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Evaluating Effects of Management on Riparian Ecosystems1
Martin N. Fogel and Peter F. Ffolliott
2
Abstract.--Computer simulation of the hydrologic
processes and biomass production is used to assess the effects
of management to determine whether such actions are a deterrent to desertification of riparian ecosystems. This
approach allows the evaluation to be made prior to implementing any action.
GENERAL SYSTEMS MODEL
INTRODUCTION
The accompanying illustration can be viewed
as a system for managing a riparian ecosystem
(figure 1). Two classes of actions serve as inputs to a particular system producing two sets
of outputs. The climatic or meteorologic inputs
to the system are largely uncontrollable by man,
whereas management has some measure of control
over land use and treatments and practices applied
to the land. The resulting outputs are grouped
into either economic or environmental indicators.
Unlike the environmental group, a quantifiable
value or utility can be assigned to the economic
outputs.
Desertification, defined herein as the loss
of the land's productive capacity, has initiated
much needed research into cause-effect relationships. The management of natural resources systems
in a holistic context has become increasingly complex because these relationships are not always
clearly understood.
In addition, tradeoffs often
need to be effected between users of the land, in
this case riparian lands as managers attempt to
respond to mandated planning requirements of
federal, state and local environmental legislation.
This study looks on evaluating those actions of
man that may cause desertification to a riparian
ecosystem.
A set of objective functions can be developed
for managing such a system to indicate the relative importance of the outputs. Although an
economic value may not be readily attached to an
environmental indicator, mathematical programming
is available for managing natural resources based
on the objective functions and constraints.
Computer simulation is developed for both
the physical and biological processes taking place
on a riparian ecosystem as a means for assessing
various options available to resource managers.
System dynamics of riparian ecosystems are simulated to determine the complex interactions be·tween environmental processes and management
decisions. With the assumption that these simulation models are reasonable facsimiles of the
real world, managers can determine the consequences
of proposed actions prior to their implementation.
This paper first presents a framework for a general
systems models, then discusses the use of a
stochastic set of daily weather variables which
serve as inputs to models of watershed hydrology
and biomass production, and finally reviews the
subject of multiobjective decision making.
To describe a riparian ecosystem in a precise
manner, a discrete-state system model is defined
formally. The elements of the model are as follows:
(1)
A time scale: t = 0,1,2, ... where t i s
in seasons
(2)
The meteorologic variable inputs are:
XMl (t)
XM2(t)
1
Paper presented at the North American
Riparian Conference [University of Arizona, Tucson,
April 16-18, 1985].
2
Professors of Watershed Management, School
of Renewable Natural Resources, College of Agriculture, University of Arizona; Tucson, Arizona
85721.
(3)
The decision variable inputs are:
XDl(t)
XD2(t)
XD3 (t)
384
daily precipitation
daily temperature and/or solar
radiation
designated land use
vegetative treatment such as
removing or thinning vegetation
structural measures such as
channel clearing or straightening
IN PUTS
OUTPUTS
ECONOMIC
CLIMATIC VARIABLES
Timber
Forage
Crops
Precipitation
Energy
MAN'S ACTIONS
ENVIRONMENTAL
Land Use
Vegetative Treatments
Structural Measures
Sediment
Water Quality
Desertification
I
I
I
I
~---------------------------J
FEEDBACK
Figure 1.--Schematic Representation of Managing a Natural
Resources (Forest) System.
(4)
(5)
The state of the system(S) is expressed
by a term to represent the state of
Hhealth" or condition of the riparian
ecosystem. It may be similar to the term
range condition which represents the
productive potential of the range rather
than the immediate availability of forage
(Stoddard et al., 1975). Another state
of the system may be the groundwater
level, which under certain conditions
may also be indicative of the productive
capacity of the land.
is excellent (EX), good (GD), fair
(FA), or poor (PR), a table such as
table 1 can be developed as the state
transition function to predict the
range condition at time (t + 1).
(6)
Yl
Y2
Y3
The state transition function F calculates
the elements of the states at time
(t + 1) as a function of the input and
the state both at time t or
S(t
+ 1)
=
F[X(t),S(t)]
The systems output function transforms
the inputs t~ the following outputs:
volume of runoff per season
volume of sediment per season
quantity of biomass produced
These outputs are a function of the state
of the system. For example, a range in
poor condition will produce more runoff
and sediment, but less biomass than a
range in good condition.
