still available in

the deep shelf areas.
64

formed when the Gulf Stream enters the Gulf of Mexico through the Yucatan Straights 

and “loops” through the basin in a clockwise direction before exiting through the Straits 

of Florida (Table 2, Figure 2). When the loop exits through the Florida Straight, it often 

becomes pinched and sheds some of its flow into a separate eddy of warm water which 

migrates backward, southwest across the GOM bringing strong loop current forces into 

active E&P areas [3]. Some oceanographers refer to the Loop Current as the equivalent of 

a hurricane beneath the water, and its impact on deepwater installations is increasing as 

operators have moved into deeper and more eddy prone areas.

The Loop Current is a persistent feature in the GOM characterized by strong surface 

current velocities (2-4 knots) with its position and intensity varying over time. The warmcore eddies that break away from the northern extremity of the Loop Current are 

characterized by intense current velocities which can cause serious impact to offshore 

operations. Typically, two to three eddies form each year. Currents influence rig 

selection, riser design, many aspects of offshore operational planning, and the design and 

installation of production systems, moorings, subsea components and pipelines. Of 

particular importance is fatigue associated with dynamic response to current loading [4]. 

For effective planning and decision making in the GOM, operators require reliable 

forecasting22 of future current conditions. 

 The National Oceanographic Partnership Program (NOPP) formulated a plan for an 

Integrated, Sustained Ocean Observing System (ISOOS) in a 1999 report to Congress [5], 

intending to move the United States from what is now a largely ad hoc and fragmented 

approach to ocean observation to a coordinated and sustained activity similar to the 

existing national weather information system [6-8]. Implementation of ISOOS will 

require investments in infrastructure and ongoing support for new and existing 

observation systems in the open and coastal ocean, and the benefits of federal investment 

will depend on the expected costs and benefits of the resulting system. The importance of 

a national network of ocean observation systems has recently been reiterated by the U.S. 

Commission on Ocean Policy [9].

The purpose of this paper is to describe the manner in which weather and ocean data is 

used in planning and decision making activities in offshore energy development and 

production and oil spill response management, and to identify and quantify the expected 

economic benefits of improved weather/ocean forecasting on these activities. For a 

description of the capabilities of each system, the NOAA website (www.csc.noaa.gov) 

maintains links to each system. The reports [10-15] provide useful summaries of 

individual systems.

The standard economic approach to valuing information requires: 

• A description of the information being valued and of the uncertainty in the

phenomena it describes; 

 

22 A number of initiatives are underway by the academic, government and commercial scientific

community to develop and verify current models of oil and gas basins around the world. The CASE

(Climatology and Simulation of Eddies) joint industry project, Oceanweather’s WANE (West Africa

Normals and Extremes) joint venture between Fugro and the Nansen Environmental and Remote Sensing

Center at the University of Bergen, and the U.S. Navy are all working on advanced ocean current modeling 

programs [4].
65

• A model of how this information is used to make decisions;

• A model of how these decisions affect physical outcomes;

• A model of how physical outcomes can be translated into economic outcomes. 

User sector representatives were identified to define the base case and improved

information scenarios, and then information was obtained regarding the natural variation

of the phenomena being described, including critical variables to nowcast/forecast, the

forecast horizon, spatial and temporal resolution. A decision model is then sketched

describing how users incorporate information into their choices and decisions. The

physical outcome describes how outcomes result from the decision parameters and the

variation in the natural phenomena. Finally, a simple economic outcome model describes 

how the physical outcomes translate into economic changes.

The outline of the paper is as follows. In §2, the methodology of the economic 

valuation is presented. In §3-6, the decision, physical outcome, and potential 

benefits of improved observation systems to energy exploration, development, and 

production activities are described, and in §7, the decision, physical outcome and 

economic outcome models related to oil spill response management are discussed. 

In §8, conclusions complete the paper.

2. Valuation Strategy

The state of knowledge of ocean data is incomplete and uncertain, and so improved

ocean/weather observation systems are expected to enhance the value of the information

and create additional network externalities [6]. Weather information is valuable, and to

the extent that improved ocean observation systems can improve the data on which

weather/ocean forecasts is based, is potentially very beneficial to energy production

activities and pollution management in the GOM. 

The potential impact of savings that may be incurred from improved ocean

observation systems was first estimated by Kite-Powell and Colgan in a study focused on 

the Gulf of Maine [16]. Kite-Powell and Colgan performed order-of-magnitude 

assessments for general categories of benefits using the following methodology:

Step 1. Value activity A that uses and/or is impacted by ocean forecasts, V(A).

Step 2. Assume that the benefit of improved ocean observation systems is 

expressed by some small factor, e(A) > 0.

Step 3. Compute the value of improved observation systems in region R,

=∑

A

V(R) e (A)V (A) .

The valuation strategy is based on estimating V(A) from public sources of information 

and hypothesizing the value of e(A) for each activity identified. The selection of e(A) is

hypothetical but not unreasonable within the framework of the model and the scope of the 

valuation. Ideally, it would be desirable to derive the value of e(A) from fundamental data 

or to ascertain the cost to achieve a desired level of e(A), but establishing such
66

relationships are beyond the state of knowledge of observation systems. Further, no direct 

link between e(A) and V(A) can be “derived” and it is difficult to “justify” e(A) on a

fundamental level. The default condition is to assume e(A) “small” (e.g., 1%, 1 day, etc.), 

and this is considered a “reasonable,” and in all instances, a conservative estimate of the

expected benefits to be incurred. 

3. Stages of Offshore Energy Development

A four-stage sequence of activity is generally followed in offshore energy

development projects:

1. Exploration, 

2. Development, 

3. Production, and

4. Decommissioning.

In the exploration stage, areas that are considered to have prospects of containing oil and 

gas reserves are drilled with exploratory wells and stratigraphic test wells. In the

development stage, the mineral deposit is prepared for commercial production. This

includes the acquisition, construction, and installation of facilities to extract, treat, gather, 

and store the oil and gas. In contrast to a single exploratory well for which drilling can

last anywhere from 2 weeks to 3 months, drilling the wells off a platform can last many

months and extend over several years. Development activities typically include drilling

and equipping development wells and service wells, and the construction and installation

of production facilities. The ongoing operation of the facility is considered the production 

phase. In production, the oil and gas is gathered, lifted to the surface, treated, processed,

and possibly, stored. When the useful life of a production platform is reached, the

equipment and structure is removed and the well casing severed and closed below the

seabed.

4. Drilling Activities

4.1. Decision Model

Offshore drilling may be subject to significant delays caused by the weather, and

weather downtime can play an important factor in the total costs of the operation. Waves 

are one of the most obvious environmental concerns for offshore operations and

constitute the primary cause of downtime and reduced operating efficiency. Weather

downtime can impact drilling operations in various ways; e.g., weather too severe for

operations involving supply boats may lead to delay if stock levels on the rig decline to a 

critical level; weather may impact anchoring up and moving time; weather may be too

severe for drilling to occur; and extreme weather may result in damaged or lost drill

strings and risers. If operating limits are exceeded because wave heights, ocean currents,

or eddies are too strong, drilling operations will be temporarily abandoned and resumed

when conditions fall within the operating capabilities of the equipment. 
67

Safe working conditions for many offshore operations may be approximately

specified by the critical values of wind speed and wave height, and for deepwater drilling 

activities, current profile, as shown in Table 3. The GOM is a fairly benign operating

environment for most of the year, but downtime due to weather can be an important

factor in determining the total drilling costs, and in the deepwater, usually plays a more

significant role because of the day rate of the drilling rig. Empirical evidence suggests

that 1-3% of drilling cost is due to waiting on weather [17], although this is subject to

significant variation depending on the time of year of drilling activity and the water depth 

of the operation. In deepwater, floating rigs are able to maintain position over the tops of 

wells through a dynamic positioning system that compensates for wind, waves, and

currents to keep the vessel stationary relative to the seabed.

Drilling activities generally follow three stages:

1. Start limits. Weather must be below these limits before an operation will start (or 

restart after abandonment).

2. Suspend limits. Work will be paused if the environment exceeds these limits.

Work recommences as soon as weather conditions drop back below the threshold.

3. Abandon limits. Task will be abandoned if these limits are exceeded. Work will

not be restarted until weather conditions fall below the start limits.

The occurrence of a hurricane warning or alarm is enough to disrupt drilling

operations, and a significant amount of operating time can be lost to “false alarms” [18-

20]. In deepwater operations, loop currents and eddies associated with them are also

common phenomena that may damage drilling strings/risers and impact the drilling

schedule [21]. In drilling operations, eddies may induce vortex-induced vibrations that

reduce the fatigue life of equipment. Eddies can hold currents of four knots or more at the 

surface and extend several hundred meters deep and measure as wide as 250 miles in

diameter. The operational limit for diver operations is half a knot or less, while

deployment of tubulars and risers can usually be safely performed in currents up to 1.5

knots. 

4.2. Physical Outcome

To a large extent, the impact of severe weather on drilling depends on the choice of

rig the operator has chosen for the operation. Many different rigs can be used to drill an 

offshore well and rig selection depends upon factors such as the type of well being

drilled, water depth and environmental criteria, the type and density of the seabed

expected drilling depth, load capacity, frequency of moves, ability to operate without

support and rig availability. 

If weather and environmental conditions are expected to be a problem, then

sophisticated all-weather semis can be used to hedge against weather downtime. The

increase in availability is achieved through the higher capital cost of the equipment;

which in turn is passed to the operator in higher day rates. Jack-ups are cheaper but are

more prone to weather delay. The choice is up to the operator: the trade-off is between

drilling availability and day rate.
68

The cost of deepwater drilling can represent a significant portion of the total field 

development costs, perhaps as much as 20-40% of total costs, and so operators pay close 

attention to the environment to minimize the magnitude of the risk. Because of the

potentially catastrophic effect a powerful eddy can have on a drilling riser, it is common

to monitor the approach of an eddy and pull the riser or circulate the stroke pipe before

the eddy actually reaches the platform. In April 2003, strong eddy currents and tropical

storm Bill and hurricane Claudette impacted several deepwater operations; e.g., Shell’s

Nakika was delayed 1 week; Total’s Matterhorn TLP was delayed 6 weeks; Heerema’s

Balder experienced several delays in BP’s Mardi Gras pipeline installation [3].

“Eddy Watch” and “Eddy Net” are monitoring systems operated by Horizon Marine 

(www.horizonmarine.com) that provides real-time ocean current maps (Figure 2).

Horizon Marine’s Eddy Watch is a weekly report published since 1984 that contains

information on eddies in the GOM. The data is gathered through 45 drifting buoys

equipped with Argos GPS satellite transmitters that float in the currents and track

movements. The buoy data is combined with infrared satellite imagery, altimetry and

remote sensing to compile the Eddy Watch report. Eddy Net is a real-time, rig-mounted 

ADCP system in 500-800 m water depth installed in 6 sites in the GOM (Figure 3) with

plans to have 20 sites by 2005. Operators also directly monitor currents through their own 

site surveys of current meters installed on boats, rigs, and platforms; e.g., Shell uses the 

ADAM system (ADCP Data Acquisition Manager). ChevronTexaco, BP, and Marathon

use ADCP on various active production facilities and drilling rigs. 

4.3. Economic Outcome Model

The Joint Association Survey on Drilling Costs estimated that the total cost of drilling 

in the GOM in 2000 was $4.6 billion [22], and over the past few years, the total annual

offshore drilling cost ranged between $3-5B. If we assume 1-3% of the total drilling cost 

is due to waiting on weather and that improved ocean observation systems can mitigate

1% of these costs, the expected annual savings due to improved ocean observation data is 

estimated to lie between $300,000 and $1.5M.

5. Development Activities

5.1. Decision Model

One of the primary goals in any construction project is predictability, but because of

the nature and location of the operation, offshore construction activities will always be

uncertain and unpredictable. There are numerous independent uncontrollable variables in 

the offshore environment, such as adverse sea conditions and weather, availability and

performance of equipment, defects in plans and specifications, and work conditions that

result in delay, and often, significant financial repercussions. Delay is a common risk in

offshore construction projects and the parties of the contract apportion risks for delays

that may be encountered. In the case of weather risk, construction contractors will

frequently quote a lump sum (base) bid that includes weather downtime, except

downtime due to named tropical storms, for work during the prime season (May 15 to

October 15). 
69

There are a wide variety of construction vessels used in the GOM and contractors

plan their operations using ocean/weather forecast to avoid adverse weather and

operating conditions. Typical offshore construction craft include crane vessels, drill

ships, dive support vessels, survey vessels, cable lay vessels, pipelay vessels, multipurpose support vessels, dredging vessels, and trawling vessels. The vessels come in a

variety of shapes and sizes, from rectangular barges to jack-ups and semi-submersibles. 

Offshore construction vessels differ from merchant ships because they do not trade

cargo between ports and their most critical operations and loading conditions occur while 

working on the high seas (and not at the start or end of their voyage). Construction

vessels also differ from passenger ships since they are much stronger and the design

standards have to satisfy a multitude of strict safety regulations.

There are guidelines for marine operations such as barge transportation, platform

mating and lift-off, etc. In barge transportation for example, weather forecasts are

normally provided at 12-hour intervals and contain forecasts for the next 24 and 48 hours, 

with the weather outlook for the coming 3-to-5 day period. Tows are designed to

withstand a 10-year return period for extreme environmental conditions for the most

exposed part of the route for the month or months during which the transportation takes

place. For long duration tows passing through areas having different characteristic sea

states, the worst sea state for the route is identified and used in the design of the cargo,

grillage, and sea fastenings [23, 24]. In installation operations, time-sensitive equipment

such as ROVs and heavy lift vessels may not be able to operate in high current.

5.2. Physical Outcome 

During construction activities, a moving vessel is installing (or removing) something

on a fixed seabed, which leads to the requirement that vessel motions be minimized as

much as possible to maximize the operational window. There are typically two options by 

which major projects are installed and completed offshore: floatover, in which the unit is

lowered into place from its transportation vessel, or heavy lift, in which the unit is lifted 

into place with large vessel-mounted cranes. The transportation and installation

limitations of the construction approaches dictate the size, weight and weight distribution

of the modules. The heavy-lift method of installation is able to complete installations in

challenging sea-states but the use of such equipment is also more costly. Lay barges for

instance are designed to operate at different wave heights, allowing the operator to

choose the barge to the sea conditions in the area. The prime risk factor is the weather,

and specifically, wave heights. A barge that can operate in 2-m wave height cost about

$250,000/day while the cost for a 5-m wave height lay barge cost about $500,000/day.

The application of reliable ocean forecasting in pipe laying is obvious. If pipeline

installation is finished late, or delayed by unexpected ocean conditions, the direct cost of 

delay expressed in terms of the day rates and the opportunity cost of nonproductive

structures and wells is likely to be substantial.

5.3. Economic Outcome Model

Order-of-magnitude savings for construction and transportation activities in the GOM 

are estimated as follows. With the occurrence of a hurricane event, weather forecasting
70

model improvement is assumed to provide a 10% or more accurate prediction of the

storm path and arrival time saving 3-5 days work time.

(1) Operator Savings – Construction

Assumptions:

• Activity: 50 installed structures/yr, 50 removed structures/yr

• Construction activity level at time of hurricane passage: 50% total structures

• Number of structures in hurricane path: 50% total structures

• Derrick barge cost: $100,000/day 

Expected Savings:

(50+50)(0.50)(0.50)(0.10)($100,000/day)(3-5 days) = $0.75-1.25M/yr.

(2) Operator Savings – Supply Vessel

Assumptions:

• Number of active supply vessels: 500/day

• Number of supply vessels in hurricane path: 50% total structures

• Supply vessel cost: $20,000/day

Expected Savings:

(500)(0.50)(0.10)($20,000/day) (3 days) = $1.5-2.5M/yr.

