APPLICATION OF LINEAR ROUTING SYSTEMS TO
REGIONAL GROUNDWATER PROBLEMS
by
Donald Hilton Evans
B.S., University of Colorado
(1966)
Submitted in partial fulfillment
of the requirements for the degree of
Master of Science in Civil Engineering
at the
Massachusetts Institute of Technology
(September 1972)
Signature of Author. .
. .
.
. . . . . . . . . .
Department of Civil Engineering, September, 1972
Certified by .
. . . . .
.
.
. ......
Certified
Thesis
b Supervisor
Accepted by . . ...
. .
.. . . . . . . . . .
Chairman, Departmental Committee on Graduate Students of the
Department of Civil Engineering
Arch&v
(NOV16 1972)
ABSTRACT
APPLICATION OF LINEAR ROUTING SYSTEMS TO
REGIONAL GROUNDWATER PROBLEMS
by
DONALD HILTON EVANS
Submitted to the Department of Civil Engineering on
August
29, 1972, in partial
fulfillment
for the degree
of Master of Science in Civil Engineering
Work in groundwater analysis goes back to the last century.
Only in the last decade, however, has there been an increase of interest
in applying a linear systems approach to the problem of routing groundwater flow. This thesis applies linear systems to the routing of
groundwater within a regional basin.
The research reported here has been devoted to the following:
1.
Developing a fast convolution technique through the use
of the Fast Fourier Transforms.
2.
Developing a method for determining the system response
parameters through linearizing the governing equation for
groundwater flow by applying Laplace transforms and using
the Method of Moments.
3.
Developing a groundwater routing model using the above
techniques applied to a regional groundwater basin.
The results from the Harmonic Analysis have been compared
with those generated by the complete solution for open channel flow.
The hydrograph generated with the use of the parameters determined from
the parameter estimation technique are compared to those resulting from
a finite difference scheme.
The techniques developed in the use of Harmonic Analysis and
parameter estimation are incorporated into a model for analyzing a
regional groundwater problem and the results discussed.
Thesis Supervisor:
Title:
Brendan M. Harley
Assistant Professor of Civil Engineering
- 2 -
ACKNOWLEDGEMENTS
This work was supported financially by the Subsecretarla de
Recursos Hdricos,
Ministerio de Obras y Servicios Pblicos,
Argentina.
Administrative support has been provided by the M.I.T. Division of
Sponsored Research through DSR 72817.
All computer endeavors were
accomplished at the Information Processing Center, M.I.T..
The author wishes to extend sincere gratitude to Professor
Brendan M. Harley of the M.I.T. Civil Engineering Department, who, in
supervising this thesis, provided assistance and guidance for this
research.
The support received from Mr. Rafael Bras, M.I.T. graduate
student, in the development of the Harmonic Analysis section of this
work is gratefully appreciated.
Additional appreciation goes to Mr. Dario Valencia and
Mr. Guillermo Vicens, Research Assistants for their helpful ideas generated through enlightening discussions.
Also to Miss Elba Rosso for
enduring the dreadful script in the typing of this thesis, and to
Mr. David Njus, graduate student, Harvard, who as penman and scholar,
with careful thought, drafted the figures herein.
Finally but not least, thanks goes to my wife, Rosemary, and
our daughter, for the loneliness endured during the period of this
thesis.
- 3 -
TABLE OF CONTENTS
Page
Title Page
1
Abstract
2
Acknowledgements
3
Table of Contents
4
I
Introduction
7
I-1
I-2
I-3
7
8
9
II
I-4
Scope of Work
10
I-5
Brief Summary of Results
11
Developments to Linear Systems Analysis
12
II-1
II-2
II-3
II-4
II-5
II-6
II-7
III
Problem Statement
Background to Groundwater Flow Modeling
Introduction to Linear Systems
Hydrograph Theory
Linear Systems
Some Typical Systems
II-3.1
Linear Reservoir Model
Application to Time Varying
II-3.1.1
12
14
16
17
Inflow
20
II-3.2
Linear Channel Model
II-3.3
Two Parameter Models
II-3.4
Three Parameter Models
Model Formulation Using Linear Systems
Groundwater Systems
Parameter Estimation
Theoretical Development to the Regional Groundwater Routing Model
Fourier Transforms in Linear Hydrologic Systems
III-1
III-2
Introduction to Fourier Analysis
Fourier Transform Technique
III-2.1
Characteristics of Subroutine FOURTRAN-Numerical Integration Technique
III-2.1.1
Test and Results for Subroutine FOURTRAN
III-2.2
Characteristics of Subroutine FOURT-Fast Fourier Transform Technique
- 4 -
22
22
25
25
28
34
41
46
46
48
49
50
56
Page
III-2.2.1
III-2.2.2
Data Requirements
Subroutine
56
FOURT--Forward
Transform
56
III-2.2.3
III-3
III-4
Subroutine FOURT--Inverse
Transform
III-2.2.4
Implications of the InputOutput Requirements
III-2.2.5
Tests and Results
Selection of an Efficient Fourier Transformation
Technique
Convoluting with Fourier Transforms
III-4.1
Defining the Nyquist Frequency, o
III-4.2
Effects of Complex Responses on the
Nyquist Frequency
III-4.3
Selection of Response Function Duration
III-4.4
III-4.5
57
57
58
58
61
64
65
67
69
III-4.6
Obtained Results
Application to Surface Routing
71
74
Application to a Regional River Basin
83
IV-1
83
III-5
IV
Selection of the Output Period
An Example-TheTheoretical Solution
57
Discussion of the Selected Regional River Basin
IV-l.1
Geology and Soil Description within
IV-1.2
IV-2
IV-3
the River Basin
Hydrologic and Agricultural
Discussion
83
86
Conceptual Discussion of a Regional Groundwater
Routing Model
87
Model Development
IV-3.1
General Linear Groundwater Routing
90
Model
IV-3.1.1
90
Discussion of the Shallow
Zone System Implemented
into the Model
102
IV-3.1.1.1
Drainage Spacing
103
ence Scheme
Parameter Estimation with the
Use of Finite
104
IV-3.1.1.2 Finite DifferIV-3.1.1.3
IV-4
Difference
Scheme 105
Linear Groundwater Routing Model Discussion
IV-4.1
Model Results
107
112
- 5 -
Page
V
Concluding Remarks
139
Summary
Conclusion
Future Work
139
139
142
V-1
V-2
V-3
References
143
List of Figures
150
List of Symbols
154
List of Tables
162
Appendices
163
A-1
A-2
A-3
Model Generated for Harmonic Analysis
Fast Fourier Transform Program
Groundwater Routing Model
B-1
Procedure for Determining the Time Period for
the Nash Model
Derivation of the System Response to a Delta
Function
Method of Moments-Parametric Analysis
B-2
B-3
C-1
Computer Implementation of the Convolution
Technique by Means of Harmonic Analysis
- 6 -
163
167
173
181
184
189
193
Chapter
I
INTRODUCTION
I-1
Problem Statement
The theory to groundwater flow representation goes back to
1856 when Henry Darcy first developed an empirical relationship for
steady-state saturated flow.
Jules Dupuit and many others have since
expanded Darcy's relationship into one representing unsteady conditions.
Through expanding knowledge in subsurface hydro-geology and
soil mechanics, the complexities of the subterranean region have
become enormous.
The 'real world' conditions which are non-
homogeneous, non-isotopic and contain cracks, fissures, etc., make
it impossible to represent, in detail, the behaviour of a subsurface
environment.
The complexities of the subsurface terrain also implies that
the response of such a system is non-linear.
However, if the neces-
sity of a non-linear solution is accepted, a unique solution for each
soil condition, recharge pattern and the many other facits of the
Thus, with the linear systems approach of
system is required.
modeling the groundwater system, we desire to find a simple but
functional procedure for determining the general behaviour pattern
of such a system.
This theme will be further discussed in Section I-3.
- 7 -
I-2
Background to Groundwater Flow Modeling
Over the past decade, a tremendous effort has been devoted
to the understanding of groundwater flow.
The techniques vary widely
but may be categorized into theoretical, analytical, experimental and
Initial attempts were theoretical, going back to the early
numerical.
1900s, when the dispersive effects of the groundwater systems were
noticed.
Since that time researchers have delved more deeply into the
relationship of the various subsurface parameters to the dispersive
effects caused by the soil characteristics.
Breitenbach [1971], in
a paper presented on groundwater simulation, pointed out the various
analytical techniques used to-day.
These analysts use Fourier
Series, Laplace Transforms, conformal mapping or graphical approximations.
The data are obtained, generally, by methods of well
withdrawals or parallel drains of a variety of configurations.
Simulation techniques used, range from physical models using
sand or other porous media, viscous fluids, electric means (relating
Ohm's Law to Darcy's Relationship) and membranes, to numerical
methods.
It is interesting to note that many modern methods or
theories have developed from other fields of study, for instance, the
well known heat flow (Carslaw and Jaeger (1959)) relation to dispersion, as well as Ohm's Law in electrical theory to mass flux
(Darcy's Law).
The background and theory involved in these areas
are discussed by Reddell and Sunada [1971].
- 8-
A Frenchman by the name of D'Andrimont introduced concepts
which lead to simulation of small groundwater basins by Toth [1962]
and Freeze and Witherspoon [1966].
With the advent of the digital
computer came a rapid increase of basin studies, for example, those
done by Bittinger, et al. [1967] and Tyson and Weber [1964] as well as
many others.
As the vastness of groundwater storage reservoirs unveils,
researchers are beginning to widen the scope of subterranean flows to
a regional basin.
Nelson and Cearlock [1967] discuss the various
methods applied to large heterogeneous systems.
Schneider [1966] and
Megnien [1964] also have done work in analysing regional flow patterns
of groundwater.
I-3
Introduction to Linear Systems
As many researchers turned to numerical methods in an attempt
to by-pass the complexities of the analytical solutions for computing
the reactivity of a groundwater system, so have many turned to the
linear systems approach.
Originally, linear systems were developed
for overland flow and were accepted in groundwater because, to quote
Kraijenhoff Van De Leur [1966], "... the unit hydrograph methods are
in complete accord with the nature of the simplifying assumptions that
have been accepted in order to find analytical solutions for the
equations describing the flow of groundwater."
-9-
The linear systems approach to routing groundwater in the
subterranean region is a subset to work done by Sherman [1932], who
advanced the unit hydrograph theory which later was used in routing of
surface flows.
It was an attempt by hydrologists to estimate the
overall effect of an 'ideal' system and compare the result to an actual
system.
The hope was that a close approximation to that system would
be obtained.
The basic assumption underlying linear system theory is
that the series of simple inputs may be used in conjunction with a
characterizing function of the system to simulate the effects of a
complex inflow pattern.
Obviously, then, the characterizing function
must implicitly contain all the variable process characteristics necessary for such a representation - an ideological condition to be sure.
Should such a simplifying technique be used at all?
A good justifica-
tion for using linear systems is provided by Rodriguez [1972] when he
says that a linear system "... may provide less information where
information is not wanted and better information where it is wanted,
all at less cost in time and effort."
The work that has evolved from linear systems in groundwater
flow can be found in Chapter II.
I-4
Scope of Work
The work carried out in this thesis will be:
- 10 -
a)
to develop the use of Harmonic Analysis within
the linear systems approach for a fast computational scheme of convolution,
b)
To use this method to develop a general model that
can be used under regional consideration,
c)
I-5
to apply the model to a regional area.
Brief Summary of Results
A convolution technique is discussed in Chapter III which
utilizes a Fast Fourier Transform program developed at M.I.T..
This
procedure was found to be highly efficient in terms of time and
In Chapter IV, a groundwater routing model is presented which
accuracy.
is capable of utilizing any configuration of system response which
might be encountered in a groundwater zone.
This model utilizes the
convolution technique in an effective procedure for analyzing such a
groundwater system.
In application of this model it was found to be
better practice to isolate the different flow processes discussed in
Chapter IV since the substantial damping effect of the groundwater
aquifer
produced time steps incompatible for aggregating those pro-
cesses into one outflow hydrograph.
Use of the fast Fourier transform
technique for predicting the response to an input provides a highly
efficient procedure for analysing both the transient and the periodic
situations.
This is found especially useful in studying the behavior
of slowly responding aquifer systems to periodic inputs.
- 11 -
Chapter II
DEVELOPMENTS TO LINEAR SYSTEMS ANALYSIS
II-1
Hydrograph Theory
In 1929 Folse presented the ideas of base-flow separation,
reduction of rainfall due to the variance of infiltration rates and
the derivation of physical constants for representing hydrologic
systems.
Sherman, in 1932, used these ideas to develop the well known
hydrograph theory.
The basic assumptions for use with the unit
hydrograph which is the result of surface runoff or effective rainfall,
are:
a)
Effective rainfall is uniformly distributed within
its duration.
b)
The effective rainfall is distributed uniformly
over the entire drainage basin.
c)
The time duration is constant for a direct runoff
hydrograph due to an effective rainfall of unit
duration.
d)
Those direct runoff hydrographs that have the same
time duration have ordinates which are directly
proportional to the total amount of direct runoff
represented by each hydrograph.
- 12 -
Note that this
implies use of the principles of linearity,
superposition and proportionability.
e)
The runoff hydrograph from a given rainfall period
reflects all the physical characteristics of the
given drainage basin.
The two significant features in linear systems application
that are invoked by the above assumptions are those of time invariance
and of superposition.
Time invariance, i.e., stationarity with time,
implies that the basin response will not vary with time - in other
words, the resulting hydrographs of an effective runoff of the same
duration will be the same.
Superposition refers to the property that
a hydrograph resulting from a given pattern of rainfall excess can
equivalently be generated by superimposing the hydrographs from separate amounts of rainfall excess that occur during each period of the
same duration.
Thus, in order to use the principle of superposition
only those systems that consist of linear elements may be considered.
The most effective way of characterizing the behaviour of such systems
is to allow the effective input to become a delta input (or unit
impulse).
The resulting output is known as the instantaneous unit
hydrograph, designated by h(O,t) or I.U.H.
h(O,t)
= 0
t < 0
h(0,t) + 0
t + 0
The properties are:
II-1
- 13 -
h(0,t)dt = 1.0 = volume of runoff.
II-2
Linear Systems
A system, as defined by Eagleson, in 1967, is any set of
inter-related components, material or conceptual, that are identified
by their state variables.
When the components are isolated from the
'real' system and provide the state variables, the result is an
'idealized' system since it excludes some of the parameters or characteristics found in the environment.
If this were not done, the task
would either be impossible or so complex that it would be economically
infeasible.
A schematic of a hydrologic system might be as shown in
Figure II-1.
The Instantaneous Unit Hydrograph, I.U.H., is the basis for
the linear systems theory since it represents the response of a system
to a unit impulse (delta function), and completely characterizes the
system.
The output resulting from the application of a known input can
be uniquely determined by convolution of the input with the I.U.H.
The characteristic function of a linear system,e.g. the I.U.H.,
can be of two types, one being time invariant and the other being time
variant.
If the system is time invariant, then the system
may be
represented by a differential equation with constant coefficients as
- 14 -
ICIIIII~~~~---~~~
I
I
I
I
I
I
I
I
I
Input
Rules
Operating
System
Natural
I
II
I
I
I
I
I
II
II
_
.~~~~~~
I
II
t
Output
II
I
Basin
Physical
I
II
I
I
I
I
Laws
I
Hydrologic System
Figure II-1
Schematic of a Hydrologic System
-- i
h (0, to-do)
I.U
q(t)
I
I
L
U-
a_. _
- -
1
Figure II-2
Linear System Approach in Deriving an outflow hydrograph
- 15 -
in Equation II-2.
ni dn - 1 q(t0
I(t)= An dn n q(t)
n dt
n-
dt
n -1
II-2
where
I(t) = Time varying input
q(t) = Time varying output
This equation implies that the response to a sum of inputs is the same
as if the inputs were individually computed and the responses summed.
The difference between a time invariant system and a time variant
system is that the coefficients for a time variant system are time
dependent.
The behaviour of a typical L.T.I. (Linear Time Invariant)
System is shown in Figure II-2.
This figure also represents the use
of the convolution integral (or Duhamel's Integral) for a causal
system, viz
q(t) =
I(T) h(t-T) d
II-3
0
where
I(T) =
Inflow Rate
h(t) = System's impulse response function
II-3
Some Typical Linear Systems
Previous sections have discussed the use of a characteristic
function which when convoluted with simple inputs will produce an
output hydrograph representative of the system.
- 16 -
This characteristic
function may consist of one, two or three parameter models that are
used to represent the system responses to an input function.
The
following sections describe the basic models that are presently used
in representing a linear system response.
II-3.1
Linear Reservoir Model
In a groundwater system, one would normally expect heterogeneous soil conditions, as well as extremely small (in relation to
those found in surface hydrology) transmissivities or diffusivities.
Therefore, one might assume that the translational effects of subsurface flow might be neglected and treat the system as a storage
reservoir.
The reservoir is what is known as a one parameter model
where the one parameter, K, is used to represent the total hydraulic
characteristics of open channels for surface flow routing or the soil
characteristics for groundwater flow.
voir is shown in Figure II-3.
The conceptual storage reser-
The linear storage is related to the
outflow by:
S = K q(t)x
where
II-4
x = 1 for linear systems
< 1 for sublinear systems
> 1 for supralinear systems
The continuity equation for the storage reservoir is given
by Equation II-5, where x = 1.
