Floods have strong impacts in the Mediterranean region and there are concerns about a possible increase in their intensity due to climate
change. In this study, a large database of 171 basins located in southern
France with daily discharge data with a median record length of 45 years is
considered to analyze flood trends and their drivers. In addition to
discharge data, outputs of precipitation, temperature, evapotranspiration
from the SAFRAN reanalysis and soil moisture computed with the ISBA land
surface model are also analyzed. The evolution of land cover in these basins
is analyzed using the CORINE database. The trends in floods above the
95th and 99th percentiles are detected by the Mann–Kendall test
and quantile regression techniques. The results show that despite the
increase in extreme precipitation reported by previous studies, there is no
general tendency towards more severe floods. Only for a few basins is the
intensity of the most extreme floods showing significant upward trends.
On the contrary, most trends are towards fewer annual flood occurrences
above both the 95th and 99th percentiles for the majority of
basins. The decrease in soil moisture seems to be an important driver for
these trends, since in most basins increased temperature and
evapotranspiration associated with a precipitation decrease are leading to
a reduction in soil moisture. These results imply that the observed
increase in the vulnerability to these flood events in recent decades is
mostly caused by human factors such as increased urbanization and population
growth rather than climatic factors.
Introduction
A number of studies have now established that extreme precipitation could
increase due to climate change, particularly in the Mediterranean (Westra et
al., 2013; Polade et al., 2017; Ribes et al., 2019; Tramblay and Somot,
2018). Changes in extreme rainfall would be caused by an increase in the
precipitable water content in the atmosphere, related to increasing
temperatures, according to the principle of Clausius–Clapeyron
thermodynamics (Drobinki et al., 2016; Pfahl et al., 2017). Nevertheless,
this relationship has a high variability in space, related to temperatures
and available humidity (Wasko et al., 2016). Several studies observed an
increase in the number of dry days associated with increased rainfall
intensities, suggesting that dry periods in these areas would become longer,
but that precipitation could be more extreme when they occur (Paxian et al.,
2015; Polade et al., 2017). Nevertheless, the increase in extreme rainfall
would not offset the decrease in precipitation totals, as the drop in
cumulative rainfall associated with the decrease in the frequency of low to
moderate rainfall is expected to predominate over the gains resulting from
the intensification of extreme precipitation (Polade et al., 2014).
Besides changes in precipitation, an increase in rainfall intensity does not
necessarily imply an increase in flood risk (Ivancic and Shaw, 2015;
Woldemeskel and Sharma, 2016). Indeed, for a given rainfall accumulation,
the runoff coefficient can be very variable in time and space in different
basins due to complex interactions between precipitation and infiltration
processes on hillslopes, which can strongly modulate flood magnitude
(Woldemeskel and Sharma, 2016; Wasko and Sharma, 2017; Bennett et al.,
2018). Most global studies on flood trends indicate a decrease in flood
intensity (Do et al., 2017; Wasko and Sharma, 2017; Sharma et al., 2018).
Yet, these trends are highly variable in space for different regions of the
globe (Yin et al., 2018; Najibi and Devineni, 2018). The attribution of
these trends is rather uncertain, while Yin et al. (2018) relate an increase
in floods with increased temperatures; Najibi and Devineni (2018) and
Hodgkins et al. (2018) conclude that trends in the flood frequency and
duration can be mostly attributed to long-term climate variability.
Nonetheless, as noted by Whitfield (2012), flood-generating processes do not
take place at the global but rather a relatively local scale, making
generalizations about flooding in future climates difficult and uncertain.
For Mediterranean basins, Blöschl et al. (2017) indicate later winter
floods and Mangini et al. (2018) noted a tendency towards increasing flood
magnitude and decreasing flood frequency. These finding are consistent with
trends detected by Mediero et al. (2014) in Spain and Giuntoli et al. (2012)
for the south of France.
