A STOCHASTIC SNOW MODEL by Lawrence Ernest Cary

A STOCHASTIC SNOW MODEL by Lawrence Ernest Cary
A STOCHASTIC SNOW MODEL
by
Lawrence Ernest Cary
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF WATERSHED MANAGEMENT
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
1974
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
I hereby recommend that this dissertation prepared under my
direction by LAWRENCE ERNEST CARY
entitled
A STOCHASTIC SNOW MODEL
be accepted as fulfilling the dissertation requirement of the
degree of
DOCTOR OF PHILOSOPHY
Date
After inspection
inspection of the final copy of the dissertation, the
following members of the Final Examination Committee concur in
its approval and recommend its acceptance:
5
/
Atira
i? 7V
17-c, ,/y>,-
his approval and acceptance is contingent on the candidate's
adequate performance and defense of this dissertation at the
final oral examination. The inclusion of this sheet bound into
the library copy of the dissertation is evidence of satisfactory
performance at the final examination.
STATEMENT BY AUTHOR
This
requirements
is deposited
rowers under
dissertation has been submitted in partial fulfillment of
for an advanced degree at The University of Arizona and
in the University Library to be made available to borrules of the Library.
Brief quotations from this dissertation are allowable without
special permission, provided that accurate acknowledgment of source
is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by
the head of the major department or the Dean of the Graduate College
when in his judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission
must be obtained from the author.
SIGNED:
i zzle44„: ,i,e_e
1/./
ACKNOWLEDGMENTS
The author wishes to express his sincere appreciation to Dr.
M. M. Fogel, dissertation director, for furnishing guidance and counsel
throughout the program. To Dr, V. K. Gupta for his assistance in the
model development, his instruction and availability for consultation,
the author is deeply indebted. Dr. Gupta's interest in the project and
desire to help were responsible, in great part, for its successful completion.
The author is also deeply indebted to Dr. C. C. Kisiel and Dr.
L. Duckstein with whom many useful discussions were held and who
furnished many valuable suggestions.
To Dr. D. B. Thorud, major advisor, the author is indebted for
continuous support throughout the graduate program.
The author also extends his appreciation to Dr. D. D. Evans and
Dr. J. L. Thames for serving on the graduate committee and for reading
and critiqueing the manuscript.
The National Weather Service is acknowledged for its collection
of climatological data, without which, studies such as this could not be
carried out.
TABLE OF CONTENTS
Page
LIST OF ILLUSTRATIONS LIST OF TABLES viii
ABSTRACT
ix
CHAPTER
1.
INTRODUCTION 1
2.
A REVIEW OF LITERATURE 5
Probabilistic Precipitation Models in General . • •
Some Pertinent Stochastic Precipitation Models • •
Probabilistic and Statistical Snow Studies 3.
18
THEORETICAL CONSIDERATIONS Storm Frequency and Magnitude
Snowpack Accumulation and Ablation Snowpack Duration and Ablation Frequency 4.
18
22
26
EXPONENTIALLY DISTRIBUTED SNOW STORM MAGNITUDES
Snow Storm Precipitation Snowpack Ablation Rate Total Snowfall Time to Snow Disappearance Snowpack Duration The Snow-Free, Snow Cycles The Snow Renewal Process Unconditional Probability Distributions
Analysis of Parameters 5.
5
8
10
APPLICATION TO A CLIMATOLOGICAL STATION
Station Selection Snow Storms Storm Magnitudes Snowpack Ablation Rate 35
35
36
38
41
42
44
48
49
51
55
55
56
68
73
iv
TABLE OF CONTENTS--Continued
Page
Total Snow Water Equivalent
The Snow-Free Periods
Snowpack Duration
Snow-Free, Snow Cycles
The Snow Renewal Process
6.
81
85
88
92
96
106
106
110
SUMMARY AND RECOMMENDATIONS
Summary of Results
Recommendations for Further Studies
APPENDIX A: INVERSION OF THE LAPLACE TRANSFORMS OF Y, T AND Tn .
APPENDIX B: PROGRAMMING THE DISTRIBUTION FUNCTIONS OF
X(t), Y AND Tn
112
REFERENCES
118
131
LIST OF ILLUSTRATIONS
Page
Figure
3.1
The snowfall, accumulation and ablation process
19
24
3.2
The snow process generated by a storm of magnitude u . .
3.3
The snow process in general
4.1
The ratio p as a function of y
53
5.1
The observed distribution functions of storm interarrival
times for three 40-day periods and the 120-day season . .
60
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
30
The observed and theoretical distribution functions of
storm interarrival times
63
The Poisson parameter estimate for six 20-day intervals,
three 40-day intervals and the 120-day season
65
Two estimates of the Poisson parameter for the 120-day
season using cumulative years of record
The observed distribution functions of storm snow water
equivalent for three 40-day periods, the 120-day season
and the theoretical distribution function
66
69
The parameter of the exponential distribution of storm
magnitudes estimated using cumulative years of record . .
72
The empirical distribution of snowpack ablation rates
with minimum rate computed using X = .097
The empirical distribution of snowpack ablation rates
with minimum rate computed using X = .123
Daily snowpack ablation rates estimated for the eight
consecutive 15-day intervals included in the 120-day
season
The observed and theoretical distribution functions of
the 120-day season total snow water equivalent
vi
76
77
80
83
vii
LIST OF ILLUSTRATIONS--Continued
Figure
5.11
5.12
Page
The observed and theoretical distribution functions of
snow-free periods 86
The observed distribution and computed distributions of
snowpack duration (Y)
5.13 The observed and theoretical distribution functions of
the snow-free, snow cycles
5.14 The observed and theoretical probability mass functions
of the number of renewals in a 20-day interval
5.15
5.16
5.17
The observed and theoretical probability mass functions
of the number of renewals in a 40-day interval
The observed and theoretical probability mass functions
of the number of renewals in a 60-day interval
90
94
98
99
100
The observed and theoretical mean value functions of the
snow renewal process
103
5.18 The observed and theoretical variances of the snow
renewal process
104
LIST OF TABLES
Table
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
Page
Mean monthly precipitation and estimated water equivalent
of mean monthly snowfall at Flagstaff, Arizona
57
Mean and variance of the number of storms and the storm
interarrival times at Flagstaff, Arizona
62
A summary of the linear regression analyses of snowpack
water equivalent on days after the termination of storms /
at Flagstaff, Arizona
74
A summary of the linear regression analysis of snowpack
water equivalent on days for eight 15-day periods within
the winter snow season at Flagstaff, Arizona
79
The observed and theoretical means and variances of
total seasonal snow water equivalent at Flagstaff,
Arizona
84
The observed and theoretical means and variances of the
snow-free periods at Flagstaff, Arizona
87
The observed and theoretical means and variances of the
snowpack duration at Flagstaff, Arizona
91
The observed and theoretical means and variances of the
snow-free, snow cycles at Flagstaff, Arizona
95
Chi-squared tests of the probability mass functions of
the snow renewal process
101
viii
ABSTRACT
The purpose of this study was to develop a stochastic model of
the snowfall, snow accumulation and ablation process.
Snow storms occurring in a fixed interval were assumed to be a
homogeneous Poisson process with intensity X. The snow storm magnitudes
were assumed to be independent and identically distributed random variables. The magnitudes were independent of the number of storms and
concentrated at the storm termination epochs. The snow water equivalent
from all storms was a compound Poisson process.
In the model, storms then occurred as positive jumps whose magnitudes equaled the storm amounts. Between storms, the snowpack ablated
at a constant rate. Random variables characterizing this process were
defined. The time to the occurrence of the first snowpack, generated by
the first storm, was a random variable, the first snow-free period.
The snowpack lasted for a random duration, the first snowpack duration.
The alternating sequence of snow-free periods followed by snowpacks of
random duration continued throughout the fixed interval. The snow-free
periods were independent and identically distributed random variables as
were the snowpack durations. The sum of each snow-free period and the
immediately following snowpack duration formed another sequence of independent and identically distributed random variables, the snow-free,
snow cycles. The snow-free, snow cycles represented the interarrival
times between epochs of complete ablation, and thus defined a secondary
ix
renewal process. This process, called the snow renewal process, gave
the number of times the snowpacks ablated in the interval.
Distribution functions of the random variables were derived.
The snow-free periods were exponentially distributed. The distribution
function of the snowpack durations was obtained using some results from
queueing theory. The distribution function of the first snow-free, snow
cycle was derived by convoluting the density function of the first snowfree period and the first snowpack duration. The distribution of the
sum of n snow-free, snow cycles was then the n-fold convolution of the
first snow-free, snow cycle with itself. The probability mass function
of the snow renewal process was evaluated numerically, from a known relationship with the sum of snow-free, snow cycles.
The snowpack ablation rate was considered to be a random variable,
constant within a season, but varying between seasons. The snowpack
durations and snow-free, snow cycles were conditioned on the ablation
rate, then unconditional distributions derived.
An application of the model was made in the case where snow
storm magnitudes were exponentially distributed. Specific expressions
for the distribution functions of the random variables were obtained.
These distributions were functions of the Poisson parameter X, the exponential parameter of storm magnitudes, Ne l , and the snowpack ablation
rate.
The snow model was compared with data from the climatological
station at Flagstaff, Arizona. Snow storms were defined as sequences
of days receiving 0.01 inch or more of snow water equivalent separated
xi
from other storms by one or more dry days. Snow storms occurred
approximately as a homogeneous Poisson process. Storm magnitudes
were exponentially distributed. Empirical distributions of snowpack
ablation rates were obtained as the coefficients of a regression
analysis of snowpack ablation. Two methods of estimating the Poisson
parameter were used. The theoretical distribution functions were compared with the observed. The method of moments estimate generally gave
more satisfactory results than the second estimate.
CHAPTER 1
INTRODUCTION
Often information is sought on future snow occurrences. The
planning of new dams on snow fed streams and the optimum operation of
present water supply systems depends upon estimates of these future
occurrences. Watershed, forest and range management plans for land
areas within snow zones can be affected by future snow events. Structural engineers require snow load data for snow zone construction. Snow
data has also been increasingly sought by planners and operators of
winter sports areas.
Many hydrologic phenomena are probabilistic in nature, since,
given the present, future events cannot be predicted with certainty.
Recently, the theory of stochastic processes has been used to describe
hydrologic processes and obtain the distribution functions of the defined random variables. Several investigators have applied stochastic
process theory to the modeling of the rainfall process, but few attempts
have been made to model the snowfall, snow accumulation and ablation
process.
Snow, when viewed as a stochastic process, poses some diffi7
culties in addition to those encountered in modeling rainfall. Unlike
rain, snow may accumulate on the ground where it remains for a variable
length of time. The snowpack may then be comprised of snow from one or
1
2
more storms. Snowpack ablation may occur soon after snow falls or after
a lag of weeks or months.
The objective of this study is to consider the process of snowfall, snow accumulation and ablation as a stochastic process. The
process is then analyzed under a set of assumptions that are largely
motivated phenomenologically.
The basic process of the occurrences of snow storms and their
magnitudes is first considered. Random variables that characterize the
snowpack resulting from these storms are defined to be the snowpack
durations and the length of the snow-free periods. The distribution
functions of these random variables are derived. The resulting stochastic model is then compared with historical data from a climatological station.
In a search of the literature, few references could be found
that dealt directly with the stochastic modeling of the snow process.
However, some of the literature regarding the stochastic modeling of
precipitation pertain to the present study. Therefore, a review of
literature is included in Chapter 2 in which probabilistic precipitation
models in general are discussed. Next, references that consider the
precipitation process as a stochastic process and that contain information pertinent to the present study are discussed. In the last section,
papers that treat various aspects of the snow process probabilistically
or statistically are reviewed.
The theoretical considerations are presented in Chapter 3. The
basic process of snow storm terminations is considered to be a
3
homogeneous Poisson process. A sequence of random variables representing storm magnitudes are defined. The storm magnitudes are assumed to
be concentrated at the storm termination epochs, independent and identically distributed and independent of the number of storms. The total
snow water equivalent in an interval is then a compound Poisson process.
The snowpack ablation rate is initially assumed to be constant through
the winter season. This is later generalized by considering the snowpack ablation rate to be a random variable, varying between seasons.
The snow process is then one of positive jumps and negative
drifts, with the slope of the drift equal to the snowpack ablation rate.
The time from the onset of the winter season to the first jump is defined
to be the first snow-free period, a random variable. The snowpack
initiated by the first storm remains on the ground for a random duration,
called the first snowpack duration. The epoch marking the end of the
first snowpack also initiates the second snow-free period of the season,
which is terminated by the formation of the next snowpack. The sequence
of snow-free periods are independent and identically distributed random
variables, as is the sequence of snowpack durations. The sums of each
pair, the snow-free period and the subsequent snowpack duration, form
another sequence of random variables, termed snow-free, snow cycles.
The snow-free, snow cycles are independent, identically distributed,
positive-valued, and represent the interarrival times between occurrences
of zero snowpack water equivalent. They are therefore the interarrival
times of a secondary renewal process, called the snow renewal process.
Based upon some results by Prabhu (1965), expressions for the distribution functions of these random variables are derived.
4
In Chapter 4, specific distribution functions, as well as expressions for the means and variances of the random variables, are obtained.
The sequence of independent and identically distributed random variables
representing snow water equivalent per storm are assumed to be exponentially distributed after the model was developed in general in Chapter 3.
The gamma distribution representing the k-fold convolution of an
exponential distribution is substituted into the general expressions for
the distribution functions of the random variables defined in Chapter 3.
In Chapter 5, data from an Arizona climatological station is used
to obtain parameter estimates. The assumptions of a homogeneous Poisson
process of storm terminations and of exponentially distributed storm
magnitudes are investigated. Snowpack ablation rates are estimated from
the daily snow records by regression analysis. Empirical distributions
of snowpack ablation rates are next obtained. Unconditional distributions of snowpack durations, snow-free periods and snow-free, snow
cycles are derived by discretizing the empirical distribution functions
of snowpack ablation rates, then numerically integrating. The unconditional theoretical distribution functions are compared with the distribution functions of observed values of the random variables. Probability
mass functions of the secondary renewal process are determined from the
distribution functions of the sums of snow-free, snow cycles. The
theoretical and observed probability mass functions of the secondary
renewal process are compared.
A summary of the results obtained in Chapter 5 is presented in
Chapter 6. This is followed by specific recommendations for further
studies. These include possible extensions of the model.
CHAPTER 2
A REVIEW OF LITERATURE
The literature contains numerous papers devoted to the application of probability theory and statistics to the general field of hydrology, and particularly, to the study of precipitation phenomena. The
purpose of the present study is to apply specific stochastic models to
the snowfall, accumulation and ablation process. Therefore, a general
review of the application of stochastic processes to hydrology has not
been undertaken.
The review has been organized into three sections. In the first
section a brief review of precipitation models in general has been presented. This has been included to indicate the numerous approaches
which have been taken in the probabilistic or statistical modeling of
precipitation. Certain references, although not directly concerned with
snow modeling, contain information which is pertinent to the present
study and are discussed in the second section. Those papers which are
devoted to the probabilistic or statistical modeling of the snow process
are presented in the last section.
Probabilistic Precipitation Models in General
In discussing stochastic models, Gupta (1973) classified them as
being either (1) empirical or purely statistical models,
5
6
(2) phenomenological or process oriented stochastic models, or (3)
physically based stochastic models.
Empirical or purely statistical models have been used extensively in hydrology. Precipitation frequency analyses are examples of such
use. The use of empirically derived distribution functions to describe
the probability of precipitation magnitudes and of precipitation magnitudes of a given duration has served as a useful tool in precipitation
analysis. Discussions of frequency analysis and empirical model fitting
can be found in Chow (1964), Weisner (1970), Viessman, Harbaugh and Knapp
(1972), and Yevjevich (1972), as well as in earlier texts.
According to Gupta (1973) there have not been many physically
based stochastic models to date. It seems that present probabilitistic
modeling of hydrologic phenomena such as precipitation is at an intermediate level, having left the state of purely empirical models and not
yet at the point where extensive use of chemical, physical and biological
laws is made to relate random variables. This intermediate level is
represented by the phenomenological, or event based, approach.
Phenomenological approaches have been frequently used to model
precipitation in the last 15 years. Although any such classification is
somewhat arbitrary, there appears to have been two general approaches.
In the first, sequences of wet and dry periods are modeled. Commonly
such sequences have been modeled as first order Markov processes
(Gabriel and Neuman 1962, Weiss 1964, Feyerherm and Bark 1965), although
higher order Markov processes have been used when first order processes
appeared to be inadequate (Hopkins and Robillard 1964, Wiser 1965,
7
Feyerherm and Bark 1967). Alternatives to the use of Markov processes
have been proposed to describe wet-dry sequences (Green 1965, 1970;
Chatfield 1966). Other investigators have used a Markov process to
describe the wet-dry sequence, and also modeled the amount of precipitation and considered the seasonal variation in model parameters (Ison,
Feyerherm and Bark 1971; Crovelli 1972; Woolhiser, Rovey and Todorovic
1972).
In the second approach, precipitation events rather than
sequences of wet and dry days are considered. How an event is defined
is dependent upon the type of precipitation process, the form of the
data and the mathematical approach taken to analyze the process (Gupta
1973). The underlying probability law governing arrivals of events is
usually determined. Then other random variables such as intensity, duration and magnitude of events, interarrival times and total seasonal precipitation are frequently considered. Gupta (1973) included a review of
phenomenological stochastic models used in hydrology in his dissertation.
A theoretical study of the precipitation process that belongs in this
category was completed by Todorovic and Yevjevich (1969). Applications
of event based approaches in modeling convective thunderstorms have been
made by Fogel and Duckstein (1969), Fogel, Duckstein and Kisiel (1971),
Duckstein, Fogel and Kisiel (1972), Lane and Osborn (1972) and Osborn,
Mills and Lane (1972). Kao, Duckstein and Fogel (1971) used an event
based approach in modeling winter precipitation in the Southwest.
Gupta (1973) developed a space-time phenomenological model of precipitation and demonstrated its use with summer rain storm data. Using what
8
could be considered an event based approach, Epstein (1966) studied
point-area precipitation probabilities using a storm cell model.
Some Pertinent Stochastic Precipitation Models
A common feature of the studies reviewed in this section is that
a rainfall event is defined as a sequence of periods (minute, hour, day)
during which precipitation occurs, preceded and succeeded by no rainfall.
The precipitation events and the event arrivals are independent random
variables. Although these criteria are satisfied by several convective
rainstorm models, these models are excluded. Only those models which
have been developed for winter season precipitation or for an entire year
are considered.
Thom (1959) described the number of excessive rainfall events
during a year by means of a Poisson distribution. His events were
magnitudes of hourly rainfall data at three stations in the mid-western
United States. The interarrival times of the events he found to be
exponentially distributed. His analysis indicated the usefulness of a
Poisson distribution in the analysis of precipitation events.
In a rainfall simulation and runoff model developed for a river
basin in Japan, Ishihara and Ikebuchi (1972) used a Markov chain to
describe the probabilities of wet and dry days at other stations given
the events at a base station. It was determined that the simulated
record did preserve the statistical characteristics of the historical
record. For periods during which similar monthly frequencies of wet
and of dry days occurred, they found that the lengths of dry periods
9
were approximately exponentially distributed while the number of precipitation days were Poisson distributed.
A theoretical study of the intermitten precipitation process was
conducted by Todorovic and Yevjevich (1969) in which a storm was defined
as continuous rain between two non-rainy intervals. A stochastic
process (C t > 0; t > 0) was defined to be the precipitation intensity at
a station. Recognizing that precipitation intensity is seldom measured,
they then defined six stochastic processes which were characteristic of
the precipitation intensity. Two random events, the first being the
event that exactly n complete storms occur in an interval and the second,
the event that the total precipitation from the n storms in the interval is an amount x, were defined. It was shown that the density and
distribution functions of the six families of random variables could be
expressed in terms of the probabilities of these two random events.
