ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-1783-2018Integrated high-resolution dataset of high-intensity European and Mediterranean flash floodsHigh-intensity European and Mediterranean flash floodsAmponsahWilliamwamponsah@knust.edu.ghAyralPierre-AlainBoudevillainBricehttps://orcid.org/0000-0002-1771-4953BouvierChristopheBraudIsabellehttps://orcid.org/0000-0001-9155-0056BrunetPascalDelrieuGuyDidon-LescotJean-FrançoisGaumeErichttps://orcid.org/0000-0002-7260-9793LeboucLaurentMarchiLorenzoMarraFrancescohttps://orcid.org/0000-0003-0573-9202MorinEfrathttps://orcid.org/0000-0001-6671-7926NordGuillaumePayrastreOlivierhttps://orcid.org/0000-0002-8396-5873ZoccatelliDavideBorgaMarcoDepartment of Land, Environment, Agriculture and Forestry,
University of Padova, Legnaro, ItalyDepartment of Agricultural
and Biosystems Engineering, College of Engineering, KNUST, Kumasi, GhanaESPACE, UMR7300 CNRS, “Antenne Cevenole”, Université de
Nice-Sophia-Antipolis, FranceLGEI, IMT Mines Ales, Univ
Montpellier, Ales, FranceUniv. Grenoble Alpes, CNRS, IRD,
Grenoble INP, IGE, 38000 Grenoble, FranceHydrosciences, UMR5569
CNRS, IRD, Univ. Montpellier, Montpellier, FranceIrstea, UR
RiverLy, Lyon-Villeurbanne Center, 68626 Villeurbanne, FranceIFSTTAR, GERS, EE, 44344 Bouguenais, FranceCNR IRPI,
Padua, ItalyInstitute of Earth Sciences, Hebrew University of
Jerusalem, Jerusalem, IsraelWilliam Amponsah (wamponsah@knust.edu.gh)5October20181041783179427March201816May20186September201813September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/1783/2018/essd-10-1783-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/1783/2018/essd-10-1783-2018.pdf
This paper describes an integrated, high-resolution dataset of
hydro-meteorological variables (rainfall and discharge) concerning a number
of high-intensity flash floods that occurred in Europe and in the
Mediterranean region from 1991 to 2015. This type of dataset is rare in the
scientific literature because flash floods are typically poorly observed
hydrological extremes. Valuable features of the dataset (hereinafter referred
to as the EuroMedeFF database) include (i) its coverage of varied
hydro-climatic regions, ranging from Continental Europe through the
Mediterranean to Arid climates, (ii) the high space–time resolution radar
rainfall estimates, and (iii) the dense spatial sampling of the flood
response, by observed hydrographs and/or flood peak estimates from post-flood
surveys. Flash floods included in the database are selected based on the
limited upstream catchment areas (up to 3000 km2), the limited storm
durations (up to 2 days), and the unit peak flood magnitude. The EuroMedeFF
database comprises 49 events that occurred in France, Israel, Italy, Romania,
Germany and Slovenia, and constitutes a sample of rainfall and flood
discharge extremes in different climates. The dataset may be of help to
hydrologists as well as other scientific communities because it offers
benchmark data for the identification and analysis of the
hydro-meteorological causative processes, evaluation of flash flood
hydrological models and for hydro-meteorological forecast systems. The
dataset also provides a template for the analysis of the space–time
variability of flash flood triggering rainfall fields and of the effects of their estimation on the
flood response modelling. The dataset is made available to the public with
the following DOI: 10.6096/MISTRALS-HyMeX.1493.
Introduction
Flash floods are triggered by high-intensity and relatively short-duration
(up to 1–2 days) rainfall, often of a spatially confined convective origin
(Gaume et al., 2009; Smith and Smith, 2015; Saharia et al., 2017). Due to the
relatively small temporal scales, catchment scales impacted by flash floods
are generally less than 2000–3000 km2 in size (Marchi et al., 2010;
Braud et al., 2016). Given the large rainfall rates and the rapid
concentration of streamflow promoted by the topographic relief, flash floods
often shape the upper tail of the flood frequency distribution of small- to
medium-size catchments. Understanding the hydro-meteorological processes that
control flash flooding is therefore important from both scientific and
societal perspectives. On the one hand, elucidating flash flood processes may
reveal aspects of flood response that either were unexpected on the basis of
less intense rainfall input or that highlight anticipated but previously
undocumented characteristics. On the other hand, improved understanding of
flash floods is required to better forecast these events and manage the
relevant risks (Hardy et al., 2016), because knowledge based on the analysis
of moderate floods may be questioned when used for forecasting the response
to local extreme storms (Collier, 2007; Yatheendradas et al., 2008).