- 17 -
o0
4-
0
rC
=I
40*
4-~~~~~~~~~~~
0
0
0
w
UQ
0
0
m
11I)
*
H
kl
i-
k
C)
w
o4--
0
6
U
0
'4-
c
4-
H-
_
4-
-
%.O
18 -
I(t) = q(t) + dt
or
I(t) = q(t)
where
S(t) = represents the reservoir storage
K
+ K d q(t)
dt
II-5
= time constant or lag between the input centroid and
the output centroid
q(t) = rate of discharge
Equation II-5 can be rewritten as:
d q(t)
+ q(t)
dt
I(t)
K
II-6
K
This is a first order linear equation and the total solution
may be determined from the homogeneous and particular solutions.
The complete solution to equation II-6 is given by
q~~~I /
q = et/
[
I e t/K dt + C]
II-7
assuming a constant input, the complete solution becomes:
q=C e-t/K + I
II-8
Introducing the boundary conditions which are:
q = 0,
t = 0
II-9
results in
C1 e
thus
II-10
+ I = 0
C1 = -I
II-11
- 19 -
The complete solution-to a constant-inflow to a storage
reservoir-then becomes
=
Q
/
I (1-e
II-12
)
Application to Time Varying Inflow
II-3.1.1
If we apply a constant input of rate I to such a linear
reservoir, the resulting outflow rate is as shown in Figure II-4, or
as given by
q
q
I
=
(t-T)
=
1 e
K
d T
II-13
where
t
)
=
I (1-et/K
<
T, the input time period of I.
If equal time periods are assumed with block inputs, I , then
the outflow rates, qn
q
=
q2 =
at the end of periods 1, 2,..... n, would be:
Ii (l-e -1/K
I2
(-e
-/K
)
) + I
e -/K
II-14
=In (-e-I/ )+In-1 e-1/K +
-
n- 2
fl-2
2/K
.... I
e
- 20 -
n-l
K
I nflow I
Outf low q
f
t
T
Figure II-4
Outflow Hydrograph Resulting from a Constant
Input to a Linear Reservoir
- 21 -
II-3.2
Linear Channel Model
Another single parameter model, known as the lag model or the
linear channel, is used for the purpose of translation of a flood wave.
The linear channel requires a constant velocity at any point in the
channel for all discharges such that the relationship between the
inflow and outflow at that point is merely:
Q (t) = I (t-T)
where
T
II-15
represents the translation in time of the flood wave with
no attenuation of the wave.
Dooge [1959] first presented the linear channel concept and
pointed out that it can also be considered to be as a cascade of an
infinite number of infinitesimal storages.
As shown in the above
section, the lag to a single reservoir (single storage) is represented by K.
be nK.
Then, if we have n reservoirs in series, the lag would
Thus if n goes to infinity as K goes to 0, while nK remains
2
constant, the variance about the mean, nK ,goes to zero, implying
that an instantaneous input of unit input will cause an instantaneous
output of the same volume after the mean travel time of nK.
II-3.3
Two Parameter Models
When considering a groundwater system, one must be realistic
- 22 -
in choosing a model for representing that system.
Common sense tells
us that a pure translation or the linear reservoir (exponential distribution) which lacks the property of having adequate 'memory' (Hillier
[1967]), will fail to represent the groundwater system.
Thus the
tendency has been to incorporate these elementary, single parameter
models into a variety of configurations.
This led to the two param-
eter models such as the Lag and Route Model or the Nash Model.
former is represented by the block diagram in Figure II-5.
The
It has the
following impulse response:
q (t) =
where
e
K
II-16
K
=
delay time of the linear reservoir.
T
=
translation time of the linear channel.
Noting the obvious increase in flexibility by applying such
models in series, Nash [1958] developed what has become known as the
Nash Model - (Also developed by Kalinin - Milynkov [1958]). Nash
conceptually applied a series of n reservoirs each of delay time K,
represented by the block diagram in Figure II-6, in order to represent the systems I.U.H..
Thus the total lag to the system can be
shown to be nK, since in a series configuration the outflow of one
reservoir is the inflow to the succeeding reservoir.
response for this model is:
- 23 -
The impulse
I(t)
q(t)
Linear
Linear
Channel
Reservoir
Figure II-5
Block Diagram of a Lag and Route Model
I
I
K
--
K2
--
'
Linear Reservoir Series
K, = K 2 = K 3 =-
.
= K
n
Figure II-6
Block Diagram of a Nash Model
- 24 -
q (t)
h
(O,t) =
1
K
t n-i
K
(-)
1
I(n)
-
e
-t/K
II-17
Notice that the Nash Model is also a modified gamma distribution.
Nash simply used the tools developed earlier by Zoch [1934] in linear
reservoirs, Clark [1945] in linear storage routing and Edson [1951]
in two parameter model development.
II-3.4
Three Parameter Models
Many three parameter models merely add the linear channel, a
translational effect,to the two parameter models.
In this paper this
is accomplished with the Nash Model as discussed above.
However,
Harley [1967] also uses the translation with a Muskingum Model, and
the Diffusion Analogy.
The advantage of the three parameter models is that they are
more capable of simulating a complex system as in the natural highly
damped groundwater system.
II-4
Model Formulation Using Linear Systems
With the basic tools now available to linear systems researchers, an infinite number of configurations become available to
represent the complex systems of the real world.
lowing models were presented by Kraijnhoff
- 25 -
Many of the fol-
[1966].
In 1955 Lyshede related a series of exponential functions to
the effect of runoff from rainfall and the basin characteristics.
This pointed out the possible use of linear reservoirs in series which
form the cascade effect of the Gamma distribution.
Singh, in 1964, used the time - area hydrograph and routed
it through two linear reservoirs in series to represent the effect of
overland and channel flows.
Singh's System is shown in Figure II-7.
Diskin, also in 1964, proposed a model using two Nash Models
in parallel, each branch consisting of a different number of equal
reservoirs and both branches having different lag characteristics in
the reservoir series such as shown by Figure II-8.
Thus by splitting
the input hydrograph, Diskin was able to develop a system that would
lag the output by:
a n
K1
+ (1-a) n
K
II-18
The I.U.H., of this system then would by represented by
h (Ot)
a
Kl(nl-
t )nl -1
1)
-t/K1
+
Kl
II-19
(1-a)
K 2 (n2 - 1)
t n 2K2
- 26 -
e -t/K
2
I(t)
Linear
Linear
Reservoir
Reservoir
q(t)
(Overland Flow) (Channel Flow)
Figure II-7
Singh's Model in Simulating Overland and Channel Flows
I(t)
(I-c) I
Coc
I
3
3
6
6
n
n
q (t)
Figure II-8
Diskin's Parallel Nash Model Configuration
- 27 -
II-5
Groundwater Systems
The Netherlands has done work in groundwater systems for
many years using the Dupuit - Forchheimer approximations.
As mentioned in Chapter I, there was skepticism in using a
linear system approach until it was realized that the basic assumptions for analytical solutions to groundwater flow were equivalent
to those used in the unit hydrograph theory.
Therefore, the
development stages of groundwater flow in linear systems are described
briefly in the following paragraphs.
In 1947, Edelman developed equations for two dimensional
groundwater flow into a unit length of channel and applied the
convolution integral to determine the effect on the groundwater flowrate of a constant infiltration rate into the phreatic zone as shown
in Figure II-9, resulting in the equation
1
Q (t)
=
KD
-P
(t-T) 2
d (-T)
II-20
p
where
2
t 2
P
=
constant percolation rate
KD
=
transmissivity
- 28 -
y = Initial Saturated Zone Thickness
P = Constant Percolation Rate to Phreatic Zone
qx = Unit Flow at x
Q(t) = Outflow Rate at Channel
, = Active Porosity
p
'a
I
Figure II-9
Edelman's One-Sided Groundwater Flow to a Unit Width Channel
I(t)
q (t)
h(O,t)
Figure II-10
Reservoir Representation of Kraijenhoff's Flow to Drainage Canals
- 29 -
1P
=
active porosity
In studying the effects of the phreatic zone in irrigation
areas, Glover [1954] developed an equation which relates the spacial
and time change of the free water surface to an instantaneous irrigation inflow, s, in equation II-21
y(xt)= s
4
IT
1 e-n
t/j
--n--/e
sinnl1x
n
L
-
n=1l,3,5
II-21
where
j
=-
2
iL
KD
From this relationship, Kraijenhoff [1958] developed the I.U.H., for
flow into parallel drainage channels.
h (,t)
e-n2/j
II-22
n=1,3,5
Expanding and setting the lags, K, equal to functions of j, results
in
h(O,t) =
8
-
1
-2 K1
1
8
e -t/Kl +---e
2
9
iT
1 e -t/K2 +
K2
II-23
1
25
8
1
12
K3
-t/K
3
We may see that this equation represents the behaviour of a system of
linear reservoirs in parallel as shown in Figure II-10.
such a system is given by equation II-24.
- 30 -
The lag
for
LAG =
K1
7
=
8
8
K1
9
1
2
K2 +..
1
34 54.. .)
~2
j (1+ + 4
=72
where
+
4
12
2
96
12
-24
II-24
= j; K 2 = j/9; K3 = j/25 etc.
De Jager [1965], Wesseling [1969] and Wemelsfelden [1963] used other
modified configurations to represent flows to parallel drains or
river channels.
In an attempt to develop the use of linear systems in groundwater application, Dooge [1960] used the concepts introduced so far
to derive coefficients in a simplifying technique.
By accepting the
work- done by Thornthwaite and Penman in estimating infiltration,
evaporation and other soil characteristics that determine the flow of
groundwater in the unsaturated zone, Dooge developed coefficients for
use under a number of conditions, these being:
a)
Water table close to the surface where there is
a direct effect on recharge by rainfall and
evapotranspiration.
b)
Water table well below the ground surface where
the recharge to the groundwater system is
accomplished only after the upper soil region
- 31 -
reaches field capacity.
c)
Composite type where the groundwater table reacts
as a shallow table until the groundwater 'storage'
decreases producing an effect more in line with
a deep water table.
Dooge's procedure is based on a constant recharge over a
given time period.
Using constant time periods and the storage
concept generated by the linear reservoir discussion in Section II-3,
he derived three routing coefficients and three coefficients required
under the conditions of negative recharge.
These basic equations
are
where
Qn
=
C
Q
=
Outflow due to contributions by the past n recharges
R
=
Recharge in period n
n
Rn + C 1 R_1
+ C2 Qn-l
II-25
Rn_1 =
Recharge in period n-1
Qn-I =
Outflow due to contributions from the past n-1
number of recharges.
The coefficients are given by:
CC
o
=1
K (1
1- K
T
-T/K
- 32 -
where
C1
=
K
T
-T/K
C2
=
e
T
=
time period of the recharge.
K
=
the linear reservoir lag coefficient.
(1-e
e-T/K
II-26
) - e
-T/K
The negative recharge computation is based on a storage
calculation at the end of period n, viz
S
n
=
R
-
n
T
(1-eT/K
) + (Q
n
- C
R )
n
1
T/K_1
II-27
If for any period this goes to zero, he calculates two additional
coefficients:
S
=
C3
=
C4
=
n
where
C3 R
n
+ C4 Q
K
1
T
eT/K_1
n
II-28
1
eT/K_ 1
Thus if one knows the parameters required by linear reservoir theory,
simplified coefficients may be calculated for routing through a groundwater system.
- 33 -
Though the computation scheme would become more complex,
one could conceptually consider using a time varying period that could
be accepted as being more realistic, i.e., to maintain a constant
input, which is acceptable under certain restrictions, and vary the
time over which the inflow is constant.
II-6
Parameter Estimation
Definitions, derivations and configurations have been
offered in the previous sections but the most important and perhaps
the most significant aspect to linear systems theory is that of
parameter estimation.
It should be obvious that a simulation proce-
dure requires a highly selective method of correlating the I.U.H.
parameters as dependent variables with the basin characteristics as
the independent variables.
Methods available for parameter esti-
mation include:
a)
Fourier Coefficients.
b)
Laguerre Coefficients.
c)
Method
of least squares.
d)
Method
of maximum likelihood.
e)
Method
of moments.
f)
Wiener - Hopf equations
O'Donnell [1960] presented an approach to develop the I.U.H.,
by means of Harmonic Analysis, which produced Fourier Coefficients.
- 34 -
He used the fact that the Fourier expansion can be used if the input/
output hydrographs are assumed periodic.
These expansions may be
represented by
Inflow Expansion:
oo
I
oo
an Cos (n-TT) +
=
n=o
(T)
b
sin (n
-)
II-29
n
n= 1
n=
I.U.H. Expansion:
0o
h (t-T)
co
a
C
=
os (m 2(t-T)) + I
T
m
m=o
m sin (m 27 T(t-T)
m=1
II-30
Outflow Expansion:
oo
Q
co
=
(t)
Cos (r 21T
A
r=o
+
r
r=o
B
r=
r 1
M
sin (r
T-)
II-31
r
By applying the convolution integral and considering the n
harmonic, he was able to derive the kernel coefficients with respect
to the input/output coefficients, thus giving:
a
o
n
1
=
-
=
2
-
A
o
m
T ao
T
annA +b n Bn
a
2
n
+
b
n
2
for n
- 35 -
1
II-32
a B -b
A
n n
n n
2
n
T an2
+
bn 2
Thus, if a long enough record is available and accurate, then a
simple means for determining the instantaneous unit hydrograph is
available.
Dooge [1965] proposed another scheme for the analysis of
heavily damped linear systems using Laguerre functions, similar to
the method proposed by O'Donnell [1960] in using coefficients derived
by means of harmonic analysis.
The equations derived by means of
the Laguerre functions are:
Input function
00
I (t) =
f
nn
a
I
n=o
(t)
II-33
Response function
00
h (t) =
I
a
n=o
n
f
(t)
n
II-34
Output function
o0
Q (t) =
I
n=o
A
n fn
(t)
II-35
- 36 -
The linkage function coefficient are given by
p-1
P
P=
O% ap-k -
k=o
k=o
ap--k
II-36
Eagleson [1965] presents the Methods of Least Squares and
the Weiner - Hopf Equation as procedures for determining the instantaneous unit hydrographs,
while Hillier and Lieberman [1967] present
the method of Maximum Likelihood and others in determining parameter
estimators.
The Method of Moments for estimating parameters was first
applied to hydrologic systems by Nash [1959].
The accuracy of this
procedure is dependant on the number of samples taken of the system
and that these samples are truly representative of the basin characteristics.
Maddaus [1969] offers what he considered to be disadvan-
tages to the use of this method, and are as follows.
a)
Non-linearity is filtered out of the lower moments
but the non-linearity tends to concentrate in the
higher moments.
b)
Inconsistency may occur between the parameters
and the assumed model resulting in negative
parameters.
c)
They tend to be biased at the extremities, thereby
causing the greatest error at the peak.
- 37 -
Since the Method of Moments is an effective parameter technique in hydrologic systems, where our concern is with the lower
moments, then any non-linearities of the natural system must reside in
the upper moments.
When applying this procedure to unit hydrograph
theory where only positive causal systems are considered, any
inconsistency producing a negative parameter would, indeed, reduce the
effectiveness of this procedure.
The effect of c) will be shown in
Chapter IV where the peak is shown to have the greatest error when
utilizing the Method of Moments.
Since the lower moments do provide
the more significant results in modeling hydrologic systems, it has
become an accepted fact that the first three or four moments only, be
used in parameter estimation.
Nash [19591 recommends the use of
dimensionless parameters in allowing an independence between the
parameters and the I.U.H.
This is accomplished by dividing all
moments exclusive of the first by the first moment.
To present an
example of the Method of Moments, the Nash Model will be considered.
The I.U.H. of a Nash Model may be represented by:
h (O,t) =
where
(t/K)l
et/K
II-37
n
=
number of equal linear reservoirs in series
K
=
the time constant or lag of a single reservoir.
- 38 -
Then the first moment about the origin, or lag of the system
is given by:
Ioo
h (O,t) t dt
0o
=
nK K
(t/K)n
1 e
(n)
d (t/K)
II-38
where
M
=
n K
=
first moment about the origin.
The second moment about the mean (or centroid) known as the
variance, is determined by the equation:
M2
where
=
M2 -
h (O,t) t2 dt
M2
0
then
where
M2
M1
II-39
=
K2 n (n+l)
=
K2 n2
=
nK 2
=
first moment about the mean
+
K2 n
-
K2 n2
- 39 -
M2
=
second moment about the mean
M2
=
second moment about the origin
By the same procedure, the third moment (Skewness) and the
fourth moment (Kurtosis) may be derived to be:
M3
=
2 n K3
II-40
M
=
6 n K4
A similar procedure may be used for the moments of the Linear
Reservoir and Lag and Route Models.
These are represented by Equa-
tion II-41 and II-42.
Linear Reservoir Moments
M1
=
K
M2
=
K2
II-41
3
M3
=
2 K
M4
=
6 K4
Lag and Route Moments
M
=
T + K
M2
=
K2
II-42
- 40 -
M3
=
2 K3
M
=
6 K4
Appendix B.3 develops moments in greater detail.
