RAINFALL FORECASTING SYSTEM OF TEFER · PDF fileRAINFALL FORECASTING SYSTEM OF TEFER PROJECT ... quantitative rainfall prediction, ... the processing of the col

RAINFALL FORECASTING SYSTEM OF TEFER PROJECT

Fatih KESKIN

State Hydraulic Works, Investigation and Planning Department, Flood Forecasting Center, 06100, Yucetepe/Ankara

[email protected]

The flood event in late May, 1998 in West Black Sea Region and also in other re-gions exposed both the strengths and weakness of the present disaster response system in Turkey. By the help of the World Bank fund, Turkey implemented a pro-ject for the disaster mitigation and forecasting system named as TEFER (Turkey Emergency Flood and Earthquake Recovery). Within the scope of the TEFER, several organizations implement in cooperation the meteorological and hydrological station network, the processing of the collected data, the meteorological and hydrological modeling works, and flood forecasting.

The flood forecasting system takes real time monitoring data of the regional me-teorology and the catchment status, and produces forecasts of the flood state of the catchment up to next 48 hours. The forecasting system is based on MIKE FLOOD-WATCH (DHI, 2004) and SCOUT (Einfalt et al., 2004). SCOUT integrates real time numerical weather prediction (NWP), radar and raingauge data to produce rainfall forecasts. The system combines the compilation of real time data with rainfall and flood forecasting and presentations of the information and results in tabular and graphical forms.

The radar data can be used in nowcasting and the numerical weather prediction data can be used in forecasting. So integration of both data is good for forecasting purposes. The aim of this paper is to explain the flood forecasting system in Turkey and the encountered problems and advantages of the rainfall forecasting system

Keywords: Scout, Rainfall, Forecasting, TEFER, Model

RIVER BASIN FLOOD MANAGEMENT 461

INTRODUCTION

Rainfall is the main source of water for the hydrological cycle. Accurate meas-urement and prediction of the spatial and temporal distribution of rainfall is not a basic issue in hydrology. The prediction technologies and the devices used as a tool in this prediction developed very much in the recent years. As a result of this devel-opment, quantitative rainfall prediction, such as numerical weather prediction (NWP), becomes more popular instead of for qualitative rainfall prediction. Data from the radar measurement where available is another important input to the rain-fall prediction as a result of the new developments in radar technology. The recent developments help to improve the quality of the flood forecasting systems where rainfall forecasting is one of the main inputs. Starting after the big flood event in 1998, Turkish Government, by the help of the World Bank fund, implemented a flood forecasting system in 2003. The system coupled atmospheric and hydrological fore-casting system and makes flood forecasts in the basins up to the next 48 hours. The installed rainfall forecasting system is discussed after giving a summary of the sys-tem.

FLOOD FORECASTING SYSTEM IN TURKEY

After the big flood event in West Black Sea Region, by the help of the World Bank fund, Turkey implemented a project for the disaster (Earthquake, Flood) miti-gation and forecasting system named as TEFER Project (Turkey Emergency Flood and Earthquake Recovery). In TEFER Project, General Directorate of State Hydraulic Works (DSI), Turkish State Meteorological Service (DMI) and General Directorate of Electric Power Resources Survey and Administration (EIEI) implement in coopera-tion the meteorological and hydrological station network, the processing of the col-lected data, the meteorological and hydrological modeling works, and flood forecast-ing (Tefer, 2002).

The contract for the TEFER, Flood Forecasting Model Development project was signed between the DSI and Danish Hydraulic Institute(DHI) Water and Environ-ment, Denmark on 30th November 2001. Sub consultants to DHI for the project are Einfalt & hydrotec GbR, Germany, and from Turkey Arti Proje Ltd and UBM United International Consultants Inc (Tefer, 2002).

The complete system that was established through this project is consisting of 3 Meteorological C Doppler Radars and 206 automatic meteorological stations (AWOS) for DMI and 129 hydrometric stations for DSI. The Radars were installed in the prov-inces of Balikesir, Istanbul, and Zonguldak. The meteorological stations were in-stalled in the western regions of Turkey. The Hydrometric stations were installed in

462 INTERNATIONAL CONGRESS ON RIVER BASIN MANAGEMENT

West Black Sea, Susurluk, Gediz, and Buyuk Menderes basins with basin areas rang-ing from 17 653 to 31 285 km2 (Keskin, 2006). The location of the basins and the loca-tions of the radars (red colored) in Turkey is given in Figure 1.

