SPATIAL APPROACH IN RIVER BASIN MANAGEMENT

USING DECISION MAKING STRATEGIES

Christian Menard

University of Applied Sciences, Geoinformation,

Europastrasse 4, A-9524 Villach/St.Magdalen, Austria

Keywords: Spatial Decision Support System, Embedded GIS, Spatial Modeling, River Basin Management.

Abstract: The information needed for River Basin Management

covers a wide range of hydrological and

environmental data and methods. Since all measurement data are spatial and time related, spatial services

fulfill the requirements in a decision making process best. In this work an open data structure for storing

spatial temporal related data is proposed. Based on the data structure the modeling process can be performed

directly in a GIS environment by using visualization and spatial analysis techniques. This concept

incorporates the functions available in a GIS environment with the modeling concepts used in River Basin

Management. The paper concludes with experimental results and gives a short outlook to future work.

1 INTRODUCTION

The development and management of water

resources requires the simultaneous consideration of

numerous relationships and impacts such as water

quality, land use, rainfall, water storage, reservoir

management, irrigation, agricultural use,

groundwater, water supply, drinking water, and

pollution. There is a great interest for optimizing the

management of river basins, and there is the need for

coordination and integration of a large amount of

spatially related information and models for decision

making (Gunatilaka, 2003).

The information needed for River B

asin

Management (RBM) covers a wide range of

hydrological and environmental data and methods.

Several needs to meet these objectives have to be

fulfilled. Among those are commitments to

sustainable development, to support decisions for the

basin for environmental measures and to consider

diverse flood mitigation options. A Decision Support

System (DSS) thus contains tools to support

hydrologists in their decision making process. From

database management or information systems via

simulation models with mathematical programming

or optimization, almost any computer-based system

could support decisions.

In this work a concept is proposed which

mbines the functions available in a GIS

environment with modeling concepts used in RBM.

The advantage is that the construction of a model

can be performed directly within a GIS environment

using spatial services. Once the model is constructed

a simulation for a given situation in the river basin

can be performed. This approach can be used for

different kinds of calculations like precipitation run

off or even river quality monitoring. The fact that a

river system is based on network structure with

edges and nodes and connection rules each part of

the network can be based on a specific model.

Once a model is calculated all results can be

sto

red in a central database as time series data. This

gives different kinds of applications the possibility

to access these data through standard interfaces. The

results can be visualized and analyzed in the GIS

environment using standard functions.

2 ENVIRONMENTAL

INFORMATION SYSTEM

To support the modeling of hydrological events an

Environmental Information System (EIS) can serve

as the central unit.

An important aspect is how the measurement

ata are transferred to this central unit. In a river

basin all kinds of monitoring stations measuring

rainfall, river quality, industrial flow or water level

319

Menard C. (2005).

SPATIAL APPROACH IN RIVER BASIN MANAGEMENT USING DECISION MAKING STRATEGIES.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics, pages 319-322

 SciTePress

are located all over the basin. These monitoring

stations can be offline or online stations. At an

offline station the data are collected manually by

means of lists. Compared to offline stations, online

stations are directly connected to the central

database via telemetry network. As soon as, for

example, the water level in an online quantity station

is measured, the actual value together with the

timestamp is sent to the central unit.

Consisting of a set of digital maps, input, and

output forms, special tools for numerical and spatial

data analysis, plausibility control, visualization and

presentation the EIS guarantees the appropriate

handling and evaluation of all measurement data.

2.1 Spatial Temporal Data Storage

The EIS is planned to consist of a centralized state

of the art database management system, providing

functions necessary for storing, monitoring and

analyzing of data. The basic data structure should be

designed in an open standard, thus any application

can access these data using standard interfaces.

For each measurement it is important to know where

the measurement was made, what was measured and

when the measurement took place.

These spatial temporal data have to be stored in

such a way that each model can easily access the

data. A data structure for flexible data storage

consists of the following entities:

• Measurement Unit (MU)

• Measurement Parameter (MP)

• Time Series (TS)

• Measurement Data (MD)

Each MU has at least a unique identifier, a name,

a location and a description. This MU can have one

or many Measurement Parameters (MP) (or

measurement sensors). Each MP (sensor) records

Measurement Data, which have at least a timestamp,

location and the measured value.

