# A dynamic runoff co-efficient to improve flash flood early warning in Europe: evaluation on the 2013 central European floods in Germany

D. Raynaud, J. Thielen, P. Salamon, P. Burek, S. Anquetin, L. Alfieri

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D. Raynaud, J. Thielen, P. Salamon, P. Burek, S. Anquetin, et al. A dynamic runoff co-efficient to improve flash flood early warning in Europe: evaluation on the 2013 central European floods in Germany. Meteorological Applications, 2015, 22 (3), pp.410-418. 10.1002/met.1469. hal-02916879

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# A dynamic runoff co-efficient to improve flash flood early warning in Europe: evaluation on the 2013 central European floods in Germany

D. Raynaud,<sup>a,b</sup> J. Thielen,<sup>b*</sup> P. Salamon,<sup>b</sup> P. Burek,<sup>b</sup> S. Anquetin<sup>a</sup> and L. Alfieri<sup>b</sup>

**ABSTRACT:** Flash floods are listed among the deadliest and costliest weather-driven hazard worldwide. Yet, only a few systems to predict flash floods run operationally in Europe. Recently, the European Precipitation Index based on Climatology (EPIC) was developed and then set up for daily flash flood early warning for an area covering most of the continent. EPIC is a purely rainfall-driven indicator based on the prediction of statistical threshold exceedance of the upstream precipitation to provide early warning up to 5 days in advance. Its main assumption is that flash floods are directly and solely related to extreme accumulations of upstream precipitation. It does not take into account any geo-factors such as slope and land use or processes such as initial soil moisture, which can have a significant impact on the triggering of such events. This study proposes an enhanced version of EPIC through a dynamic and distributed runoff co-efficient which depends on the initial soil moisture. This co-efficient, namely the European Runoff Index based on Climatology (ERIC), is used to weigh each contribution of the upstream precipitation proportionally to the initial soil moisture. The evaluation based on 1 year of daily runs proved that ERIC reaches a threat score of 0.5 if it forecasts a probability >35% of exceeding the 20 year return period of upstream runoff. This result is 0.16 higher than for EPIC. A case study of the flash flooding affecting central Europe in June 2013 demonstrated the ability of ERIC to successfully detect and locate the affected areas.

**KEY WORDS** flash flood; runoff co-efficient; early warning; probabilistic forecast; ungauged basins

Received 4 February 2014; Revised 16 May 2014; Accepted 22 May 2014

## 1. Introduction

Flash floods are phenomena characterized by both short duration and high rate between peak discharge and mean annual flow (Georgakakos, 1986). They are usually triggered by a combination of intense precipitation with high runoff production, but can also be generated by a combination with snow melt or moderate precipitation on already saturated soils. The time interval between the onset of the precipitation and the peak flow itself is defined typically as <24 h (Hill et al., 2010). The meteorological phenomena responsible for flash flooding typically have small temporal and geographical extent, such as intense convective precipitation, which sometimes are classified as mesoscale convective systems exhibiting a V-shape (Sanchez-Laulhé, 2006), or heavy continuous orographic rainfall (Llasat et al., 2000).

Flash floods can be triggered by various hydro-meteorological processes. Overland flow can be produced by either water saturation of the soil or by soil infiltration exceedance even for soils initially dry due to high rainfall intensities. Land use, soil texture (of the upper soil layer) and slope also play a key role in runoff production (Defersha et al., 2011). Urban area and karstic soil, which are quite common particularly in southern European watersheds, decrease dramatically the time of concentration (Bailly-Comte et al., 2008) making some areas particularly prone to flash flooding. Initial soil moisture also determines an important part of the runoff production when an intense storm affects a wet or a dry basin (Penna et al., 2011; Longobardi et al., 2003).

The wide range of climates and topographic or geomorphological features in Europe create numerous possibilities of flash flooding generation. Some major events have been described (Gaume et al., 2009) and analysed (Marchi et al., 2010) showing not only the large range of characteristics of these floods but also some common features such as their sensitivity to initial soil moisture.

