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Conferences & Events(September and October 2008)

EUROPEAN WATER RESEARCH DAY

8 September 2008Zaragoza, Spain

www.circa.europa.eu/Public/irc/rtd/eesd-

watkeact/library?l=/european_research

Contact name: Elena Dominguez

In the framework of the Zaragoza Interna-

tional Expo 2008, the Directorate General for

Research organises a one day event the

European Water Research Day - aimed at

presenting past, on- going and future EU re-

search water-related activities.

Organized by: European Commission, Di-

rectorate General for Research.

11th International River symposium

1 to 4 September 2008

Brisbane, Queensland, Australia

www.riversymposium.com

Contact name: Carla Mathisen

The 11th International River symposium

will explore the challenges associated with the

increased incidence of flooding and drought

Conferences & Events

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110.

Schultz, G. A. (1998), A Change of Paradigm in Water

Sciences at the Turn of the Century?, Water International,

Journal of the International Water Resources Association

23(1), pp. 37 44.

Skaggs, R. W. and Mays, L. W. (1999), Simulated

Annealing for Groundwater Restoration, Journal of Wa-

ter Resources Planning and Management, ASCE (in re-

view).

Shane, R. M., et al. (1995), The INTEGRAL PROJ-

ECT: Overview in Computing in Civil Engineering, Pro-

ceedings of the Second Congress, Vol. 1, pp. 203 205,

ASCE, June 5 - 8, Atlanta, GA.

Sprague, R.H. and Carlson, E. D. (1982), Building

Effective Decision Support Systems, Prentice-Hall, Inc.,

Englewood Cliffs: NJ

Tang, A. and Mays, L. W. (1999), Genetic Algorithmsfor Optimal Operation of Soi l Aquifer Treatment Systems,

Water Resources Management, Kluwer Academic Pub-

lishers, The Netherlands, to be published, 1999.

Topping, B.H.V, et al., (1993), Topological Design of

Truss Structures Using Simulated Annealing in Topping,

B.H.V. and Khan, A. I. (eds.), Neutral Networks and Com-

binatorial Optimization in Civil and Structural Engineer-

ing, pp. 151 165, Civil-Comp Press, Edinburgh: UK.

Unver, O., Mays, L. W., and Lansey, K. (1987), Real-

time Flood Management Model for the Highland Lakes

System, Journal of Water Resources Planning and Man-

agement 113(5), pp. 620 638.

U.S. Army Corps of Engineers Hydrologic Engineer-

ing Center (HEC) (1998), HEC-FDA Flood Damage Re-

duction Analysis, Users Manual, Version 1.0, January

1998.

U.S. Army Corps of Engineers Hydrologic Engineer-

ing Center (HEC) (1998), HEC-HMS, Hydrologic Model-

ing System, Users Manual, Version 1.0, March 1998.

U.S. Army Corps of Engineers Hydrologic Engineer-

ing Center (HEC) (1997), HEC-RAS River Analysis Sys-

tem, Users Manual, Version 2.0, April 1997.

U.S. General Accounting Office (1994), Ecosystem

Management Additional Actions Needed to Adequately

Test a Promising Approach, GAO/RCED-94-111.

U.S. Geological Survey (1998), Summary of

MODFLOW96, Users Manual.

Viessman, W., Jr., (1998), Water Policies for the Fu-

ture: Bringing It All Together, Water Resources Update,

Issue No. 111, Universities Council on Water Resources,

Carbondale, Illinois.

Vlachos, E. C. (1998), Practicing Hydro diplomacy in

the 21st Century, Water Resource Update, Issue No. 111,

Universities Council on Water Resources, Carbondale, Il-

linois.

Wanakule, N., Mays, L. W., and Lasdon, L. S. (1986),

Optimal Management of Large Scale Aquifers: Methodol-

ogy and Applications, Water Resources Research 22(4),

pp. 447 465.

Wehrends, S. C. and Reitsma, R. F. (1995), A Rule

Language to Express Policy in a River Basin Simulator in

Computing in Civil Engineering, Proceedings of the Sec-

ond Congress, Vol. 1, pp. 392 395, ASCE, June 5 - 8,

Atlanta, GA.

Wada, R. N., et al. (1986), Honolulus New SCADA

System, Journal of American Water Works Association

78(8), pp. 43 - 48.

Winston, W. L. (1994), Operations Research Applica-

tions and Algorithms, Duxbury Press, Belmont: CA.

Wurbs, R. A. (1995), Water Management Models AGuide to Software, Prentice Hall PRT, Englewood Cliffs:

NJ.

Zagona, E. A. (1995), The INTEGRAL PROJECT:

The PRYSM Reservoir Scheduling and Planning Tool in

Computing in Civil Engineering, Proceedings of the Sec-

ond Congress, Vol. 1, ASCE, June 5 - 8, Atlanta, GA.

Zagona, E. A., (1998), River Ware: A General River

and Reservoir Modeling Environment, Proceedings of the

First Federal Interagency Hydrologic Modeling Confer-

ence, April 19 - 23, Las Vegas, NV.

Zhao, B. and Mays, L. W. (1995), Estuary Manage-

ment by Discrete-Time Stochastic Linear Quadratic Op-

timal Control, Journal of Water Resources Planning and

Management 121(5), pp. 382 391.

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overall result is attainable.

Finally, lack of efficient techniques in the past

that could be used to code hydrologic and hydraulic

systems policies in computer programs might have

had negative impact on the development of com-

puter models for integrated hydrologic and hydrau-lic systems management. The advance in comput-

ing technology appears to be at a stage where it

is capable of overcoming such problems. Today, a

computer programming language specifically used

for rulesets (a set of simulation rules) have been de-

veloped at CADSWES and therefore can be help-

ful for modeling integrated hydrologic and hydraulic

systems problems, should such languages become

the requirement of the state-of-the-art for this pur-

pose.

References

Adeli H. and Hung, S. L. (1995), Machine Learning

Neural Networks, Genetic Algorithms, and Fuzzy Sys-

tems, John Wiley & Sons, Inc., New York.

American Water Works Association Research Foun-

dation (1996), Minutes of Seattle Workshop on Total Wa-

ter Management, Denver, CO.

Anderson, M. P., et al. (1993), Computer Models for

Subsurface Water in D. R. Maidment (editor in chief),

Handbook of Hydrology, McGraw-Hill, Inc., New York.

Andreu, J., Capilla, J. and Sanchis, E. (1996), AQUA-

TOOL A Generalized Decision Support System for Wa-

ter-Resources Planning and Operational Management,

Journal of Hydrology 177, pp. 269 291.

Bao, Y. X. and Mays, L. W. (1994b), New Methodol-

ogy for Optimization of Freshwater Inflows to Estuaries,

Journal of Water Resources Planning and Management

120(2), pp. 218 236.

Brion, L. M. and Mays, L. W. (1989), Methodology for

Optimal Operation of Pumping Stations in Water Distribu-

tion Systems, Journal of Hydraulic Engineering, ASCE,

117(11), pp. 1551 1569.

Bulkley, J. W. (1995), Integrated Watershed Manage-

ment: Past, Present and Future, Water Resources Up-

date, Issue No. 100, Universities Council on Water Re-

sources, Carbondale, Illinois.

Carriaga, C. C. and Mays, L. W. (1995), Optimization

Modeling for Simulation in Alluvial Rivers, Journal of Wa-

ter Resources Planning and Management, ASCE, 121(3),

pp. 251 259.

Carriaga, C. C. and Mays, L. W. (1995), Optimal Con-

trol Approach for Sedimentation Control in Alluvial Rivers,

Journal of Water Resources Planning and Management,

ASCE, 121(6), pp. 408 417.

Chambers, L. (1995), Practical Handbook of Genetic

Algorithms Applications, Vol. 1, CRC Press.

Clement, D. P. (1996), SCADA System Using Packet

Radios Helps to Lower Cincinnatis Telemetry Costs, Wa-

ter Engineering and Management 134(8), pp. 18-20

Culver, T. B. and Shoemaker, C. A. (1992), Dynamic

Optimal Control for Groundwater Remediation with Flex-

ible Management Periods, Water Resources Research

28(3), pp. 629 641.

Davis, B. E. (1996), GIS: A Visual Approach, On Word

Press, Santa Fe, NM.

DeVries, J. J. and Hromadka, T.V. (1993), Computer

Models for Surface Water in D. R. Maidment (editor in

chief), Handbook of Hydrology, McGraw-Hill, Inc., New

York.Dumont, A. and Lynn, P. (unpublished at the time of

reference), Creating a Ruleset, CADSWES, University of

Colorado, Boulder, CO.

