**FINAL DRAFT**

**The Value of Snow and Snow Information Services**

by

Richard M. Adams [^1]

Laurie L. Houston [^2]

and

Rodney F. Weiher[^3]

August, 2004

Report prepared for NOAA's National Operational Hydrological Remote
Sensing Center, Chanhassen, MN, under contract DG1330-03-SE-1097

Table of Contents

[**EXECUTIVE SUMMARY 3**](#executive-summary)

[1. Introduction 6](#introduction)

[2. Economic Concepts and Methods 10](#economic-concepts-and-methods)

[2A. Economic Value 11](#a.-economic-value)

[2B. The Value of Information Framework
12](#b.-the-value-of-information-framework)

[3. The Dimensions of Snow and Its Effects on Economic Behavior
15](#the-dimensions-of-snow-and-its-effects-on-economic-behavior)

[4. A Synthesis of the Economic Consequences of Snow
18](#a-synthesis-of-the-economic-consequences-of-snow)

[4A. Positive Economic Values of Snow (Benefits)
18](#a.-positive-economic-values-of-snow-benefits)

[4B. Negative Economic Impacts of Snow and Winter Weather (Costs)
23](#b.-negative-economic-impacts-of-snow-and-winter-weather-costs)

[4Bi. Snow Removal Costs 23](#bi.-snow-removal-costs)

[4Biii. Flight Delay Costs 24](#biii.-flight-delay-costs)

[4Biv. Costs to Utilities 27](#biv.-costs-to-utilities)

[4Bv. Flooding from Snowmelt 27](#bv.-flooding-from-snowmelt)

[4Bvi. Costs to Agriculture and Timber
27](#bvi.-costs-to-agriculture-and-timber)

[4c. How Snow Monitoring and Forecasts Can Improve on These Values
28](#c.-how-snow-monitoring-and-forecasts-can-improve-on-these-values)

[4Ci. Agricultural Benefits 28](#ci.-agricultural-benefits)

[4Cii. Utility Benefits (Hydropower, gas, and electric)
29](#cii.-utility-benefits-hydropower-gas-and-electric)

[4Ciii. Air, Road and Water Transportation Benefits
30](#ciii.-air-road-and-water-transportation-benefits)

[4Civ. Fire Management Benefits 31](#civ.-fire-management-benefits)

[4D. Importance of Climate Change on Snow Values and Forecasts
34](#d.-importance-of-climate-change-on-snow-values-and-forecasts)

[4Di. Winter Tourism 34](#di.-winter-tourism)

[4Dii. Coldwater Fishing 35](#dii.-coldwater-fishing)

[4Diii. Water Management 35](#diii.-water-management)

[4Div. Other Costs 37](#div.-other-costs)

[5. The Value of Snow: Conclusions 38](#the-value-of-snow-conclusions)

[References 42](#references)

**The Value of Snow and Snow Information Services**

# EXECUTIVE SUMMARY

Snow has substantial impacts on human behavior and activity and thus has
important economic consequences. Obtaining and distributing information
about snow and winter weather conditions has economic value, or
benefits, because of the potential to increase positive and/or decrease
negative economic impacts associated with snow and winter weather
conditions. This includes current and improved information available
from remote sensing or other techniques, as well as weather and climate
forecasts.

The overall objective of this report is to: a) identify and quantify the
positive and negative economic impacts of snow and b) assess the
potential economic benefits of snow monitoring and snow forecasts to the
American economy.

Assessing the size and scope of economic *impacts* of snow on the
nation's economy is important to assessing the economic *benefits* of
snow information, including snow coverage and predictions of snow, snow
pack, and snowmelt. In particular, snow and snow forecasts have
significant economic and scientific importance because of their impacts
on water resources and winter activities.

**Economic Impacts of Snow.**

In the western U.S. snowmelt provides over 70% of the water supply.
Accurate and timely snow forecasts and snowmelt forecasts are critical
components in the management of rivers such as the Columbia and the
Colorado.

-   Utilizing a man-made reservoir system, costs of storing water from
    snow pack serving the states of California, Oregon, Washington and
    Idaho ranges between \$2.3 billion and \$348 billion, not including
    the environmental costs of reservoirs, such as loss of fish and
    wildlife habitat.

Snow also plays a significant role in the United States tourism economy.

-   Winter tourism in just a few U.S. states adds up to more than \$7.9
    billion dollars a year.

-   The value of coldwater for fishing in three areas of the United
    States is more than \$2.3 billion dollars per year.

On average, 105 snow producing storm systems hit the lower 48 United
States each year, imposing substantial economic damages in terms of
removal costs, lost retail trade, lost wages, and lost tax revenue,
delays in ground and air transportation, flood damage during snowmelt,
damage to crops and livestock, accidents, injuries, and loss of life due
to hazardous conditions.

-   Extreme cold temperatures and winter storms in 1999 caused 74
    fatalities, 444 injuries, \$64 million dollars in property damage,
    and \$155.5 million in crop damages.

-   Average cost of snow removal for streets and highways in the United
    States exceeds \$2 billion annually.

-   Direct cost to airlines of flight delays from runway and airport
    closures (some of which are from winter storms) exceeds \$3.2
    billion annually.

Major economic impacts, both positive and negative, associated with
snowy weather include:

  --------------------------- -------------------------------------------
  **Examples of the Economic  
  Impacts (Positive and       
  Negative) of Snow and Snow  
  Events (in 2004 dollars)**  

  **Economic Benefits of      
  Snow**                      

  Winter tourism              Exceeds \$8 billion/yr in New England and
                              Rocky Mountains

  Cold water fishing          Exceeds \$2.3 billion/yr in New England

  Snow pack water storage     Up to \$348 billion/yr in western U.S.

  **Economic Costs of Snow**  

  Snow Removal                Exceeds \$2 billion/yr for U.S.

  Road closures that cause    Exceeds \$10 billion/day for closures in
  lost retail trade, wages,   eastern U.S.
  and tax revenue             

  Flight delays               \$3.2 billion annually for U.S. carriers

  Damage to utilities         Up to \$2 billion per event

  Flooding from snowmelt      \$4.3 billion for 1997 floods

  Cost to agriculture and     Up to \$1.6 billion per ice storm
  timber from frost and ice   
  --------------------------- -------------------------------------------

**Benefits of Snow Information and Forecasts.**

Although much of the cost associated with winter snow events is
unavoidable, some of these costs can be reduced given advanced notice
regarding hazardous conditions.

-   Short-term frost forecasts can save producers of high valued crops
    thousands of dollars per hectare.

-   Improved stream flow forecasts from improved seasonal forecasts will
    allow more spot market energy sales to be made in late summer and
    fall (for Columbia River hydropower, benefits were estimated at
    \$161 million per year).

-   Knowledge of soil moisture content, timing of snowmelt, the amount
    of water within snow packs, and temperature forecasts also allow
    fire managers to predict the onset, severity, and duration of fire
    seasons.

Examples of potential benefits from better forecasting of snow and snow
events include:

  --------------------------- -------------------------------------------
  **Examples of the value of  
  weather forecasts (in 2004  
  dollars)**                  

  **Agriculture**             

  Improvements in frost       Up to \$6,000/hectare/yr for fruit orchards
  forecasts                   

  **Utilities**               

  Improved long-range (1 mo   Over \$170 million/year in hydropower
  -- 1 yr) streamflow         benefits for three river systems
  forecasts                   

  Improved temperature        Over \$500 million/year from natural gas
  predictions                 and electric utility providers

  **Air, road and water       
  transportation**            

  Weather                     Exceeds \$600 million/yr at U.S. airports
  sensing/forecasting, and    
  icing diagnostics at        
  airports                    

  Improved predictions of     Exceeds \$29 million/yr from rerouting
  road ice formation and fog  trucks in U.S.

  Improved marine forecasts   Exceeds \$95 million/yr from transit time
  of winds and waves          savings and cargo loss reductions in U.S.
                              coastal waters
  --------------------------- -------------------------------------------

**Cost and Benefits of Snow Information**.

The limited number economic studies on economic benefits, as well as
detailed cost information, does not currently permit a detailed cost
benefit analysis of current and improved snow information and services.
However, we have identified potential benefits greater than \$1.3
billion annually. This compares quite favorably with preliminary
*upper-bound* cost estimates of \$150 million for NOAA's annual
investment in snow information identified in the FY 2006 Program
Baseline Assessment.

Further, for cost benefit and investment analyses, we are interested in
the value of the additional benefits from investments to improve our
snow information capabilities compared to marginal costs of those
investments. The studies evaluated in this Report indicate that the
economic *impacts* of snow on the U.S. economy, both positive and
negative, range from \$50-\$400 billion per year. This suggests strongly
that investments that make only modest improvements in snow information
will have *substantial economic payoffs.*

**Conclusions.** Although there are relatively few economic assessments
on snow forecasts compared to other weather phenomena, the existing
literature provides unequivocal evidence that:

-   snow imposes both economic costs and benefits to society;

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-   these economic effects are significant in their size relative to
    other weather phenomena;

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-   there is value in use of current information on snow coverage, water
    potential and other metrics in human decision making;

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```
-   improvements in the accuracy and lead time of snow metrics such as
    snow coverage, depth, and water content will have substantial
    economic benefits by improving individual and public decision
    making.

.

**The Value of Snow and Snow Information Services**

# 1. Introduction

Snow storms, floods, droughts, and other weather and climate-related
natural disasters cause billions of dollars in economic damages each
year, and some deaths. The International Red Cross (2003) estimated that
the cost of direct damage to infrastructure, crops, and other economic
assets from globally reported disasters was approximately \$27 billion
in 2002. Weather-related disasters have been increasing in frequency and
intensity in the past decade: from an annual average of 200 between 1993
and 1997, to 331 per year between 1998 and 2002. Ffloods affect more
people globally than all other natural or technological disasters
combined (International Red Cross 2003).

In the United States, there were 58-weather-related disasters between
1980 and 2003 in which overall damages and costs reached or exceeded \$1
billion at the time of the event (National Climatic Data Center, 2004a).
These damages were from such disasters as ice and hail storms,
wildfires, drought, and floods. Specific examples of these costly
weather related disasters include a 1998 winter ice storm in the
Northeast that caused \$1.4 billion in damages; a May 1998 severe
thunder storm and hail event in Minnesota that caused an estimated \$1.5
billion in damages; an October-November 1998 flood in Texas that dropped
10 to 20 inches of rain during two storm events and caused an estimated
\$1.0 billion in damages; and severe fire seasons in western,
south-central and southeastern states during 2000, and southern
California in late October and early November, 2003 that combined burned
more than 7 million acres and caused more than \$8.5 billion in damages
(National Climatic Data Center, 2004a ). On an annual basis, average
costs in the United States due to drought are estimated at \$6 to 8.5
billion, and flooding and hurricanes add another \$3.8 to 7.6 billion in
annual damages, in 2004 dollars (Knutson, 2001).

Some portion of the economic costs imposed by these disasters could be
reduced by using weather information, including current information
available from remote sensing or other techniques, as well as weather
forecasts and climate forecasts. Previous studies by Murphy, (1994),
Nicholls, (1996), Katz and Murphy (1997), and Houston et al. (2004)
provide a comprehensive review of studies that estimate the value of
weather information services. These studies cover a wide range of
weather issues beyond those of snow and winter weather conditions. The
interested reader is referred to these for further information. Murphy
(1994) provides one of the first reviews of value estimates of climate
and weather forecasts in an annotated bibliography that denotes the
approach and or method that each citation uses, the type of forecast,
and the sector of application. Of particular value in this publication
are the reviews on approaches and methods of forecast value assessment,
and a section on specific problems encountered by researchers that
require further study. Murphy concludes his summary by emphasizing that
estimating the value of climate forecasts is an interdisciplinary
problem requiring expertise from economics, operations research,
psychology, statistics and systems analysis, and climatology. Nicholls
(1996) provides an excellent summary of the economic and social benefits
of climatological information and services prior to 1996. This review
draws from over 90 references containing value estimates from more than
20 countries. Nearly 50 percent of the estimated values are from
developed countries, such as the United States, the United Kingdom, the
European Union, and Canada, but there are also several examples from
less developed countries such as East Africa, India, and Kenya.
Estimates of economic value are given for several economic sectors
including agriculture, energy, and manufacturing. Another excellent
review of pre-1996 studies that estimated the value of weather and
climate forecasts is found in Katz and Murphy (1997). This edited book,
entitled "Economic Value of Weather and Climate Forecasts" contains
chapters by economists, physiologists, meteorologists, and statisticians
which estimate the economic value of existing weather forecast systems
over various time horizons, from short-term time horizons such as the
next few hours to long-term time horizons such as several months or
seasons ahead. The book also contains estimates of the economic benefits
of improvements in weather forecasting systems. The review by Houston et
al. (2004) provides a review of the most recent studies that estimate
the value of weather forecasts by economic sector. This review also
determines the relative importance of weathwer forecasts to economic
sectors by comparing the values of forecasts to the gross domestic
product of each economic sector.

