2011 HapuarachchiWangPagano HydrologicalProcesses FlashFloodReview

HYDROLOGICAL PROCESSESHydrol. Process. 25, 27712784 (2011)Published online 15 March 2011 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.8040

A review of advances in flash flood forecastingH. A. P. Hapuarachchi,* Q. J. Wang and T. C. Pagano

CSIRO Land & Water, Graham Road, Highett, Victoria 3190, Australia

Abstract:Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damageto infrastructure and the environment. Research into the use of new modelling techniques and data types in flash floodforecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged fromthis research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotelysensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Overthe past decade flash flood forecast lead-time has expanded up to six hours due to improved rainfall forecasts. However thelargest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physicallybased hydrological models have been developed and used for flash flood forecasting and they have been found to give moreplausible results when compared with the results of conceptual, statistical, and neural network models. Among the threemethods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance)is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existingmodel is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash floodingis increasing due to increasing urbanisation. Copyright 2011 John Wiley & Sons, Ltd.

KEY WORDS flash floods; advances; forecasts; remote sensing; hydrological models; quantitative precipitation forecasts

Received 10 February 2010; Accepted 28 January 2011

INTRODUCTIONFlash floods can be caused by excessive rainfall or by thesudden release of water due to a dam breach or glacierlake outburst. However, as sudden-release events areuncommon and occur under extraordinary circumstancesthat require considerations outside the scope of thisreview, only rainfall-induced flash floods are consideredhere.

In general, flash floods are characterized by their rapidonset (within six hours of rainfall), which leaves verylimited opportunity for effective response. They are oftenaccompanied by other phenomena such as landslides andmudflows, and can cause bridge collapses, damage tobuildings and businesses, psychological harm to peopleand, in exceptional circumstances, fatalities. Projectionsof climate change indicate an increase in the intensityof rainfall in some parts of the world, which may leadto more severe flash flooding. In addition, changingdemography (i.e. increased urbanisation) will result inlarger segments of the population being prone to flashflooding.

In order to identify the occurrence of flash flooding,estimate the risk and implement effective mitigation mea-sures, high spatial resolution flash flood forecasting withuseful lead time is necessary. The starting point in effec-tive flash flood forecasting is accurate rainfall forecastswith useful lead times. The next step is the representation

* Correspondence to: H. A. P. Hapuarachchi, CSIRO Land & Water,Graham Road, Highett, Victoria 3190, Australia.E-mail: [email protected]

of the hydrologic and hydraulic processes within a catch-ment that determine how rainfall-runoff accumulates. Thestatic physical properties of a catchment (e.g. permeabil-ity, relief, fraction of impervious areas, land use, andsoil types) and its time-varying states (e.g. soil moisture,ground water deficit) will modulate the flash flood poten-tial of heavy rainfall (Davis, 2001). Usually, flash floodsoccur in streams and small catchments with a drainagearea of a few hundred square kilometres (Kelsch, 2001),with Davis (1998) suggesting that for the United States(US), this cut-off threshold is about 260 km2 (100 mi2).Such catchments often respond rapidly to intense rain-fall rates because of steep slopes, saturated soils, fire-induced alterations to natural drainage, and impermeablesurfaces due to soil crusting or man-made structures (i.e.pavement). It becomes necessary, therefore, to exam-ine the rainfall-to-runoff conversion process in headwatercatchments. However, these small catchments are oftenpoorly gauged or ungauged, which presents additionalchallenges in hydrological modelling.

Over the past decade, there have been increasingcalls to improve flash flood forecasts in many partsof the world, including the US, the European Union,and Australia (Penning-Rowsell et al., 2000; Handmer,2001). In response, advances in flash flood forecast-ing have been achieved through a range of develop-ments in observing capabilities and modelling techniques.This review first discusses these developments, empha-sising recent improvements in quantitative precipitationestimates (QPEs) and quantitative precipitation forecasts(QPFs), remotely sensed data products, and flow forecast

Copyright 2011 John Wiley & Sons, Ltd.

2772 H. A. P. HAPUARACHCHI, Q. J. WANG AND T. C. PAGANO

models, and their use for flash flood forecasting. This isfollowed by a focus on uncertainty estimates and urbanflash floods and the article is finished with conclusions.

