information systems for water resources planning and...

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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To cite th is article / Pour citer cet article

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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/

http://om.ciheam.org/article.php?IDPDF=94001219

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

12.1

CIHEAM - Options Mediterraneennes

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

12.2


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).

12.3


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).


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

12.5


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

12.6


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

12.8


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

12.10


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.

12.12


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 ~

i

i J, 1992. of fuzzy allocation model.

J. 118 (3): 308-323.

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.

LS, 1987.

(ed) in with Special to

No. 4, 105-120.

LS, 1991. Gestion de l’eau en l’Assemblee

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.

12.13


T and JC, i991. Economic evaluation of systems to

and quality. WF (ed) and 1991 Nat.

Conf.),'A§CE, New 597-603.

Simonovic, 1992. systems analysis: closing gap between and

J. and ASCE, 118 (3): 262-280.

Stockle CO, 1991. simulation modeling on-faim management.

WF (ed) and 1991 Nat. Conf.), ASCE, New

l ' 283-289.

JL, LS, 1991. Scheduling with limited

supply using a simulation model. and of

Systems and Special Session)

183-199.

JL and LS, 1992. an scheduling simulation

model. Ls, A, (eds) special

issue of New

JG, JT, 1992. Use of mathematical methods

complex systems. J. and ASCE, 118 (3): 281-294.

USUF, 1989. the simulation model, guide. Utah

State Foundation, Logan, UT.

Wilmes GJ, and Supalla 1990. system

management. Visions of the Symp.) ASAE

04-90, St. Joseph, 594-600.

Yeh W W-G, 1992. Systems analysis in planning and management. J.

and ASCE, 118 (3): 224-237.

Zagona EA, 1991. A decision tool managing main canal

systems. WF (ed) and 1991 Nat. Conf.)

ASCE, New 191-197.

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