In this study, the frequency and intensity of soil-cooling rains is assessed
using in situ observations of atmospheric and soil profile variables in
southern France. Rainfall, soil temperature, and topsoil volumetric soil
moisture (VSM) observations, measured every 12 min at 21 stations of the
SMOSMANIA (Soil Moisture Observing System – Meteorological Automatic Network
Integrated Application) network, are analyzed over a time period of 9 years,
from 2008 to 2016. The spatial and temporal statistical distribution of the
observed rainfall events presenting a marked soil-cooling effect is
investigated. It is observed that the soil temperature at a depth of 5 cm
can decrease by as much as 6.5 ∘C in only 12 min during a
soil-cooling rain. We define marked soil-cooling rains as rainfall events
triggering a drop in soil temperature at a depth of 5 cm larger than
1.5 ∘C in 12 min. Under Mediterranean and
Mediterranean–mountain climates, it is shown that such events occur up to nearly
3 times a year, and about once a year on average. This frequency decreases to
about once every 3.5 years under semi-oceanic climate. Under oceanic climate,
such pronounced soil-cooling rains are not observed over the considered
period of time. Rainwater temperature is estimated for 13 cases of marked
soil-cooling rains using observed changes within 12 min in soil temperature
at a depth of 5 cm, together with soil thermal properties and changes in
VSM. On average, the estimated rainwater temperature is generally lower than
the observed ambient air temperature, wet-bulb temperature, and topsoil
temperature at a depth of 5 cm, with mean differences of -5.1, -3.8, and
-11.1∘C, respectively. The most pronounced differences are
attributed to hailstorms or to hailstones melting before getting to the soil
surface. Ignoring this cooling effect can introduce biases in land surface
energy budget simulations.
Introduction
Over natural and agricultural land surfaces, the frequency and intensity of
rainfalls govern soil moisture dynamics from topsoil layers to the root zone.
While these processes are represented in land surface models (LSMs) (e.g.,
Decharme et al., 2013), sensible heat input from liquid water into the soil
and its impact on the soil temperature profile are often overlooked (Wang et
al., 2016). For example, current versions of the ISBA (Interactions between
Soil, Biosphere, and Atmosphere) LSM developed by Centre National de
Recherches Météorologiques (CNRM) neglect the precipitation-induced
sensible heat flux (PH). Rainwater temperature is rarely
measured, and raindrop temperature is not explicitly simulated in atmospheric models
(Wei et al., 2014).
The impact of neglecting this process was investigated in recent studies, in
the context of global atmospheric simulations. Wang et al. (2016) showed that
the impact on land surface temperature was relatively small in LMDZ
simulations (less than 0.3 ∘C), but they considered mean annual air
temperature differences only. Wei et al. (2014) showed that accounting for
precipitation-induced sensible heat helps in reducing biases in land surface
air temperature simulated by the Community Earth System Model 1 (CESM1).
Focusing on the winter season in the Northern Hemisphere, they found a
pronounced effect (up to 1 ∘C) on land surface air temperature in
their simulations. In both studies, it was assumed that rainwater temperature
was equal to the air wet-bulb temperature. This assumption is valid for
raindrops in thermal equilibrium with the ambient air (Kinzer and Gunn,
1951). Both studies tended to show a soil-cooling effect over France, but for
some regions at higher latitudes a soil-warming effect was simulated.
At the field scale, Kollet et al. (2009) simulated the impact of a rainfall
event on soil temperature at a depth of 5 cm (T5cm) over a
grassland site in the Netherlands. They showed that accounting for
precipitation-induced sensible heat in their simulations triggered a drop in
T5cm of more than 1 ∘C on a single rainfall event.
The simulated surface energy fluxes were markedly influenced by this
soil-cooling event during several days. In particular, the ground heat flux
was affected during nearly 1 month. They suggested that this effect could
be more pronounced in regions affected by strong convective rainfall events.
They did not measure rainwater temperature, and they assumed that it was equal
to the air wet-bulb temperature.
Rainwater temperature was measured in 1947 by Byers et al. (1949) at
Wilmington, Ohio, USA. They analyzed the data from seven storms and concluded
that three types of events could be distinguished according to rainwater
temperature values within minutes after the rain onset: close to air
temperature, close to wet-bulb temperature, and lower than air temperature by
as much as 10 ∘C. They attributed the latter kind of event to hail
melting before reaching the surface.