Figure 1 : Locations of the basins and radars

Data transfer from the radar, hydrometeorological stations is carried out to DMI

and to DSI by means of Very Small Aperture Terminal (VSAT) satellite communica-tion technology using TURKSAT 1C Satellite.

The flood forecasting system takes real time monitoring hydrological and mete-orological data of the catchment, and produces forecasts up to 48 hours of the flood state (in stage and runoff rate) of the catchment. The forecasting system is based on MIKE FLOODWATCH and SCOUT. SCOUT makes rainfall forecasts for each sub-basin with combining real time meteorological data, radar and NWP. The system combines the compilation of real time data with rainfall and flood forecasting and presentations of the information and results. It is the first time in the world that a combination of NWP data, radar data, raingauge data and a hydrological-hydrodynamic model has been combined and used for real-time operation (Einfalt et al., 2004). The forecasting process is automated, and adjusted to run every hour with the possibility of manual check and control. The hydrological model used in the system is MIKE11-NAM Module which considers the moisture content in four inter-related storages(Snow layer, surface zone, root zone and ground water) representing the physical elements of the catchment (Einfalt et al., 2004).

The flood state in the basins can be mapped by the flood mapping module named as MIKE 11 GIS and is ideally suited as a spatial decision support tool for river and flood plain management by merging the numerical river modeling and GIS


(DHI, 2004). This module is used to represent the possible inundated areas in case of big flood events. The warnings can be disseminated through internet as maps, bulle-tins and graphs by the flood warning module. The possible flood warnings are dis-seminated through the intranet of DSI for the use of Regional Directorates of DSI in the basins (Keskin, 2006).

RAINFALL FORECASTING MODEL (SCOUT)

SCOUT generates rainfall forecasts up to 48 hours in real time by the combina-tion of the radar, NWP, and raingauge data. All incoming data are quality controlled: raingauge data are checked for extreme values and hidden missing data (Maul-Kötter and Einfalt, 1998) before being used. Radar data are checked and corrected for bright band, ground clutter, anaprop, vertical profile and adjusted to rain gauges. A part of this work is done on the radar workstation at DMI by SIGMET IRIS software, the other part is performed by SCOUT (Einfalt et al., 2000).

SCOUT nowcasting is a feature tracking approach to determine cloud echo mo-tion and was first implemented in a suburban county near Paris to control the sewer network (Einfalt et al., 1990). SCOUT is based on the mass centroid method, getting the displacement vector between consecutive radar scans from the distance of the mass centers of two corresponding radar echoes. An echo is defined as such if pixels exceeding a certain threshold touch directly. In the matching process echoes are recognised in consecutive radar scans due to their features admitting a reasonable degree of change in each of the features. Thus, the history of the echoes is considered and splitting and merging echoes can be detected. Finally individual displacement vectors are applied to extrapolate each echo separately. (Einfalt et al., 2003) A com-parison between the previous forecasts and the actual measurements provides a means for a quality estimation of the current forecast. As a result, SCOUT provides forecast images and catchment specific time series for the individual sub-catchments up to 48 hours (Einfalt et al., 2004).

In the basins where radar data is not available, measurements from a raingauge network can work as a fallback strategy for getting rainfall information. Also it can be used in case of emergency where there is a problem on radar or on the network where the radar data is retreived. For the support of the forecast module in SCOUT, an approach has been developed, linking raingauge data from the raingauge net-work, mesoscale numerical modelling, and extreme value statistics for a rainfall forecast over 48 h lead time. The raingauge based forecast uses the locally measured information through the first hour of lead time by applying a spatio-temporal rain-fall analysis. This analysis procedure takes advantage of the fact that rainfields arrive


at different instants in time at the different raingauge stations. Thus, arrival time at the stations implies a direction and a speed of movement for the rainfield. A least squares algorithm computes the most suitable movement (Einfalt et al., 2003).

The radar provides a spatial view of the rainfall over the catchment and is used for nowcasting purposes up to two/three hour, the raingauge network describes the actual state in the catchment and can be used for nowcasting purposes up to one hour, if no radar forecast is available. After approximately two/three hour, the reli-ability of radar or raingauge based forecasts tends to be more uncertain, and the numerical model results, provided by the ECMWF model used by DMI, are included for the following time period. In this way, rainfall measurements during the current NWP forecast period can be taken into account for the 48-hour forecast (Einfalt, 2002). The forecasting scheme is given in the Figure 2.