6. Production Activities

6.1. Decision Model

What is considered to be severe weather varies with each platform and drill site. 

Companies develop emergency procedures for each type of rig and manned platform they 

operate, and there is no standardized shutdown or evacuation procedure in the event of an 

extreme weather event or disaster. Shut-down and evacuation procedures vary from 

company to company and depend upon the rig type and design, the location of the 

operation, and the behavior of the weather.

The decision to shutdown or evacuate and the actions taken by the crew ensure that no 

employees are injured, damage to the operation or rig is minimized, and 

drilling/production can be resumed as soon as possible after the event passes. The drilling 

superintendent and marine superintendent establish in writing specific procedures for the 

operation, evacuation, and securing of their particular rig or platform in adverse weather. 

The location and design of the rig determine the actions to be taken. Submersible, jack-up 

and semisubmersible rigs are usually not moved from location. On submersible rigs, the 

rig is typically moved across from the wellhead to prevent damage, and on jack-up rigs, 

the hull is jacked up to avoid high seas. On semisubmersible rigs, the drill string hangs 

off in the wellhead and the anchors are slackened to reduce tension. If weather is 
71

extremely severe and the rig rolls excessively, mud and bulk material may be dumped. 

Drill ships and drilling barges follow most of the same procedures but may be moved to 

inland waters out of the storm’s path.

The evacuation and shutdown action plan generally follow a well-defined sequence of 

activities: 

1. Regional tropical cyclone climatology is reviewed for area of operation.

2. National Hurricane Center analysis/forecast charts are obtained, including surface, 

upper level, and sea state (wind/wave) charts.

3. Tropical waves, disturbances, and tropical cyclones are located and plotted.

4. The closest point of approach23 and time to tropical cyclone is calculated.

5. Decisions on the course of action to follow on the latest safe departure time are

made and executed.

6. Actions are reviewed when new meteorological analysis and forecast information

becomes available.

Approximately 5-7 days before the expected arrival of the hurricane, the evacuation and 

shutdown action plan is initiated. Storm path, speed, and intensity forecast information is 

typically supplemented by in-house/consulting meteorologist and/or local weather service 

providers24. Team leaders, operational managers, and meteorologist meet twice a day to 

plan and schedule evacuation activities with primary consideration given to the latest safe 

departure time for personnel25. 

Operators are responsible for the safety of all personnel on their structures, and 2-5 days 

prior to the arrival of the storm, all nonessential personnel are evacuated during daylight 

hours. Essential personnel are the last to go and are transported to shore after wells are 

closed and topside equipment secured 1-2 days before the storm is expected to hit. In the 

1960’s operators considered 3 days the minimum time window to evacuate personnel and 

shutdown operations, while today with better and more reliable weather forecasting, 1-2 

days is considered a safe window. Shut down can be performed automatically, in fact 

nearly instantaneously, using automatic control systems on wells where it is deployed, 

 

23 The 1-2-3 Rule of Thumb is the most important aid in assessing “track error,” the distance between the 

predicted position of a storm’s center and its actual position. The 1-2-3 Rules of Thumb is derived from the 

latest 10-year average track error associated with hurricanes in the North Atlantic:

1-100 mile error radius for 24-hr forecast

2-200 mile error radius for 48-hr forecast

3-300 mile error radius for 72-hr forecast

24 The size of the private/commercial meteorological value added sector is estimated to employ

approximately 4,000 people with $400-700M in annual gross receipts [26]. Most of the firms are sole

proprietorships.

25 It is possible for crews on manned platforms to bunker down and weather out most hurricanes in the

GOM, but for safety and family concerns, all personnel are usually evacuated. The safety record associated 

with offshore production has been exceptional over the past two decades. The last major event occurred

with Hurricane Juan in 1985, where several rigs and boats capsized and in total nine lives were lost

offshore.
72

and for manned platforms, shut down is performed in stages according to facility 

requirements.

6.2. Physical Outcome 

Severe weather procedures vary according to the type of rig [25].

Submersible and jackup rigs

• On submersible rigs, move back enough from the wellhead to prevent

wellhead damage, and increase ballast so that high seas will not move the rig

off location.

• On jackup rigs, jack up the hull to avoid high seas. Jackup rigs are usually not 

moved in severe weather.

• Remove all drill pipe in the derrick on both submersible and jackup rigs.

Semisubmersible rigs

• Suspend drilling and hang off the drill string in the wellhead before the

extreme weather arrives.

• If waves are expected be extremely large, pull the upper package. Slacken lee

anchors to reduce anchor tension on windward anchors.

• Lay down and secure the cranes.

• Deballast the rig to allow waves to pass beneath the rig.

• Apply thrusters to relieve tension on the windward anchors.

• Keep a constant check on anchor tension.

• If the upper package was pulled, make sure the station-keeping equipment is

monitoring a beacon attached to the lower package.

• Make sure that the standby boat is kept downwind or abeam.

• If the weather is extremely severe and the rig is rolling excessively, dump

mud and bulk material.

For drill ships and barges, most of the above procedures are followed but need to be 

performed sooner. Drill ships and drilling barges may be moved out of the path of the 

storm and to inland waters out of the storm’s range.

Companies transport crews offshore in helicopters, crew boats, and workboats according 

to their operational guidelines. The major environmental parameters in offshore 

emergency evacuation are the wind speed and wave height, and safe working conditions 

for many offshore operations may be approximately specified by critical values of these 

parameters (recall Table 3). The limiting conditions for the operation of helicopters are 

usually defined in terms of wind speed (typically 40-50 mph), and visual flight rules 

specify that the operating minimum for single-engine helicopters is a 3-mile visibility 

with a 500-ft ceiling. The minimum operating conditions for multiengine helicopters is a 

2-mile visibility with a 300-ft ceiling. Wave height must fall below a given threshold 
73

(typically 5-8 ft) to ensure safe transfer operations with the crew or workboat. 

Evacuations are performed in the daytime and the method of evacuation depends on the 

sea state, distance to shore, climatic conditions, and availability of transportation 

equipment. Thunderstorm activity will restrict helicopter usage. The number of personnel 

involved in an evacuation depends on the type of structure: a small drilling rig may have 

a crew less than 10 while a large production platform could have over 100. In the GOM, 

there are currently about 25,000 offshore workers on any given day.

Some of the large operators in the GOM own a fleet of helicopters and will maintain 

annual contracts with service boats moored at various offshore production sites. Smaller 

operators reserve space on crew boats and helicopters26 subject to availability, but there is 

usually sufficient capacity to ensure crews are transported in a safe and timely manner. 

For the planners and managers of evacuation activities, however, work conditions remain 

stressful and difficult during a hurricane event until all personnel arrive safely on-shore.

The occurrence of an extreme weather event requires operators to decide what facilities 

to shut down and when personnel should be evacuated. Current GOM operating 

philosophy requires the evacuation of all personnel before the latest safe departure time 

and the shutdown of most, if not all, production activity. Shutting down production has 

an immediate negative economic impact on the operator, but because of the extreme risk

involved with tropical storms, a “conservative” approach is normally taken in planning 

activity. The safety record associated with offshore evacuation has been exceptional over 

the past two decades27.

6.2. Physical Outcome 

Immediately following a storm, the MMS will issue a Notice to Lessees requiring 

operators to conduct a Level X (X = I, II, III) survey for a Y-mile corridor around the 

storm path; e.g., Level I surveys are a visual inspection from the topside and the

complexity of the inspection increases with the level specified. Damage can take many 

forms [28]:

• Platforms, caissons, and flare piles can list (lean), topple, or are condemned.

• Drilling rigs, barges, and workboats can be grounded or capsize.

• Flowlines and pipelines can be damaged by a dragged anchor.

• Topsides equipment such as pumps, tank batteries, power generators, etc. may 

have water damage.

The damage incurred to a structure translates to a direct economic loss to the operator 

since many are self-insured. Operators, and to a lesser extent, insurers, absorb the cost of 

a hurricane, while service companies performing underwater inspections and emergency 

repair and construction and equipment suppliers benefit from the business derived from 

 

26 Typical dayrates for a 34 ft crew boat is $600-800/day, while for a 190 ft crew boat, $2,000-4,000/day. 

Typical helicopter rates are $1,000-1,500/hr. A crew boat can transport up to 90-130 people; a helicopter up 

to 25 depending on its size. Unscheduled, weather-related evacuations add approximately $10,000 per 

production facility and $50,000 per drilling rig over and above normal transportation cost [21].

27 The last major event occurred with Hurricane Andrew in 1992, where 164 structures were destroyed,

including 22 major platforms [27]. 
74

the event. The cost associated with a hurricane and borne by operators is thus “balanced” 

to some extent by the economic stimulus that follows in the wake of the storm.

 Starting up production and re-pressurizing wells after shutdown can be

problematic and usually takes a few days, especially when inspections need to be

performed. Engineers must spend time inspecting pipes, pumps, and process facilities

before the spigots are reopened. Wells that have been shut off can suffer from temporary

shifts in the underground pressure, reducing initial output for weeks or months. In other

fields, shutting down can help rebuild pressure and enhance production rates. The success 

of start-up operations depend in large measure on the damage caused by the storm, the

characteristics of the geologic formation, and the complexity of the wellbores. Since most 

GOM crude oil is light and in primary production, start up activities are mostly

performed without consequence, and assuming no storm damage, fixed structures may

come back on-line within 48-72 hr of evacuation. Individual wells may be off production 

for several weeks or even months. Floating production systems, which operate in the

deep waters of the GOM and where hydrates may form, may take up to one week to

resume production.

6.3. Economic Outcome Model

A company will typically include anywhere from 3-5 days of weather-related 

production losses each year in their business plans to account for the uncertainty of

weather. Operators incur the cost associated with deferred production, evacuation cost,

damage assessment, and facility repair, if any, prior to the resumption of production. 

Most of these costs, with the exception of deferred production and human life

consequences, cannot be mitigated or reduced, since offshore production facilities cannot 

be moved out of the path of the storm or otherwise avoid the storm’s impact.

The direct cost involved with a hurricane event includes shut-down cost, C1; 

evacuation cost, C2; downtime cost, C3; damage assessment cost, C4; facility repair cost,

C5; and start-up cost, C6. Improved ocean observation systems are expected to allow

some of these costs to be reduced, delayed, or possibly avoided – in particular C2 and C3

– although it is clear that no observation system cannot mitigate the actual damage of the 

event unless boats and drilling vessels are moved out of the track of the storm that

otherwise would not have been moved. Shut-down and start-up cost (C1, C6), damage

assessment cost (C4), and facility repair (C5) depend on the track and strength of the

storm and the amount of damage inflicted and are not influenced by improved ocean

observation systems except in the development design stage28.

Hurricane motion is controlled by the state of the surrounding atmosphere, and 

forecasts based upon more accurate and timely measurements of that state are themselves 

more accurate. If the forecast associated with a hurricane event can be improved, then 

 

28 The optimal design of an offshore facility, especially floating production facilities in the deepwater

GOM, requires knowledge of the response of the structure to environmental loading, which in turn, is

critically dependent on the acquisition of reliable data on current profile and wave height. It is important to 

assess seasonal and inter-annual variability in dynamic conditions, but it is seldom possible or costeffective to undertake multiple-year site-specific measurement programs in support of field development.
75

production can stay on-line a greater period of time without sacrificing safety or 

environmental considerations, and in the best case, perhaps not shutdown at all. Order-of 

magnitude estimates for evacuation and lost production savings are provided as follows.

 (1) Operator Savings – Evacuation

Assumptions:

• Manned platforms in hurricane path: 750

• Rigs in hurricane path: 100

• Evacuation cost: $10,000/platform, $50,000/rig

• Weather forecasting model improvement: 10-20% more accurate prediction

on hurricane path/zone to avoid evacuation

Expected Savings:

(750)(0.10-0.20)($10,000/platform) + (100)(0.10-0.20)($50,000/rig) = $1.25-2.5M/yr

(2) Operator Savings – Lost production

Assumptions:

• Net income margin per BOE: $5/BOE

• One-half of GOM production shut-in: 1.5 MMBOE/day

• Weather forecasting model improvement: 0.5-1 day continued production 

Expected Savings:

(1.5 MMBOE/day)($5/BOE) = $3.8-7.5M/yr

7. Oil Spill Management and Response

7.1. Decision Model

The risk of oil spills arise from activities associated with the exploration,

development, production, and transportation of offshore oil and gas resources, as well as 

from the transport of oil across the ocean to port facilities [29, 30]. During the 1970s and 

early 1980s most of the crude oil and products moved by water was associated with

inland barges or coastwise movement between U.S. production/processing and

consumption regions. By the mid-1980s, waterborne commerce of foreign imports of

crude oil and petroleum production exceeded coastwise transportation, and today is

completely dominated by foreign imports. 

Oil spills in coastal waters are especially damaging and clean up can be very

expensive. The Oil Pollution Act of 1990 [31] requires that response activities deal with

the legal constraints and interest of various political entities as it attempts to minimize

ecological damage and the quality of human life. Better knowledge of wind and water

currents will assist in the management and clean up of oil spills. 
76

Four factors influence oil spill response: the type of oil (e.g., heavy crude, distillate

fuel, etc.); the amount of oil spilled; the spill conditions, which are described by sea

temperature, ocean current, wind and weather conditions; and proximity to ecologically

sensitive areas. Once notice has been received that a spill has occurred all of these factors 

are assessed to determine the spill response.

Information to support operational decisions during an oil spill is provided through a

variety of sources. Typically, decision-making is aimed at supporting a “minimum

regret” as opposed to a “maximum win” strategy [14]. In a “maximum win” strategy, the 

best estimates of wind, currents, and the initial distribution of the pollutant is collected

and the resulting forecast taken as the threat. A “minimum regret” strategy on the other

hand uses whatever analysis techniques are available as input data. The situation unit

presents the command with not only the “best guess” of where the oil will go but also

with alternate possibilities that might present a significant threat. Reliable near-time data 

on the wind and wave conditions is essential for good decision-making.

7.2. Physical Outcome 

Oil spill response is site specific and occurs within a complex, dynamic, and

uncertain environment. The environmental effects of oil spills vary widely depending on

factors such as the amount and type of oil spilled, weather conditions, the location of the 

spill relative to natural resources, the quality and sensitivity of effected resources,

seasonal factors, and the thoroughness and speed of cleanup and restoration efforts

(Figure 4). 

Clean up operations employ one or more methods such as mechanical systems,

chemical dispersants, burning, and bioremediation depending on prevailing spill

conditions. Timing is critical to effective clean up. Floating oil spreads rapidly, and a

slow response may allow oil to spread over a large area so that boom is not effective in

containment. Floating oil also emulsifies as it mixes with water lending treatment with

dispersants ineffective after a given time window has passed.

7.3. Economic Outcome Model

There are many social costs associated with an oil spill. Many costs can be measured 

as direct economic cost such as the cost of clean up, while indirect cost such as damage

or harm to wildlife cannot be measured in a market transaction. Indirect social costs are

typically valued using “willingness-to-pay” techniques or an assessment of the loss in

consumer surplus. The estimated unit cost of a barrel of oil spilled or reaching shore

across the OCS planning areas is summarized in Table 4 [32]. The total estimated cost for 

the GOM region is assumed to range between $(888, 1445) per barrel of oil spilled. 