Another parameter that sometimes proves important is the
time to peak, which is found by taking the first derivative of the
I.U.H., and equating to zero,since in hydrology
causal systems.
the work is with
Thus:
The time to peak is that at which
d
dt
h(O,t)
=
II-43
0
Then for the various models discussed in Section II-3:
II-7
Nash Model
T
= (n-l) K
Single Reservoir
T
= 0
Lag and Route Model
T
= T
P
P
II-44
Theoretical Development to the Regional
Groundwater Routing Model
The basic equations for unsteady one dimensional flow will
be considered assuming the following conditions apply:
a)
Unconfined flow
- 41 -
b)
Incompressable fluid flow
c)
Darcy's Law applies
d)
Sloping bedrock
The continuity equation is given by:
Sc
ax
where
qi
ah
at-
=
-A
qi
=
inflow
Ax
=
area of inflow
Sc
=
storage coefficient
h
=
piezometric head
II-45
Ax
However, the response to a Dirac delta function of inflow is
desired such that the continuity equation will be:
a
+
Sc a
=
0
t >
II-46
The groundwater flow will be represented by a modified form of the Darcy
equation, incorporating the advective velocity, as in Equation II-47.
q
=
a
- K h hp m ax
- 42 -
II-47
where
q
=
groundwater flow (L3/T)
a
=
advective velocity due to the sloping
bedrock
(L/T)
h
=
piezometric head (L)
K
=
permeability (L/T)
=
mean depth of the saturated water zone
P
h
m
The advective velocity may be more adequately shown to be
a
= - K
dh
p dL
from Darcy's Law
II-48
= - K . slope
P
where the minus sign indicates the direction of flow.
Then equation
II-47 can be rewritten as
ax
a
h
ax
pm
h
aX
,
II-49
and the continuity equation II-46 will then become
a
ah
ax
- K
p
a2 h
h -
max2
+
ah
Sc-
at
= 0
II-50
or
Kp h m
a2 h
Sc
ax2
a
ah
Sc ax
- 43 -
II-51
By using Laplace Transforms, Harley [1967] shows that the resulting
system response to the Dirac delta function input (of a similar
relationship) will be:
h
(x,t)
21
=
x
*
P t
(ct- x)2p
.r
exp
II-52
4K P
thus equation II-51 results in:
/ Sc
h
(x,t)
.
=
2
K
p
nh
m
xt3k
t 3/2
2
exp
(at/c-x)2
4 K t
6~
II-53
Appendix B.2 proves a similar result for a horizontal bedrock condition.
The cumulants for the above response function are shown in
Appendix B.2 to be derived simply from the Laplace Transform.
Harley [1967] shows the first four cumulants to be given by Equations II-54
Cl
= x/c
2 K
C2
=
x
C
C3
II-54
- 44 -
12 K2 x
C3
=
C5
120 K 3 x
=
C7
Equations II-55 are the four cumulants derived from the
governing equation of groundwater flow.
Z
=
x Sc/a
Z2
=
2 K
Sc2 x/a3
h
p m
II-55
2
Sc 3 x/a 5
Z3
=
12 K 2 h
Z
=
120 K 3 h 3 SC4 x/a 7
p
m
p
m
In Chapter IV, the first three cumulants will be used to
determine the lag of a single reservoir, the number of single
reservoirs as well as the lag, T, required to simulate the system
response based on the input parameters used to compute the cumulants
shown above.
- 45 -
Chapter III
FOURIER TRANSFORMS IN LINEAR HYDROLOGIC SYSTEMS
III-1
Introduction to Fourier Analysis
A linear system is characterized by the following equation:
fi()
f
where
o
h (t-)
d
(t)
III-1
f. (t)
=
an input function
h
=
the system response function, characterized as the
(t)
response to a unit impulse
f
o
(t)
=
response of the system to the input f.(t)
1
Applying a Fourier transformation to equation III-1 yields
r o
F ()
o
t dt
f (t) e
11r
0
co
1
III-2
e
idt
dt
27r -0
-
I
f
i
(T) h
(t-T) d T
Letting s=t-T and changing the order of integration, the
above equation reduces to :
- 46 -
III-3
co
F (W)
=
h(s)
e
d s
21
I_
270
which can be further stated as
Fo(W)
0
where
=
H ()
III-4
4)
F ((
1
F. (w) =
the Fourier transform of input function
H (W) =
the Fourier transform of the system response,
(multiplied by 2)
F ()
o
=
the Fourier transform of the output.
Therefore a convolution integral can be reduced to a simple
multiplication of Fourier transforms.
Traditionally in hydrology the whole series of linear models,
such as the linear Reservoir, Muskingum, Nash and linear solutions to
the momentum and continuity equations, have been utilized by obtaining
expressions for the system response function and performing the lengthy
and time consuming numerical (or sometimes analytical) convolution in
a computer.
- 47 -
The purpose of this chapter is to combine the knowledge of
the analytical Fourier transforms of these linear systems with the
availability of numerical computer techniques to obtain Fourier
transforms in order to utilize equation II-4 to find the outflow from
a system resulting from a known inflow.
By reducing the complicated convolution procedures to a
simple multiplication and by utilizing an efficient numerical transform scheme the time required to obtain the output function should be
reduced significantly.
In this chapter two available computer programs to carry out
Fourier transformations are investigated.
One is based on traditional
finite numerical integration of the Fourier transform equations; the
other is based on the theory of Fast Fourier Transforms.
The accuracy,
speed and ease of use of each of these programs are evaluated and
compared.
III-2
Fourier Transform Technique
Two numerical, computational techniques are considered in
this paper.
One is based on the Cooley - Tukey Fast Fourier
Transform Theory which was available at M.I.T. as Subroutine
FOURT, (Appendix A-2). The other is based on finite numerical integration of the Fourier Transform equation and will be noted by Subroutine FOURTRAN (see reference Eagleson and Goodspeed (1970)).
- 48 -
The discrete form of the finite transform used in Subroutine
FOURTRAN
is:
1
F ()
=
NF
-
f(t) exp (-jw LAt) At
III-5
L=1
where
NF
=
number of points in time at which f(t) is given
f(t)
=
input function at interval At
At
=
time interval
W
=
angular frequency
This equation is evaluated at different equally spaced A's
up to some cutoff frequency, w , which must be less or equal to T/At.
If the given w0 value exceeds T/At it is automatically adjusted to
this value.
In order to return to the time domain (by taking the
inverse transform) the procedure is as follows:
III-2.1
a)
change sign of the exponential
b)
integrate over the angular frequencies (Aw)
c)
evaluate at different times (t).
Characteristics of Subroutine FOURTRAN --Numerical Integration Technique
The following are characteristics of Subroutine FOURTRAN:
- 49 -
a)
Two options are available to the user for the
output, a complex spectrum or the normalized
amplitude and phase of the spectrum.
b)
The program does not require the input function
to be periodic.
c)
The input function is assumed to start at a value
of zero,to be sampled at equal intervals,and to be
zero after the sampled period.
d)
For simplicity, the forward and inverse transform
equation are made similar by multiplying by
1// 27, thus allowing a simple transition from the
forward transform to the inverse transform.
e)
Since the complex spectrum of a time series is
symmetrical about the origin and if FOURTRAN is
used to find the inverse transform, only one
portion of the symmetrical transform is input and
thus the resulting time domain series must be
doubled in order to keep the proper scale.
III-2.1.1
Test and Results for Subroutine FOURTRAN
The tests performed on Subroutine FOURTRAN consisted of
entering and exiting the program with one function in order to
determine the effectiveness of the program in returning the identical function.
The function chosen was that of the linear
- 50 -
reservoir as presented in Chapter II.
The forward and inverse Fourier transforms from Subroutine
FOURTRAN are dependent on the following parameters:
At
=
sampling time interval
0
o
=
maximum angular frequency, Nyquist frequency
Aw =
angular frequency interval.
Table III-1 is a tabulation of the significant parameters in
the forward transform (frequency domain).
In considering Table III-1
and comparing the analytical and computational transforms, these
indicated:
a)
Accuracy increases as the integration steps decreased, i.e., At in the forward transform and
AW in the inverse transform.
b)
Figure III-1 shows the aliasing effect in the
forward transform when compared to the analytical
transform, i.e., as the transforms approach the
higher frequencies, the complex spectrum obtained,
using the program, diverges from the theoretical
transform.
c)
As the integrating variable, At, decreases the
time of execution increases significantly.
- 51 -
An
U)
H
E-4
0-
00
aD
)o
-H0
4j U)
U-
'v
4
rD
n
co
Cl
L
'
C
l
oml
0
a
O
or
0
0
0
0
O
HA
N
nr
r-
(N
x
m
v
~4-H
<
*
>1
u
3
O
3H
rzI
z
)
mn
o0
N E-
H
0
H
*
m
m
Cl
OD
co
co
o
F.
< "I
la
*
("3)
l
*
0
*
0
(d
P:
35
*
(N
*
*
o
0
H-4
o
i.
o
o
Ln
N
-
rl
O
0
0
m
m
o
o
-
.4
I
H
H
H
H
li
L
1I
r:;
-l
Hl
U)
E
3
U1
r
Lin
LO
U)
Lf
l
rl
r
(
O3
oa
03
0'
LA
Ln
uL
LA
LA
rl
rl
r
blH
N-
N
N
N
I
C
CN
C
N
ul
ur
In
in
ur
H
l
H
H
H
'v
0
>
N-
O
*
3 OH.
u
Ln
LA
LA
LA
O
O
O
O
*n
.n
EEH
U)
04-i
E
0
z
H
O
O
O
.n
.
-r*n
E-
*n
.n
O
O
.
O
.
4
Oc
Z
LA
%.
Ln
k
LA
k0
*r
(N
H
H
J
Jd J
v
N
LO
%9
rH
N
LA
H
(N
H
H
H
J
J
J
WD
v
N
P
0
4
L0
c)
LA
LO
EH
E-
H
0
cli
H
0
o0
Q4
-,
U)P4
o
o
0
0
0
0
0
0
0
Cl
Cn
m
cl
Cm
C
m
m
IV
52 -
0
F(w)
a
-
Computed Function
Annlir',nl lantirnn
UI
M
I IVI
I
= 42.0
= 0.1
= 5.0
ency = 0.9115
= 0.0139
o10-
I
10-2
I,
I,I, I
I
IlA
5
I
10
I
15
20
I
25
Frequency
(one unit = 0.0139 rad/time)
Figure III-1
Aliasing Effect in the Forward Transform--Subroutine FOURTRAN
- 53 -
attempt was made to decrease the aliasing effect
by increasing the Nyquist frequency, w ; as this
decreased the integrating step, At, however
the execution time again increased.
Increasing
A@, on the other hand, provides an inverse relationship with the execution time.
The problem
is that in order to provide good results in the
inverse transform an adequate number of points
in the frequency domain must be provided.
implies use of a smaller
This
w and therefore higher
execution times in the inverse and forward transform calculations.
The results obtained when finding the inverse of a complex
spectrum using FOURTRAN are shown in Figure III-2.
As the figure shows,
the numerical integration method of finding the Fourier transform fails
to reproduce its original input,.i.e., if a forward transform is
performed on an input and then the corresponding inverse on that transform, FOURTRAN fails to reproduce the original function.
to the finite integration technique.
with
This is due
The problem that we are faced
is when a system response is to be represented by a series system
of models as discussed in Chapter II.
In this case, the output of one
model response is the input into the next, thus requiring a multiple
use of a convolution technique.
Thus, if Subroutine FOURTRAN was
recalled a number of times, the integration error would compound itself.
- 54 -
Q
(-
too
II
C C
CO i
o
aio
I
amI
II
(D
o0O
430
0
c0
C
0
C)
E
0
E
Ew
Q)
t')
o
i
H
,-
E
I
H1.
1
H
r_:
O
Ur
E
O4
CM
zn
(d
E
Cd
d
-
- 55 -
III-2.2
Characteristics of Subroutine FOURT --Fast Fourier Transform Technique
III-2.2.1
Data Requirements
This subprogram assumes periodicity, i.e., the input values
represent one cycle of a periodic function.
The input values must be
at even time (frequency) intervals for a forward (inverse) transform
and may be real or complex.
However, when returning from the frequency
domain the data must always be complex.
If the number of data points
is a power of two this subprogram will run at its maximum efficiency.
The only data the program requires are the input values, the number of
input values and information indicating if the inverse or forward
transforms is desired.
III-2.2.2
Subroutine FOURT --- Forward Transform
The Nyquist frequency, o
,
is determined analytically as
W/At, thus defining the frequency interval, Aw, at which the transform
will be evaluated.
A property of the FOURT returned forward transform
is the symmetry of the transform about the Nyquist frequency, with that
frequency as the midpoint (plus one if the function has an even number
of points) of the transform.
range of 2(N-1)/NAt,
Since FOURT evaluates over a frequency
at frequency intervals of 2/NAt,
the Nyquist
frequency will be located at point N/2 due to FOURT's symmetric representation in the frequency domain.
The number of output points in
- 56 -
Subroutine FOURT is identical to those input.
III-2.2.3
Subroutine FOURT --- Inverse Transform
The output of the inverse transform is a regular time series
and has the same time intervals as the original input since it is
based on the same number of input points.
The resulting time domain
function must be divided by the number of points used in the calculation in order to obtain the correct results- a property of FOURT.
In
finding the inverse transform the user must ensure that the complex
spectrum is input in the symmetrical conjugate form described above.
III-2.2.4
Implications of the Input-output Requirements
As mentioned above, the number of points returned after
transforming with the subprogram FOURT is the same as input initially.
Since the program assumes a periodic function this implies that in
using the program to convolute, by multiplication of input and response
transforms, the same number of points must be in each of the transforms.
It is important to note that FOURT does not consider the
1/27 factor usually found in Fourier transforms so caution must be used
in interpreting FOURT's results.
III-2.2.5
Tests and Results
In finding the inverse Fourier transform of a FOURT obtained
- 57 -
complex spectrum, the program was able to reproduce the original time
domain function identically, thus indicating a good computational
When the theoretical Fourier transform was input in the fre-
scheme.
quency domain and the inverse was taken the results were very close to
the true function, Figure III-3.
However, the aliasing effect still
exists as shown in Figure III-4.
III-3
Selection of an Efficient Fourier Transformation Technique
Subroutine FOURT, the Fast Fourier Transformation technique
is chosen over Subroutine FOURTRAN for the following reasons:
a)
it is considerably faster
b)
the accuracy is maintained in the inverse transform when using the theoretical forward transform.
c)
the exact reproduction of the function in the time
domain is obtained when the forward and inverse
transforms are computed in succession.
III-4
Convoluting with Fourier Transforms
Implementing the algorithm to convolute by multiplying Fourier
transforms presents some immediate problems.
First, the selection of the Fast Fourier transform program,
FOURT requires that the functions being transformed be defined as
- 58 -
0
C
C
O O0
O O
C C
. o
II-
C
H
0
4-
CO
C
0
E
E
04
E
e~
.I
-H
tO
a,
E
Y
0
In
H
H
CN
c
0
LL
Ll_
- 59 -
F(w)
irt
art
i-
10-2
A
l-3
,v
5
10
15
20
25
30
Frequency
(one unit = 0.00873
rad/time)
Figure III-4
Forward Transform Indicating Aliasing Effect--Subroutine FOURT
- 60 -
periodic functions.
Selecting this period so that the resulting
output would not be affected by the assumed repeating portions of the
input and response functions, was one important task.
Secondly, the response functions of all linear models approach
zero at infinity.
Since a finite function was required in order to be
able to define a time period for the input, response and output functions
all of which must be input as the same time period, it was necessary to
develop a criteria for adequate definition of a cut off time for the
response function.
Finally, the Fourier transform of a function may include
infinitely many terms requiring that the frequency range of the transforms also go to infinity.
It is necessary, then, to develop a method
to find the Nyquist frequency,
o , which will limit the frequency band-
width to the frequencies of interest.
By fixing the Nyquist frequency,
the time increment, At, that the input function must be sampled at
in
order to detect frequencies up to w , is defined.
III-4.1
Defining the Nyquist Frequency,
o
The requirement for defining the Nyquist frequency, w , is
to ascertain the 'energy' required if the response function is sufficient to produce accurate results.
To determine the 'energy'
retained by the response function, the 'power' spectrum of the response function and NOT the input function is used for this purpose.
- 61 -
The logic here is that the frequencies of the obtained output are
limited by the dominating frequencies of the system response function.
Hydrologic systems, generally, pass a significant amount of the input
energy within the lower frequencies.
As this is also the case of the
linear models used to represent the system response, most of the
energy within the system may be retained without the consideration of
very high frequencies.
The procedure of fixing w
is illustrated using the well
known linear reservoir model whose response function is given by
h (t)
III-6
e -t/K
=1
K
where
t
=
time
K
=
the delay time constant
The normalized amplitude spectrum of this function is
given by:
H
(WK)
=
- 1
(1 + (WK)2)/
/
III-7
The power density spectrum is defined as the square of the amplitude,
or:
H
o
(WK) 1=
1
2
1+(WK)
62 -
III-8
Since a unit impulse input function is being considered, its amplitude density spectrum is given by:
1
(WK)
P
=
III-9
T
Then the energy density spectrum of the output is the resultant
multiplication of the input and response power spectrums, or:
o (WK)
1H
=
=
2-r
2-
(WK)
H
o
1P
.
(WK) 12
(WK) 12
III-10
1+(WK)
The energy of the output that must be preserved is chosen to be 98% of
Then, the Nyquist frequency,
the total energy.