While much work has been done to estimate future climatic conditions, changes in hydrological variables, including surface conditions, can also strongly modulate climatic trends (Knighton et al.,
2017). In particular, it is known that in many catchments the initial soil
moisture conditions prior to flood events play a key role in flood
generation (Brocca et al., 2008; Tramblay et al., 2010; Raynaud et al.,
2015; Woldemeskel and Sharma, 2016; Wasko and Sharma, 2017; Uber et al.,
2018; Wasko and Nathan, 2019), and its temporal change has not been much
analyzed up to now. Between two episodes of rain, the base flow of the
perennial rivers originates from the draining of the water contained in the
soils and for some basins from the aquifers. The capacity of the soil to
contain water and restore it to generate runoff depends on its
characteristics (e.g., texture, structure, porosity) but also on the amount
of water it already contains at the beginning of a rain episode. Thus, a
quasi-saturated soil will not be able to store a lot of water, which, being
unable to infiltrate the soil, will contribute directly to runoff. In most cases,
there is a nonlinear relationship between the flow rate and the initial
saturation state of the soil, usually with a threshold value of moisture
above which a rapid flow response to a rainy episode is observed (Norbiato
et al., 2008; Viglione et al., 2009; Penna et al., 2011). Difference in soil
types could induce different relationships between floods and initial
conditions (Grillakis et al., 2016; Camarasa-Belmonte, 2016). For
intermittent (seasonal runoff only) and ephemeral streams (runoff only after
a rain event), the impact of antecedent soil moisture is more complex and
strongly dependent on the soil type and geological context (in the presence
of karst in particular). In smaller basins, the impact of initial soil
moisture content is usually not significant and it increases with catchment
size (Zhang et al., 2011).
For some Mediterranean basins, the increase in heavy rainfall associated
with a reduced number of rainy days could decrease the soil water content
and therefore increase infiltration capacity, hence reducing runoff. On the
other hand, more intense rains in urbanized, impervious areas or on bare
soils that are subject to crusting effects could increase runoff and
therefore the magnitude of floods. It is therefore necessary to use
hydrological or surface models capable of representing these processes. Using the ISBA land surface scheme with
different downscaling methods, Quintana-Seguí et al. (2011) found a future increase in floods
corresponding to a 10-year return level in southern French basins, but with
different magnitudes depending on the basins. Camici et al. (2017), in a
study on the impacts of climate change on floods in central Italy, noted a
greater sensitivity of basins with permeable soils to changing climatic
conditions. Similarly, Piras et al. (2016) in Sardinia found that
impermeable and flat sub-basins are predicted to experience more intense
flood events in future scenarios, while more permeable and steep
sub-catchments will have an opposite tendency. However, there are systematic
differences between projections of changes in flood hazards in southern Europe
(Italy, Greece, Iberian Peninsula) in most European and global studies using
large-scale hydrological models (Kundzewicz et al., 2017). Indeed, some
studies point towards an increase in southern Europe (Quintana-Seguí
et al., 2011; Alfieri et al., 2015) while others suggests a decrease
(Donnelly et al., 2017; Thober et al., 2018). This is due to different GCM,
RCM, scenario and downscaling approaches but also the use of large-scale
hydrological model usually not calibrated and validated for all basins. This
type of global (or large-scale) hydrological model (e.g., LISFLOOD, VIC,
HYPE) is usually not adapted to small river basins less than 500 km2, which is the typical catchment size found in the
Mediterranean region.
Prior to making future projections on flood hazard, there is a need to
understand the main drivers of changes for floods and the links between
floods and climate characteristics (Merz et al., 2014). Indeed,
understanding the potential flood drivers and their changes may be more
relevant than predictions of uncertain flood changes as noted by Blöschl
et al. (2015). The objective of this study is to analyze trends in flood
characteristics for a large sample of French Mediterranean basins and to
relate these trends to climate and land use dynamics. This is done using
statistical tests for the detection of trends and quantile regression models
to relate high discharge quantiles to different climatic drivers.
Data
A total of 171 basins located in southern France were selected with a minimum of 20 years
of daily discharge data. The selection of basins is based on the
availability of long time series of daily discharge, and the selected basins
have no significant human influence on flow, according to a previous database
assembled by Sauquet and Catalogne (2011) and Snelder et al. (2013). The
median record length is 45 years and 56 stations have more than 50 years of
data; more than 100 stations have a complete record, with less than 5 %
missing data, between 1970 and 2010. All the catchments selected have a
Mediterranean climate, with a precipitation deficit during summer when the
low flows are recorded. These basins are experiencing flash flood events
caused by intense rainfall events, corresponding to the only region in
France where rainfall can exceed 200 mm d-1 (http://pluiesextremes.meteo.fr, last access: 26 October 2019) with the maximum occurrence between
September and November. Most basins have a catchment area lower than 500 km2 and located below 1000 m (Fig. 1). The proportion of
karstic areas for each basin has been obtained from the BDLISA database
(available here: https://bdlisa.eaufrance.fr/, last access: 26 October 2019), which provides a
delineation of karst systems in France (Schomburgk et al., 2016). For 66 basins, more than 50 % of their catchment areas consist of
carbonaceous superficial formations, indicative of karstic areas. This means
that the rainfall–runoff relationship in this type of basin can be strongly
modulated by the presence of karst (Jourde et al., 2007).
