HYDROLOGICAL PROCESSESHydrol. Process. 21, 1519–1521 (2007)Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.6726

On model integration in operational flood forecasting

Micha Werner1* andDoug Whitfield2

1 Delft Hydraulics, Rotterdamseweg185, 2629 HD, Delft, The Netherlands2 Environment Agency, NewtownIndustrial Estate, Northway Lane,Tewkesbury, GL20 8JG, UK

*Correspondence to:Micha Werner, Delft Hydraulics,Rotterdamseweg 185, 2629 HD, Delft,The Netherlands.E-mail: micha.werner@wldelft.nl

Received 20 February 2007Accepted 22 February 2007

IntroductionIn many of the papers demonstrating the suitability of a particularmodelling technique for flood forecasting, little attention is given to howthe model would actually be integrated within an operational forecastingsystem. A review of these papers shows that a great variety of techniqueshave been proposed for use in flood forecasting, ranging from what couldperhaps be considered the traditional rainfall runoff and subsequentrouting approach (e.g. Moore et al., 1990), through data driven methodssuch as the Data-Based Mechanistic models proposed by Young (2003)and Lees (2000), to ‘artificial’ intelligence approaches such as geneticalgorithms and neural networks (Liong et al., 1995; Cameron et al.,2002). While many of the latter techniques have been demonstrated tohave good potential in establishing reliable forecasts, their uptake inoperational systems has, to date, been limited. Often, this is argued tobe because of the inherent mistrust forecasters have in what they seeas essentially black box techniques. This, clearly, is an important aspectas forecasters are generally wary of using techniques that are poorlyunderstood. There may, however, be more to explain the gap betweenresearch into forecasting techniques and their uptake in practice.

Hydrological Models in the Operational Forecasting ArenaTo explore the gap further, it is important to consider the position ofhydrological models within the flood forecasting and warning process.Several authors (Parker and Fordham, 1996; Haggett, 1998) describethe flood forecasting and warning process as a series of four steps:(i) detection, (ii) forecasting, (iii) warning, and (iv) response. In thesedescriptions it is pointed out that although flood forecasting is a part ofthe chain, it holds only a supporting role due to its ability to increaselead times with which warnings can be issued when compared to basingthe warning on detection alone. The importance of this supporting roledepends, from the hydrological perspective, very much on the positionof the forecasting point within the catchment. The utility of forecastingdepends largely on the relationship between the desired lead time atthat point and the lag time of the hydrological response (Lettenmaierand Wood, 1993; Werner et al., 2005).

In an evaluation of the maturity of flood forecasting, warning andresponse systems across Europe, Parker and Fordham (1996) discussthe position of models from an organizational point of view. Manyof the criteria proposed considered the dominance of forecasting ver-sus warning, and where the emphasis was predominantly on forecast-ing, the maturity of the system was deemed low. Where the empha-sis was on institutional aspects of the issuing of warnings, and howresponse to these is organised, the system was considered well devel-oped.

Traditional Integration of Hydrological ModelsDespite the relatively modest position hydrological models hold fromthe point of view of the overall flood forecasting, warning and responseprocess, most forecasting systems have taken a model-centric approach

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in their development. This means that the forecast-ing systems have been built around the models used.Often these models are ‘pet’ models which have beendeveloped ‘in-house’. An example of this is the erst-while Midlands Region flood forecasting system (Dob-son and Davies, 1990), in the Environment Agencyin England & Wales, which was developed aroundthe rainfall runoff and the routing model used. Ini-tially, this model-centric approach makes good sense;the forecasting system is a calibrated model enhancedwith the minimum requirements to transform it intoan operational forecasting system. However, there aresome distinct disadvantages. The most important ofthese is the inflexibility to adaptation or change ofmodelling concept. Not only may change be a tech-nical challenge as the model lies at the heart of thesystem, but more importantly it is an organisationalone. Flood forecasting systems provide a service 24/7,and as a consequence, numerous people are involvedin operating these systems in real time. Changewould, therefore, require both a technical as well as atraining process and could involve significant effort.Additionally, managers of forecasting systems take anaturally conservative approach to change. In partic-ular, change to what is in effect a closed system canbe uncertain, and may require adopting science thathas not been proven operationally. Even where man-agers are amenable to change, it may then be difficultto justify the investment required when benefits areuncertain and/or not quantified. Research projects,on the other hand, suffer from this conservatism andstop short of operational implementation. As a conse-quence, introduction of emerging techniques into theoperational arena is very slow.