, must be found which
will assure that an energy loss greater than 2% does not occur.
total energy of this system is, given by:
E
=
2
f
(UK) d (K)
0
d(WK)
d
1
JO
-
1+(WK)
2
0.5
- 63 -
The
Thus, in order to keep 98% of the energy, we need to integrate over
the area of interest, as in Equation III-12 ,such that
E
=
1
U
d(WK)
7T
= 0.49
1+(wK)
III-12
[tan
(WK) - n
] = 0.49
But for n=l, Equation III-12 becomes
-1
tan
(WK)
= 1.49
III-13
thus
OK
= tan (4.68)
= tan (2680)
III-14
= 28.636
Thus, 98% of the output energy of a linear reservoir model will be
passed if the Nyquist frequency,
, is determined by the expression
III-15, which is in terms of the model parameters K.
w0
III-4.2
= 28.636/K
III-15
Effects of Complex Responses on the
Nyquist Frequency
Theoretically this procedure should be applied to each
- 64 -
model in order to obtain their respective expressions for
.
Unfortunately the power spectrums of other model response functions
get fairly complicated, especially as the number of parameters
increase.
Due to this difficulty, it was decided to use the Nyquist
frequency determined for the linear reservoir as a basis for all
linear systems used.
the selection of w
o
Making such an assumption should assure that
is on the conservative side.
Of all the linear
models the linear reservoir can pass the highest frequency components.
Figure III-5 demonstrates how the linear reservoir
normalized amplitude spectrum has higher frequency components than
Nash Models of order greater than 1 (which is the linear reservoir).
III-4.3
Selection of Response Function Duration
The time period for the system response was chosen
arbitrarily to be that time which would allow 99% of the response
to have occurred.
Again this was done by setting up an integral equation.
As
an example the linear reservoir formulation was used.
The total area
under the linear reservoir response curve is unity,so
to find the
time which should be used, the integrated system response for a linear
reservoir was equated to .99, thus representing an area of 99%.
et/K
=
.99
III-16
o
- 65 -
I
OC
Figure III-5
Normalized Amplitude Spectrum Relationships for Nash Model
- 66 -
which is
1 -e -t/K
-t/K =99
so
T
= -K-ln
r
III-17
(.01)
III-18
= 4.605 K
Again, due to the difficulty of integrating some of the more
complicated response functions, it was decided to use the linear
reservoir criteria with other linear models.
If this procedure were followed for the more complex system
response models, it would be found that the integration increases in
complexity.
To alleviate this problem, the 'lag' of the complex
system responses was used in place of the lag for the linear reservoir
response in Equation III-18.
Although a large part of the area under
the linear reservoir response curve is concentrated at the origin,
this procedure provides a conservative but efficient solution for the
response time period.
The results of this procedure when applying a
Nash Model response is found in Appendix B-1.
III-4.4
Selection of the Output Period
As mentioned in Section III-2.2 the selection of FOURT for
calculating the Fourier transformations required choosing a time period
representative of the output hydrograph.
The response and input
functions are then obliged to have the same period, so zeros must be
- 67 -
added in order to extend these functions to the required time period.
The algorithm utilized to do this is the following:
The duration of the output, T , according to convolution
theory, is the sum of the input duration, T i , and response duration Tr
so
T
where
T
o
r
=
T. +T
=
the duration of the response to the system as given
r
1
III-19
in Section III-4.2
T.
1
=
the time of input duration.
Let Ni be the number of points in the input given at intervals Ati..
N. =
1
i/At.
+
1
1
III-20
Let N be the total number of points in the output which must be at the
same interval, At., as the input, then
T
N
=
-
+
1
= Ti/At. I
+
At.1
N.
i
+
Tr/At.
1
T
r/At.
- 68 -
+
1
III-21
Therefore the number of zeros to be added to the input function is
given by:
Number of zeros
=
r/Ati
III-22
1
The time period required for the input, response and output
functions must be defined by the period Tr + Ti.
Since the system
responses will not be utilized in the time domain but only in the
forward transform, this procedure will transpose the time period into
the number of input values as required by FOURT.
For a further dis-
cussion, refer to Section III-2.2.
III-4.5
An Example - The Theoretical Solution
Having solved the implementation problems, an example was
tried.
In order to have a basis for comparison, the theoretical
solution of the example is obtained initially.
The example utilized was the response of a linear reservoir to a square wave input of amplitude I
and duration Ti.
The output function for this example can be found by
convolution.
The convolution integral for this example is:
f (t) = 1/K
o
j
_t
o
t-T
I
o
e
- 69 -
K
dT
III-23
This can be divided into two regions:
tt
f
J
1
(t)=
I
o
t-t
- (-)
K_
e
dT
For t
T.
1
III-24
=
[I
(1 - e
)t
and
T.
(t-T
f
(t) =
1
I
I
e
o
0
K
dT
For t > T.
1
III-25
=
I
0
e[
-t/K
[e
T i /K
-1]
T
i
The same result can be reached by finding the Fourier transform of the input and the response functions and multiplying them
together.
The Fourier transform of the input is given by:
T.
F ()
1
=
I
1
27
o
e
dt
t1
=
I
1
o 2
[e -j
_
III-26
]
o
-jw
- 70 -
=
(1- e-Ji0Ti)
I
-
27 (jW)
The Fourier transform of the response function is defined
as:
r co
H (w) =
2T (
)
1
J
27T
0,
e
K
-t/K
e
-jwt
dt
III-27
1
1+jWK
The Fourier transform of the output function is obtained by
multiplying these two results, i.e.
Fo ()
0
=
Fi(W)
H
(w)
III-28
=
I
o
[j
j
1
-032K
(1 - e -WTi)
21T
III-4.6
Obtained Results
A computer program was written to test the example discussed in Section III-4.5.
The program used as input, the desired forcing
- 71 -
function and the parameter, K, describing the linear reservoir model
used.
The program obtained the desired Nyquist frequency,
, the
corresponding time intervals at which the input must be given, At=r/
,
o
and interpolated the input to the desired time interval if not given
The program also evaluated the theoretical Fourier
at that At.
transform of the response function, found the transform of the input
by using FOURT, multiplied them together, found the inverse of the
resulting transform using FOURT to obtain the output function and
finally plotted the resulting data together with the theoretical
result.
A copy of the program is included in Appendix A-1.
The program was tested with a square wave input having a
maximum value of 3.0 and duration of 2 time units.
voir model used a parameter K equal to 1.5.
The linear reser-
The plot of the resulting
output function and its theoretical value can be seen in Figure III-6.
As shown, the results are extremely accurate.
The program with all the
plotting, interpolating etc., took 2.67 seconds to execute on the
I.B.M. 360-67 computer system.
It is interesting to note that even though FOURT forward
transforms showed marked aliasing effect (Figure III-4) and also failed
to reproduce, exactly, the correct function when finding the inverse
of a transform that was not its own (Figure III-3), that when the
inverse of the forward transform is taken, after convolution,.resulted
in such an accurate solution.
This is due to the fact that the
- 72 -
o Input Hydrograph
o Analytical Output Hydrograph
A Computed Output Hydrograph
3.0
2.0
/
0
~/
1.0
-I
0
a\
/
0
a'
\A
I",
0--O---
/
,
,
I
2.0
4.0
I
Aj~I
o,-b0E~mbAEmm^_OO
6.0
Time
Figure III-6
Outflow Hydrograph of a Constant Inflow Convoluted
with a Nash Model Response
- 73 -
8.0
response is most sensitive to the low frequencies.
In the areas of
low frequencies the aliasing effect and the error in finding an inverse of a non FOURT transform are minimal.
III-5
Application to Surface Routing
Harley [1967], in part, utilized two parameter simulation
models to represent flood routing in open channels.
This he ac-
complished through simplifying assumptions of the various complexities in the system.
These complexities he listed as being in the
field of physics, geometry and inflows.
The complex physics was
satisfied by taking the complete equations for open channel hydraulics;
the complex geometry was handled by assuming a uniformly wide
rectangular chezy channel; and lastly, linearization provided the
means of simplification of complex inflows.
This means that the re-
sponse of the channel may be characterized by the response to a delta
function.
In order to relate the complete linear equation to the
parameters of the simplified two and three parameter models, Harley compared the cumulants or moments, these being the Lag, M1 , the variance,
M 2 ' the skewness, M 3 and the Kurtosis, M 4 .
The parameters estimated
by this method are expressed in terms of the hydraulic parameters of
the original channel while in its reference
steady - state condition.
The lag, M 1 , was equated to the first moment about the origin and the
- 74 -
variance, M2 , was equated to the second moment about the center, these
being derived from field data.
In the derivation of these parameters, Harley used the
cumulants in the dimensionless form known as the shape factors.
These
are:
S1
=
S
C1
C2/C 2
III-29
3
S3
=
C3/C1
S4
=
C 4 /C 1 4, etc.
This in effect, removes the time scale effect from the second and following cumulants, making them dimensionless.
The two linear models that will be used to represent the
system response of Harley's complete linear channel equation will be
the two parameter models, Lag and Route Model and Nash Model.
The properties of the moments with respect to the distribution are:
M
o
=
Area
M1' =
Lag (or mean), with respect to the origin.
M2
Variance, i.e. measure of dispersion of the distri-
=
bution about the mean.
M3
=
Skewness, i.e. measure of the shift of the peak from
the midpoint of the time axis of the distribution.
- 75 -
M4
=
Kurtosis, i.e. measure of the peakness of the distribution.
Harley notes that the cumulants except the first, are invariant under
a change of origin and are expressed in the relation:
(Clt + C
Exp
t2
C
2
=
+M
t +
2
=
+ ... )
n
t + M'
1
tn
et
2
M
n
tn + ....
-
III-30
n
dE
and may be related to the moment by:
CI
= M
C2
=
M2
2
C3
=
M
C4
=
M -3M
'
III-31
3
2
He goes on to show that the cumulants for the complete solution of the
linear channel equation are:
1
2
3
x
V
o
x
1.5 V
o
- 76 -
2
(1
3
2
F
4
o
x
S x
2
1.5 V
o
o
III-32
C
=
3
4
3
F2
(
4
)
(1 +
F2
2
)
Y0
Sx
2
X
)2
)
.5 V
3
0o
4
103
F4) S x )
o
=
mean velocity in the steady state
F
=
Froude number
Y
=
depth of water in the steady state
S
=
slope of the channel bottom.
o
4
) (1 +
112
F2+
20
V
9
(1 -
F
=
4
where
40
C4
4
(1
X
5 VV )
o
4
l.5
These then can be related to the cumulants of the systems that are
selected to simulate the channel routing of the flood wave.
The
cumulants for the two parameter models, the Lag and Route and the Nash,
are presented in Section II-6.
The parameters and conditions that Harley used in the Channel Routing are as follows
Flow conditions:
Reference Discharge
=
Reference Velocity
=
4.14126 ft/sec.
Reference Celerity
=
6.21189 ft/sec.
Reference Depth
=
D (S x/y)
0
=
- 77 -
150.0
cfs.
36.22086 ft.
5.52167
Channel Parameters:
200
miles.
Length
=
Slope
=
1.0 ft/mile.
Friction coefficient =
50.0 (Chezy).
Froude No.
=
0.12129.
This configuration yields the following cumulation and shape factors
S
=
S2
=
0.120292
S3
=
0.0438913
S4
=
0.0265171
47.22126 hrs
Inflow Parameters:
Type
=
[q (O,t) =
Thomas Wave
q max
(1 - Cos(27 t/f)]
2
where
q max
f
=
wave frequency of recurrence
=
Maximum flow over t
with
Time to peak
=
48 hrs.
Peak discharge
=
200 cfs.
Base discharge
=
50 cfs.
By relating the shape factors to the cumulants, the parameters of the
linear simulation models can be related to the hydraulic characteristics.
For instance, in the case of the Nash Model we have:
- 78 -
Thus
K
=
5.68034
n
=
8.31309
The same procedure can be used to obtain the parameters for
the Lag and Route Model yielding
K
= 16.37782
T
= 30.84344.
Obviously, one must be careful of placing physical significance to these parameters.
For instance, in the case of the Nash
parameter, n, which represents the number of linear reservoirs, this
requires a fractional reservoir thus pointing out the discrepancy
between the physical significance and the parameter.
The program used to calculate the output hydrograph by means
of the harmonic analysis may be found in Appendix A-1.
Designed for use
on the IBM 370-155 computer system the program uses less than 120K of
core.
If the program was compiled (machine Language), and required a
plotted output, the results would be obtained in 2.64 secs. for the Lag
and Route Model and 3.9 secs. for the Nash Model.
The results are plotted in Figure III-7 and III-8.
Com-
paring the results obtained by Harley of the linear solution together
with those obtained using the FFT approach,
the results
of the Lag and Route Model
Figure III-7, indicates
to be as good
as or better
than Harley's solution, resulting in an RMS error of 0.00638 or better.
- 79 -
0o
4
a.
C
4
I
= oa
3 ro
OD
I
I
4
0
U)
a
w
El
44 //
EoE
"o
Io
C u J
.4
a
CM9
0
04
4
0
to
0
U)
W
4
O
UL
w)
o
U)
0 °~~-~E
o
(0
H
a
:j
U)l
H
0~~~4~~~4
S'
(U
'I-I
0
~0
sOD
o~~~ iqqO
0
O
C-
.
81
1O~~4
4-
9
-
C
LL
- 80 -
000
1
-c
4'
it
L Q
a. a
~o~
I
I
L~~~~~~~~~~~~~~~~~~
o
?/.//
I-,Z
CL
-
(0
C
CLEa 0
>%
0
O
< 4
·:/ I
O
E
I
o
I7
0/1
U)
U)
00
O
O
H
S-
o
H
>)
0
O
Va
Iw
4
0
t
>
C
00O
0(0
O
0O
C
0aD
O
4LL
- 81 -
0°
Figure III-8, indicates the results of the Nash Model also resulting
in RMS error as good as or better than 0.00089 for Harley's solution.
- 82 -
Chapter IV
APPLICATION TO A REGIONAL RIVER BASIN
IV-1
Discussion of the Selected Regional
River Basin
The river basin selected for study in this chapter is the
Rio Colorado River Basin in Argentina where water is supplied almost
entirely by the melting snows of the Andes Mountain Range.
The
tributaries that drain the catchment area within the Andes are:
the
Rio Grande, the Barrancas, the Arroyo Butaco and the Arroyito
Chachaico - Buta Ranquil.
The Rio Colorado then, carries these
waters from the Andes (about 720 west longitude) to the Atlantic Ocean
(about 620 west longitude) flowing through the great Patagonian Plain
which consists primarily of sedimentary material,
Figure IV-1.
Very
little precipitation occurs in the central region located in La Pampa
province.
The mountainous region receives the greatest precipitation,
mostly as snow.
The third region is the Eastern Coastal Region in the
province of Buenos Aires which has considerable vegetation due to the
moderate precipitation and temperate climate.
IV-1.1
Geology and Soil Description within
the River Basin
The geology in the head regions of the Rio Colorado is a
- 83 -
I
I
.1
4-)
a.
l
a,
I~~~~~~
0E
o
J
Z
AZ
in
0
(z
I.
N
0
I_
iI
'
Z
00
U
W
r.
0
go
Xi
14
I
H
W
Z4H
k4
Z
X
,_,-_
z
:3
I
-lk
- 84 -
I_' \
_
conglomerate of three primary formations, these being tertiary,
cretaceous and jurassic.
The tertiary consists of upper continental
deposits, basalts, undifferentiated eruptive rocks, acid and
mesosilicic intrusive facies, lower continental deposits and marine
deposits.
The cretaceous era consists of marine deposits, continental
deposits and marine & continental deposits, while the jurassic era
consists of marine & continental deposits and marine deposits.
Below
the headwaters of the Rio Colorado the basin dates almost entirely to
the quaternary era consisting of glacial and fluvioglacial continental
deposits, marine deposits, basalts and other undifferentiated volcanic
rocks.
There are outcrops of the tertiary, cretaceous and precambrian
eras, also.
The three regions, mountainous, central and eastern may be
used in segregating the soil types.
The mountainous area is predomi-
nately fine sandy soil and due to the lack of organic material, provides a rapid infiltrating system.
The more complex central region
may be divided into three major categories the first being a mantle
of sand as found in the mountainous region, then narrow beds of nonconsolidated river pebbles or gravels and lastly, two horizons with
the upper one consisting of a sandy silt material low in organic
material and therefore being adequately drained, and the lower
comprised of loam and clay or a silty loam which tends to retain the
salts lost from the upper horizon through leaching.
The lower horizon
sometimes appears on the surface due to the erosion of the upper.
- 85 -
A
third material which is found in the eastern region of the La Pampa
district is a hard pan material called 'Tosca' which restricts both
infiltration and root passage.
The eastern region which has soils
that are suitable for cultivation, has two soil horizons as well, the
upper being a sandy or sandy loam, while the lower is sandy with fine
silts held together by a calcareous cement.
With the high rate of
irrigation and moderate infiltration rate in this area, the water
table has risen, thereby increasing the salinity in the upper horizons.
Hard pan material has also been found in this region.
IV-1.2
Hydrologic and Agricultural Discussion
The Rio Colorado begins at an elevation of 4,800 m at the
source of the Rio Grande, and flows to the Atlantic Ocean over a
distance of 750 Kilometers.
In the upper reaches, the river averages
a slope of 2 - 0.4 m/km but decreases to an average of 0.4 m/km in
the lower reaches.
Due to the sediment transport capacity of the
river and the small slope in the eastern region, a delta was formed.
The average flow at Buta Ranquil is 143 m 3 /sec. but has a recorded
minimum flow of 44.0 m 3 /sec. and a maximum flow of 678 m 3 /sec..
The subsurface conditions have not been thoroughly studied
within this river basin.