More than 70 years after Byers et al. (1949) performed their experiment, we
were not able to find other examples in the open literature of the analysis
of ground rainwater temperature observations. Such measurements are usually
not performed in networks of ground meteorological stations.
The objectives of this study are to assess (1) the frequency of soil-cooling
rains across contrasting climatic areas in southern France using in situ
observations of atmospheric and soil profile variables, (2) the feasibility
of estimating rainwater temperature using soil temperature profile
measurements, and (3) the difference between rainwater temperature and the
ambient air temperature or wet-bulb temperature and the topsoil temperature.
We use the in situ soil temperature and volumetric soil moisture (VSM)
measurements collected by the SMOSMANIA (Soil Moisture Observing System –
Meteorological Automatic Network Integrated Application) network in southern
France (Calvet et al., 2007, 2016), over a 9-year time period from 2008 to
2016. The soil profile measurements are made at a high sampling frequency of
one observation every 12 min, and the accumulated precipitation is available
at the same frequency. This permits investigating intense precipitation
events and their impact on topsoil variables, together with the retrieval of
rainwater temperature in certain conditions.
The observations and the model simulations are presented in Sect. 2. Methods
for selecting marked soil-cooling rains and for estimating rainwater
temperature are described in Sect. 3. The statistical distribution of
soil-cooling rains and estimates of rainwater temperature are presented in
Sect. 4. The results are discussed in Sect. 5. The main conclusions are
summarized together with prospects for further research in Sect. 6.
Data
The SMOSMANIA network was implemented in southern France by
Météo-France, the French national meteorological service, in order to
monitor in situ soil moisture and soil temperature in contrasting soil and
climatic conditions at operational automatic weather stations (Calvet et al.,
2007). The SMOSMANIA network is composed of 21 stations forming an
Atlantic–Mediterranean transect shown in Fig. 1. Soil and climate types for
the 21 stations of the SMOSMANIA network are presented in Table 1. Station
full names are given in Table S1 (see Supplement). The three westernmost
stations are close to the Atlantic Ocean and present an oceanic climate.
Further east, six stations present a semi-oceanic climate. The 12 easternmost
stations are close to the Mediterranean Sea and present a Mediterranean
climate. Among them, five are located at altitudes above sea level (a.s.l.)
higher than 400 m a.s.l. over complex, mountainous terrain in the
Corbières (MTM) and Cévennes (LGC, MZN, BRN, BRZ) mountainous areas.
Two stations (MZN and BRZ) of the Cévennes area are located higher than
600 m a.s.l. and present lower mean monthly minimum and maximum
temperatures (Tmin and Tmax, respectively) than the other
stations. While Tmin can be 2 to 3 ∘C below freezing level at
wintertime at MZN and BRZ, Tmin is always higher than 1 to
5 ∘C above freezing level at the other stations. The Tmax
values do not exceed 21 and 24 ∘C at MZN and BRZ, respectively,
while Tmax at the other stations can reach 26 to 30 ∘C.
Oceanic, semi-oceanic, and Mediterranean climates are characterized by
contrasting precipitation regimes, with maximum monthly precipitation
occurring at wintertime, in spring, and in the autumn, respectively (see
Sect. 4.1). It must be noted that Mediterranean stations are often affected
by severe convective precipitation events such as thunderstorms and
hailstorms, especially in the autumn (Ruti et al., 2016). This is true for
stations located in mountainous areas, but also for those located in the
foothills of Corbières (NBN), Cévennes (PZN, PRD, VLV, MJN), and
Monts de Vaucluse (Vaucluse Mountains, CBR). Table 1 also presents the
frequency of intense soil-cooling events (see Sects. 3 and 4). Since a
noticeable fraction of observed T5cm or
VSM5cm data is missing, the number of marked soil-cooling
rainfall events could be underestimated. The missing data fraction across
seasons (Table S1) is used to correct the estimation of the possible number
of intense soil-cooling rainfall events, their frequency, and the mean time
lag between two events in Table 1.
Locations of the 21 SMOSMANIA stations in southern France. Black
dots indicate stations under oceanic and semi-oceanic climates. Red dots
indicate stations under Mediterranean and Mediterranean–mountain climates.
White letters are for names of the five stations under
Mediterranean–mountain climates. Background geographic altitudes are from
SRTM 90 m digital elevation data (http://srtm.csi.cgiar.org/, last
access: April 2019).
