Catchment-scale hydrological studies on drylands are lacking because of the
scarcity of consistent data: observations are often available at the plot
scale, but their relevance for the catchment scale remains unclear. A
database of 24 years of stream gauge discharge and homogeneous
high-resolution radar data over the eastern Mediterranean allows us to describe the properties of floods over catchments spanning from desert to
Mediterranean climates, and we note that the data set is mostly of moderate
intensity floods. Comparing two climatic regions, desert and Mediterranean,
we can better identify specific rainfall-runoff properties. Despite the large differences in rainfall forcing between the two regions, the resulting unit
peak discharges and runoff coefficients are comparable. Rain depth and
antecedent conditions are the most important properties to shape flood
response in Mediterranean areas. In deserts, instead, storm core properties
display a strong correlation with unit peak discharge and, to a lesser extent,
with runoff coefficient. In this region, an inverse correlation with mean
catchment annual precipitation suggests also a strong influence of local
surface properties. Preliminary analyses suggest that floods in catchments
with wet headwater and dry lower section are more similar to desert
catchments, with a strong influence of storm core properties on runoff
generation.
Introduction
Drylands cover one-third of the world's lands
. Their extent has been increasing in recent years due to
changing climate, land overexploitation and soil erosion, and it is expected
to expand approximately by 10 % before the end of the 21st century
. Drylands cover a wide range of
climate, tectonic, structural, lithological and phytological settings but
share similarities in hydrological processes . Hydrological
studies on drylands are lacking, despite the large importance of water
resources in these areas . In comparison with
temperate regions, drylands are generally underpopulated, with lower economic
interests and, consequently, sparser data records. Precipitation is
characterized by a very high spatial and temporal variability, and runoff
generation is localized . The lack of data, in combination
with the need of data at high resolution, is the main obstacle to the
development of hydrological studies on drylands, particularly at the
catchment scale.
Hydrology of drylands
With drylands we refer to a range of climates spanning from
Mediterranean to deserts, as presented in Sect. 2.1. Most hydrological
studies on drylands are derived using sparse data sets, with few exceptions
in the southwestern United States , Australia
, southern Spain and the eastern
Mediterranean region . In this paper we focus on
the characteristics of dryland floods. Since dryland river beds are dry for
most of the year, floods are defined as runoff events that bring water into
the channel . Localized convective storms have an important
role in these floods, and the runoff production has been shown to be
sensitive to the small-scale characteristics of convective cells
. Most convective rainfall cells in the
drylands of the eastern Mediterranean have an area up to 300 km2, move
with velocities up to 25 m s-1
and live up to 50 min . In the
Dead Sea region, the area of convective cells has been observed to increase
with latitude, following orography, while their rain intensity remained
unaffected . At the hillslope scale, soil crusting and
aggregation , stone disposition
, vegetation
and microtopography have been observed to
influence runoff generation in drylands. The combination of high rain
intensity, low vegetation interception and low soil infiltration capacity
increases the importance of infiltration excess (Hortonian) overland flow
. In semi-arid regions, describe
runoff generation as a mix combining Hortonian runoff and saturation flow
from topsoil. Because of the low soil water capacity, baseflow is low, and
most of the water infiltrating the hillslopes is evaporated
. Runoff generation on hillslopes is spatially and
temporally localized and, especially during low flows, can re-infiltrate
before reaching the base of the hillslopes . Factors such as
temporal rainfall structure, antecedent moisture and vegetation are known to
affect the connectivity of runoff sources to channels
. The presence of Hortonian overland flow
means that runoff can be generated also by small contributing areas,
resulting in high drainage density, estimated by to a
maximum of 100 km km-2. The propagation through dry
channels, however, causes part of the runoff to infiltrate along the river
bed, a process called transmission losses. The volume of water infiltrating
the channel depends on the properties of the storm (size, position), hydrograph
(volume, duration) and channel (width, porosity, initial conditions,
stratigraphy), and it is particularly important in alluvial areas
.