Only two parameters, common to all distributions, were important. These
were X i , the average number of storms per unit time interval and X 2 , the
inverse of the average amount of precipitation per storm. For sufficiently small time intervals and sufficiently small precipitation
amounts, it was then shown that both random events were Poisson
distributed.
Comparison of the theoretically derived distributions with daily
and hourly precipitation records led them to conclude that their results
were satisfactory. However, in using each rainy hour and each rainy day
as a storm, the true number of storms was apparently overestimated. Conversely, use of uninterrupted sequences of hours or days underestimated
the true number of storms in the interval.
10
An event based approach was used in the analysis of winter
(October through March) precipitation at Tucson, Arizona, by Kao et al.
(1971). A storm group was defined as a sequence of wet days uninterrupted by more than one dry day. A wet day was one receiving an amount
of precipitation equal to or greater than a stated threshold value. The
storm group interarrival times were exponentially distributed while the
numbers of storm groups were Poisson distributed. The storm group
duration was found to be described by a negative binomial distribution.
When rainfall amounts per storm were expressed as integral one-half inch
units, the amounts were geometrically distributed. Under the assumption
that the rainfall amounts per storm group were independent and identically distributed, and independent of the number of storm groups, total
precipitation was described by a compound Poisson distribution.
Kao et al. (1971) applied the model to daily rainfall data from
San Francisco. Storm interarrivals, defined as the length of time between the beginnings of successive storms, were no longer exponentially
distributed. The density function of the interarrival times was then
determined from the sum of the (assumed) independent random variables,
storm duration and length of dry spell. Using a derivation by Gupta
(as reported in Kao et al. 1971) the density function for the number of
occurrences per season was obtained.
Probabilistic and Statistical Snow Studies
A search of the literature has revealed few attempts to consider
the complete snowfall, accumulation and ablation process in an integrated
11
manner. Therefore, in this section, literature pertaining to each
aspect of the snow process is discussed by topic.
Depending upon the purpose of the investigation, two approaches
have been used in the modeling of snow accumulation and ablation. In
the first, snow depth and snow water equivalent were of primary interest.
Snow depth and water equivalent on a specified date, maximum seasonal
values and total seasonal values were random variables frequently of
interest. A second empirical approach has been the investigation of
the time seasonal snowcover first forms, length of time snow remains on
the ground and the time snow melts in the spring.
In some studies, probability distributions were fitted to the
random variable of interest. In others, relative frequencies and recurrence intervals without regard to a descriptive probability distribution were used. Areal extension of point information has frequently
been accomplished by extending the point information as isolines on maps.
Frequently, because of a lack of water equivalent data at many
climatological stations and a short period of record at other stations,
analyses of snowstorms have used snow depth. The average number of days
during the winter months receiving small amounts of snow were most common
while the average number of days receiving a given amount of snow decreased rapidly with increasing daily snowfall (Lautzenheiser 1968,
Miller and Weaver 1971). As part of their study of snow in Ohio, Miller
and Weaver used a Fisher-Tippet Type 1 distribution as a model of extreme
24-hour snowfall.
12
In a proposed stochastic snow model, Fogel, Duckstein and Kisiel
(1973) suggested a geometric distribution (with integral one-half inch
units) of water equivalent per storm event. Their proposal used definitions similar to those used in an earlier paper by Kao et al. (1971).
The number of storm groups were distributed as a Poisson random variable
and the total seasonal water equivalent was compound Poisson distributed.
A simulation model of daily snowfall records was developed by
Bolduc (1970) using a Markov chain. In order to utilize the fact that
daily precipitation records were more common than daily snowfall records,
Bolduc constructed a transition probability matrix of wet days following
wet days based upon daily precipitation records. A ratio, r, the average
ratio of snowfall to total precipitation, was then used to transform the
transition probability matrix into another matrix of snowfall transition
probabilities. A Beta distribution was used to model monthly precipitation amounts.
The depth of snow on the ground or its water equivalent is of
considerable interest. Information commonly required includes maximum
seasonal values, depth or water equivalent for a particular time during
the winter season and total seasonal values. Since snow accumulates, at
least temporarily on the ground, the depth of snow or its water equivalent at any point in time may be the result of one or more storm events.
An empirical model of snow on the ground then requires data in addition
to precipitation records.
Both snow depth and water equivalent have been treated as random
variables. Thom (1966) found that the maximum accumulated seasonal water
13
equivalent for 140 National Weather Service stations were log normally
distributed. The stations that did not receive snow every season were
described by a mixed binomial, log normal distribution to account for
the atom at the origin corresponding to the relative frequency of no
snow. Areal extension was then accomplished by using contour lines on
a map to indicate equal values of the distribution parameter estimates.
The log normal distribution of maximum seasonal water equivalent was
later used to develop snow load design criteria for the United States
(Thom 1970). The water equivalent data were expressed in terms of
weight per unit area (load, in pounds per square foot). Quantiles
(values associated with a specified probability) were then obtained.
Gumbel's extreme value distribution has been used in Canadian
studies of snow depth frequencies (McKay and Thompson 1968, Lutes 1970).
Since snow depth information was more common in the records, McKay and
Thompson first modeled snow depth, then obtained estimates of water
equivalent by using an assumed snow density. Lutes used Gumbel's
distribution to develop design snow loads. The United States Weather
Bureau (1964) selected the log normal distribution to describe the
maximum snow water equivalent on the ground for the first and second
halves of March in the North Central United States. For the analysis,
61 first order stations and 463 second order stations were used. Water
equivalent was estimated from relationships between water equivalent and
total precipitation data developed for that region. Maps of the region
showing probabilities of maximum water equivalent were then constructed.
14
In a Russian study, Kovzel (1969) found that the areal distribution of snow storage (in terms of water equivalent) was approximately
normally distributed. Kovzel expressed the snow storage characteristics
of an area in terms of mean areal water equivalent, the skew coefficient
and coefficient of variation. Kuz i min (1969), in characterizing snowcover thickness, used the coefficient of variation of thickness and
defined a modular coefficient as being the ratio of snow accumulation at
a given point to the average accumulation for different values of the
coefficient of variation.
A major source of data for water equivalent of snow on the ground
is the United States Soil Conservation Service. Three recent papers have
attempted to obtain additional information from snow course records by
fitting empirical probability distributions. For selected snow measurement dates and snow courses with five or more years of record in Oregon
and Utah, Vance and Whaley (1971) fitted a log Pearson type three
distribution to water equivalent and snow depth. Measurement dates with
entries of no snow were not included in the analysis. The relative
frequency of snow at each snow course was considered by weighting the
distributions of depth and water equivalent values by the relative frequency. In a similar empirical analysis of snow course water equivalent
for Arizona, Cary and Beschta (1973) found that a mixed binomial, log
normal probability distribution fitted the observed water equivalent at
most of 22 snow courses for the six common measurement dates. The binomial distribution adequately described the relative frequency of no
snow at the various courses. Regression analysis was used to relate
15
parameter estimates to elevation. Engelen (1972) considered snow depth
at snow courses in New Mexico and Colorado to be normally distributed
with two sets of parameters, one for the winter accumulation period and
one for the spring melt period. Engelen also considered monthly mean
values of snow depth and water equivalent as well as the coefficients of
variation as affected by time, latitude, elevation, and local influences.
Snow continues to be a difficult hydrologic variable to measure
as has been indicated by numerous studies (United States Army Corps of
Engineers 1956, United States Weather Bureau 1964, Anderson 1972, Peck
1972, Rechard 1972). One exception is the occurrence of snow. Dates of
formation and melt and length of time it remains on the ground are well
defined and relatively easy to measure (Thom 1966, McKay and Thompson
1968).
The time, measured from an arbitrary reference date, to the
occurrence of the first snowfall of the season or to the first snowfall
to exceed a given threshold value, has been found in several studies to
be approximately normally distributed (Thom 1957a, 1957b; Miller and
Weaver 1971; Cehak 1972). Cehak also determined that the date of the
first snowcover (first time snow remained on the ground) and the date of
winter cover (date of snowfall after which snow remained on the ground
for the remainder of the winter season) were approximately normally
distributed. Similarly, in Alberta, McKay and Thompson (1968) found the
date of formation of the winter snowpack to be normally distributed,
except at some stations in the southern prairie.
16
In regions where snow cover persists throughout the winter
season, duration is relatively easy to characterize. Where snowpacks
form, melt and reform several times during the winter and spring, quantification of duration is somewhat more difficult. Clapp (1967) related
the relative frequency of days per month with one inch or more of snow
to other meteorologic variables. Dickson and Posey (1967), using
climatologic data from several countries, developed maps showing the
probability of snowcover, one inch or more in depth at the end of each
month (September through May) for the northern hemisphere. Using snow
depth and duration as parameters, Potter (1965) divided Canada into
seven regions based upon the homogeneity of snowcover. For selected
stations within each region, he presented relative frequency curves of
snowcover versus month. McKay and Thompson (1968) found that the duration of snowcover at four stations in Alberta could be considered
normally distributed. For a large number of stations, Cehak (1972) used
a normal distribution of snowcover duration as a first approximation in
his study of snow conditions in the Austrian Alps.
The date by which snow can be expected to be melted has also been
treated as a random variable. As with the variables already discussed,
it requires a specific definition when used because it can be defined in
various ways. McKay and Thompson (1968) considered the date of loss of
seasonal snowcover as being the last day of cover, following which snowcover did not recur for a continuous sequence of seven days. For the
period of record, they found that the loss date was normally distributed
For the last day with snowcover and the last day with uninterrupted
17
snowcover, Cehak (1972) again used a normal distribution as a first
approximation.
In the studies of rainfall reviewed here, the theory of stochastic processes was used to develop models of storm occurrences, storm
amounts and the total precipitation occurring in an interval. However,
in describing the stochastic nature of the snow processes, reliance has
been placed on the empirical fitting of distributions to data. Random
variables such as storm amounts, snowpack depths, snowpack durations,
date of snowpack formation, date of last snowmelt and number of days
with a given amount of snow were defined and distributions fitted to
their observed values.
The theory of stochastic processes is used in the present study
to model the processes of snowfall, snow accumulation and ablation in a
unified manner. A model of the basic process of storm occurrences and
their magnitudes is developed. Subsequently, random variables characteristic of the snowpack are defined based upon considerations of the
process.
CHAPTER 3
THEORETICAL CONSIDERATIONS
In this chapter, a generalized model is developed based upon
phenomenological considerations of the actual process of snowfall, snow
accumulation and ablation. The basic stochastic process of snow storms
is described and random variables arising from the basic process are
defined. The mathematical formulations for the random variables are
then given in general.
Storm Frequency and Magnitude
A hyetograph of daily snowfall water equivalent and the accompanying snow accumulation and ablation is shown in Figure 3.1(a). It is
assumed that the water equivalent of each storm can be concentrated on
the last day of a multiday storm. Then the snow process can be represented as a process of the form shown in Figure 3.1(b).
Let (0,t] be an arbitrary time interval of interest, such as the
winter season. Denote the termination epoch of the i th storm by
T
i
.
Then the collection of termination epochs forms a discrete parameter
stochasticprocessfT.;i = 1,2,...,4. Another discrete parameter
processisthecollectionofindividualstormmagnitudesfX.;i =
1,2,...,4. It is assumed that X.; i > 1 are mutually independent,
_—
identically distributed random variables. The assumption of independence
18
19
0
20
40
60
80
100
Time, days
(a)
0
20
60
40
Time, days
(b)
80
900
Figure 3.1. The snowfall, accumulation and ablation process. -- (a) An
observed snowfall hyetograph (solid lines) and snowpack
water equivalent (dashed lines). (b) A conceptualization
of the process.
20
between individual storm magnitudes has been made in the modeling of
winter precipitation in the southwest (Kao et al. 1971) and proposed
for magnitudes of individual storm events (Fogel et al. 1973). The
assumption that they are identically distributed can be tested (Chapter
5) using a k-sample Kolmogorov-Smirnov test derived by Kiefer (1959).
Let fN(t); t > 0 1- be a counting process denoting the number of
-
complete snow storms occurring within the interval (0,t]. The random
variable N(t) can be defined in terms of fT il as N(t) = sup{i;
It is assumed that
Ti
< tl.
N(t) follows a homogeneous Poisson process with a
constant (with respect to time) intensity function X (Parzen 1962).
Todorovic and Yevjevich (1969) have described the process N(t) by a nonhomogeneous Poisson process with intensity X(t) a continuous function of
time. They found that for sufficiently small intervals, X(t) could be
assumed constant. The assumption of X(t) E X within (0,t] is made as a
simplifying assumption, although it should be pointed out that it can be
relaxed under certain conditions without loss of generality, Parzen
(1962, p. 126) presents a transformation that can be used to transform
a nonhomogeneous process into a homogeneous process.
It is next assumed that {x i ; i = 1,2,...,4 is independent of
the stochastic process fN(t); t > 01. That is, the individual storm
magnitudes are independent of the number of storms within an interval.
This assumption is made as dependence makes analytical approaches almost
intractable. It has been shown to be valid in other studies of winter
and yearly precipitation (Todorovic and Yevjevich 1969, Kao et al. 1971,
Fogel et al,) 1973). The assumption of independence between storm
21
magnitude and numbers of storms may not hold in regions where storm
durations are "long" and snowfall is uniform.
Under the above assumptions, the total water equivalent within
(0,t] resulting from a random number of storms is defined as
X(t) =
(3.1)
Then X(t) is a compound Poisson process with the distribution function
of X(t) given as
FX(t)(x) = P(X(t) =< x)
e
-Xt (Xt)n
F n(x),
n! X
(3.2)
n=0
where
F n(x) = P(Xn < x)
X
and
X
n
=X +X +
1
2
+ X .
n
Since the distribution function appearing in equation (3.2) is not
differentiable over its entire range, the density function of the random
variable X(t), denoted by dFX(t)(x) is defined only for x > O. An atom
of magnitude e
-Xt
occurs at the origin (x = 0). The theoretical con-
siderations involved here are covered in Feller (1971, Volume 2). In
order to maintain generality in this chapter, the notation dF (x) is
X
used to denote the probability density function of X, which can be of
any form.
22
Snowpack Accumulation and Ablation
The snow process as defined in this study includes snowfall,
snow accumulation and ablation. Since only snowfall is considered in
equation (3.2), additional processes must be defined to take this into
account. Snow accumulation in the model is assumed to occur as jumps
whose magnitudes equal total storm snow water equivalent and which occur
at the storm termination epochs,
Ti.
Snowpack ablation is the decrease in snowpack water equivalent
due to melting and evaporation. The ablation of a snowpack is the effect
of available energy which, in turn, depends upon many components and
meteorologic factors. The ablation rate changes through the season,
generally increasing in the early spring. The ablation rate will also
exhibit short term variations including diurnal variations depending
upon the relative magnitudes of the several energy components (United
States Army Corps of Engineers 1956). However, since the purpose of
the present study is to develop a stochastic snow model, simplifying
assumptions regarding the ablation rate are deemed warranted. It is
assumed that the ablation rate, c, is linear. This assumption has
frequently been made for daily, or longer, periods of melt in empirical
equations with reasonable results. Examples of such equations can be
found in Snow Hydrology (United States Army Corps of Engineers 1956).
In an initial regression analysis of ablation rates, a constant rate, c,
was hypothesized. The results of the regression analysis are discussed
further in Chapter 5. A generalization will be to view c as a value of
a random variable. At present, c is viewed as a constant and in the
23
next chapter, the randomness in c is incorporated by taking the derived
results as conditional probabilities with respect to c.
Another stochastic process is now defined as {Z(h); 0 < h <
where Z(h) is the point snowpack water equivalent at time 0 < h < t. In
particular Z(t) can be expressed as,
Z(t ) = z (0) + X(t) - ct + cj L[Z(h)]dh,
0
(3.3)
where
L[Z(h)] = 1 if x = 0
= 0 otherwise.
The index function LEZ(h)1 insures that the water equivalent, Z(t), will
not become negative, a physical impossibility. The process as defined
by equation (3.3) is illustrated in Figure 3.2. Since c in equation
(3.3) has been assumed a constant, it can be rewritten as
Z(t)
C
Z(0)
C
X(t)
t+ j L[Z(h)]dh
0
Or
Z (t) = u + x(t) - t + j L[Z (h )]dh
(3.4)
Note that the process {Z(t)1 given by equation (3.4) has an ablation
rate unity. In subsequent development it is assumed that c = 1.
Division of depths of snow water equivalent by the ablation rate results
in each term in equation (3.4) having units of time. To avoid introducing new notation, Z(t) is written as Z(t) = Z(t)/c and X(t) = X(t)/c.
24
-
A
-
o
o
11
(
54 `(4)Z) lioDdhlOuS Jo luOIDA!nbj aolOM
25
Prabhu (1965) has shown that when u = 0, equation (3.3) can be
alternately expressed as
Z(t) = supfX(h) - hl.
(3.5)
0 < h < t
Another random variable, T(u), is defined to be
T(u) = inffh >
Z(h) = 0, Z(T i) =
(3.6)
Equation (3.6) is interpreted as follows. If the water equivalent of
the snowpack due to a storm terminating at T 1 is u, then T(u) is the
random variable which describes when the snowpack will ablate to 0 for
the first time. However during this time a random number of storms can
occur, adding more snow water equivalent to the pack.
The accumulation and ablation processes just described are
analogous to a single server queue (M/G/l) with customer arrivals a
Poisson process, a general and as yet unspecified service time distribution and waiting time decreasing at a constant rate, c, between customer
arrivals. The waiting time is the total water equivalent of the snowpack
and service times the water equivalent from each storm. Unlike more
conventional applications of queueing theory, the analog of the number
of customers waiting service loses its physical significance as it is
the number of storms which have contributed water equivalent to the
snowpack and awaiting ablation. The waiting time (water equivalent of
the snowpack), busy period (time snow remains on the ground) and busy
26
cycle (time between occurrences of no snow) become the random variables
of concern.
Prabhu (1965, pp, 72-73) has derived the general expression for
the distribution function of T(u). This derivation is therefore omitted
and only the final expression given. The distribution function of T(u)
arises from the fact that X(t) is a compound Poisson process and that
the sequence was initiated by the occurrence of the first event, followed
by n-1 events in the interval (O, t] and is
FT
(t) = P[T(u)
t1Z(71)=
e -XT (XT)
=
T=
n-1
xu
n!
dF n(T-u).
X
(3.7)
The density function is given as
co
dF
T(u)
e
= r-i-Xt
n-1
Xu
(X°
n!
dF x n(t-u).
(3.8)
n=0
Given an initial value, u, dF T(u) (t) gives the probability the
snowpack will persist for a length of time t.
Snowpack Duration and Ablation Frequency
A random variable is defined to be Y 1 = inffh > T i ; Z(h) = 01.
Y
1
is interpreted as the length of time a point snowpack, generated by
the occurrence of one or more snow storms, persists (i.e., snowpack
duration). Another random variable, V 1 , is defined as V 1 = Tl.
27
V 1 is the first snow-free period, the time from the onset of the winter
season to the formation of the first snowpack. The second snow-free
period, V a , is then the snow-free period commencing at the end of Y 1 and
lasting until the foLmation of the second snowpack of the season. The
second snowpack will endure for a time, Y a , where Y a is defined as
Y a = inffh > V 1 + Y 1 + V a ; Z(h) = 01. The random variables V. and Y.
i > 1, are illustrated in Figure 3.3.
the first storm magnitude;
1 is initiated by
this has been denoted by X 1 with density function dF x (u). Hence, the
Recall that Y
density function of Y
1
is the unconditional density function of T(u),
for all values of X 1' and is obtained as
(y) =
dF
Yl
0
Substituting for dF
dF T(u)
(Y) dF x
(u)
1
(3.9)
T(u) (y)
Y LdF x n (y-u) dF x (u)
e-XY 1L
dF (y) = j
n!
Y
1
0 n0
1
=
-
(y) =
dF
Y1
-
n-1 y
NY (NY)j
udF n (y-u) dF x (u).
X
n!