The number of spills in the U.S. Coast Guard District 8, which includes Texas,

Louisiana, Mississippi, Alabama, and the Florida panhandle, and the volume of spills is

shown in Table 5. Roughly one half of the volume spilled came from tank vessels, and

60% of the volume involved crude or heavy oil. The 8

th District was responsible for

nearly 40% of the spills and 38% of the total volume across the United States. Eleven

percent of the total volume of oil spill occurred in the open ocean (12-200 miles), which

would normally not realize a significant improved response with enhanced ocean

forecasting. 
77

The impact of a 1% improvement in oil spill response is estimated to result in the

following cost savings:

(74,000 bbl/yr)$(888/bbl, 1445/bbl)(1-0.11)(0.01)=$(0.58M/yr, 0.95M/yr).

8. Conclusion

Oil and gas technological advancements over the past two decades have been

remarkable, but no matter how ingenious, operators still cannot overcome extreme

weather events. Weather information is valuable, and to the extent that improved ocean

observation systems can improve the data on which weather/ocean forecasts is based, is

potentially very beneficial to energy production and pollution management in the GOM.

Primary applications of ocean observation data are to provide nowcasts/forecasts of

weather, wind speed, surface wave, current, and general circulation patterns. Order of

magnitude benefits derived from ocean observation systems to energy related activities in 

the GOM are conservatively estimated to range between $8.3-15.3M (Table 6). The

actual benefits derived are expected to be a positive multiple of this factor.

Acknowledgement

The authors would like to acknowledge helpful discussions held with the 

following personnel: Ken Schaudt, Marathon; Norman Guinasso, Jr., Texas A&M 

University; Robert Martin, Texas General Land Office; Patrick Michaud, Conrad 

Blucher Institute for Surveying and Science; Mark Luther, University of South 

Florida; Greg Stone, Louisiana State University; Cort Cooper, Paul Versowsky, 

Max Regan, Sandy Furr, ChevronTexaco; David Epps, BP, Allan Alrady, J. Ray 

McDermott; Charlie Colgan, University of Maine; Hauke Kite-Powell, Woods 

Hole Oceanographic Institution. 

This paper was prepared on behalf of the National Oceanographic Partnership 

Program, Office of Naval Research. The opinions, findings, conclusions, or 

recommendations in this paper are those of the authors, and do not necessarily 

reflect the views of the Office of Naval Research. Funding for this research was 

provided in part through the Office of Naval Research.

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Draft,” Washington, D.C., 2004. Available at 

http://www.oceancommission.gov.

[10] Michaud, P.R., C. Thurlow, and G.A. Jeffress, “Collection and dissemination of 

marine information from the Texas Coastal Ocean Observation Network,” U.S. 

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M. Luther, “The evolution of a coastal monitoring system,” Sea Technology, 

February 2003, 24-31.

[13] Kelly, F.J., N.L. Guinasso, Jr., L.L. Lee III, G,F, Chapline, B.A. Magnell, and 

R.D. Martin, Jr., “Texas Automated Buoy System (TABS): A public resource,” 

Proceedings of the Oceanology International 1998 Exhibition and Conference, 

Brighton UK, 1 (1998), 103-112.

[14] Martin, R.A., F.J. Kelly, L.L. Lee III, and N.L. Guinasso, Jr., “Texas 

Automated Buoy System (TABS): Real-time currents for oil spill response,” 

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Fort Lauderdale, FL.

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National Ocean and Atmospheric Administration and Office of Naval Research, 

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80

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regulatory perspective,” OTC 13243, 579-587, Offshore Technology 

Conference, Houston, TX, April 30-May 3, 2001. 

[29] National Research Council, Oil in the Sea III: Inputs, Fates, Effects, Ocean 

Studies Board and Marine Board, National Academy Press, Washington, D.C., 

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Washington, D.C., National Academy Press, Washington, D.C., 1998.

[31] Ketkar, K.W. “The oil pollution act of 1990: A decade later,” Spill Science and 

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Marine Oil Spills: A General Purpose Model. Vol. I – Economic Analysis of 

Environmental Cost, U.S. Department of the Interior, Minerals Management 

Service, OCS Study MMS 91-043, June 1991.
Table 1. Tropical Storm and Hurricane Events in the Gulf of Mexico

Hurricane Year Magnitude

Larry 2003 0

a

Henri 2003 0

Grace 2003 0

Erika 2003 1

Claudette 2003 4

Bill 2003 0

Lili 2002 4

Isidore 2002 3

Hanna 2002 0

Fay 2002 0

Edouard 2002 0

Bertha 2002 0

Gabrielle 2001 1

Barry 2001 0

Allison 2001 0

Keith 2000 4

Gordon 2000 1

Source: National Climatic Data Center

Footnote: (a) A tropical storm is denoted by a magnitude of 0.

Table 2. Eddy Events in the Gulf of Mexico

Eddy Year Size

Titantic 2003 Huge

Sargassum 2003 Huge

Rebel 2002 Small

QE-2 2002 Small

Quick 2002 Huge

Pelagic 2002 Huge

Odessa 2001 Medium

Nansen 2001 Medium

Millenium 2001 Huge

Lazy 2000 Small

Kinetic 2000 Small

Juggernaut 1999 Huge

Indigo 1999 Small

Haskell 1999 Small

Gyre 1999 Small

Source: Horizon Marine, Inc.
82

Table 3. Limiting Conditions for Offshore Weather-Sensitive Activities in the GOM

Activity Limiting Conditions

Evacuation by crew boat WH a < 5 ft, Daylight

Evacuation by helicopter (fixed structure) WS b < 40 mph, Daylight

Deepwater drilling WS < 80 mph, WH < 8 ft, CVc < 2 knots

Tubular and riser deployment WS < 80 mph, WH < 8 ft, CV < 1.5 knots

Lifting and coupling WH < 5 ft

Evacuation by helicopter (floating structure) WS < 50 mph, WH < 5 ft, Daylight

Diving operations CV < 0.5 knots

Boom containment WH < 1 ft

Footnote: a) WH = Wave height 

 b) WS = Wind speed 

 c) CV = Current velocity

Table 4. Estimated Unit Cost Elements per Barrel Spilled and Reaching Shore

OCS Planning Area Controla 

($)

Cleanup 

($)

Property Lost 

($)

Recreation 

and Tourism 

($)

Wildlife and 

Ecologicala 

($)

Straights of Florida (64, 99) (565, 872) 272 (133, 448) b 30b

Eastern GOM (66, 103) (546, 843) 46 (90, 320) 154

Central GOM (55, 85) (650, 1002) 46 (52, 190) 154

Western GOM (58, 90) (249, 385) 46 (143, 514) 116

AVERAGE (61, 94) (503, 776) 103 (107, 368) 114

 Source: MMS

 Footnote: a) Per barrel spilled

 b) Mid-Atlantic region
83

Table 5. Number and Volume of Spills for the 8th Coast Guard District

Year Number of Spills Volume of Spills 

(1000 barrels)

1990 3,205 117

1991 3,572 14

1992 3,616 23

1993 3,477 15

1994 3,465 26

1995 3,363 36

1996 4,678 19

1997 4,699 15

1998 4,224 11

1999 3,836 18

2000 4,177 21

Average 

(1973-2000) 3,132 74

Source: U.S. Coast Guard

Table 6. Summary of Potential Benefits of Improved Ocean Observation Systems to Energy 

Development Activities and Oil Spill Response Management in the GOM

Application Nature of Benefit Annual Potential Benefits 

($M)

Drilling activity Improved operations (0.3-1.5)

Construction activity Improved operations (0.8, 1.3)

Supply vessels Improved operations (1.5, 2.5)

Evacuation Improved operations (1.3, 2.5)

Lost production Reduced production (3.8, 7.5)

Oil Spill response Improved response (0.6, 1.0)

TOTAL (8.3, 15.3)
84

Figure 1. Continental United States Landfalling Hurricanes (1950-2000) and Hurricane 

Andrew (August 1992). Source: National Climate Data Center
85

Figure 2. Loop Current Pattern and Eddy Sargassum (September 2003). Source: Horizon 

Marine, Inc.
86

Figure 3. EddyNet Data Collection Sites. Source: Horizon Marine, Inc.
87

Figure 4. Oil Spill Response Strategies
88
89

Appendix C: Commercial Fisheries in Alaska and the Pacific 

Northwest

Katharine F. Wellman and Marcus Hartley
90

The Benefits of Enhanced Coastal Ocean Observing Systems:

Commercial Fishing in the Alaska Region 

Katharine F. Wellman, PhD

&

Marcus Hartley

Northern Economics, Inc. 

June 27, 2004

Introduction

Alaskan commercial fisheries can be expected to realize benefits from enhancements to the 

Alaska Ocean Observing System (AOOS). We have identified and attempted to quantify 

potential benefits in three areas:

1) Increased harvests

2) Avoidance of overfishing

3) Enhanced business planning

In working towards an assessment of the value of improved coastal ocean observing systems 

(COOS) to the commercial fisheries of the Alaska region we have found that the use of 

COOS data in research, stock assessment and ultimately fisheries management varies 

considerably from fishery to fishery. As such we have generated a case study approach where 

we look at 3 specific Alaska fisheries. These include: (1) Bering Sea and Gulf of Alaska 

groundfish; and (2) Kodiak king crab and (3) Bristol Bay salmon. These case studies offer a 

qualitative discussion of the current and optimal COOS information scenario, decisionmaking and physical outcomes and a quantitative analysis of economic outcomes based on 

plausible scenarios. All assumptions and limitations to the economic assessment of the 

value of improved COOS are stated explicitly.

The analysis and final conclusions of this report were generated using information provided 

through interviews with Directors of the Alaska Ocean Observing System (AOOS) and 

Northwest Association of Networked Ocean Observing Systems (NANOOS) and over 25 

biologists, oceanographers, fisheries managers, and fishers. We also relied on scientific 

studies, North Pacific Fisheries Management Council Stock Assessment Fishery Evaluation 

reports, and relevant secondary literature (please see literature cited section). 

Overview of Commercial Fisheries in Alaska

This section provides a general overview of the commercial fisheries in Alaska and 

documents the importance of Alaska fisheries within the US. It provides a summary table 

showing the five major fisheries by species in Alaska and goes on to provide additional 

details for groundfish, salmon and crab, the top three fisheries by weight and value. These 

three fisheries are likely to benefit most from an enhanced ocean observing program. 
91

Commercial fisheries of Alaska are among the largest in the world and contribute the vast 

majority of US commercial fishery products and value. As seen in Table 7, landings of 

commercial fisheries in Alaska were 53 percent all US commercial landings by weight in 

both 2000 and 2001, and 26 percent of harvested value (or ex-vessel value). In addition, the 

top three Alaska ports with respect to landed weight—Dutch Harbor/Unalaska, Kodiak and 

Akutan—consistently rank in the top 6 of all US fishing ports, and the top 10 Alaska ports all 

rank among the top 50 US fishing ports29 [NMFS, and NPFMC].

Table 7. US Commercial Fishing Landings and Value

2001 2002

Regions and States

Thousand 

Pounds Thousand Dollars

Thousand 

Pounds Thousand Dollars

New England 635 646 584 685

Middle Atlantic 835 347 702 342

South Atlantic 200 176 215 173

Gulf of Mexico 1,606 798 1,716 693

Pacific (WA, OR, CA, HI) 24 55 24 52

Great Lakes 19 18 18 16

Alaska 5,036 870 5,066 812

Total, United States 9,492 3,228 9,397 3,092

Percent of US Total

Alaska Percent of US 53.1 26.9 53.9 26.2

Source: NMFS, 2003. Adapted from information contained in Fisheries of the United States, 2002.

Alaska’s fisheries are very diverse in terms of geography, species, the types of vessels and 

gears used and the way the fisheries are managed. That diversity makes it very difficult to 

generalize the effects of an improved ocean observation system. For example, biological and 

oceanographic data that is beneficial for stock assessments for one species may not be that 

useful for other species. Table 2 provides a detailed list of Alaska commercial fisheries by 

geographic location, species, and gear type.

 

29 The ports of Akutan, King Cove, and Sand Point are typically not listed among top US ports because each has 

only one fish processor/buyer, and therefore, data regarding total landed weight and value at these ports are 

confidential. However, the American Fisheries Act of 1998 opened data associated with BS Pollock fisheries to 

public scrutiny, and based on Bering Sea pollock data alone, the three communities would all be in the top 50 in 

terms of landed weight with Akutan ranking number 5 in front of Kodiak. Other ranked Alaska ports include 

Ketchikan (14), Sitka (15), Cordova (20), Petersberg (22), Homer (29), and Kenai (37).
92

Table 8. Major Commercial Fisheries in Alaska

Region Fishery

Troll Fisheries for King and Silver Salmon

Wild Seine Fisheries for Pink and Chum Salmon

Hatchery Seine Fisheries for Pink and Chum Salmon

Statewater Longline Fisheries for Sablefish

Federal Water Longline Fishery for Sablefish

Longline Fishery for Halibut

Sitka Herring Fishery (and possibly other herring fisheries)

Southeast AK King Crab Fisheries

Shellfish Dive Fisheries (Geoduck, Oyster, Urchin, Sponge)

Southeast 

Alaska—10 

Fisheries

Oyster and Mussel Mariculture Fisheries

Wild Seine Fisheries for Sockeye, Silver and Pink Salmon

Hatchery Fisheries for Sockeye, Silver and Pink Salmon

Drift Gillnet Fisheries for King, Sockeye, and Silver Salmon

Set Gillnet Fisheries for King, Sockeye, and Silver Salmon

Statewater Longline Fisheries for Sablefish

Federal Water Longline Fishery for Sablefish

Longline Fishery for Halibut

PWS Herring Fishery

Prince William 

Sound and 

Cook Inlet—

9 Fisheries

Oyster and Mussel Mariculture Fisheries

Trawl Fisheries for Pollock, Pacific Cod, Rockfish, Deep- & Shallow-water

Flatfish

Federal Water Longline Fishery for Sablefish

Longline Fishery for Halibut

Statewater Pot and Jig Fisheries for Pacific Cod

Kodiak Seine Fisheries for Sockeye and Pink Salmon

Kodiak Setnet Fisheries for Sockeye Salmon

Chignik Seine Fisheries for Sockeye and Pink Salmon

Alaska Peninsula Seine Fishery for Sockeye, Pink, Chum, and Silver 

Salmon

Alaska Peninsula Drift Fishery for Sockeye

Central and 

Western Gulf of 

Alaska—

10 Fisheries

Alaska Peninsula Setnet Fishery for Sockeye

Trawl Fisheries for Pollock, Pacific Cod, Atka Mackerel, Rockfish, Flatfish, 

including fisheries off Adak for Pollock and Pacific Cod

Federal Water Longline Fishery for Pacific Cod

Longline Fishery for Halibut

Statewater Pot and Jig Fisheries for Pacific Cod

Pot Fisheries for Red King Crab, Opilio Crab in Bering Sea and Pribilofs

Bering Sea and 

Aleutian Islands—

6 Fisheries

Pot Fisheries for Brown King Crab in Aleutians

Bristol Bay Drift Sockeye

Bristol Bay Setnet Sockeye

Kuskokwin River Chum, Silver and King Salmon

Yukon River Chum, Silver and King Salmon

Togiak Herring

Norton Sound Red King Crab

Western Alaska—

7 Fisheries

Kotzebue/Arctic Chum Salmon
93

In 2001 the ex-vessel value (the amount paid to fish harvesters) of Alaska commercial 

fisheries totaled nearly $1 billion. Typically, the fisheries are divided into 5 major species 

groups including groundfish, crab, salmon, halibut and herring. Table 3 shows the value of 

raw fish (ex-vessel value) in Alaska by species group for the years 1993-2002. Table 4shows 

estimates of participation, employment and payments to labor in the groundfish fisheries for 

2001. 

