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Real time wave propagation simulation:

model implementation for the Tagus River

P. A. Diogo, A. C. Rodrigues & A. Rodrigues

Dep. of Environmental Sciences, Faculty, of Sciences

and Tech., New Univ. of Lisbon, Quinta da Torre, 2825

Monte de Caparica; Water Institute, Lisbon.

Email: [email protected]; [email protected]; [email protected].

Abstract

A wave propagation model is applied to Tagus river, built in the FloodSurveillance and Warning System (FSWS), developed by the PortugueseWater Institute (INAG). The purpose of this project, beyond simple wavepropagation modelling, was to integrate modelling in the FSWS,minimising user interference: the modelling system should be able tomaintain it automatically and overcome input data difficulties,minimising mathematical instability and providing an effective tool forflood forecast and warning. The model is set to run at regular timeintervals, producing results accordingly. These time intervals may bechanged depending on the number of simulations needed for effectiveflood prevention. As input data, real time information, obtained fromautomatic gauging stations and from some Portuguese and Spanishreservoirs, all registered and stored in the FSWS, is used. This paper willfocus on the problems/solutions regarding real time implementation andwill try to show how such a system may help flood situations managing.The system was first tested during the winter of 1996/97 but real timeimplementation was fully ready by the winter of 1997/98. Results showedthat modelling can be a reliable tool for dealing with and managing floodsituations and future developments include application to other rivers inthe Portuguese territory, where flood situations are likely to occur.

Keywords: Flood warning system, wave propagation modelling,real time implementation, Tagus river.

1 Introduction

The need to control flow becomes more urgent as the demands on ariver as a natural resource increase. With rivers regarded as multi-purpose systems for water supply, transportation, drainage and

Transactions on Ecology and the Environment vol 19, © 1998 WIT Press, www.witpress.com, ISSN 1743-3541

36 Hydraulic Engineering Software

recreation, it is important to ensure that flows are properly andefficiently controlled and that damage due to the extreme events

including flooding or pollution, are minimised [1].The relatively low ratio of cost to benefit for a flood forecast and

warning service makes it an ideal flood-protection measure in many

areas where physical means cannot be economically justified. Thesoundest approach to the flood^ problem lies in a planned

combination of water-control structures, floodplain zoning,

insurance and adequate forecasting [2].In the case of the Tagus basin, whenever very high precipitation

occur, several dams located close to the Spanish-Portuguese border,storing high volumes of water, discharge significant flows above thecapacity of the river channel at the downstream reach [3].

In less than 15 years (from 1978 to 1990) Tagus river suffered

four severe flooding situations, as important areas at the

downstream reach of the river (just before the Tejo estuary) were

flooded [4]. During the winter seasons of 1996 and 1997, high

flows were again registered and some villages isolated.

Integrated in the Flood Surveillance and Warning System(FSWS), a one-dimensional model was implemented, using real timeinput data and producing results with regular time intervals.Methodology for real time model implementation and calibration areherein presented and results of the 1st year of application areevaluated.

2 Flood Surveillance and Warning System

The Flood Surveillance and Warning System (FSWS) was firstimplemented on the Tagus basin during the floods of December1995, using 3 automatic gauging stations (Tramagal, Almourol andOmnias) and data from Portuguese and Spanish reservoirs. Gauging

stations information was updated every 5 or 10 minutes whilereservoir information was collected hourly.

Since then the system has been growing and several newautomatic gauging stations have been installed along the Tagus andother important river basins, like Douro, Guadiana, Sado andMondego. Nowadays the system includes 19 automatic gaugingstations covering the Tagus basin and information from 5Portuguese and 4 Spanish reservoirs.


Hydraulic Engineering Software 37

The surveillance system is centred at the Water Institute (INAG),

located in Lisbon. A specific software application, developed by

INAG, communicates with all the gauging stations via modem,

stores and displays all the collected information, allowing the user to

view river data from all the country river network, and graph andprint the information. The system works in real time and may bemade available to other remote users by using a modem.FSWS is permanently registering information although some of

its functions and capabilities are relaxed during dry periods of the

year. The information collected and processed is made available to

all entities responsible for flood situation prevention and managinglike fire departments, civil protection services and municipal

authorities [3].

