Flash Flood Forecasting Based on Rainfall Thresholds

  • Lorenzo AlfieriEmail author
  • Marc Berenguer
  • Valentin Knechtl
  • Katharina Liechti
  • Daniel Sempere-Torres
  • Massimiliano Zappa
Reference work entry


Extreme rainstorms often trigger catastrophic flash floods in Europe and in several areas of the world. Despite notable advances in weather forecasting, most operational early warning systems for extreme rainstorms and flash floods are based on rainfall observations derived from rain gauge networks and weather radars, rather than on forecasts. As a result, warning lead times are bounded to few hours, and warnings are usually issued when the event is already taking place.

This chapter illustrates three recently developed systems that use information on observed and forecasted precipitation to issue flash flood warnings. The first approach is an indicator for heavy precipitation events, developed to complement the flood early warning of the European Flood Awareness System (EFAS) and targeted to short and intense events, possibly leading to flash flooding in small catchments. The system is based on the European Precipitation Index Based on Simulated Climatology (EPIC), which in EFAS is computed using COSMO-LEPS ensemble weather forecasts and a 20-year consistent reforecast dataset.

The second system is a flash flood early warning tool developed based on precipitation statistics. A total of 759 sub-catchments in southern Switzerland is considered. Intensity-duration-frequency (IDF) curves for each catchment have been calculated based on gridded precipitation products for the period 1961–2012 and gridded reforecast of the COSMO-LEPS for the period 1971–2000. The different IDF curves at the catchment level in combination with precipitation forecasts are the basis for the flash flood early warning tool. The forecast models used are COSMO-2 (deterministic, updated every 3 h and with a lead time of 24 h) and COSMO-LEPS (probabilistic, 16-member and with a lead time of 5 days).

The third system (FF-EWS) uses probabilistic high-resolution precipitation products generated from the observations of the weather radar network to monitor situations prone to trigger flash floods in Catalonia (NE Spain). These ensemble precipitation estimates and nowcasts are used to calculate the basin-aggregated rainfall (that is, the rainfall accumulated upstream of each point of the drainage network), which is the variable used to characterize the potential flash flood hazard.

Examples of successful and less skilful forecasts for all three systems are shown and commented to highlight pros and cons.


Extreme precipitation events Numerical weather predictions Flash flood early warning EPIC IDF Ensemble forecasting Reforecasts 

1 Introduction

Flash flood forecasting is an important field of applied research because flash floods are cause of major damages (Liechti et al. 2013a; Gaume et al. 2009). Several fatalities linked to flash floods have been reported around the world (French et al. 1983; Jonkman 2005; Gaume et al. 2009). Norbiato et al. (2008) adopt the term of “flash” to indicate situations where very shortly after a triggering precipitation event the level of the rivers rapidly increase. The delay between the rainfall and the peak discharge is so short that almost no mitigation action is possible. Typically flash floods are observed in small torrents showing very rapid runoff concentration delays (Norbiato et al. 2008). The torrential character of the concerned rivers might imply that cascading hazards such as mobilization of sediments and driftwood are possible. Alfieri and Thielen (2015) report on the concurrency of flash floods with landslides and debris flows. Such mass movements might increase damages downstream due to wood and debris blocking the river cross section and causing inundation of the surrounding areas.

Mitigation measures are possible if such situations are predicted some hours in advance. Javelle et al. (2010) summarizes that the challenge of accurate prediction of precipitation (Collier 2007), the very limited area of the affected basins and the reduced time available to make complex model simulations are limiting constraints for a successful application of operational forecasting systems to anticipate flash floods. Thus, although advanced early warning systems using forecasted weather radar estimates can be conceived and evaluated (e.g., Liechti et al. 2013a; Versini et al. 2014), their operational deployment might be slowed down by the delays needed to transfer data from observing systems to the meteorological services and later on the chain to the operator of hydrological models.

One way to cope with this is to provide flash flood early warning by adopting only precipitation data to estimate possible flood damage. One of the most popular approaches is the flash flood guidance (FFG) method. FFG is widely used in the United States and basically keeps track of the rainfall depth and intensity needed to trigger a flood at the outlet of a specific catchment (e.g., Georgakakos 2006). It is calculated by a hydrological model run in inverse mode.

Recent developments focus on ensemble early warning systems relying on precipitation information only. This reduces the need for calibration (e.g., Alfieri et al. 2011). One disadvantage of such approaches is the neglecting of the initial conditions (Javelle et al. 2010). This chapter presents three recent approaches devoted to the early prediction of flash floods. They are presented according to the lead time of the forecasting tools. In Sect. 2 a method deployed at the European scale is evaluated. In Sect. 3 an application designed for very small headwater basins in southern Switzerland is presented. Section 4 presents the approach used in the region of Catalonia (Spain). Section 5 summarizes the experience of the three systems and gives insight on possible further developments.

2 Flash Flood Monitoring at the European Scale with EPIC

2.1 EPIC

The European Precipitation Index Based on Simulated Climatology (EPIC, Alfieri et al. 2011) is an indicator to monitor the European domain for upcoming severe storms possibly leading to flash floods. EPIC only depends on the Quantitative Precipitation Forecast (QPF) and on the modeled river network, while all other hydrological processes (e.g., initial soil moisture, snow accumulation, and melting, among others) are not considered. Despite some important simplifications, compared to the actual processes involved, the system has no calibration parameters and can be seen as an extreme frequency analysis of the aforementioned indicator. As Guillot and Duband (1967) described in the Gradex method, the gradient of the statistical distribution of discharges tends to follow asymptotically that of rainfall, for high return periods. Consequently, the methodology here described aims to detect severe flood events by linking them to extreme rainfall accumulations at the catchment scale.

EPIC is defined as:
$$ \mathrm{EPIC}(t)=\underset{\forall di}{\max}\left(\frac{U{P}_{di}(t)}{\frac{1}{N}\sum \limits_{yi=1}^N\max {\left(U{P}_{di}\right)}_{yi}}\right), $$
where UPdi is the upstream cumulated precipitation that is the double summation of the precipitation depth (P) over the upstream area and over a certain duration di preceding the considered time t:
$$ U{P}_{di}(t)=\sum \limits_{t- di}^t\sum \limits_AP; $$
In Eq. 1, UP is calculated for each time step t and then rescaled by the corresponding mean of the annual maxima derived from a consistent dataset of N years, for the same point and rainfall duration. Although EPIC was specifically designed for forecast applications, the same formulation can be applied in real-time monitoring and in post-event analysis, using observed precipitation fields. In the latter case, one has to collect a precipitation dataset consistent with the event measurements, particularly with regard to extreme values. Reforecast datasets made available by weather forecasting centers are particularly suitable options, as they are produced with the same model version of operational forecasts.

According to the rational method for the estimation of peak flows (e.g., Chow et al. 1988), durations of accumulation di depend on the typical response time of the catchment and on the average delay in producing runoff after a rainfall event. Following the findings from Fiorentino et al. (1987) and from Viglione and Blöschl (2009), critical rainfall durations can be assumed of the same magnitude of the catchment lag time and its time of concentration. In EFAS operational runs, EPIC considers di = {6, 12, 24 h}, which are typical durations of intense storms leading to flash floods in catchments of size up to 2000 km2 (Gaume et al. 2009; Reed et al. 2007).