Despite major efforts in the detection and short-term forecasting of precipitation at high spatial and temporal resolutions, forecasting of flash floods remains difficult. The recently completed HAREN EU-FP7 research project (http://www.haren-project.eu) has demonstrated the potential benefits of real-time radar composites for flash flood detection at European scale (Berenguer et al., 2011). However, although extrapolation of radar information can be achieved a few hours ahead in time, the method cannot take into account the processes leading to generation, growth and decay of individual cells. In order to enlarge the time horizon, these processes have to be accounted for, for example through high-resolution modelling. Despite the increase in high-resolution weather forecasting products capable of resolving convection (Consortium for Small-scale Modelling (COSMO)-2, AROME, among others), there are still many processes, especially in microphysics, related to extreme precipitation that are still parameterized. Using very high resolution numerical models could lead to better flash flood forecasting (Younis et al., 2008). However, even if the structure of the storm is well predicted, uncertainty about its timing and location remains high and cannot be quantified within a deterministic approach.

A way to tackle this issue is to use ensemble prediction systems rather than deterministic forecasts. In Europe, these

<footnote><sup>a</sup> Laboratoire d'étude des Transferts en Hydrologie et Environnement (LTHE), Grenoble Université, France</footnote>

<footnote><sup>b</sup> European Commission, Joint Research Center (JRC), Institute for Environment and Sustainability (IES), Ispra, Italy</footnote>

<footnote>* Correspondence: J. Thielen, European Commission, Joint Research Centre, Ispra, VA, Italy. E-mail: jutta.thielen@jrc.ec.europa.eu</footnote>
techniques have been explored for hydrological forecasting for more than a decade for large riverine floods, for example in the European Flood Forecasting System (EFFS) FP5 research project, through initiatives such as the Hydrologic Ensemble Prediction Experiment (HEPEX: http://www.hepex.org), the COST 731 project (Rossa et al., 2010; Zappa et al., 2010) and many others (Cloke & Pappenberger, 2009). Within the scope of the IMPRINTS project (http://www.imprints-fp7.eu), ensembles have also been applied to flash floods for selected catchments (Fundel et al., 2010; Panziera et al., 2011). One aim of IMPRINTS was to complement the regional systems with a flash flood indicator on European scale which would be based on ensemble prediction system rainfall (Alfieri et al., 2011b, 2012). The methodology chosen for flash flood early warning uses an extreme value statistical analysis of predicted precipitation in small basins against a 20 year reforecast dataset to assess the severity of upcoming events. The European Precipitation Index based on Climatology (EPIC, Alfieri et al., 2011a; Alfieri and Thielen, 2012) was implemented successfully into the European Flood Awareness System (http://www.copernicus.eu/) (Bartholmes et al., 2009; Thielen et al., 2009) as an operational flash flood component. It is the first indicator running over most of the European continent and providing a flash flood forecast up to 5 days in advance. As it has been implemented operationally, EPIC was able to detect some major flash floods such as the event of November 2012 in Tuscany (Italy). It proposes a simple and robust methodology based on upstream precipitation but does not integrate any hydrologically relevant parameters such as initial soil moisture and slope.

Some tragic events, such as the 25 September 2005 flood in France (http://www.ohmcv.fr/P511_rex2005.php), have proved that soil moisture has a key role in the triggering of flash flooding. Some studies, tested on smaller catchments, took advantage of a runoff threshold instead of a purely rainfall-based approach (Norbiato et al., 2009; Javelle et al., 2010) whereas Reed et al. (2007) and Braud et al. (2010) perform proper high-resolution hydrological simulation.

The objective of this study is to maintain the large-scale coverage guaranteed by EPIC and to introduce a distributed and dynamic runoff co-efficient leading to a new flash flood indicator based on upstream runoff which will be referenced to in the following text as European Runoff Index based on Climatology (ERIC).