These previous reviews document the importance that weather information
can have in a variety of decision making processes, ranging from private
decisions of individuals, such as farmers or recreationists, to public
decisions by managers of public resources, including water supply and
transportation services. This type of information, and information
regarding snow and winter weather conditions in particular, are also
useful to managers of public resources as they prepare for flood,
drought and wildfire seasons. For example, improved snowpack information
could assist drought monitoring nationwide and provide potentially
valuable information to irrigation district managers, fire planners and
fish and wildlife agencies on soil moisture conditions and spring
snowmelt and streamflow forecasts. In addition, better estimates of
water content in snowpacks, as well as rate and timing of melt, could
assist with flood planning, and water supply forecasts. Similarly,
timing of frosts and ice conditions could aid farmers in crop management
decisions as well as aid air and ground traffic controllers in routing
decisions.

Of particular interest in this report is the value of snow and snow
forecasts. The presence of snow and the ability to accurately monitor
snow coverage and predict snow, snowpack, and snowmelt can have a
significant impact on the nation's economy. Snow and snow forecasts have
significant economic and scientific importance because of their impact
on water resources and winter activities. For many regions of the U.S.,
snow melt runoff is a vital source of water supply for drinking,
irrigation, hydropower generation, fish and wildlife habitat, and
transportation. For example, in the western portion of the United
States, snowmelt provides over 70% of the water supply (Chang et al.,
1987). The Columbia and Colorado rivers are two examples of snowmelt-fed
river systems in the Untied States (Natural Resources Conservation
Service (NRCS), 2004). The Columbia receives 75% of its flow from
snowmelt and supports a wealth of fish and wildlife resources, barge
traffic, communities and industries, and millions of acres of
agriculture (NRCS, 2004). Due to increasing water demands from large
population centers and agricultural development in the arid west, the
Colorado River system is probably one of the most intensively managed
water systems in the world. Several states and municipalities draw water
from the Colorado River. By the time the river flows into Mexico, the
quality of the water is severely degraded from return flows and salt
accumulation. Accurate and timely snow forecasts and snowmelt forecasts
are critical components in the management of such snow-melt fed rivers.
Snow and snowmelt forecasts are especially critical management tools for
the management of the Colorado River, where multistate agreements and
compacts regulate the quality and quantity of stream flow (NRCS, 2004).

Snow also plays a significant role in the United States tourism economy.
More than half of the states have winters that are cold enough to
support several types of winter recreational sports and tourism,
generating millions of dollars in revenues for each state from tourism
and related industries. Skiing and snowmobiling are the most popular
winter tourism activities. Mountain ranges such as the Appalachians, the
Rockies, the Sierras, and the Cascades provide numerous locations for
both down hill and cross-country skiing, as well as snowmobiling,
snowshoeing, mountaineering, sledding, ice climbing, and sightseeing
opportunities for winter enthusiasts. For example, Vermont has more than
200 inches of snowfall annually and offers 18 alpine ski areas, 984 ski
trails with 5,175 acres of skiable terrain and nine mountains with over
2,000 vertical feet (skipackages.com, 2004a). In the New York Adirondack
Mountains, there are hundreds of miles of ski trails and 30 ski
mountains (skipackages.com, 2004b). In the Cascade Mountains of
Washington there are 12 ski areas along the mountain range which
annually receive more than 500 inches of snow. This allows for superb
alpine skiing well into April (Washington State Tourism, 2004). One of
the nation's most popular snowmobile trail systems is the [Black Hills
snowmobile trail
system](http://usparks.about.com/gi/dynamic/offsite.htm?site=http://www.state.sd.us/gfp/sdparks/winter/snowhome.htm)
in North and South Dakota. The Black Hills offer more than 4,000 square
miles of snow: a 350-mile trail system has an 18-24 inch base of snow on
the western slopes from November until early spring (USparks about.com,
2004). Snowmelt also plays a critical role in maintaining stream
temperatures needed by salmonids and other cold water fish species. This
cool water in turn creates cold water recreational fishing opportunities
for the 34 million anglers in the U.S. (U.S. Department of the Interior,
Fish and Wildlife Service, 2002).

The overall objective of this report is to identify and quantify the
economic impacts (positive and negative) of snow and assess the possible
economic value of snow monitoring and snow forecasts to the American
economy. Specific objectives include (1) provide a brief review and
discussion of the economic concepts of value and the general economic
framework for valuing forecasts; (2) provide a synthesis of existing
studies and data to assess the economic benefits and damages associated
with snow; (3) provide a summary of the potential values of snow
monitoring and forecasts.

The remainder of this report is arranged as follows. First, we briefly
discuss economic concepts and methods. We then describe the economic
concept of value, followed by a description of how economists estimate
the value of information, such as the information contained in weather
forecasts. The extent and magnitude of snow events and their effects on
economic behavior are then described. This is followed by a synthesis of
the economic values of snow (both positive and negative), and the
importance of climate change on snow values and forecasts. The
penultimate section of the report contains a discussion on how snow
forecasts can improve on these values (increase the benefits or reduce
the damages from snow events) and how climate change can affect these
values. Finally, we conclude with some general observations about the
economic value of snow.

# 2. Economic Concepts and Methods

Economic science is an exercise in deductive logic, in which testable
hypotheses concerning the behavior of economic agents (i.e., farmers,
consumers, resource owners and markets) are deduced from a body of
theory. This body of theory is based on a series of premises proposed by
economists and philosophers dating back over two centuries to Adam Smith
(1776). These premises gradually evolved into a theoretical foundation
(primarily dealing with what economists refer to as microeconomics)
culminating in structural relationships that define the operation of
markets. This foundation was first laid out in a comprehensive and
rigorous fashion by Alfred Marshall (1920). The full set of these
theoretical relationships were then formalized by Paul Samuelson (1947),
resulting in what is sometimes referred to as modern or neoclassical
economics.

Exploring the validity of this endowed theory (through attempts to
refute its testable hypotheses) has occupied a substantial amount of
economists' time over the past fifty years, often at the expense of more
applied (empirical) research. The theoretical foundations of economics
are important because the insights gained from this theory help shape
the nature of applied (policy) research methods. It is in this
application of economic theory to real world problems where the methods
of economics become important (for example, as economists seek to
measure the economic consequences of climate and climate forecast for a
particular region or economic sector). Specifically, in applied
research, theoretical concepts are combined with actual data via an
economic assessment method, in which the economist attempts to explain
the workings of components of an economic system and/or to predict how
that system will respond to changes. Given that there are a number of
assessment methods from which to choose, economists frequently debate
the comparative advantages of each, and, as is the case in other
disciplines, economists tend to have favorites. In some cases, the
choice of assessment method can affect the type of economic information
obtained. Even with a given assessment method, results are sensitive to
specific data treatment or assumptions (Tomek, 1993).

To the non-economist, debates among economists regarding the
appropriateness of a given economic assessment method may call into
question the validity of economic analysis in general. This
interpretation would be unfortunate, as most applied economists agree on
the basic axioms upon which economic science is based, such as those
dealing with the economic concept of value, consumer and producer
behavior, and the conditions which define efficiency in markets.
Further, most would agree on the importance of including the behaviors
suggested by these axioms in any 'real world' modeling and assessment
efforts of future or prospective events, such as future weather events
and long term climate change. The essence of the debate is whether a
given method is appropriate for a particular problem or whether a method
has been employed in a technically competent fashion. To the extent that
the method (or the manner in which it is executed) can influence the
economic estimates of benefits or costs, this selection and
implementation process has potential importance to users of economic
information.

In the subsequent discussion of economic estimates pertaining to snow
events, we will not discuss the economic procedures used in the
estimation. Specifically the purpose of the review is not to critique a
particular value but to obtain a range of values of the estimated
impacts of snow and other weather phenomena on economic activity.
However, to the extent that a number of the cited studies are drawn from
the peer-reviewed literature, it is our opinion that the reported
estimates are appropriate for the purposes of this assessment.

## ***2A. Economic Value***

In economics, the definition of value is tied to the concept of
willingness to pay. For an individual, the value of an item is equal to
their willingness to pay for it in monetary terms or in terms of what
they would give up in order to obtain the item. This concept of
willingness to pay is also related to the notion of "demand", and the
elicitation of demand curves (which depict relations between demand for
a good and its price). Specifically, a demand curve can be viewed as a
marginal willingness to pay curve for the item (the term "marginal"
refers to the value of the next or incremental unit demanded). This
marginal willingness to pay usually declines with units consumed; hence,
for most goods the demand curve is downward sloping. In addition to the
direct measurement of marginal willingness to pay for a good or service,
the concept of alternative cost can be used to assign values to a
commodity in various uses. For example, with this concept, the lack of
"natural" snow for skiing, due to a drought, could be valued at the cost
of the least expensive alternative, such as man-made snow.

To an individual, snow information may have value if it results in
improved decision making, where "improved" implies that the welfare of
the individual has been increased from use of the information. In such a
case, the "value" of the information is the net gain in welfare (the
difference between welfare with and without the information). Similarly,
snow information may have social value if the use of the information by
resource managers increases net social benefits. As discussed
subsequently, there have been numerous studies that have estimated the
net economic benefits from the use of weather information. Almost
without exception, weather information has been shown to have
substantial economic value.

## 2B. The Value of Information Framework

The previous sections reviewed concepts used to measure economic costs
and benefits. These concepts can be used to assess the economic
consequences of a negative event, such as a flood, or the positive
effects of an above average snowpack on winter recreation. The estimated
economic consequences of a weather event for a particular region or a
specific economic sector, however, differ from the measures of the
**value** of a forecast to that region or sector. The reason is that a
forecast, even if perfect, typically can not eliminate all damages
associated with a negative weather event.

This section briefly discusses the conceptual framework used to estimate
the value of a weather forecast. This framework represents a
straightforward application of Bayesian decision theory and has been
used to assess the value of weather forecasts in other contexts (Baquet
et al., 1976, Katz et al., 1982; Lave et al., 1963; Sonka et al., 1986,
1987). The value of a forecast to a particular enterprise is measured by
the expected increase in economic benefits arising from the use of the
forecast in decision making. Such benefits occur when the forecast leads
to a change in economic behavior. For example, in the case of
agriculture, in the absence of a stream flow forecast, a farmer makes
planting and harvesting decisions that perform well under average
meteorological conditions. A stream flow forecasting scheme has
potential economic value, if the farmer's decisions are different for
different forecasts. This condition will be met if there are significant
differences in the meteorological conditions under different forecasts
and if these meteorological differences lead to significant differences
in growing conditions.

The basic behavioral assumption is that a farmer adopts a planting,
irrigation, and harvesting strategy that maximizes expected profits
under his current beliefs about the streamflow phase, S. One can
summarize these beliefs in the form of a probability distribution over
the possible phases, W = wet, D = dry, N = normal (average years). In
the absence of a forecast, this distribution is given by:

(1) =~W~~D~~~

where ~W~~D~~~are the long-term relative frequencies of Wet,
Dry, and Normal phases, respectively.

Suppose that a forecast X takes the form of an unqualified statement
predicting which phase will occur; that is, a forecast that comes
without a probability. Upon receiving the forecast X = x (x = W, D, or
N), the farmer updates his beliefs according to Bayes's Theorem;

(2) P~s~ = ~s~L(X\|x / P(x)

where P~s~ is the updated probability of phase S, L(x\|S) is the
probability that X = x given that the true phase is s, and:

(3) P(x) = $\sum_{s}^{}{}$~s~L(x\|s)

Given the probability of the forecast of X = x, the farmer then adopts
the strategy that maximizes expected profits under the updated
distribution:

(4)  P~s~ = P~w~ P~D~ P~N~ 

The likelihood L(X\|x) is a measure of the accuracy of the forecast. For
a perfect forecast:

> L(x\|s) = 1 if x = s, or 0 if x does not = s

In the case of a perfect forecast, once the forecast is issued, the true
phase is known with certainty and the farmer chooses the strategy that
is optimal for that phase. The value of a perfect forecast to society is
the average increase in the sum of producer and consumer economic
well-being that results from the farmer's optimizing under certain
knowledge of S, rather than under the distribution of Calculating
this value requires knowing total economic welfare for four cases: under
the optimal strategy for each of the three possible phases and under the
optimal strategy under average (of all years) conditions. The value of a
perfect forecast is then given by the difference between a weighted sum
of the first three, with weights given by the elements of , and the
value from following the optimal strategies.

