NOAA Technical Report NOS CO-OPS 044

Estimating Economic Benefits from NOAA PORTS® Installations: 

A Value of Information Approach 

Silver Spring, Maryland 

July, 2005 

noaa National Oceanic and Atmospheric Administration

U.S. DEPARTMENT OF COMMERCE 

National Ocean Service 

Center for Operational Oceanographic Products and Services
Center for Operational Oceanographic Products and Services 

(CO-OPS) 

National Ocean Service (NOS) 

National Oceanic and Atmospheric Administration (NOAA) 

U.S. Department of Commerce

The CO-OPS mission is to deliver the operational environmental products and 

services necessary to support NOAA’s Environmental Stewardship and 

Environmental Assessment and Prediction Missions. CO-OPS provides the focus 

for operationally sound observation and monitoring capability coupled with 

environmental predictions to provide the quality data and information needed to 

support the cross-cutting NOS Primary Goals of Navigation, Coastal 

Communities, Habitat, and Coastal Hazards. 
NOAA Technical Report NOS CO-OPS 044

Estimating Economic Benefits from NOAA PORTS® Installations: 

A Value of Information Approach 

Report prepared for the Center for Operational Oceanographic Products and Services 

(CO-OPS), National Ocean Service, NOAA 

Hauke Kite-Powell 

Woods Hole Oceanographic Institute Marine Policy Center 

 

 

30 June 2005 

noaa National Oceanic and Atmospheric Administration 

U.S. DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration 

Carlos M. Gutierrez, Secretary Vice Admiral Conrad C. Lautenbacher, Jr., U.S. Navy (Ret.) 

 Undersecretary of Commerce for Oceans and 

 Atmosphere and NOAA Administrator 

National Ocean Service 

Dr. Richard W. Spinrad, Assistant Administrator of the National Oceanic and Atmospheric 

Administration (NOAA) Ocean Services and Coastal Zone Management

Center for Operational Oceanographic Products and Services 

Michael Szabados, Director 
 

 

 

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NOTICE

This report has been reviewed by the National Ocean Service of the National Oceanic and 

Atmospheric Administration (NOAA) and approved for publication. Such approval does not 

imply that the contents of this report necessarily represent the official position of NOAA or of 

the Government of the United States, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for their use. Use of information from this 

publication concerning proprietary products or the tests of such products for publicity or for 

advertising purpose is not authorized.
 

 

 

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TABLE OF CONTENTS 

TABLE OF CONTENTS...............................................................................................................iii 

SUMMARY.................................................................................................................................... v 

INTRODUCTION .......................................................................................................................... 1

ECONOMICS OF INFORMATION...............................................................................................3 

QUANTIFYING ECONOMIC VALUE .................................................................................... 4 

SOURCES OF ECONOMIC BENEFIT FROM PORTS®............................................................. 7

GENERAL FRAMEWORK FOR ESTIMATING BENEFITS FROM PORTS® ..........................9 

MARINE SAFETY..................................................................................................................... 9 

Avoided groundings, commercial vessels............................................................................... 9 

Avoided distress cases, recreational vessels ........................................................................... 9 

EFFICIENCY ........................................................................................................................... 10 

Increased cargo (draft) per transit......................................................................................... 10 

Reduced delays ..................................................................................................................... 11 

Improved SAR performance................................................................................................. 12 

ENVIRONMENTAL PROTECTION: IMPROVED SPILL RESPONSE .............................. 12 

ENHANCED VALUE OF RECREATION ACTIVITIES....................................................... 13 

Boating.................................................................................................................................. 14 

Fishing................................................................................................................................... 15 

Beach visits ........................................................................................................................... 15 

ENHANCED WEATHER FORECASTS ................................................................................ 15 

General Weather and Coastal Marine Forecasts................................................................... 15 

Storm Surge Forecasts .......................................................................................................... 16 

NOTES ON APPLYING THE FRAMEWORK ...................................................................... 16 

ACKNOWLEDGEMENTS.......................................................................................................... 17 

REFERENCES ............................................................................................................................. 17 
 

 

 

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SUMMARY 

This report describes a methodology for valuing the benefits from information provided by a 

Physical Oceanographic Real-Time System (PORTS®) installation. We describe an approach to 

estimate benefits in dollar terms to the extent possible, and also discuss ways to treat nonquantifiable benefits. 