## 2. Methodology

### 2.1. Establishing modelled soil moisture–runoff relationships with EFAS

To date, the hydrological model behind the European Flood Awareness System (EFAS) is among the few calculating water balance on a daily basis for entire Europe. Although aimed at riverine floods for larger catchments, the European coverage and the daily production make EFAS results relevant as input for this study. EFAS, pre-operational since 2005, has been designed to tackle flood forecasting and early warning at the European scale. Using probabilistic weather forecasts and the hydrologically distributed model LISFLOOD (Van Der Knijff et al., 2010), it generates a probabilistic discharge forecast twice a day and up to 10 days in advance for the main European rivers. Using extreme value statistics, alert thresholds have been derived from long-term simulation (1990–2011) with the LISFLOOD model driven with observed data. Using the same model set-up and parameterization for the discharge forecast allows comparison of the forecast against the critical thresholds. Although the 5×5 km² grid spacing does not make EFAS suitable for flash flood forecasting, this long-term model simulation can also be used to establish relationships between initial moisture conditions and runoff production for each grid cell in the model domain. Equation (1) presents how the runoff co-efficient for a specific grid cell is defined in this study:

$$C_f = \frac{Ov + UpR}{Rf + Sm} \quad (1)$$

where $C_f$ is the runoff co-efficient at every grid point of the domain; $Ov$, the overland flow; $UpR$, the fast subsurface flow; $Rf$, the rainfall accumulation and $Sm$, the snowmelt contribution.

In Figure 1 the runoff co-efficient associated to each day for which the rainfall accumulation was >10 mm is plotted against its corresponding initial soil moisture. An exponential fitting (curve) on the pattern drawn by the cloud of points allows establishing of a modelled relationship for each grid cell. The example shows two locations having different land uses. In Figure 1(a), corresponding to a forest land use, the runoff co-efficient is quite stable at a value of 0.1 up to a soil moisture of 0.8 and then increases gradually. In Figure 1(b), corresponding to steep and relatively high mountains, the pattern is similar but there are two main differences: the runoff co-efficient value for dry conditions

<figure><img src="image_3.png" /><figcaption>Figure 1. Relationships between runoff co-efficient and initial soil moisture condition from LISFLOOD model outputs for a forest land point (a) and a rocky, steep mountainous area (b).</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 2. Geographical domains of LISFLOOD hydrological model and COSMO-LEPS meteorological model.</figcaption></figure>

is greater and the rising limb is shifted to lower soil moisture values. Not only can the strong link between soil moisture and runoff co-efficient be seen easily in these two examples, but the dependency on geomorphological features such as slope and land use are also implicitly taken into account by LISFLOOD. Steep slope and impermeable soil tend to increase the runoff production especially for dry and medium initial soil moisture.

## 2.2. European Precipitation Index based on Climatology (EPIC)

The methodology developed for the EPIC indicator is only described briefly as it is detailed by Alfieri *et al.* (2012). The input data of EPIC is the forecast precipitation provided by the Limited-Area Ensemble Prediction System (LEPS) of the COSMO (Marsigli *et al.*, 2005) which runs twice a day up to 132 h lead time, and covers almost all of Europe (Figure 2).

Its grid size is about 7 km and the temporal resolution is 3 h and consists of 16 ensemble members. A 20 year dataset of retrospective forecasts (reforecast) starting from 1989 is also available (Fundel *et al.*, 2010) which comprises a number of meteorological parameters including rainfall, 2 m temperature and evapotranspiration forecast with the same spatial and temporal resolution as the operational forecast.

