Development of a DSS to improve the flood control in the Spanish National River Authorities

Development of a DSS to improve the flood control in the Spanish National River Authorities M. Francés Subdirección General de Planificación Hidrológica y Uso Sostenible del Agua, Ministerio de Medio Ambiente, Madrid, Spain

J. Arbaizar Inocsa Ingeniería, S.L, Madrid, Spain

E. Ortiz HidroGaia, S.L. Tecnología del Agua y el Medio Ambiente, Valencia, Spain

ABSTRACT SAIH program is managed by The Ministry of Environment. The SAIH is a real time remote control and centralization system for obtaining, storage, processing and displaying hydraulic and meteorological parameters.

The Ministry of the Environment is developing a project to introduce new technologies at SAIH Project including the implantation of Decision Support Systems (DSS).The implantation has been based mainly on which the architecture of the System is opened as much as possible for the incorporation of all kind of hydrometeorological data as well as the implantation of hydrological and hydraulic models.The goal is to have powerful tools that integrate meteorological and hydrological data and to make forecasts in floods situations by means of hydrological and hydraulic models.

Keywords: flood control, DSS, model, real time system, flood forecasting

1

INTRODUCTION

1.1 SAIH Project SAIH program is managed by The Ministry of Environment. The SAIH is a remote control and centralization system for obtaining, storage, processing and displaying hydraulic and meteorological parameters, in real time, using the latest technology, with the purpose of improving the safety of population and properties, and to make the hydraulic infrastructures being used by the Public Administration more efficient. Among the objectives of the SAIH program, it is necessary to point out the following: • Forecast and management of floods • Optimized management of water resources • Real-time data for dam security increase •Improvement of meteorological and hydrological databases

The Ministry of the Environment has made an investment, from the beginning of the program, of 396 million euros and out of which 22 million euros dedicates to the management, operation and maintenance. The systems all together arrange approximately about 2,180 control points and 30,000 data in real time

Figure 1. Hydrographic map of Spain Basins and SAIH network.

1.2 Innovation Technologies at SAIH Project At the moment The Ministry of the Environment is developing a project to introduce new technologies at SAIH Project including the implantation of Decision Support Systems (DSS). The goal is to have powerful tools that integrate meteorological and hydrological data and to make forecasts in floods situations by means of hydrological and hydraulic models.

First stage of the project consisted of making an analysis of the state-of-the-art in the hydrological and hydraulic model, a revision of the integrating systems of hydrometeorological data in the market, as well as the revision of the world-wide experiences in similar institutions and to the own accumulated experience in the different river basin authorities.

In second stage the introduction of an interactive system (DSS) in the river basins of the Tajo, Júcar and Segura. This allows a simple and robust way of the decision making for the system management and the simulation of its scenes of volumes and levels in the interest points of the river basin.

2

THE CONCEPT

The implantation of Decision Support Systems (DSS) has been based mainly on which the architecture of the System is opened as much as possible for the incorporation of all kind of hydrometeorological data as well as the implantation of hydrological and hydraulic models. The implantation of System FEWS "Flood Early Warning System" developed by Delft Hydraulics (2007) in the SAIH of these three river basins, due to the following advantages: • It has a set of projected modules to construct a suitable system of predictions of flood.

• Philosophy of an open system.

• Modular and highly configurable character.

• The incorporation of data to the system includes temporary series and data of meteorological forecast (HIRLAM, ECWMF), data of the radar network of the INM, etc.

• The system can evaluate the quality of the data.

• Advanced graphical data visualization • The dissemination of predictions to third is via HTML intranet/internet.

With respect to the models proposal, two types of hydrological models and a hydraulic model of propagation in channels are adopted. As hydrological models, distributed models (TETIS and TOPKAPI) and one neuronal network model (ANN) and as hydraulic model, the proposal is SOBEK-RURAL.

Figure 2. Proposed DSS for Segura Basin

The advantages of this proposal are the following: • TETIS and TOPKAPI have been used and tested satisfactorily in the three river basins.

• Both models use parameters with physical meaning and its estimation and calibration are simple and robust • As they are distributed they can directly use the information provided by the Radar and the numerical predictions of the HIRLAM. • The use ANN models developed ad hoc is of easy implantation and small cost.