The environment characteristics for the 21 stations of the SMOSMANIA
network and the frequency of intense soil cooling during marked rainfall
events from 2008 to 2016. The number of intense soil-cooling rains, the
frequency, and the mean time lag between two intense soil-cooling rains are
rescaled according to the fraction of missing data across seasons (see
Table S1). Stations are listed from (top) west to (bottom) east.
In general, the soil around the stations is covered by grass. The soil
properties were measured for each station as described in Calvet et
al. (2016). In the SMOSMANIA network, VSM and soil temperature are measured
every 12 min at four depths (5, 10, 20, and 30 cm) using ThetaProbe and
PT100 sensors, respectively. The soil moisture (temperature) observations are
recorded with a resolution of 0.001 m3 m-3 (0.1 ∘C). The
data are available to the research community through the International Soil
Moisture Network website (ISMN, 2018). In this study, the sub-hourly
observations of VSM and soil temperature were used over 9 years from 2008 to
2016.
Additionally, the SMOSMANIA network consists of preexisting automatic weather
stations operated by Météo-France, measuring atmospheric variables.
We used a number of meteorological observations such as the maximum and
minimum air temperatures in an hour at 2 m, the hourly mean relative
humidity (RH) of the air, and the accumulated rainfall every 12 min. A small
fraction (less than 4 %) of the rainfall data is missing at each station.
For most of the stations, a larger fraction of the VSM and soil temperature
observations is missing. The mean fractions of missing data for all stations
are 0.11 and 0.15 for VSM and T5cm, respectively. The mean
fraction of missing data for either VSM or T5cm is 0.17. More
details on missing data for each station, including the seasonal distribution
of missing data, are given in Figs. S1, S2, and in Table S1 (see the
Supplement). For stations presenting a fraction of missing data larger than
0.1, the fraction of missing data is relatively evenly distributed across
seasons. Missing data are slightly more frequent at wintertime and in spring.
The maximum fraction of missing VSM at 5 cm is 0.23 at the SBR station, and
the maximum fraction of missing T5cm is 0.47 at the VLV
station (Fig. S1). The scaled missing data fraction of either VSM or
T5cm is shown in Fig. S2 and Table S1 for each season.
MethodsIdentification of intense soil cooling
In situ rainfall observations are used to identify various types of rainfall
events in six steps summarized in Table 2. In a first stage, a rainfall event
is defined as a continuous time series of nonzero accumulated liquid
precipitation values at time intervals of 12 min. Then we only keep the
fully documented rainfall events with available soil temperature and VSM
observations at a depth of 5 cm. Another stage of data sorting (step 3 in
Table 2) is needed to ensure that rainwater is not completely intercepted by
vegetation and is able to actually reach the soil. Because of the method used
to count rainfall events, only about 5 % of the rainfall events exceed
5 mm and have a significant impact on the topsoil VSM. Among these marked
rainfall events, we sort out (step 4) those affecting T5cm by
more than 1 ∘C. In order to assess the precipitation-induced
sensible heat flux (PH) on the topsoil temperature, we then
select marked soil-cooling rainfall events (step 5) presenting at least one
marked drop of T5cm of at least -1.5∘C in
12 min. No other meteorological factor can trigger such rapid changes in
topsoil temperature.
Steps for identifying marked soil-cooling rains, together with the
number of events for all the SMOSMANIA stations from 2008 to 2016.
StepEvent to be identifiedTotal number of eventsDefinition(and number per yearand per station)1Rainfall event123 215 (652)Continuous time series of nonzero accumulated liquid precipitation values at time intervals of 12 min2Fully documented rainfall event104 178 (551)Rainfall event with complete in situ T5cm and VSM5cm observation time series3Marked rainfall event5714 (30)Fully documented rainfall event with accumulated precipitation and changes in VSM5cm larger than 5 mm and equal to or above 0.05 m3 m-3 (VSM5cm change range ≥0.05 m3 m-3), respectively4Marked rainfall event affecting T5cm topsoil temperature1577 (8)Marked rainfall event with changes in T5cm equal to or above 1 ∘C (T5cm change range ≥1∘C)5Intense soil cooling during a marked rainfall event122 (0.65)A 12 min slot during a marked rainfall event affecting T5cm with minimum ΔT5cm (drop of T5cm within 12 min) equal to or below -1.5∘C6Rainwater temperature retrieval slot13 (0.07)Intense soil cooling during a marked rainfall event with rises in VSM5cm and VSM10cm equal to or above 0.10 and 0.05 m3 m-3, respectively
Finally, a few cases are selected (step 6) for the assessment of the
rainwater temperature retrieval (Sect. 3.2), in conditions where the topsoil
VSM profile is sufficiently affected by the rainwater.