INPUT DATA

Quantitative precipitation estimates (QPEs)The quality of any flood forecast depends to a high

degree on the quality of the rainfall input. During thepast decade, observation networks (e.g. radar and satel-lite) have been expanded, and new techniques have beendeveloped for deriving rainfall from multi-sensor obser-vations. Extensive research has gone into assimilatingradar data for producing QPEs. In general, radar pro-vides useful information on the spatial distribution ofthe precipitation field, but QPEs directly derived fromradar reflectivity measurements are subject to errors anduncertainties (e.g. Collier, 1996; German and Joss, 2003).New techniques have been developed for real-time cor-rection of systematic biases in radar precipitation fields,by adjusting radar precipitation to rain gauge measure-ments (Ahnert et al., 1986; Smith and Krajewski, 1991;Seo, 1998; Anagnostou and Krajewski, 1999; Sinclair andPegram, 2005; Mazzetti and Todini, 2009).

With the availability of high-resolution remotelysensed data, advanced algorithms for retrieving rain-fall from satellite-based microwave and infrared obser-vations have been developed (Sorooshian et al., 2000;Huffman et al., 2002; Kidd et al., 2003; Joyce et al.,2004; Kubota et al., 2007). Consequently, a number ofgeneral-use satellite-based precipitation products haveemerged (e.g. CMORPH, 3B42RT, PERSIANN, GSMaP)that allow much improved temporal and spatial resolu-tions and reduced latency. Typically, satellite-based pre-cipitation products provide QPEs of the global area of60 N60 S at 01 025 spatial resolution at 13 hintervals. Although the accuracy of QPEs varies, theyare generally useful for hydrometeorological modelling,particularly in poorly gauged catchments. Moreover,advanced techniques have been developed to improvethe accuracy of QPEs by blending multiple sources ofinformation (radar, satellite and gauged data) (Seo andBreidenbach, 2002; Gjertsen et al., 2004), thus expandingthe limits of hydrological modelling.

Burton and OConnell (2002) used radar observationfields to continuously update a function to convert frominfrared (IR) satellite observations of cloud-top temper-atures to precipitation rates. The technique was adaptedfrom the raw histogram-matching technique developed byTurk et al. (2000) and described by Grose et al. (2002).Instead of using microwave observations of precipitationfrom satellite to determine fixed relationships betweenIR satellite observations and precipitation rates, Burtonand OConnell (2002) used ground-based precipitationradar datasets to estimate an instantaneous relationship.Overall, QPEs produced by blending multiple sourcesof information have high temporal and spatial accuracy,thus, this approach deserves future research.

Quantitative precipitation forecasts (QPFs)Accuracy and lead time in rainfall prediction are the

most important components of flash flood forecasting,and recent developments enable the production of high-resolution QPFs with 16 h lead times. These techniquesinclude linear regression (Antolik, 2000), quantile regres-sion (Bremnes, 2004; Friederichs and Hense, 2007),logistic regression (Applequist et al., 2002; Hamill et al.,2004), hierarchical models based on prior climatic distri-butions (Krzysztofowicz and Maranzano, 2006), hybridapproaches that statistically combine radar and numericalweather prediction (NWP) model outputs (Golding, 2000;Ganguly and Bras, 2003; Sokol, 2006), artificial neu-ral network applications (Hsu et al., 1997; Kuligowskiand Barros, 2001; Ramirez et al., 2005), and statisticalmethods based on Bayesian techniques (Sloughter et al.,2007).

The spatial (

A REVIEW OF ADVANCES IN FLASH FLOOD FORECASTING 2773

increased the potential for more accuracy and lead timein flash flood forecasting.

Remotely sensed catchment dataOver the years, developments in observational tech-

niques have led to improvements in high-quality remotelysensed data for better defining catchment characteristics.Different sensors have been used to gather informationabout properties at the surface and in shallow layers ofthe Earth. New techniques have been developed to utilizesuch remotely sensed information for deriving physi-cal characteristics of a catchment, such as near-surfacesoil moisture, land surface temperature, snow cover andmelt, topographical characteristics, land use, and vegeta-tion cover. This data is useful for hydrometeorologicalmodelling, particularly for poorly gauged and ungaugedcatchments.

Soil moisture content is an important variable inhydrological modelling, but it is hard to measure on alarge scale via in situ measurements. However, advancesin remote sensing techniques using microwave satel-lite observations have enabled the estimation of surface(10 cm depth) soil moisture easily (Owe et al., 2001;Meesters et al., 2005). A number of studies have demon-strated fairly good agreement between soil moisturederived from microwave-based models and field observa-tions (Owe et al., 1992; Drusch et al., 2001; Jackson andHsu, 2001) as well between microwave-based models anddistributed hydrological modelling approaches (Vischelet al., 2008). More recently, a historical climatology ofcontinuous satellite-derived global land surface soil mois-ture data has been developed (Owe et al., 2008), whichconsists of surface soil moisture retrievals derived fromall available historical and active satellite microwave sen-sors from 1978 to the present. The accuracy of the derivedsoil moisture data was tested using the observed datafrom the Global Soil Moisture Data Bank (Robock et al.,2000). The comparisons between satellite and grounddata appear quite good, considering the considerable dif-ferences in spatial coverage and the vertical samplingcharacteristics between the two datasets.