Catchment size, mean altitude and fraction of karstic areas.
In addition to daily discharge data, different variables have been retrieved
from the SAFRAN-ISBA-MODCOU (SIM) hydro-meteorological model (Habets et al.,
2008). SIM is based on the SAFRAN reanalysis over France
(Quintana-Seguí et al., 2008), which is based on observed data, and provides
rainfall, snowfall, temperature and reference evapotranspiration for an
8 km × 8 km grid over France at the daily time step from 1958 until present. The
SAFRAN reanalysis is used to force the ISBA land surface scheme of
Météo-France (Habets et al., 2008), to provide among other variables
the actual evapotranspiration and the surface and root zone soil moisture at
the same spatial and temporal resolution than ISBA. Tramblay et al. (2010)
have shown that the soil moisture from the root zone simulated by ISBA is an
appropriate indicator of soil moisture prior to flood events in French
Mediterranean catchments. The catchment boundaries of the 171 basins
selected have been extracted from the HydroSheds database (https://hydrosheds.org/, last access: 26 October 2019) providing flow accumulation and flow direction
maps at the 15 arcsec resolution. Then the total precipitation,
rainfall, air temperature, actual and reference evapotranspiration from
SAFRAN and the surface and root zone soil moisture from ISBA have been
extracted and averaged over every catchment.
The evolution of land cover between 1990 and 2018 in the 171 basins was
analyzed using the Corine Landcover inventory (CLC1990 and CLC 2018). Corine
Landcover provides an inventory of 44 classes over the European region
(Büttner et al., 2002). CLC1990 and CLC2018 are respectively based on
Landsat-5 (50 m spatial resolution) and Sentinel-2 (10 m spatial resolution)
satellite images. A limitation of the CLC inventory lies in the difference
of accuracy between the CLC1990 and CLC2018 products, which may introduce an
uncertainty in the estimation of the evolution of the land cover in the
studied basins.
Methods
Two approaches are considered to evaluate trends. The first approach,
presented in Sect. 3.1 thereafter, relies on the Mann–Kendall (MK) test (Mann,
1945) applied to the annual number of flood events above two different
percentiles, the 95th and the 99th computed on the whole time
series and also on the magnitude of these events. Using two different
thresholds, which are commonly used for the analysis of floods, allows the trends on moderate (above the 95th
percentile) and more severe (above the 99th percentile) flood events to be considered separately.
The second approach presented in Sect. 3.2 is based on quantile
regression (Koenker and Basset, 1978) to estimate the temporal trend
magnitude in the 95th and 99th percentiles of daily runoff in all
stations. The quantile regression method is also used to relate the change
in runoff quantiles to changes in climate characteristics, hence providing a
way to attribute the observed changes to their potential drivers.
Hydrological years are considered, starting 1 September and ending
31 August of the next calendar year. Years with more than 5 % missing
days are removed. For the first approach based on event characteristics, a
de-clustering is required so that consecutive
daily threshold exceedances that belong to the same flood event are not included in the flood sample. A minimum
of 2 d between two flood events is selected since it is the average
duration of rainstorms in the region (Tramblay et al., 2013). This means, if
for two consecutive days the runoff is exceeding the threshold, only the
maximum value is retained. Moreover, different values between 1 and 5 d
to separate the events have been tested and did not change the trend
results.
Test for trends and regional significance
The Mann–Kendall test (Mann, 1945) is used for the trend detection.
Several studies have noted that the presence of serial correlation may
affect the results of trend analysis by increasing the variance of the test
statistic (Khaliq et al., 2009; Renard et al., 2008). To overcome this
limitation, Hamed and Rao (1998) proposed a corrected MK test statistic
considering an effective sample size that reflects the effect of serial
correlation. This correction was applied in the present study. In addition
to the MK test, the method of Sen (1968) is considered to estimate the
magnitude of trends. In the present study, trends are considered significant
at the 10 % level; however, sensitivity tests performed for p≤0.05,
p≤0.01 revealed very similar spatial trend patterns.