The major Alaska crab fisheries are now primarily in the Bering Sea (opilio tanner crab) and 

Bristol Bay (red king crab) and currently constitute approximately 15 percent of the total exvessel value of Alaska fisheries. The relative value of the crab fisheries is down from 1993 

when it generated 28 percent of total value. Historically there were major king crab fisheries 

in Kodiak and significantly larger king crab fisheries in Pribilof and St. Matthews Islands, 

but these have declined in recent years. The Kodiak fishery has been closed since 1983. 

Smaller fisheries for both king and dungeness crab continue in Southeast Alaska.

The salmon fisheries have the greatest number of participants and generate high levels of 

employment. However, in recent years the salmon fisheries have become less valuable 

because huge increases in farmed salmon—primarily from Chile and Norway—have 

saturated markets and reduce prices. In 1993 salmon generated 1/3 of the ex-vessel value of 

Alaska fisheries, but in 10 years prior to 1993 salmon accounted for as much as 67 percent of 

the value of raw fish from Alaska. Currently salmon accounts for approximately 20 percent 

of ex-vessel value.

In 1991 groundfish surpassed salmon as the largest fishery in Alaska in terms of ex-vessel 

value, and now generates over 50 percent of the value of Alaska fisheries. The groundfish 

fishery consists primarily of bottom fish including Alaska Pollock, Pacific cod, Black Cod, 

rockfish and Atka Mackerel and flatfish, including rock sole and yellowfin sole as well as 

several other types of flounders and soles. 

While Pacific Halibut is technically a flatfish it is managed and reported separately from 

other groundfish species. Most participants in the halibut fisheries also participate in either 

the groundfish fisheries or the salmon fisheries. The Halibut fishery has increased in value 

since 1995 when it was rationalized from a one-day derby fishery to an individual quota 

fishery. Currently halibut accounts for over 10 percent the ex-vessel value from Alaska’s 

fisheries.

Herring while much smaller in terms of value than the other fisheries, continues to be an 

important fishery supplementing incomes for participants in other fisheries.
94

Table 3. Ex-Vessel Value of Major Alaska Fisheries, 1993-2002

Year Crab/Shellfish Salmon Herring Halibut Groundfish Total

Ex-Vessel Value $ Millions

1993 386.1 459.7 16.6 63.0 477.9 1,403.2

1994 369.5 488.2 24.8 97.4 565.6 1,545.7

1995 318.8 558.8 44.1 67.0 646.0 1,634.6

1996 193.6 382.9 49.5 82.0 552.9 1,261.0

1997 186.7 268.8 17.2 115.5 619.9 1,208.0

1998 234.3 260.0 11.6 100.8 411.1 1,017.8

1999 286.5 365.3 15.0 123.5 487.8 1,278.1

2000 147.4 255.0 9.9 139.4 612.9 1,164.7

2001 124.4 189.9 10.5 120.2 546.9 992.0

2002 148.8 129.9 9.1 128.9 566.4 983.1

Average 239.6 335.9 20.8 103.8 548.7 1,248.8

Source: Economic Status of the Groundfish Fisheries Off Alaska, 2002, by Terry Hiatt et al. Al.

Note: Values shown are not adjusted for inflation.

Alaska’s fisheries are an important source of employment not only in the harvesting sector, 

but also in fish processing. Because of its remoteness, relatively little of the output of Alaska 

commercial fishery harvests is sold fresh. Most is processed into frozen or canned products. 

The additional value added by Alaska’s seafood processing sector brings the total output of 

Alaska’s commercial Seafood industry to over $2.4 billion annually (Northern Economics, 

2003). As seen in Table 4, fish harvesting generated employment of over 10 thousand full 

time equivalent (FTE) jobs while fish processing added an additional 15,000 FTEs. Overall it 

is estimated that direct payments to labor and owners exceeded $1.3 billion in 2001.

Table 4. Direct Economic Effect of Alaska’s Commercial Fisheries, 2001 

Species Permits Fished

Estimated 

Harvesting 

Employment 

(FTE)

Ex-Vessel 

Value 

($ Millions)

Estimated 

Processing 

Employment 

(FTE)

First Wholesale 

Value 

($ Millions)

Estimated 

Payments to 

All Labor 

($ Millions)

Crab & Shellfish 1,699 560 123.5 1,390 194.1 98.5

Salmon 7,372 4,400 188.5 6,090 537.5 290.4

Herring 815 220 10.4 190 171.2 72.6

Halibut 2,461 580 109.0 1,230 108.6 87.0

Groundfish 1,959 4,430 542.8 6,190 1,391.8 773.8

Total 14,306 10,190 974.2 15,090 2,403.2 1,322.4

Source: Northern Economics, Inc. 2003. Data provide by Alaska Department of Fish and Game and reported in 

Impact of the Seafood Industry on Alaska’s Economy.

Alaska Groundfish Fisheries

This section provides an overview of the Alaska groundfish fisheries and summarizes catch 

by species, ex-vessel and wholesale values, and employment. Also briefly discussed are the 

methods used to set annual harvest amounts. Annual harvest are currently very conservative 

and take into account the uncertainty of many predictive variables, are likely to benefit 

directly from an enhanced ocean observation program. With better information a less 

conservative harvest policy could be used, and quotas could be set higher with resulting 

increases in catch and values. The benefit estimates of the enhanced ocean observation 

program in the groundfish fisheries are discussed in Section 5.
95

Alaska’s groundfish fisheries are managed primarily by the Federal government (the 

National Marine Fisheries Service (NMFS) and the North Pacific Fishery Management 

Council (NPFMC or Council)).30 Management of the groundfish fisheries are based on 

Fishery Management Plans (FMP) (one FMP for the Bering Sea and Aleutian Islands (BSAI) 

and one FMP for the Gulf of Alaska (GOA)) summarized in the Alaska Groundfish Fisheries 

Final Programmatic Supplemental Environmental Impact Statement (SEIS) (NMFS, 2004). 

Much of the information used in this summary of the groundfish fishery is drawn from the 

2004 SEIS.

The Alaska groundfish fisheries are dominated by harvests of walleye pollock, which since 

1998 have been between 60 to 75 percent of harvests by volume (Table 5). During that same 

period harvests of Pacific cod and flatfish have averaged roughly 12 percent while harvests 

of other species are much smaller by volume. 

Table 5. Alaska Groundfish Harvests by Species Group, 1998-2002

Year Pollock Black Cod Pacific Cod Flatfish Rockfish Atka Mackerel Total

Alaska Total -- millions of pounds

1998 2,756.2 36.2 568.6 491.8 76.9 126.5 4,056.2

1999 2,395.1 33.7 534.6 534.6 97.9 124.6 3,720.5

2000 2,668.2 38.6 541.4 541.4 83.6 104.5 3,977.8

2001 3,220.7 33.3 481.5 379.2 86.2 135.8 4,336.7

2002 3,387.0 31.9 515.8 391.2 94.1 105.3 4,525.3

Source: Economic Status of the Groundfish Fisheries off Alaska, 2002, by Terry Hiatt et al. Al.

Ex-vessel and processed product values of Alaska groundfish from 1998-2002 are shown in 

Table 6. Ex-vessel value of harvests (the estimates value of raw fish harvested)31 increased

significantly from 1998 – 2000 primarily because of the rationalization of the pollock fishery 

under the 1998 American Fisheries Act (AFA). AFA allowed vessels to form cooperatives 

and effectively ended the “race for fish” in the pollock fishery. A similar increase in the 

wholesale values is seen over the same period. The additional value of the pollock fishery 

can be attributed to efficiency gains achieved through the reduction of active vessels in the 

pollock fishery and the coordination of effort through the cooperative system. A full report 

on the effects of the AFA can be found in a report to Congress compiled by the NPFMC in 

2001.

 

30 The State of Alaska has some management authority over Pacific cod and black cod when they are harvested 

in state waters.

31 Estimates of ex-vessel value include an implicit value for fish harvested by vessels that both catch and 

process groundfish (catcher processors or CPs). In reality there is no monetary transaction of involving raw fish 

with CPs, and therefore no actual ex-vessel value is recorded. Implicit values are estimated using the prices 

received for raw fish by catcher vessels when they deliver fish to processors.
96

Table 6. Value of Alaska Groundfish by Species Group, 1998-2002

Year Pollock Black Cod Pacific Cod Flatfish Rockfish

Atka 

Mackerel Total

Ex-Vessel Value of Harvest ($ Millions) 1

1998 179.6 52.9 98.8 36.2 8.0 7.9 383.6

1999 211.2 57.0 141.9 30.2 11.0 9.8 461.4

2000 298.0 75.8 157.7 41.1 9.8 9.5 592.5

2001 295.2 61.9 124.8 31.5 7.9 21.1 542.6

2002 321.6 64.4 121.7 37.2 9.7 11.2 566.2

First Wholesale Value of Processed Products ($ Millions) 2

1998 492.3 68.3 213.6 83.4 18.7 17.5 1024.8

1999 690.2 73.0 273.6 70.7 20.7 21.9 1178.1

2000 814.3 87.1 285.9 91.9 19.0 21.2 1345.8

2001 929.8 79.5 235.4 61.5 15.6 44.6 1390.8

2002 987.0 81.5 245.2 86.1 22.5 24.9 1482.8

Source: Economic Status of the Groundfish Fisheries off Alaska, 2002, by Terry Hiatt, et. Al.

1. Estimates of ex-vessel value include an implicit value for fish harvested by catcher processors.

2. Estimates of wholesale value are based on values reported by processors as product leaves the plant.

Note: Values shown are not adjusted for inflation.

It should be noted that both ex-vessel and wholesale product values are reported in Table 6. 

Reporting only the value of raw fish (ex-vessel value) significantly understates the value of 

the fisheries, particularly in the case of the fisheries that include catcher processors (CPs). 

CPs, which both catch and process fish account for approximately 54 percent of the 

wholesale value of Alaska groundfish harvests. In addition, motherships, large processing 

ships that take deliveries of raw fish at sea, account for approximately 6 percent of the 

wholesale value. The remaining 40 percent of wholesale value is generated by traditional 

shore-based processing plants. As seen in Table 7 Alaska groundfish fisheries generated over 

$600 million in direct income for fishing crews and processing labor, and boat and facility 

owners, and employ over 10,000 FTE in 2001.

Table 7. Value, Payments to Labor and Employment in Alaska Groundfish Fisheries by Sector, 2001

Sector

Value 

($ Millions)

Payments to Labor 

($ Millions)

Employment 

(FTE)

Catcher Processors 743.9 265.9 3,876.7

Motherships and Shore-based Processors 682.9 266.9 4,490.5

Catcher Vessels 288.5 115.4 2,015.7

All Sectors 1,426.9 648.2 10,383.0

Source: Alaska Groundfish Final Programmatic SEIS. NMFS, 2004.

Note the total value of all sectors does not add the value earned by catcher vessels—those values are a cost to 

motherships and shore-based processors and are included in the total wholesale value. 

Groundfish Stocks Assessment and Annual Harvest Quotas

In general, the groundfish fisheries are quota-based fisheries. The annual harvest quotas, or 

Total Allowable Catch (TAC), are determined on a species by species basis within each 

FMP. Each summer and fall, fisheries scientists review new data and augment predictive 

models to assess stocks of each species and to determine how much can be harvested without 

putting the stocks at risk of falling below the Maximum Sustainable Stock Threshold 

(MSST). The level at which each species may be harvested is known as the Allowable 
97

Biological Catch (ABC). The scientist’s recommendations for ABCs, which are generally 

quite risk averse are based on the “best available scientific data” and the amount of 

uncertainty, are forwarded to the NPFMC where they are reviewed by the senior scientists 

comprising the Council’s Scientific and Statistical Committee (SSC). The Council’s 

Advisory Panel (AP), comprised of representatives from the public and the seafood industry, 

takes the ABCs forwarded by the SSC and recommends TAC levels for each species. The 

AP’s TAC recommendations take into consideration factors such as the level of demand for 

specific species and other business/political factors. In the GOA, the recommended TACs are 

often very similar to recommended ABCs. In the BSAI however, a 2 million metric ton (MT) 

cap (4,409.2 billion pounds) limits the overall harvest of groundfish, even though the sum of 

ABCs of the various species in the BSAI far exceeds the cap.32

Table 8 shows estimates of the total biomass, spawning biomass, the ABC and actual total 

catch and exploitation rates of Alaska groundfish by major species groups in 2002. The 

estimates (Ianelli, 2003) demonstrate the relatively conservative harvest policy employed in 

the North Pacific. Overall exploitation is less than 8.5 percent of total biomass, but more 

importantly harvests are 70 percent of ABCs.

⇒ Table8. Biomass, Allowable Biological Catch, and Catch of Alaska Groundfish, 2002

Species 

Group

Total 

Biomass

(millions of lbs.)

Spawning 

Biomass

(millions of lbs.)

ABC

(millions of lbs.)

Catch

(millions of lbs.)

Exploitation 2

(percent)

Bering Sea and Aleutian Islands

Pollock 28,586 8,114 3,843 3,276 11.46

Black cod 181 65 16 4 1.99

Pacific cod 4,260 892 644 403 9.47

Flatfish 1 11,143 4,080 778 320 2.87

Rockfish 1 826 303 62 46 5.58

Atka mackerel 1,057 261 144 105 9.92

BSAI Total 46,054 13,715 5,492 4,155 9.02

Gulf of Alaska

Pollock 1,502 300 388 111 7.40

Black cod 449 161 40 28 6.30

Pacific cod 1,253 216 164 112 8.97

Flatfish 1 4,002 2,455 399 71 1.79

Rockfish 1 1,105 401 70 48 4.34

Atka mackerel NA NA 1 0 NA

GOA Total 8,311 3,533 1,183 438 5.27

Alaska Total

Grand Total 54,365 17,247 6,675 4,594 8.45

Source: Ianelli, James, 2003. “North Pacific Multi-Species Management Model” in NMFS 2004.

1. Biomass estimates of several species in this group are unavailable. 

2. Exploitation is calculated by dividing catch into total biomass. 

Alaska Salmon Fisheries

This section provides a summary of Alaska’s salmon fisheries. The summary provides 

information on the ex-vessel and wholesale value of salmon fisheries by species and 

management area, and then summarizes the forecasts of salmon returns in Bristol Bay to 

 

32 This “optimum yield” cap was approved as part of Amendment 1 to the BSAI Groundfish FMP by the 

Council and the Secretary of Commerce and implemented in 1984 to insure that fisheries would not be overharvested.
98

demonstrate the levels of uncertainty under which salmon harvesters and processors operate. 

It is surmised in Section 5 that reducing this uncertainty through an enhanced ocean 

observation program can significantly improve the value of the salmon fisheries.

Alaska’s salmon fisheries are among the worlds largest, and are managed by the Alaska 

Department of Fish and Game (ADF&G). As shown in Table 9, although the value of salmon 

has declined in recent years, the fisheries still generate over $500 million in processed 

product. Sockeye salmon are the most important species in terms of value, but pink and chum 

salmon are also very important. In 2002, sockeye generated 43 percent of the wholesale value 

of Alaska’s salmon fisheries while pinks accounted for 29 percent and chum 19 percent.

Table 9. Wholesale Value of Salmon Fisheries by Species, 2001 – 2003

Species 2000 2001 2002 2003

Wholesale Value ($ Millions)

Chinook 15.5 15.5 19.2 NA

Chum 166.9 96.6 84.0 NA

Coho 41.8 33.6 36.5 NA

Pink 165.6 219.9 154.3 NA

Sockeye 333.3 230.7 219.0 NA

All Species 723.1 596.3 513.0 NA

Source: ADF&G Commercial Operator Annual Report Data, provided by ADF&G in June 2004.