Estimation of rainwater temperature
In the SMOSMANIA network, rainwater temperature is not measured. We
investigate whether it is realistic to postulate that the rainwater
temperature can be estimated using in situ topsoil VSM5cm and
T5cm observations within a topsoil layer of depth Δz=0.1 m. We focus on marked soil-cooling rainfall events corresponding to a
drop in topsoil temperature associated with a rise in VSM values. This
condition is not always satisfied in practice because the soil temperature
probes and the soil moisture probes at a given soil depth are not located at
exactly the same place. In order to limit the impact of spatial
heterogeneities in the infiltration of rainwater into the soil, we consider
intense precipitation events able to markedly wet the topsoil soil moisture
(VSM5cm) together with the deeper soil layer
(VSM10cm) during the drop in T5cm. The in
situ VSM observations at a depth of 10 cm (VSM10cm) are used
to ensure that the rainwater really penetrates into the soil and affects the
topsoil layer as a whole. Among 122 rainfall events presenting intense
soil cooling, 101 events have available VSM10cm observations,
and only 50 events present an increase in VSM10cm larger than
0.05 m3 m-3. Within these 50 rainfall events, we only select 13
events for which the rise in VSM5cm corresponds to the rise
in VSM10cm and to the drop in T5cm within the
same 12 min slot (step 6 in Table 2). Firstly, the soil heat capacity at a
depth of 5 cm at time t (C5cmt, in units of
J m-3 K-1) is estimated using the volumetric soil moisture at a
depth of 5 cm at time t, VSM5cmt (in units of
m3 m-3):
C5cmt=CwaterVSM5cmt+Cminfmin+CSOMfSOM,
where Cwater, Cmin, and CSOM are heat capacity
values of water, soil minerals, and soil organic matter (SOM) (4.2×106, 2.0×106, and 2.5×106 J m-3 K-1,
respectively). Their corresponding volumetric fractions at a depth of 5 cm
(Table 3) are VSM5cmt, fmin, and fSOM.
Soil minerals consist of sand, clay, silt, and gravels. Values of the VSM at
saturation at a depth of 5 cm VSMsat (the porosity) are also
listed in Table 3 for all stations. While the volumetric fractions of sand
(fsand), clay (fclay), and silt (fsilt)
were directly measured at a depth of 5 cm, the volumetric fraction of gravels
(fgravel) was derived from measurements made at a depth of 10 cm
(Sect. 5.1).
Soil characteristics at a depth of 5 cm for the 21 stations of the
SMOSMANIA network. The volumetric fractions of sand, clay, and silt at 5 cm
(fsand, fclay, and fsilt) are measured,
and the volumetric fractions of gravel and soil organic matter (SOM)
(fgravel and fSOM) at 5 cm are derived from
measurements at 10 cm. VSMsat is the porosity representing VSM
at saturation (VSMsat=1-fsand-fclay-fsilt-fgravel-fSOM). Stations
are listed from (top) west to (bottom) east.
During a short time period from time t1 to t2 (12 min in this
study) of intense precipitation for which
PH dominates heat
exchanges in the topsoil layer, we can assume that the heat storage change in
the topsoil layer, in units of J m-2, corresponds to the change in
temperature of the infiltrated rainwater as
C5cmt1T5cmt1-T5cmt2Δz=2CwaterVSM5cmt2-VSM5cmt1Traint2-Traint1Δz.
One may assume that soil and incoming rainwater have reached thermal
equilibrium at time t2. Houpeurt et al. (1965) showed that thermal
equilibrium is nearly instantaneous for small soil mineral particles (e.g.,
about 10 s or less for particle size of less than 5 mm). With this
assumption, the rainwater temperature at time t2
(Traint2) is equal to the measured soil temperature at
time t2 (T5cmt2), and the rainwater temperature
just before reaching the soil at time t1 (Traint1)
can be estimated as
Traint1=T5cmt2-C5cmt1CwaterT5cmt1-T5cmt2VSM5cmt2-VSM5cmt1.
It must be noted that Eqs. (2) and (3) are valid for liquid rainwater only.