Openness in Integration of HydrologicalModels

The alternative to the model-centric approach is touncouple the running of the model, as the forecasterssee it in their daily operational use, from the actualtechnical integration of the model. In this approach,the forecasters in effect interact with what is referredto as a flood forecasting shell. This provides genericdata handling support, while allowing hydrologicalmodels to be ‘plugged-in’ as the need arises. The con-cept of such a forecasting shell has been recognizedearly on, and has been attempted in systems such asthe Regional Flood Forecasting System (RFFS) untilrecently used by the North East Region of the Envi-ronment Agency in England & Wales (Moore et al.,1990), and the National Weather Service RegionalForecasting System (NWSRFS) used across the 13regional forecasting centres of the National WeatherService in the United States (National Weather Ser-vice, 2006). Both these examples were, however, devel-oped in the 1980s and 1990s with the technology of the

day, which hampered a true open integration of mod-els. With the advent of more modern computer tech-nology, as well as the advances in software integra-tion standards, true open systems can be established,examples being the DELFT-FEWS flood forecastingsystem (Werner et al., 2004) and the MIKE FLOOD-WATCH system (Van Kalken et al., 2005). The first ofthese two exemplifies the open approach, having beenimplemented in some 18 operational forecasting sys-tems with over 30 different hydrological and hydraulicmodels from a range of model developers having beenintegrated at the time of writing.

Although this discussion of openness would appearto be more of technological than of hydrological orforecasting significance, adopting the approach hasseveral advantages, both for the practitioner and theresearcher.

Advantages in Openness—ResearchPerspectiveThe open approach equally offers advantages forresearchers. The ability to introduce advances in thescience of flood forecasting models, and method intoactual operation, can provide much required insightinto where science should be targeted. Operatingauthorities are also more likely to fund research workwhere obstacles to operational application are mini-mized, thus boosting research opportunities. Addition-ally, different models and methods can be integratedand objectively compared within the same environ-ment as described above. This can result in a moreparsimonious and scientifically objective view on theuse of models and methods, as opposed to necessaryapplication of a singular concept to all domains.

Advantages in Openness–ManagementPerspectiveFrom the perspective of the day-to-day managementof the flood forecasting system, adopting the openapproach has the advantage of uncoupling the run-ning of the forecasting system from the science usedto deliver the forecast. This means that the modelsthat encapsulate that science can be more adaptableto advances from the research community. Althoughusers will need to be made familiar with the con-sequences of change, the need for extensive retrain-ing of the forecasters or restructuring of the forecastand warning process is minimized when compared toreplacing the entire system. An additional advantageof an open approach is that due to models being inter-changeable, problems such as vendor locking can beavoided.

The concept of an open shell approach has beenadopted within the National Flood Forecasting Systemnow in use in the eight regional forecasting centres ofthe Environment Agency in England & Wales (Whit-field, 2005). This utilizes the DELFT-FEWS flood

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INVITED COMMENTARY

forecasting shell and replaces the legacy of differ-ent forecasting systems developed by the predecessorwater and river authorities (Haywood and Hatton,2001). In the first step, the forecasting methods usedin the legacy systems were integrated into the sin-gle forecasting shell. With this complete, the Envi-ronment Agency now has the freedom to select datasources and model types based on objective compar-isons using the extensive performance assessment toolsavailable within the system, without the restrictionspreviously posed due to compatibility issues. Addition-ally, the use of the same flood forecasting frameworkhas strongly encouraged knowledge transfer betweendifferent forecasting centres, allowed the developmentof standardized training, and provided the potentialto share staff during large events.

SummaryFrom the discussion above, it is clear that flood fore-casting within the context of an operational floodforecasting and warning system is very different toa scientific modelling exercise. The forecasting mod-els and methods incorporated into the system are atool in support of the warning, and not a means tothemselves. The close integration of these forecast-ing models and methods within the forecasting systemhas, however, led to quite a conservative approachin introducing advances from research into the oper-ational arena. Through adopting an open approach,this gap between research and practice can in part beclosed, as the continuity of the forecasting service canbe maintained independent of the forecasting mod-els and method used, and how these change due toscientific advance.

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