One must modify this approach in two ways in order to find the value of
an imperfect forecast. First, the optimal strategy for the updated
distribution of p in general will not correspond to the optimal strategy
for any one of the three possible phases. In the case where the forecast
takes the form of an unqualified statement about which phase will occur,
the farmer will face one of the three possible updated distributions;
that is, one for each of the three possible values of X. Calculating the
value of an imperfect forecast requires determining the optimal strategy
and total economic welfare under each of these three possible
distributions. Second, the expected economic welfare under an imperfect
forecast is still given by a weighted sum of the economic welfare under
the three possible distributions. However, the weights correspond to the
relative frequencies of the three possible forecasts, rather than the
relative frequencies of the three possible phases. These weights are
given by (2).

In our example of farmer decision making under uncertain streamflow
information, implementing this framework requires information about
differences in streamflow conditions under the three phases, the
consequences of these streamflow differences for crop yields, and
finally, the consequences of these crop yield differences for decision
making and economic welfare.

# 3. The Dimensions of Snow and Its Effects on Economic Behavior

The snow season for the contiguous 48 United States typically begins in
early October and lasts into late April. Only seven states in the U.S.
have an average snowfall of less than 15 inches (NCDC, 2004b). In high
elevations and near the Canadian border, the first snow of the season
can occur as early as September and as late as May or June (Doesken and
Judson, 1997). Alaska, western mountain ranges, northern New England and
the upper Great Lakes have the most significant snow seasons with some
weather stations in these areas recording an annual average of more than
60 days with snowfall during the winter seasons between 1961 and 1990
(Doesken and Judson, 1997). Some of the snowiest places in the world are
found in North America. For example, weather stations in Rainier
Paradise, Washington; Crater Lake, Oregon; Wolf Creek Pass, Colorado;
and Silver Lake Brighton, Utah recorded average annual snowfalls of 683,
488, 442, and 407 inches, respectively (Doesken and Judson, 1997). Most
of these places are sparsely populated; however, significant amounts of
snowfall have also been reported at weather stations that are located at
more populated cities and towns. For example, Rochester, New York, and
Buffalo, New York are the snowiest large cities in the United States,
averaging more than 90 inches of snowfall annually based on 1961-1990
National Weather Service records (National Snow and Ice Data Center,
2004). There are several other smaller cities and towns that report
average annual snow falls of more than 100 inches. These cities include
Talkeetna, Valdez, and Yakutat, Alaska; Blue Canyon and Mount Shasta,
California; Caribou Maine; Sault Ste Marie and Marquette, Michigan;
Mount Washington, New Hampshire; Syracuse, New York, and Lander, Wyoming
(NCDC, 6/2/2004).

In these northern states, and high elevation areas, snow and snowfall
play a significant role in the local and national economies. Although it
is expensive to keep roads clear of snow and ice during the winter,
winter recreation activities in some of these areas can generate
millions of dollars of economic activity, often outweighing the negative
economic impacts of snow. However, in populated cities, some
agricultural areas, and areas where it only snows infrequently, snowfall
often has a net impact on economic behavior that is negative. In
agricultural areas of the mid-west, for example, above average snow and
snowmelt can cause fields to remain saturated, which in turn may cause
delays in planting, reducing crop yields and agricultural profits.
Snowstorms can also block roads, cause ground and air traffic delays and
accidents, and even paralyze whole cities for hours or days at a time,
imposing millions and sometimes billions of dollars in damages. Some of
the more common sources of positive and negative economic values
associated with snow are listed in Table 1 below.

+----------------------+-----------------------------------------------+
| Table 1. Positive    |                                               |
| and negative         |                                               |
| economic effects of  |                                               |
| snow                 |                                               |
+----------------------+-----------------------------------------------+
| Positive Impacts     | Negative Impacts                              |
+----------------------+-----------------------------------------------+
| Winter Tourism from: | Cost of accidents and loss of life in severe  |
|                      | weather conditions                            |
+----------------------+-----------------------------------------------+
| > Skiing (Nordic,    |                                               |
| > and                |                                               |
| > a                  |                                               |
| lpine),snowmobiling, |                                               |
| > snow shoeing, ice  |                                               |
| > fishing, etc.      |                                               |
+----------------------+-----------------------------------------------+
|                      | Cost of snow removal and de-icing roads and   |
|                      | runways                                       |
+----------------------+-----------------------------------------------+
|                      | Traffic delay costs in trucking, aviation,    |
|                      | and shipping                                  |
+----------------------+-----------------------------------------------+
| Water storage        |                                               |
+----------------------+-----------------------------------------------+
| Water supply         | Cost of frost damage to agricultural crops    |
+----------------------+-----------------------------------------------+
| Cold water fish      | Flood damage during snowmelt                  |
| habitat              |                                               |
+----------------------+-----------------------------------------------+

In the following section, we discuss in more detail several positive and
negative economic effects of snow events. Specifically, we review
studies that estimate the economic consequences of such events, as well
how snow monitoring and forecasts can reduce the negative effects and
increase the positive effects of snow. We also briefly discuss how
climate change is expected to alter the economic significance of snow
and other weather events.

# 4. A Synthesis of the Economic Consequences of Snow

## 4A. Positive Economic Values of Snow (Benefits)

As stated above, there are a range of economic activities affected by
snow in general and specific snow events in particular. A prominent
source of positive economic value from snow is associated with winter
recreation and tourism. Each year people spend billions of dollars on
winter recreation and tourism activities such as skiing (alpine and
nordic), snowboarding, snowmobiling, snowshoeing, ice skating, ice
fishing, mountaineering, and sightseeing. In addition to direct
expenditures on winter recreational activities such as lift tickets,
meals, hotels and equipment, these expenditures generate billions of
dollars in indirect economic activity. Indirect economic effects refer
to secondary effects on the local economy associated with the direct
expenditures. These indirect or secondary effects include such impacts
as increased demand for labor, food preparation supplies, hotel laundry
services and a host of other inputs needed to support the tourist
industry. Many communities have conducted economic impact studies to
estimate the value of winter recreation in their local economies. For
example, Pricewaterhouse Coopers (2000) estimated that visitors to Rocky
Mountain ski resorts in Alberta, Canada in 1999 generated an economic
impact (direct and indirect) for the province of \$450 million in 2004
dollars. (Unless other wise noted, all subsequent economic estimates are
converted to 2004 dollars). Winter season visitors to Yellowstone
National Park were estimated to generate an indirect economic impact of
\$9.8 million (Taylor 1999). In North Carolina, the aggregate (direct)
revenue from 7 ski area resorts was estimated to be over \$24 million ,
resulting in a total economic impact (both direct and indirect) of \$123
million (Millsaps and Groothuis 2003). In Vermont, tourists spent
approximately \$779 million during the winter of 1999-2000, \$109
million of which was from downhill skiing. The total economic impact
from winter tourists in Vermont was estimated at \$1.2 billion (Wood and
Liang 2001). In 1998, visitors to Alaska spent over \$900 million,
resulting in a total direct and indirect spending of \$2.5 billion
(Mcdowell Group 1999). In a survey conducted in 2000-2001, the Institute
for Tourism and Recreation Research found that nonresident winter
visitors to Montana contributed more than \$352 million to the economy
(Billings Gazette, January 20, 2002). In Michigan, winter tourism
typically contributes about \$3.7 billion annually to the economy, over
one-third of total tourism revenues in the state (Detroit News, December
24, 2000)![](media/image1.png){width="1.0416666666666666e-2in"
height="6.25e-2in"}. The largest contribution comes from snowmobilers,
who use the 6,000 plus miles of trails and support system of motels and
restaurants, and spent \$175 million on snowmobiling activities in 2000
(Detroit News, December 24,
2000)![](media/image1.png){width="1.0416666666666666e-2in"
height="6.25e-2in"}. Winter tourism in just these few U.S. states adds
up to more than \$7.9 billion dollars a year (Table 2.)

In some parts of the United States, snow also provides a stable, year
long cold water source for cold water fish species, which in turn
attract anglers and generate significant economic benefits. For example,
studies in New England, Pennsylvania, and South Carolina estimate
significant economic benefits to their economies generated from trout
fishing (and other cold water species). For example, a study in New
England estimated that tourists attracted by the cold water fish species
in New England spent an estimated 24 million fishing days and generated
over \$1.3 billion in economic activity in the region in 1991,
equivalent to approximately \$1.8 million in 2004 dollars (Sinclair
2001). Trout fishing in Pennsylvania is estimated to generate about
\$500 million in economic activity annually (Commonwealth of
Pennsylvania 3/2/2004). Cold water fisheries are not limited to northern
states. A study on the economic benefits of freshwater fishing in South
Carolina reports that a cold water trout fishery generates \$19 million
in economic output annually for the state's economy (South Carolina
Department of Natural Resources, 2002). The value of coldwater for
fishing in these three areas of the United States is more than \$2.3
billion dollars per year (Table 2.).

Another significant source of positive economic values associated with
snow is the value of water storage in the form of snowpack. High
mountain ranges in the semi-arid western U.S. hold a vast winter
snowpack that provides 50 to 80 percent of each year's water supply for
western states (NRCS, June 4, 2004). This snow pack provides a service
by slowly releasing water (snow melt) over the spring, summer and fall
months, thus maintaining streamflows during a period when there is
essentially no precipitation. In the absence of a snow pack, winter
rains would need to be captured in reservoirs for later use. Thus, snow
packs may be viewed as a large, essentially costless reservoir system.
The economic value of snowpacks can be assessed by exploring the costs
of alternative storage, i.e. reservoir construction.

Many large dams and reservoirs have been constructed to capture, manage
and regulate runoff for various human uses such as hydropower,
irrigation, human consumption, and flood protection. For example, the
construction of Hoover Dam (completed in 1936) cost approximately \$165
million (nearly 2 billion in 2004 dollars). Lake Mead, the lake behind
the dam contains 28,537,000 acre-feet of water, which is enough water to
cover the state of Pennsylvania to a depth of one foot (US department of
the Interior, Hoover Dam, 2004). Thus, the average cost of storage
(ignoring operation and maintenance costs) is approximately \$70 per
acre-foot. The construction of the Glen Canyon Dam (completed in 1964)
was less expensive to construct; \$187,000,000 in 1964 dollars or
\$314,000,000 including the power plant, switchyard, town of Page, and
other associated facilities. This is approximately \$1-1.5 billion in
2004 dollars. With a total capacity of 26,215,000 acre-feet (US
department of the Interior, Glen Canyon Dam, 2004), the cost of water
storage is about \$40 per acre foot. These dams are built on premier
sites with large reservoir capacities and are much less expensive places
to store water than more recent dams that built on less desirable
locations, with smaller reservoir capacities. For example, the Parker
Water and Sanitation District of Colorado is proposing a dam and
reservoir that is estimated to cost \$100 million and hold 16,200
acre-feet of water (Parker Water District, 2004). This results in a cost
of water storage of more than \$6000 per acre-foot.

Snowpack is an inexpensive (free) way of storing water, compared to
building and maintaining dams and reservoir systems. An approximate
value of snow in the form of snowpack can be estimated with the range of
storage costs discussed above. For example, in the Pacific coast states
of California, Oregon, Washington, and Idaho, 77 million acre-feet of
freshwater is withdrawn from streams annually for public and domestic
uses, industrial and commercial uses, and irrigation (Houston, et al
2003). If one assumes that about 75 percent of the spring and summer
streamflow produced in the West originates from mountain snowpacks, then
approximately 58 million acre-feet of this water is generated from
winter snowpack. Given the range of the reservior storage costs
discussed above, the cost of storing all the water from this snowpack in
a reservoir system would be between \$2.3 billion and \$348 billion
(Table 2). If the environmental costs of reservoirs were included, such
as loss of fish and wildlife habitat, the costs of reservoirs to replace
snow pack-equivalent storage would be even greater.

