Sinclair and Pegram (2004a) provide the details of a prototype system set-up for the Mgeni and Mlazi catchments near Durban, South Africa. The implemen- tation provides several of the components of a flood forecasting system shown in figure 5.1 and these are described, along with suggested ways to "complete the puzzle"by providing the remaining components of the system.
Radar Satellite
StreamflowObservat ion s
Fig. 5.1: A schematic overview of the main components required for a successful flood forecasting system. The "best" option for each of the componentsis depen- danton factors suchas the size (hence responsetime) of the catchment, available data and skills within the team tasked with implementingthe system.
5.1 Rainfall estimation
The most important input to any flash flood forecasting system is precipitation. In Southern Africa the influence of snow can safely be ignored (except in a few select
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Fig.5.2:23 July 2004 Met-8 falsecolour image. A typical frontal system over the Western Cape Peninsula ,South Africa.
highareas) and the measurementof rainfall becomesthe most important factorin determining the input to the catchment. Asdescribed in detail in chapter 3,there are three measurementdevices available in South Africa which provide estimates of rainfall at suitable spatial and temporal resolut ion for flash flood forecasting . In theshort term any flood forecastin g system will be forced to rely on the e.
The rainfall estimates produced from rain gauges, weather radar and meteo- rologicalsatellite can be combined in an optimalway using conditionalmerging to produce the best spatialestimateof rainfallfor the catchment ofinterest. The resultingcombined rainfall estimatecan be fed into display system s for direct vi- sualization of instantaneousand accumulated rainfall (section 5.5.1). Figure 5.2 shows an example of the kind of useful visual information that may be provided in real time via remotesensing data. The estimatesare alsousedas input to catch-
in real-time using the most current available data streams.
The respon sibility for this will ultimately lie with SAWS, but a countrywide implementation at the short time-scales relevant to flash flood forecasting is still some way off. The mostpragmatic approach at thisjuncture will be site specific implementations of the appropriate merging algorithms. These specific imple- mentations will direct the rollout to a wider area and provide operational experi- ence with the algorithmsand data systems .
5.2 Catchment model
The nature of the catchment model is not dictated here as its implementation and effective useare (almost entirely)dependent on the expertise and data which are available when the forecasting system is implemented. In South Africa the nec- essary hydrological expertisedoes not exist in many of the municipal structures which are respon sible (by law, DisasterManagement Act 2002) for ensuring ap- propriate flood mitigation strategies, including floodforecasting and warningsys- tems. For useful real-time operation,the catchment models need to be informed by real-time streamflow observations(Section5.4) whichprovide a means of up- dating the models performance and improving forecasts . The concepts related to model updating are discu s ed in chapter 2.
Models should alsobesuitable for automation,or be available in a form which exposesasuitable Application Programmi ng Interface CAPI)forsystems develop- ment. Once again, the level ofsophistication in the system implementation will be highly dependent on a combination of funding and expertise.
Most of the larger Metros have by now completed the process of producing flood vulnerability assessments for the areas where they have responsibility. A key component in the vulnerability assesmentis the production of floodlines for multiple return periods. Insome casesthis may have entailed Hydrological mod- elling.In thesecasesit may be possibleto re-usethe existing model if the relevant
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datarelatedto its set-up and calibration can beretrievedfrom the consultants hired to producethefloodlines.
5.3 Rainfall forecasts
Nowcasting of rainfall fields holds considerabl e promise for improving the lead- time ofstreamfl ow forecasts. While accurate quantitative estim ates remain trou- blesome using radar and satellite information , properly forecasting the arrival times of runoff producing rainfall will likely provide some assistance to the Hy- drological forecastin g efforts. Short term rainfall nowcasting is dealt with in chapter 4, where two stochastic nowcasting models are investiga ted as a means forextendin g the inform at ion fro mcurrent best spatial rainfall field s into the fu- ture. Operation al implement ation of uch schemes in South Africa falls within the purview of SAWS. The nowcasting schemes will be well complemented by theoutputsofNWP models,which provideadvance warning ofheavy falls.
5.4 Real-time streamflow observations
Real-time streamfl ow observations, upstream of vulnerable areas provide many advantages for a flood forecasting system. Ifa Hydraulic model has been set up for the river reaches downstream of the gauging station it is possible to get an estimate of the flood levelsin real-t ime. This is po sible either through alookup system, or by running the Hydraulic model online. This is discussed in more detail insection 5.5.2.TheHydraulic modellin g effort becomesfar moreusefulif forecastsof streamflowcan be used as input. Feedback to improve Hydrological modeloutputsusingfilterin gtechniques (e.g. Kalman filters) is an important way to produce better forecas ts and thereforereal-tim e strea mflow obse rvations hold great importance.
The currentsituationis South Africa is that DWAF has instrumented a large number of flow gauging structures with telemeterin g flow gauges . The data are recorded at short time intervals (12 minutes in most cases)and transmitted to a