Contro lled b yScoutW atch

Ca tchm ent specif ictim e se rie s

In form ation sc reens

R ada r im ages(fo recasted o r m easu red )

R ada r da ta

R a ingauge data

N W P da ta

Ca tchm ent topography

Scou tFe tch(D ata Im port)

Scou tV iew(Rad ar da ta v iew er)

R ada r da ta ad ju stm en t

R adar da ta forecast

Fo recast re liab ility

48 h -fo recast

R ada r da taqua lity contro l

R a ingauge da taqua lity con tro l

Scou tP rocess

B itm aps

S C O U T fo rec astin g sc h e m eP rin te r

Figure 2 : SCOUT Forecasting Scheme

Source : Einfalt, 2002

MODEL RUNS AND DISCUSSION

The main input data for the hydrological forecasting is the rainfall. SCOUT, with combining the NWP and radar data, makes rainfall forecasts in the basins. Combin-ing the two systems and using it automatically has advantages and disadvantages. Mainly the rainfall forecasted data is used in the hydrological model automatically, so any error coming from the rainfall forecasting directly affects the accuracy of the flood forecasting. But the use of the radar data in the rainfall forecasting is very use-ful for nowcasting purposes. Radar images corrected with the ground measurements


can be used for the next 1-2 hour rainfall prediction (Einfalt et al., 2004). Especially Radar data is good for identifying convective cell storms.

In SCOUT, the radar data are corrected with the ground measurements which are retrieved automatically. Although it was planned to install a system that will work in real-time, in reality it can not be managed. The data collection system in DSI forecasting center works near real-time. For example for the DMI rainfall data, the data is collected and sent to the DSI every one hour and in some stations in every three hours depending the needs in DMI and the platform in the station. When the data comes to the DSI, it is stored in a database and exported to the models in 30 minutes intervals. The transmission of data for the use of SCOUT from field to the model takes about 1.5-2 hours. So SCOUT cannot use this data for forecasting next 1 hour, it can use this data for the next forecasting purpose for determining the direc-tion of the system only. The same situation sometimes occurs for the DSI data also. An example output view is shown in figure 3, where the image view belongs to Is-tanbul radar cloud echos of which the radar image is corrected by SCOUT with the ground observation station (green plus signs).

Figure 3 : SCOUT model output (from SCOUT view)


The model use NWP data which is produced by European Center for Medium-Range Weather Forecasting (ECMWF) for the forecast of next 2 to 48 hours. The reso-lution of the NWP data which is used for the rainfall forecasting system is very coarse (25km x 25 km) with corresponds to an area of 525 km2. The area of sub-basins is changing from 83 km2 to 1530 km2. When we think the spatial and temporal distri-bution of rainfall, for a small catchment the resolution of the NWP data is very coarse which means errors due to transformation. Also the NWP data that is used in DSI is produced once in a day, but for changing weather conditions it must be renewed in at least 6 hours time periods (Keskin, 2006).

The radar reflectivity factor Z (mm6

m-3

) is very important in radar measure-ment. Even if radar were to provide perfect measurements of the spatial and tempo-ral distributions of Z at ground level, the radar rainfall measurement problem would not be solved completely. First of all, the relationship between the radar reflectivity factor Z and the rain rate R is generally not a unique relationship. Secondly, even if it were unique, it would generally be unknown. This fundamental uncertainty in the Z–R relationship provides a lower limit to the overall uncertainty associated with radar rainfall estimation (Salek, 2004). The relation that is used in SCOUT for all of the four basins is the Z=200R1.6 which is called Marshall-Palmer Z–R relationship, (Marshall and Palmer, 1948; Marshall et al., 1955). But in reality this Z-R relationship does not hold and it is spatially variable and should not have unique values. There is an ongoing study in DMI for the determining the Z-R relationship. The adjustments in SCOUT can be done after the conclusion of the study. An example Z-R recom-mendation is given in Table 1 for stratiform events. As it can be seen it the relation changes with respect to event type.