Essaid, H. I. (1990), The Computer Model SHARP, A

Quasi-Three-Dimensional Finite Difference Model to Sim-

ulate Freshwater and Saltwater Flow in Layered Coastal

Aquifer Systems, Water-Resources Investigation Report

90-4130, U.S. Geological Survey, Menlo Park: CA.

Fedra, K. and Jamieson, D.G. (1996), The Water

Ware Decision Support System for River-Basin Planning.

2. Planning Capability, Journal of Hydrology 177, pp. 177

- 198.

Fredericks, J. W., et al. (1998), Decision Support

System for Conjunctive Stream-Aquifer Management,

Journal of Water Resources Planning and Management

124(2), pp. 69 78.

Ford, D. T. and Killen, J. R. (1995), PC-Based Deci-

sion-Support System for Trinity River, Texas, Journal of

Water Resources Planning and Management 121(5), pp.

375 381.

Goldman, F. E. (1998), the Application of Simulated

Annealing for Optimal Operation of Water Distribution

Systems, Ph.D. Dissertation, Arizona State University,

Tempe: AZ.

Goldman, F. E. and Mays, L. W. (1999), Simulated

Annealing Approach for Operation of Water Distribution

Systems Considering Water Quality, ASCE (in review).

Greene, R.G. and Cruise, J.F. (1995), Urban Water-

shed Modeling Using Geographic Information System,

Journal of Water Resources Planning and Management

121(4), pp. 318 325.

Grigg, N. S. (1998), Coordination: The Key to Inte-

grated Water Management, Water Resources Update,

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terms of hydrologic and hydraulic systems policies

or rules and because such policies can be inter-

preted and coded in computer programs, it is very

important to have these policies clearly defined

for a given watershed. It may be noted that it is

these policies that we begin with to deal with inte-grated hydrologic and hydraulic systems manage-

ment. Furthermore, the scope and areal coverage

of integrated hydrologic and hydraulic systems

management that is mandated to an institution or

water agency should be unambiguously defined.

The authors agree with the watershed approach

strategy for integrated hydrologic and hydraulic

systems management already recommended by

different institutions. This approach entails hydro-

logic and hydraulic systems policies that transcendpolitical boundaries for the purpose of integrated

hydrologic and hydraulic systems management

and, therefore, it is necessary that this approach

be acceptable by different parties so that the best

Object type User Method Category User Methods

Reservoirs

Evaporation and precipitation

No evaporation

Pan and ice evaporation

Daily evaporation

Input evaporation

CRSS evaporation

Spill

Unregulated spill

Regulated spill

Unregulated plus regulated

Regulated plus bypass

Unregulated plus regulated plus bypass

Power

Reservoirs

Power

Plant power

Unit generator power

Peak base powerLCR power

Tailwater

Tailwater base value only

Tailwater base value plus lookup table

Tailwater storage flow lookup table

Tailwater compare

Hoover tailwater

ReachesRouting

No routing

Time lag routing

Variable time lag routing

SSARR

Muskinghum

Kinematic wave

Muskingum-Cunge

MacCormack

Water User (on

AggDiversion)Return flow

Fraction return flow

Proportional storage

Variable efficiency

Table 4- Selected user methods in River Ware (after Zagona, et al., 1998)

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used for hydrologic and hydraulic systems man-

agement policy called ruleset has been developed

at CADSWES. Ruleset is a collection of rules that

control simulation (Dumont and Lynn, unpublished

at the time of reference).

7. Summary and Conclusions

Water being a precious, but limited, resource

poses the question of how to allocate a sufficient

amount to all the competing users efficiently and

effectively. An integrated hydrologic and hydrau-

lic systems management approach enables us to

have knowledge in space and time of what water

is needed for and in what amount it is needed,

thereby allowing for balancing out between the

competing needs. Through integrated hydrologic

and hydraulic systems management, viable water

policies compromising to all parties or satisfying allobjectives can be formulated.

Design of multi-dimensional, multi-objective

hydrologic and hydraulic systems projects require

formulation of sound water policies. As discussed

herein, an integrated hydrologic and hydraulic

systems management may be the most promising

means to provide the water requirements of all the

competing users, requiring the involvement of all

parties concerned. The scope and regional cover-

age of hydrologic and hydraulic systems agencies

need to be clearly defined. To this effect, a river

basin or watershed approach for regional coverage

is a sound strategy.

Computer models for integrated hydrologic and

hydraulic systems management can be very im-

portant tools that are helpful for fast computations,

easy data management and drawing conclusions

about certain water policies. Such models, gener-

ally termed as Decision Support Systems (DSS),

have been introduced recently by different institu-

tions. As computing speed and ease become more

powerful, more complex yet more comprehensive

computer models are being developed. Such com-

puter models as TERRA, River Ware, AQUATOOL

and Water Ware are examples of DSS that areused for integrated hydrologic and hydraulic sys-

tems management.

These DSS are embodied with water policies in

the form of rulesets (to use the term used in River

Ware) or expert systems (to use the term used in

Water Ware). These models have become suc-

cessful as models of integrated hydrologic and hy-

draulic systems management by the incorporation

of water policies that are formulated in a form un-

derstandable in the computation processes.

At the center of DSS are found simulation and

optimization models. A tremendous amount of

work has been done in the past to develop simula-

tion and optimization computer models that solve

problems in the areas of hydrology, hydraulics and

water resources. Effort was also made to interface

simulation and optimization computer models tosolve optimal control problems in water resources.

Although DSS are highly based on these models,

they also introduce water policy issues such as

water rights, ecosystem sustainability, amenity and

so on. These additional aspects have been incor-

porated in DSS models in such forms as rulesets

or expert systems. In this regard, much more ef-

fort is needed not only because rulesets or expert

systems have been recently introduced, but also

because the concept of integrated hydrologic and

hydraulic systems management approach is yet to

come to fruition.In conclusion, some useful computer models

in the form of decision support systems that ad-

dress integrated hydrologic and hydraulic systems

management problems have been written. Some of

these programs such as TERRA, which have been

in use for some time now, have proved the impor-

tance of DSS in integrated hydrologic and hydraulic

systems management problems. The availability of

various hydrologic and hydraulic systems mod-

els that address specific hydrologic and hydraulic

systems problems and different optimization tech-

niques, in conjunction with the advance in the infor-

mation technology, provide a wealth of resources

that are useful in designing DSS. Thus, we may

conclude that not enough work has been done to

develop DSS for integrated hydrologic and hydrau-

lic systems management. However, we have the

technical resources database management sys-

tems, simulation models, optimization techniques

and advanced computing technology and we are

faced to make use of these resources to bring out

more DSS for integrated hydrologic and hydraulic

systems management.

The requirements of writing DSS for integrated

hydrosystms models would be more complete if theideals of integrated hydrologic and hydraulic sys-

tems management are clearly defined and under-

stood, and if the policies can be easily interpreted

so as to code in computer programs. The challenge

in this regard is yet to be fully overcome. Heathcote

(1998) points out that although the concept of inte-

grated hydrologic and hydraulic systems manage-

ment is a strategy that is increasingly advocated

in the literature, it is still relatively new. Because

the concepts of integrated hydrologic and hydrau-

lic systems management can be best explained in

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els, it has been possible to develop DSS that have

manifested to address these issues. A few of these

systems have been designed not only to solve the

problem, but also to attempt to interpret the result.

Jamieson and Fedra (1996) point out that DSS

have the capabilities of predicting what may hap-pen under a particular set of planning assumptions

and of providing expert advice on the appropriate

course of action.

In summary, most of the available computer

models for hydrologic and hydraulic systems prob-

lems address only a specific issue of the general

concept of integrated hydrologic and hydraulic sys-

tems management. While they have been found

satisfactory tools to solve the particular problem

they are designed for, only a few DSS currently

available such as TERRA, River Ware, AQUA-

TOOL and Water Ware are useful as stand-alonecomputer models for integrated hydrologic and

hydraulic systems management. Therefore, it can

be inferred that because of the availability of only

a limited number of DSS for integrated hydrologic

and hydraulic systems management, the state of

practice of DSS for integrated hydrologic and hy-

draulic systems management is premature, yet

evolving.

6. Prospects for Integrated Hydrologic and hy-

draulic systems Management Models

Advances in software engineering appear to be

promising for integrated hydrologic and hydraulic

systems management models. It has enabled the

development of models that not only incorporate

easy-to-use analytical capabilities, but also offer

expert advice and intelligent interrogation facilities.

With these types of models, the artificial intelligence

involved can be provided by a mixture of optimiza-

tion techniques and expert systems that can evalu-

ate, draw preliminary conclusions and recommend

appropriate actions. This stage of development of

hydrologic and hydraulic systems models is the

emergence of what has been referred to as the fifth

generation of hydro informatics system (Jamiesonand Fedra, 1996).