2.2 EPIC in the Operational EFAS Monitoring

In the European Flood Awareness System (EFAS, Bartholmes et al. 2009; Thielen et al. 2009), ensemble weather predictions to compute EPIC are provided by the Consortium for Small-Scale Modeling (COSMO). The Limited-Area Ensemble Prediction System (LEPS) of the COSMO model (Marsigli et al. 2005) is produced twice per day at 00:00 and 12:00 UTC, with a forecasting range of 132 h. COSMO-LEPS is a 16-member ensemble covering central-southern Europe, stretching as far north as Scotland, Denmark, and Latvia. Maps are provided on a rotated spherical grid with horizontal resolution of 0.0625° (~7 km) and temporal resolution of 3 h. Climatological values are derived by a 20-year reforecast datasets starting in 1989, produced by COSMO with the same model configuration used for operational forecasts (Fundel et al. 2010). The dataset was created by initializing the model every 90 h using the ERA-Interim atmospheric reanalysis dataset (Dee et al. 2011) as initial and boundary conditions.

EPIC is run twice per day (00:00 and 12:00 UTC) using COSMO-LEPS forecasts as input, resulting in ensembles of 16 possible temporal evolutions over the forecast range. It is calculated for each pixel of the river network at 1-km grid resolution within the COSMO-LEPS domain, resulting in more than one million points. Reference values are EPIC = 0, when no rainfall is forecast, and EPIC = 1, when the cumulated upstream precipitation at a point equals the corresponding mean of the annual maxima for at least one of the considered rainfall durations. In hydrological monitoring systems, flood warning thresholds are usually set for specific return periods; hence, a more intuitive representation is to estimate the return period of EPIC and show its values for the selected events. The approach used in EFAS is described as follows:
  • At each forecast, a preliminary empirical set of rules selects the most downstream points with EPIC > 1 for at least four members out of 16 (25% probability) and with EPIC > 1.5 for at least three members. An additional criterion is set on the upstream area of points, which is bound in the range 50–5000 km2, to address the analysis on flash flood prone catchments. The lower value is bounded by the spatial resolution of the weather prediction data.

  • For the selected set of points, a 2-parameter gamma distribution is fitted to EPIC ensembles at each time step of the forecast horizon. The probability density function (pdf) of a gamma-distributed random variable x is defined as:
    $$ f(x)=\frac{x^{\alpha -1}{e}^{-x/\beta }}{\beta^{\alpha}\Gamma \left(\alpha \right)}\;\mathrm{for}\;x\ge 0\;\mathrm{and}\;\alpha, \beta >0, $$
    where α is the shape parameter, β the scale parameter, and \( \Gamma \left(\cdot \right) \) denotes the gamma function. L-moments estimators are used to fit empirical values as described by Hosking (1990). A similar approach is used and described by Alfieri et al. (2012) to fit ensemble streamflow predictions derived by COSMO-LEPS weather forecasts. Results by Alfieri et al. (2012) show that fitting raw 16-member hydrological ensembles with analytical gamma distributions leads to improvements both in the quantitative streamflow estimation and particularly in the threshold exceedance analysis.
  • A Gumbel extreme value distribution is hypothesized for the annual maxima of EPIC(di), for durations of 6, 12, and 24 h, derived from the 20-year climatology. Its cumulative distribution function F(x) takes the form:
    $$ F(x)=\exp \left(-\exp \left(-\frac{x-\xi }{\alpha}\right)\right), $$
    where α is the scale parameter and ξ the location parameter of the Gumbel distribution.
  • The two parameters of each distribution are estimated by equalling the first two sample L-moments with those of the analytical distribution (λ1, λ2):
    $$ {\lambda}_1=\xi +\alpha \gamma, $$
    $$ {\lambda}_2=\alpha \log 2, $$
    where γ is Euler’s constant: γ =0.5772. Return periods T of EPIC(di) are estimated from their analytical cumulative distributions F(EPIC(di)) in Eq. 4 and by recalling the relation T = 1/(1-F(EPIC(di))).
  • The initial set of reporting points is regrouped into three alert classes. Medium alert class (yellow color coding) includes all points having a maximum probability larger than 15% of exceeding the 2-year return period. Similarly, high (in red) and severe (in purple) alert classes include all points having a maximum probability larger than 15% of exceeding the 5-year and 20-year return period, respectively.

EPIC is calculated operationally, and results are visualized in a web interface and monitored on a daily basis to detect small-scale extreme events over Europe. Products have been designed in analogy to those developed for EFAS, as they are specifically targeted to explore and visualize probabilistic forecasts. Products shown include a map of the maximum probability of exceeding the mean annual maximum of EPIC over the forecast range (i.e., \( \underset{\forall t}{\max}\left(\Pr \left(\mathrm{EPIC}(t)>1\right)\right) \)), where different probabilities are indicated with shades of red. Also, the three layers of alert points defined above are shown with triangles of size proportional to the probability of EPIC to exceed the corresponding alert class. An example of the resulting display of EPIC on the EFAS web interface ( is shown in Fig. 1 for forecasts of the 28 February 2011 12:00 UTC for the Marche region in central Italy. At each reporting point, a time-plot displays with blue shadings the forecast return period of EPIC for a probability range of 5–95% (e.g., see Fig. 2). According to the operational alerting rules currently used in EFAS, these forecasts would have resulted in a flash flood alert for six rivers (indicated by red triangles in Fig. 1), in the Marche and Abruzzo regions. On 02 March 2011, media news confirmed widespread flooding in most rivers of the two regions, due to the most intense rainfall in 40 years (, last accessed on 11 July 2016). The location and timing of this event were skillfully captured by EPIC forecasts, together with the river basins where the most extreme features would exhibit.
Fig. 1

Map of forecast probability of exceeding the mean annual rainstorm (i.e., EPIC = 1). Model run of 28 February 2011 12:00 UTC. Color saturation of red lines is proportional to the percent probability level (see legend). EPIC reporting points and flash flood alerts are shown with pink and red triangles, respectively

Fig. 2

Forecast return period of EPIC on 28 February 2011 12:00 UTC for a reporting point at the outlet of the Potenza River in central Italy. The maximum probabilities of exceeding the three alert thresholds are shown on the left. The ensemble mean and the interquartile range are also shown with solid and dashed black lines

One can see in Fig. 2 that return period time-plots put the focus on extreme conditions, while rainfall depths below the mean annual maxima tend to converge to the 1-year return period. Vice versa, in such plots the uncertainty spread increases for extreme values, proving the usefulness of the probabilistic information but also showing the difficulty in providing accurate alerts in operational warning systems. As example, the event peak in Fig. 2 has a coefficient of variation (CV) of the predicted EPIC ensemble of CVEPIC = 0.17, while the corresponding one calculated on the ensemble of return periods, CVT = 0.87, is about five times larger.

2.3 Case Study: Flash Floods in Sardinia (Italy) in November 2013

During the night between 18 and 19 November 2013, exceptionally high rainfalls fell in Sardinia, Italy. According to the website of ARPA Sardegna, the regional environment agency, more than 350 mm of precipitation was recorded within 24 h in a considerably large region in central-east Sardinia. News reports from the BBC (, last accessed on 11 July 2016) quote up to 440 mm in 90 min, which place it among the highest measurements on record worldwide.