Table 1. Z-R RECOMMENDATIONS RELATIONSHIP Optimum for: Marshall-Palmer (Z=200R1.6)

General stratiform precipitation

East-Cool Stratiform (Z=130R2.0)

Winter stratiform precipitation - east of continental divide

West-Cool Stratiform (Z=75R2.0)

Winter stratiform precipitation - west of continental divide

WSR-88D Convective (Z=300R1.4)

Summer deep convection

Rosenfeld Tropical (Z=250R1.2)

Tropical convective systems

Source : NOAA, 2006


The meteorological data is collected from the field station at hourly intervals. The role of the forecasters (Meteorological, hydrological) is very important in qual-ity-controlling of the rain gauge data. The system uses the raw data without any human interaction in the system. Real-time hourly rain gauge data are subject to all kinds of errors (Steiner et al., 1999). Although there is an algorithm for the correction of the raw rainfall data in the model, the model can not understand the condition in the field such as data that is coming from the field station where the station (DSI stations) is not heated in winter conditions. So the data that is coming from the field can be used after proper analysis and can not be used in the radar image corrections directly.

Another error comes from the possible wind and wetness errors. In a study which is done for the South East Alps, the authors found that on average for stations at lowland and valley sites the correction is around 5%, at elevated stations between 10 and 15%, and for a few extremely windy stations on tops of mountains even over 20%, 30% or more (Kolbezn and Pristov, 1998). So again measurement errors became very important in a rainfall forecasting system especially if the system is automated.

The possible error sources are listed above which effects SCOUT results directly. Some of the errors influence the accuracy of the system too much, so a study was done to see the possible effects of the error in the model. For this study 4 events were analyzed. These events were selected from different topographies in the four basins that are mentioned above. 3 sub-basins are selected from West Black Sea Basin named as Filyos 3, Filyos 1 and Melen 10 and 1 basin is selected from Susurluk basin named as Susurluk 4. The locations of the selected sub-basins is shown in figure 4 with brown color. The sub-basins are selected near to the radar locations to reduce the possible error and where there is enough raingauge station that represents the spatial distribution of the rainfall in the sub-basin. The events starting time is se-lected as one hour before enough reflectivities (>10 mm) in the radar image occurred. The forecasts that are made for every hour for the next 48 hours are recorded. The next hour forecast is also recorded when it is available. The recorded forecasts are compared with the observed rainfall of the nearby stations which located in around the sub-basins. In the result of this study, it was seen that SCOUT produces forecasts that underestimate the station rainfall where are the stations used for comparison for the basins. The average forecast of the four events and the observed rainfall are com-pared for the selected sub-basins for the next 1, 2 , 6, 12 ,24, 48 hours. The result of the study is shown in Table 1.


Figure 4 : Locations of the selected catchments for analysis

Table 1 . Ratio of SCOUT Forecast Rainfall to Observed Rainfall Forecast Time 1 Hr. 2 Hr. 6 Hr. 12 Hr. 24 Hr. 48 Hr. Ratio of Fore-

casted/Observed Rainfall 0.43 0.25 0.16 0.09 0.11 0.13

When Table 1 is analyzed, it can be seen that the ratios are not very satisfying

and it can be seen that SCOUT produces forecasts that underestimate the point sta-tion rainfall for the basins. Especially 1 hour forecast, which comes directly from correction of radar images, is beyond the expectations. There are possible reasons of which some of them are explained above. But other than those reasons which are discussed above, the possibility of an error in the radar image is also studied. A comparison of the radar image and the observed rainfall is done for the same events. The results of this comparison showed that the average of the ratio of radar image value to the observed rainfall is about 0.54 which is also very low. The studies for the adjustments of the radar image with the distrometer are an ongoing issue in DMI. The uncertainty from the radar images can be reduced after these calibration studies

The forecast for the next 6, 12, 24 and 48 hours is not very satisfying also. The decreasing trend in the ratio is an expected trend where the uncertainty increases with lead time increase. Most of the uncertainty comes from the uncertainty in NWP data. Also the rainfall stations measure rainfall for a specific point and the forecast is done for an area. The difference should not be neglected.