For many hydrological applications it is important to know the properties of
the flow at the catchment scale, while the hydrological processes described
above are generally observed at the plot scale. Most of these processes are
involved in shaping the hydrograph at the outlet, but their relative
importance, interactions and feedbacks, often resulting in nonlinear
dynamics, are very difficult to predict . Despite the wide
variety of dryland environments, their unique processes can result in unique
flood properties at the catchment scale . Floods in arid
areas are characterized by short durations . During extreme
storms, event-based runoff coefficients and unit peak discharge can reach
values comparable to wetter climates, especially in small basins
, and respond mostly to the intensity
and volume of the storm core . The resulting flood frequency
distribution is skewed, with a high ratio of large to small floods
. Analyzing the upper tail of flood
distribution, found that arid and semi-arid areas have an
upper tail ratio, defined as the extreme peak discharge divided by the
10-year flood magnitude, which is significantly higher than wetter climates.
Objectives
A characterization of the common rainfall-runoff properties of drylands
catchments is needed, but it has to be carried out at a high spatial and
temporal resolution . A unique radar
rainfall database with 1 km2 and 5 min resolution, uniformly covering
24 years, is available for the eastern Mediterranean region
. This database, in conjunction with stream gauge
measurements, can improve the understanding of catchment-scale hydrology in
these environments. The use of operational stream gauges has high
uncertainties and limits the analysis to moderate storm events, but it allows us to analyze a large number of floods and basins and to capture their common
properties. Specifically, in this paper we are comparing catchment-scale data
from desert and Mediterranean environments in order to characterize their
unique rainfall-runoff properties. In each of these climates we try to
identify the main factors related to peak discharge and runoff generation
and which processes have an important effect at the catchment scale.
Study region and dataClimatic regime
Figure 1 shows how, under the Köppen–Geiger climate classification
, the eastern Mediterranean region under study
ranges from hot-summer Mediterranean (Csa), to hot semi-arid (BSh) and to
hot desert climate (BWh). The region is located between 30 and
33∘ N, where the descending air from Hadley and Walker circulation
cells intersects and prevents rain-bearing clouds throughout the summer
. Rain seasonality is strong, with most
of the rain occurring between October and May, and summers are dry
. Rain is caused mostly by cold fronts and postfrontal
systems associated with midlatitude cyclones over the eastern Mediterranean,
usually lasting for 2–3 d, connected with local intensive convection
. The effect of midlatitude cyclones
decreases from north to south. Less frequently and affecting mostly the
southern and eastern part of the study area, a surface low-pressure trough
extending along the Red Sea and supported by an upper level trough brings
localized convective showers in the area, mostly occurring after the daily
thermal maximum, in the afternoon (active Red Sea trough – ARST)
. On rare occasions a conveyor belt of moisture
in the mid–upper tropospheric layers causes widespread rainfall over the
whole region . Mean annual precipitation shows a
north-to-south gradient, from 700 to less than 50 mm yr-1, caused by
the different occurrence and intensity of Mediterranean cyclones and the
shape of the Mediterranean shoreline, and a west-to-east sharp gradient
caused by the shading effect of the Judean Mountains.
Köppen–Geiger climate classification from , mean
annual precipitation (1981–2010, Israel Meteorological Service) and catchment climatic classes.
Geomorphology of the region
The study area is located in the eastern Mediterranean region and can be
divided into three groups along 300 km in a north-to-south direction (Fig. 2). The
northern part is located around Mt. Carmel, between Galilee and the coastal
plain, with a hilly morphology draining west. The lithology is dominated by
carbonate units, including mostly limestones, dolostones and chalks, with
thin soils and patches of exposed bedrock . The area has a
long history of intensive cultivation that reduced soil depth and resulted
in many abandoned terraces. The landscape is dominated by cultivations and
areas covered by dwarf-shrub steppe . With few exceptions,
streams are mostly ephemeral. In the center, the Judean Mountains drain
eastward from around 1000 m above sea level (a.s.l.) to less than -400 m a.s.l.
of the Dead Sea. This creates elongated basins with a wet headwater
and dry lower section. Along the Judean ridge, the exposed Upper Cretaceous
carbonate rocks form narrow gorges crossing from the headwater to the Dead
Sea. Their eastward rainfall gradient corresponds also to a gradient of soil
development and infiltration capacity . The mountain
ridge is occupied by few large cities and cultivated areas, while the
downstream area is uncultivated and with a low vegetation cover. The south of
the region consists of the Negev, a sparsely populated rocky desert. The
geology is characterized by a heterogeneous combination of several
stratigraphical groups, with different hydraulic properties
. Loess-covered areas are more extensive in the northern
semi-arid part of the Negev rather than the central arid part, where sandy
and rocky yermic soil prevail . Urban areas are sporadic, and
most of the land is uncultivated with a low vegetation cover.