Potential sources of economic benefit from PORTS® information include: 

• Greater draft allowance/increased cargo capacity and reduced transit delays for 

commercial maritime transportation (water level information) 

• Reduced risk of groundings/allisions for maritime traffic (currents and wind information) 

• Enhanced recreational use of coastal waters boaters, windsurfers, etc. (winds, weather 

forecasts, and other information) 

• Improved environmental/ecological planning and analysis, including hazardous material 

spill response 

In the table on the following page, we summarize typical (potential) benefits from PORTS® data. 

We categorize these benefits according to the extent to which they typically can be quantified 

with varying degrees of confidence. For some benefits, there is direct observable evidence and 

benefits can be quantified with a high degree of confidence. Other benefits are likely realized at 

present but direct evidence is lacking and/or significant assumptions are required to derive 

quantitative estimates. Yet other benefits are more speculative or potential, and could perhaps be 

realized with the fuller utilization of the PORTS® data by all potential users. 

Most of these benefits are in the nature of avoided costs (increased producer surplus, or profit) 

for commercial operations and avoided costs or increased consumer surplus, including nonmarket benefits, for recreational users. 
 

 

 

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Confidence level Source of benefit Nature of 

benefit 

avoided groundings, 

commercial vessels 

avoided costs 

(surplus) 

increased draft, cargo 

loading 

efficiency 

(surplus) 

reduced delays, 

commercial vessels 

avoided costs 

(surplus) 

Usually quantifiable with 

high degree of confidence 

reasonably good 

confidence and/or direct 

evidence for benefits 

improved spill response 

(present practice) 

avoided costs 

(surplus) 

reduced distress cases, 

recreational boats 

avoided costs 

(surplus, 

value of life) 

improved weather forecasts non-market 

consumer 

surplus 

Usually quantifiable with 

lower degree of 

confidence 

more significant 

assumptions required to 

estimate benefits; less 

direct evidence 

improved storm surge 

forecasts 

avoided costs 

(surplus) 

improved spill response 

(with add’l models & 

infrastructure) 

avoided costs 

(potential; not 

realized at 

present) 

enhanced recreational 

boating 

non-market 

consumer 

surplus 

enhanced recreational 

fishing 

non-market 

consumer 

surplus 

(potential; not 

realized at 

present) 

Potential or speculative

these benefits could be 

realized with additional 

investment or a higher 

level of utilization of 

PORTS® data 

enhanced beach recreation non-market 

consumer 

surplus 

Non-quantified benefits educational use non-market 

scientific research non-market 
 

 

 

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INTRODUCTION 

NOAA Physical Oceanographic Real-Time Systems (PORTS®) are near-shore ocean observing 

systems now operating in a dozen locations around the United States 

(http://co-nos.noaa.gov/d_PORTS®.html). PORTS® installations provide near-real time 

information and, in some cases, forecasts about water levels and currents at specific points in a 

coastal water body. In some instances, they also provide information on wind speed and 

direction, salinity, bridge clearance (air gap) and on water temperature. In addition, co-located 

sensors (i.e., possibly operated by other parties and not part of the official NOAA PORTS®

installation) may provide information on wave height, visibility, and other parameters, as well as 

digital still or video images of portions of the waterbody. 

The information made available by PORTS®

 results in economic benefits because it is used by 

decision makers to make choices that affect economic well-being. To estimate the benefits that 

may accrue from a PORTS®

 installation, it is necessary to compare the outcome of these choices 

under two scenarios: the PORTS® scenario, in which the PORTS® data are available to decision 

makers; and a non-PORTS®

 scenario, in which these data are not available. The data and 

products enabled or affected by the PORTS®

 installation influence decisions made in industry, 

recreation, the research community, and public administration, changing the economic outcome 

from these activities, and thereby affecting economic well-being. The difference in outcome 

under the two scenarios is the benefit derived from the investment in PORTS®

. 