+--------------------+-----------------------+-------------------------+
| Table 2. Positive  |                       |                         |
| Economic Impacts   |                       |                         |
| of Snow            |                       |                         |
+--------------------+-----------------------+-------------------------+
| Source of Value    | Economic Values       | Regions                 |
|                    |                       |                         |
|                    | (2004 dollars)        |                         |
+--------------------+-----------------------+-------------------------+
| Winter Tourism     | \$7.9 billion per     | Alaska^1^, Montana^2^,  |
|                    | year                  | Michigan^3^, North      |
|                    |                       | Carolina^4^,            |
|                    |                       | Vermont^5^, and         |
|                    |                       | Yellowstone^6^          |
+--------------------+-----------------------+-------------------------+
| Cold Water Fishing | \$2.3 billion per     | New England^7^,         |
|                    | year                  | Pennsylvania^8^, and    |
|                    |                       | South Carolina^9^       |
+--------------------+-----------------------+-------------------------+
| Snowpack water     | \$2.3 billion to      | California, Idaho,      |
| storage            | \$348 billion per     | Oregon, and             |
|                    | year                  | Washington^10^          |
+--------------------+-----------------------+-------------------------+
| Sources: 1)        |                       |                         |
| McDowell Group,    |                       |                         |
| 1999; 2) Gazette,  |                       |                         |
| 1/20/2002; 3)      |                       |                         |
| Detroit News,      |                       |                         |
| 12/24/2000; 4)     |                       |                         |
| Millsaps and       |                       |                         |
| Groothuis 2003; 5) |                       |                         |
| Wood and Liang     |                       |                         |
| 2001; 6)Taylor     |                       |                         |
| 1999; 7) Sinclair, |                       |                         |
| 2001; 8)           |                       |                         |
| Commonwealth of    |                       |                         |
| Pennsylvania,      |                       |                         |
| 3/2/2004; 9) South |                       |                         |
| Carolina           |                       |                         |
| Department of      |                       |                         |
| Natural Resources, |                       |                         |
| 2002; 10) based on |                       |                         |
| water use data     |                       |                         |
| from Houston et al |                       |                         |
| 2003, and cost of  |                       |                         |
| dam constructions  |                       |                         |
| from US department |                       |                         |
| of the Interior    |                       |                         |
| and Parker Water   |                       |                         |
| District, 2004.    |                       |                         |
|                    |                       |                         |
| Note: For ease of  |                       |                         |
| comparison, all    |                       |                         |
| values were        |                       |                         |
| converted to 2004  |                       |                         |
| US dollars using   |                       |                         |
| the CPI from the   |                       |                         |
| Economic Research  |                       |                         |
| Service and        |                       |                         |
| historical         |                       |                         |
| exchange rates     |                       |                         |
| from               |                       |                         |
| <http://           |                       |                         |
| www.x-rates.com/>. |                       |                         |
+--------------------+-----------------------+-------------------------+

## 4B. Negative Economic Impacts of Snow and Winter Weather (Costs)

On average, 105 snow producing storm systems hit the lower 48 United
States each year (National Snow and Ice Data Center, 2004). These stroms
impose billions of dollars in economic damages, from snow removal costs,
lost retail trade, lost wages, lost tax revenue, delays in ground and
air transportation, flood damage during snowmelt, damage to crops and
livestock, accidents, injuries, and loss of life due to hazardous
conditions. For example, in the winter of 1999, extreme cold
temperatures and winter storms caused 74 fatalities, 444 injuries, \$64
million dollars in property damage, and \$155.5 million in crop damages
(NCDC 2004a.). Specific examples of each of these economic costs are
discussed below, and are presented in Table 3.

### 4Bi. Snow Removal Costs

The average annual cost of snow removal for streets and highways in the
United States exceeds \$2 billion (Doesken and Judson, 1997). This
estimate does not include the millions of dollars spent by private firms
to clear parking lots, driveways, and walks to keep businesses and other
enterprises open. For example, Rochester, New York, one of the snowiest
large cities in the United States with an average of 94 inches of snow
annually, has an annual municipal snow-removal budget of \$4.6 million
dollars, in current 2004 dollars (Doesken and Judson, 1997). Even single
events can cause millions of dollars worth of snow removal costs. For
example, a December, 1995 snowstorm in Buffalo, New York, which dropped
39 inches in 24 hours, cost the city nearly \$6 million (2004 dollars)
in snow removal costs (Doesken and Judson, 1997).

**4Bii. Road Closures that Cause Lost Retail Trade, Wages, and Tax
Revenue**

When snowstorms paralyze cities, they also cause economic damages in the
form of lost wages, lost tax revenues, and lost retail sales. For
example, a report by the Salt Institute (2000) estimated that a January
2000 snowstorm in the Washington, DC/Alexandria, VA area that closed
businesses, retail stores, and schools for two days, imposed an
estimated cost of more than \$2.5 billion per day or \$5 billion (\$5.5
in 2004 dollars) in lost wages, retail sales and government tax
revenues. Similarly, a snowstorm in North Carolina that dropped 20
inches of snow and ice in central and eastern parts of the state during
a January 2000 snowstorm was estimated to cost North Carolina businesses
about \$164 million a day (\$180 million in 2004 dollars) in lost sales
and economic activity. By week's end, estimated costs totaled more than
\$800 million, or nearly \$875 million in 2004 dollars (NC State
University News Release February 1, 2000).

The Salt Institute (2000) also cited two regional studies conducted by
Standard and Poor's DRI that estimated the cost of debilitating
snowstorms. The first was a 1996 study that estimated costs imposed on
the Eastern States from the blizzard of 1996. The blizzard cost the
Eastern states as much as \$10 billion in lost production and \$7
billion in lost sales arising from 4 days of business and government
closure. In 2004 dollars, the costs total approximately \$20 billion or
\$5 billion per day. The Chief Economist for First Union Corp., also
estimated the blizzard's cost in goods and services produced by
factories, offices, shops, and other enterprises at about \$4.8 billion
per day (nearly \$5 billion in 2004 dollars) in the Northeast corridor
of the U.S. from Virginia to Massachusetts (Salt Institute, 2000). The
second example was a 1998 study of 12 Midwestern and Eastern states
including [Illinois](http://www.saltinstitute.org/rep_il.html),
[Indiana](http://www.saltinstitute.org/rep_in.html),
[Iowa](http://www.saltinstitute.org/rep_ia.html),
[Michigan](http://www.saltinstitute.org/rep_mi.html),
[Minnesota](http://www.saltinstitute.org/rep_mn.html),
[Missouri](http://www.saltinstitute.org/rep_mo.html), [New
Jersey](http://www.saltinstitute.org/rep_nj.html), [New
York](http://www.saltinstitute.org/rep_ny.html),
[Ohio](http://www.saltinstitute.org/rep_oh.html),
[Pennsylvania](http://www.saltinstitute.org/rep_pa.html),
[Virginia](http://www.saltinstitute.org/rep_va.html), and
[Wisconsin](http://www.saltinstitute.org/rep_wi.html). They found that
if the region were paralayzed by impassible roads, \$576 million a day
in federal, state, and local tax revenues, \$1.5 billion in unearned
wages, and \$657 million in retail sales would be lost. This greatly
exceeds the more than the \$567 million (2004 dollars) these twelve
states spend for the entire winter season on snow and ice control to
keep the roadways mobile and safe (Salt Institute,2000).

Over the course of an entire winter season, such snow storms can impose
a substantial amount of economic damages at the aggregate (national)
level in terms of snow removal costs, lost retail trade, lost wages, and
lost tax revenue. For example, considering that cities such as Chicago
experience 20 snowstorms each year, or that states like New York have an
average of 40 snowstorms statewide during a typical winter, the
aggregate costs are potentially quite high.

### 4Biii. Flight Delay Costs

Every winter, snowstorms cause delayed or grounded flights and sometimes
runway and airport closures. The Air Transport Association estimates
that the direct cost of delays (some of which are from winter storms) to
airlines exceeds \$3.2 billion annually (in 2004 dollars), not including
the indirect costs of lost time to passengers or the detrimental effects
on business or general aviation (Hayhurst, 2001). CALEB Technologies
Corporation estimates that each of the world's 33 largest airlines has
about 10 major disruptions annually, at a cost of approximately \$2.1
million per incident (2004 dollars), though many of these are due to
thunder storms in the summer (Caleb Technologies, 2001).

+-------------------+----------------------+--------------------------+
| Table 3. Negative |                      |                          |
| Economic Impacts  |                      |                          |
| of Snow (Costs)   |                      |                          |
+-------------------+----------------------+--------------------------+
| Source of Value   | Economic Values      | Regions                  |
| (cost)            | (costs)              |                          |
|                   |                      |                          |
|                   | (2004 dollars)       |                          |
+-------------------+----------------------+--------------------------+
| Snow Removal      | Exceeds \$2 billion  | Entire U.S.^1^           |
|                   | per year             |                          |
+-------------------+----------------------+--------------------------+
| Road closures     | \$2.5 billion per    | Washington,              |
| that cause lost   | day                  | DC/Alexandria, VA^2^     |
| retail trade,     |                      |                          |
| wages, and tax    |                      |                          |
| revenue           |                      |                          |
+-------------------+----------------------+--------------------------+
|                   | \$180 million per    | North Carolina^3^        |
|                   | day                  |                          |
+-------------------+----------------------+--------------------------+
|                   | \$5.1 billion per    | Eastern States from VA   |
|                   | day                  | to MA^2^                 |
+-------------------+----------------------+--------------------------+
|                   | \$2.5 billion per    | IL, IN, IO, MI, MN, MS,  |
|                   | day                  | NJ, NY,OH, PA, VI, WI^2^ |
+-------------------+----------------------+--------------------------+
| Flight delay      | \$3.2 billion        | U.S.^4^                  |
|                   | annually             |                          |
+-------------------+----------------------+--------------------------+
|                   | Exceeds \$2 million  | Each of the world's 33   |
|                   | per incident         | largest airlines^5^      |
+-------------------+----------------------+--------------------------+
| Damage to         | \$2 billion (a 1994  | Mississippi^6^           |
| utilities         | ice storm)           |                          |
+-------------------+----------------------+--------------------------+
| Flooding from     | \$4.3 billion (1997  | NY and PA^7^, North      |
| snowmelt          | floods)              | Dakota^8^ and            |
|                   |                      | Minnesota^9^             |
+-------------------+----------------------+--------------------------+
| Cost to           | \$23 million per     | Australian grains^10^    |
| agriculture and   | year                 |                          |
| timber from frost |                      |                          |
| and ice           |                      |                          |
+-------------------+----------------------+--------------------------+
|                   | \$11 million (2002   | NY, and PA grapes^11^    |
|                   | frost)               |                          |
+-------------------+----------------------+--------------------------+
|                   | \$1.6 billion (1994  | Mississippi forests^12^  |
|                   | ice storm)           |                          |
+-------------------+----------------------+--------------------------+
|                   | \$6.6 million per    | Mississippi pecans^12^   |
|                   | year for next 5-10   |                          |
|                   | years from 1994      |                          |
|                   | storm                |                          |
+-------------------+----------------------+--------------------------+
| Sources: 1)       |                      |                          |
| Doesken and       |                      |                          |
| Judson, 1997; 2)  |                      |                          |
| Salt Institute,   |                      |                          |
| 2000; 3) NC State |                      |                          |
| University News   |                      |                          |
| Release           |                      |                          |
| 2/1/2000); 4)     |                      |                          |
| Hayhurst, 2001;   |                      |                          |
| 5) Caleb          |                      |                          |
| Technologies,     |                      |                          |
| 2001; 6) Lott and |                      |                          |
| Sittel, 1996; 7)  |                      |                          |
| Ahnert,           |                      |                          |
| Waldstreicher,    |                      |                          |
| and Chiaramonte   |                      |                          |
| 1997; 8) USDC,    |                      |                          |
| 1998;             |                      |                          |
| 9)Todhunter,      |                      |                          |
| 1998; 10)         |                      |                          |
| Reinheimer, 2004; |                      |                          |
| 11) Associated    |                      |                          |
| Press, 8/19/2002; |                      |                          |
| 12) Lott and      |                      |                          |
| Sittel 1996.      |                      |                          |
|                   |                      |                          |
| Note: For ease of |                      |                          |
| comparison, all   |                      |                          |
| values were       |                      |                          |
| converted to 2004 |                      |                          |
| US dollars using  |                      |                          |
| the CPI from the  |                      |                          |
| Economic Research |                      |                          |
| Service and       |                      |                          |
| historical        |                      |                          |
| exchange rates    |                      |                          |
| from              |                      |                          |
| <http://w         |                      |                          |
| ww.x-rates.com/>. |                      |                          |
+-------------------+----------------------+--------------------------+

###  

### 4Biv. Costs to Utilities 

Snow and ice storms imposes significant economic costs by disrupting
utility services. For example, an ice storm in February of 1994 in the
Southeast caused power outages in multiple states, causing more than 2
million customers to loose power (Lott and Sittel, 1996). In Mississippi
alone, 750,000 customers lost power, some for over a month. Utility
damage was estimated at nearly \$2.4 billion, in 2004 dollars (Lott and
Sittel, 1996). In addition to the effects of an individual event on
utility service in a particular region, a cold winter also greatly
increases the demand for electricity, heating oil and natural gas. This
has important economic effects on utilities, who attempt to meet the
increased demand, as well as on consumers (both residential and
commercial), who pay higher utility bills. The increase in utility bills
generally results in reduced expenditures for other items, thus slowing
economic activity throughout the economy. There is potential economic
value from snow monitoring and forecasting that can provide some
suggestion of seasonal utility demand.