1
0
Applying the identity (Prabhu 1965, p. 72)
(x)
xm dB
j v dBm (v) dB n (x-v) = m+n
m+n
0
(3.10)
28
to the integral in equation (3.10), where m = 1 since F (u) is the
X1
distribution function of the magnitude of the first event while
Fn(x)
is the distribution function of the sum of n events following the first
n-1
711' 4 dF x n+1(y)
dFY (y) =e
1
n=0
co
dF
e-XY ( )7+1
(n- I)-1! dFX 111- 1 (y).
(y) =
1
n=0
Letting k = n + 1,
CO
dF (Y) =
Y
1
e -XY
k-1
dF k (y).
( 1Xj)
x
(3.11)
k=1
The distribution function of Y
1
CO
F (y) = r Y
e
Y
1
0 k=1
is then
k-1
k!
dF k (w),
X
(3.12)
where w is a dummy variable of integration.
The total time that snow remains on the ground during the winter
season is then the sum of independent, identically distributed Y's
K(t)
K(t) _
Y
Y..
i=1
(3.13)
29
The number of times the snowpack ablates is a random variable, K(t),
therefore
YK(t)
is the sum of a random number of random variables. An
expression for the distribution function of Y K(t) is deferred until the
next chapter. It is obtained as the K(t)-fold product of the Laplace
transform of Y
1
(or Y since the Y 's are
as Y), which depends
upon the distribution function of water equivalent per storm.
The waiting time to the occurrence of a Poisson event is exponentially distributed. Since V
1
is the time from the onset of the winter
season to the occurrence of the first snowpack (generated by the first
storm), it is exponentially distributed with parameter X. Due to the
memoryless property of the exponential distribution of storm interarrival
times, the second snow-free period, V 2 , measured from the epoch of complete ablation of the first snowpack to the formation of the second
snowpack, is independent of the first snow-free period and the first
snowpack duration. Similarly, V 3 ,V 4 ,... are independent of preceding
snow-free periods and snowpack durations. Also on account of the Markov-
ian property, the snow-free periods, which are analogous to the idle
periods in an M/G/1 queue, are distributed identically as V
1
(Prabhu
1965, p. 111). The snow-free periods are illustrated in Figure 3.3 and
the distribution function of V
1
given by
(v) = 1 -
F
(3.14)
V1
As is indicated in Figure 3.3, let
T
1
= V
1
Y .
1
(3.15)
30
(5q
t(q)z) 5ioDdmous JO uolomnb2 Joi.om
31
T
1
is termed the first snow-free, snow cycle. The entire sequence of
random variables {T 1 } are similarly defined and form a sequence of snowfree, snow cycles. V
and Y
1
1
are independent random variables and since
their density functions are known, the density function of T
I
can be
obtained by the convolution of the density functions of V 1 and Y
1
t
dF
T
=
1
dF
v
(t y) dF
(Y).
-
y1
1
'0
Substituting the specific density functions and simplifying gives the
density function of T
1
as
co
(t) =
dF
Tl
S
0
r
dF
T
'
.1
t
-X l:
et
(t)
1
„k-1
- X(t - Y) - XY ( 07)
dF k (y)
X ee
x
k!
0
k=1
y k-1
k!
The distribution function of T
1
dF xk (Y).
(3.16)
is obtained by integrating equation
(3.16)
F
T
=
1
S tS
0
k e -XT
k!
Y
k-1
dF k(y)•
x
(3.17)
The total number of snow-free, snow cycles in the interval
(0,t] is a random variable, K(t). The sum of K(t) snow-free, snow
cycles gives the total length of the cycles in the interval as
32
T
K(t)
K(t)
T..
(3.18)
i=1
As with Y
functions of T
K(t) ,
the expression for the density and distribution
t) are
obtained from the
K(t)-fold convolution of the
LaplacetransformofT1(orTsincetheT:s are i.i.d. as T) and are
determined in Chapter 4.
The last process to be considered is a secondary renewal process
generatedbytherandomvariablesv.and Y i , i > 1, and is shown in
Figure 3.3. Starting from some initial time, Z(h) is positive for a
random duration (Y) and then reaches zero level. It stays at this level
. for some random duration (V) until a new storm occurs, then it again
becomespositive.sinoefT„i > represent the interarrival time
b
> 1 } forms a
renewal process. A renewal process is one in which the interarrival
times between events are positive-valued,'independent and identically
distributed random variables (Parzen 1962). If K(t) is the number of
complete renewals, that is the number of times the snowpack melts completely, is reformed, then remelts in the interval (0,t], then the
probability mass function of the secondary renewal process
dF K(t) (n) = P[K(t) = n]
(3.19)
gives the probability that the snowpack completely ablates n times
within (0,t]. The expected number of renewals, the mean value function
of the renewal process, is given as (Parzen 1962, p. 171)
33
cx
\Ln ndF K(t) (n).
m(t) = E[K(01 =
(3.20)
a=1
The number of renewals in the interval (0,t] is defined as,
K(t) = supfn;
Y T i < tl.
(3.21)
i
i=1
The probability that there will be n renewals in the interval (0,t] is
related to the cumulative probabilities of the random variables T. as
n
n+1
P[K(t) = n] = P(Y T. < t) - P
T < t),
i
_, 1
1=1
i=1
(3.22)
Based upon (3.21) and (3.22) it can be shown that
m(t) =
P
n=1
(ET.
i=1
<
=
FTn(t).
(3.23)
n=1
If the Laplace transform of both sides of equation (3.23) is taken, the
right hand side is the sum of a geometric series (Parzen 1962, p. 178),
Or
L T (s)
Lm(t) (s)1-LT(s)
(3.24)
The Laplace transform could then be inverted to yield the mean value
function.
34
Commonly in some areas (e.g., high elevations, high latitudes,
northern exposures and protected sites) a snowpack forms and remains
throughout the winter season, resulting in only one renewal. Although
the probability that there will be but one renewal at such locations is
high, it is not equal to one. Therefore the secondary renewal process
is of importance in characterizing the stochastic nature of these snowpacks as well as snowpacks that form and ablate several times during a
winter season at lower elevations. To study the stochastic nature of
snowpacks, the secondary renewal process is used in conjunction with the
random variables defined above.
CHAPTER 4
EXPONENTIALLY DISTRIBUTED SNOW STORM MAGNITUDES
In this chapter an application of the general expressions obtained in the last chapter is provided. In particular, an exponential
distribution function is assumed for the snow storm magnitudes. The
exponential distribution of snow storm magnitudes is substituted into
the general expressions, yielding specific distribution functions.
Expressions for the means and variances are obtained.
Snow Storm Precipitation
The selection of a probability distribution to describe the
amount of snow water equivalent per storm was based upon two considerations. A distribution was required that would adequately fit the data
but would not unduly complicate the analysis. Storm precipitation
amounts have been found to be approximately exponentially, or geometrically, distributed in some studies (Todorovic and Zelenhasic 1968, Kao
et al. 1971). In others, a gamma distribution was found to be an adequate model of precipitation amount per storm (Todorovic and Yevjevich
1967, Ison et al. 1971). Woolhiser et al. (1972) used an exponential
distribution to describe daily rainfall amounts. The total rainfall for
n days was then described by a gamma distribution. Similarly, Dingens
and Steyaert (1971) proposed a modified exponential distribution for
35
36
daily rainfall and a gamma distribution for k-day totals. However, for
seven day precipitation amounts, they found that the shape parameter was
equal to one which reduces the gamma distribution to an exponential
distribution.
In view of the fact that an exponential distribution has been
shown to fit precipitation per storm in some earlier studies, it is
selected as the model for snow water equivalent per storm in the study.
Recall from Chapter 3 that the precipitation amounts per storm
X., i > 1 are mutually independent and identically distributed random
variables. Thus the distribution function of X. is given as
1
F
x
(x) = 1
-
e
-
Y 1 xx
>
0,
all i > 1.
(4.1)
The density function of the total precipitation from k storms is
the k-fold convolution of the exponential density function f (x), which
X.
is a gatialta density function
k k-1
y1 x
dF k(x) = f k(x) =
x
x
e
-
y x
1
(k-1)!
(4.2)
Specific distribution functions of the random variables defined in
Chapter 3 can now be obtained by substitution of equation (4.2) for
dF k(x).
X
Snowpack Ablation Rate
Snowpack ablation is a stochastic process. However, it is
assumed to be deterministic with a rate that is a random variable, C,
which assumes one value each winter season.
37
The snowpack ablation rate is incorporated into the model as
follows. Recall from equation (3.4) that
X(t) —
X(t)
(4.3)
where X(t) was defined to be the sum of a random number of individual
storm magnitudes in equation (3.1).
N(t)
(X./c)
X(t) =
(4.4)
i=1
Substituting Xi /c for X i in equation (4.1)
F
X.
(x) = P(X./c < x)
—
= P(Xi < cx), i > 1
= 1 - e
-yx .
> 1,
,
(4.5)
where
y = cy l .
Thus the random variables X., i —> 1 can now be treated as exponentially
i
distributed with parameter y, and the snowpack ablation rate is incorporated into the model by use of the parameter y. After the specific
distribution functions have been derived, the unconditional distribution
functions and moments are obtained by integrating over the density function of C. In all subsequent discussion, equation (4.5) will represent
38
the distribution function of X., i > 1. Note that in this light,
—
equation (4.2) now represents a gamma density function with parameters
k and y, i.e.,
kxk-1e -yx
d Fxk(x) = f k(x) - Y
x
(k-1)!
Total
' Y = cYl.
(4.6)
Snowfall
Recall that the distribution function of total snowfall water
equivalent in an interval (0,t] is given by equation (3.2). Substituting
the distribution function of Xk into equation (3.2) leads to the distri-
bution function of the compound Poisson process of total snowfall in the
Interval, or total seasonal snowfall, given by
CO
F
X(t)
(x) =
k x k k-1
-Yw
e-Xt (Xt)r
Y w
edw
k! J (k-1)!
0
k=1
co
-Xt
k k
x
ey
k-1 -yw
w
e
dw.
k! (k-1)! J 0
(4.7)
k=1
The above distribution function is absolutely continuous in the interval
0< x < m, with an atom at x = 0 corresponding to the probability of no
snow, i.e.,
CO
dF
X(t)
(x) = f
X(t)
e-X t (Xt)
k!
(x) =
k k k-1
y x e-Yxdx, x > 0
(k-1)!
k=1
(4.8)
39
and
dF
(0) = e
X(t)
-Xt
(4.9)
The integral in equation (4.7) is the incomplete gamma function for
which tables are available. However it can also be integrated by substitution of the series form of e -Yw , which allows isolation of the
variable of integration. Substituting
(-1
e Yw =
-
)
3 3 3
Y w
(4.10)
j!
j=0
into equation (4.7) yields
F
X(t)
j j
k k
-Xt
(Xt) y 7 (-1) y j
e
k ! (k-1)!
j!
(x) =
j=0
k=1
w j+k-1 dw.
0
Integrating, and redefining the inner index of summation to begin at 1
results in
CO
F
X(t)
e
(x) =
-Xt
k k
(10 y (-1)
k! (k-1)!
k=1
L,
j j
y
(j-1)!
x
k+j-1
(k+j-1) •
(4.11)
j=1
Laplace transforms of random variables are commonly used in
probability theory since the transform of a distribution function is
unique and often moments can be more easily obtained from the transform
than from the density function (Feller 1971).
The Laplace transform of a compound Poisson distribution is
(Feller 1971)
40
(s) = e Xt(Lx(s) - 1) L
(4.12)
X(t)
where
L (s) is the Laplace transform of X, i.e., the precipitation
X
magnitude per event and s is the Laplace variable.
The Laplace transform of an exponential distribution is
(4.13)
.
L (s) - X
y + s
Substituting equation (4.13) into equation (4.12),
L
X(t)
(s) = e
Xt ( y
- 1).
y + s
(4.14)
The mean can then be obtained by differentiation of the Laplace transform
as can higher moments
E [ x( t) ] = ( 1 )
E[X(t)
2
(4.15)
Lx(t)(s )I s= 0 =
-
2
2 Lx(t) (s)l s. 0
] =
Os
2
2Xt
2
(Xt)
2 + 2
E[X(t) ] -
(4.16)
The variance of X(t) is then obtained from (4.15) and (4,16) as
Var[X(t)] = E[X(0 2 1 - E[X( 01 -9 =
2Xt
2
(4.17)
41
Time to Snow Disappearance
If, on a given date, the point snowpack water equivalent was observed to be Z(0), a random variable that may be of interest in certain
applications is the length of time from the observation date to the time
the snowpack ablates to 0. This random variable, T(u), was defined by
equation (3.6) and the general expression for its density function given
by equation (3.8). Substitution of equation (4.6) into equation (3.8),
gives the density function of T(u), namely
CO
f
k
-Xt-yt eyu V (Xy) [t(t-u)] k-1
= ue
k! (k-1)!
k=1
T(u)
(4.18)
When t = u, the density function reduces to (Prabhu 1965, p. 95)
f
T(u)
(u) = e -Xu ,
(4.19)
which gives the probability that the snow remains on the ground for u = t
days when no more storms occur. In the equations involving u, it is
implicit that u = Z(0)/c, and thus the right hand side becomes dimensionally correct.
An expression for the distribution function of T(u) is obtained
by substituting the infinite series form of
e-t(k y)
into equation
(4.18) and integrating,
m
F
T(u)
(0 = ue"
1
(XY)
m
k
(-1)j-1(X+Y)j-1
(j-1)!
k! (k-1)!
k=1
t
.
2
k+J-„k-1,
T
T=l1
j=
kT U)
-
UT
(4.20)
42
The Laplace transform of the distribution of T(u), from which
the moments may be obtained is (Prabhu 1965, p. 95)
L T(u) (s)
s
,
expi-uf (s+?c-Y) +2 "4s+X-Y)2 +4 1]
(4.21)
The mean and the second moment of T(u) are obtained as
E[T(u)] = (-1)L
Os T(u)
E[T(u)
2
(s)I
s=o
2
] - L
OsT(2
u)
(s)1
=
(4.22)
y X
-
2uXy y 22
u
s=o
(Y - X)
3
(y-X)
2
(4.23)
The variance of T(u) using equations (4.22) and (4.23) is given
as
2
Var[T(u)] = E[T(u) ] - E[T(u)] 2 - 2uXy
3
(Y-X)
(4.24)
Snowpack Duration
Recall from Chapter 3 that the duration of the first snowpack,
Y
1,
was defined as the length of time the snowpack initiated by the first
snowstorm of the season would persist. At higher elevations, northerly
exposures or otherwise protected sites, it would be anticipated that the
snowpack would remain for the entire winter season, ablating completely
only once, in the spring. At lower elevations, southerly exposures or
sites which receive higher net energy, the snowpack may form and ablate
one or more times. The density function of Y
1
is given by equation
43
(3.11) for the general case. Substituting equation (4.6) into equation
(3.11) yields the density function of Y 1 ,
e - XY (xy) k-lykyk-le -y y
f (Y) =
Y
1
k! (k-1)!
(4.25)
•
k=1
The distribution function of Y
1 is next obtained by integrating equation
(4.25)
F y (Y) =
1
k k-1
f y 2(k
Y X w
k! (k_.1)1
0
k=1
-
1)
' e
_ w ( x+y) d
w.
(4.26)
Substituting the infinite series form of e-w(X+y) and carrying out the
indicated integration results in
00
,
F
(y) =
k k-1
Yk!X(k-1)!
y1
k=1
00
(-1)
j-1
j-1 2k+j-2
(VEY) Y
(j-1)!
(2k+j-2) .
(4.27)
j=
The Laplace transform of the density function of Y1 is (Prabhu
1965, p. 96)
s + X + y -,/(s+X+y)
L (s) Y
2X
1
2
- 4Xy
(4.28)
As before, the mean and variance of Y 1 can be obtained by differentiating the Laplace transform. This gives
E[Y1 ] = (y-x)
(4.29)
44
E[Y
2
2y
] 3
(y - X)
Var[Y 1 ] - (Y 0
(4.30)
(4.31)
3
-
The mean and variance of Y
1
(which is analogous to the busy period in an
M/M/1 queue) as given by equations (4.29) and (4.31) are identical to the
mean and variance of the busy period as obtained by Feller (1971, p. 199).
The Snow-Free, Snow Cycles
The first snow-free, snow cycle of the winter snow season ' T 1 ,
was given as
T
1
= V + Y
1
1
The density function of T
I
was given in general by equation (3.16). Sub-
stituting the exponential distribution of snow water equivalent per
storm into equation (3.16)
m k -Xt 2k-2 k -yw
t 7
dw
ce
w
xe
f T (t) = j
k! (k-1)!
0
1
k=1
Substituting the infinite series form of e
m m
f 1 (t) =
T
k=1 j=1
integrating
(-1) j-l kyk+j-l e -Xt t 2k+j -2
7
k! (k-1)! (j-1)! (2k+j-2) .
(4.32)
45
The distribution function of T
1 is next obtained by integrating equation
(4.32). Using the substitution of the series form of e -Xt to isolate
the variable of integration yields
CO
F
T
1
(t) =
= CO
j-1 k k+j-1
e-xww2k+1-2dw
(-1-)
L, k! (k-1)! (j-1)! (2k+j-2) f
0
k=1 j=1
LJ
CO
m
co
(-1)3+m-2xk+m- 1 yk+j- 1 t2k+j+m-2
k! (k-1)! (j-1)! (m-1)! (2k+j-2)(2k+j+m-2)
k=1 j=1 m=1
(4.33)
The mean and variance of T
1
are obtained using the fact that T
is the sum of two independent random variables
'
V
1
and Y
1,
therefore the
mean and variance are the sums of the means and variances of V
E[T i ] = E[Y 1 ] + E[V i ] — x(y...20
+ (Y - X)
Var[T 1 ] = Var[Y i ] + Var[V i ] — x 2 (x)
2
3
X (y - X)
The Laplace transform of T
Laplace transforms of Y
1
and V
1
1
Y
1 and 1
(4.34)
3
(4.35)
is obtained as the product of the
since they are independent
1
46
L
(s) = L
Tl
(s)
yl
L
v
(s)
1
(s+X+Y) As+X+Y) 2
4 XYX
2X
(s+X-FY)
X+s
Asd- X+Y) 2 - 4 XY
2(X+s)
(4.36)
Inversion of equation (4.36) (see Appendix A) gives
f T
where I
1
-1
= L
1T 1
/ a
(s) = e -Xtj e -yw n wXY,
0
1-
1' 2w(Xy)']dw
(4.37)
is the modified first order Bessel function.
Equation (4.37) can be shown to be identical to equation (4.32)
by substitution of the infinite series forms of e -Yw and of I [2w(Xy) 2 ]
1
and carrying out the indicated integration.
The distribution function of T 11 ,can be obtained by inverting its
Laplace transform. Since the random variables, T i , i
1 are mutually
independent and identically distributed, the Laplace transform of T n is
given by the n-fold product of the Laplace transform of T 1 with itself
L n (s) = [(s+x y)
T
+y)2
-
4Xy]
(4.38)
'/(/
The inverse of the transform (see Appendix A) was found to be
f n(t) = L n
-1
(s) = ne
-Xt r
s n/2
j
0
0
I n [2w(xy)
e -yw (XY)
k
](dw)
n
(4.39)
47
The indicated integrations are carried out by again using the infinite
series forms of e 'Y w and of the nth order modified Bessel function, the
substitutions of which permit isolation of the variable of integration
00
fTn(t) = ne -Xt ON) n):
00
(-1)i-1(Xy)k
(k-1)! (k+n-1)! (j-1)!
k=1 j=1
t
t
.