During hailstorms, hailstones can melt at the soil surface, and part of the
heat extracted from the topsoil layer (left term in Eq. 2) is used for ice
melting. Hailstones may melt after getting to the soil surface or just
before. It can be assumed that both liquid water resulting from hailstones
melting at the surface and liquid rainwater getting to the soil together with
hailstones are very close to freezing level (Tf=0∘C) before
infiltrating the topsoil layer. In this case, the quantity of hailstones (in
kg m-2) melting after getting to the soil surface, I, can be
estimated as
I=1LfC5cmt1T5cmt1-T5cmt24-CwaterVSM5cmt2-VSM5cmt1T5cmt2-TfΔz,
where Lf=3.34×105 J kg-1 is the latent heat of
fusion. Since a fraction of the raindrops may exceed freezing temperature,
I as calculated from Eq. (4) is a low estimate.
ResultsIdentification of soil-cooling rains
The various types of rainfall events considered in this study are listed in
Table 2. Their frequency is indicated. After data sorting (step 3 in
Table 2), only 5.5 % of the fully documented rainfall events can be
considered as marked rainfall events, with accumulated precipitation and
changes in VSM5cm larger than 5 mm and
0.05 m3 m-3, respectively. At the same time, this small fraction
of rainfall events contributes as much as 57 % of the accumulated
rainfall of all rainfall events. On average, 30 marked rainfall events are
observed each year at each station.
Among these marked rainfall events, some have a notable impact on the soil
temperature and soil moisture profiles. This is illustrated by Fig. 2,
showing soil temperature and soil moisture measured at the PRD station from
21 to 25 August 2015 at depths of 5, 10, 20, and 30 cm. A sharp decrease in
soil temperature associated with an increase in soil moisture can be observed
around noon of 23 August 2015, along with a rainfall event. The most
pronounced impacts of the rain are on the topsoil variables at a depth of
5 cm, but the whole soil profile is affected by the rain, down to a depth of
30 cm. This clearly shows the effects on soil temperature of a soil-cooling
rain.
In situ soil temperature (a) and soil moisture (b)
measured at the PRD station from 21 to 25 August 2015 at depths of 5, 10, 20,
and 30 cm, together with the in situ rainfall observations
(mm 12 min-1) shown in grey.
The PRD station considered in Fig. 2 is characterized by a Mediterranean
climate. The 21 stations of the SMOSMANIA network cover contrasting climate
areas (Sect. 2) presenting a different seasonal distribution of
precipitation. Figure 3 presents the average monthly rainfall amount across
seasons (winter is from December to February) for each SMOSMANIA station over
a 9-year period of time, from 2008 to 2016. Differences in the seasonal
distribution of precipitation are very large. For the nine westernmost stations
(from SBR to SFL) the average monthly rainfall amount across seasons is
rather homogenous, although stations under oceanic (O) climate tend to have
more precipitation in winter and those under semi-oceanic (SO) climate in
spring. For the other 12 stations (from MTM to CBR) under Mediterranean (M)
and Mediterranean–mountain (MM) climate conditions, summer is generally drier
than other seasons. On the other hand, the autumn is wetter than other
seasons, especially in MM climate conditions. The maximum seasonal monthly
mean precipitation rate is 272 mm month-1 at the BRN station, and LGC,
MZN, and BRZ stations, also under MM climate conditions, present values
larger than 150 mm month-1. The differences in rain intensity
distribution are analyzed further in Fig. 4 for marked rainfall events
(step 3 in Table 2). We can see that both accumulated rainfall and rain
duration of individual marked rainfall events can be much larger for M and MM
stations than for O and SO stations, especially in the autumn and in winter.
The longest rain duration is 41 h in winter, and the maximum accumulated
rainfall during a single rainfall event is 370 mm in the autumn, at the same
BRN station. On the other hand, most of the marked rainfall events of O and
SO stations present less than 50 mm accumulated rainfall and last less than
12 h.
Average monthly rainfall (in units of mm) for the 21 SMOSMANIA
stations across seasons from 2008 to 2016. Stations are sorted from (left)
west to (right) east. Symbols “O”, “SO”, “M”, and “MM” indicate
oceanic, semi-oceanic, Mediterranean, and Mediterranean–mountain climates,
respectively. Winter season is from December to February.