As the aim is to focus on flash floods, calculations were restricted to catchments up to 5000 km² (Alfieri, 2012). Using the 1 km resolution river network, EPIC calculates at each river cell

and for each COSMO-LEPS member, the upstream precipitation (UP) associated with duration of 6, 12 and 24 h and scales it by the upstream area (Equation 2):

$$UP_{d_k}(t) = \frac{1}{N} \sum_{i=1}^{N} P_{d_k, i}(t) \quad (2)$$

where $d_k \in \{6, 12, 24\}$, duration chosen for the rainfall accumulation; $N$, number of grid cells with 1 km² area; $P_{d_k, i}$ precipitation accumulation within $d_k$ over the grid cell $i$ and $t \in [6 \text{ h}; 132 \text{ h}]$, forecast lead time.

In order to assess if the current event is extreme, a 'climatology' of the upstream precipitation at each river cell is calculated. For the three durations of accumulation up to 24 h, and on a 6 h time step, the upstream accumulation is computed using the 20 year COSMO climatology for all the points of the 1 km river network. The annual maxima associated with each duration are extracted. From the three duration intervals, the most severe compared to its climatology is selected as flash flood indicator EPIC (Equation 3):

$$EPIC(t) = \max_{\forall d_k} \left( \frac{UP_{d_k}(t)}{\frac{1}{M} \sum_{j=1}^{M} \max(UP_{d_k})_j} \right) \quad (3)$$

where $M = 20$, number of years of the climatology; $\max(UP_{d_k})_j$, maximum value of upstream precipitation during year $j$ and $d_k \in \{6, 12, 24\}$, duration chosen for the rainfall accumulation.

## 2.3. From EPIC to ERIC

The methodology proposed in this study takes advantages of EPIC's methodology and of the runoff co-efficient relationships derived from EFAS hydrological simulations. In order to do this, the mismatch between EFAS simulations at $5 \times 5 \text{ km}^2$ grid scale and the $1 \times 1 \text{ km}^2$ ERIC high-resolution outputs needs to be addressed. This is done by applying the following assumption: the runoff co-efficient dependency to initial soil moisture is supposed to be homogeneous within each of the $5 \times 5 \text{ km}^2$ cells when resampled to $1 \times 1 \text{ km}^2$. The main limitation of this assumption is related to the potential large diversity of soil textures, land uses and slopes within each cell, especially for the Mediterranean surroundings. Secondly, due to the mismatch between EFAS long simulation (1990–2011) and COSMO-LEPS reforecast (1989–2009), only 19 upstream runoff (UR) annual maxima could be extracted and used as reference:

$$UR_{d_k}(t) = \frac{1}{N} \sum_{i=1}^{N} C_{f_i}(t) \times [R_{d_k, i}(t)] \quad (4)$$

where $d_k \in \{6, 12, 24\}$, duration chosen for the rainfall accumulation; $N$, number of grid cells in the upstream area; $R_{d_k, i}$, rainfall accumulation within $d_k$ over the grid cell $i$ and $C_{f_i}$, runoff co-efficient.

$$ERIC = \max_{\forall d_k} \left( \frac{UR_{d_k}}{\frac{1}{M} \sum_{j=1}^{M} \max(UR_{d_k})_j} \right) \quad (5)$$

where $M = 19$, number of years of the climatology; $\max(UR_{d_k})_j$, maximum upstream runoff value during year $j$.
<figure><img src="image_5.png" /><figcaption>Figure 3. Mean annual upstream runoff divided by the corresponding mean annual upstream precipitation for the 12 h accumulation over Sicily.</figcaption></figure>

In contrast to EPIC, ERIC weighs the upstream precipitation contribution of each cell with its associated runoff co-efficient issued from the previous relationships and the initial moisture given by the operational EFAS forecast (Equation 4). The 16 possible soil moisture scenarios are computed using the LISFLOOD model, starting from the same initial state and evolving with their corresponding members of the rainfall forecast. Furthermore, the precipitation input (R) contains only rainfall and is reduced by the snowfall component in ERIC by using a simple downscaling of the $7 \times 7 \text{ km}^2$ COSMO-LEPS temperature forecast to $1 \times 1 \text{ km}^2$ and a threshold of $T = 1 \text{ °C}$ to distinguish rain from snow. Finally, in analogy to EPIC, ERIC is defined as presented in Equation (5).

