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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
top related