The most accurate measure of this benefit is the marginal increase in what economists call 

consumer and producer surplus. Consumer surplus is the difference between what consumers are 

willing to pay and what they actually pay. Producer surplus is the difference between the price 

received for a good or service sold and the costs of producing that good or service. Because this 

surplus is often difficult to estimate, economists also use other measures of benefit, such as the 

change in value added (contribution to GDP), or reduction in cost to achieve the same level of 

output. These measures typically are less precise estimates of true social surplus. Usually, these 

measures are estimated as annual values at the level of a firm or other economic unit, and then 

aggregated over geographic regions and industries to estimate total annual benefits. 

Benefits represent only one side of the investment decision. To estimate net benefits, or rates of 

return, it is necessary to have information on costs as well. In the case of PORTS®, there are two 

main categories of costs: the cost of data collection, processing, and archiving; and the cost of 

generating from these data the products that decision makers ultimately use. In the case of 

PORTS®, the first component (the direct capital and operating cost of the PORTS® installation) 

is usually well understood. The second component generally includes activities carried out by 

both public and private sector organizations, and these costs are likely to be more difficult to 

specify. The analysis of costs associated with the generation and use of PORTS® data is outside 

the scope of this report. 
 

 

 

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ECONOMICS OF INFORMATION 

A product, such as a real-time water level report for a harbor, represents information about the 

ocean environment. 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 and of the state of knowledge about the 

phenomena or conditions it describes. Typically, information is useful because it reduces 

uncertainty about the present or future state of nature in a particular context – for 

example, the location of a particular depth contour, or the exact water level in a dredged 

channel. 

• 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. For many CO-OPS products, including 

those based on PORTS® data, the relevant decisions involve the navigation of 

commercial or recreational vessels. Here, the critical information concerns water depth, 

current speed and direction, wind speed and direction, or other information needed for the 

safe and efficient operation of a vessel. 

• A model of how these decisions affect physical outcomes. Modeling the difference in 

outcome with and without the product in question usually requires making assumptions 

about how the decision makers will respond to the lack of the product in question. 

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

of a product is the difference between the expected value of the outcome of decisions 

using that product, and the expected value of the outcome without the product. 

Most information-based products are valuable because they reduce the user’s uncertainty about a 

factor that is important to the physical outcome (such as weather, waves, or water level). A 

standard Bayesian approach can be used to estimate the value of information contained in such 

products (see Berger 1995). In this model, a decision maker (user of the product) 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 precisely 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): 

E B a s f s ds o ( , ) ( ) ∫ =
 

 

 

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

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

maker, the optimal choice of action and the associated expected net benefit will change. To 

determine the value of the new information, we need to know something about the accuracy of 

the information and something about the frequency with which different conditions arise on 

average. (For instance, a water level forecast may be more valuable if extreme deviations in 

water level are more frequent than if they only happen once a year.) 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

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

expectation of the occurrence of event s when the new information says x, taking into account 

how “good” this information tends to be. 

The new optimal action given information 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 new information and 

EX* with it, the value of this new information to the decision maker is EX* - E0*. 

QUANTIFYING ECONOMIC VALUE 

The preceding discussion of the theoretical underpinning of the value of information does not 

address the question of how the net benefit (E) is quantified. Quantifying true economic benefits 

is difficult; and proxy measures are frequently used. 

The most appropriate measure of economic value of information resulting from a change in user 

decisions or behavior 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 a reduction in costs to businesses. 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 
 

 

 

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consumer surplus. It is the appropriate measurement because it assures that only the value in 

excess of costs is counted, making it a unique measure that avoid the artificial inflation of values 

by double counting. 

The problem with social surplus and both of its elements 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, or GDP), are widely 

available, but measure social surplus in a rather imperfect manner. 

In other situations, estimates of social surplus may be available but data to support an explicit 

model of how PORTS® information is used in economic decisions are lacking. In such cases, an 

order-of-magnitude estimate of potential value of PORTS® data may be obtained by applying a 

rule of thumb developed by Nordhaus (1996) and others: the value of weather and climate 

forecasts to economic activities that are sensitive to weather/climate tends to be on the order of 

one percent of the economic activity in question. 

Studies of economic values from investments such as PORTS® thus often face a dilemma due to 

data constraints. The most appropriate measure is the least available, while the most available 

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

benefits often must be considered approximate. 
 