The errors in the rainfall forecasting system directly affect the result of the flood forecasting systems through atmospheric-hydrologic modeling. Recent develop-ments and possible errors in the forecasting models push the forecasters to a new


approach named as ensemble approach. Especially uncertainty in the initial condi-tions of the model affects the accuracy of the forecast. On some occasions small changes to the initial conditions can produce large differences in the forecast. How-ever, at other times there is less sensitivity, and errors in the initial conditions do not greatly affect the forecast (Richardson, 2002). The use of the ECMWF data forecast directly in the model can take us to undesired conditions in the flood management. Also to say where and when there will be rainfall becomes uncertain with uncer-tainty of the model. This uncertainty makes the ensemble important on all timescales. The ensemble approach aims to provide a probability distribution for the range of possible future states, consistent with known sensitivities in the system, such as un-certainty in the initial conditions and in the formulation of the forecast model. An ensemble provides information on the probability of future rainfall events. This al-lows each user to properly assess the risks associated with their own applications and to take action accordingly. While some users will be prepared to act when there is only a small chance of adverse rainfall, others will wait until the outcome is more certain before taking a decision (Richardson D., 2002). This system is very useful in risk management. Ensemble Approach is not used in SCOUT and in flood forecasting system. The flood management and forecasting systems were developed in recent years and with ensemble approach they can help to manage the risk in a good man-ner. So in future SCOUT should be prepared such that the model can use all of the probable events and make simulations for all of probable events. Of course the use the probability should be included in MIKEFLOODWATCH also. The projects which are completed by European Cooperation in the field of Scientific and technical re-search - Earth System Science and Environmental Management (COST-ESSEM) are some examples of these approach. The new COST action 731 also aims to address issues associated with the quality and uncertainty of meteorological observations from remote sensingis by considering their impacts on hydro-meteorological outputs from advanced forecasting systems (COST ,2006).

CONCLUSION

Models are good and very useful for flood management and they can be used as a decision support tool. Especially coupling of the atmospheric and hydrological models is very popular in flood forecasting in recent years. The automation of these models can be very useful but also can decrease the accuracy of the forecasts. The user of the model must know the possible error sources that come from the model, from the data and from other sources. The model results should not be used directly or if used it must be used under user guidance. Although automatic error reduction techniques are developing recently in the models, these techniques help the user very much but can not take the place of a analyzer at the moment.


According to the interviews done with DMI staff, DMI has started some studies for reduction in some of the errors. The Z-R relation can be identified after these studies also. Even a Z-R relation for each specific sub-basin can be very useful for improving the forecast accuracy for the short range. The density of the raingauges in the basins must be revised so that basin characteristics can be presented in the mod-els. The raingauge density could also help to estimate the uncertainty in the initial conditions of the model. If the uncertainty in the initial conditions decreases, the forecast uncertainty will decrease also. The radar network is one of the important issues that should be considered. DMI has 4 radars but new radars especially for identifying convective storms must be installed.

In hydrological forecasting, “when and where is the rainfall” is the most impor-tant input for the models. So this can be satisfied by increasing the resolution of the rainfall forecast models and setting proper initial conditions. The resolution of the rainfall forecast models is getting finer such as Mesoscale Modelling 5 (MM5) which is also used in Turkey. The resolution of the model is good for the use in the hydro-logical model, but not enough. The effect of downscaling on the model should not be neglected. In recent years, the atmospheric models are used with even 2.8 km resolu-tions without downscaling. In a proper flood forecasting system, all of the rainfall forecast models (local, global) are being used. The aim is to understand the possible risk and get ready before the possible event.

A comparison table 1 which is a result of a study has been given previously. The initial model results are not satisfying. But possible reasons have been explained also. This study is done for just 4 events and the comparison can be done after selecting sufficient number of events for judgment the goodness of the model .

SCOUT is used in many parts of the world especially in Germany. The accuracy in space and time of the available data in hand is not enough in specific part of Turkey where the model is applied and because of the high elevation difference in the basins and uncalibrated radar network; the needed performance from the model can not be taken. In Germany SCOUT is one of the selected atmospheric models for rainfall forecasting. After the completion of the studies in DMI, SCOUT needs cali-bration. If the network of the radars can be increased in the near future, this will also help SCOUT to get proper results.

The importance of the probability in the forecast should not be forgotten. In the past, In many flood event, Turkish Government managed the flood, but the main thing that should be managed is the risk. Risk management can be achieved by proper hydrological and meteorological models by including the probability in the models. The importance of the user must always be remembered and even automatic models should include some parts that the user can manage and change the input data. Also the user should understand all of the possible uncertainty that should come from the models and analyze the results with these uncertainties.


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TEFER, 2002: Turkey Emergency Flood and Earthquake Recovery Project, DSI.

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RAINFALL FORECASTING SYSTEM OF TEFER · PDF fileRAINFALL FORECASTING SYSTEM OF TEFER PROJECT ... quantitative rainfall prediction, ... the processing of the col