Study area and the outline of the catchments analyzed.
Basin selection
Basins from different climatic regions were selected to highlight their
unique flood properties. The main criteria for basin selection were the
localization within the radar range and discharge data availability between
1991 and 2014, as explained in Sect. 2.4. The selection resulted in 30
basins, with an area ranging from 12 to 1232 km2 (Fig. 1). The
distinction in three groups was done following the Köppen–Geiger climate
classification (Fig. 1). Most basins are well located inside the
Mediterranean or desert climate. Because of the extremely strong
precipitation gradients, few basins extend from Mediterranean to desert
climate. These basins, named “mixed” from now on, are on the lee side of the Judean Mountains, with a wet headwater and a strong climatic gradient along the
catchment. Despite the fact that only three of such basins are available in our sample, we
decided to analyze them separately. In fact, their conditions are common to
other areas of the world, but flood properties are not well represented in
the literature. Since they include mixed climatic and hydrological
conditions, understanding their flood regime is not trivial. The resulting
basin classification includes 13 Mediterranean, 3 mixed and 14 desert basins
(Fig. 1). The characteristics of the three basin classes are reported in
Table 1. Among them are the relief ratio, defined as the ratio between the total relief of a basin and its longest dimension parallel to the principal drainage
line; and elongation ratio, which is the ratio between the diameter of a circle with
the same area as the basin and the maximum length of the basin
. While mean annual precipitation (MAP) in desert and
Mediterranean basins is largely different, area, elongation ratio and relief
ratio are comparable. In the Negev desert, report a
correlation between mean annual precipitation and number of
events per year. Mixed basins are generally more elongated and have a
frequency of floods closer to deserts.
Summary of basin properties (minimum and maximum) in the three regions.
Flood seasonality in the three climatic classes is reported in Fig. 3. As
expected at these latitudes, the seasonality is very marked, with more floods
in winter and a dry summer season. The comparison between classes does not
highlight differences in the climatic forcing. Mediterranean floods seem to
be slightly more common from December until February (78 %) than desert
floods (66 %), while transitional-season floods' frequency is higher in the desert and mixed basins. This reflects the higher frequency of ARST
circulation patterns during fall and spring . An analysis
of seasonality over basin size did not show any significant difference,
indicating that basins of different size are activated by similar circulation
patterns.
Monthly distribution of floods in the climatic classes.
Data available and uncertainties
For this study we used streamflow data from the hydrometric stations of the
Israel Hydrological Service (IHS) . The
division into single flood events was performed by IHS, in most cases when
the flow is ceased (in ephemeral channels) or when it decays to baseflow
levels. Radar data from the Shacham weather radar, located in the Ben Gurion
Airport (Fig. 2), are used. The radar is a non-Doppler C-band
system operational from the 1990s until 2014. Corrections and gauge adjustments
have been performed, resulting in a homogeneous archive of precipitation
estimates with 1 km2 and 5 min resolution from 1990 to 2014
. Rain gauge data from subsets of 808 daily stations of the
Israel Meteorological Service were later employed to locally adjust the
radar estimates, as explained in the next paragraph. For each flood, rainfall
was analyzed from 2 d before the start of runoff until its end. The
remarkable length and resolution of rainfall and streamflow databases allow
us to compare flood properties across a large sample. However, this comes at the
price of dealing with significant uncertainties at the single-event scale,
which should be kept in mind throughout the analysis. The main issues
related to radar data can be identified in shutdowns, estimation of high
intensities and local bias. During the 24 years of radar rainfall
availability the system was occasionally inactive, resulting in potentially
missing intervals during storms. Convective storms may include hail, which is
characterized by a high reflectivity that compromises the ability of the
radar to estimate precipitation intensity. Regarding local bias, despite the
corrections described in , locally, data can still have a
significant bias. A further local multiplicative mean field bias correction
is performed independently for each flood, using data from daily rain gauges
located within 20 km from each basin. The rain gauge density is higher in
the north, while for the southern basins the density drops. For consistency,