Figure 3 illustrates the simulated mean annual maxima of upstream runoff over Sicily (Italy) divided by the corresponding mean annual upstream precipitation and clearly shows the impact of slope, land use and soil moisture on runoff. Only the areas characterized by steep slopes and impermeable soils lead to high runoff production with maximum proportions between runoff and rainfall annual maxima (e.g. Etna volcano).

## 2.4. Evaluation methodology

### 2.4.1. Data collection

The pre-requisite to the evaluation of the flash flood indicator is to have, over an extended period, a list of reported flash flood events. Based on the search of media information and national hydrological reports this short database was collected for the entire COSMO-LEPS domain. Owing to the nature of the search, some flash flood events may not be included, especially for countries where not much information is available. As detailed in the following, long-term evaluation was performed from December 2009 to November 2010. During this 1 year period, 71 flash flood events were reported.

### 2.4.2. Analysing the outputs of ERIC

The ways to display and analyse the output of ERIC follow the methods proposed by Alfieri and Thielen (2012). An ERIC equal

to 1 corresponds to an upstream runoff reaching the mean annual maximum. When this threshold is exceeded over a river section by at least 2 of the 16 members (12.5%), the most downstream point of this section is selected. At this location, the ERIC value is converted into a return period fitting a Gumbel distribution on its associated annual maxima. The probabilities of exceeding the 2, 5 and 20 year return levels are computed and reported at these locations (reporting points).

Choosing a minimum probability of only 12.5% to create a reporting point downstream a tributary where $ERIC = 1$ is exceeded can create a large number of points. Thus, it is necessary to identify the areas where flash floods are most likely to strike. The data sets were first analysed to establish a more suitable minimum probability of exceedance of the 20 year return period. The range of minimum probabilities tested goes from 25 to 45% with a 5% step. Drawing circles having radius of 30 km around the reporting points then allows them to be grouped into highlighted areas (Figure 4). This radius has been chosen according to the coarser input data (rainfall forecast, $7 \times 7 \text{ km}^2$) which limits the accuracy of the indicator. When at least two reporting points fall within the radius, the correlation with observed flash flood has been found to be much higher except for isolated points. Hence, only ‘groups’ of reporting points are finally retained.

As ERIC was computed once a day during the long-term evaluation, the same area can be highlighted for several consecutive forecasts, especially if the dispersion of COSMO-LEPS is low. In order to be able to detect this type of situation, it was decided to classify the reporting points of each run in five groups according to their lead time (6–24 h), (30–48 h), (54–72 h), (78–96 h) and (102–132 h). The establishment of suitable thresholds is then done only for reporting points being issued between 30 and 48 h in advance in order to eliminate these duplicates and not count more than once a detection or false alarm corresponding to the same forecast event. The classification of reporting points in these five classes also allows assessing the performances of ERIC depending on lead-time. Using minimum probability threshold and the same methodology as previously described, the number of false alarms, hits and missed events of each of the five groups can be established.
<figure><img src="image_6.png" /><figcaption>Figure 4. Methodology of elimination and gathering of reporting points in highlighted areas. From the total set of points (a) only the ones with $P(T20 > 35\%)$ are kept (b) and are finally grouped using a 30 km radius (c).</figcaption></figure>

Finally, Bartholmes et al. (2009) showed that a key component to reduce the number of false alarms in EFAS is to issue an alert only when a severe event is predicted to be above threshold over more than one consecutive forecast run. This concept is referred to as persistence of forecasts. Such a property has also been investigated on the proposed methodology, although flash floods are known to have a shorter predictability range as they originate mostly from rapidly developing convective cells. From two consecutive runs, two groups of reporting points highlighting the same area and having the same timing are considered to be related to the same forecast flash flood event. Minor deviations in location (a few tens of kilometres) and timing (a few hours) are acceptable as the weather forecast should be more accurate with a decreasing lead-time. The forecast is considered as persistent if ERIC spots the same area in at least two runs. The hit ratio is compared between the general case considering that one forecast is enough to issue a warning and a more restrictive one requiring a persistent forecast.