### 4Bv. Flooding from Snowmelt 

Another common cost associated with snow is the damage from snowmelt
induced flooding. For example, in January 1997, a major flood event,
caused by a combination of rapid snowmelt and heavy rainfall, affected
24 counties in NY and PA. The flooding claimed 7 lives, forced the
evacuation of 110,000 people, and resulted in over \$294 million damage
(in 2004 dollars) within the region (Ahnert, Waldstreicher, and
Chiaramonte 1997). In April of 1997, a major snowmelt flood occurred
along the Red River in North Dakota and Minnesota. Total damages within
the United States section of the Red River basin were estimated at
approximately \$4.7 billion, in 2004 dollars (USDC, 1998). More than \$4
billion of these losses occurred within the Grand Forks, ND/East Grand
Forks, MN, metropolitan area, making it the most costly flood, on a per
capita basis, for a major metropolitan area in United States history
(Todhunter, 1998).

### 4Bvi. Costs to Agriculture and Timber

Globally, there are substantial economic losses each year in the
agricultural sector due to winter weather and frost damage in
particular. In Victoria and South Australia for example, it has been
estimated that the annual cost of frost damage to the grains industry is
A\$9.2 million from direct yield losses, A\$22.5 million from indirect
yield losses and A\$1.9 million from quality downgrading, or about \$23
million in US dollars (Reinheimer, 2004). In the US, there are more
economic losses to agriculture from frost damage than any other natural
hazards including earthquakes, floods, droughts, hurricanes, and
tornadoes (Snyder,2004). For example, in 2002, vegetation killing frosts
in New York, and Pennsylvania resulted in the loss of a significant
portion of the grape crop. The National Grape Cooperative Inc. estimated
that New York grape farmers lost more than \$10 million in income
because of frost, while Pennsylvania grape farmers lost over \$1 million
(Associated Press, August 19, 2002). Similarly, a February 1994 ice
storm in the Southeast damaged 3.7 million acres of commercial forests
in Mississippi with losses estimated at \$1.6 billion (2004 dollars). In
addition, 25 percent of the state's pecan crop was expected to be lost
for the following 5-10 years at an estimated cost of \$6.6 million per
year, in 2004 dollars (Lott and Sittel 1996).

## 4c. How Snow Monitoring and Forecasts Can Improve on These Values

Some of the costs associated with winter snow events are unavoidable,
but others can be reduced given advanced notice regarding hazardous
conditions. Several studies in the economic literature have attempted to
estimate the value of improvements in general weather and climatic
forecasts, and a few have attempted to estimate the value of winter
weather and climatic forecasts specifically. Examples of these studies
are presented below, by economic sector. Economic values of these
improvements in forecasts are summarized in Table 4.

### 4Ci. Agricultural Benefits 

Frost can damage or kill crops and buds of fruit trees, and extremely
low temperatures can severely damage trees. Accurate advanced warning of
frosts can reduce damages from loss of crops and fruits and reduce
uncertainty regarding the need for frost damage control, thus reducing
the unnecessary use of expensive heaters, wind machines and sprinklers.
Short-term frost forecasts can save producers of high valued crops
thousands of dollars per hectare (Hammer et al., 1996, Katz et al., 1982
and Baquet et. al, 1976). At the individual farm level, imperfect frost
forecasts have a value ranging from \$2,000 to over \$6,000 per hectare
per year (in 2004 dollars) depending on the type of fruit being produced
(Katz et al. 1982 and Baquet et al. 1976). Given that there are over 2
million hectares of orchards in the United States, frost forecasts can
have substantial economic value (USDA, 2004). Improvements in short-term
temperature forecasts could be worth as much as \$9 million (2004
dollars) to fruit producers in the United States NOAA (2002).

A recent study by Wyse (2004) investigated the benefits of improved snow
forecasts to farmers in a federal irrigation project in eastern Oregon.
Improvements in snow coverage and water content estimation for the
watershed resulted in improved farmer decision making, increasing farmer
profits by 20 percent over those realized from existing decision making
procedures.

### 4Cii. Utility Benefits (Hydropower, gas, and electric)

William et al. (1982) estimated the potential benefits of long-range
(one month to one year) streamflow forecasts for the operation of the
Oroville-Thermalito reservoir system of the California State Water
Project. Benefits included hydropower generation, water conservation for
irrigation and other beneficial uses, and decreased seepage damage to
crops. The hydropower benefits were estimated to be between \$0.4 and
0.8 million annually. Water conservation benefits were estimated at
22-24 percent increase in flow releases or approximately 200,000
acre-feet of water. The value of this water savings will depend on the
alternative beneficial use of the conserved water. For example, it may
go to a low valued crop, a high valued crop, or instream flows for
fishing or recreation. Each of these uses will have different economic
values associated with them. However, using a relatively low value of
water to agriculture of \$50 per acre foot, the annual water savings
from this one project would amount to \$10 million per year.

Hamlet et al. (2002) estimated the value of *improved* stream flow
forecasts for Columbia River hydropower, derived from improved
forecasting of the El Nino Southern Oscillation (ENSO) and the Pacific
Decadal Oscillation (PDO). Recent advances in climate forecasting have
enabled forecasters to predict streamflows in the Columbia River basin
six months earlier than forecasts that rely on actual snowpack
measurements. This allows more spot market energy sales to be made in
late summer and fall. Benefits from the improved forecasts were measured
as the value of spot market energy sales that could result from improved
streamflow estimates. The authors estimated that these sales could
increase annual revenue by approximately \$161 million per year (2004
dollars). To put this in perspective, this represents approximately
0.15% of GDP from electric, gas, and sanitary services in the United
States.

As part of a cost/benefit analysis for NOAA's geostational operational
environmental system, benefits to electric utilities and gas providers
were estimated for improvements in the satellite (imager and sounder)
systems. The improvements in the systems would reduce temperature
forecast errors (in winter and summer), and therefore increase the
accuracy of electric and gas utility load demand forecasts. NOAA (2002)
estimated the benefits from these improvements to be approximately \$7.4
million per year for natural gas providers and \$504 million per year
from electric utilities providers. Together this represents
approximately 0.3 % of the GDP from electric, gas, and sanitary services
in the United States.

### 4Ciii. Air, Road and Water Transportation Benefits

Various studies have estimated the potential benefits of improvements in
weather forecasts for transportation by air, ground and water, but most
of the studies do not estimate the value of winter forecasts separate
from other forecasts. For example, studies by Allan et al. (2001), Evans
et al. (1999), Rhoda and Weber (1996), Paul (2001), and Sunderlin and
Paul (2001) estimated the potential benefits of improvements in weather
forecasts for the air transportation industry (Allan et al., 2001; Evans
et al., 1999; Rhoda and Weber, 1996; Paul, 2001; and Sunderlin and Paul,
2001). These studies estimated that various improvements in weather
forecasts can reduce flight delays and accidents resulting in cost
savings between \$25 million and \$590 million per year, in 2004
dollars. However, these studies tended to concentrate on convective
weather systems, rather than winter weather. Similarly, a draft report
by NOAA (2003) estimated that improvements in marine forecasts would
allow vessels to decrease transit times and reduce cargo losses due to
severe weather conditions throughout the year, and could save the
industry \$95 million per year (in 2004 dollars) or approximately 0.7 %
of US GDP from water transportation.

There are a few studies that have investigated the value of improved
winter weather forecasts to the transportation sector. For example, in
Scotland, a study by Smith and Vick (1994), estimated that daily
precipitation predictions from radar imagery could help reduce road
maintenance costs (by reducing the need for de-icing equipment) by
\$35,000 per year (2004 dollars) in just one Regional Council in
Scotland In the United States, a study by NOAA (2002) estimated that
improvements in short-term ice formation and fog condition forecasts
(with at least two hours notice), could save \$29 million per year (2004
dollars) in the trucking industry by enabling the rerouting of trucks.

### 4Civ. Fire Management Benefits

Although it is difficult to estimate the value of snowpack, snowmelt,
and other relevant weather statistics as they pertain to fire
management, this type of information is an important component of fire
management. Knowledge of soil moisture content, timing of snowmelt, the
amount of water within snowpacks, and temperature forecasts allow fire
managers to predict the onset, the severity, and the duration of the
fire season. This has practical value to fire fighting agencies, who
often must commit resources to fire management well before the onset of
the traditional fire season (summer and fall months in the western U.S.)
By anticipating these capital and labor resource needs on a local or
regional level, resources could be assembled in areas of greatest likely
need, reducing the impact of subsequent fires. With early and accurate
predictions, it is thus possible to minimize the economic and ecological
damage from severe fires.