The trend in the Buenos Aires region tends
to show that basalt fractures allow the water to enter into a deeper
zone.
In the western portion of the La Pampa province a shallow
- 86 -
bedrock situation produces a high water table that is found to have
an elevation of about 2 meters below the surface.
This bedrock
strata is lower in the eastern sector of the province, thus lowering
the water table.
Agriculturally, the region is sparse but there are plans
to develop irrigation sites along the river.
Generally, the produc-
tion consists of 70% alfalfa, 19% vegetables and the rest fruit or
other minor crops.
The general procedure for irrigation of an
agricultural cultivated area is to provide enough water so as to meet
the consumptive use requirment for the crops as well as the leaching
requirment for the soil.
In the Rio Colorado Basin the growing
season spans an eight month period therefore there is a four month
period when there are no water requirements.
This procedure presents
a cyclical water demand very similar to 1/2 of a sine wave.
For the
purpose of this work it is assumed that the leaching demand is met
with the water reaching the phreatic zone in the same distribution
pattern.
IV-2
Conceptual Discussion of a Regional
Groundwater Routing Model
With the background having been discussed in the above
sections, the logic behind a regional groundwater model may be discussed.
There are three areas of interest in the groundwater area:
- 87 -
a)
A quick responding system which exists in the upper
root zone where a condition of interflow may occur.
b)
A rapidly responding system representative of the
shallow zone in the soil structure, resulting from
a drainage system in an irrigation site or as a
natural phenomena.
c)
A slowly responding system that exists in the deep
reaches of the soil structure (deep water zone),
which may represent the flow of groundwater in an
aquifer laterally to a river or parallel to the
river interacting with the river at some distance
further down stream.
The conceptual logic here may
be seen in Figure IV-2, which is a profile of the
eastern portion of the basin.
Here a loss to the
river resulting from a zone of higher permeability,
over which the river flows, will, possibly, provide
additional groundwater flow in a direction parallel
to or diverging from the river due to the geological
structure of the soil.
Or a groundwater flow may
result in an old river bed after that river was
diverted for other purposes or possibly as a result
of a change in the bedrock strata.
Figure IV-1, indi-
cates a tributary that no longer provides flow to
the Rio Colorado due to a diversion in an upstream
province.
- 88 -
z.t
II
I
I q I
,
I
I
i
I
o;
R
8
e1§!
a)
-.H
I
Lj
o
~
la0(a
0
,--I
0
0
HH
C4
1'0'
,c
Iz
9
.2
111
j i
U)
a
oI
'0
4)
U
>
F
0
o
zz
4o
(r1
_
!
(
I e.
U.l"
0
4
0
p4
4-)
Z
a
-1
Q)
E
o
2I
9
t
I-r
.14
P.,
.
C
c
IL
- 89 -
A conceptual schematic of a groundwater routing model for
an irrigation site is shown in Figure IV-3, which also indicates some
typical linear model for each process.
IV-3
Model Development
The model which will be developed in this section is similar
to one suggested by Diskin [1964], where he distributed his input into
two parallel Nash chain series thus providing a more flexible system
response.
IV-3.1
The block diagram used in this model is shown in Figure IV-4.
General Linear Groundwater Routing Model
It was found in Chapter III that the use of the Fast Fourier
Transform in Harmonic Analysis for convoluting an input with the system
response to a delta function was not only fast but also very accurate
in representing an output hydrograph.
The parameters used for the
system response in Chapter III were derived through the use of moments
and the general governing equations for open channel flow.
In a groundwater regime, there are many complex processes
that can never be completely understood either by reason of mathematical theory or by the many unknowns in the subsurface zones such as
cracks, fissures, non-homogeneous and non-isotopic conditions.
Thus
a method was needed that would be fast and represent, to the best
available means, the response of such a subsystem.
- 90 -
Considerable work
L.
'O
0
03
O -
0
0
.
..
c
,
,Oo
=7
z
O
U)
0)
c0
.
a.
o
0
0
C
C
O
o
c
0
-4
C
Hp
.-
0
C
.
_W
aa
,-
N
0C
,
O
O
4-
u
._
O
L.
0
a._
o
C
L.
c-
-o
L _
·O
-'
o
U1)
C
0
CD
-
47.~
-
l
e,,
- 91 -
O
C)
I ()
WTDR (t)
~
WTR (t)
I
-
WTDI (t)
Rp
R3
-
F
Q2 (t)
Q(t)
I(t)
RI
Inflow to Groundwater System
Slow ResponseSystem for Flow Parallel to
the River
Medium Response to River - Aquifer Relationship
Rapid Response to Drainage System
Where the Sum
WTDR, WTR, WTD Weight of Distribution,
Totals 1.0
Outflow at Some Point Downstream
Q (t)
Outflow Resulting from Groundwater Flow and
Q2(t)
R2
R3
Drainage, Laterally at the River
Figure IV-4
Block Diagram of a Regional Groundwater Routing Model
- 92 -
has been done in understanding flow through porous media either
theoretically or using an empirical systems approach.
The Dupuit
approximation and Darcy's Law have both been used in finite difference
schemes to determine what might be expected in groundwater movement.
Dooge [1960], used a linear system approach to develop coefficients
that could be used to represent flow into and out of a river system.
This was discussed in Section II-6, Parameter Estimation.
As this
uses the requirement of constant recharge over the time period, it is
felt that this is too restrictive.
Therefore, in an attempt to avoid
the constant coefficient concept as presented by Dooge and O'Donnell
(discussed in Section II-6), a procedure similar to that used by
Harley [1967] for open channel flow will be considered here for groundwater flow.
To represent the various processes that might take place,
as in a shallow water table with flow to drainage ditches as well as
a deeper system that would provide for flow to a river, lake or ocean,
we must be able to use various system responses to the same delta
function.
Conceptually, this might require the three known system
responses, the Nash, Lag and Route and Linear Reservoir Models, to act
in series, in parallel or in a complex configuration of both.
With an
increasing complexity of the system response, in order to prevent a
decrease in efficiency of the program mentioned in Chapter III, a
procedure will be used to conserve the time of computation.
- 93 -
As an
example which will be considered later, a Nash Model is used in series
with a Lag and Route Model.We would expect the time required for computation to be 4.6 seconds plus 3.9 seconds or a total of 8.5 seconds,
thus reaching an infeasible point in cost control due to a large
requirement for computer time.
Consider the prospect of manipulating the inputs and system
responses in the frequency domain for the entire record of interest
before using the Fast Fourier Transform, F.F.T., program to re-enter
the time domain.
It was shown in Chapter III that the frequency response to
a Dirac delta function for a linear reservoir is simply
where
1
H ()
=
K
=
linear reservoir time constant
w
=
angular velocity
j
=
/ 1l
IV-1
1+jWK
Since the Fourier Transform can be obtained by the relation:
F ()=e
-jwt
f(t) dt
IV-2
then the frequency response for the Lag and Route Model is simply
- 94 -
H ()
=
exp (-jWT) / (1 + jK)
IV-3
By the same procedure the Fourier transform for the Nash Model is:
H ()
=
(1 +
2K2)-n/2 exp (-jn tan- ! (WK))
IV-4
Since the Nash Model is a series of n linear reservoirs, let us put
this transform into a more suitable form.
The frequency transform
for a Nash Model may be represented as:
H ()
=
(
1
)n
IV-5
IV-5
or more illustratively by:
H (w)
(jUK)
l
l+jwK
1
(1 K) (1-..)
l+jwK
l+j3K
2
+jK)
l+jWK
3
IV-6
n
This is equally applicable to unequal linear reservoirs which would
be represented by:
H (w)
=
) 1
l+jwK 1
1
1+jWK2
)..
1
l+jwKn
n
IV-7
The important point shown above is that if we have a series
of individual components that represent the system all we need to do is
simply multiply the Fourier Transforms together to get the transform
- 95 -
of the whole system.
If we have a situation where the system response is to be
represented by parallel responses instead of in series, as shown in
Figure IV-5 we know from Chapter III, that the Fourier Transform for
the outputs,Q, can be determined in the frequency domain by multiplying the input transform, I(w), with the response transform, H(w),
or:
Qi()
=
Hi(w)
I(W)
IV-8
Q2(W)
=
H2 (W)
I 2 (W)
The inverse Fourier Transforms are given by:
q, (t)
=
Q ()
e j
ddt
IV-9
q 2 (t)
=
Q2()
e jt
d
but as shown by Figure IV-5
Q (t) =
q (t)
+
q (t)
IV-10
(Q1 +
Q2) e
- 96 -
dw
I 2 (t)
Zl(t)
q 2(t)
Q(t)
Ii(t)
Inflow Rate to System i
hi (O,t)
Instantaneous Unit Hydrograph for
System
i
qi(t)
Outflow Rate from System i
Q(t)
Total Outflow
Figure IV-5
Parallel Systems
- 97 -
therefore:
Q ()
=
Q1()
+ Q 2(W)
IV-11
Thus by merely adding the Fourier Transforms of the Output, Q, the
total output hydrograph in the frequency domain can be obtained.
Therefore, with only one operation it is possible to return the total
output hydrograph to the time domain.
Keeping these concepts in mind and returning to the program discussed in Chapter III, a relatively simple and efficient model
may be generated to determine an output hydrograph to a groundwater
routing model.
The input to such a model may be whatever one desires to
represent the inflow to a groundwater system, using possibly a different input for each leg of a parallel system or one input segregated,
by weights, for each leg of the system.
There is a restriction however to the use of the F.F.T.
program (see Appendix A-2), that is, as discussed in Chapter III, the
number of points that enter the transform program is the same number
as the points that are returned from the program.
Therefore, it is
desirable to use the total time period initially.
Then by simply
adding zeros to the shorter 'legs', the time period for each parallel
segment, can be brought up to the necessary time period.
- 98 -
There are
two important considerations to be looked at here, the time period to
use and the Nyquist frequency, w , that must be used.
As indicated
in Chapter III, these are both dependent on the system 'lag'.
The system lag must be considered by the three possible
conditions, whether it be a series system, a parallel system or a
combination system.
The lag as shown in the last chapter is re-
presented by the first moment about the origin, or for the three cases
are:
Linear Reservoir
=
KLR
Lag and Route
=
T + KL
Nash
=
nK
N
IV-12
Thus if all three models were to be in series, the system
lag, Ks would be represented by:
=
Ks
nKN
+
(T + K L )
+
KLR
IV-13
However, if the system consists of parallel members then the greatest
system lag K
would be used to give the largest time period, or
K
T
=
Max (K )
IV-14
s
- 99 -
In Chapter III, a method was developed that determined the
minimum response time period for which 98% of the area under the
distribution (or response) curve was assured.
This method was based
on the linear reservoir or the exponential distribution.
Since we are
now considering the Nash Model, the procedure for determining the time
The significant point in such a determination
period must be altered.
was the integration of the response function.
distribution this was a simple task.
For the exponential
However, the Nash Model being a
form of the gamma distribution, proved to be more complex - not so
much from the theoretical standpoint as the fact that computationally,
A small program was generated to test the
the time would increase.
significance of such a computational scheme for use within the program.
The test program and the results may be found in Appendix B-1.
It was
found that conservative results would be obtained for the time period
of the system response if the lag (first moment) of the Nash Model, nK,
was used in lieu of K as used in the computational technique for the
Linear Reservoir case.
Therefore it was unnecessary to change the pro-
cedure for determining the time period of the response of the system.
Additionally, this required the determination of the greatest 'lag' in
a series response system.
By using the greatest 'lag' of a series
system we were assured that the response time period would meet the requirements of the entire model and yet not prove to be extravangant
on the time necessary to execute the model.
- 100 -
The second requirement is that the folding frequency, or
Nyquist frequency, is great enough to retain most of the energy of the
system in the frequency domain.
As discussed in Chapter III, by using
the Nyquist frequency for a linear reservoir, i.e. n = 1, most frequency requirements would be met for any of the three types of models.
This was shown to be true by Figure III-5.
Also by acknowledging the
fact that as the number of reservoirs in series increase there is an
increased damping mechanism which reduces the effect of the higher
frequencies then a conservative estimate would be obtained if the
Nyquist frequency were evaluated in the same manner as in the Linear
Reservoir Model.
Three parameters that are used in the Groundwater Routing
Model need to be discussed.
These are the translational lag,
, the
effects of baseflow on a hydrograph, and the spacial parameter, WDTH.
For the special configuration, as indicated by Figure IV-4, implemented
into this model, the Lagged Nash Model was used
of a translational lag.
thus requiring the use
As discussed in Section II-3, the transla-
tional lag will pass all frequencies.
Therefore, no effect is pro-
duced on the outflow hydrograph except a shift in time.
The model
accounts for these lags by shifting the time array by the lag and
adjusting the points to each system accordingly.
However, it is the
input hydrographs which are shifted by this time lag while in the time
domain.
If this were not done in the time domain, it would not be
- 101 -
possible to sum two parallel legs that had different translational
lags while in the frequency mode.
introduced by this procedure.
flow in the system.
As mentioned above no error is
The second parameter indicates a base-
Normally this baseflow would be removed from the
input hydrograph and added to the outflow.
However, if the parameter
estimation technique discussed in Section II-7 is used, this procedure
requires the use of sloping bedrock and, in turn, has an advective
velocity term incorporated.
Therefore, only in the case where the
parameters are input individually, may the baseflow parameter be
utilized.
The spacial parameter, WDTH, is used to transform the unit
flow into total flow for the area considered.
Therefore it represents
the area over which the input exists.
The program listing for the Groundwater Routing Model may be
found in Appendix A-3.
A restriction in the model requires that all
'series' systems be computed by type of response model, i.e., all Nash
response models will be computed before the next response model is
considered in that series.
IV-3.1.1
Discussion of the Shallow Zone System
Implemented into the Model
There is a theoretical problem encountered in the drainage
process.
The parameters used for the routing of groundwater laterally
to the river as well as parallel to the river were determined by a
- 102 -
procedure derived from a Dirac delta function.
An irrigation area
requires that water be applied over a large surface with respect to the
travel distance to the drainage canals, thus extending the assumption
Therefore a finite difference scheme was
beyond its practical limit.
selected to assist in deriving the parameters required by the Linear
Routing Model.
IV-3.1.1.1
Drainage Spacing
The Bureau of Reclamation (R.D. Glover [1967]), developed an
empirical relationship for relating the soil characteristics with the
drainage canal spacing.
Equation IV-15,represents this relationship,
which was used in analyzing the irrigation sites in the Rio Colorado
basin to determine the drainage canal spacing, L.
L
12
P
p
DEPTH %2
Y
YMK
Max
IV-15
PERCMax
where
K
p
=
Permeability
Max lens height allowed above drainage ditch
Max
DEPTH
PERCM
Max
=
Depth to botton of drainage ditch from ground
surface
Maximum percolated water over period of interest
By using this relationship and the available soil characteristics
within the Rio Colorado basin, a mean drainage spacing of 50 meters
was calculated and used with the finite difference scheme to generate
- 103 -
an outflow hydrograph.
This hydrograph was based on an input repre-
sentative of the mean leaching requirements in the basin and a
permeability of 47 meters/month.
IV-3.1.1.2
Finite Difference Scheme
The finite difference scheme uses Darcy's Law in conjunction
with the continuity of flow equation.
The resulting finite difference
equation is given by Equation IV-16.
H.t+At - H.t =
3
I
At a..
S
jctA H.A.
iEI Sc. A.
iI
J
1
t
At Q.
IV-16
Sc. A.
3
3
3~~~~~~~
where
-
Specific permeability (L2/T)
-
(Flow area x Permeability)/L
Sc.
-
Storage Coefficient, cell j (L/L)
Qj
-
Flow input into cell j (L/T)
H.
1
-
G.W. Elevation initially, cell i (L)
H.
-
G.W. Elevation initially, cell j (L)
HT
-
Final G.W. Elevation (L)
I.
-
Index of cell adjacent to cell j
a..
13
I
- 104 -
The flow across a boundary cell may similarly be determined to be:
N
=
QF(j)
I
a.. (HT(i£j,j)
- HT(j))
IV-17
iej
where
QF(j)
=
Flow into boundary cell j
N
=
Number of cells adjacent to boundary
Figure IV-6, shows the physical interpretation of the parameters
listed above.
A program was written to solve the simultaneous differential
equations that are set up in matrix notation with the use of equation
IV-16.
The procedure used by the program in solving the simultaneous
The
equations is similar to the Gauss-Jordan method of elimination.
time of execution is extremely fast for solving a matrix with a small
numbers of nodes, however, this time increases exponentially with an
increase in the number of nodes.
Elinger [1972] uses this method to
study the entire irrigation process, inclusive of a salinity analysis.
IV-3.1.1.3
Parameter Estimation with the Use of the
Finite Difference Scheme
The configuration has two boundary cells providing the limits
for three nodes used to represent the 50 meters drainage spacing.
was found that a steady state flow condition would be established
- 105 -
It
Vertical Projection
Node J
e Area
-Bedrock
Flow Are
-
.J
.-.
--
.
Figure IV-6
Physical Schematic of the Finite Difference Scheme
- 106 -
after running the model for three years, or three irrigation cycles.
A typical hydrograph was extracted from this steady state condition
which was used, along with the Method of Moments, for estimating the
parameters required by the Linear Groundwater Routing Model.
Fig-
ures IV-7, 8, and 9, show the relationship between the parameters of
the Nash and Lagged Nash models for the number of single reservoirs,
n, the lag to a single reservoir, K, and the translational lag, T,
used in the Lagged Nash model, respectively.