• The use of hydraulic model SOBEK is immediate as it is developed by Delft Hydraulics.

3

FEWS

In response to these challenges, WL| Delft Hydraulics' Flood Early Warning System (Delft-FEWS) provides a state of the art flood forecasting and warning system. The system is a sophisticated collection of modules designed for building a flood forecasting system. The philosophy of the system is to provide an open shell system for managing the forecasting process.

This shell incorporates a comprehensive library of general data handling utilities, allowing a wide range of forecasting models to be integrated in the system through a published open interface. The modular and highly configurable nature of the system allows it to be customized to the specific requirements of an individual flood forecasting agency. Delft –FEWS can be used effectively both in simple forecasting systems, and in highly complex systems utilising the full range of hydrological and hydraulic modelling.

Delft -FEWS is a fully scalable system. It can be run as a self-contained manually driven forecasting system operating on a normal desktop computer, but can also be deployed as a fully automated distributed client-server application. The client server application allows further scaling through running time consuming tasks in parallel. The system applies the latest software standards. It has been developed in using JavaTM technology, and is fully configurable through open XML formatted configuration files. In the J2EE compliant Client-Server application, JMS is used to provide resilient communication between distributed system components.

Of paramount importance in an operational flood forecasting system is an efficient connection to external data sources. Delft FEWS provides an import module that allows importing of on-line meteorological and hydrological data from external databases.

These data include for example time series obtained from telemetry systems like observed water levels, observed precipitation, but also meteorological forecast data, radar data and

numerical weather predictions. Data are imported using standard interchange formats, such as XML, GRIB and ASCII. The import of external data also supports ensemble weather predictions, such as those provided by the European Centre for Medium Range Weather Forecasts (ECMWF) or, in Spanish case, the QPF obtained for HIRLAM (NWP model) that run at Instituto Nacional de Meteorología (INM).

The philosophy of Delft -FEWS is to provide an open system that allows a wide range of existing forecasting models to be used. This concept is supported by the general adapter module, which communicates to external modules through an open XML based published interface, effectively allowing “plugging-in” of practically any forecasting model.

An adapter between the native module data formats and the open XML interface is typically required, and such adapters are already available to support a wide range of hydraulic and hydrological models. The great advantage of this open interface is that existing hydrological and hydraulic models and modelling capabilities can easily be integrated in the forecasting system, without the need for expensive re-modelling using a specific model.

Delft -FEWS provides easy to understand, advanced graphical and map-based displays to help the user carry out the required tasks for flood forecasting in a structured way. The interactive map display allows geographic navigation, while icons give the forecaster rapid insight in warning levels being reached.

The time series display can be used to explore data further, or edit input data when necessary.

Additional insight in the dynamics of a flood event may be gained through the animated longitudinal profile and flood map displays.

Forecast results can be disseminated through configurable HTML formatted reports, allowing easy communication to relevant authorities and public through intranet and internet.

4

EXTERNAL DATA SOURCES

4.1 PRODUCTS OF INSTITUTO NACIONAL DE METEOROLOGÍA The products implemented at DSS in Spain provided by of Instituto Nacional de Meteorología of Spain are the Quantitative Precipitation Forecast (QPF) derived of High Resolution Limited Area Model HIRLAM with and images derived of weather radar network.

METEOROLOGICAL WEATHER RADAR The National Institute of Meteorology (INM) owns a radar observation system comprised of: • 15 Regional Radar Systems.

• 1 National Radar System, responsible for the centralization of the regional end products and for the generation of national mosaic images.