0
w2k+j+n-4(dw)n
0
( _ 1) j-l xkyk+j-l t 2k+j+2n-4
= n(X)nt
f T n(t)
k=1 j=
(k-1)! (k+n-1)! (j-1) ! i n
(2k+j+2n-i-3)
1=1
(4.40)
The distribution function of T
n
is
co
00
t
(-1)j-lxkyk+j-lw2k+j+2n-4
dw
n(t)
=
f
nay)ne-xwy
F T
'
--- (k-1)!(k+n-1)!(j- 1''"° In1 (2k+j+2n-i-3)
0
k=1 j=1
i=1
When
the infinite series form of e -2n.w is substituted into the distribu-
tion function of T n , the expression integrated and the inner index of
summation redefined to begin at one, the expression for the distribution
function becomes
48
CO
03
(...1) j+m-2 x k+m-2 k+j-2 t 2k+j+2n+m-4
F n(t) = n(Xy) nY
T
( k- 1) ! ( k+n- 1) ! ( j -1) ! (m-1) I ( 2k+j+2n+m- 4)
k=1 j=1 m=1
1
(4.41)
1 I (2k+j+2n-i-3)
i=1
The mean and variance of T
n
can be obtained by successive differ-
entiation of the Laplace transform, or more simply by noting that since
n .
T is the sum of n independent, identically distributed random variables,
the mean and the variance are the sums of the means and variances of T.,
E[T n ] = E[T i ] + E[T 2 1, +
+ E[T n. ]
E[T n i — (4.42)
Var[T n i = Var[T i l + Var[T 2 ] + „. + Var[T]
( 107
r,N
Var[T n ] — n L AAi ' ` Y- " / -1 , for all n > 1.
x 2 (x) 3
(4.43)
The Snow Renewal Process
The snow renewal process and the mean value function of the renewal process were introduced in Chapter 3 and their significance indicated. The mean value function could be obtained by substitution of the
Laplace transform of T, given by equation (4.36), into equation (3.24)
49
and then inverting. However a relationship exists between the probability mass function, mean value function and higher moments and the
distribution function of T n which allows them to be obtained directly
from the distribution function. For n > 1, the probability mass function
is given by eauation (3.22). The probability that no renewals occur is
given by (Parzen 1962)
P[K(t) = 0 1 = 1 - FT (t).
/
(4.44)
The mean value function and second moment are
m(t) = E[K(t)] = n[FTn(t) - F T n+1(01,
(4.45)
n=i
and
CO
E[K(0 2 ] = ): n2 EF n(t) - F n+1(t)].
T
T
n=1
(4.46)
The variance of the snow renewal process is then
Var[K(t)] = E[K(t) 2 ] - [m(t)] 2 .
(4.47)
Unconditional Probability Distributions
Although not stated in the eauations because of notational convenience, the random variables T(u), Y and Tn were all conditioned upon
the snowpack ablation rate C.
50
The unconditional distribution function of Y is obtained by
integrating the product of the conditional distribution function of Y
given by equation (4.27) and the probability density of C over all
values of C, or
co
Fyle(y1c) f (c) dc,
a
I
F(y) = (4.48)
where
a=c
.
.
minimum
The lower limit of integration, a, is explained in the next section and
is based upon theoretical considerations. An infinite ablation rate
would indicate instantaneous ablation. Instantaneous ablation would be
characteristic of sleet or snow that melted upon contact with the ground,
or rain.
The unconditional mean of Y can be easily obtained as
E[Y] =
f
E[Ylc] f c (c) dc.
(4.49)
a
The unconditional variance has been shown to be (Parzen 1962)
Var[Y] = E[Var[Ylc]] + Var[E[Ylc]]. (4.50)
The unconditional distribution functions, means and variances
of T(u) and T
n
are similarly obtained.
51
Analysis of Parameters
The probability distributions of the random variables selected
for study are all functions of two parameters, X and y, where y was
defined as
Y =
y i is the parameter in the exponential distribution of precipitation per
storm and is eaual to the inverse of the mean precipitation per storm.
Since y is the product of the snowpack ablation rate, c (inches day -1 )
and y
1
(1n.
-1
), it is the inverse of the time required for the mean snow
water eauivalent per storm to ablate to O.
The ratio defined as
P
ablation time of mean snow per storm
mean storm interarrival time
X
(4.51)
is a aualitative measure of snowpack characteristics. If p > 1, there
is a positive probability (Prabhu 1965, p. 16) that snow will remain on
the ground indefinitely. The storms are occurring at a rate that is
greater than the time reauired to ablate the mean snow water equivalent
per storm to O. A value of p > 1 (y < X) would then be characteristic of
perennial snowpacks. Such a value of p would be expected for arctic and
antarctic regions, and in temperate climates, only at a few high elevation sites. For most temperate region snow zones, p < 1 (y > X). The
value of p would, in general, increase with latitude and with elevation.
On a local basis p would be expected to be a function of local topography,
vegetation and climate. Small values of p would be associated with
52
shallow snowpacks, infrequent storms and high ablation rates. For the
case where p = 1 (y =
X), the snowpack will eventually ablate, however
all moments of the distributions of T(u), Y and Tn are infinite.
The specific model that has been presented in this chapter is
based upon the restriction that p < 1. This is then a snow model for
temperate regions where the snowpack eventually ablates entirely. Under
this restriction, the probability distribution functions of T(u), Y and
n
T are proper since the Laplace transforms of each are equal to 1 when
evaluated at s = 0, and the moments are finite and positive.
Since y > X in this model, a lower bound exists on snowpack
ablation rates,
cy i > X
C
>
yl
,
(4,52)
The snowpack ablation rate, c, is thus restricted to the interval
X
< c < m. A probability distribution of c then becomes a shifted
yl
distribution, the shift being equal to the lower bound on c.
y was expressed in terms of multiples of X. Values of p were
then computed and plotted in Figure 4.1. For a given value of X, p is
very sensitive to increasing values of y up to 4X . The value
p =
0.5 is
reached when y = 2X. Thereafter increasing y values have relatively
little affect on P' For a given storm interarrival time, the mean
snowpack duration decreases rapidly with decreasing storm magnitude and
increasing snowpack ablation rate. For values of p = 0.5 the snowpack
53
1
-
Figure 4.1. The ratio p as a function of y.
54
duration will be the dominant feature of the snow process. When p
decreases' below 0.5, the snow-free period of the winter season becomes
the major feature. For values of y > 4, the increasing snowpack ablation
rate and/or decreasing magnitude per storm exert relatively little influence on the fraction of the winter that snow remains on the ground.
CHAPTER 5
APPLICATION TO A CLIMATOLOGICAL STATION
In this chapter, data from a climatological station in Arizona
are used to obtain parameter estimates. The basic assumptions are investigated and the model is compared with data.
Station Selection
Daily precipitation measurements as well as other climatologie
data are available at first and second order stations, and at cooperator
stations. However, only at first order stations are daily measurements
of snowpack water equivalent made. Except for the estimation of snowpack ablation rates for which snowpack water equivalent measurements
were necessary, all the information required by the model for parameter
estimation (daily precipitation and daily snowfall) and for comparison
of the derived random variables with observed values, is available at
all climatological stations. To obtain the necessary estimates of
snowpack ablation rates, a first order climatological station was
selected. Although other climatological data are available at many
stations for 60 years or more, snowpack water eauivalent measurements
were initiated at many first order stations during the period 1952-1953
(Thom 1966), The daily precipitation, snowfall and snow depth records
corresponding to the longest usable record of snowpack water equivalent
were therefore used.
55
56
A station that exhibited variable snowpack characteristics from
season to season was desired so that a range in observed values of the
random variables would be obtained.
The climatological station at Flagstaff, Airzona, met these
criteria since it is located at a reasonably low elevation (6993 feet)
within an important snow zone, the Central Arizona Highlands, and is a
first order station. Although snowpack water equivalent measurements
were initiated in 1953, numerous missing data in the first few years
resulted in the selection of the 15-year period 1957 through 1971 water
years as the length of record to use in the model comparison.
The winter precipitation season in Arizona includes the period
November through April and at higher elevations 75 percent or more of
the precipitation occurs as snow (Sellers 1964). At Flagstaff snow may
occur as early as October and as late as May (excluding occasional
summer and early fall snow showers). Based upon a summary of climatologic data for Arizona by Smith (1956), Table 5.1 shows the estimated
percent of mean monthly precipitation that occurs as snow at Flagstaff.
If the criterion of 50 percent or more of the mean monthly precipitation occurring as snow is used as the basis for defining the winter
snow season at Flagstaff, the winter snow season is the period December
through March (approximately 120 days).
Snow Storms
A storm has been defined by Todorovic and Yevjevich (1969) as
continuous precipitation between two non-rainy intervals. However,
precipitation data are commonly reported as totals for a specified time
57
Table 5,1. Mean monthly precipitation and estimated water equivalent
of mean monthly snowfall at Flagstaff, Arizona.
Length of Record
Nov.
Dec.
Jan.
Feb.
Mar.
Apr.
Mean monthly
precipitation
(inches)
1898-1953
1.24
1.72
1,92
1.91
1.86
1.28
Mean monthly
snow water
equivalent**
(inches)
1897-1953
.60
1.33
2.00*
1.49
1.41
.56
78
76
Percent of
precipitation occurring as snow
48
77
104
43
* Mean snow exceeding mean precipitation is probably due to snow density
averaging less than 0.10 during January as well as measurement errors.
** Snow water equivalent estimated using snow density of 0.10.
interval such as total hourly or daily precipitation. Therefore a working definition should be one that permits a storm to be identified from
such data. Each rainy hour or day has been considered as a storm event
as has each uninterrupted seauence of rainy hours or days (Todorovic
and Yevjevich 1969). Since freauently only daily data are available
and cyclonic precipitation events often last for more than one day, a
storm is defined as an uninterrupted seauence of wet days, each day
receiving an amount of snow water equivalent equal to or greater than
.01 inch, Each storm is separated in time from other storms by at least
one dry day. A dry day is one receiving no precipitation or an amount
58
less than .01 inch. This definition is similar to that used by Kao
et al. (1971) and that proposed by Fogel et al. (1973).
During the winter season occasional rain events do occur. Since
only snow is considered in the model, rain events were not included.
Rain events were isolated by comparison of the daily precipitation
records with new fallen snow records. When a mixed event occurred, a
new snow density of 0.10 was assumed and multiplied times the depth of
new fallen snow. Although the density of new fallen snow may vary from
as low as 0.01 to 0.15, an average density of 0.10 is commonly used in
the United States (Garstka 1964, United States Weather Bureau 1969).
For precipitation events comprised entirely of snow, daily precipitation
was used as an estimate of new snow water equivalent.
The counting process fN(t); t > 01 was assumed to be a homogeneous Poisson process with parameter X in the development of the snow
model. A Poisson process is characterized by independent, exponentially
distributed interarrival times with parameter X representing the mean
number of arrivals per unit of time (Parzen 1962). In order to determine
the reasonableness of the assumption of a homogeneous process, the winter
snow season was divided into three intervals of approximately 40 days.
The interarrival times (I) as measured between storm termination epochs,
for each interval then formed a sample. The three observed distribution
functions were tested against the observed distribution function of
interarrival times for the entire season. In plotting observed distribution functions in this study, the Weibull plotting positions were used
(Chow 1964). The k-sample analog of the Kolmogorov-Smirnov test
59
derived by Kiefer (1959) was used to test the hypothesis that the interarrival times during each of the three 40-day periods were identically
distributed as the interarrival times of the entire 120-day winter snow
season. At a confidence level of a = 0.05, the hypothesis was not
rejected (fT = 1.112, cp 2
-1
(.95) = 1.584). T was computed by the method
given by Kiefer and the critical value of the statistic is from Kiefer's
Table 2. Although such a test is not conclusive it does indicate that
the assumption of identically distributed interarrival times is acceptable. The observed sample and pooled distribution functions are shown
in Figure 5.1. Two parameter estimates of the Poisson process were obtained.
The first estimate was made by the method of moments (total storms/total
days = 174/1800 = .097 storms-day
-1
). A second estimate was made using
an equation for a homogeneous Poisson process derived by Gupta (1974)
EEI
max
]
(Xt)i (-1) i+1e -Xt
i
X
i!
(5.1)
i=1
where
I
is the maximum storm interarrival time per time interval,
max
t is the length of the time interval, and
X is the Poisson parameter.
The sample mean of the observed maximum interarrival time for each season
(26.67 days) was used as an estimate of EEI max I. The parameter estimate
-1
obtained from equation (5.1) was X = .123 storms-day .
60
I.0
0
0.9
a°
4
A
A
0.8
X !
A
07
A
A
0.6
1--n
it
).<
AA
4
r•
In
A
)1(
k•
i
X I
: *• :><
0
0
0
0
A
0.3
.ç
0
0
0°
0
!
AX0
:
A '
,.; 1
* iI
0
/
o
•
iô
i
R
I 0
0 I
A Ile
0
•
i
o
2
k6
'
0
•
o
Legend:
- Pooled Sample
xxxx- Dec.1 -Jan.9
0000 - Jan.10 - Feb.I8
AALIA.— Feb.I9 - Mar.'30
0
•
0.4
A
o
A
xo
o
i
A Ao
0.5
A
x
x
• A0
Kiefer's K-sample K-S Test:
Nri----1.112<T: (.95)=1.584
--- -Maximum Deviation
O
o
4
o
8
•
02
A
X
x
0.1
0.0
10
20
30
Storm Interorrival Time; I (days)
Figure 5.1. The observed distribution functions of storm interarrival
times for three 40-day periods and the 120-day season.
40
61
Two estimates of the Poisson parameter were used because under
certain conditions, one or the other may not be a sufficient statistic.
Storms as defined in this study represent the lumping of one or more
bursts of continuous snowfall into daily snowfall and the snowfall from
consecutive days into a single storm. As more bursts of snowfall are
included in a given storm, the method of moments estimate of X would
further underestimate the true storm intensity. Under such conditions,
this estimate would no longer be sufficient since it would no longer
contain all possible information regarding the Poisson parameter. That
is, the estimate would be conditioned upon the number of bursts included
in the storms. In the second estimate of X, if storms were infrequent,
the maximum interarrival time would be long and little affected by storm
occurrences. If storms increased in frequency, the maximum interarrival
time would be shorter and increasingly dependent upon storm frequency.
Under these conditions, the second estimate of X would no longer be
sufficient.
Since the interarrival times of a Poisson process are exponentially distributed, the hypothesis that storm interarrival times were
exponentially distributed was tested using a chi-squared goodness of
fit test. At the ce = .05 confidence level, the null hypothesis that
storm interarrival times were exponentially distributed with parameter
X = .097 was not rejected (x
2
2
calculated = 13.241, x .
05, 7 d.f.
oN
14.067). Similarly the distribution with X = .123 was not rejected
(x
2
2
calculated = 3.399, x .05, 7 d.f. = 14.067). The observed and
theoretical distribution functions of storm interarrival times are
62
shown in Figure 5.2. The sample means and variances of the number of
storms and of the storm interarrival times were computed. Table 5.2
shows these sample statistics as well as the theoretical means and
variances. Since X = .097 was estimated by the method of moments, the
mean of the Poisson distribution of the number of storms is equal to
the observed mean number of storms.
Table 5.2. Mean and variance of the number of storms and the storm
interarrival times at Flagstaff, Arizona.
Observed
Number of
Storms
(Dec. 1 Mar. 30)
Storm Interarrival
Time
(Days)
Mean Variance
X = .097X = .123
•
Mean Variance
Mean Variance
11.64
10.97
11.64
11.64
14.76
14.76
8.54
64.85
10.31
106.28
8.13
66.10
That the storm termination epochs can be considered as Poisson
events is evidenced by the exponentially distributed interarrival times
and the ratio of the sample mean and variance of the numbers of termination epochs. The ratio of the mean and variance of a Poisson process is
one. For Flagstaff the ratio of the sample mean and variance was 1.06.
In the application of the model to a specific short time interval,
use of the Poisson parameter estimated for the entire season may give
63
(
!51)d
64
less satisfactory results. This is indicated in Figure 5.3 where X was
estimated for six 20-day intervals and three 40-day intervals. In this
case the larger fluctuations in parameter estimates in the 20-day intervals than in the 40-day intervals is probably the result of smaller
sample sizes. However, a greater storm frequency later in the 120-day
period is indicated but is not large enough to result in the rejection
of the assumption of homogeneity. The length of the interval within
which the Poisson parameter can be assumed constant would be a function
of the regional climate. In Arizona this may be up to 180 days while
in other regions, more or less.
When all the random variables defined on the process are considered (see below), the method of moments estimate of X generally gives
better results than does the estimate obtained from equation (5.1).
Since equation (5.1) was derived from the consideration of a homogeneous
Poisson process, this may be the result of a non-homogeneous Poisson
process over the 120-day period. However, in Figure 5.4 the two parameter
estimates are shown as estimated from cumulative years of record, 1
through 15 years. An apparent downward trend and considerable fluctuation in the value of X as estimated by equation (5.1) is shown. This
may indicate that an insufficient number of years of record was used to
estimate X using equation (5.1). The lower curve in Figure 5.4 shows
the method of moments estimate of X. After the third year's data had
been included, the estimated value of x stabilized. Addition of more
years had little apparent influence upon the parameter estimate.
65
LO
1"
1
(\I
-7
4 .1048LUDiDcj
co
o
UOSSIO cl
eq
1 0
0
o a4ow4s3
66
(.0
3
c‘i
xr 'Jo laumiod LIOSSIOd 941
co
o
d
0
o
o
d
67
Figure 5.4 also indicates that useful estimates of the Poisson
parameter may be obtainable from as little as three to five years of
data at Flagstaff. Fogel et al. (1971) found that useful estimates of
the Poisson parameter of the number of thunderstorm events per season
could be obtained with as few as five years of record and a model calibrated with 10 to 15 years. Since thunderstorm events are generally
more variable than other storm types, it is reasonable to expect that
more stable Poisson parameter estimates could be obtained for winter
cyclonic storms with the same period of record. Arizona lies essentially
in a single climatic region (Sellers 1964). It is anticipated that
similar behavior of the estimates of X would be found for other stations
in Arizona. However, some variation in the parameter estimates may
arise due to the change in the frequency of snow events with elevation.
In other climatic regions the number of years of record required may
vary.
The over or underestimation of X would influence the model re,.
sults. For example, if X > X more than the true mean number of storms
would be predicted. For a given probability, more total snow water
equivalent would be predicted by the distribution function of X(t)
using X than with X. The snow-free periods would be shorter and the
snowpack durations would be longer for a given probability level. The
number of snow-free, snow cycles and therefore the number of renewals
A
may then be fewer than would be predicted using X. If X < X the opposite
effect would be seen.
68
Storm Magnitudes
Snowstorm amounts were assumed to be independent, identically
distributed random variables. In order to test the reasonableness of
the assumption that storm magnitudes were identically distributed, the
observed distribution function of storm magnitudes occurring in the
three periods used above for storm interarrival times were compared with
the observed distribution function of all storms during the December
through March winter period. Using Kiefer's test for the three samples
and a confidence level of 0.05, it was concluded the hypothesis that the
storm magnitudes for each of the three periods were identically distributed as the observed distribution function of storms for the entire
season could not be rejected (/T = 0.566, (43, 2
-1
(.95) = 1.584). The
observed distribution functions of the three periods and of the total
winter snow season are exhibited in Figure 5.5.
The parameter of the exponential distribution of storm magnitudes, y i , was estimated by the method of moments using all storms dur-
ing the 15 years of record. The estimated value was found to be y i =
1.80 storms-inch
-1
.
The hypothesis that snow storm magnitudes were distributed ex.
ponentially with parameter y l = 1.80 was not rejected at the .05 level
2
of significance (x
2
calculated = 2.675, x .05, 3 d.f. = 7.815). Figure
5.5 shows the theoretical and observed distribution functions of the
snowstorm magnitudes.
A model assumption was that storm magnitudes could be concentrated
at the storm termination epochs. When applied using daily data, the
69
A
0.9
0.8
117
116
Legend:
• •• Pooled Sample •
xxx Dec. I - Jon. 9
000 Joni° - Fe0.19
AAA Feb.19- Moc30
04 -
Goodness of FIT Tests:
(lifer's K-sample Test:
IY ..566< 1•;' (.95)•1.594
xa Test:
Q3 -
1(lcole.
• 2 E 75 c'''Jas,3d.r.