These parameters may be
estimated from the figures and serve as input to the Linear Routing
Model, if the conditions are such that the permeability is about
47 m/month, the drainage canals are about 3 meters deep and spaced
about 50 meters.
In this derivation it was assumed that the bedrock
slope produced little or no effect to the results.
IV-4
Linear Groundwater Routing
Model Discussion
Since the parameter estimation procedure was developed with the
assumption that a delta function serve as the forcing function, consideration must be given to the actual input to the system.
If the
input area is small with respect to the distance of flow to the river,
then the errors of assumption in the derivation are acceptable.
However, if the area of application is large, e.g., a large irrigation
site or rainfall distributed over a wide area, with respect to the
lateral distance from the river, then significant errors are intro- 107 -
0
4w:
4
-
C
_<
Q o
ci_..E
.4.
Y
,H
0
0
z
''U
He
z
e
/
C *ra to
w
cr -I
to
H
O.
o
0.--
ol
M
o-J
.01
.C
0
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0
4
0
o1<l.-
a
p
p
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0
ED
p
p
p
p
i
0
It
p
p
p
I
0
p
p
p
I
V)
(ow/W)
p
p
p
O
(c'4
,'!l!qDlwJGd
- 108 -
p
p
a
Ii
0
'
p4
.4z
I
U
01
0
1p
*
-
WOOW
p
402
Cede
s.4
0
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z
p
i
-g
20
Co
zZ
,Q)
C1,
CP
0
Zi
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OC~D~u
zU)
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z
4
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C
cc)
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00
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-o-0
to
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z
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00
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P4
Z
aN
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d
4
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r--
a I
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, a I I
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A !1!qDOWJed
- 109 -
.
I .
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to
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4
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t-
-O
o
~'4
cs
w
.
z
0
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0D
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- 110 -
I
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a
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%- 0
o
duced and a re-derivation under more applicable assumptions is desirable.
For this report, it is assumed that the assumptions are indeed
met.
So far the discussion has concerned only positive outflows
from the aquifer to the river.
Unless a condition exists that would
never allow the reverse to take place, a rare situation, the more
common circunstances would require a negative outflow due to a rising
river stage or evapotranspiration.
When an irrigation site is con-
sidered, normally drainage ditches are used to allow most of the
leached water to return to the river via surface flows.
The drainage
ditches in turn restrict the height reached by the water table.
Also,
Philips [1957] shows that for bare dry soil (light clay) the evaporation loss to the water table, in terms of free water, will be about
1 centimeter per year when the water table is at a depth of 1.5 to 2.0
meters.
Therefore the most important consideration to make, when
analysing water losses to the water table would be the effects of
evapotranspiration which might set up a negative recharge condition.
This would depend on the type of crop with its depth of influence and
consumptive use requirements.
In the case of the Rio Colorado in
Argentina the normal plant growth and thus the evapotranspiration is
relatively small except in the irrigation sites.
By considering the
consumptive use of the crops within each site and the irrigation water
applied, the leaching water may be determined which is, essentially,
the water that will percolate to the groundwater system.
- 111 -
The other condition mentioned is a rise in the river stage
that will increase the 'bank storage'.
The negative outflow caused by
such a situation could be determined by checking the river stage at
each time step.
If the stage is above the water table, then change
the sign of the system response and the depth of the saturated flow,
thus allowing flow to enter the groundwater zone.
Obviously, engine-
ering judgement plays a major factor in making a change such as this.
IV-4.1
Model Results
The model was used to analyse a situation when a single irrigation area lies in relatively close proximity to a stream reach.
The
tests are divided into two segments:
a)
the prediction of the input to the drainage ditches
resulting from applied irrigation water and
b)
the prediction of the discharge to the river as a result
of this same irrigation water.
In the simulation of the drainage to the ditches a standard
drainage ditch spacing of 50 meters was assumed, while for the computation of the seepage from the total area to the river a number of
situations were examined.
These ranged from a 200 square meter site
at distances of 100 to 500 meters from the river to a 1500 meters
square area located 6750 meters from the river.
The standard input for most runs is what will be called the
- 112 -
three year irrigation cycle.
This cycle represents the leached irri-
gation water which is assumed to reach the groundwater table and
therefore represent the forcing function to the groundwater system.
This was discussed, to some degree, in Section IV-3.1.1.
A one year
input cycle represented by a depth of water applied to an area is shown
in Table IV-1.
The drainage system, represented by the block diagram in Figure IV-10, was first tested with this three year irrigation cycle.
The parameters were determined by the procedure discussed in Section
IV-3.1.1, and are based on the Lagged Nash system response model.
These parameters, as shown below:
n
=
0.7841
K
=
3.7690
months
T
=
0.5483
months
IV-18
indicate that a fraction of one linear reservoir with a mean, or lag,
of 3.769 months together with a translational lag of 0.5483 months would
represent the drainage system under study.
The input with the resulting
outflow hydrograph for this system is shown in Figure IV-11.
For comparison, the outflow hydrograph from the Linear Groundwater Routing Model, LGRM, is related to the comparable hydrograph
generated by the finite difference scheme.
Figure IV-12 presents the
typical cycle for the finite difference scheme and the LGRM.
- 113 -
Table IV-1
ONE YEAR OF THE IRRIGATION INPUT CYCLE
Month
1
Water Input (m) Over Area
0.1595
(September)
2
0.1806
3
0.2446
4
0.2711
5
0.2854
6
0.2569
7
0.2107
8
0.1804
9
0.0
10
0.0
11
0.0
12
0.0
- 114 -
I (t)
Nash
(Drainage)
--
Ir
Q(t)
Figure IV-10
Block Diagram of the Drainage System
- 115 -
400
-
---- Input:
3 year Irrigation Cycle
Ti = 36.0 months
Outflow Hydrograph
d
E
%
E
300
Parameters:
n
0.7841
K = 3.769 months
-
7 = 0.5483 months
0
200
r
1n
I
I
*
I
I.
rl
ri
,
L1
L,
I
I
r'
I
r
I
i
IA
.I
I
I
A
100
I
I
I
I
I
II
I
I
IaI
I
/
I
.I
I
I
a
,I
I
I
I
I
I
I
I
20
10
I rI
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
I
II
I
I
I
I
I
IIr
I
m
30
Time (months)
Figure IV-ll
Outflow Hydrograph to Drainage Using Nash System Response
- 116 -
--- - -
40
B
Input, 3 year Irrigation Cycle
Permeability: 20 m/mo.
n = 0.784117
K = 3.76874
mo.
T= 0.5483 mo.
d
E
ograph
-
a
10
20
Time (months)
Figure IV-12
Typical Cycle of the Linear Routing Model and
Finite Difference Scheme
- 117 -
Notice the discrepancy at the peakconfirming the discussion in Section
II-6 which states that the greatest error results at the extremities
when the parameters are generated by the Method of Moments.
This
could possibly be reduced if the fourth moment, the Kurtosis, was used
in conjunction with a four parameter model.
The typical cycles are
derived from a series of cycles which are approximately at a steady
state condition by removing the effects of the adjacent cycles.
Fig-
ure IV-13 compares these same hydrograph but represents the third and
final cycle of the output hydrograph.
The effect of the adjacent cycle
is apparent at the origin.
As discussed in Chapter III, the model is based on an aperiodic input.
However, all the inputs used in the model are represented
by a three cycle series.
In addition, irrigation sites will receive
water in a repeated pattern for perpetuity, unless changes are made to
the irrigation policy.
Because of these two reasons, it might prove
beneficial to return to the concept of a periodic signal.
The ape-
riodic signal was maintained by adding zeros (the length of the
response signal) to the input signal.
This procedure prevents the
system from being effected by adjacent signals.
Return to a periodic
signal should simply require the removal of those zeros.
Figure IV-14
indicates the result of doing so when the same input is applied to an
area with the same parameters that are representative of the drainage
system.
Notice that each cycle is identical to the next.
By com-
paring Figures IV-14 with IV-13, we can see that, over the length of
- 118 -
Input: 3 year Irrigation Cycle
Permeability: 20 m/mo.
n = 0.784117
300
K = 3.76874
'= 0.5483
o--·-
0
mo.
mo.
Model Outflow Hydrograph
Finite Difference Scheme Hydrograph
E
E 200
1--
a
,O
100
10
Time
20
(months)
Figure IV-13
Final Cycle of a Three Year Input to the
Linear Routing Model and Finite Difference Scheme
- 119 -
i
20C
160
E
E 20
80
40
10
20
30
Time (months)
Figure IV-14
Response of the Drainage System to a Periodic Input
- 120 -
40
the input period the hydrographs are identical; an interesting as well
as useful result.
The importance of this capability is readily ap-
preciated especially when steady state or periodic responses to slowly
responding systems are of interest.
The flexibility incorporated into the model for the system
response is significant in representing a more realistic situation.
In this case, as reflected in Figure IV-15, one chain of Nash elements
would represent an interflow process while a parallel chain represents
the same drainage process as presented in the above paragraphs.
For
this example 60% of the inflow is assumed to go to interflow while the
remaining 40% percolates to a deeper zone.
This was taken to be a
reasonable assumption based on the soil and hydraulic conditions of the
area.
The outflow hydrograph resulting from the two inputs and
system responses is shown in Figure IV-16.
The system parameters shown
below are selected to indicate the effect of an actual situation.
The
drainage parameters provide the same system lag as in previous paragraphs but with the translational lag effect reduced to zero.
The
interflow process parameters were chosen to yield a more rapidly responding system.
These parameters are:
Drainage Process
n
=
0.8
K
=
4.73929
T
=
0.0
- 121 -
I (t)
0.6 I (t)
0.4 I(t)
(Interflow)
(Drainage)
Q(t)
Figure IV-15
Block Diagram of a Parallel Nash System Response Representing
and Interflow and Drainage Process
- 122 -
400
Input: 3year Irrigation Cycle
m2
Area = 836.6
---- Drainage Process
Wt. Distribi ution: 0.4
n = 0.8
E
t
E
300
K = 4.379:
Interflow
Process
Wt. Distribution: 0.6
n = 1.0
29 months
K = 1.5 months
7- 0.0 months
7= 0.0 me)nths
4-
Outflow
200
/\
,,rl
I
I
100
/1
10
20
16
30
Time (months)
Figure IV-16
Outflow Hydrograph of the Interflow and Drainage Processes
- 123 -
40
Interflow Process
n
=
1.0
K
=
1.5
T
=
0.0
The effect of this combination of flows (Figure IV-17) becomes apparent when this outflow hydrograph is plotted concurrently with the
drainage system hydrograph shown in Figure IV-ll.
Since the interflow
process dominates the combined system the result is as expected, i.e.,
the peak is increased and occurs earlier than that indicated in the
drainage process (Figure IV-II).
If the lag of the combined processes
was calculated it would result in a system lag of 2.08 months.
Since
the lag of the drainage process above is 3.50 months, a difference of
about 1.4 months is expected.
A lag of this magnitude is clearly
illustrated in Figure IV-17.
Figures IV-18 and 19 show the results of two other system
configurations.
Figure IV-18 represents the outflow hydrograph when a
block input is convoluted with a system response of Nash and Lag and
Route Models in series.
As previously mentioned (Section IV-3.1) the
execution time required to convolute an input with a Nash model in
series with a Lag and Route model might be expected to be 8.5 seconds.
The result of this model with that exact system response was executed
in 3.42 seconds, inclusive of the output requirements and the plotting
routine which is a substantial savings.
- 124 -
Figure IV-19 represents a
300
------
Interflow and Draina(
Drainage Process
0
E
"IE
-
a
10
20
Time
30
(months)
Figure IV-17
Comparison of the Drainage System Outflow Hydrograph
to the Interflow/Drainage Outflow Hydrograph
- 125 -
40
i ln
ML,
01
-I
0
o
o
d51
cq
,*
U
. I
0
N
Ct
//
qtj
i./~
Pii
0~~~~
ro
Oq
..
in
N
T
C
(d
-r
W
00
cc
0
IK)
I-I
z
>1
O.-E
0
L
Cn
0
0
0A
_
0
U)
N
cn0
c0
I
-P
0
a0
- 0
!\
I0
III
._
I
II
II
I
0
!
!
!'*
C0
_
·
0o~
O
00,,@ 0
:
P4
II
If)
I
!
!
!
!
!
C
I
I
m9
I
I
I
.·
I
cN
.
Ir
·
o
I
Ir
D
0)
4I-)
0
la
It
I
I
II
II0
0
fri
II
cn
:i
CD
10
·c0
r
I~~~
CP
Co
C)
.
U)
I
I
-00
ai "Op· . E z
·
0a
a) rO
14-
w
.I
4.)
I
0r1
-
w
)
H>
cd
I ·
4)
cn
c
I
1
C
OO
01 11
0
-P
I\
0)
C)
II
1-i
a)
t_
0
.,
-
I-
CD
- 126 -
-
Iqt
,-
IcN
-
---- Input:
I
Block
Q (t)
24
Io = 20.0
Illl
Ti = 3.29
System Response
Nash-Linear Reservoir
in Parallel
20
Parameters:
Nash
I
I
I
I
I
I
I
I
I
Wt. Distribution = 0.4
n = 3.0
K =1.5
16
II
Lag =0.0
Linear Reservoir
Wt. Distribution=0.6
K-=.5
12
Outflow Hydrograph
I
1
0
I
1
I
8
II
I
I
0
0
II
II
4
-
'
/~~~~~~~
o~~~~~~
!
I
I
I
II
I
7
__I
I
2
.
II
_
4
I_
6
_
I_
I
8
10
Time
Figure IV-19
Results of a System Response Represented by Nash
and Linear Reservoir Models in Parallel
- 127 -
parallel system using a Nash model and a Linear Reservoir model for
the response characteristics with the parameters indicated.
The same three year irrigation cycle was further used as an
input to this system, representing discharge from the site back to the
river.
This system is a very slowly responding one and its behavior
contrasts sharply with the rapid response systems described above.
Figures IV-20 and 21 will serve to illustrate the results.
The input
variables again represent a sampling of data obtained from the Rio
Colorado river basin.
The system represents the lateral flow to the
river from a 1500 meters square irrigation area located about 6750
meters from the river.
Figure IV-20 is the resulting hydrograph for
a groundwater aquifer which has a bedrock slope of .01 m/m.
This
slope plays a significant part in determining the parameters (refer to
SectionIlr-3.1)as indicated by comparison with Figure IV-21 which is
the outflow hydrograph to an aquifer with a bedrock slope of .001 m/m.
One important fact must be noted here - that of the magnitude of the
time step.
In both cases the time step greatly exceeds that of the
input time period which means that the result indicates merely a
transient response of a pulsed input.
Notice that the outflow hydro-
graph in the lesser sloped system closely resembles the linear reservoir response.
This verifies the comments of Dooge [1960] who states
that the translational effect is so small in a groundwater system that
in effect:,the system may be represented by a storage reservoir.
With
such a large time step it is not possible to determine the periodic
- 128 -
C
00
0(D
000
4-
H
0
OH
4s
0
o
o
0
0
D
U)*
U)
,
>
0C~~~~~~a
O
Cow/ew tol x )
(4) 0
- 129 -
N
U)
c
E
0
0
0)
4-
C
4-
C
I-0
II
0
D
0
U,
0
E _0E 0
-
0
0
0
C
a0)
-a
4I-
I-
CI
=
cn
C
CD
oC
I
U)r-Cm
O
O
'
O0
I.
o
C: I- = cn
0
'5
E
04
o E
0--Uw,
N
00
0
0
0
In)
"
C
n
"
Y
"1
o
0
E
.r-
00
0
E
00
4)
;4
c
0
E
,-4
0
O
It
0o
la)
04
o
U)
o
0
0o
-,
:
a)
I
4)
0
43
ci)
0
Ca
0
.
o0
N
r)
(ouw/Wu 91
)
()
- 130 -
O
03
U)
cv
r-
-,
U)
signal response.
The effectiveness of the 'medium' slowly responding system
system cannot be demonstrated when such great travel distances are
considered.
Therefore, shorter distances will be used in order to
present the significance of the theoretical techniques in use.
As
mentioned earlier, the slope is a significant parameter in representing
the system response.
Figure IV-21 shows the system outflow approaching
a Linear Reservoir response when the slope was .001 m/m for a distance
of 6750 meters.
However, when this travel distance is reduced to
100 meters the outflow hydrograph responds very rapidly.
The number
of points is small which prevents an exact analysis of this hydrograph.
This technique does provide a decreasingly accurate result as the slope
approaches zero.
A slope of .05 m/m was selected to test the system;
the slope though relatively small, is sufficient to demonstrate the
effects of travel distance.
Figures IV-22, 23 and 24 exhibit the
damping effect of travel distance in the system on a cyclical input.
In the case of the two parameters, the number of reservoirs, n, increase linearly from 0.370 to 1.852, while the translational lag,
varies from 3.54 months to 17.73 months.
,
The damping effect may be
understood more clearly by considering the system lag, nK.
Notice in
Figure IV-22 that this lag amounts to 7.09 months, which is much less
than one input cycle.
As expected the response indicates three clear
cycles which rapidly approach a steady state condition.
Figure IV-25,
shows the effects of a periodic signal indicating that the aperiodic
- 131 -
0
E
0O
0
O
O
o Cq
,
C
,
o
do
oE
n
o)-o O
cn I_oO-
a)
3)
.
40.