3 The model

Natural flood waves are considerably more complex than the

simplified cases which yield to mathematical analysis, but

theoretical treatment is specially useful in studies on surges incanals, impulse waves in still water, and waves released from dams

[2].The applied model simulates flow with variable regime in

channels with no ramifications and simple topography. It is mainly

useful for wave propagation study in rivers and estuaries, irrigationchannels and basic testing of hydrodynamic waves. It's based on theSaint-Venant equations, solved by Preissman's method:

Continuity: a Q . a // 0)J J£ + b = n<7 a <9 f

momentum:,? g ^ r g^ /, K&^ *

Q = flow (nvVs) b = river width (m)ft = Boussinesq coefficient C =Chezy coefficient (m̂ / s)

h = water depth A = cross-section area (nf)

I = reference level slope R = hydraulic radius (m)(= bottom slope) q = lateral inflow (nvVs)

External boundary conditions consist of discharges from Cedilhoand water level at V. F. de Xira. Internal boundary conditions areconstituted by discharges released from two run-of-the-river type

dams and the main Tagus tributaries (Ocreza and Zezere rivers).



River bathymetry is based on topographic surveys from the earlyseventies, although bottom sediment movements have made thempartly out of date. For calculation, 380 cross-sections are used,

500 meters apart. Results of each simulation define stages and flow

along the river, with predefined time and space intervals.

4 Implementation

The model simulates wave propagation along the 190 km of the

Tagus River Portuguese reach, from Cedilho reservoir (at the

Spanish-Portuguese border) until V. F. de Xira, just before theTagus estuarine area. Flood situations are often verified along thispart of the river and several villages are affected.

As output information, the model provides stage and flow values

every 500 meters, but only 8 key cross-sections are available for the

common user, although output information on all the other cross-

sections is also stored and can be accessed by the systemadministrator. These key cross-sections correspond to sites where

some of the gauging stations are located and, therefore, where data

comparison is possible. Results are produced hourly.The model was implemented in Microsoft Power Fortran®, but

several other algorithms had to be developed. Filtering input andoutput data, an important issue not only for model stability but alsofor output validation, was implemented in Turbo Pascal 6.0®.

To keep it independent from the FSWS, a modelling shell was

developed, which may be set to active or inactive, according to thesystem administrator decision. In this way model processing errorsand inadequate outputs don'tinterfere with the FSWSperformance as filtering software"tells" the main program that no

modelling results are available.

4.1 FSWS integration

Figure 1 - Integration in FSWSThe FSWS registers stageinformation (gauging stations) with predefined time intervals andreservoirs discharges hourly. These data can be used as an input tothe model, which runs also with predefined time intervals. The

model shell accesses these data directly from the database.

FLOOD SURVEILLANCE AND WARNING

data aquisilion ,

Ii stora

I

storagcd A LT T

ER



As input data the model requires more data than can be provided

by the FSWS. For every run an initial situation regarding stage and

flow for each 500 meters and external boundary conditions are

necessary (every cross-section of the river has to be described in

terms of stage/flow). All information not available from the FSWS

is obtained from output of the previous model run (figure 1).

All the exchange of information between the model shell and the

main part of FSWS is performed using ASCII data files.

4.2 Real time implementation's methodology

Working with real time modelling and reducing user interferencerequires definition of an adequate methodology, possible to be

implemented in an automatic way (and therefore programmed). Its

final goal is to keep the model working and guarantee feasible

modelling results. It is usually found that computerised methods for

taking advantage of reported flows can became complex [5].All the tasks must be automatically performed: initial and

boundary conditions definition, model running and output resultsvalidation and storing. The implemented methodology consists of

the following steps:A) Initial conditions file construction (named here as file A),

containing flow and stage values for all cross-sections;

B) Validation of file A;C) Validation of field data, obtained from FSWS;D) File A correction by replacing the correspondent values;E) Model running, using 5 minutes time step and producing resultsfor the next 24 hours;F) Output data validation and storing, using 3 data files: one withstage and flow simulation for the next 1/2 hour (named here as fileB), the 2nd having output information to be displayed and the 3rd tobe used as an historical record of the consecutive model runs.