An important aspect towards open systems is

that for each measurement at a MU for a certain MP

an entry in the MeasurementData table is created.

Using this kind of storage enables data access from

different kinds of external models or applications

without using middleware. Furthermore, this data

structure enables the extensibility of MU’s and

MP’s.

2.2 Plausibility Control

In an EIS the data themselves are of utmost

importance. For decision supporting purposes the

data have to be complete, correct and feasible. By

transferring data from online stations or manually to

the central database, these data can be corrupted due

to sensor errors or input failures. By using wrong

data the modeling process will be strongly affected,

thus leading to wrong simulation results. In Figure 1

the data import through the plausibility layer from

different kinds of possible data sources is depicted.

In order to avoid passing wrong data to the

central database, a plausibility control is proposed.

Each entry of a measurement should pass this check.

A simple approach is to store a certain set of limits

that is used to inspect the measurement values for

each parameter. For each MP the following

attributes are possible:

• minimum threshold value,

• maximum threshold value,

• allowed deviation from the mean value

For special purposes a constant limit is not

sufficient, because the measurements for some MP’s

are time dependent. The limit values during the night

may differ from the daily limits.

In order to have temporal dependency these

limits can be modeled as a time series pair for upper

and lower boundary for each MP. These limits are

used to check the incoming data. Depending on

whether the measurements are within the allowed

range, the data are set to valid or invalid. Only valid

data are accepted for analysis and reporting.

2.3 Spatial Data Retrieval

A Geographic Information System (GIS)

incorporates all necessary tools for visualizing and

analyzing geographically related data. Results of

queries can be visualized directly on a map and data

from different geographic related assignments like

catchments areas can be handled.

Figure 1: Different kinds of possible data sources

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An EIS should combine all necessary tools for

managing and analyzing data in the central database.

All clients have the possibility to retrieve all kinds of

data from different sources (quality data, simulation

results, climate data, etc.) from this database. An

important aspect towards interoperability is that

spatial related data are stored in a database which

supports Open GIS technology defined by the Open

Geospatial Consortium (OGC) (OGC, 2005).

All data in an EIS are related to spatial temporal

information, thus all monitoring stations, catchment

areas, river reaches etc. can be visualized and

analyzed in a GIS.

Since a MU has a spatial reference which can be

static (online measurement unit) or even dynamic

(mobile measurement unit) it can be visualized on a

map using spatial services. A river network consists

of nodes and edges and relations to catchments or

reservoirs. A node, for, instance, can be a junction or

diversion, whereas an edge can be a river reach, a

channel, etc. In Table 1 different possible spatial

representations of a MU are shown. In a river

network it is evident that each network node, edge or

region has the possibility to store time series data.

Table 1: Different kinds of spatial representations

Representation Objects in a river network

point source, junction, diversion, etc.

polyline river segment, channel, etc.

polygon catchment, reservoir, etc.

3 SPATIAL DECISION SUPPORT

SYSTEM

Basically, a Spatial Decision Support System

(SDSS) attempts to provide the water-resources

managers with analytical assistance based on spatial

information in making rational choices based on

objective assessment, thereby reducing the element

of subjective opinion (Gunatilaka, 2001),

Malczewski, 1999). This requires a broader

approach, which is otherwise limited within the

narrow realms of hydrology and water resources.

For the decision making process there is the need to

include spatial and quantitative information

wherever possible on economical and environmental

considerations (Clemen, 1996). Therefore, an SDSS

can be regarded as form of artificial intelligence in

which computers are used not only to predict, what

is likely to happen given various assumptions, but

mainly to supplement management experience in

decision-making.

3.1 Spatial Modeling

The first step towards a SDSS is to describe

processes and data by means of hydrological,

hydraulic, sediment transport, meteorological and

ecological models. These models have to be

integrated into general decision making approaches.

Integrated mathematical computer models

comprising hydrological models, hydraulic models,

flood forecasting models, water balance models,

water resources management and reservoir

optimization models as well as water quality models

are in themselves tools that support decision making.