'
18115
Q2-
-
Q0
00
114
0.6
08
ID
1.2
lA
1.6
1.8
22
2.4
2.6
2.8
3.0
Storm Snow Water Equivalent, X(inches)
Figure 5.5.
The observed distribution functions of storm snow water
equivalent for three 40-day periods, the 120-day season
and the theoretical distribution function.
3.4
3.5
7.0
7.2
70
working assumption was that the total storm precipitation could be concentrated on the last day of a storm. The ablation rate in the model
does not change during the storm duration. Therefore in regions where
storms may last a number of days, during which meteorological conditions do not favor ablation, the model may underestimate the snowpack
duration and overestimate the snow-free periods. However, in Arizona,
storms seldom remain within the state for very long, seldom resulting
in more than two days of continuous, widespread precipitation (Sellers
1964). The mean storm duration at Flagstaff for storms as defined in
this study is 2.06 days. In regions where storms persist but a short
time such as Arizona, the predicted snowpack behavior may not be significantly affected by this assumption.
Although an exponential distribution was not rejected as a model
of storm magnitudes, inspection of Figure 5.5 indicated that for small
values of X, the computed distribution function gives smaller probabilities than were observed. Since the model as applied in this study
was specific for exponentially distributed storm magnitudes, application to regions where magnitudes were found to be described by'some
other distribution would require the substitution of this distribution
for the exponential distribution in Chapter 4. Conceptually this poses
no problem. Depending upon the specific distribution, the derivation of
the distributions of the random variables could become mathematically
C omplex.
To determine the number of years of record required for an
approximately stable estimate of the exponential distribution parameter,
71
the parameter was estimated using cumulative years of data and plotted
in Figure 5.6. It can be seen that there is considerable variation in
the estimated value and a general downward trend. The parameter estimate
appeared to stabilize after the 12th year had been included. Unlike
the occurrence of storms, the determination of a stable distribution
requires considerable data. Up to 50 years of data are recommended for
stable frequency distributions in mountainous areas (Weisner 1970).
However, unlike traditional precipitation frequency analyses, individual
storm amounts are considered in an event based stochastic model. This
may result in a larger effective sample size. Although a stable parameter estimate of the distribution of storm magnitudes may not have been
obtained with 15 years of record, Figure 5.6 indicated that at least the
length of record was adequate to dampen the large fluctuations in the
estimate.
Inaccuracy in the measurement of snow water equivalent could have
a significant affect upon the general goodness of fit of the model.
Precipitation, including snow, is usually measured in precipitation
gages at National Weather Service stations. Even with Alter shields
installed to reduce turbulence at the orifice, a rain gage measurement
of snowfall may be considerably less than the true snowfall (United
States Army Corps of Engineers 1956, Rechard 1972). The estimate of
y i was based upon precipitation gage measurements of snow water
equivalent. Since precipitation gage snow catches underestimate the
true amount of snowfall with the magnitude of the underestimation increasing with wind speed during snowfall, the parameter y i would be
72
uo!invisla 10Ru3uodx3
Btu
o J919WD1Dd ell/ ;0 a ICIMIS3
73
expected to be smaller than its estimate y i . The estimate of y l has a
direct influence upon the adequacy of the computed distributions of X,
X(t), Y, T and the renewal process. Since y i is the inverse of the mean
storm water equivalent, y i < y i would have a similar affect upon the
distributions of these random variables as would X > X.
Snowpack Ablation Rate
In the model the snow ablation rate was assumed to be a random
variable, C, varying between seasons. The estimation of snowpack
ablation rates was made as follows. For each winter season the snowpack
water equivalent was extracted from the records for all days between
storms, beginning the first day after a storm and ending the day prior
to the conmencement of the next storm, or when the snow water equivalent
reached the minimum value recorded. Within each winter season, the water
equivalents were adjusted to the maximum water equivalent observed during the Season. To each sequence of water equivalent values for days
between storms, the difference between the maximum observed water equivalent and the water equivalent on the first day after a storm was added
to all water equivalent values of that sequence. In this way the
sequence of observed water equivalent values for the interstorm periods
were corrected to a common value. Snowpack water equivalent was regressed on days after the end of storms. An estimate of the seasonal
average daily ablation rate was then the negative of the regression
coefficient. A summary of the regression analyses is presented in
Table 5.3.
74
Table 5.3.
A summary of the linear regression analyses of snowpack
water equivalent on days after the termination of storms
at Flagstaff, Arizona.
Yéar
Sample
Size
1957
21
.082 in.-day
1958
26
1959
Ablation
Rate (=-b)
Standard
Error
-1
.011 in.
-.86
52.25**
.171
.020
-.87
71.93**
24
.082
.034
-.46
5 • 97*
1960
60
.048
.003
-.90
247.30**
1961
20
.053
.016
-.62
11.14**
1962
72
.084
.003
-.97
1038.01**
1963
24
.054
.006
-.88
78.74**
1964
28
.190
.040
-.68
22.53**
1965
46
.134
.015
-.79
75.52**
1966
56
.068
.007
-.80
93.82**
1967
23
.051
.015
-.60
12.04**
1968
50
.041
.011
-.46
13.01**
1969
57
.171
.029
-.62
35.50**
1970
16
.117
.021
-.83
30.44**
1971
36
.053
.007
-.79
57.13**
* Significant at a = .05.
** Significant at a = .01.
75
By equation (4.52) the theoretical minimum ablation rate must
be greater than Y--
Yi
Therefore the distribution function of the random
variable C is shifted. The magnitude of the shift, c,
minimum
as 0.001 inch-day
-1
was taken
greater than the value of the ratio /— . For
Yi
Flagstaff, c „
corresponding to X = .097 was .054 and that correminimum
sponding to X = .123 was .068. In determining the observed distribution
function of ablation rates, the rates from Table 5.3 were plotted. An
ablation rate less than the minimum rate was assumed to be equal to the
minimum. Thus each observed distribution function possessed an atom at
the origin. The distribution function corresponding to X = .097 is
shown in Figure 5.7(a) while that corresponding to X = .123 is shown in
Figure 5.8(a).
In both cases, the observed distribution function was divided
into three intervals of width equal to one-third of the range. The
interval probability was determined and the distribution function
discretized by assuming the interval probability was concentrated at the
interval midpoints. In subsequent use conditional and unconditional
distribution functions were obtained using the interval midpoints and
associated probabilities. Unconditional distribution functions and
moments were obtained by weighting the conditional distribution functions and moments by each interval probability of C, then summing over
all three values. The intervals, midpoints and probabilities are also
shown in Figures 5.7(b) and 5.8(b).
76
0.0
0.00 .02
.04 .06
.08 .10
.12
.14
.16
Snowpack Ablation Rate, C(inches- day 0
.18
.20
.18
.20
-
(a)
1.0
0.8
1.7
c.) 0.6
0.4
0.2
0.0
0.00 .02.04 .06
.08
.10
.12
.14
.16
Snowpack Ablation Rate, C(inches-day ')
-
(b)
Figure 5.7. The empirical distribution of snowpack ablation rates
with minimum rate computed using X = .097. -- (a) The
distribution function. (b) The discretized density
function.
77
1 .0
0.8
a.
0.4
0.2
0.0
0.00 .02 .04 06
08 .10
.12
.14
.1 6.18
.20
Snowpack Ablation Rate, c (inches-day)
(a)
1.0
0.8
0.2
0.0
0.00 .02
.04 .06 .08
.10
.12
.14
.16
.18
Snowpack Ablation Rate (inches-day)
(b)
Figure 5.8. The empirical distribution of snowpack ablation rates
with minimum rate computed using X = .123. -- (a) The
distribution function. (b) The discretized density
function.
.20
78
Since a constant value of C within seasons was required in the
model, the affect of this assumption upon the results is of interest.
Using the same regression technique that was applied in estimating
seasonal snowpack ablation rates, the ablation rates for the eight 15-day
periods included within the 120-day winter snow season were estimated.
In the analyses, the snowpack water equivalents within each 15-day
period for the 15 years of record were adjusted to the maximum water
equivalent observed during the 15 years. The difference between the
maximum observed value and the value on the first day after a storm was
added to all observed vàlues for that particular intrastorm period.
The snowpack water equivalent for each 15-day period was then regressed
on days after the ends of storms to yield an estimate of the average
ablation rate (the negative of the regression coefficient). The results
of the regression are summarized in Table 5.4.
The snowpack ablation rates were plotted in Figure 5.9. The
average seasonal ablation rates computed from the empirical distribution
of seasonal snowpack ablation rates corresponding to X = .097 and X = .123
are shown as are the corresponding minimum values and the largest estimated rate. Except for the ablation rate for the first period, the
ablation rates follow the seasonal trend of total net energy, decreasing
from December to a low in January, then increasing with a sharp increase
in late March. The empirical seasonal distribution of the ablation rate
um = .054 to .190. All but
corresponding to X = .097 extends from c
minim
the first and last 15-day period estimates fall within this range. This
indicates that the assumption of a single ablation rate for the entire
79
Table 5.4.
Period
A suuflary of the linear regression analysis of snowpack
water equivalent on days for eight 15-day periods within
the winter snow season at Flagstaff, Arizona.
Sample
Size
Ablation
Rate
19
.028 in.-day
2
73
3
Standard
Error
-1
.0104 in,
-.55
7.28*
.186
.0281
-.62
29.55**
100
.067
.0034
-.89
391.93**
4
72
.054
.0061
-.73
80.89**
5
86
.110
.0103
-.76
112.94**
6
64
.078
.0191
-.46
16.82**
7
63
.114
.0141
-.72
65.29**
8
34
.444
.0495
-.85
80.17**
* Significant at
= .05.
** Significant at
a = .01.
80
0.40
o
Q;
0.30
6
cc
c
o
:4--0
4
‹t 0.20
cmax.=.190
0.10
0.00
15-Day Time Intervals
Figure 5.9. Daily snowpack ablation rates estimated
for the eight consecutive 15-day intervals
included in the 120-day season.
81
season could be expected to yield acceptable results even though the
ablation rate may be over or underestimated for certain short time
intervals. Since the range of the empirical distribution corresponding
to X = .123 is more narrow, less satisfactory results might be obtained.
The estimation of the minimum ablation rate by equation (4.52)
can also influence the model. A larger c , .
results in higher
minimum
ablation rates while a smaller c . .
results in lower ablation rates
minimum
as is indicated in Figure 5.9.
Although a random variable, the consistent over or underestimation of C would have a similar affect upon the random variables Y and T
as did X and y i . If the values of C were overestimated, the unconditional distribution functions of Y and T would reflect a higher ablation
rate. For a given probability the predicted snowpack duration would be
less than for lower rates of ablation. Since T = V + Y, the effect
would be less direct since the length of a snow-free, snow cycle depends
upon the snow-free period also.
Errors in the measurement of the daily snowpack water equivalent
as well as in the regression technique employed to estimate values of
C could influence the results. However, these are probably small when
used in conjunction with the assumption of a single seasonal mean daily
ablation rate.
Total Snow Water Equivalent
The sum of the individual magnitudes of storms occurring during
each winter snow season was used to obtain the observed distribution
82
function. The sample mean and variance were calculated. The compound
Poisson distribution function given by equation (4.11) was evaluated
using both Poisson parameter estimates. A Fortran program was written
for the evaluation of the distribution function. The programming is
explained in Appendix *B and a listing of the program provided. The
observed and theoretical distribution functions were plotted in Figure
5.10. Although an atom exists at the origin for each theoretical
distribution function, in both cases they were of such small magnitude
that they were not shown. P[X(t) = 0] = 9 x 10 -6 and P[X(t) = 0] =
4 x 10
-7
when X = .097 and .123, respectively. The distribution function
computed using X = .097 only approximately describes the observed
distribution function according to Figure 5.10 while the distribution
function computed using X = .123 is unsatisfactory. This observation
was substantiated by the chi-squared goodness of fit test. At the a =
.05 level, the compound Poisson distribution computed using
not rejected (x
2
calculated
7
3.038,
x2 .
05, 2 d.f.
a
=
.097 was
= 3.841). The
compound Poisson distribution evaluated using X = .123 was rejected
(x
2
calculated = 14,826, x 2
.05, 2 d.f. = 3.841).
The sample mean and
variance and the means and variances computed from equations (4.13) and
(4.15) have been presented in Table 5.5.
The individual storm magnitudes were assumed to be independent
of the number of storms. The total storm snow water equivalent was
therefore a compound Poisson process. This independence assumption may
be reasonable where the storm duration is very short in comparison to
c•
83
0 •
O 4-1
9-1 0
4J4-)
0
(111
4-1
0 Cr'
a.)
CY)
II
ti44-4
4-1 3-1
(1)
•
CO
3-1
4-1
CO '
(1)
'0 0
1-1
cr
r-4
c.) cti
•
4-)
4-0
W
O 0
4)0
U)
4-)
-0 co
cti
10 '0
C) '
•
nJ
•
r..1
0
84
Table 5.5. The observed and theoretical means and variances of total
seasonal snow water equivalent at Flagstaff, Arizona.
Observed
Mean (inches)
2
Variance (inches )
= .097
X = .123
6.44
6.47
8.20
8.67
7.19
9.11
the time between storms. In some areas, storm durations may be of such
length that they give rise to a stochastic dependence between the number
of storms and their magnitudes (Gupta 1973). The mean storm duration at
Flagstaff was 2.06 days for the 15 years of record used. The mean time
between storms (measured as the time between the last day of a storm and
the first day of the next storm) was 6.47 days. The mean interstorm
period is approximately 3.2 times as long as the mean storm duration.
Although probably not large enough to strictly meet the requirement of
independence, it is thought to be sufficiently large so that the assumption is reasonable. Without it, the derivations of the distribution
functions of the random variables would become mathematically intractable.
In spite of the possible inaccuracies in the data and the several
assumptions made, the compound Poisson distribution with X = .097 was
found to adequately describe the 120-day total snow water equivalent at
Flagstaff. There is a close agreement between the observed mean and the
mean computed from equation (4.13). The observed variance is greater
than the theoretical variance but the magnitude of the difference is
quite small (Table 5.5).
85
The Snow-Free
Periods
The snow-free periods are independent random variables, identically distributed as V with the same (exponential) distribution as the
storm interarrival times. An investigation of the snow-free periods was
undertaken since the distribution of the snow-free periods is potentially
useful information and is another check upon the applicability of the
model. Observed values of V were obtained from the daily record of the
depth of snow on the ground as the number of days between occurrences of
measurable snow depth (one inch or more). To compare the observed snowfree periods with the computed exponential distributions, all snow-free
periods for the 15 years of record were counted. If a measurable amount
of snow was reported on December 1, V 1 was recorded as 0 days in length.
For the 15 years of winter snow seasons, 81 snow-free periods were
counted. The observed distribution function and the two computed
distribution functions were plotted and are shown in Figure 5.11. The
hypothesis that the snow-free periods were exponentially distributed
with X = .097 was not rejected at the a
goodness of fit test (X
2
=
.05 level by a chi-squared
calculated = 3.379, X2
.,
05 4 d.f. = 9.488).
The exponential distribution function with a = .123 was rejected, however (x
2 calculated = 11.641, x 2
.05, 4 d.f. = 9.488). The sample mean
and variance were computed and are compared with the theoretical means
and variances in Table 5.6. That the snow-free periods are exponentially
distributed is also indicated by the near equality of the observed mean
and standard deviation (10.32 days). There is a close correspondence
86
87
Table 5.6. The observed and theoretical means and variances of the
snow-free periods at Flagstaff, Arizona.
A
Observed
Mean (days)
2
Variance (days )
X = .097
X = .123
10.65
10.31
8.13
106.45
106.28
66.10
between the sample mean and variance and the theoretical mean and vanA
ance computed using X = .097.
The snow-free periods (V) and the interarrival times between
storm termination epochs (I) in the model are identically, exponentially
distributed, with parameter X. The hypothesis that V was distributed
exponentially with parameter X = .097 and the hypothesis that I was
exponentially distributed with parameter X = .097 were not rejected at
= .05. This may indicate that V and I are identically distributed.
A
A similar conclusion cannot be drawn for the parameter estimate X = .123
since the hypothesis that the snow-free periods were exponentially
distributed with parameter X = .123 was rejected.
The length of the snow-free periods as observed from data depends
upon the definition of snowpack duration. Traces of snow depth were
considered as being snow-free when extracted from the climatological
records. If a trace of snow on the ground were not so considered, then
the lengths of these periods would be somewhat less than observed. The
significance of this possible source of observational error is not
apparent in Figure 5.11.
88
Snowpack Duration
The random variable snowpack duration (Y) was obtained as the
unconditional distribution of the random variable T(u), the time to snow
disappearance, by integrating the product of the density function of
T(u) and the density function of X
1 over all values of X 1 (equation 3.9)
Therefore only the random variable Y was selected for further exposition.
The distribution function of Y given by equation (4.27) was programmed.
The Fortran programming is described in Appendix B and a listing provided.
I
are
identically distributed as Y) it was found that the number of terms required for convergence depended upon the size of the value of the random
variable and also upon the snowpack ablation rate, C. Therefore, if,
after a predetermined number of terms, the equation failed to converge,
calculations were terminated. This did not occur until the unconditional
distribution function reached or exceeded a probability of .90. Therefore all features but the upper tail of the distribution were obtained.
The observed snowpack durations, Y, were determined from the
daily records of snow depth as the number of days snow depth was reported
as being one inch or more. Less than one inch of snow is reported as a
trace and is considered here to be snow-free. Eighty-one observations
of snowpack duration were obtained from the 15 years of record.
The total length of time that snow remains on the ground Y fl was
defined by equation (3.13). As was indicated in Chapter 4, Y n is of less
practical value than Y since the number of times the snowpack ablates to
0 is also a random variable K(t). The distribution function of Y
describes the probabilistic characteristics of the individual snowpack
89
durations. When used in conjunction with the random variable
T and the
secondary renewal process (discussed below) information as to the
probabilistic nature of snowpack duration and the number of renewals
can be obtained.
The theoretical distribution functions of Y for both parameter
estimates X = .097 and X = .123 and the observed distribution function
of Y have been plotted in Figure 5.12. Unlike earlier cases, changing
X parameter estimates has only a small apparent affect upon the theoretical distribution functions. The two theoretical distribution functions
are quite similar. Both yielded smaller cumulative probabilities than
is indicated by the observed distribution function. Neither theoretical
distribution function was rejected as a model for snowpack duration at
the cy = .05 level. For X = .097, x2 calculated = 4.275 while for X =
2
.123, x calculated = 5.005 and x2 .
05, 2 d.f. = 5.991.
The observed mean and variance and the theoretical means and
variances computed using equations (4.29) and (4.31) have been compared
in Table 5.7. The observed mean was considerably smaller than the
theoretical means while the observed variance was much smaller than the
theoretical variances.
Both theoretical distribution functions gave somewhat lower
probabilities than were observed (Figure 5.12). If this is a result of
parameter estimation in the model, it would indicate that X > X or
y i < y l . Either the mean number of storms was overestimated or the mean
snow water equivalent per storm was overestimated. In view of the be.
havior of X and y discussed above and the other results, it is felt
1
90
(A 5A) d
91
Table 5.7. The observed and theoretical means
and variances of the
snowpack duration at Flagstaff, Arizona.
Observed
Mean (days)
2
Variance (days )
X = .097
X = .123
9,90
17.89
21.17
254.39
1263.64
4698.72
that the parameter estimation does not explain the fit of the theoretical
distribution functions to the observed. The fit is probably the result
of the assumptions regarding the ablation rate or to errors in the observed values of Y. Since the theoretical distribution functions were
not rejected, the differences between the theoretical and observed
distribution functions are not statistically significant (at cy = .05).
The observed mean snowpack duration was considerably smaller
than either theoretical mean (Table 5.7). The large number of short
duration snowpacks shown in Figure 5.12 explains some of this difference.