CI,
0e
0-
II
II
II
to
E
f
Y
11
Q:
II
0
I
II
<
Q
4J
O
4-
0,
H
0
0
U)
/
0
o.1
C
C
E
N
N
-v¢
O
4
H
*0
i-.
0
0~
0~~~~~~
0
404
,~~~
.~~~~~~~
tr
10
>1
kE
0
a
I
0
ItJ
A
I
0r')
a
I
0
1h-mo...
I
CN
(ow/eW)
(4) 0
- 132 -
I
0
I
0C
E
0
o
I-
II
Cc
O
0
k
S
4,
o
/
= LO cu
0I
0
cc
oooE
0 -ou Or
0O .2
4-,:
r' O
/o
0
m
L..
0
II
II
II
II
II
0E C Y ~NC
(u 4-
C)
c
0
rn
z4
44
H
oA
0H
Q4
E
-I
O
>1
ou
O
O
2
0`1
I
O
0
,
I
000
a
I
0(D
('OW/eW )
,
I
0
Itt
(
1)3
- 133-
I
I
0N,
II
0
E
it
0E
o
0
. C
to
<nr -cO
*-E 0
= w
C
ciE
54)
ff
4-i
a,
Ls
Ul)
z
g
C
E
NE
-
w
0
0
(
rC q CDa
*-
Ci C
0a)L.
o
I
ii
II
II
on
II
E
C
0
0
_
O
LO
/
IL)
0
C,
£~ <
c4
0H
4
C.
/
10~~~
44
N~N=
InE ~I 0~4
u
_ q) H
- aE
H
e
H 'I
/*
U) CQ
rd
C2~~a
od
w
,J
('OW//w)
()
- 134 -
Input: Periodic Irrigation Cycle
Area/Weight = 200 m2
0.06
Slope = 0.05 m/m
-
Distance to River = 100 m
Parameters:
n
= 0.370
K = 19.150
0.05
m,
= 3.546
months
months
at=0.778 months
6
0.04
i
10-
E
to
x 0.03
m.
-
a
0.02
0
i=
0.01
·_
I
I
I
I
10
20
30
40
Time
(months)
Figure IV-25
Response of a Periodic Signal at 100 meters
- 135 -
signal of Figure IV-22 does indeed reach a steady state.
In Figure
IV-23 the system lag increases to 21.3 months which means that each
input cycle will produce considerable effect on the following year.
Thus, the figure indicates a much greater transient effect on the
system for the same input period.
Finally we see that a system lag of
35.5 months in Figure IV-24 almost exceeds the input time period.
In
turn the damping effect of the system on the input has all but reduced
the cyclical input to that of a simple transient.
Figure IV-26 indi-
cates that if the same input were extended to perpetuity the system would
hardly be effected by the input
configuration - an interesting result.
From Figure IV-20 we can see that as the system lag exceeds the input
time period, the transient result is relatively uneffected by the
shape of the input signal.
The third basic system of flow parallel to the river (See
Section IV-3.1) is not presented here since its behavior is expected
to be similar to that of the flow to the river but with longer distances and lesser bedrock slopes.
Therefore it was not felt neces-
sary to include it within this discussion.
The program was run on an I.B.M. 370/155 computer system.
The
core storage requirement for the program is about 120K.
We have seen that the Groundwater Routing Model is capable of
providing results to a periodic or an aperiodic signal using highly
efficient techniques.
Such a flexible, efficient model as this could
- 136 -
Input: Periodic Irrigation Cycle
Area/Weight = 200 m2
Slope = 0.05 m/m
0.24
m
Distance to River = 500
Parameters:
n = 1.852
0.20
K = 19.150
months
7' = 17.730 months
t = 3.89 months
0.16
-@
0
E
E
It
0
aa
0.12
A.....
0.08
0.04
I
II
10
II
I
_
30
20
Time
II
_I
_
40
(months)
Figure IV-26
Response of a Periodic Signal at 500 meters
- 137 -
·
IL
50
provide the Engineer or Manager with a low cost model capable of representing a wide variation of groundwater problems.
- 138 -
Chapter V
CONCLUDING REMARKS
V-i
Summary
Chapter II summarized the developments within the field of
linear systems as well as the work carried out in groundwater flow
modeling.
Chapter III developed an efficient technique of convolution
and compared the results to those obtained by a linearized solution of
the complete equation for open channel flow.
Chapter IV discussed a river basin in Argentina which was
used as an example of application of a groundwater routing model to an
actual basin.
A model was developed using a parameter estimation
technique of the system response based on the governing equation for
groundwater flow.
V-2
The results of this model were discussed.
Conclusions
The groundwater routing model discussed in Chapter IV has a
fast computational scheme which can be utilized to analyze a groundwater system.
It is a highly flexible technique, capable of any system
response desirable with little variation in computer time.
- 139 -
As the model
represents an approximation to a groundwater system it is, at best,
to be used as a tool to understanding the sensitivity of the system
being considered.
As in any technique being developed, the procedure for the
routing of groundwater has a number of benefits as well as disadvantages to other methods being used to accomplish the same ends.
The
benefits for such a system are:
a)
the convolution technique is highly efficient
b)
the cost of implementation is small
c)
any response configuration may be utilized to
represent the desired groundwater aquifer
system
d)
the input may be of any design, whether it be a
periodic or aperiodic signal
e)
in considering the longer time periods necessary
when analysing slowly responding systems, such as
very long aquifers, the procedure automatically
adjusts the time increment to the minimum level of
interest to the system which also yields benefits
in terms of inexpensive analysis of that groundwater system.
The problem areas include:
a)
a technique is required to determine the weight
- 140 -
distribution on the input to each process being
considered
b)
'parallel' flows which represent two processes,
such as a drainage system and a deep water zone,
have a wide variance in their computed time steps.
For this reason, if it is desired to combine these
flows, a separate procedure is required to do so
c)
the parameter estimation technique for the drainage
system of the routing model requires the establishment of parameters based on (1) a nomograph given
similar conditions to previously analyzed situations; (2) using known data and the Method of
Moments; or (3) the use of a finite difference
scheme, with the Method of Moments, to develop
the desired parameters.
The accuracy of such esti-
mations has not been determined
d)
the parameter estimation technique for a deep water
zone process requires the input to be over a small
area with respect to the travel distance.
Addition-
ally it is based on an advective velocity which is
dependent of the slope of the bedrock resulting in
problems to this procedure as the slope approaches
zero.
- 141 -
V-3
Future Work
To be brief, the work required in developing linear groundwater models in the future might include the following:
a)
Incorporate the lateral flow to the river with a
linear stream routing scheme, as in the M.I.T.
catchment model, in order to determine the total
groundwater outflow hydrograph at some point
downstream.
b)
Develop a technique for distributing the flow to
the various processes represented by the system
responses.
This could include a linear reser-
voir or a similar response model to represent the
infiltration process.
c)
Improve the parameter estimation process and
determine the sensitivities of the parameters when
data is available for doing so.
- 142 -
REFERENCES
Bittinger, M.W., Duke, H.R., and Longenbaugh, R.A., "Mathematical
Simulations for Better Aquifer Management", International
Association of Scientific Hydrology, No. 72, 1967, 509-519.
Breitenbach, "Groundwater Systems", in Simulation of Water Resources
Systems, Nebraska Water Resources Institute,C.E. Department,
University of Nebraska, 1971.
Carslaw, H.S., and Jaeger, J.C., Conductionof Heat in Solids, Oxford
University Press, London, 1959.
Chow, Ven Te,
Handbookof AppliedHydrology,McGraw-Hill, New York,
1964.
Clark, C.O., "Storage and the Unit Hydrograph", ASCE trans, Vol. 100,
1945, 1416-1446.
Diskin, M.H., A Basic Study of the Linearity of the RainfaZZ-Runoff
Processin Watersheds,Ph.D. Thesis, University of Illinois,
Urbana, Illinois, 1964.
Dooge, J.C.I., "A General Theory of the Unit Hydrograph",
cal Research, Vol. 64, No. 2, 1959, 241-256.
- 143 -
J.Geophysi-
Dooge, J.C.I., "The Routing of Groundwater Recharge Through Typical
Elements of Linear Storage", International Association of
ScientificHydrology,
No. 52, 1960, 286-300.
Dooge, J.C.I., "Analysis of Linear Systems by Means of Laguerre
Functions", Journal S.I.A.M. (Control), SER A, Vol. 2, No. 3,
1965, 396-409.
Eagleson, P.S., Mejia, R., and March, F., The Computation of Optimum
Realizable Unit Hydrographs from RainfaZZ and Runoff Data,
M.I.T., Hydrodynamics Laboratory Report, No. 84, 1965.
Eagleson, P.S., and Goodspeed, M.J., "A Preliminary Study of Experimental Catchments in the Alice Springs Area", Unpublished
Paper, 1967.
Edelman,
J.H.,
Over de Berekeningvan Grondwaterstrominger, Doctor's
Thesis, Delft, 1947.
Edson, C.G., "Parameters for Relating Unit Hydrograph to Watershed
Characteristics", Trans. AM. Geophys. Union, Vol. 32, No. 4,
1951, 591-596.
Elinger, M.M., and Schaake, J.C., Jr., "Physical and Economic Simulation
of an Irrigation System", Paper Presented at the Fifty-Third
Annual Meeting, A.G.U., Washington, D.C., April, 1972.
- 144 -
Freeze, R.A., and Witherspoon, P.A., "Theoretical Analysis of Regional
Groundwater Flow:
1, Analytical and Numerical Solutions to
the Mathematical Model", Water Resource Res., Vol. 2, No. 4,
1966.
Glover, R.E.,
Groundwater Movement , Engineering Monograph, No. 31,
Bureau of Reclamation, U.S. Government Printing Office, 1967.
Harley, B.M., Linear Routing in Uniform Open ChanneZ, M.Eng. Science
Thesis, National University of Ireland, Dept. of Civil Engineering, 1967.
F.B., Advanced CalcuZus for AppZications, Prentice-Hall,
Hildebrand,
Inc., Englewood Cliffs, New Jersey, 1962.
Hillier, F.S., and Lieberman, G.J., Introduction to Operations Research,
Holden-Day, Inc., San Francisco, 1967.
Italconsult
(Rome), Sofrelec
(Paris), Rio
Colorado-DeveZopment
of Water
Resources, Rome, 1961.
Jaeger, A.W., De, Hoge Afvoeren van Enige NederZandse Stroomgebieden,
Doctor's Thesis, Centrum Voor Landbouwpublicaties en Landbouwdocumentatie Wageningen, 167, 1965.
Jenkins,
G.M.,
and
Watts,
D.G.,
Spectral AnaZysis and its AppZication,
Holden-Day, Inc., San Francisco, 1968.
- 145 -
Kalinin,
G.P., and Milyukov,
P.I.,
"Approximate
Calculations
of the
Unsteady Flow of Water Masses", Trudy Ts. I.P. Issue 66,
1958.
Kraijenhoff Van De Leur, D.A., "Runoff Models with Linear Elements",
in Recent Trends in Hydrograph Synthesis, Committee for
Hydrological Research T.N.O., Central Organization for Applied
Scientific Research in the Netherlands T.N.O., Proceeding of
Technical
Meeting
21, 1966, 31-64.
Lee, Y.W., Statistical Theory of Communication, John Wiley and Sons, Inc.,
New York, 1960.
Luthin, J.N., Drainage Engineering, John Wiley and Sons, Inc.,
New York, 1966.
Lyshede, J.M.,
"Hydrologic Studies of Danish Water Courses", FoZia
Geographica Danica, Tome VI, 1955.
Maddaus, W.O., and Eagleson, P.S., A DistributedLinear Representation
of Surface Runoff, M.I.T., Hydrodynamics Laboratory Report,
No. 115, 1969.
Megnien, Claude, "Observations Hydrogeologiques Sur Le Sud-Est Du
Bassin
De Paris, Memoires Bureau de Recherches GeoZogiques
et Mineres,
No. 25, 287 pp, 1964.
- 146 -
Nash, J.E., The Form of the Instantaneous Unit Hydrograph, C.R. et
Rapports,
Assn. InternationalHydroZ.,I.U.G.G.,
Toronto,
1957, Gentbrugge, 3, 1958, 114-121.
Nash, J.E., "Systematic Determination of Unit Hydrograph Parameters",
J. Geophysical
Res.,Vol. 64, No. 1, 1959, 111-115.
Nelson, R.W., and Cearlock, D.B., Proceedings of the National
Symposium on Groundwater Hydrology, Sponsored by American
Water Resources Association, Hotel Mark Hopkins, San
Francisco, 1967.
O'Donnell, T., "Instantaneous Unit Hydrograph Derivation by Harmonic
Analysis", International Association of Scientific Hydrology,
No. 51, 1960.
Philip, J.R., "Evaporation, Moisture and Heat Fields in the Soil",
J. MeteoroZ., Vol. 14, No. 4, 1957.
Remson, I., Hornberger, G.M., and Molz, F.J.,
NumericalMethodsin
SubsurfaceHydrology,Wiley-Interscience, New York, 1971.
Reddell, D. L., and Sunado, D.K., Numerical Simulationof Dispersion
in Groundwater
Aquifer, Hydrology Paper No. 41, Colorado
State University, Fort Collins, Colorado, June 1971.
Rodriguez,
I., Class Notes:
Hydrologic Analysis and Synthesis, 1.712,
M.I.T., Spring 1972.
- 147 -
Selby,
StandardMathematicaZTabZes, Eighteenth
S.M., Editor-in-Chief,
Edition, Chemical Rubber co., 1970.
Schneider,Robert, "An Interpretation of the Geothermal Field Associated
with the Carbonate-Rock Aquifer System of Florida", GeoZ.
Soc. America,Paper Presented at the Annual Meeting in
San Francisco, 1966.
Shapiro,
G., and Rodgers, M., Editors,
Symposiumon the Prospectsfor
Simulation and Simulations of Dynamic Systems, Baltimore,
1966, Sparton Books, New York, 1967.
Sherman, L.K., "Streamflow from Rainfall by the Unit-Graph Method",
Eng. News Record, Vol. 108, 1932.
Singh, K.P., "Non-Linear Instantaneous Unit-Hydrograph Theory", A.S.C.E.,
Jour. Hydr. Div., Vol. 90, No. HY2, 1964, 313-347.
Smith, M.G., LapZace Transform Theory, D. Van Nostrand Co. LTD., London,
1966.
Toth, J., "A Theory of Groundwater Motion in Small Drainage Basins in
Central Alberta, Canada", J. Geophys. Res., Vol. 67, 1962,
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- 148 -
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Wesseling, J., "Vergelijkingen Voor De Niet-Stationaire Beweging",
Nota Voor De Werkgroep AfvlZoeiingsfactoren, 1959.
Wemelsfelder, P.J., "The Persistency of River Discharges and Groundwater Storage" IASH Publication, No. 63, Commission of
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Zoch, R.T., "On the Relation Between Rainfall and Stream Flow", Monthly
Weather Review, Vol. 62, 1934, Vol. 64, 1936, Vol. 65, 1937.
- 149 -
LIST OF FIGURES
Figure
Title
II-1
Schematic of a Hydrologic System
II-2
Linear System Approach in Deriving an Outflow
Page
15
Hydrograph
15
II-3
Conceptial Storage Reservoir
18
II-4
Outflow Hydrograph Resulting from a Constant
Input to a Linear Reservoir
21
II-5
Block Diagram of a Lag and Route Model
24
II-6
Block Diagram of a Nash Model
24
II-7
Singh's Model in Simulating Overland and Channel Flows
27
II-8
Diskin's Parallel Nash Model Configuration
27
II-9
Edelman's One Sided Groundwater Flow to a Unit
Width Channel
29
Reservoir Representation of Kraijenhoff's Flow
to Drainage Canals
29
Aliasing Effect in the Forward Transform-Subroutine FOURTRAN
53
Inverse Transform (Time Domain)--Subroutine
FOURTRAN
55
Inverse Transform (Time Domain)--Subroutine
FOURT
59
II-10
III-1
III-2
III-3
- 150 -
Page
III-4
Forward Transform Indicating Aliasing Effect-Subroutine FOURT
60
Normalized Amplitude Spectrum Relationships
for Nash Model
66
Outflow Hydrograph of a Constant Inflow Convoluted with a Nash Model Response
73
Outflow Hydrograph of a Lag and Route System
Response-FFT Technique
80
Outflow Hydrograph of a Nash System ResponseFFT Technique
81
IV-1
Rio Colorado River Basin, Argentina
84
IV-2
Profile of the Eastern Sector of the Rio Colorado River
89
IV-3
Conceptual Groundwater Routing Model
91
IV-4
Block Diagram of a Regional Groundwater Routing
Model
92
IV-5
Parallel Systems
97
IV-6
Physical Schematic of the Finite Difference
Scheme
106
Nomograph for Parameter K-Lagged Nash and
Nash Models
108
Nomograph for Parameter n-Lagged Nash and
Nash Models
109
IV-9
Nomograph for Parameter T-Lagged Nash Model
110
IV-10
Block Diagram of the Drainage System
115
III-5
III-6
III-7
III-8
IV-7
IV-8
- 151
Page
IV-ll
Outflow Hydrograph to Drainage Using Nash
System Response
116
Typical Cycle of the Linear Routing Model and
Finite Difference Scheme
117
Final Cycle of a Three Year Input to the
Linear Routing Model and Finite Difference Scheme
119
Response of the Drainage System to a Periodic
Input
120
Block Diagram of a Parallel Nash System Response
Representing an Interflow and Drainage Process
122
Outflow Hydrograph of the Interflow and Drainage
Processes
123
Comparison of the Drainage System Outflow Hydrograph to the Interflow/Drainage Outflow Hydrograph
125
Results of a System Response Represented by Nash
and Lag and Route Models in Series
126
Results of a System Response Represented by Nash
and Linear Reservoir Models in Parallel
127
Response at the River of an Input to the Irrigation Site-Bedrock Slope = .01 m/m
129
Response at the River of an Input to the Irrigation Site-Bedrock Slope = .001 m/m
130
Outflow Hydrograph of a Three Cycle Irrigation
Input at 100 meters
132
Outflow Hydrograph of a Three Cycle Irrigation
Input at 300 meters
133
Outflow Hydrograph of a Three Cycle Irrigation
Input at 500 meters
134
IV-25
Response of a Periodic Signal at 100 meters
135
IV-26
Response of a Periodic Signal at 500 meters
137
IV-12
IV-13
IV-14
IV-15
IV-16
IV-17
IV-18
IV-19
IV-20
IV-21
IV-22
IV-23
IV-24
- 152 -
Page
B-1
Time Period Approximation
183
B-2
System Response of a Delta Function
186
C-1
Flow Chart of Convolution Program
194
- 153 -
LIST OF SYMBOLS
Symbols
Description
advective velocity due to sloping bedrock (L/T)
a
1
a..