A total of 18 casualties were reported, the area near Olbia being the most severely affected. Weather forecasts used in EFAS predicted high rainfalls for Sardinia, Corsica, northern Spain, and southern France since 14 November, though forecasts were not persistent and underestimated the actual precipitation totals.

For this event, EPIC showed a medium probability for flash floods across the island about 1 day before the event (see Fig. 3). Only the forecast of 18 November 201312:00 UTC indicated a high probability of flash floods which would have resulted in an EFAS warning to authorities, though when the event was ongoing. This example highlights how the performance of EPIC are directly related to those of the underlying weather models in predicting extreme precipitation. In addition, at the grid resolution of 5–10 km the forecast uncertainty is high. Often only few members of the ensemble are able to reproduce extreme conditions potentially leading to flash floods, making the alert detection particularly challenging. A key issue in operating early warning systems based on ensemble or probabilistic forecasts is the definition of minimum probability thresholds used to trigger the alerts. Lower thresholds enable the detection of events with high forecast uncertainty, as in the case described above, and improve the hit rate of the system though at the cost of a higher average false alarm rate. Ideally, probability thresholds should be calibrated depending on the ratio between the cost of issuing an alert and the economic losses in case a flash flood occurs.
Fig. 3

EPIC flash flood forecast of 18 November 2013 00:00 UTC, at the outlet of the Cedrino River, Sardinia

2.4 Performance in Early Detection of Extreme Storms and Flash Floods

By its definition, EPIC is not designed to detect all types of floods, but rather those in small-size catchments (with a lower boundary depending on the resolution of the NWP) induced by short and intense rainfall events. The collection of quantitative discharge data and of flood thresholds in small rivers throughout Europe, for validation purpose, is a huge and painstaking task. Flash floods usually occur in ungauged catchments, where the only source of information is post-event descriptive reports. Besides, even when gauging stations are available, they are sometimes damaged and made inoperative by the rage of the flood flow. Performance in operational monitoring of the early warning system described above was assessed through a qualitative approach, by selecting the strongest recorded signals of upcoming severe events from EPIC and verifying the actual occurrence of flooding events in the areas where they were forecast.

A research work by Alfieri and Thielen (2015) analyzed 22 months of forecasts of EPIC driven by COSMO-LEPS forecasts, ending in September 2011. They obtained a threshold for flash flood alerts corresponding to 60% probability of exceeding the 5-year return period of EPIC. Such criterion enabled the detection of 363 alerts out of the overall set of reporting points (see Fig. 4), belonging to 50 distinct events. Their occurrence was investigated by searching for reported news on the internet. The main source of information used is the flooding section of the European Media Monitor (EMM,, last accessed on 19 August 2015). EMM NewsBrief was developed at the Joint Research Centre of the European Commission. It is a summary of news from the world in several languages, which is generated automatically by software algorithms. EMM news have been complemented by the Emergency Events Database (EM-DAT,, last accessed on 11 July 2016) of the Centre for Research on the Epidemiology of Disasters (CRED) and by targeted internet searches on national and regional news websites. Out of 50 events, in 42 cases reported, news of rainstorms and economic losses were found, due to a combination of floods, flash floods, surface water flooding, debris flow, landslides, hail, lightning, sea waves, or wind storms.
Fig. 4

EPIC reporting points (black dots) and flash flood alerts (red circles) between December 2009 and September 2011. COSMO-LEPS spatial domain is also indicated with a gray shaded area (From Alfieri and Thielen (2012), Copyright © 2012 Royal Meteorological Society, first published by John Wiley & Sons, Ltd.)

The remaining eight cases were not confirmed by media news as hazardous events. Reasons were attributed to errors in the event severity (two cases), to a shift in their location (three cases), and to boundary issues for regions at the edge of the simulation domain (three cases).

2.5 An Outlook to Future Flash Flood Early Warning in Europe

EPIC is used for flash flood early warning as part of the operational EFAS suite. A semiautomated procedure was set up to speed up the preparation of alert emails and reduce the component of human error due to stress and time pressure. A fully automated procedure to send flash flood alerts is envisaged for the near future, in order to maximize the warning lead time after the latest forecast results are ready. In addition, since 2014 EFAS flash flood, alert emails include information on the landslide susceptibility of the affected catchments (from Günther et al. 2013), to help identify possible hotspots of upcoming debris flow events and rainfall-driven landslides.

Research work by Raynaud et al. (2015) showed how the performance in the event detection can be improved through a modified version of EPIC based on runoff instead of precipitation. It takes into account the initial soil moisture conditions and geomorphological features to weigh the contribution of rainfall on the severity of the forecast event. Similarly, Alfieri et al. (2014) extended the formulation of EPIC to detect floods in a wider range of basin size, using a nonparametric approach relying exclusively on the output of a state-of-the-art global circulation model coupled with a land-surface scheme. They defined the extreme runoff index (ERI), which is designed to detect extreme accumulations of surface runoff over critical flood durations for each section of the river network.

3 Deterministic and Ensemble Flash Flood Early Warning in Southern Switzerland

The flash flood early warning tool deployed in southern Switzerland (canton of Ticino) is running in real time since July 2013. For some regions of the target area, real-time hydrological ensemble predictions have been established since 2007 (Zappa et al. 2008, 2013) including the use of ensemble weather radar Quantitative Precipitation Estimates (QPEs) (Germann et al. 2009; Zappa et al. 2011; Liechti et al. 2013b). Such real-time systems based on the hydrological model PREVAH (Viviroli et al. 2009) need to be calibrated against observed streamflow and are difficult to be configured for small ungauged areas.

The examples presented in Sects. 2 and 4 demonstrate the potential of early warning based on accumulated precipitation. Thus, a tailored tool based on these principles was developed for southern Switzerland, too. First-order catchments are used as baseline spatial unit to accumulate precipitation. To calculate the areas of the first-order catchments a digital terrain model with a spatial resolution of 200 m has been used and processed following the algorithms presented in Binley and Beven (1992). In the target area, 759 first-order catchments have been isolated (Fig. 5, left).
Fig. 5

Left: First-order catchments, southern Switzerland (Ticino river basin). 759 first-order catchments exist with an area between 0.02 and 29.7 km2. Right: Location of the nine MeteoSwiss rain gauge stations with a temporal resolution of 10 min

3.1 Estimation of Return Periods

In the Swiss application, return periods have been estimated following the generalized extreme value (GEV) distribution introduced by Jenkinson (1955). It incorporates three types of commonly used extreme value distributions in a single function, the Gumbel distribution (type I), the Fréchet distribution (type II), and the Weibull distribution (type III) (Martins and Stedinger 2000). The standardization of the three types simplifies the extreme value analysis substantially. No subjective decision is needed as the data itself defines the matching type of extreme value distribution through the shape parameter (Leadbetter et al. 1983).

Three parameters define the GEV: location μ, scale σ, and shape ξ. It reduces to one of the three distribution types for different ranges of the shape parameter: the Gumbel distribution with ξ = 0, the Fréchet distribution with ξ > 0, and the Weibull distribution with ξ < 0. The cumulative distribution function of the GEV is written as:
$$ G(x)=\exp \left\{-{\left[1+\xi \left(\frac{x-\mu }{\sigma}\right)\right]}^{-1/\xi}\right\}\mathrm{for}\ \xi \ne 0 $$
$$ G(x)=\exp \left\{-\exp \left[-\left(\frac{x-\mu }{\sigma}\right)\right]\right\}\mathrm{for}\ \xi \ne 0 $$
where 1 + ξ(x − μ)/σ > 0 and −∞ < μ < ∞, σ > 0, and −∞ < ξ < ∞ (Coles 2001). The parameters of the GEV were estimated by the maximum likelihood method (Aldrich 1997). As the GEV does not satisfy the regularity conditions required for the usual asymptotic properties associated with the maximum likelihood estimator to be valid, Smith (1985) gave the following guidelines:
  • ξ < 0.5, the maximum likelihood estimation is valid; it has the usual asymptotic properties.