Steps A to D consist all together of input data processing and

step F consists of output data processing.As to step E, 5 minutes time step was chosen by balancing model

execution time and results resolution. 24 hours, with hourly results,was defined as an ideal forecast time lag for flood situationsprevention.



4.2.1 Input data processing

Input consists of data obtained from automatic gauging stations,

reservoir discharges and output data from previous run of the

model. Using this information all together, an ASCII file is preparedcontaining the initial conditions for each run. Main tasks of input

data processing are:

1) Validation of previous run output file (file A);

2) Validation of gauging stations and reservoirs data;

3) File A copied to file B, used as initial conditions for the next run;

4) When available, data from gauging stations and reservoir

discharges is used to replace the correspondent values (flow and/orstage) in file B.

During data validation, 3 problems can be expected: a) after

periods of model inactivity, no output data from previous runs is

available; b) previous run produced invalid results, and c) no field

data is available. Situations a) and b) can be resumed to one case:no data from previous run is available.

After some tests it was concluded that the use of predefined initial

conditions, stored in backup files, induced mathematical instability.Instant data from gauging stations very seldom can be matched withan hypothetical flow situation.

Better results where obtained by building a new file B, byinterpolating gauging stations and discharge data along the river.Whenever interpolation is not possible then the use of predefinedbackup files can not be avoided.

4.2.2 Output data processingOutputs from the model are stored in 3 different files. The first iswhat was formerly called file A (section 4.2); the 2nd contains flowand stage results for the 8 key cross-sections of the river for the next24 hours, and the 3rd is a daily file, which registers all modelling

results each day. This last one is used to control model performanceand results validation.

Output from the model is used with two different objectives: asinput data for the next run and as information on river flowconditions, to be displayed on screen. The first consists of inputdata processing and therefore explained in section 4.2.2.

Regarding the 2™* objective, only simple validation of data isperformed: if no mathematical instability occurred then the resultsare assumed to be correct. As in any modelling study, it is up to the



person who looks at the results to decide if they are to be totally

trusted or not.

4.3 Displaying information

When validated,output results area

passed on to theFSWS main shell

and made available

o screen (Figure 2).

Whenever flooding

situations are

forecasted, values

are presented in adifferent colour anda report can be

viewed and printed, Figure 2 - Results display

informing about effects of water stage rising. For instance, within

two hours the road number 100 will be closed due to flooding.

Information about present stage and flow is also presented andthe possibility of graphing stage evolution with time, both for field

data and modelling results, helps the user understanding the

evolution of the situation.

5 Results

Implementing a real time wave propagation model adds difficultiesto simple model calibration, because many situations have to beconsidered and the modelling inputs are not perfectly controlled.Using output data from previous runs together with real time fielddata without user interference can cause mathematical instabilitiesas field data may introduce flow/stage variations with which the

model may not cope with.

5.1 Model calibration

For model calibration gauging stations data from December 1996and January 1 997 (high river flow period), and February and March1997 (regular river flow period) river flow was used. As somegauging stations were not yet implemented within those periods,



only 3 where used (Abrantes, Almourol and Omnias). Hourly dam

operation data was available for both periods and for all reservoirwithin the modelling scope.

As a calibration method,

real time situations were

simulated. By advancing the 5

computer's clock time and by *selecting data from storedgauging stations data, newinput files are created every

time the model runs.Sequential runs were

Gdddata— 8 hours

16 hoursx 20 hoias

' -—*—30 hours40 touts

10 15 20 25 30 35

Figure 3 - Stage at Abrantes

performed for 48 hours, every hour (by advancing the clock), andall simulation results registered. This procedure allows simulation

of the arriving of field data, as if it were in a real situation.

Simulating 2 days of model functioning takes from 2 minutes

(Pentium, 200 MHz processor) up to 2 hours (486, 50 MHzprocessor).

Figures 3, 4 and 5 illustrate model performance for periods ofrelatively high flow. In every graph presented the x-axis representshours passed since the model started to run as a standaloneapplication.From the 48 simulations performed within each calibration period

only partial results areshowed, as graphs wouldbecame unreadable.Nevertheless all resultsobtained where verified andcompared.