 

 

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SOURCES OF ECONOMIC BENEFIT FROM PORTS®

PORTS® data, and products derived from PORTS® data, are used by a wide range of industrial, 

recreational, and public sector organizations and individuals. They include maritime shipping 

interests, recreational boaters and fishers, and marine resource and environmental managers. 

For the purpose of this analysis, we use the following classification of benefits from PORTS®

installations: 

• Improved Safety of Shipping and Boating 

o Avoided groundings, commercial vessels 

o Avoided distress cases, recreational vessels 

• Improved Efficiency of Marine Operations 

o Increased cargo carried per ship call (greater loaded draft) 

o Reduced delays (less allowance for error/margin in piloting decisions) 

o Improved SAR performance (surface currents) 

• Improved Environmental Protection and Planning 

o Improved hazardous material spill response 

o Improved environmental restoration/conservation activities 

• Improved Recreational Experiences 

o Enhanced value from boating decisions (power, sail, windsurfing, kayaking, etc.) 

o Enhanced value from fishing decisions 

o Enhanced value from beach visit decisions 

• Improved Weather and Coastal Marine Conditions Products 

o Improved general weather forecasts 

o Improved coastal marine weather forecasts 

o Improved storm surge forecasts 

• Science and Education 

o Use of PORTS® data in scientific research 

o Use of PORTS® data in secondary education 

While this list is not exhaustive, it captures to the best of our knowledge all of the major benefits 

generated by PORTS® data.
 

 

 

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GENERAL FRAMEWORK FOR ESTIMATING BENEFITS FROM PORTS®

In the following discussion of general approaches to estimating particular benefits from PORTS®

data, we describe a process for estimating annual benefits in present dollar terms. These are 

most useful for comparison with annualized costs of the PORTS® installations. For longer time 

horizon analyses, annual values can be aggregated over time to produce discounted present value 

numbers. 

MARINE SAFETY 

Avoided groundings, commercial vessels 

Better information about water levels, currents, and winds can reduce the risk of grounding for 

commercial vessels. If it is possible to determine the reduction in grounding risk (∆rc) due to 

availability of PORTS® data for a particular class of vessels c, then the annual economic value of 

PORTS® data for that vessel class is 

AV = T × ∆rc × AC

where 

 AV = annual benefit ($) 

 T = annual number of transits for vessel class c 

∆rc = reduction in grounding risk due to PORTS® (groundings/transit) 

 AC = average cost associated with a grounding for vessel class c 

∆rc can be estimated from historical data or from a risk model. The US Coast Guard keeps 

records of vessel accidents in US waters, and often local port authorities keep their own data on 

accidents. If sufficient historical data are available, it is possible to estimate risk reduction due to 

PORTS® by comparing accident rates before and after PORTS® data come into use. Any such 

analysis must recognize that several factors determine grounding risk, in addition to 

environmental data, including channel depth and configuration, vessel characteristics, and portspecific operating procedures and constraints. The analysis must attempt to isolate, so far as 

possible, the effect of a change in available information. If the necessary historical data are not 

available, it may be preferable to estimate grounding risk from first principles. For an example 

of such an exercise, see Amrozowicz (1996).

Avoided distress cases, recreational vessels 

Recreational boaters can reduce the risk of weather-related distress situations if they have better 

information about conditions such as wind speed and direction, waves, and severe precipitation 

events in the waters they intend to visit. If it is possible to determine the reduction in this risk 

(∆rc) due to availability of PORTS® data for a particular class of recreational boaters c, then the 

annual economic value of PORTS® data for these boaters is 

AV = T × ∆rc × AC
 

 

 

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where 

 AV = annual benefit ($) 

 T = annual number of boating days involving recreational boaters c 

∆rc = reduction in distress situation risk due to PORTS® (incidents/boating day) 

 AC = average cost associated with a distress situation for boater group c 

Specific data on recreational boating accidents are difficult to obtain. The US Coast Guard 

(2004) maintains some data on recreational vessel accidents, and other local organizations that 

respond to boater distress calls may also have such data. If data are available, it may be possible 

to estimate ∆rc from the fraction of distress cases involving boaters who should not have been on 

the water given the weather conditions. For all US waters in 2003, the Coast Guard (2004) 

recorded 5,438 accidents resulting in 703 fatalities, 3,888 injuries, and about $40 million in 

property damage (the data do not include accidents resulting in less than $2,000 worth of 

damage). “Hazardous waters” and “weather” were causal factors in 356 and 184 of these 

accidents, respectively. About 12.8 million recreational boats were registered in the United 

States in 2003. 