Note: Values shown are not adjusted for inflation.

ADF&G manages the salmon fisheries on a regional basis with limits on the number of 

vessels or operations that can participate. The ex-vessel value generated in the major salmon 

management areas in recent years are shown in Table 10 for the years 2000-2003, and 

graphically for selected areas from 1993 – 2002 in Figure 2. As seen in Figure 2, the value of 

salmon fisheries has declined significantly since 1993 in all areas. The largest relative 

declines have come in Bristol Bay. Prior to 1997 Bristol Bay accounted for more value than 

any other management area. Now both Southeast Alaska and Prince William Sound generate 

as much value as Bristol Bay. Estimates of wholesale value are shown in Table 11. In 2 of 

the 3 years shown Southeast Alaska have generated more wholesale value from salmon than 

other management areas.
99

Figure 2. Ex-vessel Value of Salmon by Management Areas, 1993 – 2002

0

100

200

300

400

500

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Year

Ex-Vessel Value ($ Millions)

All Areas Southeast Bristol Bay Prince William Sound Other Areas

Source: Basic Information Tables from Commercial Fishing Entry Commission Internet site at 

www.cfec.state.ak.us. Accessed in June 2004.

Note: Values are shown in nominal dollars.

Table 10. Ex-Vessel Value of Salmon Fisheries by Management Area, 2001 – 2003

Management Area 2000 2001 2002

Ex-Vessel Value ($ Millions)

Alaska Peninsula 24.3 8.6 7.6

Bristol Bay 84.4 40.9 29.4

Chignik 12.6 8.4 4.6

Kodiak 23.1 22.1 12.2

Cook Inlet 9.8 8.5 13.0

Prince William Sound 39.3 35.4 25.9

Southeast 66.1 79.6 34.6

Other 2.4 1.2 2.0

Source: Basic Information Tables from Commercial Fishing Entry Commission internet site at 

www.cfec.state.ak.us. Accessed in June 2004.

Note: Values shown are not adjusted for inflation.

Table 11. Wholesale Value of Salmon Fisheries by Management Area, 2001 – 2003

Management Area 2000 2001 2002 2003

Wholesale Value ($ Millions)

Alaska Peninsula 61.6 34.3 33.0 NA

Bristol Bay 181.8 121.2 104.6 NA

Chignik 18.4 16.5 12.3 NA

Kodiak 72.7 72.1 43.8 NA

Cook Inlet 52.8 46.2 49.9 NA

Prince William Sound 111.0 83.5 100.9 NA

Southeast 219.8 208.7 167.1 NA

Other 4.9 13.8 1.5 NA

All Areas 723.1 596.3 513.0 NA

Source: ADF&G Commercial Operator Annual Report Data, provided by ADF&G in June 2004.

Note: Values shown are not adjusted for inflation.
100

Bristol Bay Salmon Fishery and Forecasts

The Bristol Bay sockeye salmon fishery is the world’s largest wild salmon fishery (Link, 

2002). The fishery is also highly variable in terms of run size, and yet extremely compressed 

in terms of the amount of time fish are available for harvest. Typically, harvests begin in 

earnest the third week in June and peak on July 4th. . By the end of the second week in July 

most of the fish have escaped the fishery and entered their spawning rivers. The fishery is 

also relatively remote. Bristol Bay is accessible only by air or by boat. The remoteness of the 

area, the compressed season, and the variability makes the fishery relatively expensive to 

prosecute. Processing companies have to bring in by barge all of their cans and other packing 

material for the year. Because of the compact season there is no time to attain additional 

supplies while the fishery is underway. Furthermore, all of the processing labor must be 

flown in, and again because of the compact season, it is impractical to increase or decrease 

the amount of labor in-season. The processing materials and labor are major components of 

the costs of processing. If plant managers guess wrong about the size of the harvest, too 

much or too little labor or material can spell financial disaster (Van Vacter, 2004). Thus the 

accuracy and the reliability of run forecasts can make or break the year for processors and, 

because the processors create markets for the harvesters, the harvesters incur costs as well.

Table12 shows the run forecasts and harvests for Bristol Bay from 1997 – 2004. Of particular 

importance are the range of potential run sizes and the differences between forecast inshore 

harvests and actual harvests. In 1997 for example, harvests of 24.8 million fish were forecast, 

but actual harvest came in at only 50 percent of that level. A similar shortfall was seen in 

1998. In 1999 the opposite occurred—harvests were forecast at 13.8 million fish, but nearly 

twice that amount was actually harvested. Since 2000 forecasts have been more accurate, but 

the long-term record of relatively unreliable run forecasts mean processors will likely 

continue to operate very conservatively.

Table 12. Forecasts of Bristol Bay Sockeye Salmon Runs, 1997 – 2004 

1997 1998 1999 2000 2001 2002 2003 2004

Millions of Fish

Forecast Total Run: 35.8 32.1 26.2 35.4 24.3 16.8 24.1 46.6

Escapement Goal: 8.8 9.6 11.1 11.1 7.3 7.3 7.3 11.9

Forecast Inshore Harvest: 24.8 20.6 13.8 22.3 15.6 9.7 16.8 34.7

Range of Potential Run Size NA 11 – 54 9 – 43 18 – 53 9 – 39 5 – 29 11 – 37 36 – 58

Actual Harvest 12.2 10.0 25.7 20.5 14.2 10.6 14.9 NA

Source: Bristol Bay Historical Information from ADF&G Division of Commercial Fisheries at 

www.cf.adfg.state.ak.us.

Alaska Crab Fisheries

This section provides a summary of Alaska’s crab fisheries with a focus on its notable booms 

and busts. The section shows historical values of the fisheries back through 1993, and then 

looks at harvests over successively longer periods to depict the precipitous increases and 

subsequent collapses of various crab fisheries in the state. In Section 5 we discuss arguments 

of fishery scientists that information attained in an enhanced ocean observation program 

could reduce or eliminate major crab fishery collapses such as those experienced in Alaska.
101

Historically, Alaska’s crab fisheries have generated ex-vessel values approaching those 

generated in the salmon and groundfish fisheries (see Table 33). Table13 shows ex-vessel 

values of Alaska crab harvests by species for 1993 – 2002. During the 1990’s the tanner crab 

has been the primary species, but since 2000, the value of king crab harvests have surpassed 

the value of other species.

Table 13. Ex-Vessel Value of Alaska Crab Fisheries by Species, 1993 – 2002

Year King Crab Dungeness Crab Tanner Crab Other Crab All Crab Species

Ex-Vessel Value ($ Millions)

1993 93.5 5.0 219.4 3.1 321.0

1994 59.9 5.5 241.7 5.8 312.9

1995 48.6 9.2 192.8 5.4 256.1

1996 67.8 6.0 94.4 1.9 170.1

1997 60.3 10.7 92.9 2.1 166.0

1998 60.6 4.3 139.7 0.8 205.5

1999 92.4 6.6 188.8 0.7 288.5

2000 62.2 4.5 56.7 0.0 123.5

2001 64.7 7.5 37.7 0.0 109.8

2002 81.3 8.6 42.0 0.0 132.0

Source: Basic Information Tables from Commercial Fishing Entry Commission internet site at 

www.cfec.state.ak.us. Accessed in June 2004.

Note: Values are shown in nominal dollars.

Figure 2 looks at the crab harvests by species over a 30-year period. The figure shows that 

king crab harvests peaked in 1980 and then fell to approximately 25 percent of peak levels. 

The tanner crab fishery (also known as snow crab) peaked in 1991 and then fell dramatically, 

peaked again in 1998 and since 2000 has been harvested at levels less than 15 percent of 

record harvests. As shown in Figure 3 that offers an even longer perspective, demonstrates 

how different king crab fishery areas have seen different peaks and declines. During the 

1960’s the Kodiak area was a major producer of king crab with a peak in 1965 and a 

precipitous decline by the end of the decade. The Kodiak fishery continued at low levels until 

1982 when the fishery was closed. It has remained closed ever since. Following the collapse 

of the Kodiak fishery in the late 1960s, fishing effort migrated into Bristol Bay. The Bristol 

Bay fishery expanded rapidly during the 1970s and peaked in 1980, then collapsed similar to 

the Kodiak fishery. The Bristol Bay fishery has continued albeit at relatively low levels and 

with periodic closures.
102

Figure 2. Pounds Landed in Alaska Crab Fisheries by Species, 1975 – 2002

0

50

100

150

200

250

300

350

400

450

1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

Year

Pounds (Millions)

All Species King Crab Dungeness Crab Tanner Crab

Source: Basic Information Tables from Commercial Fishing Entry Commission Internet site at 

www.cfec.state.ak.us. Accessed in June 2004.

Figure3. The Rise and Fall of Major King Crab Fisheries in Alaska, 1960 – 2002

0

20

40

60

80

100

120

140

1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

Pounds (Millions)

Kodiak Bristol Bay

Source: Westward Region Shellfish Report, 2002. ADF&G. Kodiak Alaska, 2003.

Phenomena and Now/Forecasts 

The Alaska Ocean Observing System (AOOS) is part of a growing national net work of 

integrated ocean observing systems that should improve the ability to rapidly detect changes 

in marine ecosystems and living resources, and predict future changes and their 

consequences for the public good (http://www.aoos.org). While AOOS is just in its 

developmental stage the system covers three zones including (1) the Gulf of 

Alaska/Southeastern Alaska; (2) the Aleutian Islands/Bering Sea; (3) the Artic 
103

Ocean/Beaufort Sea/Chukchi Sea. Currently data collected in these zones by various 

institutions and programs include atmospheric measurements (Doppler radars, wind profilers, 

meteorological stations, FAA Weathercams, and Satellite), oceanic measurement (NOAA 

buoys, UAF buoys, CODAR, tide gauges, NOS/NDBC water temps, and satellite), and river, 

soil and snow measurements (USDA SNOTEL Met Stations, Toolik Lake Research Station, 

USGS Streamflow data, USDA SCAN Met Stations, NWS/USGS River Stage and Flow 

Data, and NWS/USGS Snow Data Sites). Related programs include DOE/Atmospheric 

Radiation Measurement Program PWSSC Nowcast/Forecast Project, GEM Project, 

SALMON Project, GLOBTEC, and the Alaska Sea Life Center Research. The types of 

measurement and level of coverage varies across the three Alaskan zones (Please see 

http://www.ims.uaf.edu:8000/caos/zone_1.html; zone_2.html; zone_3.html for details.) 

AOOS represents a partnership that has been formed to develop a regional program in 

Alaska. Partners include the State of Alaska; federal agencies such as the National Oceanic 

and Atmospheric Administration and the Department of Interior; Academic institutions 

including the University of Alaska and University of Washington; research organizations 

such as the North Pacific Research Board, the Alaska SeaLife Center, the Prince William 

Sound Science Center, and the Barrow Artic Science Consortium; and industry groups 

including fisheries and aquaculture associations. AOOS’ goal is to provide a centralized 

location for (1) new buoys, proving wind and current speed and direction, wave height, sea 

temperature, and salinity, (2) enhancements to existing NOAA weather buoy data for 

specialized local needs; (3) processed satellite data providing Alaska-wide information on 

sea-surface temperature, ocean color (chlorophyll) and wind; (4) geographically 

comprehensive surface current data from high frequency radar; and (5) data about fish, birds 

and marine mammals, the environmental effects of human activities, and any other 

information that can be used with the physical data to predict future changes to ocean 

ecosystems. 

In general, from our research we have found that a fully developed AOOS has the potential 

to provide fishery managers with the tools to maximize the sustained use of fishery 

resources. In particular, enhanced data collection and dissemination will reduce the 

uncertainty (increase confidence) in establishing exploitations rates by, among other things 

improved predictions of recruitment failures or successes. It is well known and accepted that 

errors in forecasts of fish populations are in part due to environmental unknowns. The 

parameters that appear to be of most concern among fishery stock assessment scientists and 

managers are upwelling, temperature, currents (including tidal currents), salinity, 

chlorophyll, and the strength of oceanfronts. These are all factors that affect rates of 

maturation and migration and are more or less important depending on the fishery in 

question. In addition, it is felt that more precise33 data, that is, more data points both 

spatially (throughout the entire North Pacific Rim) and temporally would translate some 

unknowns into knowns and would enhance understanding of fish growth and predictions of 

productivity and migration patterns. 

For the past several years stock assessment scientists (Ianelli et al, 2003) have been 

evaluating the effect of bottom temperature (Tt) on survey catchability of pollock in year t:

 

33 Some of the North Pacific fisheries scientific community would love to have data collected throughout the 

year by means of an establish grid system of permanent monitoring buoys throughout the Pacific Rim which 

collect data on currents, temperature, salinity, and chlorophyll. 
104

Q(t)= U(q) +BqTt

Where Uq is the mean catchability and Bq represents the slope parameter. Bottom 

temperature was collected during the NMFS summer bottom-trawl surveys. It was shown 

that temperature affects the distribution of pollock on the shelf and by extension could affect 

the availability of the stock to survey. That is, temperature may affect the proportion of the 

stock that is within or outside of the standard survey area. These patterns were further 

examined by comparing Pollock density with selected on-bottom isotherms. This shows that 

2002 was warmer than usual and that, in general, pollock densities are rare at temperatures 

lower than 0 degrees. The latter illustrates the significant value of the understanding of the 

effect of this physical parameter on the evaluation and determination of allowable biological 

catch in fisheries management.

Another growing body of scientific evidence supports hypotheses about the direct and 

indirect effects of the environmental change on salmon production (NPAFC Science Plan 

2001-2005, BASIS). For example there is a strong correspondence between salmon catch 

and climate indices. In addition, there appears to be a correlation between water temperature, 

blooms of coccolithophorid and salmon survival (Jack Helle, Alaska Fisheries Science 

Center, Auk Bay Lab May, 2004). Over the last 6 years there have been two significant 

long-term blooms in the Bering Sea (the blooms being an indication of sterile ocean 

conditions). According to Helle, coccolithophorid blooms appear to be water temperature 

related. If the scientists have better forecasts on water temperature they could better predict 

the occurrence of these blooms and thus be better able to predict juvenile salmon survival. 

It is also hypothesized by Alaska Department of Fish and Game that physical oceanographic 

data can improve management of Cook Inlet sockeye salmon through improvements in 

season salmon run projections (Willette and Pegau, 2002). In 1999, the Alaska Board of 

Fisheries adopted a sliding range of inriver escapement goals for late-run Kenai River 

Sockeye salmon that were based upon preseason and inseason projections of the annual 

return of this salmon stock. The ADF&G offshore test fishing (OTF) provides the primary 

source of information used to project the return of this stock inseason. Achievement of 

inriver escapement goals and allocation of salmon to commercial, personal use and 

recreational user groups is thus largely dependent on the accuracy of these projections. The 

accuracy of the population estimates provided by OTF typically increases as the season 

processes. Projections made on July 20 have ranged from –5.4% to +103% of the actual run. 

Errors in OTF program estimates or run size appear to be due to interannual changes in 

migratory timing and catchability. 

The OTF program often fails to accurately predict runs that are earlier than normal. Failure 

to accurately predict very large runs can result in large escapements, loss of revenue to the 

commercial fishery, and reduced production in future years due to overgrazing of plankton 

stocks by large fry populations in rearing lakes. Failure to accurately predict weak runs can 

result in over harvest by the commercial fishery and reduced production in future years. 