A variety of models and methods have recentlybeen developed that are capable of processing remotelysensed data for estimating evapotranspiration (Basti-aanssen et al., 1998; Richard et al., 2005; Chavez et al.,2009). The surface temperature is derived from thermalinfrared and passive microwave data, and utilized to esti-mate actual evapotranspiration (Coll and Caselles, 1997).However, there is no clear consensus at present as to theaccuracy of evapotranspiration estimates derived usingremotely sensed data, and a proper validation of themethods is required before the data could be accepted.Satellite observations have also been used to monitor theareal extent of snow cover and glacier inventories. Steven(2006) examined the feasibility of the Ensemble KalmanFiltering (EnKF) data assimilation approach for snow-pack characterisation using multi-spectral remote sensingobservations. He developed a framework for estimating

the snow water equivalent using remotely sensed data.The primary findings in this study are that the assimila-tion approach is capable of providing estimates of SWEin cases with deep snow, wet snow, and light to moderatevegetation cover. These are three of the primary con-founding problems with traditional snow retrieval algo-rithms. Moreover, satellite images are being increasinglyused for deriving flood inundation extent, and for esti-mating lake and reservoir volume.

All these data are useful for hydrological modellingand consequently for flash flood forecasting, particu-larly in data-poor regions. However, it should be notedthat current remote sensing capabilities cannot replaceground-based methods for providing high-quality profiledata at a given point. Also the operational delay dueto processing time of satellite-based observations (>2 h)is not favourable for real-time forecasting. The presentadvantage of remote sensing is in mapping conditions atthe large scale (e.g. regional, continental and even global)and on a repetitive basis to detect possible changes.

FLOW FORECASTING MODELS

Data-driven modelsData-driven (e.g. neural network, statistical) models

use statistical relationships derived from rainfall andriver flow data to generate flow forecasts. Generally,these models perform better than others in situationswhere the underlying interactions and dependencies ofphysical processes are only partially understood or areunknown. These models are easy to set up and are ableto produce acceptable results with minimum input data(rainfall and discharge). Owing to this simplicity, data-driven modelsparticularly neural networks (NN)havebeen widely used for flow forecasting (e.g. Thirumalaiahand Deo, 1998; Jain and Srinivasulu, 2004; Piotrowskiet al., 2006; Sahoo et al., 2006).

Using four types of NN models (one-hidden-layerback-propagation NN, two-hidden-layer back-pro-pagation NN, radial basis function network, and FuzzyInference System Network), Sahoo and Ray (2006)demonstrated that an optimized two-hidden-layer back-propagation NN model outperforms the other three interms of prediction performance efficiency (coefficientof correlation, root mean square error and absolute meanerror). Recently developed NN models incorporate mul-tiple sources of information to produce accurate flowforecasts with useful lead times. Chiang et al. (2007)introduced a recurrent NN model that merges multi-ple rainfall sources (gauged and satellite-based data) forflow forecasting. They found that satellite-derived pre-cipitation (PERSIANN) (Hong et al., 2004) had limitedcontribution (5%) to the merging procedure in their par-ticular study. This implies the importance of gauged data,although the contribution from gauged precipitation tothe merging procedure depends on the number of gaugesand the quality of data. Nevertheless, the study exam-ined a potential method for extending satellite-derived

Copyright 2011 John Wiley & Sons, Ltd. Hydrol. Process. 25, 27712784 (2011)


precipitation to those catchments where gauge observa-tions are limited. A more complicated forecast methodthat uses NWP model forecasts, radiosonde data (direc-tional wind and pressure levels) and rain gauge data wasproposed by Kim and Barros (2001). The special fea-ture of this method is that inputs include satellite-derivedcharacteristics of storm systems such as tropical cyclones,meso-scale convective complex systems and convectivecloud clusters. Forecasts are primarily derived using aparticular NN model, which is selected based on the typeof convective weather system. Although it is claimed thatthis method could produce accurate flow forecasts withlead times up to 24 h, it requires experienced manualintervention in operational use. Categorising convectiveweather systems is labour intensive and can be subjective.

In general, the main disadvantage of using data-drivenmodels for flash flood forecasting is that they requirelong-term data records to train or calibrate. Furthermore,the derived relationships are site specific. Therefore, data-driven models are hard to apply to general flash floodforecasting, since flash floods usually occur in smallcatchments (


to poorly gauged catchments. Recent work by Mooreet al. (2006) suggests that for extreme events, the dis-tributed models may give more plausible results than thelumped models. However, physically based distributedhydrological models are computationally inefficient andneed high quality catchment data (e.g. DEM, land-useand soil maps, and soil characteristics). Thus, it remainsunclear whether the use of more complex models for flashflood forecasting has much advantage over the use ofsimple models.