the southern rain gauge density of 1/250 km2 is used, by excluding
stations in areas with higher rain gauge density. The application of mean
field bias has proven to significantly improve radar rainfall estimations
during flash floods , but large uncertainties in rainfall
volume may remain. The final radar precipitation estimations have a 0.81
correlation with daily rainfall from rain gauges and a fractional standard
error see of 0.43. Regarding streamflow data, the
uncertainties related to the use of operational stations can be identified in
the estimation of high flows, low flows and timing. When water level is higher
than the range used to calibrate the stage–discharge relation, the
extrapolation can be affected by significant error if no specific corrections
are applied. Low flows are often at the lower end of stream gauges'
sensitivity and can be affected by high relative errors. Lastly, the early
use of mechanical stream gauges, which required the digitization of charts,
resulted in inaccurate flood timing estimates that can range from few hours
up to 1 d . Records of 5534 floods are available on the
selected basins. To ensure a minimum quality of single events, we performed
an evaluation based on multiple criteria. Accepted floods need to have radar
coverage before the peak and correspondence with rain gauge cumulate
precipitation in the days before the flood. At least two rain gauges need to
be recording more than 10 % of the mean catchment rainfall during the
storm. Hard thresholds were used for automatic selection, but their values
were voluntarily loose and subjective judgment was used for controversial
events. Runoff peaks and catchment rainfall should be above a minimum
threshold (0.5 m3 s-1 and 3 mm, respectively), and the runoff
coefficient should not exceed unity. Despite significantly reducing the
sample, we believe that this selection is necessary to ensure a minimum
quality of the analyzed data. After the selection process, 1538 floods are
left: 1277 in Mediterranean basins, 73 in mixed and 188 in desert. Because of
the larger number of floods per year (Table 1) and rain gauge density, the
sample is larger for Mediterranean basins. It should be noted also that,
because of how the analysis is structured, the floods included are mostly of
moderate intensity. Flood extremes are rare in stream gauge records, and only
2 % of the analyzed floods have a unit peak discharge larger than
1 m3 s-1 km-2. This is about 10 times less than the
extreme events in this region . The highest unit peak
discharge included in the analysis is 3.4 m3 s-1 km-2. To
understand the magnitude of single events, we have estimated their return
period using the generalized extreme value distribution. The parameters of
the distribution are computed using the probability-weighted moments
on stream gauges with more than 10 years of data. We have
isolated 122 floods with more than 5 years of return period and used them as
a sample to understand the behavior of larger floods. The complete database of flood properties created for this research is available in .
Results and discussionFlood characterization: rain
To represent the characteristics of the flood-generating rainfall in the
three regions, the radar rainfall estimates are analyzed. Similarly to
, rainstorm duration is defined as the temporal duration of
the runoff-generating rainfall episodes, separated by more than 6 h of
rainfall hiatus. Maximum rain intensity is the storm maximum rain intensity
over a moving window of 1 h, recorded over an area of 3km×3km.
The area has been selected as representative to describe the high intensity
core of rain cells by . Similar analyses over
different durations (10 min, 30 min, 1 h, 2 h) and areal extents (1, 4,
9, 16 km2) showed consistent results. Antecedent rainfall is the total
depth of catchment rainfall in the 10 d before the flood-generating storm,
as calculated from the adjusted radar data. The 10 d interval is a
compromise, since in arid areas most water in the upper soil layer disappears
quickly , while in the Mediterranean region shorter intervals
can miss previous significant rainfall. Results are found to be consistent
for intervals between 10 and 30 d. Rain core depth is the amount of 10 min
rainfall that fell with intensity above 25 mm h-1, expressed in millimeters (mm)
over the basin. A similar statistic has been reported by to
be significant in explaining runoff production in arid basins together with rain
core coverage. Rain core coverage is calculated as the fraction of basin
covered by 10 min rainfall above 25 mm h-1 at any moment during the
storm. The dimensionless statistics of storm rainfall spatial position and
concentration (Δ1 and Δ2, respectively) are calculated as in
. These statistics describe rainfall organization over
the catchment as seen through the filter of the river network and are used to
highlight properties of rainfall that have an effect on runoff response.