The efforts made in the past to develop simula-

tion models have been tremendous. Almost every

specific hydrosytems problem has been modeled,

albeit the limited focus of the objective of many of

these models. In other words, many hydrologic and

hydraulic systems models were written to address

specific hydrologic and hydraulic systems problems

such as reservoir operation, water distribution, ur-

ban drainage, stream flow, and so on. However, the

painstaking task of integrating these simple models

as we see it fit is still to demand of us the commit-

ment. The parts are out, yet we are faced to put

them together to bring out the wagon.

Some promising efforts in this regard have al-

ready been undertaken. The successful develop-

ments of TERRA, WaterWare, River Ware, AQUA-TOOL and so on are very good examples. The

efforts made at the USACE Hydrologic Engineering

Center to enhance the old models to the new ones,

generally known as the Next Generation (NexGen)

models, may form one of the strong cores of DSS,

simulation models.

DSS in general are, perhaps, the most promis-

ing approach to integrate the simple models and

use for integrated hydrologic and hydraulic sys-

tems management. The three subsystems of DSS

database management subsystem, model base

management subsystem, and dialog generationand management subsystem constitute a logi-

cal construct of the concept of integrated hydro-

logic and hydraulic systems management. Figure

13 shows a representation of most of the possible

components of a typical DSS that one can aspire

for to develop. The dotted lines in the Figure show

the components that can be included in the DSS in

the future or enhancement to its current proposed

structure.

The data base management subsystem pro-

vides the opportunity for easy collection, storage

and alteration of data, including on real-time basis.

GIS and SCADA, among others, are important sys-

tems for this purpose. The proliferation of simula-

tion models and the availability of some advanced

optimization techniques provide valuable resourc-

es in dealing with different aspects of hydrologic

and hydraulic systems problems. The graphics

supported user-friendly interface environment also

helps to draw appropriate conclusions and make

necessary decisions that agree with predefined

integrated hydrologic and hydraulic systems man-

agement policies.

If there are challenges to overcome to use DSS

for integrated hydrologic and hydraulic systemsmanagement problems, one of the most difficult

challenges, perhaps, will be not having appropriate

integrated hydrologic and hydraulic systems man-

agement policies clearly defined. It may be noted

that it is possible to code any policy in a computer

program. However, no code may be written for a

policy that does not exist. Likewise, it can not be

easy to write a clear computer code for an ambigu-

ous or ill-defined policy.

A computer programming language specifically

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tecting groundwater resources.

2. Surface water pollution control: estimation of

the level of effluent treatment required to meet the

river water quality objectives.

3. Hydrologic processes: estimation of ungaged

tributary for use in the water resources planningcomponent (see No. 5 below); assessment of daily

water balance for ungaged subcatchments, and the

impact of land-use changes on runoff; and evalua-

tion of the effects of conjunctive use of surface and

groundwater.

4. Demand forecasting: Use of rule-based infer-

ence models which use generic expert system.

5. Water resources planning component con-

sisting of

a. a model capable of simulating the dynamics

of demand, supply, reservoir operations and rout-

ing through the channel system; andb. a module for reservoir site selection which

assesses ten problem classes which include:

i. landscape and archeological or historical

sites;

ii. land-use restrictions;

iii. drainage, soil and microclimate;

iv. natural habitats and associated communi-

ties;

v. water quality, aquatic biology and ecology;

vi. water resources and cost implications;

vii. reservoir construction;

viii. reservoir operations;

ix. socio-economic effects of reservoir opera-

tions; and

x. recreational provisions.

5. State of Practice of Hydrologic and hydraulic

systems Models

Although the principle of integrated river ba-

sin management models has been aspired to in

many countries, more often than not the problems

have been considered in a piecemeal fashion, with

experts from different disciplines using separate

models (water resources, surface-water pollution

control, groundwater contamination, etc.), to tackleparts of the overall problem in a reactive way (Ja-

mieson and Fedra, 1996). Uncoordinated hydro-

logic and hydraulic systems modeling efforts often

result in incompatibilities.

The new planning approaches for integrated

hydrologic and hydraulic systems management

necessitate new ways of modeling. Schultz (1998)

states that new planning tools are required to plan

and design water resources systems on the basis

of the new criteria which, include: 1) the principle of

sustainable development; 2) ecological quality; 3)

consideration of macroscale systems and effects;

and 4) planning in view of changes in natural and

socio-economic systems. He concludes that since

no planning tools following the four new criteria are

available, we are faced with a vacuum.

This argument shows that the concept of in-tegrated water resources management is a com-

prehensive representation of several components

each of which requires sufficient representation or

modeling within the whole system. Modeling needs

to be driven by coverage of all aspects of integrat-

ed hydrologic and hydraulic systems management,

not by the convenience or simplicity of the model-

ing of each aspect of the problem. Loucks (1996)

clearly puts that an integrated view of water-re-

source systems can not be compartmentalized

into either surface water or groundwater and either

water quantity or water quality just because the re-spective time and space scales make the modeling

or study of such divisions convenient.

On the contrary, as mentioned earlier in this pa-

per, computer programming generally started out

with the simplification of calculations of analytical

functions that required very long times to solve by

hand. Through time, the capability enhanced to the

level of tackling complex hydrologic and hydraulic

systems problems. It is through improvements of

the programming methodologies and new tech-

nological discoveries that more sophisticated hy-

drologic and hydraulic systems models have been

developed. Therefore, hydrologic and hydraulic

systems computer models have been approaching

the essence of integrated hydrologic and hydraulic

systems management from bottom up.

The important aspects of integrated hydrologic

and hydraulic systems problems which have been

tackled using computer programs include simula-

tion, database management systems, data collec-

tion and storage systems and so on. These efforts

have reached a level of promising prospect and

have diminished the gap between the concept of

and computer models for integrated hydrologic and

hydraulic systems management. For instance, GISgenerally provides facilities for storage and man-

agement of very large geo-information. It has been

possible to represent the terrain of the entire U.S.

as a database of Digital Elevation Model (DEM).

Automatic data collection systems such as SCADA

and radar provide readily available input data for

real-time analysis of integrated hydrologic and hy-

draulic systems problems. Some computer models

such as HEC-HMS and WMS are capable of ac-

cepting radar data.

By integrating together different computer mod-

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2. IF Meads elevation > value THEN

Meads release = meads inflow

END IF

In this approach, the user has the choice ofchanging value at run-time without rebuilding the

program. However, the policies expressed in this

fashion may be still very specific.

A more comprehensive approach is to allow

policies to be completely modifiable without requir-

ing the underlying system to be rebuilt. As such,

policies can be written in a rule language that inter-

prets the policies and be interfaced with the simu-

lation models. The policies are interpreted during

run-time, which makes the running time of the pro-

gram longer.

The general architecture of River Ware programemploys the representation of a river basin by ob-

jects. The objects that are included in River ware

include the following (Zagona, et al., 1998):

Storage Reservoir mass balance, evapora-

tion, bank storage, spill;

Level Power Reservoir Storage Reservoir

plus hydropower, energy, tail water, operating

head;

Sloped Power Reservoir Level Power Res-

ervoir plus wedge storage for very long reservoirs;

Pumped Storage Reservoir Level Power

Reservoir plus pumped inflow from another reser-

voir;

Reach routing in a river reach, diversion and

return flows;

Aggregate Reach many Reach objects ag-

gregated to save space on the workspace;

Confluence brings together two inflows to a

single outflow as in a river confluence;

Canal bi-directional flow in a canal between

two reservoirs;

Diversion diversion structure with gravity or

pumped diversion;

Water User depletion and return flow from

a user of water;Aggregate Water User multiple Water Us-

ers supplied by a diversion from a Reach or Res-

ervoir;

Aggregate Delivery Canal generates de-

mand and models supplies to off-line water users;

Groundwater Storage Object stores water

from return flows;

River Gage specified flows imposed at a

river node;

Thermal Object economics of thermal power

system and value of hydropower;

Data Object user specified data: expression

slots or data for policy statements.

Table 4 shows user methods for selected ob-

jects in River Ware.

4.3.6. AQUATOOLDeveloped at the Universidad Politcnica de

Valencia (UPV), Spain, as a result of a continuing

research over a decade, AQUATOOL is a gener-

alized decision support system that has attracted

several river basin agencies in Spain (Andreu,

et al., 1996). Andreu, et al. (1996) also note that

AQUATOOL has various capabilities that can be

used in water resource systems to:

1. screen design alternatives by means of an

optimization module, obtaining criteria about the

usefulness and performance of future water re-

source developments;2. screen operational management alternatives

by means of the optimization module, obtaining cri-

teria from the analysis of the results;

3. check and refine the screened alternatives

by means of a simulation module;

4. perform sensitivity analysis by comparing the

results after changes in the design or in the operat-

ing rules;

5. use different models, once an alternative

is implemented, as an aid in the operation of the

water resource system, mainly for water allocation

among conflicting demands and to study impacts of

changes in the system; and

6. perform risk analysis for short and medium

term operational management to decide, for in-

stance, the appropriate time to apply restrictions

and their extent.