The significance level αlocal for a statistical test is related
to a single test and is no longer valid when multiple tests are conducted
(Wilks, 2016). When the number of tests being conducted increases, more
significant values will be found. The goal of the false discovery rate (FDR)
procedure introduced by Benjamini and Hochberg (1995) is to identify a set
of at-site significance tests by controlling the expected proportion of
falsely rejected null hypotheses that are actually true. Renard et al. (2008), Khaliq et al. (2009) or Wilks (2016) demonstrated that the original
FDR is robust to cross-correlations between locations and can work with any
statistical test for which one can generate a p value. This FDR method is
applied to the MK test results to check if the trends are regionally
significant. The detected trends are regionally significant if at least one
local null hypothesis is rejected according to the global (or regional)
significance level, αglobal (Wilks, 2016). For consistency with
the local trend analysis, the global significance level is also set to
10 % in the FDR procedure.
Quantile regression
As a complementary approach to detect trends in quantiles but also to
investigate the relationship between floods and explanatory covariates, the
quantile regression (Koenker and Basset, 1978) method is applied. Quantile
regression could be seen as the extension of the ordinary least square (OLS)
regression (Koenker and Machado, 1999; Villarini and Slater, 2017). In OLS,
the conditional mean of the response variable is modeled with respect to one
or more predictors and the sum of squared errors is minimized. For quantile
regression, a conditional quantile of the response variable is modeled as
a function of predictor(s), and an asymmetrically weighted sum of absolute errors
is minimized to estimate the slope and intercept terms. In the present work,
only linear relationships are considered with one single covariate at a
time, while more complex forms of dependences could also be considered in
quantile regression. The approach has been previously used to detect trends
in extreme precipitation or floods by Villarini and Slater (2017), Yin et
al. (2018) and Wasko and Nathan (2019).
Significant annual trends at the 10 % level (Mann–Kendall test)
between 1958 and 2018 in precipitation, rainfall, frequency of dry days
(with precipitation below 1 mm), temperature, soil moisture, actual
evapotranspiration (AE) and reference evapotranspiration (ET0).
Koenker and Machado (1999) introduced the R1 goodness-of-fit measure for
quantile regression models. As for the R2 in the case of OLS,
R1 lies between 0 and 1. Unlike R2, which measures the
relative success of two models for the conditional mean function in terms of
residual variance, R1 measures the relative success of the corresponding
quantile regression models for a specific quantile, by comparison with a
restricted model (with slope =0), in terms of a weighted sum of absolute
residuals (see Koenker and Machado, 1999). Consequently, R1 constitutes
only a local measure of goodness of fit for a particular quantile rather
than a global measure over the entire conditional distribution, like
R2 . This measure can help to discriminate between different
models using different covariates (e.g., precipitation or temperature). Higher
R1 values indicate that the model fits better to observations. In this
study, this criterion is used to identify the best covariates that could
explain the temporal variations in high runoff quantiles.
Summary of the trend detection on different variables: number of
positive and negative trends significant at the 10 % level and regional
significance.
VariablePositiveNegativeRegionaltrendstrendssignificanceClimaticMean precipitation056Yes (28 basins)variablesMean rainfall149Yes (20 basins)Frequency of dry days462Yes (9 basins)Mean temperature1660Yes (165 basins)Mean surface soil moisture1132Yes (129 basins)Mean root zone soil moisture1132Yes (129 basins)Mean actual evapotranspiration1690Yes (169 basins)Mean reference evapotranspiration1360Yes (131 basins)Flood eventsNumber of floods above the 95th percentile067Yes (40 basins)Number of floods above the 99th percentile145Yes (7 basins)Flood magnitudes above the 95th percentile43NoFlood magnitudes above the 99th percentile165NoClimatic variablesCumulative precipitation during floods above the 95th percentile366Yes (16 basins)associated withCumulative precipitation during floods above the 99th percentile343Yes (5 basins)flood eventsAntecedent wetness conditions for floods above the 95th percentile1040Yes (11 basins)Antecedent wetness conditions for floods above the 95th percentile624Yes (14 basins)ResultsClimatic and land cover trends
The climate trends have been analyzed for the whole period of available
SAFRAN records, between 1958 and 2018. For each basin, the annual trends in
precipitation, rainfall, temperature, soil moisture, actual and reference
evapotranspiration have been analyzed with the Mann–Kendall test. From
Fig. 2 and Table 1, a significant decrease in annual rainfall in 56
basins, an average of -20 %, accompanied by an increase in the frequency
of dry days (with precipitation below 1 mm) for 46 basins can be seen. The snowfall is
also decreasing in the same proportions (not shown). The sole exception where
an increase in rainfall is found is for the Asse River at
Beyne-Chabrières on the western foothills of the Alps. This station has
long time series spanning from 1983 to 2009, where a +15 % trend in
annual rainfall is detected over the whole record. Yet, the detection of
this trend might be an artifact since there are several consecutive wet
years between 1992 and 2000. This trend in rainfall can also be seen for the
soil moisture trends. Associated with the precipitation decrease, positive
temperature trends are observed for almost all basins, with an average
increase of +0.5∘C during the time period 1958–2015.