### 3. Results and discussion

ERIC has been evaluated for an extended period from December 2009 to November 2010 as well as specific case studies (May 2013 central Europe, June 2013 southwestern France). Only the flash flood forecast related to the major event in central Europe is presented in detail here.

#### 3.1. Statistics from a 1 year evaluation

The evaluation on the extended period ranged from 1 December 2009 to 30 November 2010 and was performed on the entire domain. Although 1 year is short, the fact that the whole of Europe is considered leads to a sample of 71 rapid flooding events identified thanks to hydrological reports recently made available in some countries (e.g. http://www.magrama.gob.es) and news archives. This relatively large sample allows a statistical evaluation to be performed. During this period, events triggered by different initial conditions allow a wide assessment of ERIC's performances. Several major flash flood events having relatively low soil moisture were reported during these 12 months, including the ones in Thessaly (Greece) in December 2009 and in the Var department (France) in June 2010. Other events were observed during long rainy periods embedded within riverine floods (Spain in December 2009, central Europe in May 2010).

<figure><img src="image_7.png" /><figcaption>Figure 5. Numbers of hits, false alarms and hit ratio depending on the minimum probability of exceeding the 20 year return period required. Only reporting points issued from lead times between 30 and 48 h are taken into account.</figcaption></figure>

In addition, a comparison of EPIC and ERIC could be performed for the same 1 year period. The evaluation is based on the location, lead-time and probability of exceeding the 20 year return period of the reporting points described previously. Both indicators are compared with two skill scores used commonly in threshold exceedance analysis: hit ratio and threat score.

Figure 5 presents the numbers of hits and false alarms, together with the hit ratio (line) depending on the minimum probability of exceedance of the 20 year return period from 25 to 45% probability of ERIC. For thresholds over 35% there is no significant improvement of this ratio and the number of detections decreases. Thus, considering that reporting points exceeding the 20 year return period by at least 35% are related to a forecast flash flood event appears to be suitable as it combines a maximal hit ratio and as many detections as possible. With such a threshold many reporting points of the total set are removed and about 30% of them are still considered as flash floods.

The assessment of ERIC's performance depending on lead-time can be seen in Figure 6. The number of hits is highest from 24 to 72 h. The relative lower values of the hit ratio for the first lead times can be explained by the under-dispersion of COSMO-LEPS forecasts. For longer lead times, an increase
<figure><img src="image_8.png" /><figcaption>Figure 6. Numbers of hits, false alarms and hit ratio depending on lead time and for a minimum probability of exceeding the 20 year return period of 35%. Best results are found for 2 days lead time.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Figure 7. Districts affected by rapid flooding on 1 and 2 June 2013 in Germany as they were reported in the ECHO DAILY FLASH of the European Commission (http://ec.europa.eu/echo/news/echo-flash/2013/20130603_en.htm).</figcaption></figure>

of the number of false alarms due to a large spread of the meteorological input is observed and induces a hit ratio reduction below 50% at day 5. These results indicate that ERIC provides useful information, particularly up to 3 days in advance. In general, most of the uncertainty and false alarms comes from the meteorological inputs that are not really accurate in forecasting location, timing and intensity of convective structures in the medium range (Zappa *et al.*, 2010).

Table 1 presents the number of hits, false alarms, the hit ratio and the threat score for both ERIC and EPIC indicators. The hit ratio suggests that EPIC is more reliable than ERIC as it goes from 89 to <60%. Nevertheless taking into account missed events thanks to the threat score proves that the higher number of false alarms in ERIC is balanced by less missed events. The resulting score of ERIC is finally higher by 0.16 than the EPIC one.