The average cost associated with a distress situation (AC) is, at minimum, the incremental cost to 

the search and rescue organizations of assisting the boaters in question. Average property 

damage per accident in the 2003 USCG data is about $7,350. 

EFFICIENCY 

Increased cargo (draft) per transit 

Real-time information and accurate forecasts of water level (tide plus meteorological forcing) 

can allow draft-constrained vessels to enter or depart a port safely with a greater draft than they 

might otherwise. A good proxy for the economic benefit derived from this ability to carry 

increased draft is the expected cost savings associated with moving a fixed cargo volume with a 

reduced number of reduced voyages. For each trade (e.g. phosphate, petroleum) and vessel type: 

AV = (AD ×TPI × NC / AC) × ((RT × SC / KTS) + (DOC + (2AC / LR)) × PC)

where 

AV = annual benefit ($) 

AD = additional draft enabled by PORTS® information (inches) 

TPI = tons per inch immersion 

AC = average cargo carried per ship transit without PORTS® (tons) 

NC = number of transits/year affected by PORTS® 

RT = average round trip distance (nm) 

SC = operating cost at sea ($/hr) 

KTS = vessel speed (knots) 

DOC = docking and undocking time per transit (hours) 

LR = loading/unloading rate (tons/hr) 

PC = operating cost in port ($/hr) 
 

 

 

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The data to support this calculation are specific to the vessels, cargos, port facilities, and local 

conditions of each trade and port. Typical cost and speed data for broad categories of 

commercial ship in the international trades are shown in Table 1. Additional information on 

vessel costs can be found in US Army Corps of Engineers reports 

(http://www.usace.army.mil/inet/functions/cw/cecwp/cecwp_temp/egm00-6.htm). 

vessel category operating cost, 

$/24 hours 

time charter cost, 

$/24 hours 

operating speed, knots 

dry bulk handy 3,000 6,000 14.0 

 handymax 4,000 8,000 14.0 

 Panamax 5,000 9,500 14.5 

 Cape 7,000 14,000 14.5 

tanker product 6,000 12,000 14.0 

 Aframax 7,000 13,000 15.0 

 Suezmax 8,000 16,500 14.5 

 VLCC 10,000 22,000 13.0 

container 1000 TEU 5,000 9,000 15.0 

 1500 TEU 7,000 13,500 15.0 

 2000 TEU 10,000 18,000 24.0 

 3000 TEU 13,000 27,000 24.0 

 4000 TEU 16,000 35,000 24.0 

LNG 15,000 50,000 20.0 

car carrier/RoRo 8,000 16,000 16.0 

cruise 20,000 40,000 25.0 

tug/barge dry 4,000 8,000 12.0 

 tank 4,000 8,000 12.0 

Table 1: Typical vessel cost and operating speed parameters. Cost data are rough averages, 1980-2000, in 2001 

dollars, for vessels in the international trades (typically non-US flag). 

Reduced delays 

Commercial ships sometimes encounter delays entering or exiting a port. These may be due to 

draft constraints and tide windows, port operating rules governing minimum visibility or 

maximum wind or current conditions, or other factors. In some instances, PORTS® data may 

allow operators to reduce these delays. For example, specific knowledge about water levels or 

current speed may increase pilots’ comfort level with a transit and allow a vessel to move sooner 

than the absolute high water or slack tide window would otherwise suggest. 

The annual benefit from such reduced delays for vessel class c is given by 

AV = T × ∆tc × AC

where 

 AV = annual benefit ($) 

 T = annual transits by vessel class c for which PORTS® data reduce delays 

∆tc = reduction in delay due to PORTS® data (hours/transit) 

 AC = average hourly operating cost for vessel class c 
 

 

 

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As with increased draft, these benefits are generally port- and trade-specific. Representative 

operating costs are shown in Table 1. 