Similar statistics are used by to analyze the effect of
rainfall structure on flood response. Values of Δ1 higher and lower
than 1 indicate respectively storm rainfall positioned near the headwater
or close to the catchment outlet. Values of Δ2 lower than, equal to or higher than 1 indicate respectively storm rainfall concentrated, uniformly
distributed or with two peaks within the catchment. The use of dimensionless
statistics allows us to compare rainfall position between basins of different
size and shape.
Boxplots showing the distribution of event rainfall properties in the three climate classes.
The asterisk describes distributions with a statistically different median (Wilcoxon test, p value <0.05).
Black dots represent floods with more than 5 years of return period; outliers are not presented.
There is a clear pattern in most rainfall properties between Mediterranean,
mixed and desert basins (Fig. 4). In Mediterranean basins, storms have about
3 times as much rain depth and duration than deserts. The longer duration
of Mediterranean storms compared with and
can be attributed not only to including strong localized
storms but all runoff-generating storms in the region. In Mediterranean
basins maximum local rain intensities are stronger, and rainfall is
relatively uniform over the basin. Higher local intensities are expected in
Mediterranean basins when analyzing moderate events, as in the present study.
Instead, the highest rainfall intensities for extreme events have been
observed in desert areas . In deserts, duration
and cumulated rain are below 1 d and 50 mm, respectively, similar to what was reported for extreme desert floods by . As observed in
, the storm centroid is concentrated around the centroid of
the catchment, as indicated by Δ1 around values of 1.
However, in deserts it is not uncommon to have rainfall concentrated in two
different parts of the basin, as indicated by Δ2>1,
implying relatively small rain cells compared to the basins. On mixed basins,
because of their morphology and climatology, we observe a strong
concentration of rainfall in the Mediterranean headwater
(Δ1>1), which can eventually cover a relatively large portion of the basin. If
we restrict the observations to floods with return period above 5 years (dots
in Fig. 4), our sample becomes too small to draw conclusions, but we can draw
some interesting qualitative observations. Compared to other frequencies,
intense Mediterranean floods are characterized by higher rainfall depth
(median 126 mm), while the other properties are not much different. Compared
to other frequencies, intense desert floods are also characterized by
slightly higher rainfall depth (median 36 mm), but in conjunction with higher
maximum rain intensities (29 mm h-1), and rain core depth
(9 mm). This points at the importance of rainfall depth in the development of
intense floods for the Mediterranean environment, while in deserts the
characteristics of intense rainfall are also important drivers.
Boxplots showing the distribution of event runoff properties in the three climate classes.
The asterisk describes distributions with a different median (Wilcoxon test, p value <0.05). Black
dots represent floods with more than 5 years of return period; outliers are not presented.
Flood characterization: runoff
The properties of flood hydrographs in the three regions, and their
relations with rainfall at the storm scale, are reported in Fig. 5. As in
the lag time is defined as the temporal delay between the
centroid of the rainfall before the runoff peak and the time of the peak. As
noted in Sect. 2.4, note that the uncertainty we have on the hydrograph
time could affect this statistic. The runoff coefficient is calculated as the
ratio between surface runoff and rainfall, and it requires the separation of
the event hydrograph into direct runoff and baseflow. This separation was
done applying the recursive digital filter described in , a
one-parameter automated filter. This geometric method has proven to be
consistent between different catchments, even if the slower part of runoff
can sometimes be included under baseflow. The baseflow fraction is calculated
as the volume of baseflow to runoff, which in some basins can be affected by
water-retention structures. The peak flow to runoff volume ratio is obtained
dividing the peak discharge by the runoff volume, and it is a descriptor of
the hydrograph shape.
Figure 5 shows that, despite having less rainfall in shorter durations,
desert basins have event-based runoff coefficients, unit peak discharges and
propagation times comparable to Mediterranean basins. The results are similar
to , where floods in the arid southwestern United States have
been found to have unit peak discharge comparable with other climates.