Rainfall duration vs. accumulated rainfall across seasons for each
marked rainfall event at Mediterranean (M) and Mediterranean–mountain (MM)
stations (a) and at oceanic (O) and semi-oceanic (SO)
stations (b).
Characteristics of the 122 intense soil-cooling rains.
CharacteristicEvent count or fractionM or MM climate107O or SO climate15Spring17Summer82Autumn19Winter4Starting time at daytime (09:00–21:00)83 %Duration less than 2 h80 %Minimum ΔT5cm values ranging from -3 to -1.5∘C 12 min-182 %Maximum number of events per year per station2.7
The statistical distribution of δT5cm
(T5cm increase or decrease in topsoil temperature
corresponding to a rainfall event) and the T5cm change range
for marked rainfall events are shown in Fig. 5. In all climate conditions,
the topsoil layer is cooler after a marked rainfall event with a probability
of 80 %. The δT5cm difference values are larger
than 1 ∘C or smaller than -1∘C with a probability of
25 % only. More often than not, a cooling is observed in these
conditions rather than a warming. The probability of the
T5cm change range to equal or exceed 1 ∘C is a bit
larger: 28 %. This criterion was used to select marked rainfall events
affecting T5cm (step 4 in Table 2). After data sorting, we
obtain a total of 1577 events. This corresponds to about eight events per year
and per station. Because a lot of marked rainfall events can last several
hours, T5cm change range values ≥1∘C can be
explained by the diurnal cycle of the surface net radiation rather than
the mass movement of rainwater. Since obvious soil-warming rainfall events
are not detectable in our observations, we focus on soil-cooling events
characterized by a sharp decrease in topsoil temperature (e.g., in Fig. 2)
during the 12 min time interval of the soil profile observations. For this
purpose, step 5 in Table 2 permits selecting 122 intense soil-cooling rains
using minimum ΔT5cm values ≤-1.5∘C in
12 min. This corresponds to 0.65 events per year and per station.
Frequency of intense soil-cooling rains
Characteristics of intense soil-cooling rains are summarized in Table 4 and
in Fig. S23. Among the 122 identified intense soil-cooling events, 107 occur
at stations under M or MM climates and only 15 under the O or SO climates. The
spatial and seasonal distribution of these intense soil-cooling rainfall
events is shown in Fig. 6 for each station of the SMOSMANIA network. Most of
the intense soil-cooling rains (82) are in summer, while only 4 are found in
winter. The latter are all for the same BRZ station, under MM climate
conditions. In spring and during the autumn, 17 and 19 events are observed,
respectively. At six stations, no intense soil-cooling rain is observed in
9 years: three are under O climate conditions (SBR, URG, CRD), two under SO climate
conditions (CDM, MNT), and one under MM climate conditions (MZN). The PRD and
BRZ stations, under M and MM climate conditions, respectively, present the
largest number of events, with a mean rescaled frequency of intense
soil-cooling rains of 2.7 per year (Table 1). For the 12 M or MM stations
(from MTM to CBR) the mean frequency of intense soil-cooling rains is once a
year. More details are shown in Table 1. More characteristics of these
122 intense soil-cooling rains are shown in Fig. 7. These events do not
always correspond to a large amount of accumulated rainfall. Actually, about
80 % of these events present accumulated rainfall values smaller than
30 mm. About 80 % of these rains last less than 2 h, and the longest
rain duration is less than 5 h. The mean hourly rain rate per event does not
exceed 30 mm h-1 for 90 % of the events. This shows that extremely
large amounts of rain or large precipitation intensity are not required to
produce intense soil cooling. Figure 7 also shows that while 82 % of
intense soil-cooling rains present minimum ΔT5cm values
larger than -3∘C 12 min-1, very low values (down to
-6.5∘C 12 min-1) can be observed. During intense
soil-cooling rains, the minimum ΔT5cm contributes to
increase the T5cm change range. For 18 % of the events,
the minimum ΔT5cm represents more than 80 % of the
T5cm change range. For 48 % of the events, the minimum
ΔT5cm represents more than 60 % of the
T5cm change range. The statistical distributions of δT5cm and δVSM5cm are also shown in
Fig. 7. For intense soil-cooling rains, T5cm is always lower
after the rain than before. The major part (74 %) of the δT5cm values ranges between -6 and -2∘C. For
δVSM5cm, 8 % of the values are slightly negative
(the minimum observed δVSM5cm is
-0.006 m3 m-3), and 21 % do not exceed
0.050 m3 m-3. The maximum observed δVSM5cm is 0.3 m3 m-3.