The suitability of a model to a particular applica-tion depends on factors such as data availability, pro-cess complexity, temporal and spatial scales and therequired outputs of the application (e.g. forecast vari-ables such as river flow, soil moisture, and inundation).Most hydrological models perform well in humid zones,but not in arid and semi-arid zones due to highly com-plex hydro-meteorological processes. Generally, greaterspatial and temporal variations of rainfall occur in thearid zones than occur in more humid zones. Pilgrimet al. (1988) describes distinctive features of arid zonehydrology which may be different from humid zones. Hestates that in arid zones, there is a mix of hydrologicalprocesses; some humid zone processes (e.g. baseflow)are essentially absent while channel transmission lossesare critically important. Usually arid and semi-arid zonesare highly dynamic, and a prolonged wet or dry sequencemay change the character of the hydrology (and hencecalibrated model parameters). The vegetation cover issparse and the spatial extent changes largely with the sea-son in arid and semi-arid zones. Lack of organic matter inarid and semi-arid zones probably has a significant effecton many processes including interception, infiltration,evapotranspiration, and runoff. In these zones, surfacesoil largely is the first point of contact by rainfall. Thus,surface soil properties likely play a major role in runoffproduction, especially as soil saturation occurs relativelyrarely compared to humid zones. Instead, infiltration-excess runoff is a more dominant process during heavyrainfall events. Hydrophobic soils, armouring, dispersivesoils, cracks, scald, or claypan areas, sand dunes and baresurface rock are some of the features which are influen-tial in arid zone runoff production (Pilgrim et al., 1988).Most of the above mentioned physical catchment charac-teristics can be considered more realistically using physi-cally based distributed hydrological models than lumpedor statistical models; thus, physically based hydrologi-cal models are more suitable for arid and semi-arid zonehydrological modelling (El-Hames and Richards, 1998).Furthermore, a comprehensive method for flow routingis needed for arid and semi-arid zones, such as those thatemploy a detailed solution of the St Venant equations,coupled with an infiltration model to deal with transmis-sion losses.

CRITERIA FOR DECIDING FLASH FLOODOCCURANCE

Estimating flooding flowFlooding is generally associated with damaging con-

ditions due to the rising and overflowing of a body ofwater onto normally dry land, and it is often site specificand difficult to quantify. Floods can also be associatedwith exceedance of stormwater capacity in the urbanenvironment. However, urban flooding is extremely com-plex to model due to interactions with various man-madeinfrastructures such as buildings, roads, culverts, chan-nels, tunnels, and underground structures. Therefore, asimple approach often used to define thresholds for flood-ing based on overbank-flow is described herein.

The bank-full flow is assumed as the starting pointof flooding flow (Qp). However, more than bank-fullflow is usually needed to cause flood damage. Assuminga wide rectangular channel cross-section, the bank-fullflow Qbf is usually computed from channel geometryand roughness characteristics using Mannings steady,uniform flow resistance formula (Chow et al., 1988):

Qp D Qbf D bh5/3S05c /n 1where b is the channel width (m) at bank-full, h is thehydraulic depth at bank-full (m), Sc is the local channelslope (dimensionless), and n is the Mannings rough-ness coefficient. The computation of bank-full dischargerequires information on channel cross-sectional parame-ters, but measured data for these parameters is often notavailable for small streams. However, some of the param-eters can be derived using other available data such asdigital elevation maps (DEMs), land-use maps and soilcharacteristics. For example, Sc can be derived from aDEM, and n can be assumed based on river bank (landuse) and river bed (soil) characteristics. Regional relation-ships between the stream cross-sectional parameters andother catchment and stream characteristics (e.g. catch-ment area, stream length) can be used for identifying band h.

Carpenter et al. (1999) derived parameters b and hthrough regional regression relationships using a power-law relationship with the predictor variables of A (km2),Sc, stream length L (m), and average annual rainfall P(mm). They found that both b and h are related to A inthe form:

b D 1A1 2h D 2A2 3

where k and k (k D 1, 2) are coefficients. In tworegions of the US (Iowa and Oklahoma) the relationshipbetween b and A showed a higher correlation (8291%)than the relationship between h and A (4050%).