Errors in OTF program estimates of run size appear to be due to interannual changes in 

migratory timing and catchability. Migratory timing is defined as abundance as a function of 

time in a fixed geographic reference frame (Mundy, 1982). The sockeye run entering the 

Cook Inlet normally peaks on July 15, but peak migratory timing has varied from Jul6 to July 

19. According to Willette and Pegau, variations in migratory timing are likely due to a range 

of biotic and physical factors that affect rates of maturations and migrations. Ocean 
105

temperature (Burgner, 1980), the strength of oceanic fronts (Mundy, 1982), and tidal currents 

(Stasko et al., 1973) are all likely important physical factors affecting both the rate of 

maturation and migration of salmon. Catchability is defined as the fraction of the population 

captured by a unit of fishing gear. The OTF program estimates cumulative catchability to 

date from the ratio of cumulative catch per unit of effort (CPUE) obtained from the test 

fishing vessel and estimates of total return to date. Cumulative catchability varies by a factor 

of 2 among years. Variations in catchability are likely due to biotic factors (e.g. fish size) as 

well as physical factors that affect the vertical and horizontal distribution and migration rate 

of salmon (Hakoyama, 1995).

The migration of salmon into the Cook Inlet is clearly influenced by the strength and location 

of tiderips. Fishermen working the inlet are very aware of the tiderips and use the rips to 

locate and capture migrating salmon (Wilson and Tomlins, 1999). Salmon have likely 

evolved behaviors that allow them to use rip tides and associated current structures to 

minimize the energy expended to reach their natal rivers (Scholz et al. 1972). According to 

Willette and Pegau, (2004) although tiderips clearly result from strong velocity gradients, 

they also represent boundaries between water masses and may be associated with strong 

salinity gradients. 

Willette and Pegau have proposed to test several hypotheses regarding the effects of 

changing oceanographic conditions on the migratory behavior and catchability of salmon 

entering Cook Inlet. Better understanding of these effects may allow for improvement in the 

accuracy of inseason sockeye salmon populations estimates and thus improved accuracy of 

short and long-term forecasts of salmon runs. The latter suggests that with ongoing 

oceanographic data collection through AOOS ADF&G might be able to better manage for 

inriver escapement goals and maximize sustained yield thus benefiting the economy of the

Upper Cook Inlet area and nation as a whole.

Currently, OOS information is not readily used in the crab industry. Sometimes fisheries 

managers use weather data to adjust fishing seasons (for safety reasons). In addition, bottom 

temperature information is sometimes used as an indicator of species distribution and 

correlated over time to stock size. However, in general, according to our sources, the 

greatest benefit to the Alaskan king and tanner crab industry from enhanced AOOS would be 

in the long term with the collection of appropriate time series data. Biological and 

oceanographic data could be correlated with trawl surveys and allow fisheries biologists to 

better predict crab recruitment and productivity. This information could be used to develop 

harvest rate models that more adequately reflect the state of the ecosystem. According to 

Gordon Kruse (ADF&G) oceanographic conditions (upwelling, temperature and currents) are 

critical to the development of larvae for nearly all species of crab. For example, egg and 

larvae development are temperature sensitive. Larvae feed on phytoplanktons that are light 

sensitive. If winter/spring is cold then it is likely that phytoplankton blooms will occur 

before the crab larvae are developmentally ready. Better oceanographic data would allow 

fisheries scientists and fisheries managers to better predict poor and good recruitment and 

thus allow for more accurate determinations about when to close and or open a fishery. 

In addition to the above, we have learned that the value of AOOS will probably be highest in 

the rationalized fisheries. The fleet may benefit from more detailed knowledge of the 

distribution of fish by age and season. This would allow them to minimize unintended catch 

of non-targeted species (an unintended consequence but of significant value to society) 
106

minimizing fuel and crew costs. For example, if scientists knew enough to state that during 

the summer months, adult pollock will follow a particular frontal feature and can identify 

where that front is located they could provide the fleet with forecasts of prime fishing 

locations for target species. 

Finally, we have learned that there are also benefits or value from enhanced data collection 

and dissemination through AOOS to fisheries managers as relates to Essential Fish Habitat 

Provisions, Marine Protected Areas and marine mammal protection. Fisheries managers 

need and want better data to allow them to now deal with the complex spatial and temporal 

dimensions associated with ecosystem-based management tools that are the underpinnings of 

the above management tools. 

One scenario that we have not explored but for which potential economic benefits of 

enhanced OOS may be accrued is the shellfish aquaculture industry and human health and 

safety more generally. If, for example, scientists could more accurately predict blooms of 

PSP (paralytic shellfish poisoning) industry would know to terminate production activities 

and thus avoid significant health and safety consequences. PSP is a fairly significant issue in 

the Washington aquaculture industry and better information about the timing and extent of 

algal blooms could have measurable economic benefits in the near future as opposed to the 

relatively more speculative and long term future benefits of better recruitment forecasts and 

stock assessments as described above. 

Qualitative Discussion of Fishery Management Decision Making

Prediction of fish stocks or population levels plays a critical role in fisheries management 

decision-making. Prediction of stock size is critical to the underlying principals or goals of 

sustainable fisheries management. As such the role of fishery scientists is great in the overall 

decisions about harvest strategies. However, there continues to be tremendous uncertainty in 

stock size projections and thus continued potential for less than perfect decision making at 

the management level. Authors such as Solow et al (1998) have been able to use data on 

enhanced forecasts of oceanographic conditions to formulate predictions of crop yields which 

are then used by farmers to optimize cropping patterns. The Bayesian decision theory 

approach that they use could potentially provide a methodology to aid fisheries managers in 

optimizing fish harvest (given the constraints of provisions of the MSFMCA and other 

pertinent legislation and regulation and fishery management objectives through the ten 

National Standards for Fishery Conservation and Management). The Bayesian approach 

could be used as a management tool to make predictions and explore the consequences of 

alternative scenarios for a particular fishery within a particular ecosystem. The aim of this 

approach would be to develop a model as a tool for guiding decision making in a variety of 

areas including the conduct of a fishery, and the targeted collection of information to 

improve understanding of the system and its response to change (Goodman et al, 2002). 

That said it is instructive to qualitatively outline the decision model that is used in fisheries 

management decision-making in the Alaska region. Goodman et al (2002 pp.1-2) provides an 

excellent guide for lay persons of the very complex process by which harvest strategies are 

chosen. The following is taken from that report which explicitly focuses on Alaska 

groundfish species management. 
107

The current harvest decision making strategy is essentially a maximum sustainable yield 

(MSY) single-species approach, modified by some formal safeguards incorporated to ward 

against overfishing as defined from the single-species stand point, and with opportunities of a 

less-structured nature for reducing harvest rates further in response to perceived social, 

economic and ecological concerns. No quantitative standards or specific decision rules are 

stated for these latter considerations, except as they are imposed, from outside the MSFCMA, 

by the Endangered Species Act or the Marine Mammal Protection Act. 

The overfishing level (OFL) set for each stock is an estimate either of the fishing mortality 

rates associated with MSY (Fmsy) or an estimate of a surrogate for Fmsy. The OFL is 

treated in the management system as a limit that should not be exceeded except with a very 

low probability. The acceptable biological catch (ABC) set for each stock is an estimate of a 

target rate, which is intended to establish some margin between it and the OFL. The hope is 

that managing so as to achieve this target on average will accomplish the desired compliance 

with exceeding the limit (OFL) only rarely. The ad hoc downward adjustments of harvest in 

response to other social, economic, and ecological considerations takes place in the 

deliberations where the total allowable catch (TAC) is set subject to the constraint that it be 

less than or equal to the ABC.

The formulaic component of the reduction of harvest rate from the theoretical MSY harvest 

rate (from OFL to ABC) is by an amount that is often modest, when expressed as a fraction 

of the harvest rate; but in terms of the total tonnage involved, or its dollar value, the amount 

is considerable. The margin is also small relative to real natural variation, and small relative 

to the practical uncertainty about stock status or population parameters for many of the target 

stocks and indeed for most of the ecosystem. By contrast, in actual practice, the reduction in 

TAC from ABC has for some stocks and some years been quite large, but there is no explicit 

and general formula for this reduction. Many stock assessment scientists believe that this 

buffer should be better linked to uncertainty in both the measurement and process error 

(Anne Hollowed, personal conversation 2004). 

The formal and standardized quantitative portions of the process for determining OFL and 

ABC begin with the assignment of each stock to one of six “Tiers” based on the availability 

of information about that stock. Tier 1 has the most information, and Tier 6 the least. The 

so-called F40% construct plays a prominent role in some of the Tiers but not others. F40% is 

the calculated fishing mortality rate at which equilibrium spawning biomass per recruit is 

reduced to 40% of its value in the equivalent unfished stock. This is an esoteric, but useful, 

measure of the amount by which the associated fishing rate reduced the stock size, in the 

long run. The useful features of this particular measure are two-fold. First, its calculation is 

less sensitive to the details of the stock-recruitment relationship than is the calculation of 

Fmsy, so it is practical to estimate F40% for stocks that are not well enough studied for 

estimation of Fmsy. The second is that for a range of dynamics encompassing many, but not 

all, of the BSAI/GOA target groundfish stocks, for example, modeling studies have shown 

that harvesting at F35% accomplishes about the same thing as harvesting at Fmsy, so 

harvesting at the slightly lower rate, F40% established a modest margin of safety. 

Currently management of king and Tanner crab fisheries are under the jurisdiction of the 

Alaska Department of Fish and Wildlife and National Marine Fisheries Service. An annual 

stock assessment and fishery evaluation (SAFE) report is required of them by the North 

Pacific Fisheries Management Council. The SAFE summarizes among other things, 
108

guideline harvest levels (GHL) and analytical information used for management decisions or 

changes in harvest strategies. According to the 2003 SAFE for King and Tanner Crab 

Fisheries sin the Bearing Sea and Aleutian Islands, the Federal requirements for determining 

the status of the stocks are the minimum stock size threshold (MSST) and the maximum 

fishing mortality threshold (MFMT). These requirements are contained in the Fisheries 

Management Plan (FMP). The MSST is 50% of the man total spawning biomass (SB or 

TMB= total mature biomass) for the period 1983-1997, upon which the maximum 

sustainable yield (MSY) was based. A stock is overfished if the SB is below the MSST. The 

MFMT is represented by the sustainable yield (SY) in a given year, which is the MSY rule 

applied to the current SB (the MSY control rule is F=0.2 for king crabs and F=0.3 for Tanner 

and snow crabs). Overfishing occurs if the harvest level exceeds the SY in one year. GHLs 

are developed from joint NMFS and ADF&G assessment of stock conditions based on 

harvest strategies developed by ADF&G. 

Regular trawl and hydoraccoustic survey results for five stocks (Pribilof blue king crab, St. 

Matthew blue king crab, Bristol Bay red king crab, eastern Bering Sea Tanner crab (c. 

bairdi), and eastern Bering Sea snow crab (c. opilio) are compared to thresholds established 

by the State of Alaska harvest strategies and regulations. ADF&G uses these thresholds to 

determine if a fishery should be opened and to calculate GHL. For example, the Bering Sea 

Tanner crab fishery was closed in 1997 due to near record low stock abundance in the 1997 

NMFS survey and poor performance in the 1996 fishery. ADF&G will reopen the fishery 

when the female biomass is above the threshold and the fishery GHL is above the minimum 

identified in the rebuilding harvest strategy or MSY biomass defined in the FMP as 189.6 

million pounds of total mature biomass. 

The traditional approach to fishery science, that is, the primary input to the harvest strategy 

outlined above, has been to assess the state of the stocks on a single-species basis, using 

catch and biological data as input to the models and then forecast what will happen if things 

(usually total catches) stay as they are or get changed somewhat. This leads to decisions 

being made based on expected outcomes. Some sense of the robustness of decisions can be 

made by running the forecasts with different assumptions or from different starting points but 

this sort of exploration has traditionally been limited and ad hoc. A more recent approach is 

instead to create models of the fishery systems and to use computer simulations to test 

systematically what would happen if different management strategies (combinations of data 

collection, assessment and decisions following specific rules) were adopted. This sort of 

analysis is aimed at systematically revealing how different management approaches compare 

in meeting sets of objectives. Unfortunately given the scope of this work we are not able to 

explore and implement this approach at this time. In general, however, we have been able to 

ascertain that enhanced AOOS offers the potential greatest benefits at the scientific level 

where science can monitor, assesses forecasts within bounds and generally inform and 

support decision making. At this point stock assessment scientists feel limited in their 

capabilities and feel that more atmospheric, oceanic, biological and ecosystem data would be 

of tremendous benefit. Once such data is made more available they feel several years of 

improved predictions (better than correct 60% of the time) will be required in order to gain 

the trust of fisheries managers and the fishing community and ultimately have their 

recommendations for such metrics as total allowable catch (TAC) to be readily accepted. 
109

Assessment of the Economic Value of an Enhanced Ocean Observation Program 

An enhanced ocean observation program has the potential to provide significant benefits to 

Alaska commercial fisheries. This section discusses three areas where better information and 

less uncertainty could generate higher values:

1) Better information could improve the reliability of forecasts that in turn will enhance 

the ability of fishery businesses to plan their fishing seasons and profitably prosecute 

their fisheries. In Alaska salmon fisheries it is estimated that reducing the uncertainty 

due to unreliable run size and timing forecasts, would lead to increases in net 

revenues for salmon processors by $77 million per year

2) Better information and more certainty could allow a more aggressive harvest policy in 

the Alaska groundfish fisheries without placing stocks under undue risk of 

overfishing. Improvements due to better information could generate an estimated 

$504 million in additional wholesale value per year.

3) Better information and more certainty could allow a more aggressive harvest policy in 

the Alaska groundfish fisheries without placing stocks under undue risk of 

overfishing. Improvements due to better information could generate an estimated 

$504 million in additional wholesale value per year.

Enhanced Business Planning – Alaska Salmon Fisheries

Analysts at Northern Economics interviewed key informants at three of the top five Alaska 

salmon processors—Don Giles at Icicle Seafoods, Terry Gardiner at Norquest, and Norm 

Van Vacter at Peter Pan Seafoods. Processors were asked to describe how improved certainty 

in run forecasts would help their operations with particular reference to Bristol Bay. All three 

agreed separately, that if an enhanced ocean observation system could significantly improve 

run size and timing forecasts, then the benefits to their bottom lines would be very 

significant. In general it was felt that improvement in profits from Bristol Bay could range 

from $25 to $50 million per year.

Improvements would come in three areas: 1) from cost savings and efficiency gains, 2) from 

increased processing amounts when forecast runs are high, and 3) from higher wholesale 

prices on average.

Cost savings and efficiency gains would be generated if processors could rely on run size and 

run timing forecasts, and if the range of the forecasts was smaller. The savings would be 

realized in the amount of processing materials and labor that are deployed to the facility. 

With greater certainty the amount of material and labor can be optimized. 

With high levels of uncertainty processors tend to be conservative in their planning. In years 

with high run size forecasts most processors will plan on harvests in the low end of the 

forecast range. The costs of underestimating labor and material needs are lower than the costs 

of overestimating. Once the material and labor is acquired and deployed they become sunk 

costs, and if the runs fail to materialize then operating losses are likely. Thus if there is 

greater certainty and reliability in the forecasts, processors will learn to be more aggressive in 
110

their planning and will be able to process additional volumes rather than letting harvestable 

salmon escape.

Finally, the lack of certainty on run size and timing reduces processors’ ability to work with 

buyers. The inability to guarantee a buyer that a certain quantity of product will be delivered 

on a certain date limits the price that buyers are willing to pay. Currently Bristol Bay 

processors are generally unable to pre-sell the majority of their product.

For purposes of this study, we assume that the enhanced ocean observation system can in fact 

lead to improved reliability of run forecasts, and that improved net revenues for processors in 

the Bristol Bay salmon fishery will amount to at least $25 million per year. Similar 

improvements can also be expected for salmon fisheries in other areas, but the magnitude of 

those improvements will depend on several factors. In particular we believe that five key 

factors determine the potential for improved net revenue resulting from greater certainty. 