However, the small spatial and temporal scales of flash
floods, relative to the sampling characteristics of typical
hydro-meteorological networks, make these events particularly difficult to
monitor and document. In most of the cases, the spatial scales of the events
are generally much smaller than the sampling potential offered by even
supposedly dense raingauge networks (Borga et al., 2008; Amponsah et al.,
2016). Similar considerations apply to streamflow monitoring: often the
flood responses are simply ungauged. In the few cases where a stream gauge
is in place, streamflow monitoring is affected by major limitations. For
instance, peak water levels may exceed the range of available direct
discharge measurements in rating curves, causing major uncertainties in the
conversion of flood stage data to discharge data. In other cases, stream
gauges are damaged or even wiped out by the flood current: in these cases,
only part of the hydrograph (usually a segment of the rising limb) is
recorded.
The call for better observations of flash flood response has stimulated the
development of a focused monitoring methodology in the last 15 years over
Europe and the Mediterranean region (Gaume et al., 2004; Marchi et al., 2009;
Bouilloud et al., 2009; Calianno et al., 2013; Amponsah et al., 2016). This
methodology is built on the use of post-flood surveys, where observations of
traces left by water and sediments during a flood are combined with accurate
topographic river section survey to provide spatially detailed estimates of
peak discharges along the stream network. However, the important thing to
note here is that the survey needs to capture not only the maxima of peak
discharges: less intense responses within the flood-impacted region are
important as well. These can be contrasted with the corresponding generating
rainfall intensities and depths obtained by weather radar re-analysis, thus
permitting identification of the catchment properties controlling the
rate-limiting processes (Zanon et al., 2010). The large uncertainty affecting
indirect peak discharge estimates may be constrained and reduced by
comparison with peak discharges obtained from hydrological models fed with
rainfall estimates from weather radar and raingauge data (Amponsah et al.,
2016). Post-flood surveys typically start immediately after the event and are
carried out in the following weeks and months (Gaume and Borga, 2008), during
the so-called Intensive Post-Event Campaigns (IPEC, in the following), before
possible obliteration of field evidence from restoration works or subsequent
floods.
The aim of this paper is to outline the development of the EuroMedeFF
dataset, which organises flash flood hydro-meteorological and geographical
data from 49 high-intensity flash floods, whose location stretches from the
western and central Mediterranean, through the Alps and into Continental
Europe. The database includes high-resolution radar rainfall estimates, flood
hydrographs and/or flood peak estimates through IPEC, and digital terrain
models (DTMs) of the concerned catchments. Collation of the EuroMedeFF
dataset is a challenging task (Borga et al., 2014), due (i) to the lack of
conventional hydro-meteorological data which characterises these events
(owing to the small spatio-temporal scales at which these events occur), and
(ii) to the fact that extreme events are, by definition, rare. Collecting
rainfall and flood data by means of opportunistic post-flood surveys required
the mobilisation of a group of researchers (ranging in size from 5 to more
than 20 persons) for an extended period of time (ranging from a few days to
some weeks). In addition to this, high-quality weather radar estimates of
extreme events such as the ones triggering flash floods are not easy to
gather, due to the number of sources of error affecting radar estimation
under heavy precipitation and in rough topography environments (Germann et
al., 2006; Villarini and Krajevski, 2010). Owing to these reasons, the
EuroMedeFF dataset of 49 flash flood events comprising high-quality radar
rainfall estimates, flood hydrographs, surveyed flood peaks at ungauged
sites, and digital terrain models is simply unprecedented in size in Europe
and in the Mediterranean in terms of (i) number of events, (ii) variety of
provided data, and (iii) the degree of integration. Given the quality and
resolution of the rainfall input, the archive provides unprecedented data to
examine the impact of space–time resolution in the modelling of
high-intensity flash floods under different climate and environmental
controls. Since results from previous modelling studies are quite mixed, much
of the knowledge being either site-specific or expressed qualitatively, the
availability of the EuroMedeFF data archive may open new avenues to
synthesise this knowledge and transfer it to new situations.
The criteria for the EuroMedeFF database development and a summary table and
spatial locations of the collected flash floods are presented in Sect. 2.
Section 3 describes the components of the flash flood datasets, whereas the
methods used to generate the rainfall and discharge datasets are presented in
Sect. 4. Section 5 discusses the main features of the dataset, based on
climatic regions and the two methodologies for discharge data collection
(stream gauges and indirect estimates from post-flood analysis). General
remarks on the scientific importance of the EuroMedeFF database are provided
in the Conclusions section, whereas a link to the freely accessible
EuroMedeFF database is provided in the Data Availability section.
Criteria for EuroMedeFF database development
The EuroMedeFF database includes data from high-intensity flash flood events
from different hydro-climatic regions in the Euro-Mediterranean area. To be
included in the dataset, the following data availability was ensured:
(i) digital terrain model (DTM) of resolutions 5–90 m of the impacted
region/catchment; (ii) weather radar rainfall estimation with high spatial
and temporal resolutions, and (iii) discharge data from stream gauges and/or
post-flood analyses. Rainfall data are provided at a time resolution of
60 min or less and as “best available rainfall products” (i.e. estimates
which include the merging of radar and raingauge estimates).