Improved SAR performance 

The effectiveness and efficiency of search and rescue (SAR) operations often depend critically 

on knowledge of surface currents and wind speed and direction in the area where a search is 

being orchestrated. This is relevant only if knowledge of currents and wind are known to be a 

factor in local SAR operations. This is generally the case offshore (see Allen 2004), and may be 

the case in some regions served by PORTS®. If PORTS® data can contribute to better SAR 

performance in a region, the benefit can be estimated as follows: 

AV = I × ∆t ×OC

where 

 AV = annual benefit ($) 

 I = annual number of SAR cases in which PORTS® data improve effectiveness 

∆t = average time savings per SAR operation due to PORTS® data (hours) 

 OC = average hourly operating cost for SAR assets during a SAR operation 

If the greater effectiveness of SAR operations leads to a reduction in lives lost after SAR 

notification, this is a quantifiable benefit as well and can be estimated as the product of the 

expected annual number of lives saved and the statistical value of life. The US Coast Guard 

typically uses $3 million for the value of a statistical life; studies suggest that values as high as 

$7 million may be appropriate; we recommend using $4 million based on a review by Viscusi 

(1993). 

Note that any estimation of benefits due to improved SAR performance must take into account 

the reduction in recreational boating distress cases described above. 

ENVIRONMENTAL PROTECTION: IMPROVED SPILL RESPONSE 

More effective spill response, including deployment of spill response equipment and dispersants 

or other measures, can result in reduced environmental cost from hazardous material spills. 

Booms and skimmers can be deployed to limit the transport of spilled material and to recover the 

material from the water. Dispersants may be used to separate spilled petroleum into smaller 

droplets, reducing shoreline impact, but leaving the material in the water column for longer 

periods. Decisions about dispersant use, for example, therefore involve tradeoffs between 

exposure of shoreline or benthic communities, and exposure of organisms in the water column; 

and models that predict contaminant concentrations with and without dispersant use are of value 

in spill response. In situ burning of spilled materials raises similar issues, with one of the 

tradeoffs being air quality effects. 

To estimate accurately the value of PORTS® data in spill response and planning, it is necessary 

to carry out extensive simulations of likely spill scenarios and responses with and without 

PORTS® data. These simulations typically require circulation models, transport and fate models 

for the spilled material, environmental resource data, and physical/biological damage models, 

and economic damage models. Natural resource damage assessment models include the Natural 
 

 

 

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Resource Damage Assessment (NRDA) Process (http://www.darp.noaa.gov/about/nrda.html) of 

NOAA’s Damage Assessment and Restoration Program. The benefit derived from the PORTS®

data is given by: 

AV [(R E ) (R E )] PR

ST

= ∑ 0 + 0 − P + P ×

where 

 AV = annual benefit ($) 

 ST = range of representative hazardous material spills 

 R = spill response costs incurred by responding agencies 

 E = environmental damage cost due to spill 

 PR = expected number of spills per year 

 0 subscript = without PORTS® data 

 P subscript = with PORTS® data 

ENHANCED VALUE OF RECREATION ACTIVITIES 

Information about marine conditions can enhance recreational activities in several ways, as the 

following example illustrates (the example is adapted from Kite-Powell et al. 2004). While the 

subject here is a surfer’s decision, similar arguments hold go/no go decisions by boaters, fishers, 

and other beach visitors. 

Consider a surfer who wants to go to the beach for a day’s surfing. 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, or from severe harmful algal blooms.) 

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 bacteria 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 hazardous, 

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 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 
 

 

 

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(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. 

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. PORTS® can provide fine scale (both 

temporal and spatial) information on physical 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 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 PORTS® data. 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. 

Boating 

Boaters’ decisions potentially affected by PORTS® information include (a) the decision not to go 

boating on a given day because PORTS® data suggest that conditions are unfavorable, (b) the 

decision to go boating on a given day when they might otherwise not have because PORTS® data 

suggest that conditions are favorable, and (c) the decision to change the timing or destination of a 

boating trip. 