The very large observed and theoretical variances overshadow the difference in means and show that large fluctuations in the snowpack duration
must be expected. In view of the large variances, the means are of
relatively little importance in the characterization of the snowpack
duration. The difference between observed and theoretical variances can
be attributed, at least in part, to the calculations of variances conditioned on small values of C. Recall from Chapter 4 that when c =
the moments of Y are infinite. When values of C near the theoretical
92
minimum value are used, the resultant conditional variances are very
large (Figures 5.7 and 5.8). Since low ablation rates occurred more
frequently, the variance conditioned on c
1 received greater weight in
the calculation of the unconditional variance.
When the observed values of V and Y were determined, a snow-free
day was one with either no snow on the ground or a trace. In the monthly
climatological data, the depth of snow was reported as a trace when it
decreased below a depth of one inch. Snow density increases rapidly
soon after snowfall and then continues at a slower rate. During melt
the snowpack density maY be as high as .45 to .50 (Garstka 1964). The
amount of snow water equivalent remaining on the ground when the snow
depth is reported as a trace may be significant. The actual snowpack
duration may then be underestimated from records when days with only a
trace of snow are considered to be snow-free. If the days when a trace
of snow contained appreciable snow water equivalent could be determined
and included in the observations of Y, then the observed distribution of
Y may be closer to the computed distribution than was indicated in
Figure 5.12.
Snow-Free, Snow Cycles
n
Unlike the random variable Y ,the random variable giving the
total length of snow-free, snow cycles (i.e., the sum of n cycles) dur-
ing a season, T 'a , assumes additional importance since the probability
mass function, mean value function and variance of the secondary renewal
process can be directly obtained from the distribution function of T.
n
The distribution function of T, given by equation (4.41) was programmed
93
(see Appendix B). As in the evaluation of the distribution function of
Y, a convergence problem existed for large values of T and C. If the
distribution function did not converge after a given number of terms,
calculations were terminated. However, the probability of 0,90 was
equaled or exceeded in the unconditional distribution before the equation
no longer converged, so all but the upper tail was obtained.
The observed values of T were determined as the sums of the
paired observations of V and Y. The resultant 81 values of T for the
15 years of record were then plotted to form the observed distribution
function. The theoretical distribution functions for each estimate of
X and the observed distribution function have been presented in Figure
5.13. Up to approximately t = 35 days, the theoretical distribution
functions bracket the observed distribution function. Thereafter both
theoretical distribution functions gave lower cumulative probabilities
than estimated by the cumulative relative frequencies. The chi-squared
goodness of fit test was used to test the hypotheses that each of the
theoretical distribution functions fit the observed values of the random
variable. At the oe = .05 level, neither theoretical distribution func-
tion was rejected. For the distribution function computed using X = .097,
2
2
= 12.592. For the distribution
= 7.133 while X .05 ,
calculated
X
6 d.f.
^
2 calculated = 10.247 and the critical
function computed using X = .123,
value was the same as in the first test.
As before the sample mean and variance of T and the theoretical
means and variances computed using equations (4.34) and (4.35) were
placed in Table 5.8. The sample mean was somewhat less than the
94
a)
a)
0
•
95
Table 5.8. The observed and theoretical means and variances of the
snow-free, snow cycles at Flagstaff, Arizona.
Observed
Mean (days)
2
Variance (days )
X = .097
X = .123
20.56
28.20
29.30
331.10
2370.06
4466.22
theoretical means. The variances computed from equation (4.35) were an
order of magnitude greater than the sample variance, indicating that,
at least for the 15 years of record at Flagstaff, the length of the
snow-free, snow cycles was much less variable than would be predicted
by the model of the snow process.
As with the snowpack duration, the distribution of the snow-free,
snow cycles depends upon the two parameters X and y as well as the distribution of snowpack ablation rates. Since T = V + Y, and V depends only
upon x, the affects of the estimates of x and y i on T and the distribution of C would be generally similar to the affects the estimates have
upon Y.
The observed values of T were determined as sums of pairs of V
and Y values. The possible underestimation of Y and overestimation of
V values from the daily snow depth records would result in changing
observed values of T.
The observed mean of the snow-free, snow cycles was somewhat
less than either theoretical mean but due to the inclusion of the
96
snow-free periods, this difference was relatively less than that noted
for Y. The lack of agreement between observed and theoretical variances
of T can be attributed, at least in part, to the calculations of conditional variances, conditioned on small values of C as in the calculations
of the variances of Y. The large variances also indicate that at
Flagstaff, large fluctuations in the length of the snow-free, snow
cycles must be anticipated. However the model shows much greater fluctuations than is indicated in the historical record. In view of the
large variances, the mean is of relatively little value in describing
the behavior of the snow-free, snow cycles.
The Snow Renewal Process
The relationships between the probability mass function of the
snow renewal process, {K(t); t > 0} , and the distribution function of
n
T , equations (3.22) and (4.44), were used to evaluate the probability
mass function. Then equations (4.45) and (4.47) were used to compute
the mean value and variance.
The distribution functions of T
n
for n = 1,2,...,10 were computed
for each estimate of x. For n = 10, the probabilities were of such small
magnitude (P[T i° < 60] = .0008 when X = .123 and .0002 when X = .097)
that calculations were terminated. The largest value of T for which con.
vergence was obtained was t = 60 days when X = .123 and 70 days when
X = .097.
The 120-day period was divided into twelve 10-day intervals and
the number of times the snowpack depth decreased to less than one inch
in each interval was recorded. The probability mass functions for 20,
97
40 and 60 days were determined using each
20-day period, each 40-day
period and each 60-day period beginning December 1.
The 120-day winter
snow season then consisted of six 20-day, three 40-day or
two 60-day
periods. These were pooled to yield a single sample for the 20, 40 and
60-day periods.
The probability mass functions for t = 20, 40 and 60 days and
for each estimate of X were then evaluated. The distribution function
of the sum of ten snow-free, snow cycles was the largest calculated,
therefore the probability mass function of a maximum of nine renewals
could be determined. This was adequate, however, since the maximum
number of renewals observed during any 60-day period at Flagstaff was
seven. The observed and theoretical probability mass functions for the
20, 40 and 60-day periods are shown in Figures 5.14, 5.15, and 5.16,
respectively. The results are variable. One theoretical probability
mass function does not seem to give a consistently closer fit to the
observed relative frequencies than the other for a given time period,
nor is there a consistent pattern between the three time intervals.
Both theoretical probability mass functions generally follow the observed mass function. A chi-squared test was used to compare the
goodness of fit of the observed relative frequencies with the theoretical
probability mass functions. A summary of these tests has been presented
in Table 5.9. The probability mass function computed using the method
of moments estimate of X was rejected (at 0 = .05 level) for the 20-day
,
period, but not for the 40 or 60-day periods. The probability mass
function computed using x estimated by equation (5.1) was rejected for
the 20 and 60-day periods.
98
q-1
o
X 0
0
H
v -
6
ro
6
cv
6
(u= (o z)>1)d
_
6
0
6
0
O
99
CD
acr
rn ch
> N
n n
.0
0 4:e.C4s*C
I
4:51
X
X 0
X 0
x o
O
r°.
(u= ot7)>Od
(
100
X 0
10 0
Cf)
(0
o
X 0
I
.r-i
0
0
;-1
a
,
IX
re)
0
•
y•-1
Ct1
C.)
5 -4
9-1
0
4-1
0
14
0
0
w
.0
(0
'0 0
0
c0
0
0
4•4
$-4
CI)
01
ctl
OX
0
3
0
0
H
)0-
006.
ttl
CNJ
O
(u=(09))Od
a)
101
Table 5.9. Chi-squared tests of the probability mass functions of the
snow renewal process.
Time Interval
Critical Values
Calculated
X = .097
2
20 days
x2
Values
X = .123
= 3841
6.46
4.72
40 days
2
= 3 841
'
x .05, 1 d.f.
3.40
3.12
60 days
2
= 5991
X .05, 2 d.f.
2.41
6.05
X .05, 1 d.f.
The mean value function and the variance of the snow renewal
process were computed using equations (4.50) and (4.52). The observed
mean value function and variance were computed for 10, 20, 30, 40, 50
and 60-day periods. All of the consecutive 10 through 60-day periods
within the 120-day winter snow season were pooled to yield a single
estimate of the mean value function and variance for each period. One
exception to the above procedure was the 50-day period. Since the 120day season could not be evenly divided into 50-day perios, the two
periods December 1 through January 19 and January 30 through March 20
were used.
Over all time periods, the observed mean value function and the
mean value function corresponding to X = .097 are in closer agreement
than are the observed and that corresponding to X = .123. However, for
A
the first two time periods, 10 and 20 days, X = .123 gives a better fit
102
(Figure 5.17). The observed and theoretical variances have been presented in Figure 5.18. The variance of the observed snow renewal process is consistently larger than the variances computed from equation
- (4.52). The difference increases with the length of the time period.
That the computed probability mass function (X = .097) was re-
jected (at ce = .05) for 20-day intervals but not for longer intervals
may indicate that in short time intervals, the snow model may give
results which inadequately describe the probabilities of the number of
times the snowpack ablates. Since the snow renewal process was the last
step in the model development, it is dependent upon all of the assumptions made throughout the development. The over or underestimation of
parameters and the assumption of a single seasonal daily snowpack
ablation rate are reflected in the probability mass functions of the
renewal process and in the mean value function and variance.
The manner in which the number of renewals within an interval
were observed may have influenced the relative frequencies. As in the
observation of Y, when the snowpack depth decreased to a trace, complete
ablation was assumed. If the traces contained significant snow water
equivalent and were considered, the resultant relative frequencies of
the number of renewals may have been altered.
The renewal process is asymptotically normally distributed
(Parzen 1962). As the time interval becomes large, the probability mass
function of the renewal process approaches the normal density function.
Although a 60-day interval is probably not of adequate length for the
number of renewals to be approximately normally distributed, comparison
103
3.0
2.0
1.0
0.0
10
20
30
40
50
60
Time Intervals (Days)
Figure 5.17. The observed and theoretical mean value functions of the
snow renewal process.
104
10
40
20
30
Time Intervals (Days)
50
60
Figure 5.18. The observed and theoretical variances of the snow' renewal
process.
105
of Figures 5.14, 5.15 and 5.16 reveals that as the interval length increases, the observed and computed probability mass functions become
less skewed.
In Chapter 4, the ratio p was defined by equation (4.51). The
ratio then represented the expected fraction of time that the ground was
snow covered. When p was computed using X = .097, a value of 0.59 was
obtained. For X = .123, p = 0.67. The ratio of the sample means of Y
and T yielded a sample estimate of
= 0.48. For the 15 years of record,
the ground was snow covered an average of 48 percent of the 120-day
season. The average amount of time the ground was snow covered as estimated from the ratio
was somewhat greater than observed. In view of
the large variances of Y and T observed in the Flagstaff data and computed, the ratio p would be expected to vary over a considerable range.
Therefore the ratio p could only be used in a very qualitative description of the characteristics of the Flagstaff snowpack.
CHAPTER 6
SUMMARY AND RECOMMENDATIONS
In Chapter 6 a summary of the results of the snow model application to an Arizona climatological station is presented. Recommendations
for possible future studies are made.
Summary of Results
A stochastic model of the snowfall, accumulation and ablation
process was developed. Random variables characteristic of the snow process of snow storm occurrences and storm magnitudes that were independent
and identically distributed. Distribution functions of the random
variables were derived in general, and then in the case where storm
amounts were exponentially distributed. An Arizona climatological station was selected. Parameter estimates were made, basic assumptions
investigated and the model results compared with data.
The climatological station at Flagstaff, Arizona, was used since
it is located within an important snow zone and the snowpack observations necessary for model application were available. During the 120day long period December 1 through March 30, snow storms, uninterrupted
sequences of days receiving .01 inch or more of snow water equivalent
preceeded and followed by dry days, occurred approximately as a homoge-
nous Poisson process. The snow storm magnitudes were exponentially
106
107
distributed with parameter y l . Storms were usually of short
duration,
therefore the assumption of independence between storm
occurrences and
magnitudes was thought to be reasonable.
Two estimates of the Poisson parameter X were used. The first
was determined by the method of moments while the second was developed
from the consideration of the maximum interarrival times of a homogeneous
Poisson process. The method of moments estimate generally gave better
results than did the second estimate of X. The difference between the
two parameter estimates could be due to non-sufficiency of the estimates,
a non-homogeneous Poisson process or small sample size. It was concluded that the sample was of insufficient size for a stable estimate of
x using the second method.
The compound Poisson distribution with Poisson parameter estimated by the method of moments was found to adequately describe total
snow water equivalent at Flagstaff. The compound Poisson distribution
with the Poisson parameter estimated by the second method did not.
The observed snow-free periods were exponentially distributed
with parameter x estimated by the method of moments. In theory, the
snow-free periods and the storm interarrival times are identically,
exponentially distributed. This was also indicated in the observed
distributions, where the snow-free periods and storm interarrival times
were both described by an exponential distribution with parameter X
estimated by the method of moments. The exponential distribution of
snow-free periods with the parameter estimated by the second method was
rejected, therefore a similar conclusion could not be drawn.
108
Thetheoret icaldistributionsofthesnowpackciuratio ns
(Y ) and
.
ofthesnow-free,snowcycles (T. )fit the observed distributions
at the
chosen level of significance. These distributions
were functions of the
Poisson parameter x, the parameter y i from the exponential distribution
of storm magnitudes and the snowpack ablation rate, C. Both Poisson
parameter estimates were used in each of the theoretical distributions.
Both estimates yielded similar results for the snowpack durations and
for the snow-free, snow cycles. For large values of the random variables
and high ablation rates, the infinite series in the expressions for the
distribution functions did not converge when an acceptable number of
terms were used. Cumulative probabilities of at least .90 were obtained
before this occurred, so all but the upper tails were obtained.
The distributions of Y, T and T
n
were conditioned upon the snow-
pack ablation rate which appeared in the distribution functions as y =
cy l . Estimates of the mean daily ablation rate for each season were
obtained from a linear regression analysis of snowpack water equivalent
data. The empirical distributions of ablation rates were formed from
the regression coefficients. The unconditional distributions were subsequently found by discretizing the empirical density functions of the
snowpack ablation rates and numerically integrating. Even though a
single ablation rate was assumed to apply through each winter season,
the model results were generally satisfactory. One exception was that
theoretical variances much larger than the observed variances were
obtained when ablation rates near the theoretical minimum were used.
109
The probability mass functions of the snow renewal
process,
giving the probabilities of the number of times the snowpack
ablated
completely in an interval, were determined from the distribution func-
tion0fT.'s for 20-day, 40-day and 60-day intervals. When the distributionfunction o fT . I s with X estimated by the method of moments was
used, the computed probability mass functions for the 40 and 60-day
intervals adequately fit the observed relative frequencies. The computed
probability mass function of the 20-day interval was rejected. The
theoretical probability mass functions with X estimated by the second
method did not fit the relative frequencies of the observed number of
renewals in either the 20 or the 60-day periods.
The theoretical mean value functions and variances of the snow
renewal process were computed from the distribution function of T.'s.
1
The mean value function computed using the method of moments estimate of
X showed close agreement with the observed mean value function. The
theoretical mean value function computed using X estimated by the second
method generally yielded larger values than the observed. The theoretical variances of the snow renewal process were somewhat lower than the
observed variances and the magnitude of the difference increased with
the length of the time interval.
It was concluded that the snow model developed in this study
could give useful results when the underlying assumptions were met, at
least approximately. Applications to other regions would require verification of the adequacy of the assumptions.
1 10
Recommendations for Further Studies
The model developed in this study possesses several shortcomings
that warrant further research. In the derivation of the distribution
functions of Y, T and T n , the infinite series forms of e
the modi-
fied Bessel function were used. For large values of these random
variables, at high ablation rates, the infinite series in the distribution functions failed to converge with an acceptable number of terms.
Further investigation is needed into alternate methods of determining
the distribution functions.
Two methods of estimation of the Poisson parameter were used.
Under certain conditions, the estimates may not be sufficient. The
conditions under which they are not could be the subject of further
study. Alternate methods and alternate storm definitions could be
included.
The model is sensitive to the snowpack ablation rates. The
empirical distributions of snowpack ablation rates were determined by
regression analysis. The regression coefficients were then considered
to be realizations of the albation rate. The use of regression coefficients as values of a random variable may be misleading since different
results may be obtained when other methods of estimating the ablation
rate are used. In future applications, the assumption of a single
ablation rate might yield acceptable results. This single ablation rate
could be estimated by regression analysis. Other methods of determining
ablation rates and ways of including variable (within season) ablation
rates need to be explored.
111
In the study, the model was compared with data from only
one
station. The applicability of the model to other stations in Arizona
should be checked since the frequency of snow storms, storm magnitudes
and ablation rates may vary from station to station. In addition, the
space extension of the model would require parameter estimates and
estimates of the ablation rates. When applying the model to stations
outside the climatic region in which it was developed, consideration
should be given to the several assumptions made in its development.
A potential use of the model is in the determination of the stochastic properties of streamflow. A possible approach to obtaining the
distribution function of discharge, Q(t), would be the application of
unit hydrograph theory. The snowpack water equivalent, Z(t), is analogous to a rainfall hyetograph. Convoluting Z(t) with the kernal function
representing ordinates of the unit hydrograph would define a new random
variable, Q(t), the discharge or ordinate of the streamflow hydrograph.
A significant difference between this approach and classical unit hydrograph theory is the snowmelt lag time, which would require investigation.
APPENDIX A
INVERSION OF THE LAPLACE TRANSFORMS OF Y, T AND T n
The Laplace transforms of the density functions of Y, T and Tt
are inverted. The inversions were carried out using Table of Laplace
Transforms (Roberts and Kaufman 1966). All references with respect to
Laplace transform pairs and operations with Laplace transforms are from
this table.
Inversion of the Laplace Transforms of Y
The Laplace transform of Y was given by Prabhu (1965) as
2
_
LY(s) - (s+X+y) - As+X-Py) - 4XY
2X
(A.1)
From the table of operations, Part II of Roberts and Kaufman, inverse
transform pair (1)
ag
-1
(s) = af(t)
(A.2)
where
a=
I.
X
The numerator of equation (A.1) is nearly of the standard form (inverse
transform pair 84, p. 215)
/22
s - is - a
(A.3)
112
L
113
Applying the shifting theorem given by transform pair 3, p. 169
g
-1
(cs - b) = c
-1 b/c t
e
f(t/c)
(A.4)
Let
b =
+ y)
c= 1
t = y
Then equation (A.4) becomes
g-1(cs - b) = ef(y) (A.5)
Rewriting equation (A.1) in the form given by equation (A.2)
1
LY (s) =-5-[(s+x+y) -/(s+y+x) 2
,.
-
4]
(A.6)
Applying the results of the shifting theorem given by equation (A.5) to
the portion of the right hand side of equation (A.6) in brackets
-
(s)
Y
=
Y(X+y)
e
2X
[s -/s 2 -
(A.7)
where
a = 2(Xy)
2
.
Using transform pair 84, p. 215, the inverse of equation (A.7)
e -Y(x+Y)2(NY, 1/2
k
1 [2(XY) Y]
Y
,
L'(s)
- 2X
Y
where 1 1 (u)is the modified Bessel function of order 1.
(A.8)
114
Inversion of the Laplace Transform of T and T n
The transform inversion of L (s) differs somewhat from the inverT
sion of the transform of Y in that the Laplace variable appears in the
denominator, The Laplace transform of T was given as
L (s) _ (s+x+y) - 1(s+x+y) 2
2(X+s)
-
4 XY
(A.9)
Applying transform pair (3), p. 169 from the table of operations
g
-1
(as-b)= a
-1eb/a t
f(t/a)
(A.10)
Letting
a= 1
b =
Then
g
-1
e;.xt
(A+s) —
(A.11)
f(s)
2
L -1eXt
(s) —
L - [ s+N -
- 4 NY]
(A.12)
From transform pair (13) in the table of operations
L
-1 1
f(u) (du) 1'
g(s) =
(A.13)
0
When n = 1
L
-
1 _g_(2)
.
f(u) du
(A.14)
But
f(u) = L
-1 g( s )
(A.15)
115
and
g(s) = (s+y) - As+y) 2 - 4xy
(A.16)
Again using transform pair (3), p. 169
h(y)
e -yt (s v/S2
4xy)
(A.17)
where
a= 1
b=-y
Then
L
e - Y t L -1 (s -/s 2 - 4xy)
(A.18)
By transform pair (84), p. 215
L -1-(s
470 2(
)
-
-
r
)2
I
1
[2t(xy)]
(A.19)
Substituting the right hand side of (A.19) into (A.18)
i
_ -1 . . ...