These are:

1) The relative remoteness of the area

2) The length of the season

3) The current variability in run forecasts

4) The need for non-resident labor

5) The ability of the particular fishery to improve

For each of these factors, we scored the major salmon fisheries in Alaska on a scale of 0 to 4 

relative to Bristol Bay. A score of ‘4’ indicate a factor is on par with that factor in Bristol 

Bay. A lower score means the factor is less important for the particular area. Each factor was 

assigned an equal weighting and the average score was calculated to determine the relative 

increase in wholesale value. For example the Alaska Peninsula fishery was assigned an 

overall average score of 3, and therefore the increase in wholesale value is 75 percent of the 

increase assumed for Bristol Bay where increases were 24 percent. Thus the wholesale value 

of Alaska Peninsula fisheries is expected to improve by $5.9 million per year ($33 million × 

75 percent ×24 percent = $5.9 million). Over all areas it is estimated that improved certainty 

could add $77 million annually in wholesale net revenues for salmon processors. 

Table 14. Estimated Improvement in Salmon Processor Annual Net Revenues Due to Improved Certainty 

of Run Size

Management Area

Bristol 

Bay

Alaska 

Peninsula Chignik Kodiak

Cook 

Inlet

Prince 

William 

Sound Southeast All Areas

Factor Score (0 to 4, with 4 meaning the factor is equivalent to Bristol Bay)

Remoteness 4 4 4 2 1 1 1 2.4

Short season 4 3 1 1 1 1 0 1.6

Variability 4 2 1 2 1 2 2 2.0

Non-Resident Labor 4 3 2 0 2 1 2 2.0

Room for Improvement 4 3 0 2 1 2 2 2.0

Overall Average 4 3 1.6 1.4 1.2 1.4 1.4 2.0

Proportional Score 100 75 40 35 30 35 35 63

Results Annual Wholesale Value ($ Millions)

Base Case Wholesale 

Value 104.6 33 12.3 43.8 49.9 100.9 167.1 511.6

Net Revenue Increase 

from Improved Certainty 25.0 5.9 3.7 6.1 5.2 10.6 20.5 77.0

Source: Developed by Northern Economics, Inc. Estimates of wholesale value are from Table 11.
111

These benefits are measures of producer surplus as most of the improvement would be a 

result of reduced costs to harvesters and producers. There might also be some improvement 

in product quality as fish are harvested earlier in the season and a shift to fillets (with greater 

value added) from headed and gutted (lower value added) also leading to improvements in 

both producer and consumer surplus. Finally, because of increased product quality and 

greater value added, total processed product output (1st wholesale value) from the region 

would increase resulting also in increased regional economic impacts. 

Increased Harvests – Alaska Groundfish Fisheries

As indicated in Section 2, stock assessments and harvest quotas for groundfish take into 

account the amount of uncertainty in each of the utilized species. Scientists recommend 

relatively low ABCs for species with high levels of uncertainty in key variables, but will 

recommend relatively high ABCs for species where there is more certainty. It is suggested 

(Anne Hollowed, NMFS/REFM) that use of temperature data can be used to reduce stock 

size forecast error. Currently NMFS is correct only 60% of the time in their annual pollock 

stock assessment. The risk adverse nature of groundfish harvest strategies is furthered by an 

absolute limit on TACs in the Bering Sea of 2 million metric tons. Without this “OY Cap” 

harvests in the Bering Sea could significantly increase.

For purposes of this study fishery scientists from NOAA’s Alaska Fishery Science Center 

were asked what harvests levels of groundfish might look like if enhancements to AOOS led 

to significant improvements in their ability to accurately predict (on a relative scale) stock 

sizes and recruitment of major groundfish species. Scientists indicated that constraints 

imposed within their stock assessment models to account for uncertainty could be reduced 

and that recommended ABCs would increase significantly for many of the groundfish 

species. Furthermore if there was a longer track record of improved stock assessments it is 

surmised that political decisions to limit overall harvests would eventually be removed and 

that TACs would approach recommended ABCs.

While not developed specifically for this study the “North Pacific Multi-Species 

Management Model” developed by Dr. James Ianelli includes an assessment of groundfish 

harvests under the assumption that uncertainty in stock levels and recruitment are greatly 

reduced, and that artificial caps on harvests are eliminated. The model was originally 

developed for use in the Alaska Groundfish Fisheries Final Programmatic Supplemental 

Environmental Impact Statement (NMFS 2004) and the assessment of harvests with the 

assumption of high levels of certainty corresponds to Alternative 2.1 in the SEIS. The model 

results for the base case and the case with improved information are shown in Table 15. The 

results demonstrate the possibility that with improved information catch and value in the 

fisheries can be improved significantly. The long run average increase in wholesale value 

projecting out 20 years from the base year (2002) is over $500 million annually. While 

projections of overall biomass are expected to decline, constraints in the model assures that 

exploitation rates do not cause stocks to fall below sustainable levels.
112

Table 15. Estimated Value of Improved Information in Alaska Groundfish Fisheries

Scenario Base Case With Improved Information Difference

Total 

Biomass Catch

Whsle. 

Value

Total 

Biomass Catch

Whsle.

Value

Total 

Biomass Catch

Whsle. 

Value 

Year

Pounds 

(Millions)

Pounds 

(Millions) $ Millions

Pounds 

(Millions)

Pounds 

(Millions) $ Millions

Pounds 

(Millions)

Pounds 

(Millions) $ Millions

2003 43.3 4.4 1,360.7 43.3 6.3 2,048.5 0.0 1.9 687.8 

2004 42.0 4.4 1,354.9 38.5 5.4 1,768.5 -3.5 1.0 413.6 

2005 41.8 4.1 1,262.9 37.0 4.9 1,629.9 -4.8 0.8 367.0 

2006 42.0 4.0 1,224.5 37.0 5.0 1,638.2 -4.9 1.0 413.6 

2007 42.4 3.9 1,204.1 37.5 5.2 1,705.9 -4.9 1.3 501.8 

6-year Avg. 42.3 4.2 1,281.4 38.7 5.4 1,758.2 -3.6 1.2 476.8 

Long-Run Avg. NA 4.1 1,248.3 NA 5.4 1,753.1 NA 1.3 504.8 

Source: Biomass and catch estimates are taken from Alaska Groundfish Final Programmatic SEIS. NMFS, 2004; 

estimates of wholesale value are estimated by Northern Economics based on the average wholesale value 

per ton of harvest from 2002 as shown in Table 6

In order to assess the benefits from improvements in coastal ocean observing systems for the 

groundfish fisheries we assume that better data results in stock assessments that are 

significantly more accurate and that scientists are better able to make long range (3-10 year) 

projections because of the enhanced ability to predict spawning success. This would greatly 

increase the confidence that scientists, decision makers, and the interested public (including 

environmentalists) have in the process. We assume as a result that decision makers can drop 

the 2 million OY Cap in the Bering Sea and Aleutian Islands (BSAI) and allow TAC in both 

the Gulf of Alaska (GOA) and BSAI to rise to Maximum Sustainable Yield levels (currently 

MSY levels are generally higher than ABC levels). Harvest of groundfish could nearly 

double under this scenario. Therefore, our initial model results for groundfish (which 

assumes no price effect with significantly increased harvest supply; i.e., wholesale prices are 

perfectly elastic in our model) indicate that wholesale groundfish revenues of total output 

(gross revenues) would increase by over $1 billion per year for the first five years. Over time 

the increase would stabilize at levels of approximately $400 million greater than the current 

fishery is projected to generate as the stocks are fished down to stable MSY levels. Please 

note that additional increases in wholesale value may also result from reductions in 

incidental catch and subsequent discards as well. We have not been able to measure this 

affect. Regardless, because of the assumption of perfect elasticity, our total estimate is an 

upper-end estimate. There would most likely be a price effects resulting from such a 

significant increase in harvests.

Avoidance of Overfishing -- Kodiak King Crab Fishery

The chief crab scientist for ADFG, Gordon Kruse, indicates that an aggressive harvest policy 

in the face of uncertainty about recruitment is the primary culprit in the collapses of crab 

fisheries (Kruse, 2004). According to Kruse, successful reproduction of various crab stocks 

requires not only sufficient numbers of spawning adults, but also on favorable ocean 

conditions, currents and temperatures in particular. While estimates of spawning adults can 

be attained using catch data and trawl surveys, ocean conditions linked to successful 
113

reproduction are not easily monitored. An enhanced ocean observation program would 

significantly improve scientists’ ability to successfully predict reproduction events. 

Kruse goes on to say that the collapse of king and tanner stocks can be linked to recruitment 

failures that occurred over several successive years. Even though the spawning biomass was 

adequate for sustainable harvests, ocean current and temperatures caused reproduction 

failures. Because new year classes were not being produced as assumed, overfishing resulted. 

The first major failure occurred in the Kodiak king crab fishery. By the time scientists 

realized that recruitment failures were occurring, the stock was fished below minimum stock 

size thresholds levels from which the stock has never recovered. In the case of the Bristol 

Bay king crab collapse, scientists and managers recognized the pattern from Kodiak and 

scaled back harvests enough to keep the stock above the minimum threshold, and thus the 

fishery continues albeit at much lower levels. 

Kruse believes that an enhanced ocean observation system could have provided scientists 

with enough additional information, that the total closure of the Kodiak king crab fishery 

could have been avoided. At a minimum, with the additional information, a scaled back king 

crab fishery could have been maintained at levels proportional to current levels in Bristol 

Bay. Thus this study assumes that an enhanced ocean observation system could have 

prevented overfishing in Kodiak and that the Kodiak fishery would continue today.

Figure 4 is a copy of Figure3 except that hypothetical harvests are assigned to the Kodiak 

fishery assuming they are proportional (based on peak harvest years) to harvests in the 

Bristol Bay king crab fishery. The heavy dashed line shows the projected catches. The 

additional catches would have generated approximately $62.7 million annually in wholesale 

value per year at 2002 prices. While this estimate is relatively speculative (the collapse of the 

Kodiak crab fishery cannot be prevented after the fact) it does provide insight into the 

potential benefits of enhanced oceanographic information systems if it results in overfishing.

Figure 4. Projected Kodiak King Crab Harvests if Better Information Were Available

0

20

40

60

80

100

120

140

1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

Harvest (millions of pounds) 

Bristol Bay Kodiak Actual Kodiak Projected

Source: Data from Westward Region Shellfish Report, 2002. ADF&G. Kodiak Alaska, 2003. Projections of 

Kodiak from 1982 – 2002 were developed by Northern Economics.
114

Conclusions

 Our estimates of the potential value of an enhanced AOOS to Alaska commercial fisheries 

include: 

• $77 million annually in increased net revenue in Alaska salmon fisheries,

• $504 million annually in increased total wholesale value in Alaska groundfish 

fisheries, and 

• $63 million annually in lost wholesale value that might have been avoided.

These three types of improvement should not be viewed as additive. The groundfish figure is 

an estimate of increased value of total output to processors, of which perhaps 20 25%34 might 

be considered a net increase in revenues above costs to both harvesters and processors. The 

remaining 75-80% represents economic impacts that potentially could be generated 

throughout the region. The estimate of benefits to the salmon industries reflects cost 

reductions and higher output values, or a change in net revenues or producer surplus. The 

value assigned to the crab fishery is an estimate of foregone output or avoided cost/losses of 

which again, only 20-25 percent might be considered a net increase in revenues above costs 

to both processors and harvesters. 

Furthermore it should be noted that in developing our estimates of the value of enhanced 

AOOS information we found it difficult to specify quantitative decision and physical 

outcome models. Many of the scientists we talked to were able to speculate about the 

benefits of more/better defined and accessible AOOS data but were only in early stages of 

considering how to add oceanographic parameters to their stock 

assessment/recruitment/escapement models. Regardless, these scenarios are very difficult to

model due to the complexity of nonlinear interactions in biological systems and the broad 

array of influential parameters. Development of management decision making that takes 

environmental, ecological and ecosystem effects into account will require considerable 

amounts of monitoring, understanding of the behavioral relations among fishers, the fish they 

catch and the prey of the harvested species (Langton and Haedrich, 1997). As such our 

analysis takes a significant leap between decisions to open or close a fishery and economic 

outcomes and it is difficult to say, therefore, with confidence that additional biological and 

oceanographic data generated through enhanced AOOS will with complete certainty lead to 

better decision outcomes in fisheries management. In addition, in the long term, as the 

complexity of information requested from fisheries scientists increases, and as more and 

more complex models are utilized, predictability and certainty may not necessarily increase. 

In fact, the more complex the models, the more they have to depend, in practice, on 

assumptions and presumptions rater than data. This may have implications for the value of 

information provided by AOOS overtime. 

 

34 The 20-25 percent is a rule of thumb estimate of the portion of total output that is likely to 

be considered a “return on investment” by owners of the processing facilities and harvesting 

vessels, after fixed (including facility maintenance and replacement of production units—e.g. 

machinery, engines, etc) and variable costs.
115

That said, in the spirit of this project, as illustrated in the previous sections, we have 

attempted to estimate the benefits that improvements in AOOS could generate. In particular 

we have shown that enhanced AOOS could generate over $500 million annually in additional 

value in select Alaska’s commercial fisheries (e.g. Alaska groundfish fisheries). However, 

there are several factors, as explicitly outlined below, that may lead to the uncertainty around 

those estimates. 

1) It is unknown whether the proposed changes to the ocean observation program will 

actually deliver more and “better” data.

2) Assuming better data is delivered, it is unknown whether or when those data will be 

integrated into stock assessment and run forecast models.

3) Assuming better data are integrated into the models it is unknown whether the new 

data will actually improve the reliability of the models.

4) Assuming the reliability of the models is improved, it is unknown whether or when 

the improvements will be accepted by managers or industry members.

These types of uncertainties lead to a more conservative expected value of information 

estimate. To assess benefits to the Alaska groundfish fishery, for example, of enhanced data, 

a value of information model (using hypothetical probabilities) might be expressed as 

follows:

1) There is a 75% probability that groundfish scientists will be able to use the data to 

refine their analyses

2) There is a 50% probability that what scientists think today will be borne out by their 

further analyses and data

3) There is a 50% probability that the NPFMC and NOAA NMFS will lift the 2 million 

MT TAC cap, once a track record is established

4) There is a 50% probability that groundfish stocks will be in the same shape they are 

in 25 years from now 

An expected value model combines the above probabilities multiplicatively. Thus based on 

the hypothetical probabilities described above, there is only a 9 percent chance (0.75 × 0.5 × 

0.5 × 0.5), that the improvements generating $504 million (in today’s dollars) will be 

realized. 

In addition to the probabilistic model described above, the fact that the benefits of the 

enhanced ocean observation system are not expected to be realized for many years after the 

system is upgraded means that the cost and benefits stream must be discounted to present 

values. If we assume conservatively, for example, that:

1) The cost of the observation system upgrades in Alaska are $100 million and occur in 

2005,

2) The cost of operating and maintaining the system for the next 50 years (until 2055) 

are $10 million per year,

3) The benefits of the program to the groundfish fishery ($504 million per year) begin to 

accrue 15 years (in 2020) after the system is upgraded and continue until 2055,
116

4) The social discount rate is 7.5 percent; 

Given the above assumptions, the net present value of enhancements to AOOS to Alaska 

groundfish fisheries is reduced to only $17.5 million. If we further assume there is only a 9 

percent probability that the benefits of an enhanced system for the groundfish fishery will be 

realized then, the net present value becomes negative.