Statistical distributions of δT5cm(a, b) and T5cm change range (c, d) during 5714 marked
rainfall events for Mediterranean (M) and Mediterranean–mountain (MM)
stations (a, c) and for oceanic (O) and semi-oceanic (SO)
stations (b, d). The percent value is the ratio of the count of each
bin to the total count in all climate conditions. Bin width is 1 ∘C.
Bins for values larger than 1 ∘C or smaller than -1∘C
are in red.
Statistical distribution of the 122 intense soil-cooling rains among
stations of the SMOSMANIA network across seasons. Red, orange, green, and blue
bars are for winter, spring, summer, and the autumn, respectively. Symbols
“O”, “SO”, “M”, and “MM” indicate oceanic, semi-oceanic,
Mediterranean, and Mediterranean–mountain climates, respectively. Stations
are listed from (left) west to (right) east.
Statistical distribution of the 122 intense soil-cooling rains in
terms of (Sect. 3.3) accumulated rainfall (a), rain
duration (b), rain rate (c), minimum ΔT5cm(d), δT5cm(e),
and δVSM5cm(f), with bins of 10 mm,
60 min, 10 mm h-1, 1 ∘C, 1 ∘C, and
0.05 m3 m-3, respectively.
Estimation of rain temperature from soil temperature and soil
moisture observations
Among the 122 intense soil-cooling events, we found 13 cases presenting
simultaneous marked changes in T5cm, VSM5cm,
and VSM10cm, within a 12 min slot (step 6 in Table 2).
Observed values of VSM5cm, T5cm, 2 m air
temperature Tair at time t1 and t2, and 2 m wet-bulb
temperature Twb at time t1 for these 13 example rains are
listed in Table 5, together with the accumulated precipitation of the
considered rainfall events and the peak rainfall intensity
(mm 12 min-1) of each rain. Among these 13 cases, 2 are under SO
climate conditions (PRG and SFL) and 11 are under M and MM climate
conditions (from LZC to CBR in Table 5). The latter include five cases from the
same station, PRD. From a seasonal perspective, 2 cases occurred in spring
(cases 9 and 12), 1 during the autumn (case 3), and the other 10 cases
occurred in summer.
The estimated rain temperatures (Train) for 13 intense
soil-cooling events, together with the in situ observations of
VSM5cm, T5cm, Tair at time
t1 and t2, and Twb at time t1. The time lapse from
time t1 to t2 is 12 min. For rain events 9 and 12, Eq. (4) is used
to estimate the amount of melting hail at the soil surface. Train
values lower by -5∘C than Tair at time t1 are in
bold. Stations are listed from west (top) to east (bottom).
Table 5 also shows the rain temperature estimates at time t1
(Train) derived from Eq. (3) and the amount of melted hail
derived from Eq. (4). Interestingly, the only two cases occurring in spring
(cases 9 and 12) correspond to melting hail events. Taking the PRD station
case 9 as an example, the measured precipitation amount is 9.3 kg m-2
during a rainfall event of 36 min, with an average rain rate of
15.5 mm h-1. From time t1 to t2, the T5cm
topsoil temperature decreases very fast from 17.9 to 12.6 ∘C
(-5.3∘C in 12 min), and the air temperature also decreases from
17.5 to 15.0 ∘C (-2.5∘C in 12 min). During the same
time lapse, VSM5cm increases by +0.13 m3 m-3.
Storms with hail were reported in the press and in social media at many
places of southern France on 23 April 2016, including close to the PRD region
(Infoclimat, 2016). Using Eq. (4), one can estimate the amount of water
originating from melting hail: about 1 kg m-2. Soil temperature and
soil moisture time series for all these 13 rain examples are shown in
Figs. S3–S15. Thunderstorms and hail were also reported for case 12
(Infoclimat, 2010).
Figure 8 shows the estimated Train vs. Tair and
T5cm at time t1. While most of the Tair
values range between 16 and 22 ∘C, except for case 12
(Tair=4.3∘C), the estimated Train values present
a larger variability, from 0 to 22.5 ∘C. Excluding the two spring
cases (9 and 12), the standard deviation of Train values in
Table 5 is 4.6 ∘C, which is much larger than for Tair,
1.9 ∘C.