An alternative statistically based definition of floodingflow is the flow of a certain return period, which considersthe risk and uncertainty associated with flooding. Hender-son (1966) showed that there is a good statistical relation-ship between the bank-full flow and a flow with a return



b = a1Ab1

h = a2Ab2

Derive regional regressionequations for b and h

Estimate bank-full flow ata particular location

Derive regional regressionequations for Q2, Q3,

Q4,Qn

Compute Q2, Q3,Q4,Qnat a particular location

Long-term rainfall data andadequate discharge dataavailable to calibrate a

hydrological model

Generate long-term river flowdata using a hydrological

model

Compare with available flood flow data and local information

Qbf = bh5/3 S 0.5 / n

Q2 = a3Ab3Scl

c

River cross-sectionalinformation available atbank-full condition at

some locations

Adequate observeddischarge data available

at some locations toderiveQ2, Q3, Q4,Qn

Figure 1. Different methods to estimate bank-full flow at a data-poor location, depending on available data

period of between one and two years. Many researchershave since used this range to assume bank-full flow (e.g.see discussion in Riggs, 1990). For instance, two-yearreturn period flow (Q2) can be calculated using histor-ical observed flow records. In the absence of observedflow data, a hydrological model-simulated flow seriescan be used (Reed et al., 2007). Carpenter et al. (1999)conducted a regression analysis (similar to the above) toderive Q2 based on regional data (A, Sc and L). Theyfound that Q2 is related to A and Sc with 85% correlationcoefficient in the form:

Q2 D 3A3Sc 4where 3, 3 and are coefficients. However, deriv-ing bank-full flow at each grid-cell of a catchment ischallenging, since a river cross-section may vary greatlyover short distances and may also change in time withthe occurrence of floods. Therefore, assuming Q2 as thebank-full flow at every grid-cell is questionable. Figure 1presents a flow chart for estimating bank-full flow at adata-poor location of a catchment, depending on availabledata.

Flow comparison methodGiven a forecast flow at a particular point in a catch-

ment (such as the catchment outlet), there needs tobe a criterion for deciding whether flooding should beexpected. The simplest approach is to compare the mod-elled flow value with the observed flooding threshold.An alternative statistical-distributed modelling approachis to compare the modelled flow value with the long-termsimulated record (i.e. climatology) of the model (Reedet al., 2007).

Figure 2 shows the flow chart of the statistical-distributed modelling approach for flash floodforecasting. In the historical mode (Figure 2), a grid-based distributed hydrological model (calibrated) is runusing archived multisensory QPE data to generate a long

term flow series at each grid cell in a particular catch-ment. Basing on the statistical characteristics derivedfrom the long-term simulated flow data, a unique flowfrequency curve is developed for each cell using a flowfrequency analysis method.

In the real-time mode, for any forecast period (e.g. 12,24 h), the model produces many grids of flow forecasts(e.g. hourly) of the catchment. Using these flow grids,a grid containing peak flow forecast at each cell isprepared for a given forecast time. The grid of peakflow forecasts is then converted to a flow frequency gridbased on the statistical characteristics of the frequencycurves derived in the historical mode. Finally, the flowfrequency forecasts at each cell is compared with aparticular threshold flow frequency value, determinedfrom local characteristics, to assess the level of severityof the event and then to identify flash flood risk areaswhere warnings need be issued. The rationale for this isthat model simulations can be biased and a comparison

FlowFrequency

curves

Frequencythresholds

Local/regionalknowledge

Simulatedhistorical

peaks

Grid-basedhydrological

model

ArchivedQPE

Historical

Real-timeQPE/QPF

Maxforecastpeaks

StatisticalPost-processor

Forecastfrequencies

Initial hydromodel states

Grid-basedhydrological

model

Real-time

Compare

Figure 2. Flow chart of the statistical-distributed modelling approach forflash flood forecasting (from Reed et al., 2007)



Figure 3. Simplified representation of the flash flood guidance system and the main sources of uncertainty considered (Ntelekos et al., 2006). Grayfonts mark the variables for which uncertainty was quantified and thick dark gray arrows mark the propagation of uncertainty throughout the system.WSR-88D is radar data of the Next-Generation Weather Radar (NEXRAD) network. SAC-SMA is the Sacramento soil moisture accounting model.

Thresh-R is threshold runoff

with bank-full flow in exceedance frequency diminishesthe effect of the bias. However, this approach requireslong historical data to run simulations to establish boththe simulated and observed flow frequency distributions.

Rainfall comparison methodInstead of comparing the flow forecast with flooding

flow, one can compare the rainfall required over a specificarea to produce flooding flow at its outlet with the rainfallforecast. This method is commonly known as flash floodguidance (FFG), and is defined as the volume of rainfallof a given duration and distributed uniformly over a smallcatchment (


to be uniformly distributed over an area above the flowpoint to produce bank-full flow.