AQUATOOL has been accepted by the Sagura

and Tagus river basins agencies in Spain as a stan-

dard tool to develop their basin hydrologic plan and

to manage the resource efficiently in the short to

medium term (Andreu, et al., 1996).

4.3.7. Water Ware

This decision support system is a comprehen-sive model for integrated river basin planning. It

has the capabilities of combining geographical in-

formation systems, database technology, modeling

techniques, optimization procedures and expert

systems (Jamieson and Fedra, 1996). The aspects

of integrated river basin management that this DSS

incorporates are briefly as follows (Fedra and Ja-

mieson, 1996).

1. Groundwater pollution control: simulation of

flow and contaminant transport, and reduction of

the level of contaminant in the aquifer and/or pro-

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reservoirs having a total capacity of approximately

13.63 billion m3 (11,080,000 acre-ft) are found in

the basin.

4.3.2. TERRA (TVA Environment and River Re-

source Aid)TERRA is a DSS developed for the Tennes-

see Valley Authority (TVA) and the Electric Power

Research Institute (EPRI) (Reitsma, et al., 1996).

It was developed for the management of the TVA

river, reservoir and power resources. TERRA has

the following characteristics:

1. consists of geo-relational data base;

2. serves as the central data-storage and re-

trieval system;

3. records the TERRA information flow;

4. supports interfacing specialized data man-

agement software;5. has various visualization tools; and

6. checks the data entering the database or

data from both resident and non resident models

against various sets of operational constraints (en-

vironmental, recreational, special/emergency, navi-

gational and so on).

TERRA consists of the three essential compo-

nents of a DSS, namely, 1) management of the state

information of the TVA river basin, 2) the models for

conducting simulations and optimizations, and 3)

a comprehensive set of reporting and visualization

tools for studying, analyzing and evaluating current

and forecast states of the river system.

4.3.3. PRSYM (Power and Reservoir System Mod-

el)

This model is used for river, reservoir and power

systems. It provides a tool for scheduling, forecast-

ing and planning reservoir operations. It integrates

the multiple purposes of reservoir systems such as

flood control, navigation, recreation, water supply,

and water quality, with power system economics by

solving the problem based on pure simulation, rule-

driven simulation or a goal programming optimiza-

tion (Zagona, et al., 1995).Shane, et al. (1995) note that PRSYM repre-

sents a major advance in modeling flexibility, adapt-

ability and ease of use, which enable the users to:

1. Visually construct a model of their reservoir

configuration using icon programming with icons

representing reservoir objects, stream reach ob-

jects, diversions, etc.;

2. Select appropriate engineering functions,

standardized by the industry, to reflect object char-

acteristics needed for schedule planning, e.g., res-

ervoir and stream routing methods;

3. Replace outdated functions with improved

versions developed by industry;

4. Develop and include functions that are unique

to their system;

5. Experiment with operating policies; and

6. Use data display and analysis objects to cus-tomize data summary presentations.

4.3.4. Conjunctive Stream-Aquifer Management

This DSS is used for conjunctive management

of surface water and groundwater under the prior

appropriation water right (Fredericks, et al., 1998).

It has the three components which are typical of

a DSS: database management subsystem, model

base management subsystem, and a dialog gen-

eration and management subsystem or user inter-

face. It is possible to prepare input data files for this

DSS using GIS. The overlay of the GIS raster orgrid database with other aquifer grid data enabled

the finite groundwater model MODFLOW to readily

read these data.

4.3.5. River Ware

Developed by the Center for Advanced Deci-

sion Support for Water and Environmental Sys-

tems (CADSWES) at the University of Colorado,

this DSS was designed for a general river basin

modeling for a wide range of applications (Zagona,

1998). It has three fundamental solution methods:

simple simulation, rule-based simulation and opti-

mization.

To abate the problems of complicated water

policies, a different programming language (from

the usual programming languages such as FOR-

TRAN and C/C++) called River Ware Rule Lan-

guage (RWRL) is used. Policy descriptions can

be designed as structured ruleset in RWRL. Once

these policy descriptions are saved as ruleset files,

a simulation may be guided by the ruleset (Dumont

and Lynn, unpublished). Furthermore, the policies

can be modified between runs, without requiring

the simulator to be changed or rebuilt (Wehrend

and Reitsma, 1995).Wehrend and Reitsma (1995) gave the follow-

ing examples of how water policies can be formu-

lated and interpreted.

1. IF Meads elevation > 1229.0 THEN

Meads release = Meads inflow

END IF

This approach gives a conditional water policy,

which may be considered to be easy enough to be

incorporated in a general simulation model.

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4. Decision Support Systems (DSS) as Tools for

Integrated Hydrologic and hydraulic systems

Management

4.1. DEFINITION OF DSS

Decision support systems (DSS), as might be

inferred from the name, do not refer to a specificarea of specialty. It is not easy to connote a specific

definition to DSS based on their uses. Reistma, et

al. (1996) point out that although some consensus

exists as to the purpose of DSS, a single, clear,

and unambiguous definition is lacking. Generally,

however, a DSS gives pieces of information, some-

times real-time information, that help make better

decisions. Sprague and Carlson (1982) defined

a DSS as an interactive computer-based support

system that helps decision makers utilize data and

models to solve unstructured problems.

4.2. BASIC STRUCTURE OF DSS

DSS generally consists of three main compo-

nents: 1) state representation, 2) state transition,

and 3) plan evaluation (Reitsma, et al., 1996).

State representation consists of information about

the system in such forms as databases and geo-

graphic information systems. State transition takes

place through modeling such as simulation. Plan

evaluation consists of evaluation tools such as multi

criteria evaluation, visualization and status check-

ing (Reitsma, 1996). The above three components

comprise the database management subsystem,

model base management subsystem and dialog

generation and management subsystem, respec-

tively. Figure 10 depicts these subsystems includ-

ing their specific purposes and functions. Some

examples of DSS for different integrated hydrologic

and hydraulic systems management are presented

later in this Section.

Jamieson and Fedra (1996) elaborated on the

basic structure of the Water Ware DSS (Figure 11).

It is shown in this Figure that each subsystem is

made up of different components. The data man-

agement subsystem can use different tools such

as GIS as well as other simplistic data. The modelbase subsystem basically consists of simple simu-

lation models, optimization techniques and expert

systems (also sometimes known as rule-based

simulation models). The dialog generation and

management subsystem helps in visualization and

making decisions through interactive user inter-

face.

The structure of DSS discussed above has,

perhaps, made them the best structured and most

promising computer models for integrated resource

management. These models are believed to con-

tribute largely to this objective. Reitsma, et al.

(1996) pointed out that the next few years will

be most interesting for DSS. This stems from the

fact that DSS are promising computer models for

integrated hydrologic and hydraulic systems man-

agement and the advance in the computing andinformation technology is remarkable.

4.3. EXAMPLES OF DSS FOR INTEGRATED

HYDROLOGIC AND HYDRAULIC SYSTEMS

MANAGEMENT

4.3.1. Trinity River Basin, Texas

One of the integrated DSS in regional hydrolog-

ic and hydraulic systems management was devel-

oped for the Trinity river in Texas (Ford and Killen,

1995). This DSS has the capability of integrating

three major hydrologic and hydraulic systems prob-

lems. Accordingly, it has three components whichperform the following tasks: 1) retrieve, process and

file rainfall and streamflow data; 2) estimate basin

average rainfall and forecast runoff; and 3) simu-

late reservoir operation in order to forecast regu-

lated flows basinwide. Each of the tasks is done by

the DSS subsystems which use existing models.

The first subsystem, data-retrieval, processing and

filing subsystem, retrieves data that are collected

from an existing precipitation and streamflow gauge

network, and stores the data using a time-series

database-management system (DBMS) designat-

ed as HEC-DSS. The second subsystem, rainfall

estimating and runoff forecasting subsystem, uses

the following computer programs: 1) PRECIP to

compute catchment areal-average rainfall, and 2)

HEC-1F for forecasting runoff. The third subsys-

tem, reservoir simulation subsystem, uses HEC-5

that is customized and fitted to basin conditions.

Figure 12 shows different components of this

DSS that are used for forecasting streamflow.