Consequently, widespread increasing trends in reference and actual
evapotranspiration rates over all basins are observed, similarly to in
Vicente-Serrano et al. (2014) in Spain or Rivoire et al. (2019) for the
whole Mediterranean region. The combined decrease in precipitation with
increased evapotranspiration yields a decrease in soil moisture for the
surface and the root zone layers. Yet, it must be stressed here that the
soil moisture in the present study is not observed but simulated from the
ISBA land surface model. However, the detected trends are in accordance with
previous studies over southern France such as Vidal et al. (2012) or Dayon et
al. (2018).
Urban, agricultural and forest cover by catchment from the CORINE
database for the year 2018 and differences between 1990 and 2018 (upward
black triangles indicate an increase, and downward blue triangles a decrease;
the triangle sizes are proportional to the absolute changes between 1990 and
2018).
With regard to land cover (Fig. 3), most basins have low urban areas (below 10 %)
and the basins with the highest coverage are found mostly in the southeast.
An increase in urban areas of up to +20 % of total catchment surface can be
seen between 1990 and 2018, mostly for basins located close to the
Mediterranean coast and in particular those in the Provence-Alpes-Côte-d'Azur
region. The class representing discontinuous urban fabric represents 73 %
of urbanized areas and increased by +36 % between 1990 and 2018.
The increase in urbanized areas could have a strong impact on runoff
generation, in particular for small basins, with the increase in impervious
surfaces favoring surface runoff. In contrast, the agricultural and forest
land cover can reach 100 % of the basin surface, in particular in the
western Tarn regions for agriculture. We can notice a reduction in forest
cover in the Northern Cévennes areas associated with an increase in
agricultural surfaces. When looking in detail at the original
classification, for some catchments of size 500 km2 or less,
the percentage of vineyards could exceed 70 % of the total catchment areas, particularly for basins located in the Occitanie region. For almost all
basins, the percentage of vineyards has decreased between 1990 and 2018. The
other dominant land use classes related to agriculture are pastures
(27.8 % of all catchments), complex cultivation patterns (21.9 %) and
land principally occupied by agriculture with significant areas of natural
vegetation (27.7 %). Forested areas are mostly represented by broad-leaved
forest (35 %), coniferous forest (19 %) and mixed forest (14.4 %)
classes. It must be noted that the land cover change analysis is hampered by
the short duration of the land use maps available, 28 years between 1990 and
2018, and different sensors during this period possibly lead to the
different attributions to some land use classes.
Flood trends
To analyze flood trends, all flood events above the 95th or 99th
percentiles of daily runoff computed on the whole time series are extracted.
As noted in the method section, a declustering approach has been implemented
to avoid introducing an autocorrelation signal in the samples due to several
consecutive threshold exceedances belonging to the same event. The trend MK
test is applied to the number of annual exceedances above these two
thresholds and also on the magnitude of the threshold exceedances. From
Fig. 4 a general tendency towards a decrease in the annual
number of flood events above the 95th percentile can be seen, which is significant in 67
catchments, and to a lesser extent also in the number of events above the
99th percentile in 45 catchments. These trends are regionally significant
according to the FDR procedure and particularly over the northern ridge of
the Cévennes mountainous areas. According to the Sen slope method to
estimate the decrease in the annual number of events above the 95th
percentile, for most basins the trends are ranging between -0.5 and -1 event
per decade. For the most extreme cases the trends can reach up to -2.5
events per decade. Since for all catchments the number of events above the
95th percentile per year is 4.5 on average (min =2, max =6, after
de-clustering), the magnitude of these trends can be considered moderate.