The group of INM radar systems operates as a coordinated network, and generates images on a national scale by based on the composition of the images received from each individual system. By the moment the radar characteristics (the Institute is involved a renovation project of radar network) is: Operational cycle: 10 minutes Amplitude Mode: 6 rpm 20 elevations 240 km range 2 km resolution NUMERICAL WEATHER FORECAST OF NWP MODELS Model HIRLAM (High Resolution Limited Modelling Area) is a regional weather forecasting numerical model (limited area) that is operative in the INM from 1995. It is the result of a cooperation project between several European countries (Finland, Sweden, Norway, Denmark, Holland, Ireland, Iceland and Spain, more France like collaborator), to develop short time weather forecasting numerical model models of numerical prediction. The cooperation is in the R + D framework,

whereas each country has its own operative workflow. Nevertheless, there is a model (system) of reference that is maintained and updated regularly. At the moment, the INM uses east model for three operative areas denominated ONR, HNR and Cnn. Daily ones (00, 06, 12 and 18 UTC) with a maximum range of prediction of 72 hours for area ONR, being of 36 hours for HNR and CNN. 4.4 PRODUCTS DERIVED OF REMOTE SENSING. EUMETSAT The METEOSAT Second Generation (MSG) series consists of three satellites planned for operation between 2000 and 2012. Compared to the current METEOSAT program, MSG sensor technology will provide substantially improved spectral and temporal coverage.

Whilst these improvements were driven by the increasing observation requirements of numerical weather forecasting, they will also result in much improved data for other applications, especially over land. There are different Satellites Application Facilities (SAF), utilizing specialist expertise from the Member States, SAFs are dedicated centres of excellence for processing satellite data and form an integral part of the distributed EUMETSAT Application Ground Segment.

Each SAF is led by the National Meteorological Service (NMS) of a EUMETSAT Member State in association with a consortium of EUMETSAT Member States and Cooperating States, government bodies and research institutes. The lead NMS is responsible for the management of each complete SAF project.

There are currently five SAFs providing products and services on an operational basis: SAF on Support to Nowcasting and Very Short Range Forecasting Ocean and Sea Ice SAF Climate Monitoring SAF Numerical Weather Prediction SAF Land Surface Analysis SAF

NOWCASTING SAF The Nowcasting SAF provides operational services to ensure the optimum use of meteorological satellite data in Nowcasting and Very Short Range Forecasting. The NWC SAF Consortium is Instituto Nacional de Meteorologia (Operations Leading Entity); Meteo France; Sveriges meteorologiska och hydrologiska institut and ZentralAnstalt für Meteorologie und Geodynamik.

The products implemented at DSS in Spain are Precipitation Products: Convective Rainfall Rate and Total Precipitable Water.

The main use of the Convective Rainfall Rate product is the monitoring of convective systems, i.e. of their rain intensity. This product provides the rate of precipitation estimated for convective clouds (in millimetres per hour).

The Total Precipitable Water product provides information on the humidity in the atmosphere.

It is given as the total amount of liquid water, in millimetres, if all the atmospheric water vapour in the column from the Earth's surface to the "top" of the atmosphere were condensed.

High values in clear air often create conditions for the development of heavy precipitation and thus flash floods.

LAND SAF The Satellite Application Facility on Land Surface Analysis (LSA SAF) has been initiated in June 1999, with the purpose of developing techniques to retrieve products related with land, land-atmosphere interactions, and biospheric applications, using data from EUMETSAT satellites; METEOSAT Second Generation (MSG, launched in August 2002), and the first Meteorological Operational Polar satellite of EUMETSAT (EPS/MetOp-A, launched in October 2006).

The spectral characteristics, time resolution and global coverage offered by MSG and EPS allow for their use in a broad spectrum of land surface applications. The broad scope of the LSA SAF is therefore to increase the benefits from MSG and EPS data related to land, land-

atmosphere interactions and biospheric applications by developing techniques that will allow a more effective use of data from the two EUMETSAT satellite series (MSG and EPS).

Activities in the observation and characterisation of land surface processes are especially relevant in several fields of applications such as: weather and climate modelling, natural hazard forecasting and monitoring, ecosystem monitoring and hydrology.