The value of decisions not to go boating is captured in part by the preceding discussion of 

boating safety benefits. Decisions of type (b) and (c) can be valued according to the economic 

surplus generated by a boating day in the region of interest. [Citations.] The value of PORTS®

to these boating decisions is 

AV = NBD × SBD

where 

 AV = annual benefit ($) 

 NBD = number of additional positive boating days/year due to PORTS® data 

 SBD = economic (consumer) surplus generated by one boating day 
 

 

 

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Fishing 

Similar to boating, a fishing experience (whether from shore or from a boat) can be enhanced by 

PORTS® data if these data permit an improvement in the probability of catching a fish. Fish 

capture probabilities can be related to currents and to water temperature, among other factors. 

For an application to Florida salt water fishing, see Wieand (2004). As with boating, the 

important parameters are the number of fishing days enhanced by PORTS® data, and the 

incremental economic surplus associated with this enhancement. 

The incremental benefit derived from recreational fishing activities due to PORTS® data can be 

estimated as follows: 

AV = NFD × ICV

where 

 AV = annual benefit ($) 

 NFD = number of fishing days/year on which catch probability is enhanced 

 ICV = economic (consumer) surplus generated by enhanced catch probability 

Beach visits 

The derivation of value for beach visits follows closely the surfer example described above. For 

an application of this approach to valuing improved information for beach use decisions in 

California, see Pendleton (2004). 

The benefit due to enhanced beach recreation resulting from PORTS® data can be estimated as 

follows: 

AV = NBD × BDV

where 

 AV = annual benefit ($) 

 NBD = number of additional beach days/year enabled by PORTS® data 

 BDV = economic (consumer) surplus generated by one beach day. 

ENHANCED WEATHER FORECASTS 

General Weather and Coastal Marine Forecasts 

Observations of atmospheric conditions, such as winds, are an input to general weather forecast 

models. As such, PORTS® data may be used by the National Weather Service to improve both 

general weather forecasts and coastal marine weather forecasts for the region surrounding the 

PORTS® installation. The value of improved coastal marine forecasts is reflected in the 

improved recreational boating experience of local boaters, as discussed above. The improved 

general weather forecasts benefit all users of weather forecasts in the area. 

Estimates of the value of significant increases in weather forecast quality lie around $16 per 

household per year (Lazo and Chestnut 2002). However, these estimates assume significant 

improvements in weather forecast quality over present baselines, which may be difficult to 
 

 

 

16

achieve in general: 12 updates/day instead of 4, one-day forecast accuracy 95% instead of 80%, 

and geographic resolution of 3 miles instead of 30 miles. Also, it may be difficult to attribute 

specific improvements in local or regional weather forecasts to PORTS® data. On the other 

hand, in some nearshore locations, conditions over the water have significant effects on local 

weather, and PORTS® observations may play a critical role in local forecasts. 

Storm Surge Forecasts 

Storm surges are associated with large storm events, such as hurricanes, and can cause extensive 

damage. Much of this damage cannot be avoided by an improved forecast, but marginal 

improvements in response activities (securing boats and structures, evacuating areas) may be 

possible or less costly with a more accurate and timely forecast. 

NOTES ON APPLYING THE FRAMEWORK 

In each of the benefit categories discussed above, it is possible to estimate the potential value of 

PORTS® data by assuming that all potential users of the information in fact make use of it as 

described. This potential value is an upper bound of sorts on what is likely to be the value 

actually realized during a given year, since the number of actual users is likely to be less than 

100% of potential users, 100% of the time. Potential value is often easier to estimate than actual 

value because estimating potential value does not require data on how many users actually use 

the PORTS® data, and how often. 

In situations where data or model limitations do not permit the application of the benefit 

frameworks described above, it may be possible to estimate at least the general scale of potential 

benefit by applying a “one percent proxy rule.” Formulated by Nordhaus (1986) and other 

economists on the basis of experience with a number of forecast/nowcast value of information 

studies of industries and activities sensitive to weather, this rule suggest that the value of weather 

nowcast/forecast information to economic activity sensitive to weather conditions is generally on 

the order of one percent of the economic value generated by the economic activity. There is, of 

course, no guarantee that this rule will hold in all cases; but where no better estimate can be 

constructed, it provides an order of magnitude estimate of value that is likely to be reasonable. 
 

 

 

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ACKNOWLEDGEMENTS 

Funding for this work was provided by the NOAA Center for Operational Oceanographic 

Products and Services under the guidance of Richard Edwing and David McFarland. Rodney 

Weiher assisted in the administration of the project. 

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