For the 99th percentile the magnitude of trends are similar, with a
maximum decrease of -1.4 events per decade, and for most stations on average
-0.4 events per decade (with an average annual number of 1.6 events above
the 99th percentile, after de-clustering). In addition to the trends in
the annual number of events, there is also a weak signal of an increase in
the magnitude of floods, in particular above the 99th percentile for 16
stations, yet these trends are not regionally significant.
Significant trends at the 10 % level (Mann–Kendall test) in the
annual number of flood events above the 95th and 99th percentiles (a, b) and in the magnitude of these threshold exceedances (c, d). Blue
triangles indicate a decrease and red triangles an increase. The size of the
triangles indicates the relative changes.
Besides this event-based analysis, the temporal trends in the 95th and
99th percentiles of the daily runoff time series have been investigated
using quantile regression. The approach is complementary but different to
the testing of trends on the annual occurrence and the magnitude of the
events, since quantile regression allows the possible changes on
the quantiles of daily runoff time series to be evaluated. This analysis reveals that for a
majority of catchments, a decreasing trend in these two percentiles is
detected. The procedure is to apply a quantile regression of the percentile
of interest with time as a covariate, and to validate if the slope of the
quantile regression model is significantly different than zero at the 10 %
level, a bootstrap resampling approach (Efron, 1979) has been considered. For
the 95th percentile, a decreasing trend is found in 147 stations and an
increase is found in only 12 stations. For the 99th percentile, 89 stations with negative trends and 15 with increasing trends are found. The relative
changes in the 95th and 99th percentiles are ranging for most
stations between 0 and -0.5 as shown in Fig. 5. The number of detected
trends with quantile regression for the 95th and 99th percentiles
is larger than the number of trends detected with the MK test. However, for
many basins the trends in the 95th and 99th percentiles are of
small magnitude, and only for the largest trends does the MK test also detect
significant changes in the annual number of events above these thresholds.
Histogram of the relative changes in the 95th and 99th
percentiles estimated from the quantile regression models, with time as
covariate (with a slope significantly different than zero at the 10 %
level).
In an attempt to relate the detected trends to catchment characteristics,
the Student t test has been used to compare the catchment descriptors
between the group of basins with or without trends. The catchments where
decreasing trends in flood occurrence are detected tend to be larger
catchments (mean size of 369 km2 vs. 253 km2
for the catchments with no significant trends), with a lower proportion of
karstic areas (33 % vs. 41 %) and urban areas (1.7 % vs. 3.79 %).
Also, more decreasing trends are detected in agricultural catchments than in
forested areas. Yet, no clear link can be found between land cover changes
and flood trends, probably due to the short duration of the land cover
dataset available. The only exception is trends in urbanization, with
a lower increase in urbanization (+0.77 % average increase in urban
areas) in catchments where floods are decreasing by comparison with
catchments with no flood trends (+1.41 % average increase in urban
areas). It must be noted that there is a strong spatial variability of the
observed trends highlighting the complex interplays between the different
catchment characteristics, as similarly noted by Snelder et al. (2013) over
France. For example, the magnitude of the detected trends is not correlated
with the different catchment properties. This implies that it would be very
challenging to propose a typology of basins with similar changes in floods
according to catchment properties.
Changes in event precipitation and antecedent soil moisture conditions
For each event, the cumulative catchment precipitation average is computed
as the sum of nonzero consecutive rainy days, on a time window up to 10 d prior to the flood event. The antecedent soil moisture is taken as the
root zone soil moisture corresponding to the day prior to the start of the
rainfall event. Figure 6 show the Mann–Kendall test results for these two
indicators for floods above the 95th and the 99th percentiles. An
increase in precipitation associated with floods using both thresholds is
observed (for 34 catchments for the 95th percentile and 36 catchments
for the 99th percentile), associated with a decrease in antecedent soil
moisture conditions prior to floods in up to 40 catchments for floods above
the 95th percentile. There is a correlation between the reduction in
antecedent soil moisture prior to flood events and the decrease in the
annual number of flood events above the 95th percentile (r=0.44), and
also to a lesser extent for the number of floods above the 99th
percentile (r=0.34). Consequently, as observed in Australia by Wasko and
Nathan (2019), it can be hypothesized that the decrease in antecedent soil
moisture is an important driver leading to the reduction in the annual
number of floods, despite the increase in event precipitation already
pointed out by several studies in this region (Tramblay et al., 2013; Ribes
et al., 2019; Blanchet et al., 2018). Indeed, for 12 catchments an increase in event rainfall is detected when for the same catchments a decrease in the
annual number of events above the 95th percentile is also observed. It
is also the case for 11 catchments for the events above the 99th
percentile, with an increase in event rainfall accompanied by a decrease in
the annual number of events. However, as shown before, the increased event
precipitation for several basins is probably the cause of higher flood
magnitudes for the most severe events (above the 99th percentile).
