information systems for water resources planning and...
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Information systems for water resources planning and management:applications to irrigation
Pereira L.S.
Etat de l'agriculture en Méditerranée : Ressources en eau : développement et gestion dansles pays méditerranéens
Bari : CIHEAMCahiers Options Méditerranéennes; n. 1(1)
1993pages 198-212
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Pereira L.S. Information systems for water resources planning and management: applications
to irrigation. Etat de l'agriculture en Méditerranée : Ressources en eau : développement et gestion dans
les pays méditerranéens . Bari : CIHEAM, 1993. p. 198-212 (Cahiers Options Méditerranéennes; n.
1(1))
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http://www.ciheam.org/http://om.ciheam.org/
TO
Luis Santos (*)
Summary
This some aspects of systems, mainly systems analysis in
planning and management. a on methodologies and
techniques, examples of application to
management. These case studies include scheduling simulation models and
decision designed to help optimize decisions on when and
how much to as well as and to decisions on allocation
and Case studies also the use of design
of systems and management of canal systems.
Examples make evident the usefulness of systems, but also the need of
institutional solutions implementation and communication between
and
Cette communication fait la de quelques aspects des systemes
en l’analyse des la planification et gestion des en eau . Ainsi, un les methodes et techniques utilides, sont donnes quelques
exemples d’application en Etudes de cas des modeles de
simulation de la conduite des et des-systkmes de 8 la decision
(*) and of of of Lisbon, and hvited
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les 8 et aussi bien
que les et B ddcisions les allocations et les
la etudes de cas
les de et
i la gestion de systhes Les l l’utilite potentiel de l’analyse de systhes en même temps qu’ils font le besoin
de .solutions et, aussi, la
la et les
, l
~
~ systems Two main type of used in
systems
of
systems analysis building
decision tools.
Systems analysis is a of using models
by decomposing them of
. of - physical, economical, social, ecological - and objectives to be
attained -
systems two main of
, The
of
can be made in a simple by
that decisions may not be influenced by
blame on the models judgement has not been the best.
a
The statement of qwst
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maximized. To do so be involved with the conceptual development of the
models and not only be the clients a final while can not be left alone,
without any feedback about outcomes.
advances in systems analysis methodologies come but no
is visible on its use by decision despite developments in computational
facilities and the gap between and may
be Challenges have to be faced by both analysts (Loucks, 1992) and
new have to be done filling the gap between This is
in and planning and management.
AN ON
Systems analysis can be applied to a of in and
management. These decisions, and
management, allocation, quality management,
impacts assessment. any case an optimal solution is but the
optimal can be in satisfying specific
by Loucks (1992), Yeh (1992), (1992), Simonovic (1992),
et al (1992) and (1992) give a complete on systems analysis
methods and application in
is one of most widely used techniques
in (Yeh, 1992), including
when dealing with non equations. This optimization technique is common
in when the objective function is in A
example is given by and (1991) evaluating impacts of on
quality. is also common to optimize pipe
1992).
Techniques like dynamic and non
also common, with both and. stochastic
As they management, namely
allocation management (Simonovic, 1992).
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of mathematical Optimization models have been developed in the last decade
to to
to
et al,
The use of to decisions. These
models a of situations and A simulation
model can be
to make a decision. This is typically the case
et al, 1992). ~ it et
al, 1991).
a it
l of This is
(Ait et al, 1990, Zagona and
1991) of The is
At
systems a holistic and
design models, optimization models, data bases
in a
This facilitates the use of
Exemples of
et et al, 1990) änd management at et
et et al,
1991).
CIHEAM - Options Mediterraneennes
OF
levels and planning scales
and management, levels of decision must be
1987):
a)- The level. decide on systems, methods and
management These decisions combine to define the
management. .of the depend upon the of
such decisions. decisions and influenced
by decisions and levels.
b)- The level. belongs to the maintenance and man-
agement The decisions, in the sche-
duling, affect and to the management decisions.
Thus they should be to the adequate conditions that enable the best
and should also make effective the
policies and the involvement o€ in the management l
c)- The basin level.
policies and influence allocation and quality
of the and of associations should be involved in the
of policy making and conflicts,
Exemples given bellow to the two levels of decision. These examples
case studies of being developed. the level of decision, the
basin it is that of institutional on the
technical ones 1987; 1991). A of tools to
decisions in Loucks (1992), Simonovic (1992) and et al
(1992), just to quote some of most
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scheduling simulation
of give on simulation models which could help
decisions about when and how much to The included in et
al (1992) give a on these and explain a of models and
As exemple, the model is selected and 1992;
et al, 199 1).
The model a soil balance in combination with
an Thus scheduling is evaluated two the
and the yield loss.
The has a with and English
Which the
- to select, modify update the data files on and
and effective
- to choose the simulation options (i) fulfilling
(maximal yield), (ii) applying deficit using a selected
(iiij with available depths and at fixed (iv) optimizing
the dates when supply is limited;
- to calculations with new new assumptions the computa-
tional thus
- to make new computations as well as
data.
This simulation model is a tool selecting
management, helping the to plan of than
one location. developinents include: the development of a to help
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designing using simulated of and
management the development of to simulate the
demand in an system aiming at and the
combination with a system to make the utilization of
at scale.
support systems for irrigation management
The usefulness of management is 'evident: the an
system if the is designed this can, in
time, decide and decisions on the timeliness and depths of
These developed pivots management (Wilmes et al,
1990) but tend to be applied to conditions. This is the case an EC
aiming at developing an management the .
and as well as
- vegetable and field The system is expected to be applied by at
level, not in collective systems.