However, it should be noted that the highest unit peak discharges are 1 order of magnitude lower than what was observed in . For
extreme floods, desert basins of our area have been observed to have peak
discharges even higher than the northern Mediterranean region
. Lag times are comparable, but
observed that the correlation between travel time and discharge is
significantly different between our Mediterranean and desert regions.
Figure 5 highlights instead a significant difference in peak to runoff ratio,
number of runoff peaks and flood duration, indicating differences in
hydrograph shape between the two climates. Analyzing extreme storms,
reported similar runoff coefficients between the arid
east and the western Mediterranean, with a mean value of 0.32. As expected, in Fig. 5 runoff coefficients for moderate floods are lower but in a few cases comparable to extreme events. Most desert basins are known not to have any
baseflow, as confirmed by the very low baseflow fractions observed. Instead,
in Mediterranean basins, baseflow can last for few days after the storm and
significantly contribute to the total runoff volume, bringing the annual
runoff coefficient to higher values than arid areas. The mixed region shows
flood properties closer to desert than Mediterranean in terms of flood
duration, hydrograph shape and baseflow component, but with even lower runoff
coefficients. The low runoff coefficients are in line with what reported by
for an experimental area nearby. As we did in Sect. 3.1, if
we restrict the observations to floods with more than 5 years of return
period (dots in Fig. 5), we can draw some qualitative result. Intense floods
of both Mediterranean and desert areas show higher durations (median around
4.5 and 1.5 d, respectively) and runoff coefficients (median 0.28 and 0.17)
compared to events with lower frequency. Despite the higher values, the
relation of the factors between the two climates does not change much.
Compared with lower intensities, floods with high peak discharge in
Mediterranean areas are characterized by higher rainfall volumes, duration
and runoff coefficients, indicating a much higher runoff volume and a longer
development. Instead, floods with higher peak discharge in deserts have
mostly higher runoff coefficients, while the shape of the hydrograph does not
appear to be very different from more frequent events.
Pearson correlations between unit peak discharge and other parameters among floods of different
regions. The asterisks indicate statistically significant correlations (Wilcoxon test, p value <0.05).
Main factors related to peak discharge and runoff generation
The unit peak discharge and runoff coefficient are two important parameters to characterize basin response. In order to highlight what factors are related to these two parameters, we analyzed their correlations with other storm
and basin properties separately for each region (Fig. 6). From this analysis
we cannot detect causality, but we can characterize different behaviors
between the regions and suggest possible interactions. The parameters
considered are MAP; relief ratio as in Sect. 2.3; amount of rainfall in
the 10 d before the flood-generating storm (Ant. Rain); mean catchment
storm rainfall (Rain depth); maximum local hourly rain intensity on
3km×3km, as in Fig. 4 (Rain Intensity); rainfall
depth of the storm core, as in Fig. 4 (rain core depth); and rain position, as in
Fig. 4.
Unit peak discharge in Mediterranean basins is correlated mostly with
rainfall depth, rain core depth and antecedent conditions (Fig. 6a),
suggesting an importance of soil moisture for the development of the flood
peak. suggest that this is true for moderate storms, while
during intense storms most of the basin is saturated and runoff peak is
correlated to rainfall intensity. In desert and mixed basins the high
correlation with rain properties, and in particular storm core, suggests that
representing the most intense fraction of rainfall is key to correctly
represent flood peak, as shown by and
. In desert basins, and to a minor extent in Mediterranean,
mean catchment rain is correlated with the depth of the storm core, while in
mixed catchments it is not. For this reason, it is interesting to note that
rain core depth in mixed basins is much more important than mean catchment
rainfall. in a similar setup, but on a single arid basin,
also found a high correlation between storm core properties and peak rate.
Rain position does not seem to have a significant impact on peak discharge in
any of the analyzed cases.
Correlations of the unit peak discharge and runoff coefficient for each basin with more than 15 floods.
Each dot represents one basin and filled dots indicate statistically significant correlations (Wilcoxon test, p value <0.05).
In Mediterranean basins, high runoff coefficients are related to high rain
depth, high MAP and high antecedent rain (Fig. 6b), indicating a strong
importance of soil moisture in runoff generation. As observed in other
semi-arid regions, this could support a combined flow generation, where
runoff is produced after the partial saturation of the topsoil
. High soil moisture can also improve the connectivity of
runoff-generating areas . In Mediterranean areas
rainfall volume is often observed to influence runoff coefficients
. found no correlation
between the runoff coefficient and rain depth for extreme events in Mediterranean
and arid environments, suggesting that during extreme events rain intensity
has a stronger influence on the amount of runoff produced.
for extreme floods data in different climatic regions and
with a simulation approach in a semi-arid area found an
influence of antecedent conditions on the runoff coefficient, and both suggested
that the effect could be higher for moderate floods. Rainfall depth in desert
basins has a low impact on runoff coefficients while the properties of the
storm core are more important, in accord with what was found by
and . These characteristics could be attributed to runoff
generated by infiltration excess, as is often observed in arid environments
. The relatively high negative
correlation with MAP is also interesting. In arid regions rainfall is often
pointed out as the most important variable for hydrological models
. argue that runoff generation
and rate are primarily controlled by surface properties rather than by the
storm rain amount. Our results indicate that the storm core properties and
annual rain amount are definitely important for runoff production, but with
significant differences between arid and Mediterranean regions. A possible
explanation is that, in arid environments, a slight increase in annual
precipitation causes modifications in the soil and development of vegetation
patches. This is linked with a significant increase in infiltration capacity
; a
significant reduction of runoff connectivity ; and,
consequently, a reduction of the runoff coefficients of single storms. The
relation between the runoff coefficient and rainfall distance from the outlet is
harder to interpret. found a negative correlation between
rainfall distance from the outlet and runoff coefficient, suggesting of the
presence of transmission losses. In the overall data this relation is not
clear, but large differences in transmission losses among desert basins have
been observed , and an analysis on single basins could be
more appropriate.
In Fig. 6 we have treated the three regions as homogeneous, correlating
different factors with the main hydrograph parameters. To verify this
assumption, we can analyze the correlations on single basins, even if data
availability limits this analysis only to a fraction of them. In Fig. 7 we
have selected basins with more than 15 floods and analyzed the correlations
presented for Fig. 6. Additionally, most studies of flood characterization in
dry areas are based on a limited number of basins or events. This analysis
allows us to understand what the variability in the results among basins of
the same region can be.
The correlations with unit peak discharge seem relatively consistent with the
findings of the previous section. The dispersion among basins is smaller in
Mediterranean areas, maybe also because of the larger average number of
events, and is larger in the mixed and desert regions, but the directions of
the correlations are relatively consistent. This indicates that the processes
shaping peak discharge are similar within the climatic regions. The
correlation with the runoff coefficient seems consistent among Mediterranean basins but
shows a wider variety of behavior among desert basins. The influence of rain
properties on the runoff coefficient of desert basins is very scattered. The runoff coefficient is well correlated with rainfall properties in a few basins, while
in others it is not. This could indicate a difference in processes shaping
runoff coefficients within desert areas. A special note can be made for the
correlation with rain distance from the outlet. Even if statistically
non-significant, one basin does show a negative correlation (-0.37), as it
would be expected in basins with substantial transmission losses. This basin
is the Zin, where estimated transmission losses to be
comparable at an annual scale with catchment runoff, and more than 40 % of the
streams run over alluvium. However, nearby basins that should also be
affected show a slight positive correlation, proving this analysis to be
somehow unreliable for the detection of transmission losses in the current
setup.
Conclusions
The observation of flood properties at the catchment scale points out the
integrated effect of processes that are often observed at the plot scale but
whose interactions to shape the catchment response are difficult to predict.
The steep climatic gradient in the study area allowed us to compare flood
properties in different climates within the same monitoring systems. The fact
that radar and stream gauges belong to the same networks ensures a relative
data homogeneity and allows us to highlight the differences in runoff response
between climates. It should be noted that these differences are limited to
moderate intensity floods since extreme events are very rare in stream gauge
network. Despite the large uncertainties, which are inevitable in this kind of
analysis, the large sample allows us to recognize a few common patterns for
moderate floods in the regions analyzed, verifying the consistency across
multiple basins.