  • 1 > ξ > 0.5, the maximum likelihood estimation has a result, but the standard asymptotic properties are not fulfilled.

  • ξ > 1, maximum likelihood estimation is unlikely.

For extreme values ξ ≥ 0.5 is rare. Therefore, the theoretical restriction of the maximum likelihood method is normally no obstacle in practice (Coles 2001). For the analysis of series of yearly maxima from natural processes shape parameters above 0.5 indicate problems with the data or different processes involved. For analysis, the block maxima approach was chosen. Homogeneity and stationarity were tested by trend analysis using the Mann-Kendall test (Kendall 1970) and the Run test (Wald and Wolfowitz 1940).

3.2 Combined Use of Station Information and Gridded Data for IDF Estimations

Similar to EPIC, also the early warning tool used in southern Switzerland adopts intensity-duration-frequency (IDF) curves. Different data sources are evaluated according to the IDF formulation described in Koutsoyiannis et al. (1998). This IDF equation relates the three indicators related to heavy precipitation: intensity, duration, and return period. IDF are generally created to assess the frequency of a certain rainfall intensity for a certain event duration. Once the relation is established, then a real-time product can be evaluated with respect to its probability of recurrence. In a first step, intensity-duration-frequency (IDF) curves for each catchment have been calculated based on:
  • Rain gauge data for the period 1980–2013 from the monitoring network SwissMetNet of MeteoSwiss. Nine stations within the target area observing precipitation with a temporal resolution of 10 min have been used (Fig. 5, right).

  • The gridded RhiresD dataset (Schiemann et al. 2010; MeteoSchweiz 2013). RhiresD has a spatial resolution of 1 km and a temporal resolution of 1 day. It has been calculated based on approximately 420 rain gauge measurements covering the entire territory of Switzerland. It is available from 1961 to 2012.

  • Thirty-year hindcast of the COSMO-LEPS numerical weather prediction model. The hindcast is available for the time period between 1971 and 2001. The used data has a temporal resolution of 1 day. The grid size is 10 km (Fundel et al. 2010; Jörg-Hess et al. 2015).

The gridded data have been downscaled (nearest-neighbor, Fundel and Zappa 2011) to generate local integral time series for each of the evaluated first-order catchments. Finally, IDF curves have been calculated for each precipitation dataset (single gauges, RhiresD, and COSMO-LEPS) and first-order catchment. A GEV fit (see Sect. 4.1) has been estimated for every duration for which the relation between precipitation intensity and the return period is of interest (from 10 min up to 10 days in the specific case of the early warning system for southern Switzerland).

Since only the single gauges provide information on sub-daily precipitation intensities, a methodology has been developed to combine the gridded datasets with the information stemming from the ground stations (Knechtl 2013). The information of the 10-min rain gauge measurements has been adopted to extend the catchment IDF functions. Knechtl (2013) associated each catchment to one of the local rain gauges. The association is realized basing on the similarity between the IDF of the stations and of the sub-areas in case of durations of 1–10 days and return periods of 2–20 years. The assumption is that similar IDF in this interval is a sign that the IDF is similar for all durations and thus the local information provided by the station can be used to integrate the information from the two gridded datasets.

Figure 6 is an example of the resulting spatial intensity distribution based on the gridded precipitation datasets RhiresD (left) and COSMO-LEPS (right) for the duration 2 h and the return period of 2 years. For the RhiresD dataset, the weakest intensities occur in the eastern and southern part of the canton of Ticino. Medium intensities are shown in the northern part and in the central Ticino. The strongest intensities are in the north of the Lago Maggiore. Furthermore, there is a strong intensity gradient between the Italian catchments and the Swiss catchments in the west of the Lago Maggiore.
Fig. 6

Catchment-specific rainfall intensities [mm/h] for a duration of 2 h and a return period of 2 years. Left: RhiresD. Right: COSMO-LEPS

Concerning the COSMO-LEPS hindcast (right-hand side of Fig. 6), relatively weak intensities occur in the southern Ticino, medium intensities occur in the northern Ticino, and highest intensities occur in the east and western part of the target area.

The spatial distribution of precipitation intensities between the RhiresD and the COSMO-LEPS dataset is clearly different. These differences are due on how these datasets have been elaborated. The COSMO-LEPS hindcast dataset contains the COSMO-LEPS-related model biases, and the RhiresD contains biases owed to the limitations of the spatial representativity of rain gauges. Furthermore, the strong intensity gradient between the Italian and Swiss catchments in the west of the Lago Maggiore only appear for the RhiresD dataset whereas there is no such gradient for the COSMO-LEPS dataset. The resulting IDF equations for the Italian catchments are erroneous due to missing data in the RhiresD dataset outside Switzerland. To a weaker extent, the same phenomenon occurs in the southeast where Italian sub-catchments show also a weaker intensity compared to the neighboring Swiss sub-catchments for the RhiresD dataset. This limitation could be eliminated only if a homogenous transnational dataset would be available.

In the presented application, the availability of separate IDF analysis for different data sources allows switching between different IDFs in the case of real-time operations. Alerts based on observations are triggered by comparison to the RhiresD IDF, while alerts for the next days are triggered by comparison to the COSMO-LEPS IDF. This reduces the problem of inhomogeneity and bias between the raw COSMO-LEPS output and the observation-based products (Fundel et al. 2010).

3.3 Operational Implementation Forced by Deterministic and Ensemble NWP

The obtained IDFs associated to COSMO-LEPS and RhiresD long time series are the core of the deployed early warning system. Real-time forecasts and gridded observations are obtained from MeteoSwiss. Three datasets are used:
  • COSMO-LEPS forecast. COSMO-LEPS is a Limited-Area Ensemble Prediction System developed and run by the Consortium for Small-Scale Modeling (Marsigli et al. 2005). This probabilistic numerical weather forecast model has 16 members. It is available for lead times up to 132 h at a spatial resolution of 7 km (Addor et al. 2011; Zappa et al. 2008). Forecasts are initialized at 12:00 UTC and are delivered approximately 10 h later. Thus, analyses are completed only for 120 h starting from 00:00 UTC of the next day, 12 h after initialization.

  • COSMO-2 is a high-resolution realization of COSMO-LEPS and includes explicit calculation of small-scale convection. The grid resolution of the model is 2.2 km and the forecast lead time is 33 h with a temporal resolution of 1 h. A new forecast is calculated every 3 h (Zappa et al. 2008; Ament et al. 2011).

  • CombiPrecip is the observation-based dataset used within the Swiss flash flood early warning tool. This dataset combines rain gauge with radar measurements. The aim of this dataset is to combine the quantitatively accurate gauge measurement data with the radar measurement that covers a large area (Sideris et al. 2014). It is operationally available at a temporal resolution of 1 h. Different accumulations can be computed.