(1)
In mathematical terms:
The function F is actually a vector set
of functions which, predicts the future
condition or state from the current S,
the meteorologic variables XM(t) and the
decision variables XD(t). Thus,
S(t
+ 1) = F[S(t),XM(t),XD(t)]
Y(t)
(7)
(2)
It is not necessary for the transition
function to be given in quantifiable
terms. In range management, for example,
assume that the meteorologic variables
making up the weather are denoted by
either favorable, average, or unfavorable
and that the decision variable is translated into a high or low grazing intensity. Then, if the range condition class
385
=
G[XM(t), XD(t), S(t)]
(3)
Finally, the feedback function reviews
the output variables and the state of
the system at time (t) and then makes
decisions which become input variables
at time (t + 1) or
XD(t
+ 1) = K[Y(t)]
= K(G[XM(t),XD(t),RC(t)]
(4)
subwatersheds, each no larger than approximately
10 km2. Using runoff volumes and peak flow rates
determined by this technique, sediment yields are
estimated by the Modified Universal Soil Loss
Equation (1972).
SIMULATION MODELS
As seen in figure 1, two sets of outputs are
produced from the riparian ecosystem, each of which
requires a separate simulation model or output
function. First, a hydrologic model is used to
produce tJ.he environmental indicators of what is
occurring on the land, such as the water yield,
the sediment yield, and the water quality indices.
Then, a biomass production model is used to
determine the vegetative yield of the system. To
drive both sets of simulation models, daily
meteorologic variables are used.
In addition to its applicability to ungaged
watersheds, the Soil Conservation Service method
can incorporate the state such as the range condition as a factor in estimating the runoff
potential of a riparian ecosystem.
Biomass Production Modeling
Net primary production (NPP) is the total
aboveground dry weight biomass produced per unit
area in a growing season. According to Gilbert
(1975), net primary production is assumed to be
the difference between photosynthesis rate (PS)
and the respiration rate (RS). While most models
are on an annual bas~s, Gilbert (1975) developed
a daily model for estimating PS and RS which uses
daily estimates of available soil water content
and temperature and is based on the following
functional relationships:
Simulating Weather Variables
It is well established that the climate of
semiarid regions are variable in both time and
space. Drought to flood conditions can occur in
a very short interval of time. Historical hydrometeorologic records often do not include these
extremes and any subsequent analysis based on this
incomplete record may be misleading. To avoid
this problem, previously developed techniques for
generating a synthetic time series of daily precipitation and temperature (and/or solar radiation)
are used. Event-based, stochastic precipitation
models developed by Fogel et al., (1974) and
Duckstein et al., (1975) at the University of
Arizona serve as inputs to watershed hydrologic
models.
Photosynthesis rate
Respiration rate
A daily temperature model developed by Hekman
(1977) was used to provide the energy inputs.
Modeling Riparian Hydrology
While several hydrologic models have been used
for natural resource systems, most of them require
more data than are normally available from riparian
ecosystems for calibration and validation purposes.
For this study, the U.S. Soil Conservation Service
(1972) procedure for estimating runoff from rainfall
has been found to be satisfactory for ungaged watersheds which can be divided into relatively homogenous
Weather
G~~dition/Grazing Intensi~
Favorable
High Low
Average
High Low
Unfavorable
High
Low
EX
GD
EX
GD
EX
FA
GD
GD
GD
EX
FA
GD
FA
GD
FA
FA
GD
FA
FA
PR
FA
PR
PR
FA
PR
PR
PR
PR
£(temperature, soil
water content, aboveground green plant
biomass, and maximum
PS)
g(temperature, aboveground
green plant biomass, and
maximum RS)
The water content of the soil is simulated
on a daily basis by calculating the infiltration
(storm rainfall less runoff) and estimating daily
evapotranspiration as a function of soil water
content and potential evapotranspiration (PET).
This latter term, PET, is calculated using daily
temperature and/or solar radiation.
MULTIOBJECTIVE DECISION MAKING
According to Horton and Campbell (1974), "to
properly manage riparian and phreatophyte zones
requires a knowledge of (1) the present community
relationships, (2) the possibility of developing
different vegetation types, and (3) the individual
reactions of the various species that occupy the
zone or that might be introduced under management."
This paper does not address this level of management
directly but recognizes that the above information
must be incorporated into the decision-making process.
Table 1.--Range Condition as a Function of Weather
and Grazing Intensity.
Range
Condition
at time t
=
The purpose of this paper is to lay out an
overall framework for evaluating management alternatives. The section that follows, therefore,
386
effects of uncertainty about either scientific and
technical information or public values on the
evaluation of alternatives.
presents this decision-making aspect while cognizant of the fact that riparian areas may be
used for a variety of uses. Thus, the problem
becomes one of evaluating management alternatives
under a multiobjective situation.
Managing Riparian Zones: A Continuous Review Process
In referring to the feedback function K, it
is obvious that managing resources is in systems
methodology, a continuous review process. By
knowing the condition at any given time, the
system's outputs (forage production, water, and
sediment yields, etc. and the future condition
can be predicted. Management actions, then, can
be taken accordingly through the use of the feedback function.
In the context of planning, ESAP defines the
subjective benefit resulting from the achievement
of stated goals or objectives. It is assumed that
the decision-maker's utility function can be
specified numerically by obtaining his utility
value for each criterion, and then combining these
single utilities into one overall utility function.