Three criteria have been considered for the development of the EuroMedeFF
database.
Flood magnitude. A unit peak discharge of 0.5 m3 s-1 km-2
(this parameter is termed Fth) is considered as the lowest value
for defining a flash flood event. This means that, for an event to be
included in the database, at least one measured flood peak should exceed the
value of Fth. The authors are aware that, depending on climate and
catchment size, a unit peak discharge of 0.5 m3 s-1 km-2
can correspond to a severe flash flood (for instance, in the inner sector of
the alpine range) or a moderate flash flood (for instance, in many
Mediterranean basins). A value of 0.5 m3 s-1 km-2 can be
considered as a lower threshold for flash floods across a variety of climates
and studies (Gaume et al., 2009; Marchi et al., 2010; Tarolli et al., 2012;
Braud et al., 2014). For the sake of simplicity, we adopted the same value of
Fth in all the studied regions. Since the identification of the
flash floods included in the database is primarily driven by the local
observed impact, for most floods the lowest unit peak discharge is much
higher than Fth.
Spatial extent. The upper limit for a catchment impacted by the
flood is 3000 km2 (this parameter is termed Ath). The same
meteorological event may have triggered multiple floods
(e.g. September and October 2014 floods in
France which have affected several catchments of about 2000 km2 – Ardèche, Cèze, Gard,
and Hérault). In this case, we report several events for the same date,
corresponding to different specific catchments with areas less than
Ath.
Storm duration. The upper limit for the duration of the
flood-triggering storm is up to 48 h (this parameter is termed
Dth). The rainfall duration is identified by defining a minimum
period duration with basin-averaged hourly rainfall intensity less than
1 mm h-1 over the impacted catchment to separate the time series in
consistent events. The methodology is similar to Marchi et al. (2010) and
Tarolli et al. (2012), where the duration is defined as “the time duration
of the flood-generating rainfall episodes which are separated by less than
6 h of rainfall hiatus”. We made this threshold explicit to reduce
subjectivity. Here, the minimum duration depends subjectively on
hydro-climatic settings and basin size. The reported Dth is the
duration of the rainfall responsible for each event flood peak, separated
from other rainfall events that may have occurred before or after the main
event depending on the characteristics of the largest involved catchment. In
a number of cases in which the features of the flash flood response were
specifically affected by wet initial soil moisture conditions, rainfall data
are provided for a longer period than the storm duration. This enables us to
account for antecedent rainfall in the analyses.
Location of the flash floods in the central and western
Mediterranean, the Alps, and Inland Continental Europe; inset is the eastern
Mediterranean (Israel). The length of the arrow represents the area of the
largest basin. Colour indicates the magnitude of the largest unit peak
discharge. Direction represents the timing of the flash flood occurrence.
In general, the preliminary selection of flash floods was based on rainfall
data (amount, intensity) from meteorological agencies and qualitative field
recognition of flood response. This led to the exclusion of a number of
low-intensity events. Post-flood reconstruction of peak discharge was carried
out for events that passed this preliminary screening. Several of these
events were not included in the dataset because they failed to meet the
requirements in terms of flood magnitude, spatial extent and storm duration.
Given these constraints, the EuroMedeFF database includes 49 high-intensity
flash floods: 30 events in France, 7 events each in Israel and in Italy, 3
events in Romania, and 1 event each in Germany and in Slovenia.
Figure 1 shows the location of the basins impacted by the flash floods
included in the data archive and provides information on the basic features,
such as timing of occurrence over the year, size of the largest affected
river basin and highest unit peak discharge. The figure shows that the timing
of the floods varies gradually from the south-west, where the floods occur
mainly in the September to November season, to the east, where the floods
occur mainly in the period from autumn to late spring. The shift in
seasonality is paralleled by a decreasing basin size and unit peak discharge
from south-west to east. These findings are supported by the work of Parajka
et al. (2010), who analysed the differences in the long-term regimes of
extreme precipitation and floods across the Alpine–Carpathian range, and of
Dayan et al. (2015), who analysed the seasonality signal of atmospheric deep
convection in the Mediterranean area.
Table 1 reports summary information of the EuroMedeFF database. In the table,
each event is labelled as an “EventID”, which comprises the
impacted catchment/region and the year of occurrence, e.g. ORBIEL1999 (cf.
event 1 in Table 1). The “EventID” is used in the archive to
uniquely identify the event. The table is ordered first on a country basis,
followed by the date of flood peak for each country, from past to most recent
events. For each of the 49 events, the table reports the river basin and the
country, the date of the flood peak, the climatic region, the number of river
sections for which discharge data are available (in terms of both indirect
post-flood estimates and streamgauge-based data), with indications of the
sections with streamgauge information, the range of basin area for the
catchments closed at the studied river sections, the storm duration, the
range of unit peak discharges and the indication of earlier works on the
event. In a few cases, more than one flash flood event is reported for the
same river basin.