TRACE (Trinity River Advanced Computing Envi-

ronment) is the forecasters interface of the DSS. It

executes programs PRECIP, HEC-1F and HEC-5

with the proper input. It also serves as a file man-

ager, input processor and DBMS interface. Further-more, it executes, behind the scenes, programs

PREFOR and PREOP to complete the HEC-1F

and HEC-5 files, respectively. The DBMS-interface

component of TRACE executes program EX-

TRCT to create working copies of data records,

program DISPLAY to graph data, and program

DWINDO to tabulate and edit data (Ford and Kil-

len, 1995).

The size of the Trinity river basin for which this

DSS was developed is approximately 4.6 million

ha (17, 800 sq. mi.). Seven multipurpose major

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SYSTEMS MANAGEMENT

No doubt that the first computer models devel-

oped to solve hydrologic and hydraulic systems

problems targeted specific problems such as catch-

ment runoff simulation, stream flow characteriza-tion, water quality monitoring, and so on. With the

enhancement of computing efficiency and speed

over the past several years, more sophisticated

and user friendly computer models for hydrologic

and hydraulic systems problems have been devel-

oped. However, the objective of most of the com-

puter models was not to address the problems of

integrated hydrologic and hydraulic systems man-

Model name Developed by Model purpose Remarks

LINDOLindo

Systems, Inc.

Solves linear, quadratic

and integer programming

problems

A user friendly Linear Interactive

and Discrete Optimizer

(hence, the name LINDO).

LINGOLingo Allegro

USA, Inc.

Solves linear and nonlinear

programming problems

A sophisticated matrix generator;

helps the user create large

constraints objective function

terms by writing one line code.

GRG2 Univ. of Texas

Solves nonlinear

programming problems

Uses the generalized reduced

gradient algorithm to find theoptimal solution.

GINOSolves nonlinear

programming problems

This model is a microcomputer

version of GRG2.

GAMS

GAMS

Development

Corporation

Solves linear programming

problems

MINOS Saunders andMurthagh

Solves linear and nonlinearprogramming problems

Uses different algorithms when the

problem has linear objective function

and constraints, nonlinear objectivefunction and linear constraints, and

nonlinear objective function and

constraints.

GAMS/ZOOMSolves mixed integer program-

ming problems

Adapted ZOOM

(Zero/One Optimization Method).

GAMS/MINOSSolves linear and nonlinear

programming problems

Adapted MINOS (Modular In-Core

Nonlinear Optimization System).

Table 3- Summary of some of the most popular optimization models in the U.S.

agement inasmuch as a consensus exists as to the

definition of integrated hydrologic and hydraulic

systems management given in Section 2.

More recently, computer models that attempt

to provide support for decision makers have been

brought into the picture. One can safely say thatsuch computer models, generally termed as de-

cision support systems (DSS), have manifested

themselves at this time as promising models for

integrated hydrologic and hydraulic systems man-

agement. The following topic discusses the DSS

applications for integrated hydrologic and hydraulic

systems management.

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(about 13 hours) on the same computer to obtain

the optimal solution for a three cycle operation.

Sakarya, et al. (1998) have compared two

newly developed methodologies, a mathematical

programming approach and a simulated anneal-

ing approach, for determining the optimal opera-tion of water distribution system considering both

quantity and quality aspects. Both methodologies

formulate the problem as a discrete-time optimal

control problem. The mathematical programming

approach interfaces the GRG2 model (Lasdon and

Warren, 1986), a generalized reduced gradient

procedure, with the U.S. Environmental Protection

Agency EPANET model (Rossman, 1994) for water

distribution system analysis. The simulated anneal-

ing approach is also interfaced with the EPANET

model. The study showed that while different re-

sults were obtained for total pump operation hours,the total 24 hr energy costs were comparable.

3.4. COMPUTER BASED INFORMATION SYS-

TEMS

3.4.1. Supervisory Control Automated Data Acqui-

sition (SCADA)

SCADA is a computer-based system that can

control and monitor several hydrologic and hy-

draulic systems operations such as pumping, stor-

age, distribution, wastewater treatment and so on.

Several such systems have been developed in the

past for different water supply agencies. For in-

stance, the Metropolitan Sewer District of Cincin-

nati planned to integrate a SCADA system in the

1980s to monitor its wastewater treatment plants

and pump stations. This system was planned for

an area which consisted of seven major treatment

plants, 30 package wastewater plants serving indi-

vidual subdivisions and about 130 pump stations

(Clement, 1996). A SCADA system developed

in 1986 for Honolulu, Hawaii, had the capability

of controlling and monitoring 57 source pumping

stations, 126 storage reservoirs, and 73 booster

pumping stations (Wada, et al., 1986). In general,

SCADA systems are designed to perform the fol-lowing functions:

acquire data from remote pump stations and

reservoirs and send supervisory controls;

allow operators to monitor and control water

systems from computer controlled consoles at one

central location;

provide various types of displays of water

system data using symbolic, bar graph, and trend

formats;

collect and tabulate data and generate re-

ports; and

run water control software to reduce electrical

power costs.

Remote terminal units (RTUs) are used to pro-

cess data from remote sensors at pump stations

and reservoirs. The processed data are transmitted

to the SCADA system also by the RTUs. Converse-ly, supervisory control commands from the SCADA

system prompt the RTUs to turn pumps on and off

and open and close valves.

3.4.2. Geographic Information System (GIS)

All hydrologic processes relate to space mak-

ing it plausible to associate geo-information with

hydrologic processes. Survey of some of the recent

literature shows several attempts that have been

made to incorporate GIS into hydrologic analyses.

Greene and Cruise (1995) classify these attempts

into four groups: 1) calculation of input parametersfor existing hydrologic models; 2) mapping and dis-

play of hydrologic variables; 3) watershed surface

representation; and 4) identification of hydrologic

response units. Since several GIS database layers

can be overlain, GIS can be a very useful tool to

integrate the analyses of hydrologic processes of

watersheds.

The study by Greene and Cruise (1995) formed

a GIS database of such hydrologic/hydraulic vari-

ables as storm water inlet locations, soil moisture

characteristics of layered soils, etc. to determine

the discharge hydrograph at desired outlet points.

The results obtained from this analysis showed

reasonable accuracy.

3.4.3. GIS as a Tool for Flood Damage Analysis

Buffering applications in GIS delineating the

area in a river system that is affected by a flood

of certain magnitude help to perform sensitivity

analysis to the risk from flooding. This can be done

in two major ways. First, a series of what if ques-

tions can be analyzed before the flooding occurs.

Putting in various flood levels and analyzing can

help forecast the associated damages thereby as-

sisting the management body to make better deci-sions before the flood occurs. Second, if landscape

coverage is readily available in a GIS database, the

effect of the disaster from a flood event can be ana-

lyzed very quickly, thus permitting the management

body to respond rapidly. Such analyses can save

lives and property (Davis, 1996). Figure 9 shows

how rivers and buffered flood zones can be visual-

ized or represented on GIS desktop.

3.5. PROSPECTS OF COMPUTER MODELS FOR

INTEGRATED HYDROLOGIC AND HYDRAULIC

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string of length n can be looked upon as a solution

vector for the problem (Murthy, 1995). Five tasks

are required in the performance of a GA to solve

the optimization problem: encoding, initialization of

the population, fitness evaluation, evolution perfor-

mance and working parameters (Adeli and Hung,1995).

The decision variable vector is encoded as a

chromosome using mostly binary number coding

method. Therefore if there are m decision variables

and if each decision variable is encoded as an n-

digit binary number, then a chromosome is a string

of n x m binary digits as shown in Figure 7.

A population of chromosomes is initialized

which require randomly generating the initial popu-

lation in such a way that all values for each bit have

equal probability of being selected. The fitnessmeasure at every feasible solution is equal to the

objective function value at that point. Thus, fitness

evaluation is used to determine the probability that

a chromosome will be selected as a parent chro-

mosome to generate new chromosomes. Evolu-

tion performance involves selection, crossover and

mutation. Selection chooses the chromosome to

survive for a new generation. Crossover is used to

recombine two chromosomes (parent strings) and

generate two new chromosomes (offspring strings)

based on a predefined crossover criterion. Muta-

tion serves as an operator to reintroduce lost al-

leles into the population based on a predefined

mutation criterion. Working parameters guide the

genetic algorithm and include chromosome length,

population size, crossover rate, mutation rate and

stopping criterion.

Simulated Annealing (SA). SA stems from an

algorithm that is used for the application of statisti-

cal thermodynamics concepts to combinatorial op-

timization problems. A solution to a combinatorial

optimization problem is based on a statistical me-

chanics in which the best solution is obtained from

a large set of feasible solutions.In essence, it is a type of local search (descent

method) heuristic that starts with an initial solution

and has a mechanism for generating a neighbor

of the current solution. For minimization problems,

if the generated neighbor has a smaller objective

value, it becomes the new current solution; other-

wise the current solution is retained. The process

is repeated until a solution is reached with no pos-

sibility of improvement in the neighborhood (Murty,

1995).