The system that provides the highest degree of
utility for all the objectives is designated as
the preferred alternative.
SUMMARY
This paper presents an approach to managing
natural resource systems that relies on the computer simulation of the physical and biological
processes that occur on such systems. It is
well recognized that some of the models or techniques have not, as yet, been validated, making
some of the results suspect. It is also recognized that these techniques are not widely used,
probably because the real world demands political
compromises which often require solutions other
than those that rely entirely on mathematical
equations and computer algorithms. However, such
subjective decisions can be incorporated into the
decision-making process through the use of decisionaiding techniques as ELECTRE and ESAP. In other
words, political compromises can also be programmed
on the computer, and with this understanding, the
approach presented herein may one day be accepted.
It is evident that the decisions so made,
formally or otherwise, are of the multiobjective
or multicriteria type. Managers attempt to maximize economic returns while simultaneously trying
to maintain good or better condition and to minimize erosion or other adverse environmental
effects.
Through simulation of the system, it is
possible to determine if (as the result of man's
actions) a course is taken towards or away from
desertification. One indicator of such a state is
range condition, which can be used to identify
the state of this process, as desertification is
synonomous with a lowering of the productive
capacity of the land.
Multiobjective Decision Making
LITERATURE CITED
Multiobjective optimization has been applied
to water resource management for two decades, with
some of the earlier techniques reviewed by Cohan
~nd Marks (1975).
Many of these can be used in
land management in general and to riparian zones
specifically.
Cohan, J. L. and D. H. Marks. 1975. "A Review
and Evaluation of Multiobjective Programming
Techniques." Water Resources Research.
Volume II, No. 2. pp. 208-220.
David, L. and L. Duckstein. 1976. "Multicriterion Ranking of Alternative LongRange Water Resources Systems.n Water
Resources Bulletin. Vol. 12, No~
pp. 731-754.
As implied earlier, the environmental outputs of a natural resource system are often not
quantifiable in monetary terms. Some type of
ranking, weighting, or other means for distinguishing between objectives are needed to facilitate
trade-offs by amounts of the objectives. Examples
of mathematical programming techniques for accomplishing this include goal programming, compromise
programming, surrogate worth trade-off method and
ELECTRE (1976).
Duckstein, L., M. M. Fogel and D. R. Davis. 1975.
"Mountainous Winter Precipitation: A Stochastic
Event-based Approach." Proceedings, AGU
Symposium on Precipitation Analysis for
Hydrologic Modeling. Univ. of California,
Davis. June 26-28, 1975, pp. 172-188.
A straightforward procedure that has its
roots in multiattribute utility theory, as presented by Keeney and Raiffa (1976), is an iterative
computer algorithm called Evaluation and Sensitivity
Analysis Program (ESAP). As used by West and Husaini
(1982), this program has the advantage of being able
to handle large data sets and can provide a systematic procedure for evaluating alternative plans of
action that can be relatively different in scope.
Another important advantage of ESAP is the direct
manner in which it recognizes and deals with the
387
Fogel, M. M., L. Duckstein and J. L. Sanders. 1974.
"An Event-based Stochastic Model of Areal
Rainfall and Runoff." Proceedings, Symposium
on Statistical Hydrology. USDA/ARS Misc. Pub.
No. 1275. pp. 247-261.
Gilbert, B. J. 1975. "Grassland Simulation Model."
Range Science Series No. 17, Dept. of Range
Science, Colorado State Univ., Ft. Collins,
Colorado.
Hekmart Jr., L. H. 1977. "Simulation Evaluation of
Water Yield Response to Vegetation Management
on a Forested Watershed in Arizona." Unpublished Ph.D. dissertation. University of
Arizona.
Soil Conservation Service. 1972. "Hydrology."
Section 4, National Engineering Handbook.
USDA/SCS, Washington, D.C.
Stoddard, L. A., A. D. Smith and T. W. Box. 1975.
"Range Management." McGraw-Hill, New York.
Horton, J. S. and C. J. Campbell. 1974. "Management of Phreatophyte and Riparian Vegetation
for Maximum Multiple Use Values. USDA Forest
Service Research Paper RM-117. 23 p.
West, S. A. and S. W. Husaini. 1982. "Evaluation
and Sensitivity Analysis Program: Pilot
Application ~o Saudi-Islamic Environmental
Assessment." Journal of Engineering and
Applied Sciences. Vol. 1, No. 3. pp. 237-250.
Keeney, R. L. and H. Raiffa. 1976. "Decisions
with Multiple Objectives: Preferences and
Value Tradeoffs." John Wiley and Sons, New
York.
Williams, J. R. and H. D. Brendt. 1972. "Sediment
Yield Computed with Universal Equatio0:."
Journal of the Hydraulics Division, ASCE,
Vol. 98, No. HY 12. pp. 2b87-2098.
388