Budyko plot for the study basins (P: mean annual precipitation,
AET: mean annual actual evapotranspiration, PET: mean annual potential
evapotranspiration). In case of multiple nested catchments, only data for the
largest one are reported.
Summary information on the flash flood database.
No. ofRangeRange ofEvent IDRegion/catchmentDate ofClimaticriver sectionsin watershedStormunit peakPreviousimpacted (country)flood peakRegion(no. of streamarea (km2)durationdischargestudiesgauges)(h)(m3 s-1 km-2)1ORBIEL1999Orbiel (France)13 Nov 1999Mediterranean21 (1)2.5–239290.80–13.00Gaume et al. (2004)2NIELLE1999Nielle (France)13 Nov 1999Mediterranean16 (0)5–125336.00–20.00Gaume et al. (2004)3VERDOUBLE1999Verdouble (France)13 Nov 1999Mediterranean29 (1)0.35–350301.30–77.14Gaume et al. (2004)4VIDOURLE2002Vidourle (France)9 Sep 2002Mediterranean25 (2)13–110261.33–22.22Delrieu et al. (2005)5GARDONS2002Gardons (France)8 Sep 2002Mediterranean66 (6)1.6–1855251.99–50.00Delrieu et al. (2005)6CEZE2002Ceze (France)8 Sep 2002Mediterranean12 (4)7.3– 1120250.94–19.18Delrieu et al. (2005)7VALESCURE2006Valescure (France)19 Oct 2006Mediterranean4 (4)0.27–3.93342.31–6.94Tramblay et al. (2010)8GARDONS2008Gardons (France)21 Oct 2008Mediterranean33 (9)0.27–1521210.68–34.55Naulin et al. (2012, 2013); Vannier et al. (2016)9CEZE2008Ceze (France)21 Oct 2008Mediterranean21 (3)0.95–1120210.71–22.16Naulin et al. (2012, 2013)10ARGENS2010Argens (France)15 Jun 2010Mediterranean35 (1)3–2550230.73–10.00Payrastre et al. (2012); Le Bihan et al. (2017)11ARDECHE2011Ardeche (France)3 and 4 Nov 2011Mediterranean14 (14)16–2263310.66–9.88Adamovic et al. (2016)12ARDECHE2013Ardeche (France)23 Oct 2013Mediterranean15 (14)2.2–2263170.70–8.1813ORB2014Orb (France)17 Sep 2014Mediterranean7 (3)3.5– 335112.08–20.3114VIDOURLE2014Vidourle (France)18 Sep 2014Mediterranean8 (3)15–770180.89–17.6715HERAULT2014Herault (France)17 Sep 2014Mediterranean10 (4)1–1305291.08–23.0016GARDONS2014-AGardons (France)18 and 20 Sep 2014Mediterranean28 (21)0.27–1855180.63–26.6717ARDECHE2014-AArdeche (France)19 Sep 2014Mediterranean16 (15)3.4–2263130.98–12.8518ARDECHE2014-BArdeche (France)10 and 11 Oct 2014Mediterranean17 (15)3.4–2263410.54–2.9219LEZMOSSON2014Lez Mosson (France)7 Oct 2014Mediterranean20 (4)0.38–30670.76–46.15Brunet et al. (2018)20GARDONS2014-BGardons (France)10 Oct 2014Mediterranean30 (13)0.27–1855290.81–22.3721CEZE2014Ceze (France)11 Oct 2014Mediterranean6 (3)77–1120151.04–7.1422ARDECHE2014-CArdeche (France)3 and 4 Nov 2014Mediterranean16 (16)3.4–2263150.77–7.4523ARDECHE2014-DArdeche (France)14 and 15 Nov 2014Mediterranean14 (14)3.4–2263160.97–2.4724ARDECHE2014-EArdeche (France)27 Nov 2014Mediterranean12 (12)3.4–226370.54–0.9925LERGUE2015Lergue (France)12 Sep 2015Mediterranean11 (3)7.5–1850210.68–20.50Brunet and Bouvier (2017)26VALESCURE2015-AValescure (France)12 Sep 2015Mediterranean4 (4)0.27–3.93260.87–4.38Tramblay et al. (2010)27ARGENTIERE2015Argentiere (France)3 Oct 2015Mediterranean14 (0)1.3–2964.45–18.2128BRAGUE2015Brague (France)3 Oct 2015Mediterranean16 (0)0.6–41.563.03–23.4329FRAYERE2015Frayere (France)3 Oct 2015Mediterranean6 (0)1.3–21.464.44–18.2530VALESCURE2015-BValescure (France)28 Oct 2015Mediterranean3 (3)0.27–3.93381.33–22.22Tramblay et al. (2010)31ZIN1991Zin (Israel)13 Oct 1991Arid1 (1)233.532.28Greenbaum et al. (1998);Lange et al. (1999);Tarolli et al. (2012)32NEQAROT1993Neqarot (Israel)23 Dec 1993Arid1 (1)699.541.00Tarolli et al. (2012)33NORTHDEADSEA1994Teqoa (Israel)5 Nov 1994Arid- Mediterranean1 (1)14241.12Tarolli et al. (2012)34NORTHDEADSEA2001Darga, Arugot (Israel)2 May 2001Arid-Mediterranean2 (2)70–23540.82–1.78Morin et al. (2009); Tarolli et al. (2012)35RAMOTMENASHE2006Taninim, Qishon (Israel)2 Apr 2006Mediterranean11 (0)0.75–2282.29–29.33Morin et al. (2007); Grodek at al. (2012)36HAROD2006Harod (Israel)27 and 28 Oct 2006Mediterranean12 (1)1.2–10050.58–10.00Rozalis et al. (2010); Tarolli et al. (2012)37QUMERAN2007Qumeran (Israel)12 May 2007Arid5 (0)8.5–45.333.35–12.96Rozalis et al. (2010); Tarolli et al. (2012)38STARZEL2008Starzel (Germany)2 Jun 2008Continental17 (0)1–119.580.81–11.74Ruiz-Villanueva et al. (2012)39SORA2007Selška Sora (Slovenia)18 Sep 2007Alpine-Mediterranean18 (2)1.9–21216.51.58–10.85Zanon et al. (2010)40FEERNIC2005Feernic (Romania)23 Aug 2005Continental1 (1)1685.52.22Zoccatelli et al. (2010)41CLIT2006Clit (Romania)30 Jun 2006Continental1 (0)3644.86Zoccatelli et al. (2010)42GRINTIES2007Grinties (Romania)4 Aug 2007Continental1 (0)5241.92Zoccatelli et al. (2010)43SESIA2002Sesia (Italy)5 Jun 2002Alpine-Mediterranean6 (6)75–2586221.33–4.7844FELLA2003Fella (Italy)29 Aug 2003Alpine-Mediterranean7 (5)24–623120.52–8.37Borga et al. (2007)45ISARCO2006Isarco and Passirio (Italy)3 and 4 Oct 2006Alpine2 (2)48–7512.50.75–1.07Norbiato et al. (2009)46MAGRA2011Magra (Italy)25 Oct 2011Mediterranean36 (3)0.5–936241.70–28.19Amponsah et al. (2016)47VIZZE2012Vizze (Italy)4 Aug 2012Alpine3 (1)45–108180.93–1.55Destro et al. (2018)48SARDINIA2013Cedrino-Posada (Italy)18 Nov 2013Mediterranean18 (1)4–627124.98–25.64Niedda et al. (2015); Righini et al. (2017)49LIERZA2014Lierza (Italy)2 Aug 2014Alpine-Mediterranean8 (0)1.5–12.41.512.03–27.59Destro et al. (2016)
We used the Budyko diagram (Budyko, 1974) to characterise the climatic
context of the catchments included in the EuroMedeFF database (Fig. 2). The
Budyko framework plots the evaporation index (i.e. the ratio of mean annual
actual evaporation to mean annual precipitation, AET /P) versus the
aridity index (i.e. the ratio of mean annual potential evapotranspiration to
mean annual precipitation PET /P). The mean values of these variables
were calculated for each river basin, so the number of points plotted in
Fig. 2 is smaller than the total number of flash floods in the database.
Figure 2 also reports the empirical Budyko curve (dotted curve; Budyko,
1974), which fits well with the upper envelope (continuous curve) of the data
included in the data archive. Not surprisingly, the catchments under Arid or
Arid-Mediterranean climate display typically water-limited conditions, with
the aridity index, PET /P>1. Continental, Alpine and
Alpine-Mediterranean catchments lie in the energy-limited sector of the
Budyko plot, with aridity index, PET /P<1, indicating wet climate.
Mediterranean catchments often display water-limited conditions, although
less severe than catchments under Arid and Arid-Mediterranean
climate.
The EuroMedeFF dataset
The EuroMedeFF dataset consists of high-resolution data on rainfall,
discharge, and topography. The information in the data archive is categorised
into three main groups: generic, spatial, and
discharge data.
Generic data
The “Readme” text file contains generic data on the date of the
flash flood occurrence, the name of the impacted catchment and the country
and administrative region of the catchment. Detailed generic information on
the spatial data (DTM and radar) and discharge data (flood hydrographs and
IPECs) are also elaborated in the file. Also, the coordinate systems and grid
sizes of the spatial data, and the time resolutions and reference of the
radar and flood hydrographs, are summarised.
Spatial data
Topographic data. Digital terrain model (DTM) with a grid size
of 5–90 m. For each event, DTM data are provided in compressed ASCII raster
files, with label “EventID_DTMXX”, where XX is the grid
size in metres. The DTM is provided in the local
country coordinate system, with a file
(DTMXX_WGS84_LowLeft_corner) reporting the coordinates of the
lower left corner in the WGS84 coordinate system. All the data relative to
one country are in the same coordinate system.