This algorithm has the disadvantage that the lo-

cal search stops at a local minimum (see Figure 8).

This can be avoided by running the local search

several times starting randomly from different initial

solutions. By doing so, the global minimum can be

taken as the best of the local minima found.

A better approach to find the global minimum

was introduced in 1953 by Metropolis et al. (Murty,

1995). In this attempt, annealing was applied to the

search of minimum energy configuration of a sys-

tem after the system is melted. At each iteration,

the system is given a small displacement and the

change in the energy of the system, , is calculat-

ed. < 0, the change in the system is accepted;

otherwise, the change is accepted with probability

exp (- /T) where T is a constant times the tem-

perature.

This optimization technique has been applied todifferent problems in engineering, such as ground-

water restoration (Skaggs and Mays, 1999), op-

eration of water distribution systems (Sakarya, and

Mays, 1999; Goldman and Mays, 1999), for water

quality purposes (Sakarya, et al., 1998).

3.3.4. Comparison of Heuristic Search Methods

(GA and SA) to Other Optimization Techniques

Whereas the heuristic search methods involve

trial solutions, mathematical programming and

DDP/SALQR follow some given procedures. On

the other hand, mathematical programming and

DDP/SALQR require derivative information. The

optimal solution found by mathematical program-

ming approach may result in a very short operating

time during one time interval that can not be fol-

lowed for practical purposes. In the simulated an-

nealing approach, this problem can be minimized

by setting minimum period of operation (Sakarya,

et al., 1998).

The mathematical programming approaches

find the optimum solution in much shorter operating

times than the heuristic search approaches. Tang

and Mays (1999) have developed a new methodol-

ogy for the operation of soil aquifer treatment sys-tems, formulated as a discrete-time optimal control

problem. This new methodology is based upon

solving the operations problem using a genetic al-

gorithm interfaced with the one-dimensional unsat-

urated flow model HYDRUS (Kool and van Genu-

chten, 1991). The same problem has been solved

by Tang, et al. (1996) using SALQR interfaced with

the HYDRUS model. The computer time for a ten

cycle operation with the SALQR algorithm was re-

ported as 654 CPU seconds, while with the genetic

algorithm, it needed about 46600 CPU seconds

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bays and estuaries (Bao and Mays, 1994b; Zhao

and Mays, 1995).

Various computer codes are available that solve

either linear programming problems, nonlinear pro-

gramming problems or both. Table 3 gives a sum-

mary of some of the more popular optimization mod-els in the U.S.

3.3.2. Differential Dynamic Programming

Differential dynamic programming (DDP) is a

stage wise, nonlinear programming procedure that

has been successfully applied to hydrologic and

hydraulic systems problems that are based on dis-

crete-time optimal control, such as multi-reservoir

operation, groundwater hydraulics and so on (Mays,

1997).

A modified form of DDP, known as Succes-

sive Approximation Linear Quadratic Regulator(SALQR), has been used for optimization problems

in which nonlinear simulation equations are made

linear in the optimization step (Culver and Shoe-

maker, 1992).

Example applications of DDP have been made

by Carriaga and Mays (1995) to reservoir release

optimization to control sedimentation, and SALQR

to operation of multiple reservoir systems to control

sedimentation in alluvial river networks by Nicklow

and Mays (1998); to operate soil aquifer treatment

systems by Tang, et al. (1999); and to optimal fresh-

water inflows to bays and estuaries by Li and Mays

(1995)

3.3.3. Genetic Algorithms and Simulated Annealing

Genetic Algorithms (GA). Genetic algorithms are

non-conventional search techniques patterned after

the biological processes of natural selection and

evolution (Tang and Mays, 1999). GA can be use-

ful for the selection of parameters to optimize the

performance of a system and for testing and fitting

quantitative models (Chambers, 1995). Every solu-

tion of the optimization problem is represented in

the form of a string of bits (integers or characters)

that consist of the same number of elements, say n.Each candidate solution represented as a string is

known as an organism or a chromosome. The vari-

able in a position on the chromosome and its value

in the chromosome are called the gene and the al-

lele, respectively. For example, if n = 3, a general

chromosome is x = (x1, x2, x3) where x1, x2, and

x3 are the genes on this chromosome in the three

positions (Murthy, 1995).

Genetic algorithms for optimization problems are

developed by first transforming the problem into an

unconstrained optimization problem so that every

G (Q, s) = 0 (21)

h (Q, s) = 0 (22)

Where Q is inflow to an estuary, s is the salinity

of the estuary and H is the fish harvest. Eqs. (21) Are

the hydrodynamic transport equations that relatethe salinity at a given point in an estuary to inflow

whereas Eqs. (22) Are regression equations that re-

late inflow to fish harvest. The last two equations are

the bound constraints that define the limitations on

freshwater inflows and salinity.

3.3. INTERFACING OPTIMIZATION AND SIMULA-

TION MODELS

The general form of the objective functions and

the constraints in hydrologic and hydraulic systems

problems including the foregoing examples can be

linear, non-linear or differential equations. Each ofsuch equations needs different approaches for solu-

tion. Several computer codes have been written for

each of these types of formulations.

For those hydrologic and hydraulic systems op-

timization problems which involve solving general

governing differential equations of mass, energy and

momentum (as is the case with most of the above

formulations), the approach used can be solving the

optimization problem directly by embedding finite dif-

ferences or finite element equations of the govern-

ing process equations (Mays, 1997). This approach

is relatively tedious to apply to real world problems.

Alternatively, an appropriate process simulator can

be used to solve the constraints process simulation

equations when they need to be evaluated for the

optimizer. Consequently, the following general and

simpler optimization problem can be used.

Minimize F (u) = f(x (u), u) (23)

Different techniques have been successfully

applied to solve optimization problems that are for-

mulated in the above form. The most common tech-

niques are given below.

3.3.1. Mathematical ProgrammingMathematical programming includes linear pro-

gramming and nonlinear programming problems

(Jeter, 1986). Herein we will refer to the mathemati-

cal programming approach as interfacing simulation

models with nonlinear programming codes such

as GRG2. This programming technique has been

found useful in several hydrologic and hydraulic sys-

tems problems such as groundwater management

systems (Wanakule, et al., 1986), water distribution

systems operation (Brion and Mays, 1989; Sakarya

and Mays, 1998), optimizing freshwater inflows to

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tion of a discrete-time-optimal control problem is

stated as

Subject to

, t = 1, 2, T. (4)

Where is the vector of the state variables attime t, is the vector of the control variables at time

t, and T is the number of decision times.

A few possible optimization formulations for dif-

ferent hydrologic and hydraulic systems problems

are given below.

3.2.1. Groundwater Management Subsystems

The general groundwater management problem

can be expressed mathematically as (Mays, 1997)

Optimize Z = f (h, q) (5)

Subject to

G (h, q, c) = 0 (6)W (h, u) 0 (7)

Where h and q in the objective function are vec-

tors of heads and pumpages (or recharges), re-

spectively. C is a parameter that measures quality

such as chlorine content and so on. Eqs. (6) Are the

general groundwater flow constraints, which repre-

sent a system of equations governing groundwater

flow and transport. Eqs. (7) may be taken as addi-

tional constraints which can be included to impose

restrictions such as water demands, operating rules,

budgetary restrictions and so on. It may be noted

here that the lower and upper bounds on pump ages

may or may not exist whereas those on the head

can be the bottom elevation of the aquifer and the

groundwater surface elevations for the unconfined

cells respectively.

3.2.2. Real-time Operation of River-Reservoir

Systems for Flood Control

Mays (1997) states the optimization problem for

the real-time operation of multireservoir systems un-

der flooding conditions as

Minimize Z = f(h, q) (8)

Subject toG (h, Q, r) = 0 (9)

(15)W(r) = 0 (10)

Where h and Q are the vectors of water surface

elevations and discharges, respectively. Eqs. (9) Are

the hydraulic constraints defined by the Saint-Venant

equations for one-dimensional gradually varied flow

and other boundary conditions. Eqs. (10) are other

constraints such as operating rules, target storage,

storage capacities, and so on.

The objective of the optimization in this case can

be to minimize (a) the total flood damages, (b) de-

viations from target levels, (c) water surface eleva-

tions in the flood areas, or (d) spills from reservoirs

or maximizing storage in the reservoirs.