The is composed by
- an commanding the input and output using a language;
- a knowledge consisting of (i) an goals
into optimization calls, (ii) a to select the computational se-
quence and models to be utilized, (iii) an solving optimization func-
tions, and (iv) a which the mathematical optimization
into the language;
1 The is the of and associates
Alhens.
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- a models commanded by the knowledge simulation
models can be embedded which called by a model the
of to be solved, At models &
1992), a time scheduling model
the soil balance and
1992), and sub - models this one, including a salinity model;
- a data composed by a data base management system commanding sev-
systems to data, and
data like thematic systems.
this is a challenging activity a
which justifies the involvement of institutions of
is expected that be accessible to
like videotex. This should enable to
optimize planning of and to decide in time. The
simulation capabilities with the thematic and spatial database would allow
and to optimize and allocation. Uses of
to those of simulation models but optimization capabilities, multiple choice of
models, and capacities of databases give to a application and
As using simulation models, a subsist: the implementation in field
This not only the models be'adequately validated but that
institutional be built, involving and extension in
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of farm surface irrigation systems
-The design of is not a task to be it
happen when these With
options have to be simultaneously: the level basin
the slope; the length and width of the field units; the
of the supply, like a field ditch with
flow equipment, cablegation automation devices.
A can he of to help and the
best decisions. Thus a systems must embed
- a data base including on field hy-
the system and dis-
main
- a design model and level basin like
(USUF, 1989);
- a land leveling design model;
- the supply systems;
- computation of the economic impacts of the design solutions;
- a of solutions based on investment costs,
and impacts on yields.
This is being developed the based in field
This would to:
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- the selection of the most method: with con-
tinous flow, level basin ïn-igation;
- the land levelling;
- the definition of the design like length, slope, and
time and, level basins, the the length and width of the
basin, and the time;
- the choice of the supply system, size of conduits and automation devices;
- the compuvation of mainly application effi-
ciency and
- the investment and costs;
- the advantage of the system.
of irrigation systems management
As many attempts being developed and implemented to
and management of collective systems (Ait et al, 1990;
et al, 1990; Zagona and 1991; Gates et al, 1991).
canal systems, time and to
much than in systems (optimization tools these systems
in to this
objectives of decision tools to help deciding
deliveties that match demand. This would fa-m conditions allowing the
implementation of flexible scheduling, and conditions to
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tools nwnagement of canal systems include
models and Taking as example the case study by et al (1990),
it can be that a system has to include.
- a database the canal conveyance and systenl, including
the the of
past and the of actual A would be
has to be used by the models;
- a demand model in advance the outflows at main tulnouts in the
system in case no imposed to own de-
mands. if demand is enough time in advance, the
demand model the demands at
- a model which computes the demand and helps to decide .
of demand exceeds the capacity of the system' '
- a canal simulation model which computes times..The simulation
helps to establish the inflow that satisfy the
Canal systems management tools can be used not only to
but also they may be utilized with devices to command
if sensed state available in time. The data
base can also be used with an the to the demands. Also the
database can be use billing using the on actual advanced
of the system using optimization can be as The
advantages of using any kind of these management tools evident when with
systems, the of and is the
basic condition equity and
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The above shows a of methodolo&es and on using
systems to decisions in planning and management,
in Applications to management, as
wee1 as design of systems, make evident the potential of such decision
tools, but also two challenges making full use of them: the gap
between and and institutional
Abdellaoui Oulhaj A and Essafi 1990. of scale
collective on demand a holistic O.
Lahlou (ed) Time Scheduling of de
New vol. 59-78.
VF, Shayya and Cao L, 1990. An system the
design of systems. Visions of the Nat.
Symp.), ASAE 04-90, St. Joseph, 340-347.
and G, 1991. Canal models WF (ed)
and 1991 Nat. Conf.), 238-243.
Clemmens AJ, Gooch 1991. canal system unsteady flow modelling. WF (ed) 1991 Nat. Conf.) ASCE, New
244-252.
1990. conjunctive
and systems. Visions of the Nat. Symp.), ASAE
04-90, St. Joseph, 239-247.
Gates WE and Fontane 1991. planning
WF (ed) and
1991 Nat. Conf.) ASCE, New 567-575.
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1992. Systems analysis in ~
l to J. 118 (3): 238-248.
G, Jones JW 1991. A decision system
of yield, WF (ed)
and 1991 Nat. Conf,), 198-204.
Simulating
model. LS, A, Ait (eds) special
insue of New (in ~
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i J, 1992. of fuzzy allocation model.
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Lane LJ, Ascough JC and Hakousou TE, 1991. decision
systems with embedded simulation models. WF (ed)
and Conf.), ASCE, New 445-451.
Loucks systems in planning. J.
and ASCE, 118 (3): 214-223.
GT, 1992. quality modelling decision making. J.
and ASCE, 118 (3): 295-307.
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(ed) in with Special to
No. 4, 105-120.
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de la de de Oct. 1991).
LS, 1992. Special issue
of 41, New (in
LS, JL, to time
management: modeling in a system. O. Lahlou (ed)
Time Scheduling of de 1 0 , New
vol. 147-166.
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T and JC, i991. Economic evaluation of systems to
and quality. WF (ed) and 1991 Nat.
Conf.),'A§CE, New 597-603.
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J. and ASCE, 118 (3): 262-280.
Stockle CO, 1991. simulation modeling on-faim management.
WF (ed) and 1991 Nat. Conf.), ASCE, New
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JL, LS, 1991. Scheduling with limited
supply using a simulation model. and of
Systems and Special Session)
183-199.
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