The same analysis on persistent forecasts shows that on one hand the hit ratio is similar for EPIC with 88% and increased for ERIC, reaching 78%. On the other hand, the threat score is largely deteriorated when not considering the non-persistent forecast. This evaluation has been done using one forecast run per day. Since spring 2013, COSMO-LEPS is available twice a day. Thus, it is likely that more events would be spotted using the persistence criterion with recent forecasts. Finally, it is important to underline once again that the results presented here are probably underestimating the number of false alarms because there is no comprehensive flash flood database on the European scale, but they do give a first estimate of the indicators' performances.

## 3.2. Case study: the June 2013 flash flood in Germany

From the end of May to the first days of June 2013, a low pressure system located over northern Italy remained stationary

for 2 days (Blösch *et al.*, 2013). In this type of situation, also known as the ‘Genova low’, the warm and humid air from the Mediterranean reaches central Europe, turns back south and results in intense precipitation over Switzerland, Germany, Austria and the Czech Republic through an orographic blocking over the northern side of the Alps. The rainfall accumulation for this event reached in some areas 100 mm in 24 h and 300 mm in 72 h. On 1 and 2 June, flooding started in the upstream tributaries of the Elbe and Danube rivers with flash flood features. Overall, the flood event caused 23 fatalities (http://eur-lex.europa.eu/: draft amending budget N°9 to the general budget 2013) and insured losses were estimated at about $27 billion (http://floodlist.com/insurance/june-floods-cost-27-billion). Figure 7 presents the districts affected by rapid flooding on 2 June in Germany together with the areas where the rainfall accumulation exceeded 100 mm. In Switzerland, the St Gallen region was the most affected whereas the western Czech Republic also suffered massive flooding which spread rapidly downstream to Prague. Finally, some casualties were also reported in the western part of the Czech Republic due to rapid rise of water level.

Figure 8 presents the output of ERIC for the three daily consecutive forecasts starting from 30 May. The criteria

<table><caption>Table 1. Numbers of detections, false alarms, missed events, hit ratios and threat scores of ERIC and EPIC indicators: comparison between persistent and non-persistent forecasts.</caption><thead><tr><th></th><th></th><th>Detection</th><th>False alarm</th><th>Missed events</th><th>Hit ratios</th><th>Threat score</th></tr></thead><tbody><tr><td rowspan="2">ERIC</td><td>Either persistent or not persistent</td><td>53</td><td>36</td><td>18</td><td>0.59</td><td>0.50</td></tr><tr><td>Either persistent or not persistent</td><td>25</td><td>7</td><td>46</td><td>0.78</td><td>0.32</td></tr><tr><td rowspan="2">EPIC</td><td>Either persistent or not persistent</td><td>25</td><td>3</td><td>46</td><td>0.89</td><td>0.34</td></tr><tr><td>Either persistent or not persistent</td><td>7</td><td>1</td><td>64</td><td>0.88</td><td>0.1</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>Figure 8. Three runs of ERIC on 30 May, 31 May and 1 June 2013 1200 UTC. The ERIC's time series converted in return periods are also displayed and correspond to the black reporting point which is issued for the three runs.</figcaption></figure>

established previously are applied: a minimum probability of 35% of exceeding the 20 year return period and a minimum of two reporting points over the same area. On 30 May, three areas were highlighted by ERIC, two in southern Germany (Danube) at the border with Switzerland and Austria and one in Saxony-Anhalt (North Germany, Elbe). On 31 May the same areas were forecast. Both numbers of reporting points and probability of exceedence are increased. A fourth one appeared in the western Czech Republic. Also, the day before the rapid flood event, the ERIC forecast remained persistent.