In operational mode, COSMO-2 is nudged to real-time data of a weather radar precipitation obtained from CombiPrecip.

Figure 7 presents the setup of the real-time tool consisting of an “early detection” component based on COSMO-LEPS, which is evaluated against the COSMO IDF for accumulated rainfall of 1–5 days. A second “nowcast” component consists of the combination of the latest 12 hourly fields of CombiPrecip and the forecasted precipitation fields of COSMO-2 for the next 12 h. The accumulated rainfall is evaluated for different intervals (from 1 to 12 h) and depending on the source of the data either the IDF curves of RhiresD or the one based on COSMO-LEPS is used. The nowcast component is updated each hour, while the early detection component runs once every day.
Fig. 7

Sketch of the real-time Swiss flash flood early waning tool consisting of an early detection component (right part) and a nowcast component (left side). See main text for further information

Knechtl (2013) evaluated this setup against observed events. These events are either discharge peaks in gauged sub-areas or reports of damages caused by flash flood events. The hypothesis that it is possible to detect hydrological events with the flash flood early warning tool could be partly confirmed. The highest skill is obtained if the return period of CombiPrecip is assessed at hourly time scale. With this, it was possible to confirm most of the damage events that occurred in 2010 and 2011. The prototype tool is affected by several false alarms. This is because initial conditions of the soils are not considered.

3.4 Case Study in October 2014

The early warning system presented in Fig. 7 is operational since July 2013. In the period between the start of operations and December 2014 damages related to floods (Hilker et al. 2009) have been reported for more than 20 calendar days (Andres et al. 2015). Numerous damage events occurred on 13 October 2014. In this section, we present the situation as seen by the tool for this particular day. Figure 8 shows the outcomes of the early detection component as available on 10 October 2015 (about 72 h ahead of the event). The analysis of the probabilistic COSMO-LEPS rainfall fields indicates that there is locally some probability to exceed the 2-year return period (T2) for 24, 48, and 72 accumulated rainfall. The 24-h maps indicate that in the northwest areas the intense rainfall should already occur on 11 October 2014. In the southeastern areas, the main rainfall has to be expected for 13 October 2014. The 48-h maps present a band extending from southwest to northeast with probabilities of about 30% (five to six COSMO-LEPS members) to exceed T2 for the period 12–13 October 2014. A similar interpretation can be achieved when inspecting the map of 72-h accumulated rainfall for the period 11–13 October 2014.
Fig. 8

Early detection of flash floods in southern Switzerland on the basis of operational COSMO-LEPS daily rainfall fields. The examples display alert based on the probability of exceeding the return period of 2 years (T2) for each accumulation interval (24, 48 and 72 h). The situation relates to a forecast issued on 10 October 2014 and indicating possible floods for the next 4 days (see text for additional information)

The event itself was characterized by a period of intense precipitation between 01:00 UTC and 13:00 UTC on 13 October 2014. Figure 9 presents three different visualizations of rainfall information during this 12-h interval. The top panel (source MeteoSwiss via, last accessed on 19 August 2015; Heil et al. 2014) presents the accumulated rainfall from the weather radar QPE for a duration of 12 h. The lower left panel presents the evaluation of the CombiPrecip accumulated rainfall during the same time interval under consideration of the RhiresD IDF (Fig. 6, left). The lower right panel of Fig. 10 illustrates the evaluation of the COSMO-2 forecast available at 20:00 UTC of 12 October 2014 (5 h prior to the beginning of the relevant accumulation interval). The return period of the COSMO-2 accumulated rainfall was assigned after comparison to the COSMO-LEPS IDF (Fig. 13, right).
Fig. 9

Heavy precipitation event in southern Switzerland on 13 October 2014. Visualization of analyses of accumulated precipitation between 01:00 UTC and 13:00 UTC. Top panel: accumulated rainfall from the weather radar QPE of MeteoSwiss. Bottom left panel: evaluation of the CombiPrecip accumulated rainfall. Bottom right panel: evaluation of the COSMO-2 forecast available at 20:00 UTC of 12 October 2014. See also Fig. 7 and the text for further information

Fig. 10

Visualization of locations affected by floods and debris flow (blue dots) and correspondent alerts issued by the flash flood early warning tool for southern Switzerland on 13 October 2014. Top row: alerts related to CombiPrecip accumulated rainfall. Central row: alerts related to COSMO-2 rainfall predictions. Bottom row: probabilistic alerts based on COSMO-LEPS. See Fig. 7 and the text for full information

Since CombiPrecip is a “post-processed” version of the radar QPE, the general shape of the evaluated CombiPrecip rainfall and of the radar accumulated QPE is very similar. The early warning tool highlights several spots with high precipitation intensities. These spots are within an ellipse stretching from southwest to northeast. This is very similar to the indications obtained from the early prediction obtained on 10 October 2014 by evaluating COSMO-LEPS (Fig. 9). The COSMO-2 prediction presents a very high rainfall intensity exceeding the return period of 5 years in many areas of the target region. The location of the spots with highest intensity is about 30 km more south than the observed fields (radar QPE and CombiPrecip). Nevertheless, the information of COSMO-2 was available with some hours of advance and could have been used to anticipate the event and trigger mitigation measures.

All flood and debris flow events occurring in Switzerland are documented in a database collecting information from newspapers and online sources (Hilker et al. 2009). Andres et al. (2015) evaluated the damages occurred in the target area on 13 October 2014 and provided the exact coordinates of the locations affected by damages. This information has been used to roughly evaluate the potential of the early warning tool (Fig. 10).

In Fig. 10, the damage locations are plot within the field of seven flash flood indicators (panels (a) to (g)):
  • (a) Return period of 24-h accumulated CombiPrecip precipitation for the event day (13 October 2014)

  • (b) Return period of 24-h accumulated CombiPrecip precipitation for the day antecedent to the event

  • (c) Maximal return period of hourly rainfall intensity obtained from CombiPrecip during the event day

  • (d) Return period of 12-h accumulated COSMO-2 precipitation forecast for the run started at 00:00 of the event day

  • (e) Return period of 24-h accumulated COSMO-2 precipitation forecast for the run started at 00:00 of the event day

  • (f) Probability of exceeding a return period of 2 years for 24-h accumulated rainfall on the event day as obtained from the 16-member forecast of COSMO-LEPS delivered 1 day ahead

  • (g) Probability of exceeding a return period of 2 years for 24-h accumulated rainfall on the event day as obtained from the 16-member forecast of COSMO-LEPS delivered 2 days ahead

The inspection of Fig. 10 indicates that both the rainfall observed 1 day ahead (Fig. 10b) and the maximal 1-hour intensity of CombiPrecip during the event (Fig. 10c) would have been poor indicators for locating the damage spots. The 24-h cumulated CombiPrecip rainfall (Fig. 10a) seems to be better correlated to the locations affected by damages. This might indicate that the events are not triggered by local high-intensity events. The cause for the event is most probably due to long-lasting rainfall, as also indicated by the early prediction component of the tool (Fig. 9). Figure 11 also indicates that the event is characterized by high-intensity 24-h accumulated rainfall. The COSMO-2 evaluation (Fig. 10e) well identifies the regions that were finally affected by damages.
Fig. 11

Network of hydrometeorological sensors available in Catalonia: The circles indicate the location of the 4 C-band radars of XRAD and the C-band radar of the Spanish Agency of Meteorology (AEMET); the gray triangles and white diamonds show the location of the rain gauges and stream-level sensors, respectively

The probabilistic early predictions with the tool based on COSMO-LEPS present higher probability of high 24-h rainfall on the event day in the case of the forecast delivered 2 days in advance (Fig. 10g), while the evaluation available 1 day ahead (Fig. 10f) shows lower probability of exceeding a 2-year return period for 24-h cumulated rainfall in the areas affected by damages according to the newspaper reports collected by Andres et al. (2015).