+--------------------+----------------------------+---+----------------+---+
| Table 4. Value of  |                            |   |                |   |
| weather forecasts  |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Type of Weather    | Economic Value of Weather  |   | Source         |   |
| Information        | Information, in 2004       |   |                |   |
|                    | dollars                    |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| [Agricultural      |                            |   |                |   |
| ben                |                            |   |                |   |
| efits]{.underline} |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Frost forecast     | Perfect forecast:          |   | Katz et al.    |   |
|                    |                            |   | (1982)\*       |   |
|                    | \$6,210/hectare/yr for     |   |                |   |
|                    | apple orchards             |   |                |   |
|                    |                            |   |                |   |
|                    | \$3,781/hectare/yr for     |   |                |   |
|                    | pear orchards              |   |                |   |
|                    |                            |   |                |   |
|                    | \$2,076/hectare/yr for     |   |                |   |
|                    | peach orchards             |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Frost forecast     | Imperfect forecast:        |   | Baquet et al.  |   |
|                    | \$2,642/ha-yr for pear     |   | (1976)\*       |   |
|                    | orchards                   |   |                |   |
|                    |                            |   |                |   |
|                    | Perfect forecast:          |   |                |   |
|                    | \$4,203/ha-yr for pear     |   |                |   |
|                    | orchards                   |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Precipitation and  | Imperfect: 20% increase in |   | Hammer et al.  |   |
| frost timing       | profit for wheat producers |   | (1996)\*       |   |
|                    | in Australia               |   |                |   |
|                    |                            |   |                |   |
|                    | Perfect: 15% of value of   |   |                |   |
|                    | perfect forecasts is       |   |                |   |
|                    | achieved by present        |   |                |   |
|                    | forecasts                  |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved satellite | \$9 million/year derived   |   | NOAA (2002)    |   |
| imager and sounder | from improvements in frost |   |                |   |
| which improve      | mitigation                 |   |                |   |
| short-term (3-hr)  |                            |   |                |   |
| temperature        |                            |   |                |   |
| forecasts          |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
|                    |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| [Utilities         |                            |   |                |   |
| ben                |                            |   |                |   |
| efits]{.underline} |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved           | \$0.4 and 0.8 million/year |   | William et al. |   |
| long-range (one    | in hydropower benefits and |   | (1982)         |   |
| month to one year) | 200,000 acre-feet of water |   |                |   |
| streamflow         | savings                    |   |                |   |
| forecasts for the  |                            |   |                |   |
| operation of the   |                            |   |                |   |
| O                  |                            |   |                |   |
| roville-Thermalito |                            |   |                |   |
| reservoir system   |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved           | \$161 million/year derived |   | Hamlet et al.  |   |
| long-range weather | from more efficient        |   | (2002)         |   |
| forecasts of ENSO  | reservoir operations and   |   |                |   |
| and PDO            | hydropower sales on spot   |   |                |   |
|                    | markets                    |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved           | \$10 million/year in       |   | Maurer and     |   |
| long-range         | hydropower benefits (1.8%  |   | Lettenmaier    |   |
| streamflow         | increase)                  |   | (2004)         |   |
| forecasts for the  |                            |   |                |   |
| Missouri River     |                            |   |                |   |
| Basin              |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved satellite | \$7.4 million/year derived |   | NOAA (2002)    |   |
| imager and sounder | from load forecasting      |   |                |   |
| which will improve | efficiency for natural gas |   |                |   |
| max and min        | providers in the US        |   |                |   |
| temperature        |                            |   |                |   |
| predictions        |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved satellite | \$504 million/year Derived |   | NOAA (2002)    |   |
| imager and sounder | from load forecasting      |   |                |   |
| which will improve | efficiency for electric    |   |                |   |
| max and min        | utility providers in the   |   |                |   |
| temperature        | US                         |   |                |   |
| predictions        |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Table 4. Value of  |                            |   |                |   |
| weather forecasts  |                            |   |                |   |
| (continued)        |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Type of Weather    | Economic Value of Weather  |   | Source         |   |
| Information        | Information, in 2004       |   |                |   |
|                    | dollars                    |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| [Air road and      |                            |   |                |   |
| water              |                            |   |                |   |
| transport          |                            |   |                |   |
| ation]{.underline} |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Terminal           | \$580 million/yr           |   | Sunderlin and  |   |
| Convective Weather |                            |   | Paull 2001\*   |   |
| Forecast           | derived from delay         |   |                |   |
|                    | reductions                 |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Integrated Icing   | \$33.7 million/yr          |   | Paull 2001\*   |   |
| Diagnostic         |                            |   |                |   |
| Algorithm          | from reduced accidents     |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improvement of     | \$10 million per year for  |   | Leigh 1995\*   |   |
| Terminal Aerodrome | all Quantas international  |   | and            |   |
| Forecasts          | flights\*                  |   | [              |   |
|                    |                            |   | http://www.esi |   |
|                    | [imperfect:](http://www.e  |   | g.ucar.edu/HP_ |   |
|                    | sig.ucar.edu/HP_rick/attri |   | rick/esig.html |   |
|                    | butes.html#imperfect)\$6.8 |   | Accessed March |   |
|                    | million/year               |   | 1](http:       |   |
|                    |                            |   | //www.esig.uca |   |
|                    | [perfect:](http:           |   | r.edu/HP_rick/ |   |
|                    | //www.esig.ucar.edu/HP_ric |   | esig.html Acce |   |
|                    | k/attributes.html#perfect) |   | ssed March 1), |   |
|                    | \$8.4 million/year\        |   | 2004           |   |
|                    | derived from the Quantas   |   |                |   |
|                    | Airlines avoidance of      |   |                |   |
|                    | carrying extra fuel        |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Integrated         | \$176 million/yr from      |   | Allan et al.   |   |
| terminal weather   | improved decision making   |   | 2001\*         |   |
| system services    | resulting in reduced       |   |                |   |
|                    | gridlock and reduced       |   |                |   |
|                    | delays                     |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Weather sensing    | \$590 million/year from    |   | Evans et al.   |   |
| and forecasting    | national delay reduction   |   | 1999\*         |   |
|                    | benefits (i.e. \$16.7      |   |                |   |
|                    | million SEA, \$25.7        |   |                |   |
|                    | million LAX, \$119 million |   |                |   |
|                    | SFO)                       |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Weather systems    | \$25 million/year from     |   | Rhoda and      |   |
| processor (WSP)    | reduced flight delays as a |   | Weber 1996\*   |   |
| modifications      | result of national         |   |                |   |
|                    | deployment of WSP          |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Type of Weather    | Economic Value of Weather  |   | Source         |   |
| Information        | Information, in 2004       |   |                |   |
|                    | dollars                    |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved satellite | \$29 million/year derived  |   | NOAA (2002)    |   |
| imager and sounder | from rerouting             |   |                |   |
| which will improve | efficiencies in the        |   |                |   |
| predictions of     | trucking industry          |   |                |   |
| short-term ice     |                            |   |                |   |
| formation and fog  |                            |   |                |   |
| conditions         |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Improved satellite | \$95 million/year to       |   | NOAA (2003)    |   |
| imager and sounder | commercial shipping from   |   | Draft          |   |
| which will improve | transit time savings and   |   |                |   |
| marine forecasts   | cargo loss reductions      |   |                |   |
| of winds and waves |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| Daily              | > [imperfect:](http://     |   | Smith and Vick |   |
| precipitation      | www.esig.ucar.edu/HP_rick/ |   | 1994           |   |
| predictions from   | attributes.html#imperfect) |   |                |   |
| radar imagery      | > \$35,000 per year,       |   |                |   |
|                    | > derived from deciding    |   |                |   |
|                    | > whether to apply         |   |                |   |
|                    | > de-icing materials for a |   |                |   |
|                    | > single Regional Council  |   |                |   |
|                    | > in Scotland              |   |                |   |
+--------------------+----------------------------+---+----------------+---+
| \*Values were      |                            |   |                |   |
| obtained from      |                            |   |                |   |
| Exhibit A.2. of    |                            |   |                |   |
| Stratus Consulting |                            |   |                |   |
| report by Lazo et  |                            |   |                |   |
| al. 2003,          |                            |   |                |   |
| additional summary |                            |   |                |   |
| information for    |                            |   |                |   |
| many of these      |                            |   |                |   |
| values is also     |                            |   |                |   |
| available at       |                            |   |                |   |
| http://            |                            |   |                |   |
| www.esig.ucar.edu/ |                            |   |                |   |
| HP_rick/esig.html. |                            |   |                |   |
|                    |                            |   |                |   |
| Note: For ease of  |                            |   |                |   |
| comparison, all    |                            |   |                |   |
| values were        |                            |   |                |   |
| converted to 2004  |                            |   |                |   |
| US dollars using   |                            |   |                |   |
| the CPI from the   |                            |   |                |   |
| Economic Research  |                            |   |                |   |
| Service and        |                            |   |                |   |
| historical         |                            |   |                |   |
| exchange rates     |                            |   |                |   |
| from               |                            |   |                |   |
| <http://           |                            |   |                |   |
| www.x-rates.com/>. |                            |   |                |   |
+--------------------+----------------------------+---+----------------+---+

## 4D. Importance of Climate Change on Snow Values and Forecasts

A series of studies by the US Global Change Research Program (2000),
report that global warming will likely result in less precipitation
falling as snow, existing snowpack melting sooner and faster, and runoff
from snowpack shifting from late spring and summer to late winter and
early spring. According to these studies, changes in the amount, timing,
and distribution of snowfall (and rain) and runoff are also probable.
This could lead to changes in, winter recreation opportunities, water
availability, and competition for water resources. Changes are also
likely in the timing, intensity, and duration of floods, droughts, and
fires, with related changes in water quality. This will have a
significant impact on water management, in terms of water supply for
irrigation and flood protection, and on drought and wildfire planning.
Climate change will also impact natural mountain environments in several
ways. Changes in snowpack and temperature could lead to the loss of
glaciers and mountain meadows. Forests could be affected by fire,
insects and disease disturbance, and tree species could change,
affecting fall foliage. These ecosystem changes would subsequently cause
changes in animal and plant species. To date there has been no research
to examine how changes in mountain landscapes may affect tourism;
however a new study of this issue has just begun in a sample of Canadian
parks (Scott 2003).

Depending on how well each community and industry can adapt, these
changes in climatic conditions have the potential for negative impacts
on U.S. economies. The few studies that have estimated some of these
impacts on winter tourism, coldwater fishing, and water management are
discussed below.

### 4Di. Winter Tourism

Studies estimating the economic impact of climate change on winter
tourism, and the ski industry in particular, have been conducted for
several countries (Australia: Galloway 1998, Konig 1998; Austria:
Breiling et al. 1997; Canada: McBoyle and Wall 1992, Lamothe and Periard
1988; Scotland: Harrison et al. 1999; Switzerland: Konig and Abegg 1977,
Elasser and Burki 2002; United States: Bloomfield and Hamburg 1997). All
of these studies projected negative consequences for the industry (Scott
2003). For example, in Austria (the most tourism dependant developed
country in the world), where 50% of the country's tourism income comes
in the winter, a warming trend (between 1960 and 1990) has caused the
loss of 15 winter sport season days each year (Breiling, 1998). These
lost winter sport days have cost the country an estimated 1.5% drop in
GNP, and as much as a 6% drop in gross local product (Breiling, 1998).

### 4Dii. Coldwater Fishing

Climatic changes affecting snowpack and timing of snowmelt could also
greatly affect conditions of many cold water fish habitats. Reductions
in snowpack will result in warmer streamflow temperatures. This in turn
would reduce the value of recreational fishing days, since warm water
fishing generally has a lower economic value per day of fishing than
coldwater fishing (Martin et al., 1974; Vaughan and Russell, 1982). One
study of the Appalachian Mountains region of North Carolina estimated
that economic losses from the recreational trout fishery due to climate
change would result in an annual economic loss of \$61-584 million (in
1995 dollars), or \$75-722 million in 2004 dollars (Ahn et al. 2000).
This cost may be slightly offset by increases in warm water fishing
recreation, but since warm water fishing is generally less valuable, the
net effect is still likely to be negative.

### 4Diii. Water Management

Snowpack is the primary source of warm-season streamflow for most of the
western United States and thus represents an important source of water
supply (McCabe and Dettinger 2002**).** Melting snow provides over 70%
of the water supply for the western United States (Chang et al., 1987).
Western U.S. seasonal snowpack forecasts under a warming climate depict
a severe loss of snowpack. For example, in California, by 2030,
temperature is projected to rise about 0.6 °C, resulting in a minor
decrease in April snowpack at lower elevations. By 2090, a projected
temperature increase of 2.1 °C would result in a loss of about half of
the average April snowpack storage, reducing spring runoff by 5.6 cubic
km and increasing winter flood peaks (Knowles and Cayan 2002), These
changes in snowpack, and timing of snowmelt could increase the cost of
storing water in the west, and in some regions, increase flood frequency
and amplitude.

Climate change will also impact water management in other areas of the
United States that have water resources dependent on snowmelt. For
example, in the Missouri River Basin, Stone et al. (2001) estimate that
climate change could result in an increase of water yields across the
northern and northwestern portions of the Basin by 70 percent. However,
they also estimate that water yields in some areas in the southeastern
portions of the Basin may decrease by more than 80%, resulting in a net
decrease in water yield at the mouth of the Basin of 10 to 20 percent
during spring and summer months. This net decrease in water during the
agricultural season has the potential to decrease agricultural
production in the area, or to increase water supply and management costs
as reservoir operators and water managers transport water from one
region to another to maintain agricultural productivity.

Climate forecasts for winter weather associated with El Nino events are
also important features in water management, especially in the west. For
example, Pagano et al. (2001) estimated that the Salt River Project in
central Arizona was able to save \$1 million by making long-lead
decisions regarding the need (or lack thereof) for groundwater pumping
to augment reservoir water supplies, based on El Nino related climate
information that predicted absence of precipitation in January, followed
by late season rains that would increase streamflows. This example
illustrates how advanced knowledge of climatic conditions can help
reservoir and water managers prepare for various changes in climatic
conditions and reduce the potential economic costs of regional changes
in water availability and potential costs of lost production by adapting
to the changing conditions in advance.

Pagano et al. (2001) also note that although climate forecasts are a
useful tool for managers, the operational water management community
currently underutilizes them. This is probably also true for other
resource managers as well. To remedy this situation they suggest 1) the
formation of closer relationships between forecasters and users, 2)
introducing forecast use demonstration projects, 3) increasing
institutional flexibility, and 4) creating a regional forum to
facilitate information transfer between the hydro-climatic research
community and operational water managers. These recommendations would
also be useful for other resource managers.

### 4Div. Other Costs

Rising water temperatures and changes in ice cover are of particular
importance to the ecology of lakes, streams, and their biological
communities, and are likely to affect how ecosystems function. For
example, rising temperatures in surface waters would likely make many
freshwater systems unihabitable for coldwater fish species such as
salmon and trout that are already near the temperature threshold of
their viable habitat. Rising temperatures also reduce ecosytem health by
reducing the dissolved oxygen in water. The natural ecosystems of the
Arctic, Great Lakes, Great Basin, Southeast, and the prairie potholes of
the Great Plains appear highly vulnerable to the projected changes in
climate (National Assessment Synthesis Team, 2000). However, assessing
the economic value (or cost) of such changes is complicated by the fact
that change is constant and nature is constantly adapting. What may be
devastating to one species is likely to encourage the expansion of
another that will fill the niche in the system that is disturbed. This
is particularly true for agricultural production. For example, Adams et
al.(1990) project that climate change in will have a positive impact on
the agricultural economy in the northern regions of the United States.

Another difficult cost to assess is the cost of snow related deaths.
Snow-related accidents result in the deaths of hundreds of people in the
United States each year, primarily from traffic accidents, overexertion,
and exposure, but deaths from avalanches have been steadily increasing
(Doesken and Judson, 1997). While there are ethical issues associated
with any attempt to assign a monetary value to a particular individuals
life, statistically-based studies suggest that the value of a life saved
in the U.S. (from a premature death) is in the range of \$5 to\$ 7
million (in 2004 dollars). Such values are routinely used by the U.S EPA
and other regulatory agencies in the design of regulatory policies. A
similar range of values could be used to assign a value to the benefits
of snow information that resulted in the saving of human lives.

# 5. The Value of Snow: Conclusions

Snow has a substantial effect on human behavior and activity and thus
has important economic consequences. Obtaining and distributing
information about snow and winter weather conditions has the potential
to increase positive economic values and decrease negative economic
values associated with snow and winter weather conditions.

The discussions presented in this report included a synthesis of the
economic effects of both snow and other weather related phenomena.
Although there are relatively few economic assessments on snow forecasts
in the literature compared to other weather phenomena, the existing
literature provides unequivocal evidence that 1) snow imposes both
economic costs and benefits to society; 2) these economic effects are
significant in their size relative to other weather phenomena; 3) there
is value in use of current information on snow coverage, snowpack water
potential and other snow metrics in human decision making and 4)
improvements in the accuracy and lead time of snow metrics such as snow
coverage, depth, and water content will improve individual and public
decision making.