In Mediterranean basins runoff peaks and coefficients are connected mostly with rainfall
volume, with antecedent precipitation having also a significant role. This can resemble runoff
generation through the saturation of the topsoil or a landscape with limited connectivity of
runoff sources, as described in . Remarkably, most Mediterranean basins show
similar correlations with flood properties, indicating relatively uniform runoff processes.
Despite the lower rainfall duration and depth, floods in desert areas display unit peak
discharges, lag time and event-based runoff coefficients comparable with the Mediterranean region.
Peak discharge in deserts is strongly correlated with rainfall properties, particularly storm
core. Drivers of runoff coefficients, instead, seem to be strongly site-specific. A possible
explanation from plot-scale experimental studies, supported by our results, is that hyper-arid
catchments are more impermeable than arid ones. A slight increase in mean annual rainfall allows
a significant increase in infiltration capacity and a decrease in the event runoff coefficients.
This variability can be difficult to capture through experimental studies, where only few basins
are analyzed, and should be considered in experimental designs.
Despite the small sample, we can draw some indications also for mixed basins. These basins show
unique rainfall properties, with volumes and durations between Mediterranean and desert but highly
concentrated in the headwater. They often lack baseflow at the outlet and display very low runoff
coefficients. Storm core properties are related to peak discharge, while, because of the strong
rainfall concentration, mean catchment rainfall does not appear to be significant.
Evidence of transmission losses at the catchment scale seems difficult to find even in areas where
they are known to be significant. A possible explanation is that the sample is not large enough to
isolate the effect of transmission losses over runoff volume from other effects, but this does not
imply that they are not present.
Floods with higher return period have different properties compared to events with lower intensity.
In Mediterranean climate these floods are characterized by higher rainfall volume, while in deserts intense
floods are also characterized by higher local rainfall intensity and higher volume of the storm core.
Runoff coefficients of intense floods are significantly higher for both climates.
Considering the conclusions above, the main processes of runoff production at
the catchment scale in Mediterranean areas are connected to rainfall volume and
soil moisture. To model floods accurately it is important to capture rainfall
volume, its temporal distribution and the pattern of hydrological properties
within the landscape. The use of rainfall-runoff models developed for
temperate areas, including runoff production from saturation excess, could be
effective for these areas. In desert catchments, instead, runoff production
is mainly connected with local rain intensity and with soil properties.
Rainfall resolution below 10 km2 is fundamental to describe
the core of convective cells well, and a rain gauge network is generally not
able to provide such a resolution. Hydrological models should include runoff
production through infiltration excess, together with a spatial description
of local rainfall intensities and soil properties.
The study of rainfall-runoff properties over the eastern Mediterranean
presented important challenges. The main ones can be identified as the
availability of data in different geographical areas and the high data
uncertainties. Regarding rainfall uncertainties, soon dual-polarization radar
could improve the estimation of strong rainfall and separate the occurrence
of hail. Regarding discharge uncertainties, estimations have been reduced in the
last decades, but errors related to the estimation of high flows are still
expected to be significant in the coming years .
Standardized methods for post-event estimation, such as the ones proposed by
, could help to reduce and quantify these uncertainties
for high flows.
Data availability
Processed data are available through the HyMeX archive
(, 10.6096/MISTRALS-HyMeX.1501).
Author contributions
FM and DZ prepared the data set. DZ and EM conceptualized and performed
the analysis. DZ wrote the manuscript with contributions from MA and YR. FM
and JAS provided insights on the rainfall data and the relations with intense
events. All authors reviewed the manuscript and contributed to its
development.
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 study was funded by the NSF-BSF grant BSF 2016953, by the Israel
Science Foundation (grant no. 1069/18), by the BARD project (IS-5124-18) and
by a Google gift grant. This study is a contribution to the PALEX project
Paleohydrology and Extreme Floods from the Dead Sea ICDP Core and is a
contribution to the HyMeX program.
Financial support
This research has been supported by the United States–Israel Binational Science Foundation (grant no. 2016953), the Israel Science
Foundation (grant no. 1069/18), and the United States–Israel Binational
Agricultural Research and Development Fund (grant
no. 5124-18).
Review statement
This paper was edited by Giuseppe Tito Aronica and reviewed
by two anonymous referees.
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