Radar rainfall data. Corrected and raingauge-adjusted radar rainfall
data are provided with a 1 km or less grid size and temporal resolution
appropriate for the flood (typically 60 min or less). For each event, radar
data are provided in compressed ASCII raster files, with label
“EventID_RADAR”. Radar data are provided, consistent with the DTM
data, in the local country coordinate system with a file
(Radar_WGS84_LowLeft_corner) reporting the coordinates of the
lower-left corner in the WGS84 coordinate system. At least, all the data
relative to one country are in the same coordinate system. The time reference
for the radar data is provided as
yymmddHbMb- yymmddHeMe, with
Hb, Mb referring to the beginning and He,
Me to the end of the considered time period.
The spatial data (DTM and radar) are provided in ASCII format. The
coordinates for radar and DTM data as well as locations of streamgauge and
IPEC sections are consistently provided in both local (country-specific) and
WGS84 systems. The main advantage of WGS84 is that it avoids possible
conversion problems from local coordinate systems while providing a
homogeneous coordinate system throughout the database.
Discharge data
Flood hydrographs. For each event, the location of the available
streamgauge stations, upstream area of the basin draining to the station and
observed hydrographs are provided in the Excel file
“EventID_HYDROGRAPHS”. The coordinates are consistent with the
local country coordinate system given for the spatial data, and are also
provided in the WGS84 coordinate system. The time reference system for the
hydrograph data are consistent with that used for the radar data.
Post-flood data. Comprehensive data on post-flood surveys through
IPEC are provided in the excel file “EventID_IPEC”. For each
section, the location of the surveyed cross section, the area of the basin,
the indirect estimation method used and peak discharge estimates are
provided. When possible, the following further parameters are reported: flood
peak time, wet area, slope, roughness parameter, mean flow velocity, Froude
number, geomorphic impacts (in three classes – Marchi et al., 2016), and the
estimated peak discharge uncertainty range (Amponsah et al., 2016).
Coordinates of the surveyed sections are consistent with the local country
coordinate system given for the spatial data, and are also provided in the
WGS84 coordinate system.
Summary statistics for drainage areas for the EuroMedeFF database
under different climatic regions.
No. ofMean drainage25th–75thClimatic regionscasesarea (km2)quantiles (km2)Mediterranean6061817.5–113.7Alpine and Alpine-Mediterranean441508.6–97.2Inland Continental2037.62.2–48.6Arid and Arid-Mediterranean1014813.5–210.7
Summary statistics for drainage areas for the EuroMedeFF database
based on the two classes of discharge assessment (stream gauges vs. indirect
methods).
Raw radar data were provided by several sources and elaborated following
different procedures depending on the quality and type of available radar and
raingauge data, in order to obtain the best spatially distributed
precipitation estimate for each event. In general, original reflectivity data
in polar coordinates have been used as raw radar data. A set of correction
procedures, taking into account the highly non-linear physics of radar
detection of precipitation, and procedures for the raingauge-based
adjustment, were used. The procedures include the correction of errors due to
antenna pointing, ground echoes, partial beam blockage, beam attenuation in
heavy rain, vertical profile of reflectivity and wet radome attenuation, and
a two-step bias adjustment that considers the range-dependent bias at yearly
scale and the mean field bias at the single event scale. Radar and raingauge
rainfall estimates were merged using the same procedure: a mean field bias
calculated at the event accumulation scale using rain gauges located in or
around the study catchment. Additional details on the procedures can be found
in Bouilloud et al. (2010), Delrieu et al. (2014), Marra et al. (2014), Marra
and Morin (2015), Boudevillain et al. (2016), and in the references
therein.
For French events 7, 26 and 30 in Table 1, only rainfall data from one local
rain gauge are available. These floods have been kept in the database because
of the interest in including flood response data for very small basins
(<1 km2) and because the small catchment size of the Valescure basin
(4 km2) causes the absence of radar rainfall data to be less detrimental
than for floods that hit larger catchments. Note that the available rain
gauge is located within the considered 4 km2 basin. In addition, as the
radar closest was quite far from the catchment, located in a zone with
complex topography, radar data accuracy was not guaranteed.
Discharge estimation methods
Discharge data in the EuroMedeFF database derive from both streamflow
monitoring stations and post-flood indirect estimates of flow peak through
IPEC. Streamflow data, permitting recording of flood hydrographs, thus
enabling assessment of not only discharge, but also time response and flood
runoff volume estimation, were checked for the uncertainties affecting rating
curves at high-flood stages by using hydraulic models and topographic data.
Discharge data from reservoir operations, water levels and use of the
continuity equation, when available, were also included in the database after
accurate quality control.
Different methods have been used for the indirect reconstruction of flow
velocity and peak discharge from flood marks, such as slope area, slope
conveyance, flow-through-culvert, and lateral super-elevation in bends.