Significant trends at the 10 % level (Mann–Kendall test) in
cumulative precipitation during flood events above the 95th and
99th percentile (a, b) and in the soil moisture initial conditions (c, d). Blue triangles indicate a decrease and red triangles an increase.
The size of the triangles indicates the relative changes.
Explanatory covariates for high runoff quantiles
To test the influence of different covariates on the variation of the
95th and 99th percentile values, quantile regression models using
time, temperature, soil moisture from the root zone, actual
evapotranspiration (AE), reference evapotranspiration (ET0) and
precipitation have been compared. The goal here is not to select the best
covariates for each station but to identify relevant covariates at the
regional scale. Since climatic covariates could influence the hydrological
response at different timescales (Mediero et al., 2014; Villarini and
Slater, 2017; Wasko and Nathan, 2019), three different aggregation periods to
compute moving averages have been compared. On the event scale, the
different covariates have been averaged with a 3 d time lag preceding each
event. At the monthly timescale representing the seasonal variability, the
covariates have been averaged for the 30 d preceding the events. For the
annual timescale the covariates have been averaged for 365 d preceding
the events. At the event scale, the precipitation represents the intensity
of rainfall during the event rather than the preceding soil moisture. On the other
timescales, for the monthly and annual aggregation periods the precipitation
is here a proxy for soil moisture and its long-term variability. To test
which covariate provides the best reproduction of the observed 95th and
99th percentiles of the daily discharge time series, the R1 metric
is computed, for each covariate, between the quantile regression model built
with the covariate and a constrained model with a constant slope (0).
Distribution of the R1 coefficients for different covariates
for the 95th or 99th percentiles of daily runoff, averaged at (a, b) the event scale (3 d), (c, d) the monthly scale, and (e, f) the annual timescale.
The results are plotted in Fig. 7. A similar pattern can be seen for both
percentiles, with decreasing R1 values for longer time aggregation
periods for the covariates. On the event scale, both precipitation and
soil moisture are outperforming other covariates, including time. The same
results are found for the annual timescale, yet with a different
interpretation because annual precipitation is representing the average
level of soil moisture storage rather than event rainfall. The link observed
between the 95th and 99th percentiles with annual precipitation or
soil moisture is an indication that the long-term decrease observed for
these two variables (Fig. 2) could be the cause of the observed decrease
in the frequency of floods above these two percentiles. At the monthly timescale, the cumulative precipitation plays the most important role when the
effects of soil moisture, actual evapotranspiration and temperature are
similar. For almost all covariates, there is an improvement by comparison to
the quantile regression model using time only.
Overall, the R1 coefficients are decreasing with increasing slopes and
basin mean elevation. However, these two variables are correlated
(r=0.61). This is an indication that antecedent soil moisture condition
may have a lower influence on flood generation in mountainous areas,
probably due to shallower soils and steeper slopes. For event-based soil
moisture and precipitation, there is an inverse relationship with basin
size: for small basins (less than 500 km2) event soil
moisture and precipitation are good predictors for the time variations of
the 95th and the 99th percentiles, with R1 values up to 0.6,
while for larger basins the R1 values are much lower, reaching a maximum
of 0.2 for some basins. When averaged at the monthly or annual time step,
the relation is opposite, with a larger influence of soil moisture and
antecedent precipitation for larger basins with higher R1 coefficients.
This finding is fully consistent with results obtained for different regions
of the globe (Zhang et al., 2011; Ivancic and Shaw, 2015; Woldemeskel and
Sharma, 2016; Wasko and Sharma, 2017), highlighting the buffering effects of
large basins with the capacity to store more water than smaller basins.
Conclusions
The results obtained in the present study show that despite the increase in
extreme precipitation events reported by previous studies over the same
domain (Ribes et al., 2019) there is not a general increase in flood
occurrence. Only for a few basins is the intensity of the most extreme floods
showing significant upward trends. On the contrary, a global tendency
towards fewer annual flood occurrences is observed for events of moderate
intensity, i.e., those above the 95th percentile. The same signal, with a lower
magnitude, is also seen for higher floods above the 99th percentile.