Martina et al. (2006) introduced a flood forecastingmethod for given river sections, based on the directcomparison of QPF with critical rainfall threshold values,without the need for an online real-time forecastingsystem. The estimation of critical rainfall thresholdsrequires derivation of a joint probability function ofrainfall totals over the contributing area and water stages(or discharges) at a relevant river section. To derivethe probability function, a long series (10 000 years)of hourly average rainfall data is generated using astochastic model, and used to force a hydrological modelto generate soil moisture and water stage data. Soilis categorized into three antecedent moisture classes(AMCs) as dry, moderately saturated, and wet (basedon 033 and 066 percentiles), and the correspondingrainfall and water stage is sorted into the three AMCsfor determining joint probability density functions. Themost appropriate rainfall threshold is determined basedon the minimum expected cost under uncertainty using aBayesian utility function. This approach is simple and canbe used by non-technical operators. Its main limitation isthat rainfall forecast uncertainty is not considered.

Flash flood susceptibility assessment procedureWhile more complex hydrometeorological models are

used for quantifying flash floods, a simple flash floodsusceptibility assessment can be considered as a usefulfirst step in determining the contributing factors to theflash flood vulnerability of a catchment using limiteddata. It is particularly useful in an urban context. Collierand Fox (2003) proposed a simple decision-supportprocedure for assessing the vulnerability of catchments toflooding due to extreme rainfall. The procedure considersthe following:

1. The likelihood that a heavy rain event will becomestationary and protracted over the area

2. The availability of significant precipitable water in thelower atmosphere

3. The likelihood that heavy cells embedded in the mainarea will move in parallel to the main watercourse, inwhich case, the flood peak is likely to be enhanced

4. The steepness of the catchment leading to a short timeto peak

5. The soil moisture condition of the catchment6. The likelihood of unimpeded flow to the main water-

course: whether or not significant vegetation and chan-nel debris are likely to be problematic, and whether ornot there are constrictions in the channel that will facil-itate a build-up of water for later release as a wave(surge)

7. Snowmelt

The variables listed above include both hydrometeoro-logical and catchment morphological characteristics. Inan assessment, these variables are identified and each

variable is assessed on a scale of 14, where a scoreof 4 strongly favours flood development. The individualscores of all variables are summed, and the flood categoryis determined based on the overall score. These variablesneed to be continuously updated in real time. Collier andFox (2003) found the procedure to be a useful indicatorof flash floods in tests on a number of major flood events.

One of the challenges with this procedure is assessingwhether a precipitation system will become stationary.In practice, this can be accomplished by integrating real-time radar reflectivity data, which indicates the generaldirection of cell tracks and cell splitting throughout anevent.

Dale et al. (2004) found that the main weakness of theprocedure is that all variables have equal weights in thescore, yet naturally some variables are more influentialthan others in flash flood formation. For example, therainfall depth over a relatively short period on a steepcatchment may result in an extreme event. However,low scores for factors such as urban extent, snow depthand direction of motion result in a lowering of thefinal score to a value considered as major rather thanextreme. Therefore, appropriate weights to differentvariables need to be assigned carefully through theexperience gained by many trials. The procedure could beimproved significantly by splitting the fixed, antecedentand real-time variables, and overlaying the risk levels(Dale et al., 2004).

UNCERTAINTY ESTIMATES

Flash flood forecasts are necessarily uncertain due toinput data errors, and modelling errors. Understandingof the uncertainty from all these sources is particularlyimportant for decision making in flood warning.

Historically, probability theory has been the primarytool for representing uncertainty in mathematical models(Ross, 1995). With the introduction of fuzzy set the-ory (Zadeh, 1965) uncertainty could be represented usingnon-probabilistic approaches. Zadeh (1978) developed abroader framework for uncertainty representation calledpossibility theory, which is also known as a fuzzy mea-sure. Basing on these theories, various methodologieshave been developed for the treatment of uncertaintyin flood forecasting, such as the Generalised LikelihoodUncertainty Estimation (GLUE) by Beven and Binley(1992), the Bayesian Forecasting System by Krzyszto-fowicz (1999) and a methodology based on fuzzy exten-sion principle by Maskey et al. (2004).

Krzysztofowicz (2002) developed a Bayesian systemfor probabilistic river stage forecasting. In this system,short-term probabilistic river stage forecasts were pro-duced using probabilistic QPFs as input to a determin-istic hydrological model. The overall system includes aprecipitation uncertainty processor, a hydrologic modeluncertainty processor, and an integrator. The system iscomputationally efficient and capable of quantifying thetotal uncertainty that exists at the forecast time. Notably,



Figure 4. Probabilistic stage forecasting with 95% confidence interval (from Chen and Yu, 2007)

the general methodology developed in this study can beapplied to any hydrological model. One of the weak-nesses of this method is that it considers only the totalprecipitation amount as the dominant source of uncer-tainty. The assumption of a single dominant source ofuncertainty is not universally valid.