3.2.3. Reservoir System Operation for Water

SupplyThe optimization for this kind of hydrologic and

hydraulic systems problem can be expressed as

(Mays, 1997)

Maximize Benefits = (11)

Subject to

, t = 0, , T - 1 (12)

, t = 1, , T (13)

, t = 1, , T (14)

, t = 1, , T (15)

, t = 1, , T (16)

Where St and Ut are the vectors of reservoir

storage and releases and t represents discrete time

period. Eqs. (12) define the system of equations of

conservation of mass for the reservoirs and river

reaches. and are respectively the vectors of reser-

voir storage at the beginning of time period t + 1 and

t, is the vector of hydrologic inputs and is the vec-

tor of reservoir losses. Eqs. (13) and (14) define the

bound constraints on reservoir releases and storage

respectively.

3.2.4. Water Distribution System Operation

Mays (1997) defines the optimization problem

for water distribution system operation in terms of

the nodal pressure heads, H, pipe flows, Q, tank wa-

ter surface elevations, E, pump operating times, D,

and water quality parameter, C, as follows.

Minimize energy costs = f (H, Q, D) (17)

Subject to

G (H, Q, D, E, c) = 0 (18)

W (E) = 0 (19)

Where Eqs. (18) And (19) express the energyand flow constraints and the pump operation

constraints. The remaining equations express the

bound constraints on the nodal pressure head,

3.2.5. Freshwater Inflows to Bays and Estuaries

The optimization problem is to minimize fresh-

water inflows, or to maximize harvest or both, ex-

pressed mathematically as

Optimize Z = f (Q, s, H) (20)

Subject to

86

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Table 2. Contd.

4. Storm water systems

SWMM

STORM

Metacalf and Eddy,Inc., University of

Florida and Water

Resources Engineers

under the auspices of

EPA

HEC

Simulation of urbanrunoff quantity/quality

Simulation of storage,

treatment, overflow

and runoff

Can simulate hydrographs andpollutographs which can be used

as input to river and reservoir water

quality models.

Can simulate the interations of

rainfall/snowmelt, runoff, dry-weather

flow, pollutant accumulation and wash-

off, land surface erosion, treatment

and detention storage. Water quality

parameters include suspended and

settleable solids, biochemical oxygen

demand, total nitrogen, orthophos-

phate, and total coliform.

5. Water distribution/quality

EPANET

KYPIPE2/

KYQUAL

QUAL2E

WQRRS

U.S. Environmental

Protection Agency

University of Kentucky

Texas Water Develop-

ment Board

HEC

Water quality and

hydraulics in water

distribution

Flow and water quality

in pipe networks

Water quality

Water quality for river-

reservoir systems

Performs extended period simulation

of hydraulic and water quality condi-

tions. In addition, water age, source

tracing and chlorine decay can be

simulated.Consists of several pack-

ages for different purposes. Simulates

both steady state flows and extended

period simulation along with water

quality in pipe distribution networks.

Allows simulation of 15 water quality

constituents, including dissolved oxygen,

biochemical oxygen demand, tempera-

ture, organic nitrogen, and so on.

A package of three programs: Stream

Hydraulics Package (SHP), Stream

Water Quality (WQRRSQ) and Reser-

voir Water Quality (WQRRSR).

6. Bay/Estuary Systems

SHARPUSGS

Freshwater-saltwater

flow

A quasi-three dimensional, finite

difference models that simulates

freshwater and saltwater flow in

layered coastal aquifer systems.

7. Flood Mitigation/Forecasting Systems

HEC-FDA HECFlood damage reduc-

tion analysis

Part of the Next Generation (NexGen)

models developed by the HEC. Per-

forms plan formulation and evaluation

for flood damage reduction studies.

87

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Table 2. Contd.

FLDWAV

UNET

FESWMS-DH

Hydrologic Research

Laboratory of the Na-

tional Weather Service

R. L. Barkau

USGS, Water Resourc-

es Division, for FederalHighway Administration

(FHWA)

Dynamic routing of

flood

One dimensional

unsteady open channel

flow

Two-dimensional riverflow

FLDWAV combines the capabilities

of DWOPER and DAMBRK models

which are one dimensional unsteady

flow models based on an implicit finite

difference solution of the St. Venant

equations.

Used for unsteady flow through a full

network of open channels with external

or internal boundary conditions.Based up on RMA-2 model which is

a finite element model used for either

steady or unsteady flow.

2. Ground-water systems

MODFLOW

UN Groundwa-

ter Software

Package (GW1- GW11)

PLASM

WHPA

SUTRA

USGS

UN Department of

Technical Coopera-

tion for Development,Natural Resources and

Energy Division

Illinois State Water

Survey

EPA

USGS

Simulation of two- or

three-dimensional

saturated flow

Varies; depends on

which model is used

Simulation of two

dimensional unsteady

flow

Delineation of

Wellhead Protection

Areas, defined by the

Safe Water Drinking

Act (1986)

Fluid movement and

solute and energy

transport

Three dimensional, finite difference

groundwater model.

Each model in the packet solves a spe-

cific groundwater flow problem.

Has capabilities for simulating two-di-

mensional unsteady flow in hetroge-

neous anisotropic

aquifers under water table, nonleaky

and leaky artesian conditions.

Delineates capture zones and

contaminant

fronts assuming steady-state

horizontal flow in the aquifer.

Consists of four particle tracking

modules.

Can be used to analyze groundwater

contaminant transport and aquifer

restoration problems.

3. Surface-ground water systems

MODBRANCH USGS Combining surface and

groundwater flow

Formed by coupling together two

simulation models: MODFLOW-96

(latter version of MODFLOW) and

BRANCH (a steady and unsteady

surface water flow model).

The term Optimize in Eq. (1) refers to either

maximization or minimization whereas the con-

straint equations dictate the feasibility of the objec-

tive with respect to each and all of the constraints.

the process simulation equations basically consist

of the governing physical equations of mass, en-

ergy and momentum.

Many hydrologic and hydraulic systems prob-

lems can be formulated as discrete-time-optimal

control problems. The basic mathematical defini-

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Various optimization techniques in general and

their application to various hydrologic and hydrau-

lic systems problems in particular have shown re-

markable progress over the past three decades.

The progress of the application of these techniques

has gone alongside with the revolution of computermodels and as such similar explanations can be

given to the development of simulation models

and optimization techniques over the past three or

more decades. Figure 6 gives the development of

the application of optimization techniques to hydro-

logic and hydraulic systems problems, in an anal-

ogy that is similar to Figure 1, which was given for

Table 2- Taxonomy of some of the most popular hydrologic and hydraulic systems simulation models in the US

1. Surface water systems

Model name Developed by Model purpose Remarks

a) Watershed

runoff system

HEC-1

HEC-HMS

TR-20

HYMO

A & M Water-

shed Model

WMS

US Army Corps of

Engineers Hydrologic Engi-

neering

Center (HEC)

HEC

US Department of

Agriculture Soil Conservation

Service (SCS) and Agricul-

tural Research Service

US Department of AgricultureAgricultural Research Service

and Texas A & M University

USACE Waterways Experi-

ment Station

Brigham Young University

Precipitation- runoff

processes

Precipitation- runoff

processes

Precipitation-runoff

processes

Precipitation-runoff

processes

Precipitation-runoff

processes

Precipitation-runoff

processes

Streamflow hydrographs at desired locations

in the river basin are computed.

Part of the Next Generation (NexGen) models

developed by the HEC. Surpasses HEC-1.

New capabilities include a linear distributed

transformation that can be applied with grid

(e.g., radar) rainfall data, optimization options,

and so on.

Uses the SCS curve number method and

SCS curvilinear dimensionless unit

hydrograph to develop the runoff response.

Includes option to compute watershed sedi-

ment yields using a modified version of the

universal soil loss equation.

Accepts radar readings as well as

conventional gauged rainfall data. Capabili-

ties also include standard step method water

surface profile computation.

Automatically delineates watershed

boundaries using TINs.

b) Streamflow

systems

HEC-2

WSPRO

HEC-RAS

HEC

US Geological Survey

(USGS)

HEC

Water surface profile

in rivers

Water surface profile

in rivers

Water surface profile

in rivers

Computes water surface profile for

gradually varied flow.

Uses the standard step method solution

of the energy equation.

Part of the NexGen models. Surpasses

HEC-2. Current version performs one

dimensional steady state flow; future

versions will perform unsteady flow and sedi-

ment transport calculations.

simulation models.

The general formulation for optimization prob-

lems in water resources can be expressed in terms

of state (or dependent) variables (x) and control (or

independent) variables (u) as (Mays, 1997; Mays

and Tung, 1992)

Optimize f(x, u) (1)

Subject to process simulation equations

G(x, u) = 0 (2)

And additional constraints for operation on the

dependent (u) and independent (x) variables

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Some of the earliest simulation models included

in Table 2 such as HEC-1 and TR-20 are lumped

parameter hydrologic rainfall-runoff models. These

models, which were developed in the late 60s and

early 70s, continue to be the accepted standards.

There have been many advances in the distributedwatershed modeling over the past several years

that now permit the more comprehensive and so-

phisticated distributed modeling. The development

of collection and management of overwhelming

data required to derive these models have been

made easier with the emergence of more user

friendly software and geographic information sys-

tems (GIS).

The Watershed Modeling System (WMS, for-

merly known as GeoShed) developed at Brigham

Young University (Nelson, et al., 1995) is a graphi-

cally based software tool with an interface to HEC-1 and an interface to CASC2D, a two-dimensional,

grid-based, distributed hydrologic model. In addi-

tion, features include triangulated irregular network

(TIN) generator from scattered and digital elevation

model data source, automated watershed and sub-

basin delineation from TINs. CASC2D, developed

through the U.S. Army Corps of Engineers, is a

physically based rainfall/runoff model which uses

rectangular grid cells to represent the distributed

watershed and rainfall domain (Julien, et al., 1995).

This model uses a two-dimensional diffusive wave

equation to simulate overland flow and a one-di-

mensional diffusive wave equation to simulate

channel flow.

3.1.3. Real-time Rainfall Runoff Analysis Using GIS

and Radar Data

Watershed rainfall-runoff computation requires

determination of the general hydrologic processes

within the watershed. This, in turn, requires not only

the topographic information of the watershed but

also information about other hydrologic variables

such as the temporal and spatial distribution of pre-

cipitation. Use of GIS has made it possible to rep-

resent spatial distribution of elevations using DigitalElevation Models (DEM). Three principal methods

are available in most GIS models for structuring a

network of elevation data: 1) square-grid networks;

2) contour-based networks; and 3) triangulated ir-

regular networks (TIN) (Moore, et al., 1991).

Precipitation data can be obtained by means of

remote sensing such as radar at desirable time in-

tervals so that real-time runoff (flood) simulation can

be performed. Using the DEM data (available for

the entire United States from the USGS), GIS can

compute the aspect (direction of maximum slope)

at a given location within the watershed. With other

hydrologic parameters for abstraction, infiltration,

routing and so on available in GIS or other data-

base systems, the watershed runoff processes can

be easily simulated. In effect, this approach can be

used to forecast flood events at desired locationson a real-time basis provided that instantaneous

rainfall data can be directly obtained using radar or

other means. Figure 2 shows a general procedure

that can be used for modeling a general real-time

operation (adapted from Loucks, 1996).

The WMS discussed in Section 3.1.2 is an ad-

vanced model used for a more comprehensive wa-

tershed modeling system. This model incorporates

digital terrain modeling, GIS data, and analytical

hydrologic models in a single environment. It has

the capabilities of automatically delineating water-

shed and sub basin boundaries from TIN and thencomputing geometric parameters such as area,

slope and runoff distances

for each basin. Figure 3 shows the representa-

tion of a watershed by grids for which different data

can be stored in GIS. WMS can determine differ-

ent parameters of the watershed from the stored

grid data. HEC-1 is directly interfaced in WMS for

performing rainfall/runoff analysis (Nelson, et al.,

1995).

As shown in the WMS interface in Figure 4,

runoff hydrographs at desirable locations can be

computed and viewed. This can be a very useful

tool especially in dealing with flood mitigation ef-

forts. If one or more detention facilities exist within

the watershed, it may be possible to adjust release

policies on a real time basis such that threatening

flood peaks can be reduced.

3.1.4. Real-time Flood Management Model for the

Lower Colorado River Authority

Developed at the University of Texas at Austin

by Unver, et al. (1987) for the Lower Colorado River

Authority (LCRA), this model can be used for flood

routing and rainfall-runoff modeling on a real-time

framework. It has several modules that interactwith one another. Real-time data that are managed

by the data management module of this model in-

clude rainfall collected at recording gages, stream

flow collected at automated stations, headwater

and tailwater elevations at each dam, information

on which rivers and reservoirs are to be simulated

in flood routing, and current reservoir operations.

The models subsystems constitute the three basic

subsystems of a DSS. Figure 5 depicts the struc-

ture of the model as given by the LCRA.

3.2. OPTIMIZATION FORMULATIONS

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analytical structure or mathematical formula but also

capable of reducing and incorporating water policies

into the analytical structure are required. Further-

more, these models may be required to interpret the

result of the computations, give conclusions based

on the result and make appropriate recommenda-tions based on the conclusions reached.

A review of the computer models for solving hy-

drologic and hydraulic systems problems show that

although tremendous work has been done in the

past to develop such models, only a few models

exist that address the overall framework of prob-

lems associated with integrated hydrologic and hy-

draulic systems management. A few of the reasons

may be attributable, among others, to:

1. the lack of clear definition and better under-

standing of integrated hydrologic and hydraulic

systems management;2. the variation of water needs with space and

time; and

3. the evolution (revolution) of computer pro-

gramming.

Most of the existing hydrologic and hydraulic

systems simulation models solve problems that

can be readily expressed in a form of mathematical

functions. Similarly, hydrologic and hydraulic sys-

tems optimization models search for optimal solu-

tions of problems defined by mathematical func-

tions. To use such models for integrated hydrologic

and hydraulic systems problems, they must also

have the capability of considering different water

policies and incorporating them into the solution.

Computer modeling approaches that at least

partly tried to address some of the concepts of

integrated hydrologic and hydraulic systems man-

agement are highly based on interfacing simple

computer models programmed and used for the

analysis of specific hydrologic and hydraulic sys-

tems problems. At the core of some advanced com-

puter models used for integrated hydrologic and

hydraulic systems management lie simple simula-

tion modules, rule-based simulation modules (also

known sometimes as expert systems) and optimi-zation modules of hydrologic and hydraulic systems

problems. While many simulation and optimization

modules have been developed and interfaced over

the years by different institutions and agencies,

the incorporation of rule-based simulation mod-

ules in computer models for integrated hydrologic

and hydraulic systems management appears to

have emerged as a sound approach recently. By

incorporating rule-based simulation modules, it has

become easier to manage decisions that involve

several factors and water policies.

The following section discusses some of the

computer models that emerged in the US over

the past few decades for the simulation of various

types of hydrologic and hydraulic systems prob-

lems. Real time event hydrologic models are dis-

cussed in this Section and subsection 3.2 discuss-es the basic mathematical structure of optimization

models, which may be viewed as generic functions

that can be customized to specific hydrologic and

hydraulic systems problems.

3.1. SIMULATION

3.1.1. Development of Hydrologic and hydraulic

systems Simulation Models

In the advancement of information technology,

hydrologic and hydraulic systems simulation mod-

els have generally gone through an evolutionary

process. Figure 1 depicts the evolution of hydro-logic and hydraulic systems models as classified

into five generations (derived from the explanation

given by Jamieson and Fedra, 1996). The first gen-

eration codes (models) which tremendously simpli-

fied calculation of analytical functions through ge-

neric computer codes are but mediocre by todays

standards. One may draw an analogy between the

coming into being of these codes and the transition

of computation methods from using the slide rule to

scientific calculators. In both cases, similar jobs are

done but the new method highly reduced the time

required for numerical computations. The succeed-

ing generations of models successively enhanced

the robustness of the models and/or the ease with

which the model can be used. The fifth generation

of models are embodied with artificial intelligence

that not only perform analytical computations but

also draw some preliminary conclusions and rec-

ommend appropriate actions.

3.1.2. Taxonomy of Hydrologic and hydraulic sys-

tems Simulation Models

Over the past few decades, water resources

professionals have witnessed the development

of quite a number of hydrologic and hydraulicsystems simulation models. Wurbs (1995) points

out that a tremendous amount of work has been

accomplished during the past three decades in

developing computer models for use in water re-

sources planning and management. The majority

of these models, perhaps most of the earliest com-

puter models to be developed for water resources

problems, may be viewed as simulation models.

Taxonomy of some of the popular hydrologic and

hydraulic systems simulation models in the US are

summarized in Table 2.

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Fund and the National Geographic Society clearly

recognized the critical need for the watershed ap-

proach for integrated hydrologic and hydraulic sys-

tems management rather than political jurisdiction

or boundaries. Similarly, the Environmental Advisory

Board (EAB) of the US Army Corps of Engineers(USACE) recommended in 1994 to use the water-

shed/ecosystem approach as the holistic, integrated

concept on which to base (water resources) plan-

ning (Bulkley, 1995). Furthermore, the US General

Accounting Office (1994) listed the importance of

the watershed approach for integrated manage-

ment. Accordingly, watershed boundaries:

1.are relatively well defined;

2.can have major ecological importance;

3.are systematically related to one another hier-

archically and thus include smaller ecosystems;

4.are already used in some water managementeffor