Overall, there are much more trends detected for the annual occurrence of
floods than for their intensity. It should also be emphasized that the
magnitude of these trends remains moderate, and consequently these trends are only noticeable over long time
periods. The decrease in soil moisture seems to be an important driver for
these detected changes, and indeed in all basins an increase in temperature and
evapotranspiration associated with a decrease in precipitation is leading to
a reduction in soil moisture over time. For several basins, the soil
moisture decrease can offset the increase in extreme precipitation and
generate less frequent floods. These changes are mostly observed for larger
agricultural basins, with low urbanization and karstic areas. Wasko and
Sharma et al. (2017) previously noted the importance of catchment size for
the influence of soil moisture on flood runoff due to higher potential of
soil moisture storage. The trends detected in the present work are
consistent with those found in other Mediterranean regions such as Spain
(Mediero et al., 2014) and Australia (Wasko and Nathan, 2019). An important
finding of the present work is that with the same large-scale climatic
drivers (in terms of temperature, evapotranspiration and precipitation) the
flood trends in the basins can be different. This shows the importance of
basins characteristics to buffer climatic variability. Indeed, even if
similar patterns of changes in the 95th and 99th percentiles are
found, the analysis of individual catchments is revealing spatial
differences even for neighboring basins caused by different topography, soil
and land cover combinations. This is a factual demonstration of the
commentary of Whitfield (2012), who stated that it would be very difficult, if
not scientifically irrelevant, to make general statements about the
plausible future evolution of flood risk.
These results, which show a lack of a generalized upward trend in floods, should
be put into perspective with the observed increase in the vulnerability to
these episodes. Indeed many reports such as Llasat et al. (2013) indicate an
increase in the number of floods inducing damages between 1981 and 2010 in
southern France and northern Spain, which they attribute to an increased
vulnerability and land use changes. In the French Mediterranean regions, 66 % of the total cost of flood damage is
concentrated in private properties in France (Vinet, 2011), and the total assets lost due to floods are rising,
as they are in many other regions (CCR, 2018; Paprotny et al., 2018). The areas close
to the Mediterranean have seen a population increase and an extension of
urbanized areas, driven in part but not solely by the increase in touristic
activities (Vinet, 2011; Vinet and De Richemond, 2017). Bouwer (2011)
concluded after a review of 22 disaster loss studies “that there are no trends
in flood losses, corrected for changes (increases) in population and capital
at risk, which could be attributed to anthropogenic climate change”.
Therefore, it can be concluded that, at least for southern France, as noted
previously by Neppel et al. (2003), the increasing cost of damages caused by
floods is rather due to the increase in socio-economic vulnerability rather
than a climate change signal towards an increase in the severity of floods.
Nonetheless, the evolution of flood frequency and intensity is a key
question for risk prevention. Flood-related mortality in the Mediterranean
basin is conditioned both by hazard drivers (e.g., rainfall intensity,
discharge) but also by social drivers (e.g., behaviors, characteristics of
buildings) as shown in different studies (Ruin et al., 2008;
Vinet, 2011; Boudou et al., 2016). Deeper knowledge in rainfall and flood
trends must be crossed with exposure (e.g., population in flood prone zones)
and vulnerability data (e.g., eldering of population in the future) to
anticipate evolution in human mortality in relation to flash floods in the
Mediterranean basin (Petrucci et al., 2019). As pointed out in previous
research projects (Merz et al., 2014; Meyer et al., 2013) there is a need to
integrate climate change scenarios with socio-economic change scenarios to
better quantify changes in flood risk. To achieve this task, it is necessary
to develop databases on vulnerability and exposure to be analyzed in
conjunction with hydrometeorological data (Saint-Martin et al., 2018).
Data availability
The dataset compiled in this work are made available to the research community upon
request.
Author contributions
YT performed the analysis and wrote the paper. LM extracted the basin attributes and performed analyses. LN and FV contributed to the writing. ES contributed to the database collection and to the writing.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Hydrological cycle in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS inter-journal SI)”. It is not associated with a conference.
Acknowledgements
This work is a contribution to the HYdrological cycle in The Mediterranean
EXperiment (HyMeX) program, through INSU-MISTRALS support.
Review statement
This paper was edited by Eric Martin and reviewed by Patrick Arnaud and one anonymous referee.
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