3.5 An Outlook to Future Flash Flood Early Warning in Switzerland

An initial assessment of the value of the early warning tool demonstrated the potential of precipitation-based indexes for flash flood prediction. The current situation concerning the tool implemented in southern Switzerland can be summarized in four points:
  • Real-time collection of forecasts is still ongoing since July 2013. The next step will be the verification of this first period of real-time operations.

  • The realization of a second prototype in northern Switzerland is planned.

  • Further efforts will focus on using probabilistic extrapolation of weather radar QPE (Mandapaka et al. 2012; Foresti and Seed 2014) for hydrological applications (Liechti et al. 2013a).

  • Finally, real-time hydrological information as obtained from the application of high-resolution distributed models should be used to estimate initial conditions and reduce the number of false alarms observed in this prototype application (Knechtl 2013). This will make use of concepts linked to the mapping of dominant runoff processes (Antonetti et al. 2015).

4 Flash Flood Detection in Catalonia

The flash flood forecasting system adopted in Catalonia (Corral et al. 2009; Alfieri et al. 2011; Versini et al. 2014) is based on rainfall estimates and forecasts generated from weather radar observations. This system, named FF-EWS, is designed as a tool for monitoring the hazard of intense rainfall situations in the context of a flash flood early warning system.

Radar rainfall inputs depict the evolution of the rainfall field with a resolution (of the order of 1 km and 5–10 min) adapted to monitoring the precipitation phenomena that produce flash floods. Also, extrapolation of radar rainfall observations has been successfully used for forecasting the evolution of the rainfall field for a few hours.

With these rainfall inputs, flash flood hazard assessment is based on the assumption that the rainfall accumulated upstream of a point of the drainage network (i.e., the basin-aggregated rainfall) can be used to characterize the flash flood hazard, especially for high return periods, when the pdf of discharges and the pdf of precipitation tend to have the same slope (Guillot and Duband 1967).

4.1 Probabilistic Rainfall Inputs

Quantitative Precipitation Estimates (QPEs) and Quantitative Precipitation Forecasts (QPFs) are produced from weather radar observations with the Integrated Tool for Hydrometeorological Forecasting (Corral et al. 2009). The use of probabilistic rainfall inputs allows us to characterize the uncertainty in QPE and QPF and assess its impact in the estimated hazard. Most of the existing techniques to generate radar-based probabilistic rainfall products are based on the ensemble approach (e.g., Bowler et al. 2006; Llort et al. 2008; Germann et al. 2009; Villarini et al. 2009; Berenguer et al. 2011; Panziera et al. 2011; Quintero et al. 2012): they produce a number of realistic rainfall scenarios (members) compatible with radar observations and that at the same time respect the spatial and temporal structure of the rainfall field. The latter is a crucial aspect to properly assess the impact of rainfall uncertainty on hazard assessment.

Ensuring the quality of the QPE maps is fundamental to guarantee the good performance of the system. This requires processing radar observations with a chain of algorithms to reduce the effect of the sources of uncertainty affecting radar QPE (e.g., Corral et al. 2009; Villarini and Krajewski 2009). In the Integrated Tool for Hydrometeorological Forecasting (Corral et al. 2009), the production of QPE maps includes (1) mitigating the effects of the interception of the radar beam with the terrain (Delrieu et al. 1995), (2) eliminating non-meteorological echoes (Berenguer et al. 2005; Park and Berenguer 2015), (3) identifying precipitation types in volumetric radar data, (4) extrapolating elevated radar observations to the surface with a vertical profile of reflectivity that depends on the type of precipitation (as described by Franco et al. 2006, 2008), and (5) converting reflectivity into rain rate using a relationship also adapted to the type of precipitation. From instantaneous rainfall maps, rainfall accumulations are computed with a resolution of 1 × 1 km2 considering the motion of the precipitation systems and the evolution of rainfall intensities between consecutive radar scans.

Radar QPE ensembles (EQPE) are obtained by perturbing the deterministic QPE with the method of Llort et al. (2008), which considers the space-time structure of errors affecting radar QPE. Each member of the EQPE is a possible realization of the unknown precipitation field given radar measurements.

Deterministic rainfall nowcasting is based on Lagrangian extrapolation (i.e., advection of most recently observed rainfall map with the estimated motion field, neglecting the evolution of rainfall intensities), which has proven to generate useful rainfall forecasts for lead times up to a few hours (see, e.g., Germann et al. 2006; Berenguer et al. 2005, 2012). It is composed of two modules for:
  • Rainfall tracking: The algorithm implemented to estimate the motion field of precipitation is based on matching three rainfall maps within 24 min with a modified version of the COTREC algorithm (Li et al. 1995).

  • Extrapolation of rainfall observations: The last observed rainfall field is advected in time according to the motion field estimated with the mentioned tracking technique. The motion field is kept stationary in time along the series of generated forecasts.

The uncertainty in rainfall nowcasting is characterized with the SBMcast technique (Berenguer et al. 2011). It generates an ensemble of realistic future rainfall scenarios that evolve from the most recent QPE field, assuming the String of Beads model (Pegram and Clothier 2001) to characterize the space-time variability of the rainfall field.

4.2 Flash Flood Hazard Assessment

Hazard assessment uses estimated and forecasted 30-min rainfall accumulations. This accumulation period is thought to be relevant at point scale (e.g., for urban drainage or in sensitive points of the road network).

For each point of the drainage network, the rainfall inputs available at a given time are used to compute the basin-aggregated rainfall accumulated over a duration corresponding to the concentration time of the catchment (the computations are made for durations between 0.5 and 24 h and for catchments between 4 and 2000 km2).

Hazard assessment (expressed in terms of probability of occurrence or as return period) is based on comparing the computed rainfall accumulations with the values of the available intensity-duration-frequency (IDF) curves. In the case of basin-aggregated rainfall, point IDF values are reduced with a scaling factor that depends on the area of the drained catchment. Hazard assessment is recalculated every time a new QPE map is available with a resolution of 1 × 1 km2 and for lead times between t + 0 and t + 3 h.

Because of the simplicity of this flash flood hazard assessment approach the results sometimes do not reproduce what could be obtained with a system based on a complete rainfall-runoff simulation. The simplification of relating the probability of occurrence of rainfall with the probability of occurrence of discharges neglects some hydrological variables that have an important role in the catchment response (such as the initial moisture state of the catchment or the presence of accumulated snow). On the other hand, the main advantage of the implemented approach is that it does not use parameters that require calibration. This is an important advantage for an operational implementation over large domains, where the aim of the system is to detect flash flood events in small and medium catchments that are often ungauged.

4.3 Implementation in Catalonia (NE Spain) and Case Studies

The FF-EWS is used operationally for flash flood hazard assessment at the control center of the Catalan Water Agency (Barcelona, Spain) for monitoring the evolution of rainfall situations that might lead to flash floods in Catalonia (NE of Spain – see Fig. 11).