L g(s) - e -yt 2(Xy) I [2t(Xy) 2 ]
t
1
(A.20)
Substituting equation (A.20) into equation (A.14)
t
g(s)
0
e-Yu 2(xy)15E2u(Xy)1/21du
1
(A.21)
and subsequently, substituting equation (A.21) into equation (A.12)
L
-1
T
(s) = e
-Xt f
0
e
(xy) I [2u(Xy) 1/2 ]du
1
(A.22)
which is the inverse of the transform of T as given by equation (A.9).
116
The Laplace transform of T n was given as
A
2
s+X+y) - 4 Xy
L n (s) — [(s+X+Y) -2(x+s)
T
(A.23)
As in the inversion of the transform of T, two shifts are necessary to
put the transform into a standard form. The first shift is carried out
as it was in equations (A.10) through (A.12) resulting in
L n
-1
e -Xt -1[(s+y) - j(s+y)
(s) —
2
- 4Xy]
n
(A.24)
2n
By equation (A.16),
L
-1 1 g(s)
n
s
j f(u) (du) n
—
(A.25)
0
But
f(u) = L
and
-1
g(s)
•
g(s) = Us+Y) - As+0 2 4 XY1 n .
(A.26)
Using transform pair (3), p. 169
h(s+y) = e - Y t (s -/s 2 - 4Xy) n
where
a= 1
b=-y.
Then
C ig(s) = e - Y t L -1 (s - i/s 2 - 4Xy) n(A.27)
117
By transform pair (36), P. 226
L-1 (s -
- 420;)
n
n2(Xy)k
In[2(Xy)kt].
(A.28)
Substituting equation (A.28) into equation (A.25)
2n(W)2
[2(Xy)'ul(du)n.
un
L
-1 h g(s) = j
sn
0
(A.29)
Substituting equation (A.29) into equation (A.24)
L n -1 (s) = n e -Xt
T
e -yu ( xy ,
)
n /2 in [2u(xy) 1/2
](du)n.
0
(A.30)
APPENDIX B
PROGRAMMING THE DISTRIBUTION FUNCTIONS OF X(t), Y AND T n
Due to the complex nature of the expressions derived for the
distribution functions of X(t), Y and T n , the University of Arizona CDC
6400 computer was used to obtain numerical results. Computer programs
for each distribution function were written in FORTRAN. The programming
techniques are briefly explained below, followed by a listing of each of
the three programs.
The Distribution Function of X(t)
The distribution function of X(t) was written in the following
form
(yitx)
F
X(t)
(x) =
k
00
v
- '1 —
k! (k-1)! L (j - l)! (k+j-1)
k=1
i=1
(B.1)
In order to minimize roundoff errors due to the potentially large magnitudes of the numerators and denominators, the following approach was
used. Beginning with the inner summation, the first term was stored.
Each succeeding term was computed as the product of the immediately preceding term and a multiplier. For example, the first term of the inner
summation is
-
y i
(B.2)
118
119
When j = 2, the term is
-Y 1 • (j-1)
2
Yl x
(B.3)
When j = 3, the term is (B.3) times
• Similarly, the terms for
j = 4,5, ... were computed as the preceding terms times this multiplier.
After all terms of the inner summation were computed and stored
(up to the predetermined maximum number of terms) the outer summation was
computed. Again, the terms were computed by determining the multiplier,
then computing the terms for k = 1,2,... in the same way they were computed for the inner summation. The terms of the outer summation were
also stored in a temporary storage location. The distribution function
was then evaluated by multiplying the outer temporary storage variable
corresponding to k = 1 times every inner storage variable corresponding
to j = 1,2,3, ... . The results were summed and the procedure repeated
for k = 2. A cumulative sum was used to sum over all k. The product
(k+j-1) in the denominator of the inner summation was included in the
last step since it depended upon k. Upon termination of the summation
at the maximum k, the exponential e
-xt
was included.
The number of terms required for convergence (to 4 decimal
places) depended upon the value of the random variable, X(t). The
approximate number of terms required for convergence was determined
experimentally for selected values of the random variable. These results
formed an algorithm which was used to compute the number of terms in
the inner and outer summations for a given value of X(t).
120
Double precision variables were used to further minimize errors
due to roundoff. In the last step the mean and variance
were computed.
The Distribution Function of Y
In programming the distribution function of Y, a
similar approach
was used. The distribution function of Y was written as
CO
F (y) =
Y
1
k k-1 2k-1
Y X Y
k! (k-1)!
(-1)j-1(X+Y)j-iYi-1
(j-1)! (2k+j-2)
( B. 4)
k=1
The first term of the inner summation was stored. The second
term was computed as the product of the first and the multiplier. Each
succeeding term was similarly calculated. The outer summation was computed in the same way. The remainder of the evaluation was carried out
in the same manner as was the evaluation of X(t). Double precision
variables were used to further minimize roundoff errors. The unconditional distribution function was computed by weighting the conditional
probabilities by the probability of the snowpack ablation rate, C, and
summed over all values of the ablation rate.
The number of terms in the outer and inner summations required
for convergence depended upon the value of Y. In addition, the number
of terms also depended upon the snowpack ablation rate, C. The number
of terms required for convergence were computed experimentally for
selected values of Y and each value of the snowpack ablation rate. The
results formed an algorithm for the calculation of the required number
of terms.
121
In the last steps of the program, the conditional and unconditional means and variances were computed.
The Distribution Function of T n
The evaluation of the distribution function of T n was accomplished
using the same prograuning techniques employed for X(t) and Y. The
distribution function was written as
k-1 2k+2n-2
F n(t) = n(Xy)n 7 ON) t
T
L, (k-1)!(k+n-1)!
k=1
j=
(-1)1-1(yt)j-1
I (2k+j+2n-i-3)
1=1
(...1)m-1(x)m-1
•
(m-1)! (2k+j+2n+m-4)
(13.5)
m=1
The presence of a third summation required the employment of additional
temporary storage. Otherwise, each summation was evaluated by computing
each term as the product of the immediately preceding term and the
multiplier. The product in the denominator of the middle summation was
determined by computing the product for a given value of n over all
values of j and k. The results were stored in a temporary two-dimensional
array. The array was then called inside the do loops used to evaluate
the distribution function. Double precision variables were again used
to further minimize roundoff errors.
The conditional and unconditional distribution functions were
evaluated as they were in the program for Y. The conditional and unconditional means and variances were calculated.
122
Discussion
The time required for compilation and execution of the programs
increased with the increasing complexity of the program. The program
for the evaluation of X(t) (TPRECIP) required approximately 6.4 seconds
and 31060B core storage. Program SEVREV, which evaluated the distribution function of Y required approximately 19 seconds and 31603B core
storage. Program SIX (the distribution function of Tn) required 1636
seconds and 64747B core storage. The time required increased tremendously in program SIX since the distribution functions of Tn (n = 1,2,...,10)
were computed for 19 values of T and 3 values of C, and required the
evaluation of a triple summation for each value of T and C.
The number of terms required in the summations in Y and Tn increased nonlinearly as functions of the value of Y (or T) and of the
snowpack ablation rate. For large values of Y (approximately 40 days)
and T (approximately 60 days) and high ablation rates (c = .17 inchday
-1
) the probabilities no longer converged within acceptable computing
time limits. Calculations were terminated when this occurred. In both
programs, for each estimate of X, cumulative probabilities equal to or
greater than .90 were obtained. In spite of the failure of convergence,
all of the distributions except the upper tails were evaluated.
123
PROGRAM TPRECIP (INPUT,OUTPUTITAPE1=INPUT,TAPE2=OUTPUT)
C PROGRAM TO EVALUATE THE DISTRIBUTION FUNCTION OF THE COMPCUND
C POISSON PRCCESS OF TOTAL ACCUMULATED WATER EQUIVALENT TO TIME T
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
LIST OF PROGRAM VARIABLES, PARAMETERS AND INDICES
GAM
- PARAMETER FPCM THE EXPONENTIAL DISTRIBUTION OF WATER EQUIVPER STORM
- INNER SUMMATION INDEX
- MAXIMUM NUMBER OF TERMS IN INNER SUMMATION
- OUTER SUMMATICN INDEX
- MAXIMUM NUMBER OF TERNS IN OUTPR SUMMATION
- CCMPUTED NUMBER OF TERMS IN OUTER SUMMATION
- COmPUTED NUMBER OF Tr_RMS IN INNER SUMNATICN
- NUMBER OF VALUES CF THE RANDOM VARIABLE, X
- LENGTH OF THE TIME INTERVAL, DAYS
- TOTAL WATER EQUIVALENT TN THE INTERVAL OF LENGTH T
- PARAMETER FROM THE POISSON DISTRIBUTION OF THE NUMBER OF
STORMS IN THE INTERVAL T
THE COMPUTED MEAN OF TOTAL SNOW WATER EQUIVALENT IN THE
X MEAN
INTERVAL T
XVAR - THE COMPUTED VARIANCE OF THE TOTAL SNOW WATER EQUIVALENT IN
THE INTERVAL T
ALENT
J
JJ
K
KK
LIOI)
LJ(I)
N
T
X(I)
XLAM
-
DOUBLE PRECISION DFX(20),TEMPB,E,GX,TLOTEMP(150),ATEMP(150),XG,TL
1XG,Z
DIMENSICh LK(20), LJ(20),X(20)
C SET JJ AND KK EQUAL TO DESIRED UPPER LIMITS
JJ = 150
KK = 50
C
READ IN PARAMETERS, LENGTH OF INTERVAL,T, AND NUMBER OF VALUES OF X
READ (1,100) XLAM,CAM,T,N
100 FORMAT(3F10.0,I3)
C
INITIALIZE ARRAYS
00 77 I=1.N
X(I)=0.
OFX(I)=0.
77 CONTINUE
READ(1,101) (X(I),I=1,N)
101 FORMAT(8F10.0)
TL = XLAM*T
C = OEXP(- TL)
C 00 LOOP TO ITERATE X VALUES
00 10 I=10
IF(X(I) .GT. O.) GO TO 999
OFX(I) = E
GO TO 10
ARE CALCULATED
C THE NUMBER OF TERMS OF THE OUTER AND INNER SUMMATIONS
* 6
999 LJ(I) = 20 +
LK(I) = 15 + X(I)
.
LK(I) ARE LESS THAN OR
C IF THE COMPUTED NUMBER OF TERMS, LJ(I) AND
TERMS, THE TOTAL NUMBER OF
C EQUAL TO THE MAXIMUM ALLOWABLE NUMBER OF
C TERMS ARE SET EQUAL. TO THE COMPUTED NUMBER
KKKK = KK
124
IF( LK(I) .LE. KK) KKKK=LK(I)
JJJJ = JJ
IF(LJ(I) .LE. JJ) JJJJ = LJ(I)
GX = GAM*X(I)
XG = -GX
TLXG=TL*GX
C 00 LOOP TO COMPUTE ELEMENTS OF INNER SUMMATION OF DISTRIBUTION FUNCTION
BTEMP(1) = 1
00 43 J=2 1 JJJJ
BTEMP(J) = BTEMP(J-1 ) * (XG/(J-1))
43 CONTINUE
C
DO LOOP TO COMPUTE ELEMENTS OF OUTER SUMMATION OF DISTRIBUTION FUNCTION
ATEMP(1) = TLXG
DO 53 K=2,KKKK
KTK = K * (K-1)
ATEMP(K) = ATEMP(K-1) * (TLXG / KTK)
53 CONTINUE
C 00 LOOP TO ITERATE K
DO 12 K=1,KKKK
KKK = K-1
TEMPS=
DO 13 J=1,JJJJ
b.
TEMPB = TEMPB + BTEMP(J) / (KKK+J)
13 CONTINUE
OFX(I) = OFX(I) + ATEMP(K)*TEMPB
12 CONTINUE
DFX(I) = OFX(I) * E
• 10 CONTINUE
C
C COMPUTING MEAN AND VARIANCE OF TOTAL SEASONAL WATER EQUIVALENT
XMEAN = (XLAM*T)/GAM
XVAR = (2.*XLAM*T)/(GAM*GAM)
PSTM= XLAM*T
C
PRINTOUT OF RESULTS
WRITE(2,400) (LK(I),LJ(I),I=1,N1
400 FORMAT(1H ,I3,5X,13)
WRITE(2,200) XLAM,GAM,PSTM,T,XMEAN,XVAR
200 FORMAT(1N1,15X,*CUMULATIVE DISTRIBUTION FUNCTION OF TOTAL SEASONAL
,20X,*GAMMA 1
I WATER EOUIVALENT4/1H0,20X,*LAMB0A*,14X,*=*,F11.4/1H
1*,13X,*=*,F11.4/1H ,20X,*MEAN STORMS/SEASON =*,01.1.4/1H ,20X,*SEA
4 ,011.4/1H t2
3 30 N LENGTH*,7X,*=*,F10.3 1 1H ,20X,*MEAN SNOW WE*,8X,*=
,011.41
4
SNOW
WE
=
40X,*VARIANCE OF
WRITE (2,201)
201 FORMAT(1H0,22X1*WE,INCHES * ,4Xt * CUMULATIVE * / 36X,*P R OBABILITY*)
WRITE(2,202) (X(I),OFX(I),I=1,N)
202 FORMAT(1H0,20X,F5.215X, 013 . 6)
STOP
END
125
PROGRAM SEVREV(INPUT,OUTPUT,TARE1=INPUT,TAPE2=OUTPUT)
C PROGRAM TO EVALUATE THE DISTRIBUTION FUNCTION OF THE SUN OF N
C PACK DURATIONS, Y
C LIST OF VARIABLES, PARAMETERS, AND INDICES
C NUNN = NUMBER OF RENEWALS CF THE SNOW PROCESS
C NUMC = NUMBER OF INTERVALS SELECTED FROM THE DISTRIBUTION
C NUMY = NUMBER OF VALUES OF THE RANDOM VARIABLE Y
= MAXIMUM NUMEER OF TERMS IN THE OUTER SUMMATION OF
KK
C JJ
= MAXIMUM NUMBER OF TEPMS IN THE INNER SUMMATION OF
COMPUTED NUMBER OF TERMS IN THE INNER SUMMATION
C JUJJ
COMPUTED NUMBER OF TERMS IN THE OUTER SUMMATION
C KKKK
C XLAM = PARAMETER FROM THE POISSON PROCESS OF STORMS
= PARAMETER FROM EXPONENTIAL DISTRIBUTION OF STORM
C GAM
C Y(I) = VALUES OF THE RANDOM VARIABLE
OF C
THE OF OF Y
THE OF OF Y
-
MAGNITUDE
DOUBLE PRECISION OFYN(24,3),UNYN(24),TEMPK(60),TEMPJ(250),NFAC,SUM
1K,SUMJ,SLOPE,AVAL
DIMENSION Y(25),C(5),PC(5),YMEAN(5),YVAR(5),GL(5),GPL(5),CG( 5 )
C SET MAXIMUM NUMBERS OF TERMS IN SUMMATIONS, NUMBER OF SNOWPACK
C DURATIONS, NUMBER OF VALUES OF SNCWPACK ABLATION RATES, AND NUMBER
C OF VALUES OF THE RANDOM VARIABLE Y
NUMN = 1
NUMY = 24
NUMC = 3
KK = 60
JJ = 250
C
C
C
C
C
C
INPUT PARAMETERS, VALUES OF Y, SNOWPACK ABLATION RATES, AND
ABLATION RATE PROBABILITIES
REA0(1,100) XLAM I GAM
100 FORMAT(2F10.0)
READ(1,101) (Y(I),I=1,NUMY)
READ(1,111) (C(I),I=1,NUMC)
READ(1,101) (PC(I),I=1,NUMC)
101 FORMAT(8F10.0)
COMPUTE VALUES WHICH ARE USED IN THE PROGRAM
DO 55 L=1,NUMC
CG(L) = GAM 4 C(L)
GL(L) = XLAM*CG(L)
CG(L)
GPL(L) = XLAM
55 CONTINUE
DO LOOP TO ITERATE THE NUMBER OF RENEWALS, N
DO 999 N=1,NUMN
INITIALIZE ARRAYS
DO 11 I=1,NUMY
UNYN(I) = 0.0000
DO il j=1,NUMC
OFYN(I,J) = 0.0000
11 CONTINUE
CALCULATING N FACTORIAL
NFAC = 1
IF( N .LE. 1) GO TO 87
DO 9 J=2,N
9 NFAC = NFAC*J
87 CONTINLE
126
C
DO LOOP TO ITERATE Y VALUES
DO 13 I=100 U9Y
YSO = Y(I) *Y(I)
DO LOOP TO ITERATE C VALUES
DO 19 L=1,NUmC
C COMPUTE Nut-19ER OF TERMS IN SUMMATIONS
C
777
778
22
27
C
KKKK = 10 + 40*C(L) + Y(I)*.2
IF(Y(I) .GT. 40.) GO To 777
ZJ = ALOG(Y(I))+ C(L)*ALOG(Y(I))
GO TO 778
ZJ = ALoG(Y(I))*1.04 + C(L)*ALOG(Y(I))*1.7
JJJJ = (EXP(ZJ)/2) * 2
IF(KKKK .GT. KK) KKKK = KK
IF(JJJJ .GT. JJ) JJJJ = JJ
CGY = Y(I) *CG(L)
YSQLG = YSO*GL(L)
SUMK = 0.
00 21 K=1,KKKK
IF(K .GT. 1) GO TO 22
TEMPK(1) = 1. / NFAC
GO TO 27
TEMPK(K) = TEmPK(K-1)*(YSQLG/((K-1)*(K+N - 1)))
KW = 2*K+N-3
YLG = Y(I) * GPL(L)
SUmJ = 1. / (KN+1)
TEMPJ(1) = 1.0000
DO 23 J=2,JJJJ
TEMPJ(J) = TEMPJ(J-1)*((-YLG)/(J - 1))
SUMJ = SUMJ + (TEMPJ(J) / (KN +J))
23 CONTINUE
SUNK = SUNK + TEMPK(K) * SUMJ
21 CONTINUE
OFYN(I,L) = N*(CGY**N) *SUNK
UNYN(I) = UNYN(I) + OFYN(I,L)*PC(L)
19 CONTINUE
13 CONTINUE
COMPUTING mEANS AND VARIANCES
XN = N
UNPIN = 0.