Amongst these methods, the most commonly used for the implementation of the
dataset presented in this paper is the slope conveyance, which consists of
the application of the Manning–Strickler equation, under assumption of
uniform flow, and requires the topographic survey of cross-section geometry
and flow energy gradient, computed from the elevation difference between the
high water marks along the channel reach surveyed (Gaume and Borga, 2008;
Lumbroso and Gaume, 2012).
Although the identification of river cross sections suitable for indirect
peak discharge assessment has sometimes proved not easy (flood marks can be
hardly visible or obliterated by post-flood restoration works), and discharge
reconstruction in cross sections that underwent major topographic changes is
affected by major uncertainties (Amponsah et al., 2016), an appropriate
choice of the cross sections permitted us to achieve a spatially distributed
representation of flood response for most studied events. Specific details on
the IPEC procedures can be found in the references provided in Table 1.
Discussion
Overall, 680 peak discharge data are included in the archive: 32 % (219)
were recorded by river gauging stations or based on data from reservoir
operations, and 68 % (461) from IPEC surveys. We followed the geomorphic
impact-based linear error analysis of the slope conveyance discharge
determination presented in Amponsah et al. (2016) for the uncertainty
assessment of the IPEC peak flood estimates. Table 2 reports the number of
river sections for each of the climatic regions and the corresponding summary
statistics of the upstream drainage area. Almost 90 % of the included
discharge data are from the Mediterranean region, which is consistent with
increasing collation and analysis of flash flood data in this region compared
to other climatic regions in Europe (e.g. Gaume et al., 2009; Marchi et al.,
2010). The area of the basins included in the archive ranges from 0.27 to
2586 km2. Table 2 shows that flash flooding may impact larger basins in
the Mediterranean, Alpine and Arid regions than those considered in the
Inland Continental region. This supports earlier findings from Gaume et
al. (2009).
Table 3 reports summary statistics of the upstream drainage area for the two
discharge assessment methods (stream gauges and indirect methods). As
expected, stream gauges correspond to larger areas, whereas post-flood
surveys play major roles in documenting peak discharges for smaller drainage
areas (Borga et al., 2008; Marchi et al., 2010; Amponsah et al., 2016).
Nevertheless, the database also includes discharge data from a few measuring
stations deployed in small research catchments. This allows reduction of the
uncertainty related to the estimation of peak discharge in very small
catchments (Braud et al., 2014).
Unit peak discharges versus drainage areas for the studied flash
floods. The envelope curve for the upper limit of the relationship is
reported.
Unit peak discharges versus drainage areas based on climatic
regions: (a) Mediterranean catchments,
(b) Alpine-Mediterranean and Alpine catchments, (c) Inland
Continental, and (d) Arid and Arid-Mediterranean catchments. The
envelope curve for each climatic region is reported.
Unit peak discharges versus drainage areas based on discharge
assessment methods: (a) stream gauges and (b) indirect
methods. The envelope curves for the upper limits for each method are
reported.
The relationship between the unit peak discharge (i.e. peak discharge
normalised by the upstream drainage area) and the upstream area was
investigated for the EuroMedeFF database to identify the control exerted by
catchment size on flood peaks (Fig. 3) and to analyse its variation among the
four main climatic regions (Fig. 4a–d). Not surprisingly, the unit peak
discharges exhibit a marked dependence on watershed area. The envelope curve,
representing the observed upper limit of the relationship, was empirically
derived as a power-law function for all the floods as well as for the four
different main climate regions. The envelope curve representative of all the
floods is similar in shape to that reported by Gaume et al. (2009) and Marchi
et al. (2010) in previous analyses in the same hydro-climatic context.
However, the multiplier reported here is larger than that reported in earlier
analyses, due to the inclusion of recent more intense cases documented in
large catchments. Inspection of the multiplier and exponent coefficients of
the envelope curves reveals that the same exponent provides a good fit for
the different climatic regions, whereas the highest multiplier is reported
for the Mediterranean region, with an intermediate value for the
Alpine-Mediterranean and Alpine basins, and the same lowest value for Inland
Continental, Arid-Mediterranean and Arid basins. For small basin areas (1 to
5 km2), Mediterranean and Alpine catchments are shown to experience
similar extreme peaks.
Figure 5a–b show the relationship between unit peak discharge based on the
two discharge assessment methods and watershed area in a log–log diagram,
together with the envelope curves. Indirect estimates of peak discharges show
similar dependence of unit peak discharge on catchment size to that reported
in Fig. 3, showing that the information content of the overall envelope curve
is dominated by the flood obtained based on post-flood campaigns. Indeed,
peak data from streamgauging stations show a clearly different exponent of
the envelope curve (-0.12) when compared to post-flood indirect peak flow
estimates (and to the ones previously shown in Fig. 3). The highest values of
the peak discharge are often missed by the gauging stations because of
insufficient density of streamgauge networks and/or damage to the stations
during floods. This sampling problem is more severe in small basins: as a
consequence, both the value of the multiplier and the exponent of the
envelope equation are lower in Fig. 5a than in the plots that include
post-flood peak discharge estimation in ungauged streams (Figs. 3 and 5b).
The EuroMedeFF dataset is publicly available and can be
downloaded from
http://mistrals.sedoo.fr/?editDatsId=1493&datsId=1493&project_name=HyMeX&q=euromedeff
(last access: 2 October 2018). The dataset is also made available with the
following unique DOI provided by the HyMeX database administrators:
10.6096/MISTRALS-HyMeX.1493 (Amponsah et al., 2018).
Conclusions
We presented an observational dataset that provides integrated
fine-resolution data for high-intensity flash floods that occurred in Europe
and in the Mediterranean region from 1991 to 2015. The dataset is based on a
unique collection of rainfall and discharge data (including data from
post-flood surveys) for basins ranging in size from 0.27 to 2586 km2.
The archive provides high-resolution data enabling a number of flash flood
analyses. It allows the analysis of the space–time distribution of causative
rainfall, which may be used to investigate methodologies for rainfall
downscaling. The data may foster the investigation of the rainfall–runoff
relationship at multiple sites within the flash flood environment. This may
lead to the identification of possible thresholds in runoff generation which
may be related to initial conditions, rainfall rates and accumulations, and
catchment properties. Moreover, it allows investigations to clarify the
dependence existing between spatial rainfall organisation, basin morphology
and runoff response. The archive may be used as a benchmark for the
assessment of hydrological models and flash flood forecasting procedures in
various hydro-climatic settings. The availability of fine-resolution rainfall
data may be used to better understand how rainfall spatial and temporal
variability must be considered in hydrological models for accurate prediction
of flash flood response. Furthermore, the availability of multiple flash
flood response data along the river network may be exploited to better
understand how calibration of hydrological models may be transferred across
events and sites characterised by different severity.
Finally, inspection of the data included in the archive shows the relevance
that indirect peak flow estimates have in flash flood analysis, particularly
for small basins. This shows the urgency of developing standardised methods
for post-flood surveys in order to gather flood response data, including flow
types, flood peak magnitude and time, damages, and social response. This is
key to further advancing understanding of the causative processes and
improving assessment of both flash flood hazard and vulnerability aspects
(Calianno et al., 2013; Ruin et al., 2014).
Compilation of the flash flood data from Italy, Germany, Slovenia and
Romania was done by WA, LM, DZ and MB. The Israeli data were compiled by EM
and FM, whereas the French data were compiled by IB and OP, with
contributions from P-AA, BB, CB, PB, GD, J-FD-L, EG, LL and GN. The initial
draft of the paper was written by WA with the contributions by MB for
Sects. 1 and 5, EM, FM, LM, IB, OP, GD, EG, DZ and MB for Sects. 2 and 3, FM
for Sect. 4.1, and LM for Sect. 4.2. All authors contributed through their
revision of the text.
The authors declare that they have no conflict of
interest.
Acknowledgements
This paper contributes to the HyMeX programme (www.hymex.org, last access: 2
October 2018). Collation of hydrometeorological data and processing of data
collated through post-flood surveys for the Italian events have been done in
the framework of the Next-Data Project (Italian Ministry of University and
Research and the National Research Council of Italy – CNR). Part of the data
was acquired through the Hydrometeorological Data Resources and Technologies
for Effective Flash Flood Forecasting (HYDRATE) project (European Commission,
Sixth Framework Programme, contract 037024). Part of the data provided in
this dataset was acquired during the FloodScale project, funded
by the French National Research Agency (ANR) under contract no. ANR 2011
BS56 027. We also benefited from funding by the MISTRALS/HyMeX programme
(http://www.mistrals-home.org, last access: 2 October 2018), in
particular for post-flood surveys. The rainfall reanalyses for the French
events were provided by the OHM-CV observatory (observation service supported
by the Institut National des Sciences de l'Univers, section Surface et
Interfaces Continentales and the Observatoire des Sciences de l'Univers de
Grenoble). We thank Arthur Hamburger, whose work was supported by Labex
OSUG@2020 (Investissements d'avenir – ANR10 LABX56) and the SCHAPI. We also
thank the SCHAPI, EDF-DTG, for providing some of the French streamgauge
discharge data, and Meteo France for providing French raingauge and radar
input data. For Israel, raw radar data were obtained from E.M.S. (Mekorot
company), raingauge data from the Israel Meteorological Service, streamgauge
data from the Israel Hydrological Service, and post-flood estimates from
reports by the Soil Erosion Research Station at the Ministry of Agriculture
and Rural Development. The HyMeX database teams (ESPRI/IPSL and
SEDOO/Observatoire Midi-Pyrénées) helped in making the dataset
available in the HyMeX database. Edited by:
David Carlson Reviewed by: two anonymous referees
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