Comparing the forecast (Figure 8) with the German restricts actually affected by flash flooding (Figure 7) proves that ERIC produced good results for Germany in this case where both location and timing of the event were accurate. The area including four reporting points with a persistence of three runs at the border between Austria and Switzerland could have been validated as the St Gallen region that was affected by flash flooding. Also, Figure 8 clearly depicts the areas in the Czech Republic where casualties were reported. In comparison to ERIC, EPIC highlighted a smaller area and did not show any persistence (Figure 9).
<figure><img src="image_11.png" /><figcaption>Figure 9. Comparison between ERIC (a) and EPIC (b) forecasts on 1 June 1200 UTC. No persistence is observed with EPIC. The reporting points displayed depend on the thresholds chosen for each indicator: 20 year return period exceeded by a minimum of 35% of members for ERIC and 5 year return period exceeded by a minimum of 60% of members for EPIC.</figcaption></figure>

## 4. Summary and conclusion

The work presented here proposes a methodology to introduce runoff into the European flash flood indicator, European Precipitation Index based on Climatology (EPIC), developed by Alfieri *et al.* (2012) which is based on threshold exceedance of the upstream precipitation only. A distributed and dynamic runoff co-efficient is used in this work, to take into account initial soil moisture and geomorphological features and weigh the contribution of the rainfall to flooding. Results from a 1 year analysis show that European Runoff Index based on Climatology (ERIC) correlates well with reported flash flood events when the probability of exceeding the 20 year return period for the upstream runoff is >35%. The threat score proves that the runoff-based indicator has better performances than the purely rainfall-based one.

The computation time was not significantly increased by the current methodology compared to EPIC and is of the order of 3 h on a single node of a Linux cluster. Thus, ERIC could also be used operationally as part of an early warning system such as the European Flood Awareness System (EFAS). As the LISFLOOD model covers the entire European continent, the geographical domain has not been reduced compared to EPIC. In future, an extension to the south of LISFLOOD's model domain is planned which would allow using ERIC on the Algerian and Tunisian coasts. Further enlargement of all input data domains would also lead to an entire coverage of the Mediterranean region which is particularly exposed to flash flood and for which ERIC has already proved its skills in the northern part. The scalability of the methodology makes it furthermore suitable for small-scale application. Currently, ERIC uses soil moisture–runoff relationships on 5×5 km² grid spacing, meteorological input on 7×7 km² from the Consortium for Small-scale Modelling-Limited-Area Ensemble Prediction System (COSMO-LEPS) and aggregates the upstream runoff using the 1×1 km² river network. Its downscaling would be possible over smaller areas if both rainfall and hydrological information was available and would prevent from performing hydrological simulations.

Some further improvements could be investigated in the future. First, so far, the soil moisture which determines the dynamic runoff co-efficient is only updated once a day to guarantee the consistency between ERIC's climatology and the operational

forecast. Building a LISFLOOD long-term simulation on a 6 h time step may allow detecting of events with duration of 12 or 24 h having dry initial soil moisture but with the soil reaching saturation during the very first time steps. Such an improvement would increase the time of computation of the annual maxima but would not affect the ERIC forecast. Second, so far, the return period estimation is calculated by fitting a Gumbel distribution to the raw ERIC ensemble of 16 members. For rainfall and runoff return level, a three-parameter GEV (generalized extreme value) distribution may be capable of providing more realistic results, particularly for return periods larger than the reforecast climatology (i.e. 19 years). Some tests have been done to assess if using a different distribution allows mitigation of the over-estimation of the return periods exceeded but the fitting suffers from a limited sample size of 19 values. Further improvement of this indicator could be achieved by enlarging the temporal length of both LISFLOOD and COSMO-LEPS climatologies, particularly if larger return periods (e.g. 50, 100 years) were tested.

In summary, this study indicates that the runoff-based flash flood indicator ERIC shows a good detection rate of flash flood across Europe. It provides early information on the probability of occurrence of flash floods with best performance between 1 and 3 days in advance.

## Acknowledgements

This work has been carried out with the help of colleagues in the Climate Risk Management unit, Institute for Environment and Sustainability, Joint Research Center. In particular, the authors wish to thank Beatriz Revilla-Romero and Philippe Roudier for their support as well as Alessandra Bianchi and Hadewij Sint for their help in GIS.

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