DO 33 I=1,NUMC
DIF = CG(I) - XLAN
YMEAN(I) = XN / DIE
YVAR(I) = XN*((CG(I)+XLAM)/(CIF * DIF * DIF))
UNMN = UNMN + ymEAN(I)*PC(I)
33 CONTINUE
C COMPUTE UNCONDITIONAL VARIANCE
UNVAR = 0.0
DO 99 I=1,NUMC
UNVAR = UNVAR+YvAR(I)*PC(I) +(YMEAN(I)-UNMN)*PC(I)
99 CONTINUE
C PRINTOUT RESULTS
WRITE(2,211) N
211 FORMAT(1H1,20X,*NUMBER OF RENEWALS =*,I3)
wRITE(2,200)
200 FORHAT(1H0,7X,*CON0ITICNAL AND UNCONDITIONAL PROBABILITIES OF SNOW
AN
I REMAINING ON THE GRCUND*//15x,*SEAS0NAL SNCwPACK ABLATION RATE
20 PROBABILITY.)
WRITE(2,202) (C(J),J=1,NUMC)
202 FORMAT(1H 1 5X 1 *RATE*,7X, 4 (F6.4, 5 X) 0
WRITE(2,203) (PC(J),J=1,NUMC)
127
203 FORMAT(tH ,5)(,*PRO3*;7X,4(F6.4,5X))
WRITE (2,209)
205 FORMAT(1H ,25X,*CONDITIONAL*,14X,*UNCONOITIONAL * /5X, * Y,DAYS * ,1 2 X
1PROFIABILITY OF Y*,10X,*PRO3ADILITY OF Y*)
WRITE(2,210) (Y(I),(OFYN(I,J),J=1,NUMC),UNYN(I);I=1,NUMY)
210 FORMAT(1H ,5X,F4.0,6X,4011.4)
WRITE(2,220) (YMEAN(I),I=1,NUMC),UNMN
220 FORMAT(1111,5X1*MEAN*,4X14F11.4)
WRITE(2,230) (YVAR(I),I=1,NUMC),UNVAR
230 FORMAT(1110,5X1*VARIANCE*15F11.4)
999 CONTINUE
STOP
END
,*
128
PROGRAM SIX(INPUT,OUTPUT,TAPEi=INPUT,TAPE2=OUTPUT)
C PROGRAM TO EVALUATE THE CONDITIONAL AND UNCONDITIONAL DISTRIBUTION
C FUNCTION OF THE SUM OF N SNOw-FREE, SNOW CYCLES, T
C LIST OF VARIABLES, PARAMETERS AND INDICES
C M
C J
- INDEX OF INNER SUMMATION
- INDEX OF MIDDLE SUMMATION
C K
- INDEX OF OUTER SUMMATION
- MAXIMUM NUMBER CF TERMS IN INNER SUMMATION
C MM
C JJ
- MAXIMUM NUMBER OF TERMS IN MIDDLE SUMMATION
C KK
C HUNT C NUMC C N C XLAM -
MAXIMUM NUMBER OF TERMS IN OUTER SUMMATION
NUMBER OF VALUES OF THE RANDOM VARIABLE, T
NUMBER OF VALUES OF SNOWPACK ABLATION RATE
NUMBER OF SNOW-FREE, SNOW CYCLES
PARAMETER FROM THE POISSON DISTRIBUTION OF THE NUMBER OF
C STORMS
- PARAMETER FROM THE EXPONENTIAL DISTRIBUTION OF WATER EQUIVC GAM
C ALENT PER STORM
C C(I) - SNOWPACK ABLATION RATE ARRAY
C PC(I) - ARRAY FOR SNOWPACK ABLATION RATE PROBABILITIES
C T(I) - ARRAY FOR VALUES OF THE RANDOM VARIABLE T
COMPUTED NUMBER OF TERMS IN INNER SUMMATION
C MMMM
C JJJJ
COMPUTED NUMBER OF TERMS IN MIDDLE SUMMATION
C KKKK - COMPUTED NUMBER OF TERMS IN OUTER SUMMATION
DIMENSION C(5),PC(5),T(25),CONMN(5),CONVAR(5)
DOUBLE PRECISION OFTN(20,5),UNDF(20),PROD(200,30,NFAC,TEMPA,SUMA,
ITEMPB,SUMB,TEMPC,SUMC
C SET MAXIMUM NUMBER OF TERMS AND NUMBER OF VALUES OF C, Tf AND N
MM = 150
JJ = 200
KK = 36
NUMC = 3
NUMT = 19
NN = 10
C INPUT LAMBOA,GAMMA,C1PROBABILITY OF C
READ(11100) XLAM,GAM
100 FORMAT42F10.0)
READ(1,101) (C(J),J=1,NUMC)
READ(1,101) (PC(J),J=1,NUMC).
READ(1,101) (T(J),J=1,NUMT)
101 FORMAT(8F10.0)
C CO LOOP TO ITERATE N VALUES
00 999 N=1,NN
C PRINT HEADINGS
WRITE(2,209) N
209 FORMAT(1H1,20X,*NUMBER OF RENEWALS = 41 ,13)
WRITE (2,200)
200 FORMAT(1H0,7X,*CONDITIONAL AND UNCONDITIONAL PROBABILITIES OF THE
1SNOW RENEWAL PROCESS*//15X,*SE4SONAL SNOWPACK ABLATION RATE ANO FR
10BABILITY*)
WRITE(2,2021 (C(J),J=1,NUMC)
202 FORMAT (1H ,5X,*RATE*,7X,4(F5.3,6X))
WRITE(2,203) (PC(J),J=1,NUMC)
203 FORMAT(1H ,5X,*PRO3*,7X,4(F5.316X))
WRITE(2,210)
210 FORMAT(1H ,25X,*CONDITIONAL*,14X,*UNCONDITIONAL*/6X,47,0AYS*,12X1*
1PRO8ABILITY OF T*,10X.*PROBABILITY OF T*)
129
00 121 I=1,NWIT
UNDF(I) = 0
DO 121 J=1,NUMC
OFTN(I1J) = 0
121 CONTINUE
C
COMPUTE AND STORE N FACTORIAL
NFAC =
IF(N.E0. 1) GO TO 91
DO 88 II=21N
88 NEAC = NFAC
II
81 CONTINUE
C COMPUTE AND STORE N- FOLD PRODUCT (2K+2I+J-4)
00 77 J=1,jj
DO 77 K=11KK
KLN = 2 4 K+J+2*N
PROD(J,K) = KLN -4
IF(N.EQ. 1) GO TO 77
DO 87 II =20
PROD(J 1 K) = PROD(J,K)*(KLN-II-3)
87 CONTINU P
77 CONTINUE
C DO LOOP TO ITERATE T VALUES
CO il I=1 1 NUNT
TL = XLAM * T(I)
TIN = TL**N
C DO LOOP TO ITERATE C VALUES
DO 22 L=1,NUMC
CG = GAM*C(L)
C
C
C
COMPUTE NUM2ER OF TERMS IN THE SUMMATIONS
IF COMPUTED NUMDERS OF TERMS ARE LESS THAN THE SPECIFIED MAXIMUM
THE TOTAL NUMBERS OF TERMS ARE SET EQUAL To THE COMPUTED NUMBER
KKKK = 8+L+T(I)*.25
MMMM = 10 + T(I)*.4
IF(T(I) .GT. 40.) GO TO 777
ZJ =ALOG(T(I))+(C(L)/3.1)*ALOG(T(I))
GO TO 779
777 IF(T(I).GT.60. .ANO.C(L).GT. .16) GO 10 789
ZJ=ALOG(T(I))+(C(L)/1.6)*ALOG(T(I))
GO TO 778
789 ZJ = ALCG(T(I)) + C(L) * ALOG(T(I))
778 JJJJ = (EXP(ZJ)/2)*2
IF(KKKK .GT. KK) KKKK = KK
IF(JJJJ .GT. JJ) JJJJ = JJ
IF(MMMM .GT. MM) MMMM = MM
TGAM = T(I)*CG
TGN = TGAM**N
TGLN = N*TGN*TLN
C
DO LOOP TO ITERATE OUTER SUMMATION
SUmA = 0.0
00 33 K=1,KKKK
IF(K .GT. 1) GO TO 37
TEMPA = 1. / NEAC
GO TO 38
37 TEMPA = TEMPA*I(TGAM*TL)/((K - 1) * (K 4 N - 1)))
38 CONTINUE
-
C
DO LOOP TO ITERATE MIDDLE SUMMATION
SUMB = 0.0
130-
DO 44,J=1,JJJJ
IF(J .GT. q) GO TO 60
TEMPB = 1.0000
GO TO 61
60 TEMPO = TEMP8*((-TGAM)/(J-11)
61 CONTINUE
C
DO LOOP TO ITERATE INNER SUMMATION
KJN = 2*K +J+2*N-4
TEMPC = i.00ao
SUNG = TEMPC/(KJ)441)
DO 55 M=2,MMM4
TEMPC = TEMPC*((-TL)/(M-1))
SUNG = SUMC +(TEMPC/(KJN+m))
55 CONTINUE
SUMB = SUMO + SUMC*(TEMPB/PROD(J,K))
44 CONTINUE
SUMA = SUMA + TEMPA*SUMB
33 CONTINUE
OFTN(I l L) = SUMA*TGLN
UNDF(I) = UNOF(I) + OFTN(I,L)*PC(L)
22 CONTINUE
C
PRINTOUT FINAL RESULTS
WRITE(2,205)(T(I), (OFTN(I,L), L=1,NUMC),UNDF(I))
205 FORMAT(1H0,5X 1 F4.0,6X,5011.4)
11 CONTINUE
C
COMPUTE UNCONDITIONAL AND CONDITIONAL MEANS AND VARIANCES
UNMEAN = 0
XN = N
DO 66 J=1,NUMC
CG = C(J) * GAM
OIE = CG - XLAM
ODIF = DIF*DIF*DIF
SUM = CG + XLAM
CONMN(J) = XN* (CG/(XLAM*DIF))
CONVAR(J) = XN* t(XLAM*XLAM*SUM+ODIF)/(XLAM * XLAM*Q0IF))
(JNMEAN = UNMEAN + CONMN(J) * P0(J)•
66 CONTINUE
C
COMPUTE UNCONDITIONAL VARIANCE
UNVAR = 0
Do 67 J=1,NUMC
TOIF = CONMN(J)-UNMEAN
SQ = TOIF*TDIF
UNVAR = UNVAR+CONVAR(J)*PC(J) + SQ*PC(J)
67 CONTINUE
WRITE(21206)((CONMN(J),J1,NUMC) , UNMEAN )
206 FORMAT(1H0,5X.*MFAN*,6X,5F11.4)
WIITE(2,207)((CONVAR(J),J=1,NUMC),UNVAR)
207 FORMAT(1H0,5X.*VARIAhCE*.2Xl5F11. 4 )
999 CONTINUE
STOP
ENO
REFERENCES
Anderson, H. W. 1972. Water yield as an index of lee and windward
topographic effects on precipitation. Distribution of Precipitation in Mountainous Regions. Proceedings Geilo Symposium,
Vol. 2:346-358.
Bolduc, S. 1970. Use of Markov chain in the study of snow occurrences.
Ph. D. Dissertation. Stanford University, 139 pp.
Cary, L. E. and R. L. Beschta. 1973. Probability distributions of snow
course data for central Arizona. Hydrology and Water Resources
in Arizona and the Southwest 3:8-16.
Cehak, Konrad. 1972. Statistical considerations about snow conditions
in the Austrian Alps. Distribution of Precipitation in Mountainous Regions. Proceedings Geilo Symposium 2:198-204.
Chatfield, C. 1966. Wet and dry spells. Weather 21(9):309-310.
Chow, V. T. 1964. Statistical and probability analysis of hydrologic
data. Section 8 in Handbook of applied hydrology. V. T. Chow,
Editor-in-chief. McGraw-Hill Book Co., Inc., New York, 1418 pp.
Clapp, P. F. 1967. Specification of monthly frequency of snow cover
based on macroscale parameters. J. Appl. Met. 6(12):1018-1024.
Crovelli, R. A. 1972. Stochastic models for precipitation. Intl.
Symp. on Uncertainties in Hyd. and Water Res. Systems 2:284-298.
Dickson, R. R. and J. Posey. 1967. Maps of snow-cover probability for
the northern hemisphere. Monthly Weather Review 95(6):347-353.
Dingens, P. and H. Steyaert. 1971. Distribution for k-day rainfall
totals. Bull. Intl. Assoc. Sci. Hyd. 16(3):19-24.
Duckstein, L., M. M. Fogel and C. C. Kisiel. 1972. A stochastic model
of runoff producing rainfall for summer type storms. Water
Resources Res. 8(2):410-421.
Engelen, G. B. 1972. A graphical and statistical approach to the
regional study of snowpack in mountain areas with special
reference to Colorado and New Mexico. Paper presented Intl.
Symp. on the Role of Snow and Ice in Hydrology. Banff. 13 pp.
131
132
Epstein, E. S. 1966. Point and area precipitation probabilities.
Monthly Weather Review 94(10):595-598.
Feller, William. 1971. An introduction to probability theory
and its
applications, Vol. 2. John Wiley and Sons, Inc., New York,
669 pp.
Feyerherm, A. M. and L. D. Bark. 1965. Statistical methods
for persistent precipitation patterns. J. Appl. Met. 4(3):320-328.
Feyerherm, A. M. and L. D. Bark. 1967. Goodness of fit of a Markov
chain model for sequences of wet and dry days. J. Appl. Met.
6(5):770-773.
Fogel, M. M. and L. Duckstein. 1969. Point rainfall frequencies in
convective storms. Water Resources Res. 5(6):1229-1237.
Fogel, M. M., L. Duckstein and C. C. Kisiel. 1971. Space-time validation of a thunderstorm rainfall model. Water Resources Bull.
7(2):309-316.
Fogel, M. M., L. Duckstein and C. C. Kisiel. 1973. A stochastic snow
model to evaluate reservoir operation. Paper presented at the
54th Annual Meeting Am. Geophys. Union., Washington, D. C.,
20 pp.
Gabriel, K. R. and J. Neuman. 1962. A Markov chain model for daily
rainfall occurrences at Tel Aviv. Quart. J. Royal Meteorological
Soc. 88:90-95.
Garstka, W. U. 1964. Snow and snow survey. Section 10 in Handbook of
applied hydrology. V. T. Chow, Editor-in-chief, McGraw-Hill
Book Co., Inc., New York, 1418 pp.
Green, J. R. 1965. Two probability models for sequences of wet or
dry days. Monthly Weather Review 93(3):155-156.
Green, J. R. 1970. Generalized probability model for sequences of wet
and dry days. Monthly Weather Review 98(3):238-241.
Gupta, V. K. 1973. A stochastic approach to space-time modeling of
rainfall. Ph, D. Dissertation. The University of Arizona,
Tucson, 154 pp.
Gupta, V. K. 1974. Personal Communication. Department of Hydrology
and Water Resources, The University of Arizona, Tucson.
Hopkins, J. W. and P. Robillard. 1964. Some statistics of daily rainfall occurrence for the Canadian prairie provinces. J. Appl.
Met. 3(5):600-602.
133
Ishihara, T. and S. Ikebuchi. 1972. Stochastic structures in space and
time of daily precipitation and their simulation.
Proceedings
2nd Intl. Symp. in Hyd. 615-627.
Ison, N. T., A. M. Feyerherm and L. D. Bark. 1971. Wet period precipitation and the gamma distribution. J. Appl. Met. 10(4):658-665.
Kao, S. E., L. Duckstein and M. M. Fogel. 1971. A probabilistic model
of winter rainfall. Paper presented at the Fall National Meeting Am. Geophys. Union., San Francisco, 24 pp.
Kiefer, J. 1959. K-sample analogues of the Kolmogorov-Smirnov and
Cramer-V. Mises tests. Ann. Math. Stat. 30:420-447.
Kovzel, A. G. 1969. A method for the computation of water yield from
snow during snowmelt period. Floods and Their Computation,
Vol. 2, Intl. Assoc. Sci. Hyd. Publication 85:598-607.
Kuz 1 min, P. P. 1969. Snowmelt and water yield from snowcover. Floods
and Their Computation, Vol. 2, Intl. Assoc. Sci. Hyd. Publication
85:591-598.
Lane, L. J. and H. B. Osborn. 1972. Hypotheses on the seasonal
distribution of thunderstorm rainfall in southeastern Arizona.
Proceedings, 2nd Intl. Symp. in Hyd. 83-94.
Lautzenheiser, R. E. 1968. Snowfall, snowfall frequencies and snowcover
data for New England. Proceedings 25th Annual Meeting, Eastern
Snow Conf. 85-94.
Lutes, D. A. 1970. Snow loads for the design of roofs in Canada. Proceedings 38th Annual Meeting, Western Snow Conf. 61-67.
McKay, G. A. and H. A. Thompson. 1968. Snowcover in the prairie
provinces of Canada. Trans. A.S.A.E. 11(6):812-815.
Miller, M. E. and C. R. Weaver. 1971. Snow in Ohio. Ohio Agricultural
Research and Development Center. Ohio Agric. Expt. Sta. Bull.
1044, 23 pp.
Osborn, H. B., W. C. Mills and L. J. Lane. 1972. Uncertainties in
estimating runoff-producing rainfall for thunderstorm rainfallrunoff models. Proceedings Intl. Symp. on Uncertainties in Hyd.
and Water Res., Tucson 1:189-202.
Parzen, Emanuel. 1962. Stochastic processes. Holden-Day. San
Francisco. 324 pp.
Peck, E. L. 1972. Snow measurement predicament. Water Resources Res.
8(1):244-248.
134
Potter, J. G. 1965. Snow cover. Canada Department
of Transport,
Meteorological Branch, Toronto, 69 pp.
Prabhu, N. U. 1965. Queues and inventories. John Wiley
and Sons, Inc.,
New York, 275 pp.
Rechard, P. A. 1972. Are existing measurements of precipitation
input
to watersheds adequate for management decisions? Natl. Symp.
on
Watersheds in Transition, Fort Collins, 98-106.
Roberts, G. E. and H. Kaufman. 1966. Table of Laplace
transforms.
W. B. Saunders and Co., Philadelphia, 367 pp.
Sellers, William. 1964. The climate of Arizona. In Arizona Climate
by Christine R Greene and William D. Sellers. Univ. Arizona
Press 5-64, 503 pp.
Smith, H. V. 1956. The climate of Arizona. Arizona Agric. Expt. Sta.
Bull. 279, 99 pp.
Thom, H. C. S. 1957a. Climatological analysis of snowfall thresholds.
Archiv fur Meteorologie, Geophysik und Bioklimatologie,
Serie B 8(2):195-202.
Thom, H. C. S. 1957b. Probabilities of one-inch snowfall thresholds
for the United States. Monthly Weather Review 85(8):269-271.
Thom, H. C. S. 1959. A time interval distribution for excessive rainfall. J. Hydraulics Div. Proc. ASCE 85(HY7):83-91.
Thom, H. C. S. 1966. Distribution of maximum annual water equivalent
of snow on the ground. Monthly Weather Review 94(4):265-271.
Thom, H. C. S. 1970. Development of snow load design criteria for the
United States. Proceedings 38th Annual Meeting, Western Snow
Conf. 49-51.
Todorovic, P. and V. Yevjevich. 1967. A particular stochastic process
as applied to hydrology. Intl. Hyd. Symp., Fort Collins,
1:298-303.
Todorovic, P. and V. Yevjevich. 1969. Stochastic process of precipitation. Colorado State Univ. Hydrology Papers No. 35, 61 pp.
Todorovic, P. and E. Zelenhasic. 1968. The extreme values of precipitation phenomena. Intl. Assoc. Sci. Hyd. Bull. 8(4):7-24.
United States Army. 1956. Snow hydrology. Summary report of the snow
investigations. North Pac. Div., Corps of Engineers, 437 pp.
135
United States Weather Bureau. 1964. Frequency of maximum water equivalent of March snow cover in North Central United States. U.
S.
Department of Commerce, Weather Bureau Tech. Paper No. 50, 24
pp.
United States Weather Bureau. 1969. Manual of surface observations.
U. S. Department of Commerce. E.S.S.A. Circular N, 7th ed.
Vance, H. M. and B. L. Whaley. 1971. Snow frequency analysis for Oregon
and Utah. Proceedings 39th Annual Meeting Western Snow Conf.
34-38.
Viessman, W., T. E. Harbaugh and J. W. Knapp. 1972. Introduction to
hydrology. Intext Educational Publishers, New York, 415 pp.
Weiss, L. L. 1964. Sequences of wet or dry days described by a Markov
chain probability model. Monthly Weather Review 92(4):169-176.
Weisner, C. J. 1970. Hydrometeorology. Chapman and Hall, Ltd.,
London, 232 pp.'
Wiser, H. H. 1965. Modified Markov probability models of sequences of
precipitation events. Monthly Weather Review 93(8):511-516.
Woolhiser, D. A., E. Rovey and P. Todorovic. 1972. Temporal and
spatial variation of parameters for the distribution of N-day
precipitation. Proceedings of the 2nd Annual Symp. in Hydrology,
Fort Collins.
Yevjevich, V. 1972. Probability and statistics in hydrology. Water
Resources Publication, Fort Collins, 302 pp.
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement