ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-9-221-2017A high space–time resolution dataset linking meteorological forcing and
hydro-sedimentary response in a mesoscale Mediterranean catchment (Auzon)
of the Ardèche region, FranceNordGuillaumeguillaume.nord@univ-grenoble-alpes.frBoudevillainBricehttps://orcid.org/0000-0002-1771-4953BerneAlexisBrangerFlorahttps://orcid.org/0000-0003-4273-8938BraudIsabellehttps://orcid.org/0000-0001-9155-0056DramaisGuillaumeGérardSimonLe CozJérômehttps://orcid.org/0000-0003-1243-6955LegoûtCédrichttps://orcid.org/0000-0003-2958-4815MoliniéGillesVan BaelenJoelVandervaereJean-PierreAndrieuJulienhttps://orcid.org/0000-0002-0031-1672AubertCoralieCaliannoMartinDelrieuGuyGrazioliJacopohttps://orcid.org/0000-0002-7097-3946HachaniSaharHornerIvanHuzaJessicaLe BoursicaudRaphaëlRaupachTimothy H.https://orcid.org/0000-0003-3336-7610TeulingAdriaan J.https://orcid.org/0000-0003-4302-2835UberMagdalenahttps://orcid.org/0000-0002-0237-0588VincendonBéatriceWijbransAnnetteUniv. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, FranceEnvironmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandIrstea, UR HHLY, Hydrology-Hydraulics, Villeurbanne, FranceLaMP, CNRS/UBP, Clermont Ferrand, FranceUniversité Côte d'Azur, CNRS, ESPACE, FranceInstitut de géographie et durabilité, Université de Lausanne, Lausanne, SwitzerlandÉcole Nationale d'Ingénieurs de Tunis, Université de Tunis, El Manar 1, TunisiaHydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the NetherlandsInstitute of Earth and Environmental Science, University of Potsdam, Potsdam, GermanyCNRM UMR 3589 (Météo-France & CNRS), Toulouse, Francenow at: Amec Foster Wheeler Environment and Infrastructure, 1425 Route Transcanadienne, Dorval, H9P 2W9, Québec, CanadaGuillaume Nord (guillaume.nord@univ-grenoble-alpes.fr)22March20179122124919July201623September201616December201622January2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://essd.copernicus.org/articles/9/221/2017/essd-9-221-2017.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/9/221/2017/essd-9-221-2017.pdf
A comprehensive hydrometeorological dataset is presented spanning the period
1 January 2011–31 December 2014 to improve the understanding of the
hydrological processes leading to flash floods and the relation between
rainfall, runoff, erosion and sediment transport in a mesoscale catchment
(Auzon, 116 km2) of the Mediterranean region. Badlands are present
in the Auzon catchment and well connected to high-gradient channels of
bedrock rivers which promotes the transfer of suspended solids downstream.
The number of observed variables, the various sensors involved (both in situ
and remote) and the space–time resolution (∼km2, ∼ min)
of this comprehensive dataset make it a unique contribution to research
communities focused on hydrometeorology, surface hydrology and erosion. Given
that rainfall is highly variable in space and time in this region, the
observation system enables assessment of the hydrological response to rainfall
fields. Indeed, (i) rainfall data are provided by rain gauges (both a research
network of 21 rain gauges with a 5 min time step and an operational network of
10 rain gauges with a 5 min or 1 h time step), S-band Doppler
dual-polarization radars (1 km2, 5 min resolution), disdrometers
(16 sensors working at 30 s or 1 min time step) and Micro Rain Radars
(5 sensors, 100 m height resolution). Additionally, during the special
observation period (SOP-1) of the HyMeX (Hydrological Cycle in the
Mediterranean Experiment) project, two X-band radars provided precipitation
measurements at very fine spatial and temporal scales (1 ha,
5 min). (ii) Other meteorological data are taken from the operational
surface weather observation stations of Météo-France (including 2 m
air temperature, atmospheric pressure, 2 m relative humidity, 10 m wind
speed and direction, global radiation) at the hourly time resolution
(six stations in the region of interest). (iii) The monitoring of surface
hydrology and suspended sediment is multi-scale and based on nested
catchments. Three hydrometric stations estimate water discharge at a
2–10 min time resolution. Two of these stations also measure additional
physico-chemical variables (turbidity, temperature, conductivity) and water
samples are collected automatically during floods, allowing further
geochemical characterization of water and suspended solids. Two experimental
plots monitor overland flow and erosion at 1 min time resolution on a
hillslope with vineyard. A network of 11 sensors installed in the
intermittent hydrographic network continuously measures water level and water
temperature in headwater subcatchments (from 0.17 to 116 km2) at a
time resolution of 2–5 min. A network of soil moisture sensors
enables the continuous measurement of soil volumetric water content at
20 min time resolution at 9 sites. Additionally, concomitant
observations (soil moisture measurements and stream gauging) were performed
during floods between 2012 and 2014. Finally, this dataset is considered
appropriate for understanding the rainfall variability in time and space at
fine scales, improving areal rainfall estimations and progressing in
distributed hydrological and erosion modelling.
DOI of the referenced dataset: 10.6096/MISTRALS-HyMeX.1438.
Introduction
The Mediterranean area is prone to intense rainfall events,
sometimes triggering flash floods that may have dramatic consequences (Ruin
et al., 2008). Flash floods are the consequence of short, high-intensity
rainfalls mainly of spatially confined convective origin and often enhanced
by orography (Borga et al., 2014). Therefore, flash floods usually impact
basins less than 1000 km2 (Marchi et al., 2010). In medium-scale
Mediterranean catchments, the control exerted by the amount of rainfall and
its intensity and variability on the generation of runoff and the erosional
processes operating at different scales is of major importance (Navratil et
al., 2012; Marra et al., 2014; Tuset et al., 2016). Assisting stakeholders in
implementing efficient soil conservation and river management measures
implies understanding the processes and the factors that control surface
runoff, developing modelling approaches able to provide reliable flow
separations, localizing sediment sources and sinks, and predicting the space–time
dynamics of sediment and associated contaminant within the catchment. This
requires taking into account the space–time variability in rainfall events,
using spatially distributed models coupling hydrology and mass transfers.
Although the interest for distributed models is recognized for understanding
the inner behaviour of the catchment (i.e. pathways and transit times), many
studies have shown that their reliability does not meet the expectations.
Indeed, the water and sediment discharges simulated by distributed models at
the outlet of the catchment are generally poorer than the results simulated
by lumped models (Jetten et al., 2003; Reed et al., 2004; de Vente el
al., 2013). To date there are various difficulties that hinder the potential
of distributed models (e.g. Cea et al., 2016) such as the large number of
parameters, the definition of some parameters which are difficult to measure,
the high non-linearity of the equations, the interaction between input
parameters, the uncertainty in the experimental measurements and input data,
the space–time variability in the physical processes, and the lack of
comprehensive field data available for initialization and calibration.
Thus, the deployment of
multi-scale observation systems over a period of several years in medium
catchments and the release of the collected datasets as open data with
metadata on how the data have been collected and quality-assured, as well as
their associated uncertainties (Weiler and Beven, 2015), are of crucial
importance to address the current limitations of distributed models.
High space–time resolution (∼km2, ∼ min) datasets
linking meteorological forcing and hydro-sedimentary response are rare in
scientific literature because of the high number and diversity of types of
sensors required for measuring rainfall and surface hydrology. The already
published datasets consist of first-order catchments (“Tarrawarra data
set”, southeastern Australia: Western and Grayson, 1998), catchments where
the observation period is exceptionally long (“Reynolds Creek Experimental
Watershed”, northwestern USA: Slaughter et al., 2001; “Walnut Gulch
Experimental Watershed”, southwestern USA: Renard et al., 2008; Stone et
al., 2008; “Goodwater Creek Experimental Watershed and Salt River Basin”,
midwestern USA: Baffaut et al., 2013), or catchments located in
snow-dominated mountains (“Reynolds Creek Experimental Watershed”,
northwestern USA: Reba et al., 2011; “Dry Creek Experimental Watershed”,
northwestern USA: Kormos et al., 2014). In mesoscale catchments, such
datasets are scarce (“Walnut Gulch Experimental Watershed”, southwestern
USA: Goodrich et al., 1997; “Iowa River Basin”, north central USA: Gupta et
al., 2010), especially in the Mediterranean region.
This study is part of the FloodScale project (Braud et al., 2014), which is a
contribution to the HyMeX programme (Hydrological Cycle in the Mediterranean
Experiment, Drobinski et al., 2014), a 10-year multidisciplinary programme on
the Mediterranean water cycle. A three-level nested experimental strategy was
planned for the HyMeX programme:
A long-term observation period (LOP) lasting about 10 years (2010–2020)
to gather and provide observations of the whole coupled system that support
analysis of the seasonal-to-interannual variability in the water cycle
through budget analyses.
An enhanced observation period (EOP) lasting about 5 years (2011–2015),
for both budget and process studies.
Special observation periods (SOPs) of several months, which aimed at
providing detailed and specific observations to study key processes of the
water cycle in specific Mediterranean regions, with emphases put on heavy
precipitation systems and intense air–sea fluxes and dense water formation.
The FloodScale project (2012–2015) fits into the EOP and encompasses SOP1
(Ducrocq et al., 2014), which took place from 5 September to 6 November 2012
and was dedicated to heavy precipitation and flash floods. This study focuses
on nested scales that range from the hillslope to the medium catchment scale,
all belonging to the Cévennes – Vivarais Mediterranean
Hydrometeorological Observatory (OHMCV) (Boudevillain et al., 2011). It is
located in Ardèche, in a region with a high gradient in annual rainfall
(e.g. Molinié et al., 2012). The observation system was operated by
different teams from various countries during SOP1 and the EOP: IGE, IRSTEA
Lyon, EPFL, Wageningen University, LaMP and Météo-France. The dataset includes precipitation and
weather data, soil moisture data, runoff and soil erosion data, hydrologic
and suspended sediment response data, surface water quality data, and GIS
data.
The duration of the observations presented here (4 years, from 1 January 2011
to 31 December 2014) allows the characterization of the standard catchment
behaviour and provides the opportunity to observe less ordinary events with
processes that are specific to flash floods and to characterize possible
threshold effects that are not observed in small to moderate events. The
observation strategy is reinforced by the deployment of conventional and
polarimetric radars that provide precipitation measurements at spatial scales
not properly resolved by rain gauge networks (Berne and Krajewski, 2013). A
special effort was dedicated to soil moisture measurements and stream gauging
during floods. These opportunistic observations made possible by a real-time
warning system enable watching transient processes like runoff, monitoring
the increase in water content in soil and gauging high discharges in small
to medium catchments, which is challenging due to the very short response
times of such systems. This allows documenting the upper ends of
stage–discharge rating curves that are generally extrapolated at high values.
The paper presents the acquired datasets to make them accessible to the
scientific community and make their use easier and wider. The authors are
convinced that the published datasets can serve as a benchmark for
hydrological distributed modelling applied to the Mediterranean area. The
paper is organized as follows. Section 2 presents the location of the studied
catchment and its context (geology, climatology, land use, pedology).
Section 3 describes the observation system (instruments and measured
variables) and is organized into three subsections: (i) hydrometeorological
data, (ii) spatial characterization data, and (iii) hydrological and sediment
data. Finally, in Sect. 4, the first studies that provide preliminary answers
to the scientific questions selected in the introduction are highlighted.
Catchment description
The Auzon catchment (116 km2) is located in a region traditionally
called “Bas Vivarais” between the plains of the Rhône Valley to the
east (minimum elevation: 40 m a.s.l.) and the Ardèche Mountains
to the west (maximum elevation: 1550 m a.s.l.). The Auzon River is a
left-bank tributary of the Ardèche River, which drains from north to south
(Fig. 1). The Auzon catchment ranges in elevation from 140 to
1019 m a.s.l. This mid-elevation area includes the volcanic plateau
of Coiron to the north (approximately one-third of the catchment area),
standing as a barrier. The latter closes the horizon over the sedimentary
piedmont hills to the south (approximately two-thirds of the catchment area).
The Coiron Plateau is a vast basaltic table ranging in elevation from 600 to
1000 m that has the appearance of an oak leaf lying on the
marly-limestone bedrock of the Bas Vivarais (Grillot, 1971; Naud, 1972).
Intense volcanic activity between 7.7 and 6.4 million years BP (early
Pliocene) produced stacked lava flows and pyroclastic flows that gradually
filled a former valley. This phase of volcanic activity is contemporaneous
with the volcanism phase of the Mont Mézenc. The current morphology of
the region is the result of significant Quaternary erosion, which has notched
the edges of the lava flows delimiting narrow digitations separated by marly
thalwegs. An inverted relief is now observed where the present surface of the
plateau corresponds to the former valley bottom. The sedimentary substratum
is composed of Cretaceous marls and limestones from the Upper Jurassic. The
former valley bottom is now raised with respect to the young valleys carved
by streams and one can observe gorges with steep slopes of marls with a typical
badlands aspect.
Location of the OHMCV pilot site. The two main catchments studied in
the FloodScale project (Braud et al., 2014) – Gardon (2062 km2 ) in
the south and Ardèche (2388 km2) in the north – are outlined by
the bold red line along with the main rivers and the operational hydrometric
stations (blue dots). The small research catchments are shown with orange
boundaries. The Auzon catchment, which is the object of this study, is framed
by a black rectangle which defines the spatial extension of Fig. 3. The two
S-band operational radars with the range circles at 50 and 100 km
(dashed circles) are also shown. The 500 m contour lines are displayed in
the background.
The region is exposed to both oceanic and Mediterranean climatic influences.
The terrain of the region is partly mountainous and plays a major role on
rainfall properties. The highest average daily rainfall intensities are
located in the higher areas, while the highest average hourly rainfall
intensities are located over the plain (Molinié et al., 2012). Average
yearly rainfall ranges between 850 and 900 mm throughout the Auzon
Basin, which represents an
intermediate value between the plains of the Rhône Valley to the east
(500 mm) and the Ardèche Mountains to the west (2000 mm).
On the Coiron Plateau, forest vegetation has almost completely disappeared
(Figs. 2 and 3b). Oaks, chestnut trees and associated shrub flora only remain
on the marly slopes and basalt screes. Mediterranean grazed open woodlands
with broom (Sarothamnus purgans), boxwood (Buxus sempervirens) and sloe tree (Prunus spinosa) cover almost all the
rocky outcrops (Bornand et al., 1977). Grasslands and crops are located in
well-drained depressions.
On most of the limestone formations and marly formations with steep slopes,
natural vegetation is dominant. This vegetation consists of downy oak woods
(Quercus pubescens), garrigues and Mediterranean open woodlands
where stunted oaks are associated with broom (Sarothamnus purgans),
boxwood (Buxus sempervirens), juniper and dry grasslands (thyme,
Aphyllanthes and Brachypodium). On marly-limestone
formations with low slope, the vegetation has been cleared and gave way to
traditional crops (cereals, vines, etc.) and grazed grassland. Overall,
according to the CORINE Land Cover 2006 classification, the Auzon catchment
consists of forest (26.7 %); pastures under agricultural use
(17.1 %); vineyards (19 %); moors, heathland and sparsely vegetated
areas (14.3 %), crops (9.5 %); natural grasslands (11 %); and
urban areas (2.3 %).
Typical landscapes of the Auzon catchment: (a) volcanic
plateau of Coiron with a mix of grassland and open woodlands in the north
part of the catchment, (b) deep valleys descending from the Coiron
Plateau presenting steep slopes of marls with badlands aspects and covered by
deciduous forest, (c) southern boundary of the Coiron Plateau
characterized by the presence of cliffs, (d) toposequence on
marly-limestone formations with regosols on steep marly slopes in the
foreground followed by cultivated clayey soils with vines and ending with
rocky outcrops and lithosols on limestones with garrigue,
(e) hillslopes with vineyards on clayey soils drained by a river
incised in the marly-limestone bedrock and surrounded by a zone of riparian
vegetation, and (f) garrigue and Mediterranean open woodland on
karstified limestones in the western part of the catchment leading to the rapid
drying-up of the Auzon River.
Location maps of the Auzon catchment and instruments for
(a) rainfall, (b) meteorology, and (c) hydrology.
Three different backgrounds are represented: (a) elevation (25 m
bare-earth DEM, source: IGN), (b) land use (30 m resolution images
derived from Landsat images, source: UMR Espace), and (c) (1:100000
soil map, source: INRA). Icons for X-band radars from the IFLOODS project website
(http://ifis.iowafloodcenter.org/ifis/more/ifloods/) (Demir et
al., 2015).
The brown soils on basalt material cover the majority of the volcanic edifice
of the Coiron Plateau (Fig. 3c). They gather soils supported by basaltic rock
(16a), scoria and tuffs (16b), colluvium of anthropogenic origin (16c),
screes and talus fans (16d). These four main soil families constitute a very
homogeneous group with very close physico-chemical characteristics.
Cartographic differences are based on the nature of the parent rock, the
topographic location and human intervention; those factors determine the
depth, the heterogeneity and the texture of soils (Bornand et al., 1977).
Soil depths are generally less than 2 m. The soil matrix consists
mainly of clay and fine silt. The stony load is variable according to the
drainage of the medium.
In the piedmont hills beneath the Coiron Plateau, there are rendzina
(9), clayey-stony
soils of variable depth (20–70 cm) on marly limestones and regosols
(33a) due to erosion on marls characterized by deep gullies (badlands) that
constitute a significant source of suspended material during floods. In less
steep terrain, there are generally cultivated soils (13a), loam and clay
loam that are irregularly deep,
decarbonated at the surface, well structured and supporting cereal crops and
vines. At the south of the Auzon catchment, there are lithosols and regosols
(34a), rocky outcrops and shallow brown calcareous soils (30–40 cm deep) on
marly limestones. On the western edge of the Auzon catchment, rocky outcrops
and lithosols (39a and b) on Jurassic limestone formations are highly
dominant. These karstified formations are responsible for the natural
drying-up of the Auzon River, frequently observed in its downstream reach.
Finally, on the edges of the main rivers (Claduègne and Auzon),
calcareous alluvial soils (2) or coarse textured alluvium (1) are present.
The multi-scale observation system presented in this study (Fig. 3) is based
on nested catchments: the Gazel catchment (3.4 km2), the
Claduègne catchment (44 km2) and the Auzon catchment
(116 km2). Rainfall and weather observations include both
operational and research instruments that are located both inside the
catchments and in their immediate vicinity. Hydrological observations are
mainly concentrated over the Claduègne catchment.
Data description
Table 1 presents the hydrometeorological variables, gives the characteristics
and the number of instruments, and indicates whether the measurements belong
to an operational network or a research network of observation. Table 2
presents the soil and surface water variables (hydrological and sediment
data) and gives the characteristics and the number of instruments. Table A2
in Appendix A contains a chart
that describes the period of measurement of each instrument (rainfall,
meteorology, soil and surface water) and specifies the number of instruments
deployed in the field. All the data presented here have undergone careful
(mostly manual) quality assurance.
Hydrometeorological dataRainfallRadars
The region of interest is covered by two operational S-band radars (Fig. 1):
a conventional radar (Thomson MTO 2000S) located in Bollène (about
40 km away) and a polarimetric radar (Selex Meteor 600S) located in
Nîmes (about 90 km away). Their visibility over the Auzon
catchment is, however, hindered by the topography and the lowest beam is at
about 2 km above the ground. These operational radars, managed by
Météo-France, provided data (radar reflectivity and rain rate
estimates) over the entire period of interest. To complement these radars and
monitor the small-scale variability in precipitation, two additional X-band
research radars were deployed during HyMeX SOP1 (Fig. 3a), providing
measurements at a resolution of about 100 m × 100 m. A
fast-scanning radar (WR-10X+), managed by LaMP, provided rapid plan
position indicator (PPI) scans (every 3 min) at one elevation.
EPFL-LTE managed a mobile X-band polarimetric (MXPol) radar, which provided a
combination of range height indicator (RHI) and PPI scans of polarimetric
variables every 5 min. These two research radars enabled the
monitoring of low-level precipitation over the Auzon catchment. Their maximum
range should vary between 30 and 40 km (the range represented in
Fig. 3a is only qualitative). Finally, five Micro Rain Radars (MRRs),
provided by CNRM, LaMP and OSUG, were deployed in combination during the
autumns of 2012 and 2013 at three locations in the region of interest to
document the vertical profile of precipitation. These Doppler FW-CW
vertically pointing radars measuring the Doppler spectra enable study of the
vertical structure of rainfall as well as the associated microphysical
processes in relation with the orography (Zwiebel et al., 2015). More
detailed information about the operational and research radar systems
involved in HyMeX can be found in Bousquet et al. (2015). The operational
radar processing algorithms are described in Tabary (2007), while the data
from the MXPol radar are processed following the steps described in
Schneebeli et al. (2014) and Griazioli et al. (2015). The characteristics of
MXPol are given by Schneebeli et al. (2013) and Mishra et al. (2016).
Overview of the instruments used to gather the hydrometeorological
variables in the region that encompasses the Auzon catchment (Ardèche,
France) between 2011 and 2014. Note that RHI means “range height
indicator”, PPI means “plan position indicator”, Op means “operational”,
and Res means “research”. The
column “Number” indicates the maximum number of instruments in operation at
the same time.
CompartmentOp/resInstrumentVariableUnitNumberObservation frequencyIntegrationmethodRainfallOpS-band Doppler andReflectivitydBZ25 minInstantaneouspolarimetric radarCumulative rainfallmm2ResX-band Doppler andHorizontal reflectivitydBZ15 minInstantaneouspolarimetric radar (MXPol)Differential reflectivitydBZ1Combination of RHI and PPI scansDifferential phase∘1Doppler power spectradBm1Cross-spectradBm1ResX-band fast-scanningradar (WR-10X+)ReflectivitydBZ13 minInstantaneousResMicro Rain Radar (MRR-2)ReflectivitydBZ310 s resolution–1 min averageIntegratedResDisdrometer (Parsivel 1)Drop size distributionmm-1m-31130 s or 1 minIntegratedDrop velocity distribution11Precipitation ratemmh-111ResDisdrometer (Parsivel 2)Drop size distributionmm-1m-351 minIntegratedDrop velocity distribution5Precipitation ratemmh-15OpRain gauge (Météo-France,Cumulative rainfallmm101 h (Météo-France),IntegratedSPC Grand Delta)5 min (SPC)ResRain gauge (Hpiconet)(R3039 1000 cm2)Cumulative rainfallmm205 minIntegratedMeteorologyOpTemperature probe (PT100)Air temperature∘C61 hInstantaneousResBaro-Diver (DI500)Air temperature∘C42 minInstantaneousMini-Diver (DI501)Atmospheric pressurecmH2O42 minInstantaneousOpHumidity probe (HMP45D)Relative humidity%RH51 hInstantaneousOpBarometer (PTB220)Atmospheric pressurehPa11 hInstantaneousOpWind sensor (DEOLIA 96,Wind speedms-121 hInstantaneousAlizia 312)Wind direction∘21 hInstantaneousOpPyranometerShortwave/longwaveWm-211 hInstantaneous(Kipp & Zonen Inc.)radiation
Overview of the instruments used to gather the hydrological and
suspended sediment variables in the Auzon catchment (Ardèche, France) or
its close vicinity between 2011 and 2014.
CompartmentInstrumental deviceInstrumentVariableUnitNumberLocationObservationfrequencySurface waterHydrometric stationsPressure probe (PLS)Water levelm1Gazel2 minDischargeLs-11Gazel2 minRadar level sensor (Cruzoe)Water levelm1Claduègne10 minDischargem3s-11Claduègne10 minRadar level sensor (RLS)Water levelm1Auzon5 min or 1 hDischargem3s-11Auzon5 min or 1 hLSPIV/camera (VW-BP330)Water surface velocityms-11Auzon5 min or 1 hRadar surface velocity sensor (RG-30)Water surface velocityms-11Claduègne10 minAcoustic Doppler velocimeter (IQ Plus)Water velocity profilems-11Claduègne10 minConductivity and temp. probe (CS547)Water conductivityµScm-12Gaz., Cla.f2 or 10 minWater temperature∘C2Gaz., Cla.f2 or 10 minSuspended solids probe (Visolid IQ 700)TurbiditygL-1SiO22Gaz., Cla.f2 or 10 min3700 portable samplerSediment concentrationgL-12Gaz., Cla.f10 or 40 minWater erosion plotsHS flume with level sensor (Thalimedes)DischargeLs-12Le Pradel1 min3700 portable samplerSediment concentrationgL-12Le PradelvariableStream sensor networkMini-Diver (DI501)Water levelm7a2 minWater temperature∘C7a2 minCTD-Diver (DI271)Water levelm4b2 or 5 minWater temperature∘C4b2 or 5 minWater conductivityµScm-14b2 or 5 minDischargeLs-14b2 or 5 minSoilSoil moisture networkThetaProbeSoil volumetric water contentm3m-36 transects × 25 pointscpre- and post-event10 HSSoil volumetric water contentm3m-39 profiles with 5 sensorsd20 minThetaProbe (ML2X)Soil volumetric water contentm3m-31 profile with 4 sensorse1 hSoil temperature∘C1 profile with 4 sensorse1 h
a The group of stations represented by green stars in Fig. 3c (bz1, mi1, mi2, mi4, sg2, sj2, sj3).
b The group of stations represented by blue stars in Fig. 3c (sg1, sj1, mi3, vb1).
c The points represented by blue circles in Fig. 5.
d The points represented by red plus signs in Fig. 5.
e The points represented by a black cross in Fig. 5.
f Abbreviation for “Gazel, Claduègne”.
Disdrometers
A network of 16 OTT Parsivel disdrometers (optical spectropluviometers), 11
of which are of the first generation and 5 of the second generation, covers
the southern part of the Auzon catchment (Figs. 3a and 4) and extends lightly
more to the west, up to Saint-Etienne de Fontbellon, referred to as STEF in
Fig. 3a. At least five devices were available at the same time from
15 November 2011 (see Table A2
in Appendix A for the period of operation of the instruments). Moreover, a
2-D video disdrometer (2-D VD) was deployed at Le Pradel (south of the Gazel
catchment) in autumn 2012 and 2013 for an inter-comparison of measurements
(Raupach and Berne, 2015). All Parsivels except Saint-Etienne de Fontbellon
and Pradel-Grainage are colocated with rain gauges from the network described
in the next paragraph (Fig. 4). The correction technique described by Raupach
and Berne (2015) using the 2-D VD as a reference disdrometer has been applied
to Parsivel data, improving the consistency of recorded drop size
distribution (DSD) moments.
Location of the 19 Hpiconet rain gauges (red dots)
and 14 disdrometers (yellow crosses) deployed over a 7 km × 8 km
area in the southern part of the Auzon catchment. Where rain gauges are
clustered with interdistances of 10 up to 500 m, inset maps present
the configuration of the deployment at the local scale. The names of the
location of the instruments are indicated in black. For the “Le Pradel”
site, two sub-sites are indicated and the location of the soil erosion plots
is represented with red lines. The boundaries of the Gazel and Claduègne
catchments are displayed in black. Orthophotos are displayed in the
background (source: IGN France 2009).
Rain gauges
An operational network of 10 rain gauges (6 managed by Météo-France
and 4 managed by the Flood Forecasting Service – SPC Grand Delta) is present
over the Auzon catchment or its close vicinity (Fig. 3a). It provides data at
an hourly time step (Météo-France rain gauges) and 5 min time step
(SPC Grand Delta rain gauges). Additionally, a research network of 21 rain
gauges is implemented over the Auzon catchment (Fig. 3a). It provides data at
5 min time step. Nineteen rain gauges were initially deployed over a
7 km × 8 km area located in the southern part of the catchment
(Fig. 4) and 2 additional rain gauges were subsequently installed in the
northern part of the Claduègne catchment. This network, called Hpiconet,
was designed for sampling rainfall at spatial scales ranging from tens of
metres to tens of kilometres and at temporal scales ranging from
1 min to 1 day. This offers the opportunity to address the issues of
the definition of the scales of variability in rainfall and the origins of
this variability that are still open questions within the hydrometeorological
community (Fraedrich et al., 1993; Fabry, 1996; Krajwesky et al., 2003). The
rain gauges are distributed over 11 main locations with a mean interdistance
of about 2 km. At some locations, several rain gauges are clustered
with interdistances of 10 to 500 m as seen in the inset maps of
Fig. 4. All rain gauges are identical and consist of a Précis
Mécanique tipping bucket of 0.2 mm (model 3039) with a collecting
area of 1000 cm2. Each rain gauge is connected to a HOBO or Campbell
data logger. A first step of the data quality control consists of comparing
the total rain amount recorded by the tipping bucket–HOBO system with that
collected at the outlet of the rain gauge with a 30 L plastic tank.
The comparison of these two measurements shows that the relative difference
remains below 10 % for all the rain gauges and lower than 5 % for
most of them. Calibration is performed if an error of more than 5 % is
detected. In a second step, the temporal evolution of cumulated sums of
rainfall of close stations are plotted against each other. Clogged buckets
never occurred thanks to the frequent maintenance (at least once a month).
Other meteorological data
Most of the meteorological variables originate from one of the operational
surface observing networks of Météo-France. Six stations are located
in the Auzon catchment or its close vicinity. All of them continuously
provide hourly measurements. The measured variables at each station differ
depending on the network to which each station belongs. There are four types
of networks for Météo-France stations present in this study: the
“aeronautical synoptic” network (including LANAS-SYN), the RADOME-RESOME
network with automatic stations also known as the Automated Regional Network
(including BERZEME-RAD), the network of automatic stations surveyed in real
time (including ALBA-SA and ESCRINET) and the network of automatic stations
examined in delayed time (including AUBENAS-SA et MIRABEL-SA). Each observing
network has its own purposes, so the variables needed are different. For
instance, LANAS-SYN is the only station that provides surface pressure.
Figure 3b shows the location of each station together with the variables they
measure.
Air temperature and relative humidity
Each of the six stations considered is equipped with a WMO-standard
meteorological shelter, which is installed 1.5 m above ground. Both
an air temperature sensor (PT100) and a relative humidity sensor are mounted
in the shelter so that they are protected from solar radiation.
Atmospheric pressure
Atmospheric pressure is measured by a digital barometer (PTB220) at one of
the six stations considered.
Wind speed and direction
Wind measurements are conventionally performed at 10 m above ground
surface level and on open ground at three of the six stations considered.
Wind speed is given by a cup anemometer, while wind direction is measured
thanks to a vane mounted on a pole that has pointers indicating the principal
points of the compass.
Global radiation
Several measurements of radiation can be performed. One out of the six
considered stations is equipped with a pyranometer (CM11) providing global
solar radiation values.
Additionally, one Baro-Diver and three Mini-Divers were deployed over the
Claduègne catchment to complement the stream sensor network to
measure the atmospheric pressure and the air temperature with an observation
frequency of 2 min (Fig. 3b). However, these four sensors are not
located in a shelter and are therefore subject to solar radiation.
Spatial characterization data
Characterization data are used to define the topography, pedology, geology,
land use, landscape and hydrological properties of the Auzon catchment. These
data provide the fine-scale detail required for modelling and hydrological
assessment. The coordinate system of reference used in this study is the
Réseau géodésique français (RGF) 1993 (official in France,
based on IAG-GRS80 ellipsoid, very similar to WGS 84). The projection is
Lambert conique conforme. Table 3 presents the main GIS descriptors available
for the region of interest in this study.
GIS descriptors
For the Claduègne catchment, a 1 m bare-earth digital elevation model
(DEM) was derived from an aerial lidar dataset acquired in 2012 and processed
by Sintégra (Braud et al., 2014). For the Auzon catchment, the 25 m DEM
released by IGN France in 2008 is available. A combination of these two
latter DEM was performed using ArcGis based on re-sampling and
interpolation to produce a 5 m DEM over the Auzon catchment from which the
catchment boundaries were derived using TAUDEM D8 incorporated in ArcGis. A
map (scale 1:50000) of the geology of the region including the Auzon
catchment was released by the Bureau de Recherches
Géologiques et Minières (BRGM) in 1996 and digitalized in vector
format from 2001 (Elmi et al., 1996). A map (scale 1:100000) of the
pedology of the region including the Auzon catchment was released by INRA
(Bornand et al., 1977). In addition, the Ardèche soil database at scale
1:250000 produced by Sol-Conseil and Sol Info Rhône Alpes provides a
vector map of the region synthesizing a large amount of information on soil
(soil class and unit, horizon, thickness, etc.) and bedrock. Very high
resolution images were acquired and processed to provide detailed land use
maps: 5 m resolution satellite images (QuickBird images) taken in 2012 for
the Claduègne catchment and 30 m resolution satellite images (Landsat)
taken in 2013 for the Ardèche catchment. The orthophotography database
released by IGN France in 2009 provides aerial images of the Auzon catchment
at a resolution of 0.5 m. In addition, vector data of the drainage
network, catchment boundaries, instrument locations, administrative
boundaries and road network are available.
Infiltration tests
A field campaign aiming at documenting the variability in surface hydraulic
properties was conducted in May–June 2012 in the Claduègne catchment.
The measurements were performed at 17 points throughout the catchment
(Fig. 5), which were selected from the cross-analysis of pedology, land cover
and geology maps following the method of Gonzalez-Sosa et al. (2010). The
tested hypothesis is that land use has a major influence on the observed
hydraulic properties rather than the soil texture. Two techniques were used:
the mini disk infiltrometer and the double-ring infiltrometer using the
Beerkan method (Braud et al., 2005). With the exception of two points, both
instruments were used at each location. Between one and three repeated
measurements were performed. Soil texture was then analyzed at the INRA
laboratory of soil analyses in Arras (France). The results of this campaign
are described in Braud and Vandervaere (2015).
List of GIS descriptors available for the Auzon catchment.
GIS descriptorDataTypeDateLegendAuthorAccessTopography1 m bare-earth DTMa of Claduègne catchmentRaster2012Lidar campaignSintégra géomètresPublicexperts25 m bare-earth DTM of Auzon catchmentRaster2008BD TOPO, ArdècheIGN FranceMarketed productc5 m bare-earth DTM of Auzon catchmentRaster2014Combination of data based onIGEPublicresampling and interpolationGeologyMap of Auzon catchment at scale 1:50000Vector1996BD Charm-50, AubenasBRGMMarketed productcPedologyMap of Auzon catchment at scale 1:100000Raster1977Pedological map of France at 1:100000, PrivasINRAMarketed productcMap of Auzon catchment at scale 1:250000Vector2001IGCS – Référentiel RégionalBRGM/ChambreMarketed productcPédologique, BDSol-ArdècheAgricultureSoil propertiesSoil depth for each SCUbVector2015Processed from the Ardèche soil database at 1:100000IRSTEA LyonPublicMaximum soil water storage for each SCUVector2015Processed from the Ardèche soil database at 1:100000IRSTEA LyonPublicSoil texture of superficial layer for each SCUVector2015Processed from the Ardèche soil database at 1:100000IRSTEA LyonPublicSoil stone content for each SCUVector2015Processed from the Ardèche soil database at 1:100000IRSTEA LyonPublicInfiltration testsInfiltration campaign Claduègne catchmentVector201252 sampled pointsIRSTEA LyonPublicLand use5 m resolution images of Claduègne catchmentVector/raster2012Processed from QuickBird imagesUMR EspacePublic30 m resolution images of Auzon catchmentRaster2013Processed from Landsat imagesUMR EspacePublicOrthophotography0.5 m resolution images of Auzon catchmentRaster2009BD ORTHO, ArdècheIGN FranceMarketed productcSurfaceCatchment boundariesVector2014Processed from the 5 m bare-earth DEMIGEPublicinformationwith TAUDEM D8 toolDrainage network (stream)Vector2010BD CARTHAGESandre eaufrancePublicDrainage networkVector2008BD TOPO, ArdècheIGN FranceMarketed productc(permanent and intermittent)InstrumentsVector2014PointIGEPublicRoad networkVector2008BD TOPO, ArdècheIGN FranceMarketed productcAdministrative boundariesVector2008BD TOPO, ArdècheIGN FranceMarketed productc
a DTM: digital terrain model.
b SCU: soil cartographic unit.
c Not released in this study.
Hydrological and sediment data
Almost all the instruments deployed in the field to measure the soil and
surface water compartments were installed for research purposes. There was
virtually no hydrological observation before 2011 in the Auzon catchment,
except the water erosion plots and a site for soil moisture measurement
(SMOSMANIA network). Most of the instruments were installed in the framework
of the FloodScale project and the HyMeX EOP (2011–2014). The
instruments are located mainly in the Gazel and Claduègne sub-catchments
(Fig. 3c), where it was decided to put the major efforts.
Location of infiltration tests and soil moisture measurements in the
Gazel and Claduègne catchments. The soil moisture measurements include
both manual and continuous measurements. The black rectangle shows the
position of the zoom provided at the top left of the figure. The pedology
(1:100000 soil map; source: INRA) is displayed in the background.
Surface waterHydrometric stations
Three hydrometric stations with natural controls are located respectively on
the Gazel, Claduègne and Auzon rivers (Fig. 3c). The Claduègne and
Auzon stations are situated at a bridge in order to facilitate the access and
the manipulations during floods; they were installed respectively in October
2011 and June 2013. Water level is measured using H radar (Table 2). The
Gazel station is situated on a natural reach without any construction; it was
installed in April 2011. Water depth is measured using a hydrostatic pressure
probe. The common variables provided by these three stations are water level
and stream discharge. The observation frequency is respectively 2, 10, and
5 min for the Gazel, Claduègne and Auzon stations. The logged
values are time-averaged measurements (typically 30 values over less than
1 min), with their dispersion (standard deviation, minimum and
maximum values). A significant effort was dedicated to the establishment of
the stage–discharge relationships during the period 2012–2014. Many on-alert
campaigns were carried out to perform discharge measurements at high flows.
All the discharge measurements with their estimated uncertainties at the
95 % level of confidence are presented in Table 4. Different
techniques and instruments, including salt dilution, current meter, surface velocity radar (SVR) (Welber
et al., 2016), acoustic Doppler current profiler (ADCP), acoustic Doppler
velocimeter (ADV) and large-scale particle image velocimetry (LSPIV), based
on images recorded by a fixed camera (Le Coz et al., 2010; Dramais et
al., 2015), were used depending on the type of river and the hydraulic
conditions. The BaRatin framework (Le Coz et al., 2014) combining Bayesian
inference and hydraulic analysis was used to build steady, multi-segment
stage–discharge relationships and to estimate the associated uncertainty
(95 % confidence interval).
List of the discharge measurements carried out at three
hydrometric stations (Gazel, Claduègne, Auzon) between 2011 and 2014. The
gauging techniques/instruments include salt dilution, current meter, surface velocity
radar (SVR), acoustic Doppler current profiler (ADCP), acoustic Doppler
velocimeter (ADV), large-scale particle image velocimetry (LSPIV), and
Manning–Strickler.
Additional variables are provided by these stations. At the Gazel and
Claduègne stations, different physico-chemical variables of the surface
water are measured continuously: temperature, conductivity and turbidity.
Generally, for all inter-flood periods (marked by very low turbidity) the
turbidity value was set to 0 during the procedure of data quality control
even if there was a value different from 0 in the raw data. Indeed, the
turbidimeters used in this study are designed for a high turbidity range
(typically 1000 to 10 000 ppm) and are not capable of accurately
measuring low turbidity (typically less than 50 ppm). Sequential
samplers, triggered by the data logger, collect water and suspended sediment
samples when threshold values of water level and turbidity are exceeded. The
suspended sediment concentration (SSC), which represents the mass of solid
divided by the volume of liquid (expressed in gL-1), is estimated
by measuring the volume of liquid present initially in the sample and
weighting the solid fraction after drying it at 105 ∘C. The detailed
procedure was given by Navratil et al. (2011) and resulted in a median
relative uncertainty of 15 %. Some selected samples are analysed using a
laser diffraction particle size analyser (Malvern Mastersizer/E) to
characterize the particle size distribution. At the Claduègne station,
water surface velocity is measured continuously using the non-contact radar
technology based on the principle of the frequency shift due to the Doppler
effect. The continuous measurement of water velocity has become increasingly
common in the US and in Europe, especially for operational hydrometric
agencies, as it allows for the index velocity method to be applied (Levesque
and Oberg, 2012). This approach is particularly relevant to small rivers
subject to flash floods where flow is highly unsteady. It represents a useful
tool for extrapolating stage–discharge rating curves over a range of flows
for which the use of conventional gauging methods is impractical or unsafe
(Nord et al., 2014). At the Claduègne station, an acoustic Doppler
velocity meter was fixed to the channel bed during the period from September
2013 to November 2014 to measure detailed velocity profile (100 cell maximum)
at the same observation frequency as water level and surface velocity. This
system provides an alternative continuous measurement of flow velocity in the
water column from the bed up to the water surface.
Overland flow and water erosion
Two erosion plots were monitored on a hillslope with vineyard at “Le
Pradel” (Figs. 2e and 4) during the period from December 2009 to October
2013. The erosion plots, considered as two duplicates, are 60 m long and
2.2 m wide and they extend over the entire length of the hillslope. The
width of the plots corresponds to the distance between two vine rows oriented
in the direction of the main slope. The vine rows are located on the edges of
each plot. The two plots are parallel and spaced by approximately 5 m. The average slope in the longitudinal direction is about 15 %.
The vegetation cover between the vine rows varied between years but remained
very sparse. The brown calcareous soils underlain by marly limestones are
composed of 34 % of clay, 41 % of silt and 25 % of sand
particles. The Gazel River is located about 40 m away from the
monitored hill foot. The transition between the cultivated hillslope and the
river is marked by a riparian vegetation zone and a cliff of about
10 m. This monitored hillslope is included in the catchment, whose
outlet corresponds to the Gazel hydrometric station, with the idea of
investigating the fate of solid particles eroded from the hillslope to the
river.
A rain gauge and a disdrometer were located at about 30 m from the
erosion plots (Fig. 4). The two plots were equipped similarly. Runoff was
collected in the bottom part of the hillslope. The water depth was measured
every minute with a 1 mm resolution using a gauge (OTT Thalimedes) within an
H flume designed following the US Soil Conservation Service recommendations.
The stage–discharge rating curve was built experimentally and allowed for
calculation of discharge with a median relative uncertainty of 10 %. A
sequential sampler containing 24 bottles of 1 L capacity sampled water and
eroded particles within the H flume. When critical thresholds of water depth
or water depth variation were exceeded, the data logger triggered the
sampling of water and eroded particles. Thus, the time intervals between each
two samples were irregular, depending on the shape of the hydrograph. SSC
were estimated following the procedure given above in the section “Hydrometric
stations”. While the discharges were available continuously, the sediment
fluxes were only calculated for the times where SSC values were available.
Many samples were analysed using a laser diffraction particle size analyser
(Malvern Mastersizer/E) to characterize the particle size distribution. More
details about the description of the plots, the topographical data available,
and the monitored runoff and erosion events are given by Grangeon (2012) and
Cea et al. (2016). The infiltration and runoff processes over this hillslope
were previously studied by Nicolas (2010).
Stream sensor network
A stream sensor network composed of four CTD-Divers
(conductivity–temperature–depth) and seven Mini-Divers (temperature–depth)
was deployed on the Gazel and Claduègne catchments (Fig. 3c). These
compact instruments (Table 2) for autonomous measurement and record were
installed in small metallic boxes
(177 mm × 81 mm × 57 mm) embedded in the riverbed
in the case of bedrock rivers and anchored vertically to the wall or any
other fixed element in the rest of cases. The lids of the boxes were
perforated to ensure water permeability. In addition, in the case of the
CTD-Divers, a hole of 3 cm in diameter was formed at each end of the
boxes to let the water circulate and ensure a significant renewal of water
inside. The instruments were installed in the intermittent hydrographic
network, delineating 10 sub-catchments of 0.17–2.2 km2 and one
sub-catchment of 12.2 km2, which contains the whole area with
volcanic geology of the upstream part of the Claduègne catchment. The
selected sites are mainly headwater sub-catchments where the landscape
properties are considered homogeneous in terms of geology, pedology and land
use (Fig. 3). The underlying assumption in the choice of the measurement
sites was that the delineated sub-catchments were homogeneous hydrological
units and could lead to different responses for the same rainfall forcing.
Taken together, these selected sub-catchments constituted a representative
sample of the landscapes encountered in the Gazel and Claduègne
catchments. Given that rainfall is highly variable in space and time in this
region, the observation system enables assessment of the hydrological
response to rainfall fields.
The CTD-Divers and Mini-Divers measure the total pressure as they are not
compensated for (cableless instruments). An independent measurement of
atmospheric pressure is therefore necessary for accurate barometric
compensation and consecutive calculation of the hydrostatic pressure or water
depth. Initially (in September 2012), only one barometer (a Baro-Diver) was
installed in the area following the manufacturer's recommendation, which
specifies that, in general, in relatively flat open terrain, the pressure
measurement has a maximum range of 15 km. However, the error in the
measurement of water level was important in our case (about 2 cm),
mainly due to the error in the atmospheric pressure which varies
significantly throughout the area due to the relief and the differences in
climate between the Coiron Plateau and the piedmont hills. As a consequence,
three additional Mini-Divers (used as barometers) were progressively deployed
from November 2012 to April 2013 in the Gazel and Claduègne catchments to
reduce the measurement error of water level to about 1 cm. In order
to compensate for the total pressure values measured by the CTD-Divers and
Mini-Divers, it is necessary to calculate the atmospheric pressure at all
points of the stream sensor network. For this, we rely on the four points of
atmospheric pressure measurement available and we choose between the
following two options according to criteria of distance and difference of
altitude between the calculated point and the measuring points:
linear interpolation of atmospheric pressure between the two closest
points of measurement based on the difference of altitude with the calculated
point;
meteorological method of correcting pressure (National Advisory
Committee for Aeronautics, 1954) based on the nearest point of pressure and
temperature measurement by applying a standard temperature gradient
(-6.5 Kkm-1).
The results are not very sensitive to the method used, with the most sensitive
factor being the density of atmospheric pressure measurement over the
spatial extension of the stream sensor network.
When possible, controlled sections are chosen to allow the establishment of a
stage–discharge relationship based on stability and sensitivity of the
control points. This is the case for three points of the stream sensor
networks: mi3, sj1, and vb1 (Fig. 3c). Mi3 is located on a concrete,
broad-crested artificial control, sj1 on a natural weir and vb1 in a circular
concrete culvert. Many on-alert campaigns were carried out to perform
discharge measurements at different flow conditions at these points and at
two additional points (bz1 and sg1) for which stage–discharge relationships
could be established in the future.
SoilSoil moisture
Infiltration excess runoff was thought to be the dominant process (e.g.
Nicolas, 2010) in the Gazel and Claduègne catchments. The observation
strategy thus focuses on the documentation of the soil infiltration capacity
and initiation of ponded conditions at the surface. The monitoring of soil
moisture in the Gazel and Claduègne catchments is a task that has two
components:
mobile (manual) soil moisture measurements at the surface before/after
rainfall events (good areal representativity);
deployment and maintenance of fixed sensors (continuous monitoring but
point values).
A series of mobile soil moisture measurements were conducted in the Gazel
catchment during HyMeX SOP1 (Huza et al., 2014). The measurements were
taken on six fields, distributed over the whole catchment (Fig. 5). Fields were
selected to appropriately represent the catchment, while still capturing
inter-field variability and the influence of different topographical
features. Vineyards were not selected because the soil was dominated by
stones, making it impossible to sample without breaking the sensor. This
resulted in all selected fields being pastures and grasslands. Within each of
the selected six fields, a transect path of 50 m was measured. Along
the 50 m transects, a measurement was taken at spatial intervals of
2 m and all measurements were done at the same location for each of
the measurement days. Point volumetric soil moisture measurements were done
using a portable three-prong (6 cm rod length) ThetaProbe ML2X sensor
(Delta-T Devices Ltd, Cambridge, UK), which employs the frequency domain
reflectometry (FDR) technique, and the internal default conversion tables (to
convert output voltage to volumetric soil moisture content) were used. On
each measurement day, all fields were measured within a few hours to minimize
the influence of evaporation and drainage processes. The strategy was to
select measurements days that aligned with high-precipitation events and to
capture both pre-event and post-event soil moisture conditions whenever
possible. During SOP1, 16 measurement days were completed on the six
different transects. This produced approximately 2500 soil moisture
measurements. The accuracy given by the manufacturer is
±0.01 cm3cm-3.
Nine sites (Figs. 3 and 5) with different land uses (two vineyards, four
pastures, one piece of fallow land, two small oak woods) were selected for
the installation of fixed sensors. Three sites are located in the Gazel catchment (two
in the mi3 and one in the mi4 sub-catchments), three other sites are located in its close vicinity, and all are representative of the piedmont hills landscapes
(Fig. 2d and e). The three last sites are
located in the bz1 sub-catchment or its close vicinity and are representative
of the Coiron Plateau landscapes (Fig. 2a). These choices of localization
were motivated by the presence of the stream sensor network with the
objective to make the most direct connection possible between rainfall
forcing and hydrological response in small catchments relatively homogeneous
in terms of geology and land use. The nine sites were equipped in 2013 with
five sensors for continuous soil moisture measurements – two at about
10 cm, two at 20–25 cm and one at 30–50 cm depth – in
order to document soil saturation (Braud et al., 2014). These five sensors
are connected to the same data logger and the observation frequency is
20 min (Nicoud, 2015; Uber, 2016). The selected sensors are
capacitive probes (Decagon 10 HS) which employ the FDR technique, and the
internal default conversion tables (to convert output voltage to volumetric
soil moisture content) were used. The range of volumetric soil moisture
content of the instrument is between 0 and 0.57 cm3cm-3 and the
accuracy is ±0.03 cm3cm-3 according to the manufacturer.
Additionally, a station of SMOSMANIA (Soil Moisture Observing System –
Meteorological Automatic Network Integrated Application) is located in the
close vicinity of the upstream part of the Claduègne catchment, on the
Coiron Plateau (Figs. 3c and 5). SMOSMANIA is based on the existing network
of operational weather stations of Météo-France (RADOME). Among the
21 stations of this network that compose an Atlantic–Mediterranean transect
in the southern part of France, Berzème is the station of interest for
this study. The land cover around the station consists of fallow, cut once or
twice a year. Four probes measuring soil moisture (ThetaProbe ML2X) were
installed at the following depths: 5, 10, 20 and 30 cm.
Soil temperature
Soil temperature is measured at the station of SMOSMANIA (Berzème) at the
following depths: 5, 10, 20 and 30 cm. The accuracy is
±0.5 ∘C.
First applications using the datasetAreal rainfall estimation
Areal rainfall estimations are important for water budget assessment and the
understanding of the internal catchment behaviour. Geostatistical techniques
(rain gauge ordinary kriging, as well as merging of radar and rain gauge data
through kriging with external drift) were used to obtain quantitative
precipitation estimates (QPEs). The uncertainty of these QPEs was calculated
using the methodology presented by Delrieu et al. (2014). The QPEs were
produced for a wide range of spatial and temporal resolutions
(15–360 min, 1–300 km2) for a
30 km× 32 km window encompassing the Auzon catchment in
order to assess the effect of adding high-resolution rainfall data on the
quality of the QPE for small scale hydrology applications. Rainfall estimates
and error structure were compared for four scenarios with varying rainfall
datasets (operational rain gauges, operational + research rain gauges,
operational rain gauges + radar, all data) for the 25 largest rainfall
events of 2012 and 2013. For all the scenarios, the results show that the
error of the QPE increases with higher spatio-temporal resolutions. For the
technique of kriging with external drift (merging radar and rain gauge data),
there is a significant reduction in QPE error compared to the technique of
ordinary kriging (using only rain gauge data), and this reduction is still
more sensitive at higher spatio-temporal resolutions. Taking into account the
data of the research rain gauge network (dense rainfall data) results in a
reduction in QPE error. This reduction is similar to the decrease when adding
the radar data; however, the spatial structure of the errors and the rainfall
estimates of these scenarios show large differences.
Additionally, significant effort has been dedicated to the production of
rainfall re-analysis (QPE) for the
2007–2014 period (Boudevillain et al., 2016) based on the operational radar
and rain gauge data for a window of 32 000 km2 including the major
catchments of the Cévennes region (Doux, Eyrieux, Cance, Ardèche,
Cèze, Gardons, Vidourle, Hérault). These QPEs were produced with a
daily and hourly time resolution and for two types of geographic
discretization: (1) Cartesian meshes of 1 km2 for a regular grid
covering the study area and (2) “hydrological” units corresponding to the
discretization of the major catchments in subcatchments of homogeneous size
in the range of 5, 10, 50, 100, 200, and 300 km2. The uncertainty of
these QPEs was also calculated using the methodology presented by Delrieu et
al. (2014). An example of these QPEs is shown in Fig. 6 for the region of the
Auzon catchment during the 4 November 2014 event between 13:00 and
14:00 UTC, which corresponds to the peak of rainfall preceding the peak of
discharge measured at the Auzon station (Fig. 8). Figure 6a and b illustrate
the added value of radar data to capture the spatial structure of rainfall
even if, as for this case study (Fig. 6c and d), QPE error standard deviation
may be sometimes larger than for ordinary kriging. Boudevillain et al. (2016)
showed that, in general, QPE errors are significantly reduced with the
technique of kriging with external drift. The data presented in this section
are made available in the public dataset associated with this paper.
Ordinary kriging (OK) estimates from the operational rain gauge network
(top) and kriging with external drift (KED) estimates from radar–operational rain
gauge merging (bottom) for 4 November 2014 between 13:00 and 14:00 UTC. The
graphs on the left display the estimation of hourly rain amounts (mm) and the
graphs on the right display the corresponding error standard deviations (mm).
The results are provided for a raster grid of 1 km2 resolution.
Stage–discharge rating curves and their uncertainty for the Auzon
station: blue, prior rating curve based on hydraulic analysis only (no
gaugings); red, rating curve established with traditional gaugings only;
green, rating curve established with all gaugings, including high-flow
non-contact gaugings. Solid lines represent the rating curves. Shaded areas
represent the corresponding uncertainty 95 % confidence intervals.
Event of 4 November 2014 on the Auzon catchment – hydrograph and
associated uncertainty: blue, with prior rating curve (no gaugings); red,
with rating curve established with traditional gaugings only; green, with
rating curve established with all gaugings. Solid lines represent the
hydrographs. Shaded areas represent the corresponding uncertainty 95 %
confidence intervals.
Improvement in the quantification of flood hydrographs and
reduction of their uncertainty
The Gazel–Claduègne–Auzon experimental data were also used to develop a
methodology to quantify and reduce the uncertainty of flood hydrographs. This
methodology is based on the non-contact stream gaugings performed during the
on-alert campaigns (see “Hydrometric stations” in Sect. 3.3.1) and on the
BaRatin framework (Le Coz et al., 2014). At the Auzon hydrometric station,
during the 2014 campaign, 11 LSPIV gaugings could be performed through the
automated station. They were completed by 10 SVR gaugings. These gaugings
have higher uncertainties than the traditional dilution or velocity–area
methods, but have the advantage of being feasible safely even under
hazardous, high flow conditions. These gaugings were incorporated as
observations in the BaRatin methodology, which was further developed by
adding the propagation of stage uncertainty and rating curve uncertainty to
discharge time series (Horner, 2014; Branger et al., 2015). BaRatin is based
on hydraulic analysis of the flow conditions at the stations, which are used
as priors. The Bayesian framework then calculates the posterior rating curve
and its associated uncertainty by incorporating the uncertain gaugings.
Figure 7 shows that for the Auzon station, the new gaugings contributed to
establish a rating curve significantly different from the prior rating curve,
and different from the one which could have been obtained using traditional
gauging methods only. The difference is particularly important for high flow.
The rating curve uncertainty is also significantly reduced. Figure 8 shows
the hydrograph and the associated uncertainty for the different rating curves
presented in Fig. 7 for the 4 November 2014 event, the largest flood recorded
during the period 2011–2014. The estimation of the flow volume during the
event is 24 % underestimated without the non-contact gaugings. This
improved accuracy in peak discharge and flow volume estimation is valuable
not only for the validation of hydrological models but also for more applied
purposes (flood forecasting, mapping of flood risk
areas, water resources), along with estimation of
rainfall uncertainty.
Distributed physically based soil erosion modelling
The impact of model simplifications on soil erosion predictions was tested by
applying the GLUE methodology to a distributed event-based model at the
hillslope scale (Cea et al., 2016). In this paper the authors analysed how
the performance and calibration of a distributed event-based soil erosion
model at the hillslope scale is affected by different simplifications on the
parameterizations used to compute the production of suspended sediment by
rainfall and runoff. Six modelling scenarios of different complexity were
used to evaluate the temporal variability in the sediment flux at the outlet
of a 60 m long cultivated hillslope. The six scenarios were calibrated
within the GLUE framework in order to account for parameter uncertainty, and
their performance was evaluated against experimental data registered during
five storm events. The Nash–Sutcliffe efficiency (NSE), the percent bias
(PBIAS) and coverage performance ratios showed that the sedimentary response
of the hillslope in terms of mass flux of eroded soil can be efficiently
captured by a model structure including only two soil erodibility parameters
which control the rainfall and runoff production of suspended sediment.
Increasing the number of parameters makes the calibration process more
complex without increasing the predictive capability
of the model in a noticeable manner.
Data availability
As an example of the kind of data made available in this paper, Fig. B1 in
Appendix B shows an overview of rainfall, discharge and turbidity for the
entire record (period 2011–2014) at the Claduègne hydrometric station.
The measurement period is characterized by a wide variety of water
conditions: a dry year in 2012, a wet year in 2014 and an intermediate year
in 2013, as well as some intense rainfall events in spring 2013,
autumn–winter 2013–2014 and autumn 2014. Note that the 4 November 2014
flood is a 5–10-year return period flood for the Claduègne River (this
return period was roughly estimated from archive photos and interviews with
residents and farmers who live and work near the river).
Overview of the URL links and DOIs that allow for access to the
public datasets presented in this study. The datasets are organized by
instrument.
CompartmentInstrumental device/Op/ResDataset nameMistral data accessStatus withinDOIIncluded ininstrumentMistrals/HyMeXbundleddatabasebdatacRainfallX-band Doppler andResMXPol-EPFL-LTERadarhttp://mistrals.sedoo.fr/?editDatsId=721Public after registration10.14768/MISTRALS-HYMEX.721nopolarimetric radar (MXPol)X-band fast-scanningResLe Chade LaMP X Band radarhttp://mistrals.sedoo.fr/?editDatsId=796Public after registration10.14768/MISTRALS-HYMEX.796noradar (WR-10X+)Micro Rain Radar (MRR-2)Micro Rain Radar LaMP Le Pradelhttp://mistrals.sedoo.fr/?editDatsId=855Public after registration10.14768/MISTRALS-HYMEX.855noMicro Rain Radar LaMPhttp://mistrals.sedoo.fr/?editDatsId=1112Public after registration10.14768/MISTRALS-HYMEX.1112noSt-Étienne-de-FontbellonMicro Rain Radar OSUGhttp://mistrals.sedoo.fr/?editDatsId=1158Public after registration10.14768/MISTRALS-HYMEX.1158noSt-Etienne-de-FontbellonMicro Rain Radar OSUG Montbrunhttp://mistrals.sedoo.fr/?editDatsId=1159Public after registration10.14768/MISTRALS-HYMEX.1159noDisdrometer (Parsivel 1)ResDSD network, Pradel-Vignesahttp://mistrals.sedoo.fr/?editDatsId=436Public after registration10.17178/OHMCV.DSD.PVI.11-14.1zip1DSD network, Mont-Redonahttp://mistrals.sedoo.fr/?editDatsId=679Public after registration10.17178/OHMCV.DSD.MRE.12-14.1zip1DSD network, Pradel-Grainagehttp://mistrals.sedoo.fr/?editDatsId=745Public after registration10.6096/MISTRALS-HyMeX.745zip1EPFL-LTE Disdrometers (at leasthttp://mistrals.sedoo.fr/?editDatsId=899Public after registration10.6096/MISTRALS-HyMeX.899zip16 instruments for Fall 2012and Fall 2013)Disdrometer (Parsivel 2)ResDSD network, Saint-Etienne-de-Fontbellonahttp://mistrals.sedoo.fr/?editDatsId=74410.17178/OHMCV.DSD.SEF.12-14.1zip1DSD network, Villeneuve-de-Berg-1ahttp://mistrals.sedoo.fr/?editDatsId=680Public after registration10.17178/OHMCV.DSD.VB1.12-14.1zip1DSD network, Villeneuve-de-Berg-2ahttp://mistrals.sedoo.fr/?editDatsId=681Public after registration10.17178/OHMCV.DSD.VB2.11-14.1zip1DSD network, Villeneuve-de-Berg-3ahttp://mistrals.sedoo.fr/?editDatsId=682Public after registration10.17178/OHMCV.DSD.VB3.12-14.1zip1Rain gauge (SPCOpSPCGD French rain gaugesahttp://mistrals.sedoo.fr/?editDatsId=1444Public after registrationnozip1Grand Delta)(Berzème,Escrinet, Pont d'Ucel, Vogüe)Rain gauge (Hpiconet)ResHpiconet rain gauge networkahttp://mistrals.sedoo.fr/?editDatsId=656Public after registration10.17178/OHMCV.RTS.AUZ.10-14.1zip1Rainfall reanalysisResPluviometric reanalysis Cévennes-Vivaraishttp://mistrals.sedoo.fr/?editDatsId=1183Public after registration10.17178/OHMCV.REA.CEV.07-14.1zip1MeteorologyBaro-DiverResLimnimeter network, Gazel andhttp://mistrals.sedoo.fr/?editDatsId=994Public after registration10.17178/OHMCV.LIM.CLA.12-14.1zip1Claduègne catchmentsSurface waterHydrometric stationsResGazel and Claduègne hydro-sedimentary stationsahttp://mistrals.sedoo.fr/?editDatsId=993Public after registration10.17178/OHMCV.HSS.CLA.11-14.1zip1Acoustic Doppler Velocimeter IQ Plus, Claduègnehttp://mistrals.sedoo.fr/?editDatsId=1349Public after registration10.17178/OHMCV.ADV.CLA.13-14.1zip1LSPIV gauging stations (Auzon hydrometric station)ahttp://mistrals.sedoo.fr/?editDatsId=996Public after registration10.17180/OBS.OHM-CV.ARDECHEzip1Water erosion plotsResRunoff and erosion plots, Pradelhttp://mistrals.sedoo.fr/?editDatsId=1347Public after registration10.17178/OHMCV.ERO.PRA.10-13.1zip1Stream sensorsResLlimnimeter network, Gazel andhttp://mistrals.sedoo.fr/?editDatsId=994Public after registration10.17178/OHMCV.LIM.CLA.12-14.1zip1networkCladuègne catchmentsSoilThetaProbeResSoil Moisture Gazelhttp://mistrals.sedoo.fr/?editDatsId=1179Public after registration10.6096/MISTRALS-HyMeX.1179zip110 HSResSoil moisture sensor network, Gazelhttp://mistrals.sedoo.fr/?editDatsId=1350Public after registration10.17178/OHMCV.SMO.CLA.13-14.1zip1and Claduègne catchments
a Means that the instrument(s) is (are) still running at the date of publication
of the paper and at least for the period of LOP (long-term observation
period, 2010–2020) of the HyMeX program.
b A simple registration (name, affiliation, e-mail) is necessary
to create an account (http://mistrals.sedoo.fr/User-Account-Creation/).
An e-mail with login and password is instantaneously sent which enables direct
access to public data.
c To facilitate the use of the data and avoid downloading each
individual datasets, a bundling service was provided: the “zip1” file (with
the same structure as Table 5). The bundled data present the advantage of
gathering data in ASCII and Cartesian format, in a single coordinate system
and in the same time zone (UTC). The bundled data are selected for the spatial
and temporal windows presented in the paper since some individual datasets
have different extents. The “zip1” file (602 MB) is accessible directly
(without registration) by clicking on the purple icon of the “data access”
section of the page
http://mistrals.sedoo.fr/MISTRALS/?editDatsId=1438.
Overview of the URL links and DOIs that allow access to the public
GIS descriptors presented in this study.
GISDataDataset nameData accessStatus withinDOIIncluded indescriptorMistrals/HyMeXbundleddatabasebdatacTopography1 m bare-earth DTM ofDigital Terrain Model (DTM) Lidarhttp://mistrals.sedoo.fr/?editDatsId=1178Public after registration10.6096/MISTRALS-HyMeX.1178zip1Claduègne catchmentof Claduègne catchment5 m bare-earth DTM ofDigital Terrain Model (DTM) ofhttp://mistrals.sedoo.fr/?editDatsId=1389Public after registration10.6096/MISTRALS-HyMeX.1389zip1Auzon catchmentthe Auzon catchment regionSoil propertiesSoil depth for each SCUaSoil properties Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1385Public after registration10.6096/MISTRALS-HyMeX.1385zip1Maximum soil water storageSoil properties Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1385Public after registration10.6096/MISTRALS-HyMeX.1385zip1for each SCUSoil texture of superficialSoil properties Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1385Public after registration10.6096/MISTRALS-HyMeX.1385zip1layer for each SCUSoil stone content for each SCUSoil properties Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1385Public after registration10.6096/MISTRALS-HyMeX.1385zip1InfiltrationInfiltration campaignInfiltration campaign Claduègnehttp://mistrals.sedoo.fr/?editDatsId=1321Public after registration10.6096/MISTRALS-HyMeX.1321zip1testsCladuègne catchmentcatchment, Ardèche, FranceLand use5 m resolution imagesLandcover map Claduègne catchmenthttp://mistrals.sedoo.fr/?editDatsId=1381Public after registration10.14768/MISTRALS-HYMEX.1381zip1of Claduègne catchment30 m resolution imagesLandcover map Ardeche, Cèze andhttp://mistrals.sedoo.fr/?editDatsId=1377Public after registration10.14768/MISTRALS-HYMEX.1377zip1of Auzon catchmentGardon BassinsCatchment boundariesSurface information Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1390Public after registration10.6096/MISTRALS-HyMeX.1390zip1SurfaceDrainage networkSurface information Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1390Public after registration10.6096/MISTRALS-HyMeX.1390zip1information(stream)InstrumentsSurface information Auzon catchmenthttp://mistrals.sedoo.fr/?editDatsId=1390Public after registration10.6096/MISTRALS-HyMeX.1390zip1
a SCU: soil cartographic unit.
b A simple registration (name, affiliation, e-mail) is necessary
to create an account (http://mistrals.sedoo.fr/User-Account-Creation/).
An e-mail with login and password is instantaneously sent which enables
direct access to public data.
c To facilitate the use of the data and avoid downloading each
individual datasets, a bundling service was provided: the “zip1” file (with
the same structure as Table 6). The bundled data have the advantage of
gathering data in ASCII and Cartesian format in a single coordinate system
and in the same time zone (UTC). The bundled data are selected for the
spatial and temporal windows presented in the paper since some individual
datasets have different extents. The “zip1” file (602 MB) is accessible
directly (without registration) clicking on the purple icon of the “data
access” section of the page
http://mistrals.sedoo.fr/MISTRALS/?editDatsId=1438.
Overview of the URL links and DOI that allow access to the
additional datasets presented in this study. The datasets are organized by
instrument.
CompartmentInstrumental device/Op/ResDataset nameMistrals data accessStatus withinDOIIncluded ininstrumentMistrals/HyMeXbundleddatabasebdatacRainfallS-band Doppler andOpOperational Weather Radar ARAMIS, BOLLENE,ahttp://mistrals.sedoo.fr/?editDatsId=705Associated scientistsNoNopolarimetric radar5 min reflectivity and radial wind speedOperational Weather Radar ARAMIS, BOLLENE,ahttp://mistrals.sedoo.fr/?editDatsId=695Associated scientistsNoNo5 min cumulative rainfall in mmOperational Weather Radar ARAMIS, NIMES,ahttp://mistrals.sedoo.fr/?editDatsId=708Associated scientistsNoNo5 min reflectivity and radial wind speedOperational Weather Radar ARAMIS, NIMES,ahttp://mistrals.sedoo.fr/?editDatsId=699Associated scientistsNoNo5 min cumulative rainfall in mmFrench Radar composite 5 min cumulativeahttp://mistrals.sedoo.fr/?editDatsId=703Associated scientistsNoNorainfall in mmMicro Rain Radar MRR-2ResMicro Rain Radar CNRM Le Pradelhttp://mistrals.sedoo.fr/?editDatsId=1110Associated scientistsNoNoRain gauge Météo-FranceOpOperational surface weather observationahttp://mistrals.sedoo.fr/?editDatsId=627Associated scientistsNozip2stations over France – hourly dataMeteorologyWeather stationsOpOperational surface weather observationahttp://mistrals.sedoo.fr/?editDatsId=627Associated scientistsNozip2stations over France – hourly dataSoilThetaProbe ML2XOpSMOSMANIA – Soil moisture andhttp://mistrals.sedoo.fr/?editDatsId=469Associated scientistsNozip2temperature, France
a The instrument(s) is (are) still running
at the date of publication of the paper and at least for the period of LOP
(long-term observation period, 2010–2020) of the HyMeX programme.
b The validation of the HyMeX data policy
(http://mistrals.sedoo.fr/HyMeX/Data-Policy/HyMeX_DataPolicy.pdf) is
necessary to access datasets under “associated scientists” status. There
are two options. (1) The user had registered previously for access to
public data only. In this case, he/she must go to his/her account
(http://mistrals.sedoo.fr/Your-Account/) and complete the HyMeX
registration application. (2) The user registers for the first time. He/she
must go to http://mistrals.sedoo.fr/User-Account-Creation/, not
checking the box corresponding to “Simple registration (name, affiliation,
country) for direct access to public data only”, and follow the
registration.
c To facilitate the use of the data and avoid downloading each
individual dataset, a bundling service was provided: the “zip2” file (with
the same structure as Table 7). The bundled data have the advantage of
gathering data in ASCII and Cartesian format in a single coordinate system
and in the same time zone (UTC). The bundled data are selected for the spatial
and temporal windows presented in the paper since some individual datasets
have different extents. The “zip2” file (4 MB) is accessible clicking on
the blue icon of the “data access” section of the page
http://mistrals.sedoo.fr/MISTRALS/?editDatsId=1438.
All the public datasets presented in this study are listed in Tables 5 and 6.
In addition, all the datasets under the “associated scientists” status of
the HyMeX data policy
(http://mistrals.sedoo.fr/HyMeX/Data-Policy/HyMeX_DataPolicy.pdf)
presented in this study are listed in Table 7. Data collected by a specific
instrument and a network of instruments are listed in Tables 5 and 7, while the
GIS descriptors are listed in Table 6. This granularity, also chosen for the
DOI attribution (see the column “DOI” in Tables 5, 6, and 7), enables each
individual dataset to be associated with a principal investigator who is very
familiar with the data and who will be an essential resource for any user in
case of need. The added-value dataset corresponding to the results of the
rainfall re-analysis (QPE) for the
2007–2014 period (Boudevillain et al., 2016) based on the merging of
operational radar and rain gauge data has been added to Table 5 as it
represents a useful dataset for many hydrological studies. All the individual
datasets listed in Tables 5, 6, and 7 have a specific metadata record (see
URL links in corresponding tables) in the HyMeX Database
(http://mistrals.sedoo.fr/HyMeX/) maintained by the ESPRI/IPSL and
SEDOO/Observatoire Midi-Pyrénées in France. The metadata record
includes many fields, amongst which the dataset name, the period of
observation, the principal investigator in charge of the dataset, a
description of the data, the geographic coordinates of the instruments, a
description of the instruments and the measured variables, the format of the
data and the status of the data. The metadata record also has a “data
access” section. For the public datasets (Tables 5 and 6), a simple
registration (name, affiliation, e-mail) is necessary to create an account
(http://mistrals.sedoo.fr/User-Account-Creation/). This simple
registration generates an automatic e-mail with login and password which
enables direct access to public data. For the additional datasets under
“associated scientists” status (Table 7), the validation of the HyMeX data
policy
(http://mistrals.sedoo.fr/HyMeX/Data-Policy/HyMeX_DataPolicy.pdf) is
necessary to access the data. There are two options.
(1) The user had registered previously for access to public data
only. In this case, he/she must go to his/her account
(http://mistrals.sedoo.fr/Your-Account/) and complete the HyMeX
registration application.
(2) The user registers for the first time. He/she must go to
http://mistrals.sedoo.fr/User-Account-Creation/, not checking the
box corresponding to “Simple registration (name, affiliation, country) for
direct access to public data only”, and follow the registration.
Additionally a bundling service was performed to facilitate the use of the
data. The bundled data include the most commonly used data in
hydrometeorological and hydrological studies. The bundled data have the
advantage of gathering data in ASCII and Cartesian format in a single
coordinate system and in the same time zone (UTC). The bundled data are
selected for the spatial and temporal windows presented in the paper (see the
extent of Figs. 3 and 6) since some individual datasets have different
extents. For the remaining datasets, not included in the bundled data, the
effort to prepare the data was judged too laborious and their potential more
restricted. Such datasets remain accessible individually even though they are
not necessarily in the same format and with the same extent (polar vs.
Cartesian and coordinate system). The bundled data are organized in two
independent ways: (i) for the public datasets which refer to Tables 5 and 6,
the “zip1” file (602 MB) is accessible directly (without registration)
clicking on the purple icon of the “data access” section of the page
http://mistrals.sedoo.fr/MISTRALS/?editDatsId=1438; (ii) for the
additional datasets subject to the HyMeX data policy, the “zip2” file
(4 MB) is accessible by clicking on the blue icon of the “data access”
section of the page http://mistrals.sedoo.fr/MISTRALS/?editDatsId=1438.
Alternatively, there are other ways to access and visualize the data: the
SEVnOL system, maintained by IGE, on the OHMCV website
(http://www.ohmcv.fr) and the BDOH database
(https://bdoh.irstea.fr/OHM-CV/) maintained by IRSTEA and managed by
the data producers (IRSTEA, IGE). SEVnOL and BDOH are complementary tools to
the bundling service proposed in this study (through the release of the
“zip1” and “zip2” files).
BDOH was developed for the management of long-term time series and enables
the following features: visualization of data, downloading of data,
interpolation of time steps for export, import and export to multiple
formats, and automatic calculation of derived time series. SEVnOL is a web
interface developed to view and extract data, metadata and products in
several formats (XML, CSV, NetCDF) over a user-defined spatial and temporal
window (Boudevillain et al., 2011).
Conclusion
A high space–time resolution dataset linking
hydrometeorological forcing and hydro-sedimentary response in a mesoscale
catchment is presented. The Auzon catchment (116 km2), a tributary
of the Ardèche River, is subject to precipitating systems of
Mediterranean origin, which can result in significant rainfall amount. The
data presented cover a period of 4 years (2011–2014), including the
HyMeX-SOP1 field campaign (Ducrocq et al., 2014) and the ANR FloodScale
project (Braud et al., 2014), which aims at improving the understanding of
processes triggering flash floods. The multi-scale observation system
presented is part of the OHMCV (Boudevillain et al., 2011). The operational
and research networks provide high space–time resolution data
(< 1 km2, 5 min) for studying the microphysics of
precipitating systems and producing QPE
particularly adapted to fine-scale
hydrological studies. The measurement of the other meteorological variables
relies almost exclusively on the operational network (1 h time resolution).
Validation data are both spatially distributed and multi-scale. They include
point measurements of soil moisture (fixed sensors in continuous mode and
mobile sensors during rain events), runoff and erosion measurements on
hillslope, water level measurements in the intermittent hydrographic network
of headwater catchments (11 points of measurement) and hydrometric
measurements (discharge, water conductivity and temperature) at the outlet of
three nested catchments (3.4, 44 and 116 km2). Discharge measurements
were made at high water levels during on-alert campaigns to establish
stage–discharge relationships. It is hoped that using this dataset will lead
to advances in understanding hydrological processes leading to flash floods
and improving distributed hydrological models.
Abbreviations used in this article.
CNRMCentre National de Recherches Météorologiques, National Centre for Meteorological ResearchEPFLÉcole Polytechnique Fédérale de Lausanne, Swiss Federal Institute of TechnologyESPRI/IPSLEnsemble de Services Pour la Recherche de l'Institut Pierre Simon Laplace,Research oriented services of the Pierre Simon Laplace InstituteIGEInstitut des Géosciences de l'Environnement, Institute of Environmental GeosciencesIRSTEAInstitut national de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture,National Research Institute of Science and Technology for Environment and AgricultureLaMPLaboratoire de Météorologie Physique, Laboratory of Physical MeteorologyOSUGObservatoire des Sciences de l'Univers de Grenoble, Observatory of Sciences of the Universe of GrenobleSEDOOService de DOnnées de l'Observatoire Midi-Pyrénées,Data management service of the Midi-Pyrénées Observatory
Summary of the period of operation of the
instruments. The number of instruments in operation is also indicated given
that many instruments belong to a network of sensors. Op/Res column indicates
whether the instruments belong to an operational observation network (“Op”)
or a research observation network (“Res”).
* The instrument(s) is (are) still running at the date of
publication of the paper and at least for the LOP (long-term
observation period, 2010–2020) of the HyMeX programme. For each
year from 2011 to 2014, the number of operating instruments is indicated by
fortnightly period (the months are numbered from 1 to 12). Fields
with numbers indicates the periods when instruments operate, whereas blank fields indicate periods without any
measurements.
Overview of the (a) 6 h accumulated rainfall,
(b) discharge (10 min time step) and (c) turbidity
(10 min time step) for the entire record (period 2011–2014) at the
Claduègne hydrometric station. The rainfall data presented were taken
from the operational Météo-France rain gauge “Mirabel-SA” displayed
in Fig. 3. Note the 4 November 2014 flood is a 5–10-year return period flood for the Claduègne
River.
Guillaume Nord, Brice Boudevillain, Alexis Berne,
Guillaume Dramais, Cédric Legoût, Gilles Molinié, Joel Van
Baelen, and Jean-Pierre Vandervaere were principal investigators responsible
for specific instruments or networks of instruments which resulted in the
main individual datasets presented in this study. Isabelle Braud was the
leader of the FloodScale (2012–2015) ANR project. Flora Branger, Isabelle
Braud, Jérôme Le Coz, Guy Delrieu, Guillaume Nord, and Jean-Pierre
Vandervaere were responsible for work packages within the FloodScale project
which contributed significantly to the design of the observation system
presented in this study. Simon Gérard, Martin Calianno, and Coralie
Aubert helped in installing and maintaining the observation system. Julien
Andrieu prepared the land use maps. Guillaume Nord prepared the manuscript
with contributions from all co-authors. Flora Branger and Ivan Horner helped
with elaborating Figs. 7 and 8. Guillaume Nord, Brice Boudevillain, and
Isabelle Braud managed the process of data preparation and DOI attribution to
the individual datasets for subsequent submission to Earth System Science Data. Guillaume Nord and Brice Boudevillain contributed to the
bundling of the data. Brice Boudevillain performed the selection of the
individual datasets for the spatial and temporal windows presented in the
paper. All co-authors contributed to the data collection or evaluation of the
individual datasets presented in this study.
The authors declare that they have no conflict of
interest.
Acknowledgements
The FloodScale project is funded by the French National Research Agency (ANR)
under contract no. ANR 2011 BS56 027, which contributes to the HyMeX
programme. It also benefits from funding by the MISTRALS/HyMeX programme
(http://www.mistrals-home.org). OHMCV is supported by the Institut
National des Sciences de l'Univers (INSU/CNRS), the French Ministry for
Education and Research, the Environment Research Cluster of the
Rhône-Alpes Region, the Observatoire des Sciences de l'Univers de
Grenoble (OSUG)
and the SOERE Réseau des Bassins Versants (Alliance Allenvi). The
development of the BDOH database was supported by Irstea internal funding and
the Rhône Sediment Observatory (OSR) project, partly funded by the Plan
Rhône. The equipment of the erosion plots and part of the Gazel
hydrometric station was funded by the
Rhône-Alpes Region. The Sontek-IQ Plus instrument was funded by the
French National Research Agency (ANR) under contract no. ANR-12-JS06-0006
(SCAF project). The authors thank the providers of operational data:
Météo-France and the SPC Grand Delta. The contract of Simon
Gérard was funded by the Institut National des Sciences de l'Univers
(INSU/CNRS). The PhD thesis of Annette Wijbrans was funded by Rhône-Alpes
Region. We warmly thank colleagues of IGE (especially Romain Biron who
participated at the beginning of the project), including students and
technical staff (with special thoughts to Matthieu Le Gall), who helped in
installing and maintaining the observation system. We also thank Isabella Zin
and Jérémy Chardon for providing the analogue rainfall forecasting.
IGE is part of Labex OSUG@2020 (ANR10 LABX56), which funded the contract of
Martin Calianno, who also assisted with the first installations. The HyMeX
database teams (ESPRI/IPSL and SEDOO/Observatoire Midi-Pyrénées) and
the team of the OSUG data centre helped in accessing the data and attributing
DOIs to the individual datasets. In addition, the authors acknowledge the
EPLEFPA Olivier de Serres; the municipalities of Lavilledieu, Lussas,
Mirabel, Saint-Germain, Saint-Etienne de Fontbellon,
and the middle school of Villeneuve de Berg; and the
local landowners and neighbours for hosting the experiments. Timothy H.
Raupach acknowledges the support from the Swiss National Science Foundation.
Edited by: K. Elger
Reviewed by: two anonymous referees
ReferencesBaffaut, C., Ghidey, F., Sudduth, K. A., Lerch, R. N., and Sadler E. J.:
Long-term suspended sediment transport in the Goodwater Creek Experimental
Watershed and Salt River Basin, Missouri, USA, Water Resour. Res., 49,
7827–7830, 10.1002/wrcr.20511, 2013.Berne, A. and Krajewski, W. F.: Radar for hydrology: Unfulfilled promise or
unrecognized potential?, Adv. Water Resour., 51, 357–366,
10.1016/j.advwatres.2012.05.005, 2013.Borga, M., Stoffel, M., Marchi, L., Marra, F., and Jakob, M.: Hydrogeomorphic
response to extreme rainfall in headwater systems: Flash floods and debris
flows, J. Hydrol., 518, 194–205, 10.1016/j.jhydrol.2014.05.022, 2014.
Bornand, M., Legros, J., and Moinereau, J.: Notice explicative de la carte
pédologique de France à 1/100 000, Privas, INRA, Service d'étude
des sols et de la carte pédologique de France, 255 pp., 1977.Boudevillain, B., Delrieu, G., Galabertier, B., Bonnifait, L., Bouilloud, L.,
Kirstetter, P.-E., and Mosini, M.-L.: The Cévennes-Vivarais Mediterranean
Hydrometeorological Observatory database, Water Resour. Res., 47, W07701,
10.1029/2010WR010353, 2011.Boudevillain, B., Delrieu, G., Wijbrans, A., and Confoland, A.: A
high-resolution rainfall re-analysis based on radar-raingauge merging in the
Cévennes-Vivarais region, France, J. Hydrol., 541, 14–23,
10.1016/j.jhydrol.2016.03.058, 2016.Bousquet, O., Berne, A., Delanoe, J., Dufournet, Y., Gourley, J. J.,
Van-Baelen, J., Augros, C., Besson, L., Boudevillain, B., Caumont, O., Defer,
E., Grazioli, J., Jorgensen, D. J., Kirstetter, P.-E., Ribaud, J.-F., Beck,
J., Delrieu, G., Ducrocq, V., Scipion, D., Schwarzenboeck, A., and Zwiebel,
J.: Multifrequency Radar Observations Collected in Southern France during
HyMeX-SOP1, B. Am. Meteorol. Soc., 96, 267–282,
10.1175/BAMS-D-13-00076.1, 2015.
Branger, F., Dramais, G., Horner, I., Boursicaud, R. L., Coz, J. L., and
Renard, B.: Improving the quantification of flash flood hydrographs and
reducing their uncertainty using noncontact streamgauging methods, EGU
General Assembly, Vienna, Austria, 12–17 April 2015, EGU2015-5768, 2015.Braud, I., De Condappa, D., Soria, J., Haverkamp, R., Angulo-Jaramillo, R.,
Galle, S., and Vauclin, M.: Use of scaled forms of the infiltration equation
for the estimation of unsaturated soil hydraulic properties (Beerkan method),
Eur. J. Soil Sci., 56, 361–374, 10.1111/j.1365_2389.2004.00660.x,
2005.
Braud, I., Ayral, P.-A., Bouvier, C., Branger, F., Delrieu, G., Le Coz, J.,
Nord, G., Vandervaere, J.-P., Anquetin, S., Adamovic, M., Andrieu, J.,
Batiot, C., Boudevillain, B., Brunet, P., Carreau, J., Confoland, A.,
Didon-Lescot, J.-F., Domergue, J.-M., Douvinet, J., Dramais, G., Freydier,
R., Gérard, S., Huza, J., Leblois, E., Le Bourgeois, O., Le Boursicaud,
R., Marchand, P., Martin, P., Nottale, L., Patris, N., Renard, B., Seidel,
J.-L., Taupin, J.-D., Vannier, O., Vincendon, B., and Wijbrans, A.:
Multi-scale hydrometeorological observation and modelling for flash flood
understanding, Hydrol. Earth Syst. Sci., 18, 3733–3761,
10.5194/hess-18-3733-2014, 2014.Braud, I. and Vandervaere, J. P.: Analysis of infiltration tests performed in
the Claduègne catchment in May–June 2012, contribution to WP3.4
“Documentation and mapping of soil hydraulic properties, soil geometry and
vegetation cover of small catchments” of the FloodScale (2012–2015) ANR
project, 66 pp., available at:
http://mistrals.sedoo.fr/?editDatsId=1321, last access: 1 October 2015.Cea, L., Legout, C., Grangeon, T., and Nord, G.: Impact of model
simplifications on soil erosion predictions: application of the GLUE
methodology to a distributed event-based model at the hillslope scale,
Hydrol. Process., 30, 1096–1113, 10.1002/hyp.10697, 2016.
Delrieu, G., Wijbrans, A., Boudevillain, B., Faure, D., Bonnifait, L., and
Kirstetter, P. E.:. Geostatistical radar-raingauge merging: a novel method
for the quantification of rainfall estimation error, Adv. Water Res., 71,
110–124, 2014.Demir, I., Conover, H., Krajewski, W. F., Seo, B.-C., Goska, R., He, Y.,
McEniry, M. F., Graves, S. J., and Petersen, W.: Data Enabled Field
Experiment Planning, Management, and Research using Cyberinfrastructure,
J. Hydrometeorol., 16, 1155–1170, 10.1175/JHM-D-14-0163.1, 2015.de Vente, J., Poesen, J., Verstraeten, G., Govers, G., Vanmaercke, M., Van
Rompaey, A., Arabkhedri, M., and Boix-Fayos, C.: Predicting soil erosion and
sediment yield at regional scales: Where do we stand?, Earth-Sci. Rev., 127,
16–29, 10.1016/j.earscirev.2013.08.014, 2013.Dramais, G., Le Coz, J., Le Boursicaud, R., Gallavardin, A., Benmamar, D.,
and Hauet, A: Suivi hydrométrique par mesure vidéo sur le bassin
versant de l'Ardèche, Irstea, 10.17180/OBS.OHM-CV.ARDECHE, 2015.Drobinski, P., Ducrocq, V., Alpert, P., Anagnostou, E., Béranger, K.,
Borga, M., Braud, I., Chanzy, A., Davolio, S., Delrieu, G., Estournel, C.,
Filali Boubrahmi, N., Font, J., Grubisic, V., Gualdi, S., Homar, V.,
Ivancan-Picek, B., Kottmeier, C., Kotroni, V., Lagouvardos, K., Lionello, P.,
Llasat, M. C., Ludwig, W., Lutoff, C., Mariotti, A., Richard, E., Romero, R.,
Rotunno, R., Roussot, O., Ruin, I., Somot, S., Taupier-Letage, I., Tintore,
J., Uijlenhoet, R., and Wernli, H.: HyMeX, a 10-year multidisciplinary
program on the Mediterranean water cycle, B. Am. Meteorol. Soc., 95,
1063–1082, 10.1175/BAMS-D-12-00242.1, 2014.Ducrocq, V., Braud, I., Davolio, S., Ferretti, R., Flamant, C., Jansa, A.,
Kalthoff, N., Richard, E., Taupier-Letage, I., Ayral, P.-A., Belamari, S.,
Berne, A., Borga, M., Boudevillain, B., Bock, O., Boichard, J.-L., Bouin,
M.-N., Bousquet, O., Bouvier, C., Chiggiato, J., Cimini, D., Corsmeier, U.,
Coppola, L., Cocquerez, P., Defer, E., Delanoë, J., Di Girolamo, P.,
Doerenbecher, A., Drobinski, P., Dufournet, Y., Fourrié, N., Gourley, J.,
Labatut, L., Lambert, D., Le Coz, J., Marzano, F., Molinié, G., Montani,
A., Nord, G., Nuret, M., Ramage, K., Rison, B., Roussot, O., Said, F.,
Schwarzenboeck, A., Testor, P., Van-Baelen, J., Vincendon, B., Aran, M., and
Tamayo, J.: HyMeX-SOP1, the field campaign dedicated to heavy precipitation
and flash-flooding in the North-Western Mediterranean, B. Am. Meteorol. Soc.,
95, 1083–1100, 10.1175/BAMS-D-12-00244.1, 2014.
Elmi, S., Busnardo, R., Clavel, B., Camus, G., Kieffer, G., Bérard, P.,
and Michaëly, B.: Notice explicative de la carte géologique de la
France à 1/50000, Aubenas, Éditions du BRGM Service géologique
national, 170 pp., 1996.
Fabry, F.: On the determination of scale ranges for precipitation fields, J.
Geophys. Res., 101, 12819–12826, 1996.Fraedrich, K. and Larnder, C.: Scaling regimes of composite rainfall time
series, Tellus A, 45, 289–298, 10.1034/j.1600-0870.1993.t01-3-00004.x,
1993.
Gonzalez-Sosa, E., Braud, I., Dehotin, J., Lassabatère, L.,
Angulo-Jaramillo, R., Lagouy, M., Branger, F., Jacqueminet, C., Kermadi, S.,
and Michel, K.: Impact of land use on the hydraulic properties of the topsoil
in a small French catchment, Hydrol. Process., 24, 2382–2399, 2010.Goodrich, D. C., Lane, L. J., Shillito, R. M., Miller, S. N., Syed, K. H.,
and Woolhiser, D. A.: Linearity of basin response as a function of scale in a
semiarid watershed, Water Resour. Res., 33, 2951–2965,
10.1029/97WR01422, 1997.
Grangeon, T. : Etude multi-échelle de la granulométrie des particules
fines générées par érosion hydrique : apports pour la
modélisation, PhD thesis, Université Joseph Fourier, France, 204 pp.,
2012.Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from
polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech.,
8, 149–170, 10.5194/amt-8-149-2015, 2015.
Grillot, J. C.: Contribution à l'étude géologique et
hydrogéologique du massif des Coirons (Partie Occidentale), PhD thesis,
Applied geology, Université des Sciences et Techniques du Languedoc,
France, 97 pp., 1971.Gupta, V. K., Mantilla, R., Troutman, B. M., Dawdy, D., and Krajewski, W. F.:
Generalizing a nonlinear geophysical flood theory to medium-sized river
networks, Geophys. Res. Lett., 37, L11402, 10.1029/2009GL041540, 2010.
Horner, I.: Quantification des incertitudes hydrométriques et bilans
hydrologiques sur le bassin versant de l'Yzeron (ouest lyonnais), MS thesis,
AgroCampus Ouest, France, 52 pp., 2014.Huza, J., Teuling, A. J., Braud, I., Grazioli, J., Melsen, L. A., Nord, G.,
Raupach, T. H., and Uijlenhoet, R.: Precipitation, soil moisture and runoff
variability in a small river catchment (Ardèche, France) during HyMeX
Special Observation Period 1, J. Hydrol., 516, 330–342,
10.1016/j.jhydrol.2014.01.041, 2014.Jetten, V., Govers, G., and Hessel, R.: Erosion models: quality of spatial
predictions, Hydrol. Process., 17, 887–900, 10.1002/hyp.1168, 2003.Kormos, P. R., Marks, D., Williams, C. J., Marshall, H. P., Aishlin, P.,
Chandler, D. G., and McNamara, J. P.: Soil, snow, weather, and sub-surface
storage data from a mountain catchment in the rain–snow transition zone,
Earth Syst. Sci. Data, 6, 165–173, 10.5194/essd-6-165-2014, 2014.Krajewski, W. F., Ciach, G. J., and Habib, E.: An analysis of small-scale
rainfall variability in different climatic regimes, Hydrolog. Sci. J., 48,
151–162, 10.1623/hysj.48.2.151.44694, 2003.
Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., and Camenen, B.:
Performance of image-based velocimetry (LSPIV) applied to flash-flood
discharge measurements in Mediterranean rivers, J. Hydrol., 394, 42–52,
2010.Le Coz, J., Renard, B., Bonnifait, L., Branger, F., and Le Boursicaud, R.:
Combining hydraulic knowledge and uncertain gaugings in the estimation of
hydrometric rating curves: A Bayesian approach, J. Hydrol., 509, 573–587,
10.1016/j.jhydrol.2013.11.016, 2014.
Levesque, V. A. and Oberg, K. A.: Computing discharge using the index
velocity method, U.S. Geological Survey, Techniques and Methods 3-A23,
148 pp., 2012.Marchi, L., Borga, M., Preciso, E., and Gaume, E.: Characterisation of
selected extreme flash floods in Europe and implications for flood risk
management, J. Hydrol., 394, 118–133, 10.1016/j.jhydrol.2010.07.017,
2010.
Marra, F., Nikolopoulos, E. I., Creutin, J. D., and Borga, M.: Radar rainfall
estimation for the identification of debris-flow occurrence thresholds,
J. Hydrol., 519, 1607–1619, 2014.Mishra, K. V., Krajewski, W. F., Goska, R., Ceynar, D., Seo, B.-C., Kruger,
A., Niemeier, J., Galvez, M. B., Thurai, M., Bringi, V. N., Tolstoy, L.,
Kucera, P., Petersen, W., Grazioli, J., and Pazmany, A.: Deployment and
performance analyses of high-resolution iowa XPOL radar system during the
NASA IFloodS campaign, J. Hydrometeorol., 17, 455–479,
10.1175/JHM-D-15-0029.1, 2016.
Molinié, G., Ceresetti, D., Anquetin, S., Creutin, J. D., and
Boudevillain, B.: Rainfall Regime of a Mountainous Mediterranean Region:
Statistical Analysis at Short Time Steps, J. Appl. Meteorol. Clim., 51,
429–448, 2012.National Advisory Committee for Aeronautics: Manual of the ICAO Standard
Atmosphere Calculations by the NACA, Technical Note 3182, Washington,
available at:
http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19930083952.pdf,
last access: 10 June 2015, 1954.
Naud, G.: Contribution à l'étude géologique et
hydrogéologique du massif des Coirons (Partie Orientale), PhD thesis,
Université de Provence, France, 153 pp., 1972.
Navratil, O., Esteves, M., Legout, C., Gratiot, N., Némery, J., Willmore,
S., and Grangeon, T.: Global uncertainty analysis of suspended sediment
monitoring using turbidimeter in a small mountainous river catchment,
J. Hydrol., 398, 246–259, 2011.
Navratil, O., Evrard, O., Esteves, M., Legout, C., Ayrault, S., Nemery, J.,
Mate-Marin, A., Ahmadi, M., Lefevre, I., Poirel, A., and Bonte, P.: Temporal
variability of suspended sediment sources in an alpine catchment combining
river/rainfall monitoring and sediment fingerprinting, Earth Surf. Proc.
Land., 37, 828–846, 2012.
Nicolas, M.: Etude expérimentale et numérique du ruissellement de
surface: effets des variations d'intensité de la pluie. Application à
une parcelle de vigne en Cévennes-Vivarais, PhD thesis, Université
Joseph Fourier, France, 217 pp., 2010.
Nicoud, C.: Study of the relationship between initial soil moisture and
hydraulic response for evaluation of flood severity, MS thesis,
Université Joseph Fourier, France, 44 pp., 2015.Nord, G., Gallart, F., Gratiot, N., Soler, M., Reid, I., Vachtman, D.,
Latron, J., Martín Vide, J. P., and Laronne, J. B.: Applicability of
acoustic Doppler devices for flow velocity measurements and discharge
estimation in flows with sediment transport, J. Hydrol., 509, 504–518,
10.1016/j.jhydrol.2013.11.020, 2014.Raupach, T. H. and Berne, A.: Correction of raindrop size distributions
measured by Parsivel disdrometers, using a two-dimensional video disdrometer
as a reference, Atmos. Meas. Tech., 8, 343–365,
10.5194/amt-8-343-2015, 2015.Reba, M. L., Marks, D., Seyfried, M., Winstral, A., Kumar, M., and
Flerchinger, G.: A long-term data set for hydrologic modeling in a
snow-dominated mountain catchment, Water Resour. Res., 47, W07702,
10.1029/2010WR010030, 2011.Reed, S., Koren, V., Smith, M., Zhang, Z., Moreda, F., Seo, D. J., and DMIP
Participants: Overall distributed model intercomparison project results,
J. Hydrol., 298, 27–60, 10.1016/j.jhydrol.2004.03.031, 2004.Renard, K. G., Nichols, M. H., Woolhiser, D. A., and Osborn, H. B.: A brief
background on the U.S. Department of Agriculture Agricultural Research
Service Walnut Gulch Experimental Watershed, Water Resour. Res., 44, W05S02,
10.1029/2006WR005691, 2008.
Ruin, I., Creutin, J., Anquetin, S., and Lutoff, C.: Human exposure to
flash-floods-relation between flood parameters and human vulnerability during
a storm of September 2002 in Southern France, J. Hydrol., 361, 199–213,
2008.Schneebeli, M., Dawes, N., Lehning, M., and Berne, A.: High-resolution
vertical profiles of polarimetric X-band weather radar observables during
snowfall in the Swiss Alps, J. Appl. Meteorol. Clim., 52, 378–394,
10.1175/JAMC-D-12-015.1, 2013Schneebeli, M., Grazioli, J., and Berne, A.: Improved Estimation of the
Specific Differential Phase Shift Using a Compilation of Kalman Filter
Ensembles, IEEE T. Geosci. Remote, 52, 5137–5149,
10.1109/TGRS.2013.2287017, 2014.Slaughter, C. W., Marks, D., Flerchinger, G. N., Van Vactor, S. S., and
Burgess, M.: Thirty-five years of research data collection at the Reynolds
Creek Experimental Watershed, Idaho, United States, Water Resour. Res., 37,
2819–2823, 10.1029/2001WR000413, 2001.Stone, J. J., Nichols, M. H., Goodrich, D. C., and Buono, J.: Long-term
runoff database, Walnut Gulch Experimental Watershed, Arizona, United States,
Water Resour. Res., 44, W05S05, 10.1029/2006WR005733, 2008.
Tabary, P.: The new French radar rainfall product. Part I: Methodology,
Weather Forecast., 22, 393–408, 2007.Tuset, J., Vericat, D., and Batalla, R. J.: Rainfall, runoff and sediment
transport in a Mediterranean mountainous catchment, Sci. Total Environ., 540,
114–132, 10.1016/j.scitotenv.2015.07.075, 2016.
Uber, M..: The Impact of Initial Soil Moisture on the Hydrologic Response of
Two Flash Flood Prone Catchments in Southern France, MS thesis, University of
Potsdam, Germany, 80 pp., 2016.Weiler, M. and Beven, K.: Do we need a Community Hydrological Model?, Water
Resour. Res., 51, 7777–7784, 10.1002/2014WR016731, 2015.Welber, M., Le Coz, J., Laronne, J. B., Zolezzi, G., Zamler, D., Dramais, G.,
Hauet, A., and Salvaro, M.: Field assessment of noncontact stream gauging
using portable surface velocity radars (SVR), Water Resour. Res., 52,
1108–1126, 10.1002/2015WR017906, 2016.Western, A. W. and Grayson, R. B.: The Tarrawarra Data Set: Soil moisture
patterns, soil characteristics, and hydrological flux measurements, Water
Resour. Res., 34, 2765–2768, 10.1029/98WR01833, 1998.Zwiebel, J., Van Baelen, J., Anquetin, S., Pointin, Y., and Boudevillain, B.:
Impacts of orography and rain intensity on rainfall structure. The case of
the HyMeX IOP7a event, Q. J. Roy. Meteor. Soc., 142, 310–319,
10.1002/qj.2679, 2015.