1A
constant coefficients of inflow cosine term or Laguerre
functions in time period i
specific permeability from node i to node j (L2/T)
coefficient for differential equation [Appendix B]
Ai
constant coefficients of outflow cosine term or Laguerre
functions in time period i
b.
constant coefficients of inflow sine term in time period i
B
coefficient for differential equation [Appendix B]
Bi
constant coefficients for outflow sine term in time period i
c
coefficient for differential equation [Appendix B]
c
wave celerity in open channel flow (L/T)
1
1
1
Ci.
constant coefficient of integration
C.
j
1
]
jth
cumulant
- 154 -
D
differential operator
DEPTH
distance from ground surface to invert of drainage system (L)
E
total 'energy' of the system
f.(t)
input function
f
(t)
Laguerre function
(t)
output function
1
f
n
o
Froude number
F
F.
1
I
moment of the input [Appendix B]
discreteforward transform of input function
F. (W)
Fourier transform of input function
F ()
Fourier transform of output function
G.
I
h
piezometric head (L)
1
1
h
m
h(t)
moment of output [Appendix B]
mean height of saturated water zone (L)
impulse response
- 155 -
h(O,t)
instantaneous unit hydrograph
h(t-t)
I.U.H. lagged by time T
H.
1
I
H.
1
initial groundwater elevation in cell i (L)
H.
initial groundwater elevation in cell j (L)
moment of response function
]
HT
Final groundwater elevation (L)
H( )
Laplace transform of piezometric head [Appendix B]
H(W)
response function
H ()
normalized amplitude spectrum
I
constant inflow or input rate
I.1
inflow or recharge for period i
Ij
index to cell adjacent to cell j
I
input amplitude of constant input
o
)
0
I (t)
time varying input
inflow rate at time T
I(t-T)
inflow distribution lagged by T
- 156
I(W)
input function (frequency mode)
j
parameter used in empirical equations [Chapter II]
j
K
time constant of a linear reservoir (T)
K.
reservoir lag coefficient for system i (T)
K
lag coefficient of a Lag and Route Model (T)
1
L
KLR
lag coefficient of a Linear Reservoir Model (T)
K
lag coefficient of a Nash Model
K
permeability
(T)
N
P
(L/T)
K
total 'lag' to a series system (T)
KT
maximum system lag for use in a parallel system (T)
KD
transmissivity (L3 /T)
L
drainage spacing (L)
L
travel distance of flow in aquifer (L)
M.
1.
I
moment about the centroid
- 157 -
M!
It h moment about the origin
n
constant
n
time period
n
number of equal linear reservoirs in series
n.1
number of equal linear reservoirs in series i
N
number of cells adjacent to boundary node
N
number of points in output hydrograph from subroutine FOURT
NF
number of points within time period of discrete Fourier
transform
N.
number of input points to subroutine FOURT
I
1
p
constant percolation rate
P 6 (W)
impulse amplitude function
PERCM
maximum percolated water over period of interest (L3)
q
outflow
q
groundwater flow resulting from the diffusion analogy
inflow due to advective velocity
- 158 -
q(t)
time varying output
qi(t)
outflow for period i
Q
outflow resulting from a constant inflow
Qj
flow input into cell j (L/T)
volume outflow during period n
QF(j)
flow into boundary cell j
Q(s)
Laplace transform of outflow function
Q(t)
outflow at time t
Qi(t)
outflow for period i
Q( )
output function (frequency mode)
R.
volume recharge to aquifer during period i (L3 )
s
Laplace operator
5
time transformation variable (T)
S
instantaneous irrigation inflow
S
reservoir storage
1
S
n
reservoir storage for period n
- 159 -
S(t)
reservoir storage at time t
SiI
shape factors with index i
S
storage coefficient (L/L)
S
c
o
slope of channel bottom (L/L)
t
time
Ti1
time period for input function (T)
T
time period for output function (T)
o
T
P
T
r
(T)
time to peak (T)
time period for system response (T)
new time period [Appendix B] (T)
V
o
mean longitudinal velocity in the steady state condition
of open channel flow (L/T)
x
travel length of flow (L)
x
superscript to outflow rate
X
area of function outside time period [Appendix B]
- 160 -
y(x,t)
Yo
Max
free surface height at x and time t (L)
depth of water in the steady state of open channel flow (L)
maximum lens height allowed above drainage ditch (L)
Zi
Ith cumulant to Groundwater Routing Model
a
weight distribution parameter
a.1
constant coefficients for kernel cosine term or Laguerre
function in time period i
Bi
constant coefficients for kernel sine term in time period i
r(n)
gamma function of n
A
increment operator
11
active porosity
T
translational lag of a linear channel (T)
o
energy density spectrum of the output
0
L
L
angular frequency (rad/T)
0
Nyquist, or folding, frequency (rad/T)
- 161 -
LIST OF TABLES
Table
Title
Page
III-1
Fourier Transform (Forward) Results--Subroutine FOURTRAN
52
One Year of the Irrigation Input Cycle
114
IV-1
- 162 -
APPENDIX A
Computer Programs
A-1
MODEL GENERATED FOR HARMONIC ANALYSIS
- 163 -
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Ir
APPENDIX B-1
Procedure for Determining the Time Period for the Nash Model
Nash Model =
1 t n-l
- (B-.1
K K
-t/K
P(n)
The integral of the Nash Model, giving the area under the
distribution, is given by:
1
Area
xe
AK
o
1
t n-l
-t/K
K
r(n)
e-T/K
!_
n
-
_
_
r !(-1)
+l
rhn
r-eps
oo
o(n-l-r)
B-1.2
The steps to optimize the time of convergence for obtaining a percentage of the area follows:
a)
Start with the T for a single reservoir as an approximation
i.e.
b)
B-1.3
T = - K · ALOG (.01)
Using T from step a, determine the area under the curve
for Time Period T using equation B-1.2 above.
c)
If X =
(1 - Results
of b)) is less than
- 181 -
.01, or 1% of
the total area, then use the present Time Period T.
Otherwise determine the ordinate at time T by using equation B-l.l.
Then assuming a rectangular area as shown in
Figure B-1.
Solve for At (the time increment to T).
X
=
At
d)
B-1.4
(T)
Increment T by At and return to b)
T'
=
T + At
Results
TIME PERIOD
N
K
3
Nash Integration
Linear Reservoir Theory
5
44.07
69.08
5
!5
61.42
115.13
5
:2
24.57
46.05
6
5
213.97
230.03
8
5
69.21
138.16
!5
83.60
184.20
10
- 182 -
f (x)
t
T
Figure B-1
Time Period Approximation
- 183 -
`I-
APPENDIX B-2
Derivation of the System Response to a Delta Function
Assumptions:
a)
Two dimensional flow
b)
No advective velocity (i.e. horizontal bedrock)
c)
One side of response is considered,
XI.
Governing Equation:
a2 h
hm x2
where
qi
K AX
p
Scah
K dt
p
B-2.1
h
=
mean water saturated zone height
h
=
water saturation elevation
Sc
=
storage coefficient
K
=
permeability
qi
=
flow due to advective velocity
m
P
Dynamic Equation:
(Darcy's Equation)
q
=
-K h p m ax
B-2.2
0
B-2.3
Continuity:
Dq
ax
+ sc ah
at
- 184 -
A diagram of the system considered is shown in Figure B-2.
Taking the Laplace transform of Equation B-2.1, we obtain:
A
)2 H
2
(x,s) - Bs H (x,s) + B h (x,O) = C Q(x,s)
Assuming q (O,t) =
6
(t)
then
Q (O,s) =
1
when t = 0, for all S
Q (x,s) =
0
B-2.4
x>O
the coefficients represent:
A=hm
B = Sc/Kp
C = 1/K Ax
P
assuming
h (x,0) =
0
then the characteristic equation for the system response to the delta
function in the frequency domain is
Ar2
-
Bs =
0
thus
r2
=
Bs/A
or
r
=
+
Bs/A
The homogeneous solution to the differential equation B-2.2 is
H
(x,s)
=
C1 e
-
vBSAlx
+
185 -
C2 e
-
Bs/A IXI
B-2.5
-7--n .... ,-4.
Figure B-2
System Response to Delta Function
186 -
S (t)
But Hildebrand [1962], for finite intervals, shows that
lim h(x,t)
t:which implies
Thus
=
s - oo
that C
El(x,s)
B-2.6
lim s H(x,s)
CO
=
C2
=
e
0
-
BS/A Ix
B-2.7
Applying the boundry condition @ X = 0 to equation B-2.5
H(O,s)
=
C2 =
C Q(O,s)
=
B-2.8
C
To check this result:
A.
a2H
H (x,s)
A )
Bs
lIXI)
C2 exp (A
- Bs H(x,s)
=
- B
A D H(xs)
~x2
-
Bs H(x,s)
H(x,s)
=
C exp (-
C 2 exp (-
B
A
IXi)
B-2.9
therefore
Thus
=
's/A
0
Ixi)
B-2.10
From Selby [1970] (Page 497, Eq. 82), the response to the delta
function
is
- 187 -
c v 'WB IxlI
h (x,t)
7- t3/2
2
but
B-2.11
4t
A = hm
B
=
Sc/K
C
=
1/K Ax
P
1
K Ax
so
exp (- B/A IXIx
h(x,t)
p
2 A-
SC
Kh
pm
lxl
(Sc/Kphm)Ilx
exp (-
t3 /2
B-2.12
4t
See Appendix B-3.2 for the moment derivation of this system response.
- 188 -
APPENDIX B-3
Method of Moments-Parametric Analysis
B-3.1
Moments Analysis
The method of moments is a curve fitting procedure used in
linear systems.
Moments are normally referenced about the mean or
about the origin, but may be referenced about any point.
th
The n
n
where
order moments about the origin are defined by:
-0
F(t) =
tn F(t) dt
B-3.1
function or distribution
Thus, the first moment about the origin would be:
M,
~o
1 ='
Ffi
c
B-3.2
t F(t) dt
If the function, F(t) were a probability distribution then
the Zeroth moment (area) would he unity, i.e.
'
= Iooto
=
B-3.3
F(t) dt
1
- 189 -
The nth order moments about the centroid are defined as
Ia: (t-M1 )n F(t) dt
Mn
The interrelationship between the moments at the two referenced points may be given as the binomial theorem.
A complete
analysis of moments and cumulants is given by Harley [1967], i.e.
M
n
0)
1 M )-i
=
i=o
(- M')
1
B-3.4
M
n
-
j
=
(J) M
i=o
i
i
j-i
(Mi)
For example:
M2
= M
+
2
M
B-3.5
or
M;
=
M
Mz
=
M-
+
21
3M
M 2 + M13
M;2
B-3.6
M3
3 =
M
3 -
3M' M 2 - M'
j2
3
1
The effectiveness of the Methods of Moments in linear systems
stems from the fact that a simple relationship exists between the
input, response and output functions in the lower order moments.
- 190 -
If
we represent F as the input moments, H as the response moments and G
as the output moments, we would have:
First Moment
G
=
F
+
H1
This simple property is true for the first three moments,
beyond that the interactions become more complex.
B-3.2
Derivation of Moments Using Laplace Transforms
The Laplace transform of the flow function is given by
Jo
e
Q (x,s)
-st
dt
B-3.7
(x,t) dt
B-3.8
q(x,t)
o0
then
dQ (x,s)
=
ds
I
Q(x,s)
d
st q
o
n
also
-t
e
-st
t)n
q(x,t) dt
dsn
B-3.9
=
(-1)
ft
e
q(x,t) dt
o
n
thus
d
Q
(x,s)
ds
=
s=o
(-1)
J
n
t
o
- 191 -
q(x,t) dt
B-3.10
Equation B-3.10 can also be stated as
dn Q(x,)
dsn
where
n
= (-1)
M
B-3.11
s=o
th
M
|
= n
Moment about the centroid
Thus, the derivative of the Laplace transform evaluated at
s = 0 are the moments.
The cumulants may be determined similarly by taking the derivative of the logarithm of the Laplace transforms and evaluating at
s = 0.
Applying this technique to Equation B-2.10
M1
=
-d
(log H (xs))
=
- log C + (-
o
jxj)
B-3.12
A
=
log C
Note that any moments/ cumulants, excluding the first, will
result in the moments being infinite.
This result led us to model
the differential equation as discussed in Section II-7.
- 192 -
APPENDIX C-1
Computer Implementation of the Convolution Technique
by Means of Harmonic Analysis
The program developed to implement the convolution technique
using the harmonic analysis concept requires only one subroutine for
operation.
However, the program presently includes a plotter routine
and an interpolation routine to compare the computed hydrograph with
a known test hydrograph.
Appendix A-1.
A listing of this program may be found in
The plotter and interpolation routines are not discus-
sed here but the user may implement his own programs to satisfy this
requirement if deemed necessary.
The subroutine essential for this
program is subroutine FOURT, the FFT program developed at M.I.T..
Subroutine FOURT is fully explained by comment statements in Appendix
A-2 but additional information may be found in Chapter III.
C-l.1
Program Input/Output Procedures
As Figure C-1 indicates, the program simply generates an
input function, computes the analytical response functions which are
placed in the format required by FOURT, manipulates the transforms of
the input and response functions and returns the resulting output
function to the time domain.
In this case the parameters to the sys-
tem response models are assumed known and are input to the program.
- 193 -
PARAMETERS /
I
',
GENERATE
FUNCTION
INPUT
COMPUTE VARIABLES
AND TIME PERIOD OF
OUTPUT
I
PRINT VARIABLES
AND PARAMETER
TRANSFORM INPUT
(CALL FOURT)
SELECT
RESPONSE
2
LINEAR
RESERVOIR
MODEL
LAG
3
ROUTE
MODEL
CALCULATE
RESPONSE
FUNCTION AND PLACE
IN PROPER FORMAT
MULTIPLY
TRANSFORMS
COMPUTE
INVERSE TRANSFORM
(CALL FOURT)
PRINT
RESULT
CALL EXIT
Figure C-1
Flow Chart of Convolution Program
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NA c
H
MOD)EL
C-l.l.l
Input Requirements
Two cards are used to input the necessary parameters for the
input and the system response.
These are:
Card 1
Variables
NO,NRES
Description
NO (Col. 1-3) is the model desired to
represent the system response and may
be the integer values 1, 2, or 3. 1
represents the Linear Reservoir model,
2 the Lag and Route model, and 3 the
Nash model as discussed in Chapter III.
Format
I3,F10.0
NRES (Col. 4-13) indicates the number
of equal linear reservoirs used in
series by the Nash model. If this system response is not required then this
real variable may be ignored.
Card 2
K,KRES,LAG,BFL
K (Col. 1-10) a real variable indicating
the first moment or 'lag' to the Linear
Reservoir model.
KRES (Col. 11-20) a real variable indicating the system lag required to determine the time period of the response
function. For the Linear Reservoir and
the Lag and Route models this is the
same value as K above. For the Nash
model, however, this would represent the
value resulting from NRES · K(nK) or the
lag of the Nash model.
LAG (Col. 21-30) the real variable representing the translational lag of the
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4F10.0
Lag and Route model. This variable may
be set to zero if this model is not required.
BFL (Col. 31-40) is the real variable
indicating a baseflow of the input
hydrograph. This variable, too, may be
set to zero or left out if the input
condition warrants it.
C-l.l.l.l
Input Function
The function indicated in the program listing is that of a
Thomas wave, being:
qMax
f(t)
where
2
[(1 - Cos (--))]
C-1
f
q
=
maximum amplitude of the input
f
=
time period of the input
a function more suitable to ones needs is easily substituted at this
point in the program.
C-1.1.1.2
Subroutine FOURT
The listing for this program may be found in Appendix A-2.
The calling sequence is discussed by the comment statements in that
listing.
The user of the convolution program need not understand the
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variables required by the calling statement of FOURT since all the
requirements are satisfied by the main program, thus FOURT is not
discussed except as found in Chapter III.
C-1.1.2
Output Presentation
The input function that will be transformed into the frequency domain is printed along with the corresponding time for each
point to be used by the FFT program.
The resulting output function is printed with the same correct
time array after the output is transformed into a time series from the
frequency mode.
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