4000350030002500200016001000500

Measured MowH hoiars16 hours30 hows

10 15 20 25 30 35 40 45 50 55 60 65 70 75hours 5.2 System performance

Figure 4 - Flow at AlmourolDuring system implementation several unexpected problems cameup. Most of them had to do with incorrect filtering of input datawhich led to frequent model crashes. After some weeks of testing,errors were minimised as new filtering was added.

As the whole system is not dependent on the model, modellingerrors do not interfere with FSWS performance but the model isdependent on the state of the FSWS: if insufficient data is available


30 55 40 45 60 65 60 65 70 75 60


then the model performance is reduced and can lead not only to

incorrect results but also to mathematical instability.

Report production based on

simulation results is easily

available and is user friendly.

This feature is not yet

available for all users as thetable containing Stage - Effectinformation has to be

carefully verified. It's notacceptable to report flooding F^e 5 - Stage at Omnias

situations just because this table is not updated.The real time modelling shell has been able to maintain it

automatically and overcome data insufficiencies with minimum

simulation results degradation. Nevertheless results quality differ as

input data availability varies. This may constitute a problem ascommon users are not able to evaluate model performance and just

have to decide to accept the results or not. Until now self validation

of simulations is not implemented but automatic information on latemodel performance would be an interesting tool for all users.

After mathematical instability or periods of inactivity, the systemtakes a few time to recover and to produce reliable results again.

This time is dependent on the general state of the system (registered

flows and data availability) and on the number of simulationsperformed per hour. This adaptation to field data can take up to 5 or

6 simulations.

6 Conclusions

Simulations results show good stage and flow simulation wasachieved. It was also evident that reliable results are always limited

in time which can be defined as the wave propagation time from themost upstream section to the analysed section. This is due to thenon-availability of field data, which was not yet registered. Forexample, model is not able to predict how dam discharges will vary

and therefore simulations for the upstream river reach first 500

meters is only valid for short time period.Results have shown that modelling may be a useful flood

prevention tool and have helped detecting areas where topographic

surveys require updating. During the last two years, it proved to be



a useful tool for the flood management. In particular, it gave

support to a coordinated action between Portuguese and Spanish

authorities in regard to the dam operation during the flood period.

Displaying immediate simulation data to decision makers mayeffectively help flood situations managing as preventing measurescan be implemented much sooner. However it is necessary to create

tools for evaluating results as most users are not familiar with

modelling limitations.

Calibration of a real time model application cannot be performed

as a simple model application. Special attention has to be given to

input data filtering and processing, as an automatic system must beprogrammed to carefully evaluate available data and be able to

replace data gaps. This procedures must also be calibrated, asmodelling performance is highly dependent on the options taken.

Wave propagation time can be independently (off line) calibratedbut all procedures should be tested by simulating real timesituations.

Future developments include an indication of modelperformance in previous model applications to other Portugueserivers where flooding requires particular attention.

7 References

[1] PRICE, R. K, "A mathematical model for river flows", I -Theoretical development, Report n. INT 127, Hydraulics ResearchStation, Wallington England, December 1975, revised September1977.[2] KINSLEY, R. K. Jr., Max A. Kohler and Joseph L. H. Paulhus;

Hydrology for Engineers, McGraw-Hill, London, 1988.[3] INAG, "O sistema de vigilancia e alerta de cheias", Direc^ao deServices de Recursos Hidricos, Institute da Agua, Lisboa, Mar^o1997.[4] RODRIGUES, R. (1994a) "Algumas consideragoes sobre ascheias do Tejo em Portugal e a influencia das albufeiras emEspanha. In, T Congresso da Agua, Vol. 2, APRH, p.II-9 a 11-19,

Lisboa.[5] SITTER, W. T., and K. M. Krouse: Improvement of Hydrologicsimulation by Utilising Observed Discharge as an indirect input(Computed Hydrograph Adjustment Technique - CHAT), NOAATech. Memo. NWS Hydro-38, February 1979.


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