A first step towards the development of a proba-bilistic FFG forecast method (Figure 3) was presentedby Ntelekos et al. (2006). They considered uncertaintyin the threshold-runoff calculation methodology, in thehydrologic models parameters, and initial states. Dif-ferent sources of uncertainty were propagated to studytheir joint effect on the FFG System (Figure 3). Theirresults confirmed that flash flood guidance estimates aremost uncertain under dry initial soil moisture conditions.The uncertainty of threshold-runoff dominated the out-come of the numerical experiments, while the hydrologicmodel uncertainty had limited influence on FFG results.The threshold-runoff is particularly sensitive to stream-top width, hydraulic depth, the drainage area, and thelength of the main stream; a decrease in their uncertaintylevels would automatically translate to a decrease to theoverall threshold-runoff uncertainty.

Chen and Yu (2007) proposed a different type of proba-bilistic river stage forecast method that consists of a deter-ministic forecast derived from support vector regressionand a probability distribution of forecast error based onfuzzy inference. This method can account for total uncer-tainty involved in the forecasting process. The resultsindicated that for short lead time (13 h) forecasts, thepredicted confidence intervals were smooth and theiramplitudes were small, thus useful for practical appli-cations. However, for forecasts with lead-times greaterthan 3 h, the confidence intervals exhibited undesirablefluctuations (Figure 4).

Yatheendradas et al. (2008) investigated the predictiveuncertainty of a physically based distributed hydrologicalmodel KINEROS2 (Smith et al., 1995) driven by high-resolution radar rainfall input for flash flood forecastingin a semi-arid catchment. They considered rainfall esti-mates, model parameters, and initial moisture conditionsas the sources of uncertainty. The GLUE approach andvariance-based Sobol global sensitivity analysis method(Sobol, 1993), which accounts for factor interactions at

all orders, was used for uncertainty analysis. Follow-ing are the main conclusions drawn from the results(Yatheendradas et al., 2008).

1. The uncertainty in the radar rainfall estimates almostcompletely dominated the uncertainty in the modelledresponse

2. Initial hillslope soil moisture significantly influencedthe modelled response

3. The predictive uncertainty in the modelled flash floodresponse is often likely to be much higher than whatwould be considered acceptable for accurate flash floodforecasting

In general, good uncertainty estimates of flash floodforecasts can add credibility to the forecast system. How-ever the development of methodologies capable of incor-porating forecast uncertainty into the decision-makingprocess (whether to issue flood warning or not) remains amajor challenge. Even the simple assignment of appropri-ate exceedance thresholds presents problems: a too lowthreshold would lead to a high number of false alarms;conversely, a too high threshold would lead to a lowprobability of detection and a greater number of surprises.In either circumstance, miscalculation of risk could haveconsiderable social and economic consequences.

URBAN FLASH FLOOD FORECASTING

Urban areas typically have high risks of flash floodingdue to the presence of large impervious areas, block-ing of stormwater flow or insufficient drainage capacity,and sometimes location (vicinity of a river). Blockage ofcanals and stormwater drainage systems by debris andsediment may significantly increase the risk of flooding.During an urban flash flood event, streets can becomefast-moving rivers that can cause damage to infrastruc-ture, while water-filled basements and viaducts can causefatalities. Some structures such as highways, railwaylines, large buildings, concrete walls, and pavements canact as temporary embankments that may trap water forseveral days.



Flash flood(FF) forecast

RainfallComparison

method

Flow comparisonmethod

FF Guidance

Direct flowcomparison

FF susceptibilityassessmentprocedure

Flow frequencycomparison

Flow forecastmodel

Forcing Data

Rainfall forecasts

Remotelysensed data

Historicalclimatology

Geomorphologicdata

Neural Networks

Statisticalmodels

Conceptualmodels

Distributedphysically-based

models

Figure 5. Different approaches available for making a flash flood forecast

Increasing urbanisation due to population growth,combined with potential climate change impacts, meansthat there is an increased risk of more frequent andsevere urban flash floods. As land is converted fromfields or woodlands to residential areas, roads, andparking lots, its capacity to absorb rainfall is diminished.On average, urbanisation results in a two- to six-foldincrease in runoff compared to what would occur onnatural terrain (Ramachandra and Kumar, 2008). Inaddition, the high population densities and related socio-economic infrastructure in urban areas usually leads tomore significant damage from flash flood events than thatassociated with flooding in non-urban areas.