It is not possible to assign an aggregate economic cost or benefit
estimate to the use of snow coverage or snow forecasts in various
decision making contexts, due to the limited number of original economic
research studies specific to this issue. Furthermore, it is not possible
to perform a benefit-cost analysis on the products of the Remote Sensing
Center without cost information on their suite of products. However, the
general pattern of values identified here suggests that the value of
snow information is likely to be very high. For example, summary table 5
contains sector-level estimates of the economic impacts (benefits and
costs) of snow and related winter events. Economic values in excess of
one billion dollars are reported for many of these sectors and events.
Specifically, the economic damages and benefits associated with snow and
snowmelt gleaned from the literature are large relative to many other
weather phenomena. For example, the studies evaluated here suggest that
the aggregate positive impacts (benefits) of snow to the U.S. economy
range from \$50 to \$400 billion per year (in 2004 dollars). The range
in values is a function of how much value one chooses to assign to the
water content of snow packs in the western United States, with the upper
value reflecting the "reservoir value" provided by existing snowpack.
Conversely, the empirical evidence indicates that the aggregate negative
impacts (costs) of snow events to cities, transportation, and utilities,
ranges from \$15 to \$50 billion per year. The range of cost estimates
reflects underlying assumptions used in the quantification process, most
importantly the frequency and intensity of snow events.

  --------------------------- -------------------------------------------
  **Table 5. Examples of the  
  Economic Impacts            
  (Benefits/Costs) of Snow    
  and Snow Events (in 2004    
  dollars)**                  

  **Economic Benefits of      
  Snow**                      

  Winter tourism              Exceeds \$8 billion/yr in New England and
                              Rocky Mountains

  Cold water fishing          Exceeds \$2.3 billion/yr in New England

  Snowpack water storage      Up to \$348 billion/yr in western U.S.

  **Economic Costs of Snow**  

  Snow Removal                Exceeds \$2 billion/yr for U.S.

  Road closures that cause    Exceeds \$10 billion/day for closures in
  lost retail trade, wages,   eastern U.S.
  and tax revenue             

  Flight delays               \$3.2 billion annually for U.S. carriers

  Damage to utilities         Up to \$2 billion per event

  Flooding from snowmelt      \$4.3 billion for 1997 floods

  Cost to agriculture and     Up to \$1.6 billion per ice storm
  timber from frost and ice   
  --------------------------- -------------------------------------------

Summary Table 6 contains estimates of the value of improved weather
forecasts, with emphasis on winter events. Although there are no
published reports on the specific value of snow forecasts, the table
clearly demonstrates that forecasts of winter weather events can have
substantial economic value to a range of economic sectors if used in
decision making. Specifically, the results in Table 6 suggest that a
conservative estimate of the value of current forecast information
across these sectors (excluding agriculture) would exceed exceed 1
billion dollars per year. Continued improvements in forecast accuracy
and lead time are expected to increase these values.

  --------------------------- -------------------------------------------
  **Table 6. Examples of the  
  value of weather forecasts  
  (in 2004 dollars)**         

  **Agriculture**             

  Improvements in frost       Up to \$6,000/hectare/yr for fruit orchards
  forecasts                   

  **Utilities**               

  Improved long-range (1 mo   Over \$170 million/year in hydropower
  -- 1 yr) streamflow         benefits for three river systems
  forecasts                   

  Improved temperature        Over \$500 million/year from natural gas
  predictions                 and electric utility providers

  **Air, road and water       
  transportation**            

  Weather                     Exceeds \$600 million/yr at U.S. airports
  sensing/forecasting, and    
  icing diagnostics at        
  airports                    

  Improved predictions of     Exceeds \$29 million/yr from rerouting
  road ice formation and fog  trucks in U.S.

  Improved marine forecasts   Exceeds \$95 million/yr from transit time
  of winds and waves          savings and cargo loss reductions in U.S.
                              coastal waters
  --------------------------- -------------------------------------------

Given the substantial economic effects of snow, even small improvements
in decision making from the use of snow coverage and other snow metrics
are expected to lead to substantial economic gains to a broad range of
economic sectors. For example, even modest improvements in streamflow
forecasts are of potential value to the 25 million irrigated
agricultural acres in the western United States. The one study to
address the relationship between snow coverage and improved streamflow
accuracy showed gains in irrigator profits of approximately 20 percent
(compared with a strategy of using historical water year probabilities).
Similarly, small improvements in monitoring the location and depth of
snowfall are likely to have value to the transportation sector and
cities in terms of allocating snow removal equipment and other
resources. The literature also suggests that snow is one weather
phenomenon that is likely to be affected substantially by global climate
change. If global climate change occurs, as some GCM-based analyses
suggest, the value of information on snow metrics is expected to
increase, given the increased level of climatic uncertainty embedded in
most climate change scenarios. This should enhance the value of and
demand for snow metrics.

In summary, the potential economic value of snow monitoring and
forecasting appears to be at least equivalent to the value of an ENSO
monitoring and reporting system, which has been estimated to be over one
billion dollars per year (Adams et al, 2001). While we do not have
sufficient information to perform a comprehensive benefit-cost analysis,
it is likely that the value to society of a snow monitoring and
reporting system is substantially greater than the costs of this system.

# References

Adams, R.M., C. Rosenzweig, J. Ritchie, R. Peart, J.D. Glyer, B.A.
McCarl, B. Curry, and J. Jones. 1990. "Global Climate Change and U.S.
Agriculture". Nature. May (219-224).

Ahn, S. De Steiguer, J. Palmquist, R. and Holmes, T. (2000). "Economic
analysis of the

potential impact of climate change on recreational trout fishing in the
southern Appalachian Mountains". Climate Change, 45, 493-509.

Ahnert, Peter R.*,* Jeff S. Waldstreicher, and John F. Chiaramonte.
1997. "Integrating new data and technology into operations: forecasting
the snowmelt flood of January 19-20, 1996". Paper presented at the
American Meteorological Society's 13th International Conference on
Interactive Information and Processing Systems (IIPS) for Meteorology,
Oceanography, and Hydrology, February 2-7, 1997, in Long Beach,
California.

Alan, S.S., S.G. Gaddy, and J.E. Evans. 2001. "Delay Causality and
Reduction at the New York City Airports Using Terminal Weather
Information Systems". Lexington, MA:MIT Lincoln Laboratory. 2001

Associated Press. August 19, 2002. "May frost drained juice from grape
crop in Pa. ![](media/image2.png){width="1.0416666666666666e-2in"
height="5.2083333333333336e-2in"}The spell was sporadic, leaving some
growers with bare vines and others with flush ones".
![](media/image2.png){width="9.027777777777777e-3in"
height="0.10486111111111111in"}By Charles Sheehan, Associated Press
Posted on Mon, Aug. 19, 2002.

Baquet, A.E., A.N. Halter, and F.S. Conklin. 1976. "The value of frost
forecasting: A Bayesian appraisal". American Journal of Agricultural
Economics, 58:511-520.

Billings Gazette, January 20, 2002. "Tourism's cold cash keeps Montana,
Wyoming warm in winter". by Brett French Gazette outdoor writer
(<http://www.billingsgazette.com/index.php?display=rednews/2002/01/20/build/local/zmain.inc>).

Bloomfield J., and S. Hamburg. 1997. "Seasons of change: global warming
and New England's White Mountains". Washington D.C.: Environmental
Defense Fund.

Breiling M, P. Charamza, and O. Skago. 1997. "Klimasensibilitat
osterreichischer bezirke mit besonderer berucksichtigung des
wintertourismus". Report 97:1. Institute for Landscape Planning, Alnarp,
Austria.

Breiling, Meinhard. 1998. "The roll of snow cover in Austrian economy
during 1965 and 1995 and possible consequences under a situation of
temperature change". University of Tokyo, Graduate School of
Agricultural and Life Sciences, Department of Biologic al and
Environmental Engineering, Laboratory of Land Resource Sciences. Paper
presented at Conference of Japanese Snow and Ice Society, Niigata.
October 1998.

Caleb Technologies, Press release January 12, 2001. "CALEB™ CrewSolver™
Helps Continental Recover Newark Operations Quickly".

http://www.calebtech.com/press/pr_01122001.shtml, accessed June 28,
2004.

Commonwealth of Pennsylvania 3/2/2004)
http://www.governor.state.pa.us/governor/cwp/view.asp?a=3&q=436446

Chang, A. T. C., J. L. Foster, P. Gloersen, W. J. Campbell, E. G.
Josberger, A. Rango and Z. F. Danes. 1987. Estimating snowpack
parameters in the Colorado River basin. In: Proc. Large Scale Effects of
Seasonal Snow Cover, IAHS Pub. No. 166, 343-353.

Doesken, Nolan J. and Arthur Judson, 1997. "The Snow Booklet: A Guide to
the Science, Climatology, and Measurement of Snow in the United States".
Colorado Climate Center, Department of Atmospheric Science, Solorado
State University, Fort Collins, CO. p. 86.

Detroit News, December 24, 2000. "Snow boosts winter tourism State
officials expect a long, lucrative season as people visit skiing lodges,
trails". By Jeremy Pearce, The Detroit News,
http://www.detnews.com/2000/metro/0012/25/c01-166744.htm

Elsasser, H. and R. Burki. 2002. "Climate change as a threat to tourism
in the Alps". Climate Research, 20:253-257.

Evans, J.E., T.J. Dacey, D.A. Rhoda, R.E. Cole, F.W. Wilson, E.R.
Williams. 1999. "Weather Sensing and Data Fusion to Improve Safety and
Reduce Delays at Major West Coast Airports". Lexington, MA:MIT Lincoln
Laboratory.

Galloway, R.W. 1988. "The potential impact of climate changes on
Australian ski fields". In: Pearman Gl (ed) Greenhouse: Planning for
climatic change. CSIRO, Melbourne, Australia, 428-437.

Hamlet, Alan F., Daniel Huppert, and Dennis P. Lettenmaier. 2002.
"Economic Value of Long-Lead Streamflow Forecasts for Columbia River
Hydropower". Journal of Water Resources Planning and Management,
March/April 2002:91:101.

Hammer, G.L., D. P. Holzworth, and R. Stone. 1996. "The value of skill
in seasonal climate forecasting to wheat crop management in a region
with high climatic variability". Australian Journal of Agricultural
Research 47:717-737.

Harrison, S.J., S.J. Winterbottom, C. Sheppard. 1999. "The potential
effects of climate change on the Scottish tourist industry". Tourism
Management, 20:203-211.

Hayhurst, John. 2001. Air Traffic Management News Room and Resources,
"Developing the Next Generation Air Traffic Management System".
Testimony of John Hayhurst President, Air Traffic Management, The Boeing
Company before the U.S. House of Representatives Committee on Science
Space and Aeronautics Subcommittee July 19, 2001.
http://www.boeing.com/atm/resources/testimony.html, accessed June 23,
2004.

Houston, Laurie L, Richard M. Adams, and Rodney F. Weiher. 2004. "The
Economic Benefits of Weather Forecasts: Implications for Investments in
Rusohydormet Services". Report prepared for the World Bank under NOAA
Contract. Submitted April 2004. p. 28.

Houston, L. L., W. Michio, J.D. Kline, and R.J. Alig. 2003. "Past and
future water use in Pacific Coast States". Forest Service, Pacific
Northwest Research Station, General Technical Report, PNW-GTR-5888. p.
37.

International Red Cross. 2003. "World Disasters Report 2003".
International Federation of Red Cross and Red Crescent Societies.
http://www.ifrc.org/publicat/wdr2003/ accessed, June 21, 2004.

Katz, R.W., A.H. Murphy, and R.L. Winkler. 1982. "Assessing the value of
frost forecasts to orchardists: A dynamic decision-making approach,
Applied Meteorology, 21:72-82.

Katz, R.W. and A.H. Murphy, Economic Value of Weather and Climate
Forecasts. Cambridge University Press, 222 pp, 1997.

Knowles, Noah, and Daniel R. Cayan. 2002. "Potential effects of global
warming on the Sacramento/San Joaquin watershed and the San Francisco
estuary". Geophysical Research Letters, 29(18).

Knutson, C. 2001. "A comparison of droughts, floods and hurricanes in
the U.S". National Drought Mitigation Center, Lincoln, NE, USA.
http://enso.unl.edu/ndmc/impacts/compare.htm.

Konig, U. 1998. "Tourism in a warmer world: implications of climate
change due to enhanced greenhouse effect for the ski industry in the
Australian Alps". Wirtschaftsgeorgraphie und Raumplanung, Vol. 28,
University of Zurich, Zurich, Switzerland.

Konig, U. and B. Abegg. 1977. "Impacts of climate change on tourism in
the Swill Alps". Journal of Sustainable Tourism f(1):46-58.

Lamonthe, and Periard. 1988. Implications of climate change for downhill
skiing in Quebec". Climate Change Digest 88-03, Environment Canada,
Ottawa, Canada.