Development phase of the LSA SAF, which started in September 1999, has been carried out within 5 years. During this stage, the LSA SAF consortium developed an operational system capable of delivering a set of Land products on an operational basis. The Initial Operations of the LSA SAF saw the completion of the development of the MSG based products including the validation activities, the set-up of the operational capabilities, the user support and the first set of operational provided products. The Consortium in the Continuous Development and Operations Phase: Instituto de Meteorologia (IM), Portugal; Royal Meteorological Institute (RMI), Belgium; Météo-France (MF), France; University of Karlsruhe, Germany; Institute of Dom Luiz , Portugal; University of Valencia (UV), Spain and Finnish Meteorological Institute (FMI), Finland. The following Targeted Products and Applications generated by LSA SAF parameters will be derived from MSG and EPS measurements and implemented at DSS in Spain are: Land Surface Temperature, Soil Moisture, Snow Cover, Evapotranspiration, Vegetation Index and Leaf Area Index 5

RAINMUSIC

In the framework of the MUSIC project (Contract no.

EVK1-CT-2000-00058), University of Bologna developed innovative techniques for combining weather radar, weather satellite and rain gauge derived precipitation data, taken as independent measurement sources, in order to provide reliable rainfall estimates. Three basic independent sources of precipitation estimates

are considered: rain gauges, meteorological radar and satellite images. Each estimate is affected by biases and by errors of different sources and nature. Given the independent nature of the sources of errors, MUSIC project developed three new algorithms based upon the conjunctive use of block Kriging and of the Bayesian combination that allow for the substantial elimination of the bias and the reduction of the variance of the estimation errors, thus increasing the reliability of the precipitation estimates.

Three Bayesian combinations were developed: a combination between radar and rain gauges, a combination between rain gauges and satellite and a combination between radar rain gauges and satellite.

The Bayesian combinations were developed to work in real world situations. In order to deal with real world data and to work in real-time situations, a FORTRAN code was written to apply the Bayesian combinations to rain gauges, radar and satellite data and to manage all the parameters involved in the application of the new techniques. The resulting software was called RAINMUSIC. The rain gauges and radar Bayesian combination (GRBC) is based on the use of block-Kriging and Kalman filter and it’s a modification of the combination technique presented by Todini (2001). A scheme of the GRBC can be found in Figure 3.

Figure 4. Example of rain gauges and radar Bayesian combination (GRBC) at Segura Basin.

6

Figure 3. Scheme of the rain gauges and radar Bayesian combination (GRBC).

The next figure show an example of three time steps of hourly cumulated rainfall Bayesian combination at Segura Basin with radar data provided by INM and raingauges of SAIH network of Segura Basin make with RAINMUSIC Software implemented in DSS.

TOPKAPI

TOPKAPI (Todini et al, 2001) (TOPographic Kinematic Approximation and Integration) is a physically based rainfall-runoff model applicable at different spatial scale, ranging from the hill slope one to the catchment one, and in the perspective to the GCMs one, maintaining at increasing scales physically meaningful values for the model parameters.

The parameterization is relatively simple and parsimonious.

The TOPKAPI model is based on the idea of combining the kinematic approach with the topography of the basin; the latter is described by a Digital Elevation Model (DEM), which subdivide the application domain by means of a grid of square cells, whose size generally increases with the overall dimensions, due to

the constraints imposed by the computing resources; consequently, increasing the application scale of the model implies an increase in the dimensions of the cells, which at catchment scale may amount to several hundred meters per side. Each cell of the DEM is assigned a value for each of the physical characteristics represented in the model. The flow paths and slopes are evaluated starting from the DEM, according to a neighbourhood relationship based on the principle of the minimum energy cost, namely the maximum elevation difference; it takes account of the links between the active cell and the four surrounding cells connected along the edges, due to the finite difference approach underpinning the model; the active cell is assumed to be connected downstream with a sole cell, while it can receive upstream contributions from more than one cell, up to three ones.

The integration in space of the non-linear kinematic wave equations results in three ‘structurally-similar’ zero-dimensional nonlinear reservoir equations. The first represents the drainage in the soil, the second represents the overland flow on saturated or imperious soils and the third represents the channel flow.

The parameter values of the model are shown to be scale independent and obtainable from digital elevation maps, soil maps and vegetation or land use maps in terms of slopes, soil permeability, topology and surface roughness.

the distributed model, while in the lumped model it is performed on the basin or sub-basin as a whole, in this way arriving at a problem unconstrained by spatial dimensions. The equations obtained for the local scale and for the lumped scale are structurally similar; what distinguishes them are the coefficients, which in one case have local significance and, in the other, summarize the local properties in a global manner.

The TOPKAPI model proposed is structured around three basic modules which represent, in turn, the soil water component, the surface water component and the channel water component (drainage network component) respectively.

7

TETIS

The TETIS model is a conceptual distributed model where each grid cell represents a tank model with six tanks connected among them. A conceptual scheme of the vertical movement of the water at each cell can be observed in Figure 6. The relationships among different tanks are different for every case, but always simple relationships were used. A more detailed description of the model appears in Vélez et al.

(2002) and Francés et al. (2007).

Figure 5. Visualization of outputs of TOPKAPI model in Segura Basin

The integration of the fundamental equations is performed on the individual cell of the DEM in

Figure 6. Runoff production in TETIS model and conceptual scheme of vertical movement at cell scale

The first tank corresponds to the snow package, which can either exist or not. The snow melting process used in the model is the classic DegreeDay method, which is widely used in the scientific literature. The second tank corresponds to the static storage, where the only flow exiting is the evapotranspiration.

This tank also represents the initial abstractions and pond surfaces. The surface storage is the third tank, where the available water that is not infiltrated can be drained superficially as surface runoff. The soil infiltration capacity has been associated with the soil saturated hydraulic conductivity. The fourth tank represents the gravitational storage; the percolation process is modelled according to both soil saturation conditions and the transport capacity, in vertical sense; the remaining water is available to feed the interflow. The fifth tank corresponds to the aquifer, where the vertical flow represents the system underground losses and the horizontal flow is the base flow. The last tank represents the channel at the cell, where each cell is connected to the downstream cell according to the drainage network. Indeed, it is a three-dimensional model. All cells drain towards the downstream cell until they reach the channel. Once the channel is reached the flow routing is performed according to the GKW (Geomorphological Kinematic Wave) methodology.

The time series required during the model execution are discharge, rainfall, evapotranspiration, snow water equivalent and temperature in case that the snow exists.

Cartographic information uses raster format maps. Digital Terrain Model (DTM) and soil properties (available water and saturated hydraulic conductivities) are obtained based on soils studies, land use, geological maps, edaphologic information, hydrogeological data and other environmental topics that could be interesting and are available at the study area.

The infiltration model and the flow channel routing model proposed in TETIS include a few correction factors which correct globally for the different soil properties maps instead of each cell value of the calibration maps, thus reducing dramatically the number of factors to be calibrated. This strategy allows for a fast and agile modification in different hydrological processes. These correction factors can be

found using an automatic calibration. In general, if the TETIS model has been calibrated adequately these values must not change along the basin, thereby allowing extrapolation to ungauged subbasins.

The routing along the channel network was carried out using the GKW, where nine geomorphologic parameters are required, which can be obtained from potential laws. The coefficients and the exponents of these potential relationships can be obtained with a geomorphologic regional study for hydrological homogeneous zones and it’s possible to use the geomorphologic parameters recommended in scientific literature (VÊlez, 2001), since a geomorphologic regional study for hydrological homogeneous zones was not carried out.

All storages have relevancy inside the initial soil moisture conditions (state variables): snow pack supplied as snow water equivalent in mm.

The static storage is given as a percentage of the maximum capacity of the static storage.

The initial soil moisture in the surface storage, the gravitational storage and the aquifer are given as a water column in mm. Finally, the initial condition in the channel is supplied to the model as a percentage of the bankfull section. These initial condition variables are global, e.g. they have the same value for all cells and must be adjusted or calibrated by means of automatic or trial and error procedures.

8

NEURAL NETWORK

The application of artificial neural networks (ANNs) to various aspects of hydrological modelling has yielded to interesting and promising results during recent years. In particular, rainfall-runoff models and real time forecasting models based on ANN’s schemes have received special attention. Their ability to incorporate in a systematic approach non linear relationships between variables represents an attractive aspect in favour of ANNs modelling.

An ANN is a massively parallel-distributed information processing system that has certain performance characteristics resembling biological neural networks of the human brain.

Each net is characterized by its architecture, which is represented in terms of an organized number of hidden layers and nodes, input nodes, activation functions and the direction of the information’s flux through the network (see Figure 7).