In this region, the littoral and pre-littoral mountain ranges (approximately parallel to the coast) act as natural barriers to the warm and humid air from the Mediterranean Sea favoring the genesis of convective processes that lead to intense rains. Yearly rainfall accumulations range from 400 to 1200 mm, but some individual events contribute significantly to the yearly totals (the 10-year return period daily accumulation exceeds 100 mm, and almost every year accumulations over 200 mm in 24 h are recorded somewhere in the Spanish Mediterranean coast). The response times of the mountainous catchments are rather short due to the steep slopes of the streams and the urbanization of the flood plains especially near the coast (where some ephemeral streams have become streets, which increases the risk of flash floods). These factors combined with heavy rainfall events are the ingredients that lead to flash floods in this region.

Operationally, the FF-EWS uses the observations of the rain gauges and stream-level sensors of the Automatic Hydrological Information System and of the four C-band radars of the XRAD (the radar network of the Meteorological Service of Catalonia – see Fig. 11). These are processed with the Integrated Tool for Hydrometeorological Forecasting to generate the radar-based QPE and QPF ensembles. The IDF curves applied to estimate of the return period of the measured rainfall are those used for river planning and in flooding studies at the Catalan Water Agency (ACA 2003).

4.3.1 Case Study: 12–14 September 2006

This case was a typical autumn event during which several mesoscale convective systems crossed Catalonia from southeast to northwest. The maximum rain gauge accumulations were reported in Constantí (near Tarragona) with 267 mm and in the area of the Gulf of Roses (north of Girona) with 256 mm, 216 mm in 24 h (Fig. 12a).
Fig. 12

Results obtained with the radar-based hazard assessment system during the event of 12–14 September 2006. (a) Scatterplot of radar QPE (R) versus rain gauge measurements (G) and (b) radar QPE accumulation for 13 September 2006 (the circles indicate rain gauge accumulations); (c) hazard assessment for the entire event (the red dashed ellipses indicate the areas where floods were reported)

This event caused significant material losses (intense flooding in urban areas in the regions of Tarragona and Barcelona and failure of the road and railway networks due to flooding and landslides) and one casualty. The intense rainfall produced flash floods in ephemeral torrents near the coast and in some sub-basins of the main rivers as, for example, in the lower part of the rivers Llobregat (5000 km2) and La Muga (850 km2).

The estimated rainfall accumulation map for 13 September 2006 is shown in Fig. 12b. The comparison between radar QPE and the available rain gauge records shows a reasonable agreement, except for the largest accumulations (probably more affected by attenuation of the radar signal due to intense rain).

Figure 12c shows the summary of the maximum hazard level estimated at each point of the drainage network throughout the event and how the system was able to successfully detect the importance of the event and identify the areas most affected by intense rainfall and flash floods (marked with red dashed circles in Fig. 12c; see also Fig. 11 of Barnolas et al. 2008). Figure 13 shows the variety of hazard assessment products generated by the system every time a new radar QPE map is available: besides the hazard assessment based on radar observations (Fig. 13a), the system forecasts the expected evolution of the hazard level based on radar QPF (the hazard level forecasted on 13 September 2006 at 15:00 UTC for a lead time of 2 h is shown in Fig. 13b). Additionally, the system estimates the uncertainty in the forecasted hazard level in terms of probability to exceed a given return period (Fig. 13c, d show, respectively, the probability to exceed a return period of 2 and 5 years for the case shown in Fig. 13b). As presented in Sect. 4.1, this product describes how the uncertainty in rainfall QPE and QPF affects the hazard level estimated with the FF-EWS system.
Fig. 13

Hazard assessment for 13 September 2006 at 17:00 UTC: (a) obtained radar rainfall observations; (b) obtained for a lead time of 2 h. (c) and (d) Probability of exceeding a return period of 2 and 5 years, respectively, as estimated from probabilistic rainfall forecasts with a lead time of 2 h

4.3.2 Case Study: 02 November 2008

On 02 November 2008 in the early morning, a convective system coming from the Mediterranean moved toward the interior of Tarragona. The intense rain and strong winds caused significant damages in the coastal area. Some flash floods occurred in the streams near the coastal city of Salou (the closest rain gauge recorded 50 mm between 01:00 and 03:00 UTC). Also, the Segre river flooded some areas in the region of Lleida. The maximum rain gauge accumulation exceeded 130 mm in the village of Prades, with over 90 mm recorded on 02 November 2008 between 02:00 and 05:00 UTC. In the area of Girona, the rainfall was more sustained along the day, and maximum accumulations reached up to 125 mm, resulting in numerous calls to the fire brigades for flooding in cities like Girona or Olot.

The radar QPE accumulation shows a good correspondence with collocated rain gauge measurements (Fig. 14a, b). Figure 14c shows that the system identified significant hazard in the main spots where floods occurred. In the zone of Tarragona, the area affected by large rainfall accumulations is a 15-km wide band along which an intense convective cell developed and propagated. The system was able to identify significant hazard level in the torrents around Salou, where the most damaging flash floods occurred (see Figs. 14c and 15a). Figure 15be show the evolution of the hazard map forecasted for 06:00 UTC. The first signal of possible hazard in this area was detected 1.5 h ahead (i.e., with the forecast generated at 04:30). Thirty minutes later, the hazard map forecasted with a lead time of 1 h is almost identical to what was finally diagnosed from radar observations (Fig. 15a).
Fig. 14

Same as Fig. 13, but for the event of 02 November 2008

Fig. 15

(a) Hazard estimated on 02 November 2008 at 06:00 UTC. (be) Evolution of the hazard assessment product for lead times of 2.0, 1.5, 1.0, and 0.5 h

For this case, the European Flood Awareness System’s flash flood component (EPIC; see Sect. 2), forecasted the possibility of a significant event in the coastal streams of Tarragona almost 3 days in advance (based on the NWP forecasts produced on 31 September 2008 at 12:00 UTC). However, the differences in the exact location where the different members of the NWP Ensemble Prediction System used in EPIC forecasted the intense rainfall event resulted in a wide region with some (low) probability of flash flood occurrence (Alfieri et al. 2011).

This case illustrates the complementarity of a system like EPIC (based on NWP forecasts) and the radar-based flash flood hazard system (with a time horizon of a few hours): once a system like EPIC has determined the possibility of flash floods over a certain area a few days ahead, radar-based QPE and QPF allow us to increase the resolution of hazard assessment in the context of flash flood monitoring.

4.3.3 Case Study: 17–19 June 2013

This event caused significant losses all throughout the central Pyrenees (both in the southern and the northern sides). In Catalonia, the northwestern counties were the most affected, with serious flooding in the head of the Garona and Noguera Pallaresa catchments, in the Pyrenees (with mountains above 3000 m amsl). Many of the mountain torrents and the rivers Garona and Noguera Pallaresa overflowed and flooded several villages producing important damages: some roads collapsed, the bridges of Salardú, Arties, and Llavorsí collapsed, several houses were destroyed by the waters in Arties and Bossòst, and 400 people were evacuated (see In the county of Val d’Aran, the total damages were estimated in 100 M€. Daily rainfall accumulations reached 115 mm in Vielha (the capital of this county), with maximum rainfall intensities of 12 mm in 30 min.