Lave, L.B. 1963. "The value of better weather information to the raisin
industry". Econometrica, 31(1):151-164.

Lott, J. Neal., and Mathew C. Sittel, 1996. "The February 1994 Ice Storm
in the Southeastern United States". National Climatic Data Center
(NCDC), http://www1.ncdc.noaa.gov/pub/data/special/iwais96.pdf, accessed
June 20, 2004.

McBoyle, G.R. and G. Wall. 1992. "Great lakes skiing and climate
change". In: Gill, A. and R. Hartmann (eds) Mountain resort development.
Centre for Tourism Policy and Research, Simon Fraser University,
Burnaby, Canada, pp 70-81.

Marshall, Alfred. 1920. "Principles of economics; an introductory
volume". London, Macmillan. 871 p.

Martin, w., R. Gum, and A. Smith. 1974. "The demand for and value of
hunting, fishing, and general rural outdoor recreation in Arizona". The
Agricultural Experiment Station Technical Bulletin 211, 56p. University
of Arizona, Tucson.

McCabe, G.J., and M.D. Dettinger. 2002. "Primary Modes and
Predictability of Year-to-Year Snowpack Variations in the Western United
States from Teleconnections with Pacific Ocean Climate". Journal of
Hydrometeorology, 3(1):13--25.

http://ams.allenpress.com/amsonline/?request=get-abstract&issn=1525-7541&volume=003&issue=01&page=0013

McDowell Group, 1999. "Alaska visitor industry economic impact study".
Prepared for: Division of Tourism Alaska Department of Commerce and
Economic Development.

Millsaps, S. W., and P.A. Groothuis. 2003. "The economic impact of the
North Carolina ski areas on the economy of North Carolina 2002-2003
season". Prepared for the North Carolina Ski Areas Association.

Murphy, A. H. "Socio-economic value of climate forecasts". Report
PES-94-1 to Economics Group, Office of the Chief Scientist, NOAA.
Prediction and Evaluation Systems, Corvallis, Oregon. May 1994 32 pp.

National Assessment Synthesis Team. 2000. "Climate Change Impacts on the
United States:\
The Potential Consequences of Climate Variability and Change Overview: 
Water". US Global Change Research Program, Published in 2000.
<http://www.usgcrp.gov/usgcrp/Library/nationalassessment/overviewwater.htm>,
accessed, June 8, 2004

National Climatic Data Center (NCDC). 2004a. "Billion Dollar U.S.
Weather Disasters, 1980-2003". National Oceanic and Atmospheric
Administration, National Climatic Data Center, February 03, 2004.
http://www.ncdc.noaa.gov/oa/reports/billionz.html, accessed June 8,
2004.

NCDC. 2004b. "U.S. Snow Monitoring, Number of Days with Snow Maps".
http://www.ncdc.noaa.gov/oa/climate/research/snow/days.html, accessed
June 8, 2004.

NCDC. 6/2/2004. "Comparative Climatic Data Publication for over 250
Cities: Snowfall, average total in inches".
http://www.ncdc.noaa.gov/oa/climate/online/ccd/snowfall.html, accessed
June 2, 2004.

National Oceanic and Atmospheric Administration (NOAA), Geostationary
operational environmental satellite system (GEOS) GOES-R sounder and
imager cost/benefit analysis (CBA) -- Phase III. Prepared for the
Department of Commerce by National Oceanic and Atmospheric
Administration (NOAA), 2003. National Environmental Satellite, Data, and
Information Service (NESDIS), Office of Systems Development.

NOAA. 2002. "Geostationary operational environmental satellite system
(GEOS) GOES-R sounder and imager cost/benefit analysis (CBA)". Prepared
for the Department of Commerce by National Oceanic and Atmospheric
Administration (NOAA), National Environmental Satellite, Data, and
Information Service (NESDIS), Office of Systems Development, November
15, 2002.

Natural Resource Conservation Service (NRCS, June 4, 2004), National
Water and Climate Center "Snow surveys and water supply forecasting,
Agricultural Information Bulletin536".
http://www.wcc.nrcs.usda.gov/factpub/aib536.html, accessed June 4, 2004.

National Snow and Ice Data Center.2004. "2004. Snow Facts".
http://www-nsidc.colorado.edu/snow/facts.html, Accessed June 6, 2004.

Nicholls, J.M.1996. "Economic and social benefits of climatological
information and services: a review of existing assessments". World
Meteorological Organization, Geneva, Switzerland. WMO/TD-No. 780. 38 pp.

North Carolina State University. 2000. "Economic, Ecological Impact of
Recent Snows May Be Felt for Months, Feb. 1, 2000, NC State University
News Release
http://www2.ncsu.edu/ncsu/univ_relations/news_services/press_releases/00_01/18.htm

Pagano, Thomas C., Holly C. Hartmann, and Soroosh Sorooshian. 2001.
"Using climate forecasts for water management". American Water Resources
Association 37(5)[, pages
1139-1153](http://www.awra.org/jawra/toc/toc_37_5.html).

Parker water and sanitation district, Colorado. http://www.pwsd.org/,
accessed June 12^th^, 2004.

Paull, G. 2001. "Integrated Icing Diagnostic Algorithm (IIDA) safety
benefits analysis accident case studies". Prepared for the FAA by MCR
Federal, Inc. Bedford. MA. 2001

Pricewaterhouse Coopers. 2000. "The economic impact of downhill skiing
at Alberta's rocky mountain ski resorts". Presented to: Alberta Economic
Development. Department of Economics Appalachian State University.

Reinheimer, Jason. 2004. "Breeding for reproductive frost tolerance in
barley" Research Update for Advisers - Southern Region". South
Australian Barley Improvement Program, University of Adelaide, Waite
Campus, Adelaide
http://www.grdc.com.au/growers/res_upd/south/04/reinheimer.htm

Rhoda, D.A. and Me. Weber. 1996. "Assessment of Delay Aversion Benefits
of the Airport Surveillance Radar (ASR) Weather Systems Processor (WSP)
-- Project Report". Lexington, MA:MIT Lincoln Laboratory.

Samuelson, Paul. A. 1947. "Foundations of Economic Analysis". Cambridge,
Harvard University Press. 447 p.

Salt Institute. 2000. "Recent Snowstorm cost over \$5 billion... snow
fighting agencies still not prepared to meet customer demands". January
25, 2000. <http://www.saltinstitute.org/pubstat/storm1-25-00.html>.

Scott, Daniel, 2003. "Climate change and tourism in the mountain regions
of North America". Presented at the 1^st^ International Conference on
Climate Change and Tourism, Djerbga, Tunisia, 9-11 April 2003.

Sinclair, M. 2001. "The tourism sector and climate change in the
Northeast: The need for a green resort effort". Proceedings of the
Conference of the New England Governors and the Eastern Canadian
Premiers. Fredericton, New Brunswick, Canada. 29 March 2001.

Skipackages.com. 2004a.
http://www.skipackages.com/vermontskipackages.htm. Accessed June 8,
2004.

Skipackages.com. 2004b. http://www.skipackages.com/newyork.htm. Accessed
June 8, 2004.

Smith, Adam. 1776. "An Inquiry into the Nature and Causes of the Wealth
of Nations".\
Fifth edition (1789), London, Methuen & Co., Ltd.

Smith, K. and Vick, S.D. 1994. "Valuing weather radar benefits for
winter road maintenance: a practical case example". Meteorological
Applications, 1:173-177.

Snyder, R.L. 2004. "Worldwide frost problems and frost protection".
[International Society for Agricultural Meteorology - Needs for
agrometeorological solutions to farming
problems](http://www.agrometeorology.org/needs.html), May 14 2004.
http://www.agrometeorology.org/needs.html, accessed June 21, 2004.

Sonka, S.T., P.J. Lamb, S.B. Hollinger, and J.W. Mjelde. 1986. "Economic
use of weather and climate information: concepts and an agricultural
example". Journal of Climatology, 6:447-457.

South Carolina Department of Natural Resources. 2002. "New
Delayed-Harvest Waters". Written by Greg Lucas, *Edited by Bill Byrd.*
News Release #02 -- 156, July 8, 2002.
http://www.byrdultrafly.com/dhsc.htm

Stone, [Mark
C.](http://www.awra.org/jawra/authors/author_StoneMarkC.html), [Rollin
H.
Hotchkiss](http://www.awra.org/jawra/authors/author_HotchkissRollinH.html),
Carter M. Hubbard, [Thomas A.
Fontaine](http://www.awra.org/jawra/authors/author_FontaineThomasA.html),
Linda O. Mearns, and Jeff G. Arnold. 2001. "Impacts of Climate Change on
Missouri River Basin Water Yield". American Water Resources Association
37(5) [pages 1119-1129](http://www.awra.org/jawra/toc/toc_37_5.html).

Sunderlin, J. and G. Paull. 2001. "FAA Terminal convective Weather
Forecast Benefits analysis". Prepared for the FAA by MCR Federal, Inc.
Bedford, MA.

Taylor, David T. 1999. "Economic importance of the winter season to Park
County, Wyoming". University of Wyoming, Cooperative Extension Service,
College of Agriculture, Department of Agricultural and Applied
Economics. 10 Pages.

Todhunter, Paul E. 2001. "A hydroclimatological analysis of the Red
River of the north snowmelt flood catastrophe of 1997". American Water
Resources Association 37(5)[, pages
1263-1278](http://www.awra.org/jawra/toc/toc_37_5.html).

Todhunter, Paul. 1998. "Flood Hazard in the Red River Valley: A Case
Study of the Grand Forks Flood of 1997". North Dakota Quarterly
65(4):254-275.

Tomek, W.G. 1993. "Confirmation and replication in empirical
econometrics". American Journal of Agricultural Economics. 75:6-14.

United States Department of Agriculture (USDA) National Agricultural
Statistics Service, "2002 Census of Agriculture". Volume 1, Chapter 1:
U.S. National Level Data, Table 1. Historical Highlights: 2002 and
Earlier Census Years.
http://www.nass.usda.gov/census/census02/volume1/us/index1.htm, accessed
June 29, 2004.

USDA, Natural Resource Conservation Service
http://www.wcc.nrcs.usda.gov/factpub/fcst_s2.htm accessed June 2, 2004

U.S. Department of Commerce. 1998. "Red River of the North 1997 Floods".
Service Assessment and Hydraulic Analysis, NOAA, NWS, 35 pp.

U.S. Department of the Interior, Fish and Wildlife Service, and
Department of Commerce, U.S. Census Bureau. 2002. "2001 national survey
of fishing, hunting, and wildlife-associated recreation". issued October
2002.

U.S. Department of the Interior, Bureau of Reclamation. Hoover Dam

http://www.usbr.gov/lc/hooverdam/, accessed June 6^th^, 2004.

U.S. Department of the Interior, Bureau of Reclamation. Glen Canyon Dam

http://www.usbr.gov/uc/news/gcdfacts.html, accessed June 6^th^, 2004.

USparks about.com. 2004. "South Dakota's Best Winter State Parks

The Top Choices Offer Snowmobiling, Winter Camping, and Ice Fishing".  
http://usparks.about.com/blbestwinter-sd.htm. Accessed June 4, 2004.

United States Fish and Wildlife Service. 2003. "Net economic values for
wildlife-related recreation in 2001, addendum to the 2001 national
survey of fishing, hunting, and wildlife-associated recreation". Report
2001-3, September 2003.

Vaughan, W. and C. Russell. 1982. "Valuing a fishing day: An application
of a systematic varying parameter model". Land Economics 58(4):540-463.

Washington State Tourism. 2004. Experience Washington, the official site
of Washington State Tourism. Things to do-skiing and snowboarding.
http://www.tourism.wa.gov/TTD_Activity_T32.html. Accessed June 4, 2004.

William, W-G. Yeh, M. ASCE, Leonard Becker, and Robert Zettlemoyer.
1982. "Worth of Inflow Forecast for Reservoir Operation" Journal of
Water Resources Planning and Management Division, ASCE, October 1982,
108-WR3:257-269.

Wyse, Barbara. 2004. "The effects of water reductions on farm-level and
regional economic activity: the case of Malheur county, Oregon".
Unpublished MS thesis, Oregon State University, Corvallis, OR.

Wood, N.E., and K. Liang. 2001. "The economic impact of tourism in
Vermont: Fall". Prepared for the Vermont Department of Tourism and
Marketing by The University of Vermont, Department of Community
Development and Applied Economics, and Vermont Tourism Data
Center-School of Natural Resources. p. 17.

[^1]: Professor, Agricultural and Resource Economics, Oregon State
    University, Corvallis, OR

[^2]: Post-graduate Research Assistant, Oregon State University,
    Corvallis OR

[^3]: Chief Economist, National Oceanic and Atmospheric Administration,
    Washington, DC