The vast majority of the networks are trained with the popular back-propagation learning algorithm BPNN, which is also used herein, with the MSE (mean squared error function) as the objective function.

Figure 8. Watershed and outputs of ANN model implemented in Rambla Benipila in DSS of Segura Basin

9

Figure 7. Visualization of outputs of TOPKAPI model at Segura Basin

The number of hidden layers is fixed to one, which is sufficient to approximate any complex nonlinear function with the desired accuracy [Hornik et al., 1989]. Following a typical and well tested configuration in the literature, the activation function of the input and output layer has been set to a linear function, while the hidden layer incorporates the non linear sigmoid function, providing the ability of the network to capture possible complex non linear relationships between inputs and target outputs.

A robust ANN model has been implemented in DSS of Segura Basin, at the moment one little flash-flood watershed has been implemented, the Rambla Benipila, the watershed and the forecast results can be observed in Figure 8

SOBEK

SOBEK is an integrated software package for river, urban or rural management. Seven program modules work together to give a comprehensive overview of waterway systems keeping you in control. SOBEK has been developed by WL | Delft Hydraulics in partnership with the National Dutch Institute of Inland Water Management and Wastewater Treatment (RIZA), and the major Dutch consulting companies.

The software calculates (easily, accurately and fast) the flow in simple or complex channel networks, consisting of thousands of reaches, cross sections and structures. You can define all types of boundary conditions, as well as define lateral inflow and outflow using time series or outputs of hydrological models.

The computation of the water levels and discharges in the SOBEK-flow-network is performed with the Delft-scheme. This scheme solves the Saint-Venant equations (continuity and momentum equation) by means of a staggered grid. In this staggered grid the water

levels are defined at the connection nodes and calculation points, while the discharges are defined at the intermediate reaches or reach segments.

In general, numerical approximations must satisfy the following requirements: Robust, i.e. effective or capable of dealing with a wide range of practical problems, without producing numerical instabilities, Efficient, i.e. efficient use of computational resources such as processor time, Accurate, i.e. sufficient accuracy given the modelling purpose.

For the Delft-scheme robustness has been the most important design aspect. By the range of practical problems to be dealt with the following problems are included: • Drying and flooding • Super-critical flow including hydraulic jump.

The used procedure guarantees a solution. In certain flow conditions the time step is reduced temporarily by a time step estimation procedure to avoid numerical instability.

The Delft-scheme uses a so-called minimum degree algorithm with an iterative simulation technique. This is highly efficient in case of large networks and long time series.

The SOBEK model of Segura River implemented at DSS of Segura Basin has 190 cross sections, 728 calculation points and a total length of river (including tributaries) of 82 Km. between Contraparada discharge station and the Mediterranean Sea.

Figure 9. Schematic view of the 1D SOBEK model of Segura River included at DSS.

10 CONCLUSIONS The Ministry of the Environment is developing a project to introduce new technologies at SAIH Project including the implantation of Decision Support Systems (DSS).

The implementation, commissioning and evaluation of the DSS will be done during 2008.

11 REFERENCIAS Francés F., et al., 2006: Split-parameter structure for the automatic calibration of distributed hydrological models. Journal of Hydrology. doi:10.1016/j.jhydrol.2006.06.032.

Hornik K., Stinchcombe M. and White H., (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366.

Todini, E., 2001. Bayesian conditioning of radar to rain-gauges, Hydrol. Earth System Sci., 5:225-232.

Todini, E. and Ciarapica, L., 2001. The TOPKAPI model. Mathematical Models of Large Watershed Hydrology, Chapter 12, edited

by Singh, V.P. et al., Water Resources Publications, Littleton, Colorado.

Vélez, J. I.; Vélez J. J. y Francés, F., 2002: Modelo distribuido para la simulación hidrológica de crecidas en grandes cuencas. XX Congreso Latinoamericano de Hidráulica IAHR. La Habana, Cuba. Published in CD. ISBN 959-7160-17-X..

WL │Delft Hydraulics (2007). The DELFTFEWS Configuration Guide. Version 1.1