Modelling urban flash floods is particularly difficultdue to the absence of natural flow paths and the pres-ence of man-made structures. Snell and Gregory (2002)applied a flash flood forecast model for rural and urbancatchments in New Mexico, using information on theroad networks to alter the flow direction map derivedfrom a DEM alone, since streets serve as major drainageconduits in urban catchments. Flow direction cells thatdid not correspond to the street drainage network wereedited to correspond to the drainage network. However,the results indicated that additional work is necessary toimprove the estimates of runoff and discharge. A recentstudy of flash flood forecasting in the Dead Run water-shed of Baltimore County in Maryland, USA (Javieret al., 2007), confirmed that a major limitation on theaccuracy of flash flood forecasting in urban areas isimposed by stormwater management infrastructure. Themodel analyses also suggest that there is potential forimproving model forecasts through the use of informationon initial soil moisture storage. The authors further com-mented that distributed hydrological models and high-resolution radar rainfall can provide important elementsof site-specific flash flood forecasting systems in smallurban watersheds.

Very high-resolution DEMs such as LiDAR (LightDetection And Ranging) (Lin, 1997) are useful forurban flash flood modelling. Haile and Rientjes (2005)examined the effects of LiDAR DEM resolution inflood modelling, and concluded that it has a significanteffect on inundation extent, flow velocity, flow depth,and flow patterns across the model domain. Therefore,the spatial resolution of flash flood forecasts in urbanareas requires special attention. Though urban flash floodmodelling is very important, no existing model is capableof addressing all these issues to reliably forecast flashfloods in urban catchments.

CONCLUSIONS

This review discusses recent advances in flash floodforecasting, including new developments in QPEs, QPFs,remotely sensed data, flow forecast models and methods,forecast uncertainty estimates, and flash flood forecastsin the urban context. Summary of different approachesdiscussed in this review for flash flood forecasting isshown in Figure 5. The main conclusions drawn fromthis review are listed below.

1. Effective flash flood forecasting with useful lead timesis one of the most challenging areas in hydrology,particularly due to the uncertainties associated withrainfall forecasts. Figure 6(a) shows the current qual-ity versus the importance of QPF, QPE, and remotelysensed data. Quality means the ability of the informa-tion source to portray the actual scenario. Importancereflects the contribution of this product towards over-all flash flood forecast accuracy, if the product reachedits full potential. Improvements are being made inremotely sensed data retrieval techniques (radar- andsatellite-based) and in merging rainfall data derivedfrom different sources (gauged, radar, satellite, NWP



Impo

rtanc

e

Current quality

(a) Data

QPF

RSD

QPE Tuni

ng n

eede

d

Ease of use

(b) Flow forecasting models

PDM

LHM

DDM

RCM

FCM

FSA

Relia

bility

Operational simplicity

(c) Criteria for decidingflash flood occurrence

LHM = Lumped hydrological modelsFSA = Flash flood susceptibility assessment procedure

QPF = Quantitative precipitation forecastsRSD = Remotely sensed data and deriving techniquesQPE = Quantitative precipitation forecastsPDM = Physically-based distributed hydrological models

RCM = Rainfall comparison methodsFCM = Flow comparison methods

DDM = Data driven models

Figure 6. Current states of data, flow forecasting models, and criteria for deciding flash flood occurrence used for flash flood forecasting

model forecasts). These improvements have facili-tated the production of sufficiently accurate QPFs with16 h lead-times and high quality QPEs. However fur-ther improvements in lead-time (1224 h) and accu-racy of QPFs is essential (Figure 6(a)) for providingmeaningful flash flood forecasts. Observed informa-tion such as rain gauge, radar, and satellite-based dataare only partially useful for producing QPFs, whereas,high-resolution-quality NWP products have the poten-tial to improve the quality of QPFs significantly

2. Flash floods usually occur in small catchments(


flash floods in the future. Thus, flash flood forecast-ing in urban areas needs special attention in order toreduce severe losses. Unfortunately, no existing modelis capable of making reliable flash flood forecasts inurban watersheds.

ACKNOWLEDGEMENTS

The authors thank Jim Elliott, Soori Sooriyakumaran, andtheir colleagues at the Bureau of Meteorology, Australia,for their support and guidance on this review. CathyBowditch of CSIRO is gratefully acknowledged for edit-ing this paper. We thank the two anonymous reviewersfor their valuable comments on this manuscript. Permis-sion for figure use (Figure 2) came from Seann Reed,Alexandros Ntelekos (Figure 3), and Shien-Tsung Chen(Figure 4). This review was undertaken as part of theWater Information Research and Development Alliance, ajoint initiative of the CSIRO Water for a Healthy CountryFlagship and the Bureau of Meteorologys Water Divi-sion. The funding support from the CSIRO OCE ScienceLeader Scheme is also gratefully acknowledged.

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