This event is presented to illustrate two of the limitations of the presented hazard assessment system:
  1. 1.

    The location of the affected area falls beyond the limits of what can be considered the maximum range for radar Quantitative Precipitation Estimation (as can be seen in Figs. 11 and 16, the closest radar is located over 100 km away) and is a very mountainous area affected by visibility problems from the radar perspective due to beam blockage and beam overshooting (e.g., Pellarin et al. 2002). Consequently, the quality of radar QPE in these areas is in general rather poor and underestimates rain gauge accumulations (see Fig. 16a, b).

  2. 2.

    Although this was quite a significant rainfall event, the severe response of the basin was strongly influenced by the accelerated melting of a good part of the snow accumulated in the mountains at the end of spring (after the event, the snow depth recorded in stations located above 2200 m showed a reduction of up to 700 mm).

Fig. 16

Results obtained with the radar-based hazard assessment system during the event of 17–19 June 2013. (a) Radar QPE accumulation between 17 June 2013 at 20:00 UTC and 19 June 2013 at 24:00 UTC; the red dashed ellipse indicates the location where radar QPE significantly underestimated precipitation and where floods occurred (the black dots correspond to the villages mentioned in the text) (from west to east, Salardú, Vielha, Arties, Bossòst and Llavorsí). (b) Radar-rain gauge blending accumulation. (c) Scatterplot of radar QPE versus rain gauge measurements (the blue diamonds correspond to the locations where radar QPE significantly underestimated rainfall accumulations). (d) Hazard assessment for the entire event based on radar-rain gauge blending in the domain indicated in panel (b)

These factors resulted in serious underestimation of the hazard level in this area using radar-based QPE (not shown here). To improve the quality of radar QPE in such mountainous areas, several authors have proposed to use small X-band radars to fill the gaps of regional radar networks (e.g., Beck and Bousquet 2013; Campbell and Steenburgh 2014). When these small radars are not available (as in the case of the Catalan Pyrenees), the estimated QPE field can be improved by blending radar QPE maps with rain gauge observations (e.g., Velasco-Forero et al. 2009; Schiemann et al. 2011; Sideris et al. 2014 and references therein). These are constrained by rain gauge observations and reproduce the structure of the rainfall field as depicted by radar. The blended radar-rain gauge QPE (obtained with the technique of Velasco-Forero et al. 2009) has been implemented to recalculate the hazard level along the event. Figure 16c, d shows, respectively, the resulting total rainfall accumulation and the summary of the maximum hazard level estimated along the event. Now, the areas most affected by floods show significant flood hazard level (above a return period of 5 years in some parts of the drainage network). However, the damages indicate that the real magnitude of the event was significantly higher (the return period was most likely above 25 years), with an important role of snow melting (not considered in the current version of the hazard assessment system).

4.4 An Outlook to Future Flash Flood Early Warning in Catalonia

The hazard assessment system presented here uses radar-based probabilistic QPE and QPF inputs. The system is used in real time in the control center of the Catalan Water Agency for monitoring the evolution of rainfall events that might lead to flash floods. Some of its outputs are also disseminated to the general public through the website “Water in Real Time” (AETR –, last accessed on 17 July 2016).

The main advantage of the system is its high resolution (1 km and 6 min), which enables very precise determination of the areas at risk at the expense of shorter lead times (in Catalonia, up to 3 h). Consequently, extending this time horizon with NWP models with lead times beyond 1 day is necessary to enable earlier preparedness and trigger effective emergency and response plans. The best practice is to use the radar-based FF-EWS in the context of monitoring potentially hazardous rainfall situations as detected with systems based on NWP (as suggested by Alfieri et al. 2011; Versini et al. 2014).

Current work in progress focuses on extending the coverage of the system for flash flood hazard assessment in the context of the EC Civil Protection project European Demonstration of a Rainfall- and Lightning-Induced Hazard Identification Nowcasting Tool (EDHIT,, last accessed on 17 July 2016). This project is implementing a similar system at European scale based on the European radar mosaics generated by the EUMETNET project OPERA (Huuskonen et al. 2014). The goal of EDHIT is to demonstrate the potential of the European radar mosaic for rainfall-induced hazard assessment in the context of Civil Protection. A prototype of the system has been operating since mid-2012, and the first analyses show promising results. Figure 17 shows an example of the regional hazard assessment based on radar observations during the event of May 2012 in central Europe, compared with the results obtained with an operational numerical weather prediction system.
Fig. 17

(a) Hazard level overlaid on hourly rainfall accumulations for 01 June 2013 at 17:00 obtained from radar observations (left) and from rainfall forecasts with a lead time of 3 h (right). (b) Correlation between hourly accumulations estimated from the OPERA radar mosaic and rainfall forecasts obtained with the HIRLAM run corresponding to 01 June 2013 at 00:00 UTC (dashed blue line) and with the analyzed nowcasting system run on 01 June 2013 at 00:00, 08:00, 16:00 UTC and on 02 June 2013 at 00:00, 08:00 UTC (red lines)

In parallel, current research focuses on how to account for soil moisture conditions in hazard assessment: the chosen approach is based on adapting the rainfall thresholds used for hazard assessment to the soil moisture conditions depicted with the rainfall-runoff model LISFLOOD (van der Knijff et al. 2010), applied operationally at European scale in the context of EFAS.

5 Conclusions

Three early warning systems for flash flood early warning have been presented in this chapter. The systems show a number of similarities though making different uses of probabilistic information. EPIC and the Swiss tool use probabilistic forecasts of COSMO-LEPS and consistent hindcast datasets for obtaining spatially distributed information on possible upcoming floods. The Catalan FF-EWS system makes full use of ensemble weather radar QPE and QPF, while the Swiss tool uses an advanced weather radar product combined to high-resolution numerical weather predictions for the nowcasting of flash floods.

A clear challenge in flash flood forecasting based on NWP is the accurate prediction of the location and timing of extreme storms, given their features of small-scale, short, and intense events. Probabilistic approaches and ensemble forecasting can help us address this issue, so that if some chance of extreme events is predicted, regional systems are triggered to monitor the evolution of the event as it approaches and develops, making use of more detailed information of regional monitoring networks and of the expertise of local flood forecasters.

Interestingly, ongoing developments of all the three systems are aimed to integrating initial soil moisture conditions and to better characterize the timing and magnitude of the flood events.

All three systems present and target applications in areas extending across different countries. Problems of data homogeneity are often not easy to address, particularly when merging data from different national and regional networks and in the blending of point observations with gridded output from numerical models. EPIC is not affected by such issue as it is based on modeled precipitation from a single data product. On the other hand, both the Swiss tool and the large-scale application of the Catalan approach might suffer from using transnational data. Hence, the implementation of these latter applications in different areas requires a careful and tailored design based on the local data availability.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Lorenzo Alfieri
    • 1
    Email author
  • Marc Berenguer
    • 2
  • Valentin Knechtl
    • 3
  • Katharina Liechti
    • 3
  • Daniel Sempere-Torres
    • 2
  • Massimiliano Zappa
    • 3
  1. 1.Directorate for Space, Security and MigrationEuropean Commission – Joint Research CentreIspraItaly
  2. 2.Center of Applied Research in HydrometeorologyUniversitat Politècnica de CatalunyaBarcelonaSpain
  3. 3.Swiss Federal Research Institute WSLBirmensdorfSwitzerland

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