Transferability of Models and Results

While the approach taken in this analysis may be transferred to other fisheries around the 

U.S. it is not necessarily appropriate to transfer the estimates generated. Fisheries around the 

nation are each unique and managed as such. In addition the uncertainties inherent in value of 

information models across fisheries will be unique. We have learned that the complexity of 

the biological and stock assessment models that are the basis for harvest rates and fisheries 

management generally make it very difficult to transfer applications even between fisheries 

within a region. We believe that best approach to modeling the value of enhanced COOS to 

fisheries across the United States will be through explicit case examples that illustrate the 

sign and potential magnitude of benefits.
117

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M.A. Johnson and S.R. Okkonen (eds.), University of Alaska, Fairbanks, Alaska. 

Van Vacter, Norm, 2004. Dillingham Plant Manager, Peter Pan Seafoods. Personal 

Communication with Northern Economics, Inc. May 2004.
119

Appendix D: Recreational Boating on the Great Lakes

Tom Pelsoci

Delta Research Corp.
120

DECISION MODEL

TO ESTIMATE THE VALUE OF IMPROVED WAVE HEIGHT FORECASTS FOR 

GREAT LAKES RECREATIONAL BOATING

Case Study 

Coordinated Regional Benefit Studies of Coastal Ocean Observing Systems 

(Great Lakes Region)

Tom Pelsoci

Delta Research Corp.

Chicago, Illinois

June 30, 2004

Funded through the National Ocean Partnership Program, supported by the National 

Oceanic and Atmospheric Administration, National Science Foundation, Office of Naval 

Research, U.S. Environmental Protection Agency, US Geological Survey, Mineral 

Management Service, U.S. Coast Guard, Defense Advanced Research Projects Agency, 

Department of State, the U.S. Army Corps of Engineers, and the National Aeronautics & 

Space Administration.
121

Executive Summary

The Great Lakes Forecasting System (GLFS) provides lake surface and wave height forecasts 

for each of the Great Lakes. 

Based on the assumption that GLFS forecast accuracy can be further improved through the 

deployment of enhanced ocean observing technologies and / or analytical practices, this 

study posits a decision model to estimate the economic value of better information (improved 

forecast accuracy) for Great Lakes recreational boaters.

Per Table 1, the decision model indicates that for every 1% of additional forecast accuracy, 

Great Lakes recreational boaters can be expected to enjoy 97,000 additional boating days and 

$21 million of incremental economic benefits.

On the basis of conservative assumptions, improving forecast accuracy from 80% to 85% 

could result in 483,000 additional boating days and $ 103.5 million of incremental economic 

benefits.

Table 1: Economic Impact of Improved GLFS Forecast Accuracy ($ Million)

FORECAST

ACCURACY

ADDITIONAL

BOATING DAYS

INCREMENTAL 

ECONOMIC BENEFIT

80% BASELINE BASELINE

81% 96,689 $ 20,711,000

82% 193,377 $ 41,421,000

83% 290,066 $ 62,132,000

84% 386,755 $ 82,843.000

85% 483,444 $ 103,554,000
122

Wave Phenomena and Wave Forecasts

Discussions with marina operators, staff of recreational boating associations, and editors of 

recreational boating journals and outlets indicate that weather forecasts are considered to be 

very important by Great Lakes recreational boaters. In particular wave heights and wave 

periodicity (choppiness) are key concerns relative to safety and enjoyment. 

While the type of vessel (sailing vs. motor boat), the size of the vessel, and the skill and 

experience of the operator are important sources of variation, expert input would indicate that 

in seas with 18” wave heights and higher, coupled with choppy conditions, amateur sailors 

and motor boaters will have a less than pleasurable experience and may find conditions 

unsafe. 

Since recreational motorboats are generally less seaworthy than sailing vessels and are “made 

to go fast”, wave height is a particularly important constraint relative to enjoyable recreation. 

We therefore posited that

- “Good” surface conditions are associated with wave heights under 18” and

- “Bad” surface conditions are associated with wave heights at or over 18”.

Wave height along with wave periodicity information is forecasted by the NOAA Great 

Lakes Forecasting System (GLFS), on an hourly basis, for a 5 km grid (except for Lake 

Superior where the grid is 10 km). 

The initial implementation of the GLFS for Lake Erie was completed in 1993. GLFS has 

since been extended to the other four Great Lakes. GLFS operates with two components (1) 

an atmospheric input module (a step coordinate Eta model) to force a (2) numerical ocean 

module (Princeton Ocean Model).

Great Lakes marine forecasting is relatively difficult due to “the presence of small scale 

coastal features which can generate local convergence / divergence regions and the presence 

of strong air-lake fluxes, rapid upwelling, and seiches. The Great Lakes Forecasting System 

uses the output from the Eta model as input to the wave or ocean model and there is no 

feedback between the waves that develop and the winds that generate them” (Sousounis).

Wave Height Observations

The National Buoy Data Center (NBDC) operates 12 moored buoys in coastal and offshore 

waters of the five Great Lakes. Additional surface information is also collected by on-shore 

C-MAN stations (Figure 1)
123

Figure 1: Great Lakes Surface Data Collected by NDBC Buoy’s

Based on NDBC 1981 to 2001 time-series of empirically observed wave patterns, the 

arithmetic mean wave heights for the Great Lakes during the five and half month recreational 

boating season have a tendency to exceed 18 inches 70 percent of the time (Table 2). 

Table 2: Mean Wave Heights for Great Lakes May 1- October 15, 1981-2001 (inches)

MAY JUNE JULY AUGUST SEPT

OCT

(1-15)

Lake Erie -

Sandusky 15.76 11.82 15.76 19.7 27.58 31.52

Lake Michigan 

South 15.76 11.82 19.7 23.64 35.46 39.4

Lake Michigan 

North 19.7 15.76 19.7 23.64 35.46 43.34

Lake Huron 

Central 19.7 15.76 19.7 23.64 35.46 43.34

Lake Superior 

Central 19.7 15.76 11.82 19.7 35.46 47.28

Source: National Data Buoy Center, Historical Time Series
124

However, when reviewing actual (as opposed to mean wave heights), 2002 and 2003 hourly 

data for three of the twelve NDBC buoys, only 40 percent of hourly readings (during the 

May to October period) exceeded 18”. 

Since it can be further assumed that the bulk of recreational boating takes place during the 

months of June, July, and August, hourly readings for these 3 months indicate that wave 

height exceeds 18” only 31.5 percent of the time.

Accordingly, on the basis of the above empirical evidence, we assumed that lake 

surface conditions are “Good” 68.5 % of the time and “Bad” 31.5 % of the time.

Decision Model and Physical Outcome Model

The following assumptions were used for a base-case decision model.

- GLFS forecasts are able to correctly identify “Good” lake surface conditions 80% of 

the time and fail to do so 20% of the time.

- GLFS forecasts are able to correctly identify “Bad” lake surface conditions 80% of 

the time and fail to do so 20% of the time.

- During subsequent analysis, forecast accuracy was deemed the independent variable 

and frequency of “Good” forecasts was stepped in 1% increments from 80% to 85%

In addition, three decision rules were posited to drive recreational boater behavior:

- Forecast of “Good” lake surface conditions will result in a decision to “go out” by 

100% of boaters, planning to go boating or inclined to go boating any particular day 

(this is equivalent to a condition of “all other things being equal”.

- 15% of boaters who have appropriate expertise to navigate in difficult lake conditions 

or who lack such expertise and recklessly ignore forecasts of “Bad” lake surface 

conditions and “go out” anyway.

- 85% of boaters, who tend to be more cautious or realize that “Bad” wave conditions 

do not make for an enjoyable experience, will act in conformity with available 

forecasts. When the forecast indicates “Bad” lake surface condition, these 85% of 

boaters will stay in harbor. 

Finally, as “the Great Lakes Forecasting System uses outputs from the Eta model as input to 

the wave (or ocean) model and there is no feedback between the waves that develop and the 

winds that generate them” (Sousounis), for purposes of modeling recreational boater 
125

behavior, we assumed a condition of independence between wave forecasts and observed 

wave conditions.

Reflecting the above assumptions, a base-case decision model is indicated in Figure 

2.

As forecast accuracy is incremented (relative to a base case decision model) for each one 

percent improvement in forecast accuracy, the probability of the combination “Good” 

Surface Conditions – Incorrect Forecast – Decision to Stay in Harbor (i.e., the probability of 

lost boating days) decreases as indicated in Table 3

Figure 2: Decision Model, Boater Behavior as Function of Forecast Accuracy 

Wave Height Wave Wave Decision Boater Probability

(Empirically Forecast Forecast Parameter Behavior of Physical

Observed and Accuracy (Physical Outcome

Given) (Independent Outcome)

 Variable)

 0.15 Go Out Anyway 0.0378

Correct F/C 

 0.80

“Bad” 0.85 Stay in Harbor 0.2142

Over 18”

(0.315)

 

 0.20 Incorrect F/C Follow F/C & Go Out 0.0630

 1.00

 0.80 Correct F/C Follow F/C & Go Out 0.5480

 1.00

“Good” 

Under 18”

(0.685) 0.15 Go Out Anyway 0.02055

 0.20

Incorrect F/C

 0.85 Stay in Harbor 0.11645

 Forego Enjoyable

 Boating Day
126

Table 3: Improving Forecast Accuracy and the Probability of Lost Boating Days 

Forecast Accuracy Probability of Lost Boating Days

Base-case 80% 0.11645

81% 0.1106275

82% 0.104805

83% 0.0989825

84% 0.09316

85% 0.0873375

Economic Outcome Model

The Economic Outcome Model incorporates the following additional assumptions:

- For Michigan, Ohio, Wisconsin, and Illinois, Great Lakes boating days are derived 

from the number of recreational boats registered statewide. It is assumed that these 

boats average 12.1 boating days per year (NSRE) and that 50 percent of boating days 

are spent on the Great Lakes, as opposed to rivers and smaller lakes.

- For New York, Pennsylvania, Indiana and Minnesota, boating days are derived from 

the number of boats registered in Great Lake coastal counties. It is assumed that these 

boats average 12.1 boating days per year (NSRE) and that 90% of boating days are 

spent on the Great Lakes. This adjustment is due to the extensive saltwater 

component in New York and Pennsylvania and to the very large internal (non-Great 

Lake) component in Indiana and Minnesota.

- Based on the above approach, boating days for the five Great Lakes are estimated at 

16.6 million days per year (Table 4, Column 4).

Based on a 1999 Study of the economic impact of recreational boating in Ohio (Hushak), 

daily expenses for travel, meals, boat-fuel, marina fees, etc. are assumed to average $ 187 per 

day for all Great Lakes boating days. This average value excludes capital investments for 

boat acquisition and major upgrades and is adjusted to 2003 dollars to yield $ 214 per day.
127

Table 4: Economic Impact as a Function of Forecast Accuracy

Forecast

Accuracy

Probability

of Lost 

Boating 

Days 

Delta:

Decreased 

Probability 

Lost Days 

Great 

Lakes

Boating 

Days Per 

Year

Increase 

in Great 

Lakes 

Boating 

Days 

Average 

Value of 

Boating 

Day ($)

Economic

Benefits 

from 

Improved 

Forecasts)

(1) ((2) (3) (4) (5) (6) (7)

80% 0.11645 0 16,606,049 0 214.2 0

81% 0.1106275 0.0058225 16,606,049 96,689 214.2 $ 20,711,000

82% 0.104805 0.011645 16,606,049 193,377 214.2 $ 41,421,000

83% 0.0989825 0.0174675 16,606,049 290,066 214.2 $ 62,132,000

84% 0.09316 0.02329 16,606,049 386,755 214.2 $ 82,843,000

85% 0.0873375 0.0291125 16,606,049 483,444 214.2 $ 103.55mill

Table 4, Columns 5 and 7 indicate that for every 1% of additional wave height forecast 

accuracy, Great Lakes recreational boaters are likely to enjoy 97,000 additional boating days 

corresponding to $21 million in incremental economic benefits. 

On the basis of conservative assumptions, improving current wave height forecast accuracy 

from 80% to 85% would thereby generate 483,000 additional boating days and $ 103.5 

million of incremental economic benefits.

Applicability to Other Regions

The above approach may be applied to other coastal regions if the assumptions, appropriate 

for the Great Lakes region, are adjusted to reflect conditions in other coastal regions. In 

particular,

- Assumptions about feedback loops in ocean surface forecasting systems,

- Wave height time-series observations for a relevant recreational season,

- Assumptions about number of boating days and the value of boating days, etc. 
128

References

Great Lakes Commission. 2000. Recreational Boating and the Great Lakes – St. 

Lawrence Region.

Gunderson, J. and Kreag, G., 1990, Estimated Economic Impact of Recreational Fishing 

on Minnesota Waters of Lake Superior, Minnesota SeaGrant. Minneapolis MN.

Hushak, L. 1999. Recreational Boating in Ohio: An Economic Impact Study. Ohio 

SeaGrant Publication. Columbus OH.

Leeworthy,V. and Wiley, P. 2001. Current Participation Patterns in Marine Recreation

National Survey on Recreation and the Environment 2000. National Oceanic and 

Atmospheric Administration. Washington, DC.

National Marine Manufacturers Association, 2000, 1999 U.S. Recreational Boat 

Registration Statistics.

National Survey on Recreation and the Environment. 2000. National Oceanographic and 

Atmospheric Administration. Washington DC.

NMFS or National Marine Fishery Service. 2001. Marine Recreation Fishing 

Expenditure Survey < http://www.st.nmfs.gov/st1/econ/implan.pdf >

Sousounis, P. Improving Marine Forecasts Over the Great Lakes The Comet Outreach 

Program, < http://www.comet.ucar.ed/outreach/9915807.htm >

Schwab, D. J., et. al., February 1999. Forecast Verification for Eta Model Winds Using 

Lake Erie Storm Surge Water Levels. Weather and Forecasting

Schwab, D. J. and Bedford, K.W. 1999. “The Great Lakes Forecasting System” Coastal 

and Estuary Studies 56, pages 157-173. American Geophysical Union.

Wisconsin SeaGrant, 2003, Coastal Business and Economic Development, 

< http://seagrant/wisc.edu/greatlakes/glnetwork/economic.html >

U.S. Coast Guard (U.S. Department of Transportation), 2000, Boating Statistics 2000, 

Comdtpub P16754.14

Vilaplana, J. and Hushak, L. 1999. Outdoor Recreation at Lake Erie. Ohio Sea Grant 

Program. Columbus, OH.
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WHOI-2005-03

Estimating the Economic Benefits of Regional Ocean Observing Systems April 2005

Hauke L. Kite-Powell, Charles S. Colgan, Katharine F. Wellman,

Thomas Pelsoci, Kenneth Wieand, Linwood Pendleton, Mark J. Kaiser, WHOI-2005-03

Woods Hole Oceanographic Institution

Woods Hole, Massachusetts 02543 N00014-02-1-1037

Technical Report Office of Naval Research

We develop a methodology to estimate the potential economic benefits from new investments in regional

coastal ocean observing systems in US waters, and apply this methodology to generate preliminary estimates

of such benefits. The approach focuses on potential economic benefits from coastal ocean observing

information within ten geographic regions encompassing all coastal waters of the United States, and within

a wide range of industrial and recreational activities including recreational fishing and boating, beach

recreation, maritime transportation, search and rescue operations, spill response, marine hazards

prediction, offshore energy, power generation, and commercial fishing. Our findings suggest that annual

benefits to users from the deployment of ocean observing systems are likely to run in the multiple $100s of

millions of dollars per year. The project results should be considered first-order estimates that are subject

to considerable refinement as the parameters of regional observing systems are better defined, and as our

understanding of user sectors improves.

ocean observing system

economic benefits

coastal ocean

139

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