In order to transform the outputs from these models

into practical decisions, they need to be combined

with other type of information, such as details of

infrastructure, possibilities for control, spatial

information etc. In an SDSS these tools combined

with spatial data can be integrated in a GIS

environment. A model can be an internal model,

which runs on the same machine in the decision

support environment, or an external model, which is

an external application like HEC-1 or HEC-HMS

(Cunge, 1992). Important for an external model is

that a preprocessor prepares the data necessary for

the external model and a post processing task which

retrieves the result data back to the central database

(Fürst, 2005).

Figure 2: Modeling Workspace.

A model can be controlled by means of model

parameters. A Model Parameter Set (MPS) contains

all attributes necessary to initialize and control a

model calculation. As input data for a model

calculation all time series data in the EIS are valid.

After successful calculation of a model the results

are stored again as time series data. One model can

be defined as network and can have one or more

predecessor and one or more successor models.

Using this principle, different models are using time

series data from the EIS. All connected models

together with the MPS’s build up a Modeling

Workspace (MW), which is depicted schematically

in Figure 2.

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3.2 Simulation Results

When starting a MW, a Simulation Run (SR) is

created. Each model in the MW is started in respect

to its correct order. A model can only be started if

the necessary input data are already calculated by

the previous model. In this example a precipitation

run off simulation is used. The area of the MW

consists of three sub basins S1-S3 two sources A1,

A2 one junction A3, a sink A5 and three reaches B1-

B3 (see Figure 3). As river reach several models can

be used like Simple Lag, Modified Puls,

Muskingum, etc. In this example the Muskingum

routing model was used for the reach B3.

First the run off from the two sub basins S1 and

S2 are calculated and combined at the junction A3.

Next an external calculation of Muskingum routing

from A3 to the sink A5 is performed using HEC

routines. Finally the run off from S3 is calculated

and combined with the routed flow to the sink A5.

One MW can be started for different MPS’s,

thus resulting in several simulation results. The MW

can be represented directly in a GIS environment

(see Figure 3).

In Figure 4 the results of a simulation run is

presented where the simulated flow at sink A5 can

be visualized as time series. In addition to the

simulated flow the observed flow can be displayed.

4 CONCLUSION AND OUTLOOK

An Environmental Information System contains tools

to manage data from online or offline monitoring

stations in a river basin. All data in a river basin are

spatial temporal related. GIS functions and external

tools can be combined for hydrologic modeling and

will support hydrologic experts in decision finding

processes.

Future work will concentrate on concepts for

integrating digital elevation models (DEM) for

watershed management, thus allowing stream and

sub basin delineation.

REFERENCES

Clemen, Robert T. (1996). Making Hard Decisions: An

Introduction to Decision Analysis, 2nd Edition,

Duxbury Press, Belmont, CA.

Cunge, J.A., Erlich, M., Negre, J.L. and Rahuel, J. L.

(1992). Construction and assessment of flood

forecasting scenarios in the hydrological forecasting

system HFS/SPH. Proc. BHR Group 3rd Internat. Conf.

on Floods and Flood Management, Florence, Kluwer

Academic, pp 291-312.

Figure 3: Modelling Workspace for a preci

itation run of

model

Gunatilaka, A., Dreher, J. & Pitono, J (2001). River basin

management strategies for Indonesia – Extension of

the Brantas model to Central Java, Proc. International

Water Association Conference on ‘Water & Waste-

water Management for Developing Countries’, October

2001, Kuala Lumpur, pp 360 –369.

Gunatilaka A., Dreher J. (2003), Use of real-time data in

environmental monitoring: current practices, Water

Science & Technology Vol 47 No 2, pp 53–61.

Malczewski J

.,(1999), GIS and Multicriteria Decision

Analysis

, New York, Wiley, John & Sons,

Incorporated.

Fürst (2004), GIS in Hydrologie und Wasserwirtschaft,

Wichmann Verlag, Heidelberg.

OCG (2005), http://www.opengeospatial.org.

Figure 4: Observed and simulated flow at sink A5.

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