Estimated Train for the 13 cases listed in Table 5 vs.
observed ambient Tair(a) and observed
T5cm(b), with levels of grey indicating air
relative humidity (RH, dimensionless) and VSM5cm to
VSMsat ratio (dimensionless) values at time t1.
For the 13 storms listed in Table 5, Train (T5cm)
tends to be lower (higher) than Tair, with a mean difference of
-5.1∘C (+6.0 ∘C). On average, Train is
cooler than topsoil by -11.1∘C. Train is cooler than
Twb by -3.8∘C. For cases 2, 11, and 13,
Train is close to Tair. For cases 1 and 3,
Train is close to Twb. The other cases present
Train values much cooler than Tair and
Twb. In particular, Train is cooler than
Tair by more than 5 ∘C for five cases (4, 5, 7, 9, 10),
among which three cases (7, 9, 10) occurred at the PRD station. At time t1,
RH ranges from 68 % to 97 % and
VSM5cmVSMsat ranges from 20.7 % to
63.8 %. Soil-cooling rate ΔT5cm values range from
-5.3 to -1.5∘C in 12 min. The corresponding air cooling values
are less pronounced, ranging from -2.5 to 0.0 ∘C. For case 9,
VSM5cmVSMsat increases only by 27 %
during the considered 12 min slot. This is a relative small increase
compared to other cases, which is less than the median value of 29 % and much less
than the maximum value of 58 % observed for case 10 at the same station.
Despite the moderate soil wetting in case 9, T5cm values
presented the most pronounced decrease (-5.3∘C). The
T5cm value at time t2 also presented the largest
difference with the estimated rain temperature (+12.6 ∘C).
DiscussionHow accurate are rain temperature estimates?
In this study, an attempt is made to estimate rain temperature using
observations in the topsoil layer.
In Eqs. (2) and (3), it is assumed that the precipitation-induced sensible
heat flux dominates heat exchanges in the topsoil layer. Since soil
properties are known, the mean PH value can be estimated from
Eq. (2) for the intense soil-cooling events used to retrieve
Train (see Table 5). For the 10 events of Table 5 occurring at
summertime, this flux ranges from 408 to 1009 W m-2, with a mean value
of 648 W m-2. These PH flux values are very high and
represent large fractions of absolute values of the net radiation
Rnet (i.e., the amount of energy available for surface heat
exchanges, driven by the incoming solar radiation, that could be simulated
without accounting for PH). They are probably often much larger
than Rnet because the Rnet energy budget component is
generally small during rainfall events, in relation to the low incoming solar
radiation. Moreover, 7 events out of 10 occur at nighttime or at dusk (see
Supplement), i.e., in small Rnet value conditions. The
Rnet variable is not measured at SMOSMANIA stations. Typical
measured summertime values of the maximum daily Rnet over the
grassland site of Meteopole-Flux in southwestern France (Zhang et al., 2018)
range from about 200 W m-2 during cloudy rainy days to about
700 W m-2 in clear sky conditions. At nighttime, absolute
Rnet values rarely exceed 100 W m-2.
Because Eqs. (3) and (4) include soil heat capacity (Eq. 1), the static soil
properties must be known together with the time-evolving VSM and soil
temperature. In particular, the volumetric fraction of gravels is the most
variable static soil characteristic in Table 3: fgravel ranges
from 0 to 0.41 m3 m-3 at CRD and BRN stations, respectively.
Among stations of the 13 rain retrieval cases in Table 5, fgravel
ranges from 0.05 to 0.34 m3 m-3 at PZN and PRD, respectively. The
fraction of gravels was not measured at a depth of -5 cm. Instead, values
given in Table 3 are derived from gravimetric measurements made at -10 cm.
In order to assess to what extent uncertainties on fmin values may
affect the retrieved Train values, two numerical experiments
(Exp1 and Exp2) were made using other soil characteristics than those listed
in Table 3 (control experiment):
Exp1 used the reassembled static soil volumetric fractions of soil
minerals and SOM at a depth of 5 cm assuming fgravel=0 m3 m-3. This was equivalent to considering fine earth only, and
the resulting fSOM and fmin fractions were larger and
smaller than in the control experiment, respectively. This tended to increase
C5cmt (Eq. 1) and to decrease Train (Eq. 3).
Exp2 used the measured soil characteristics at a depth of 10 cm (Calvet
et al., 2016). The impact of Exp2 on fSOM, fmin,
C5cmt, and Train varied a lot from one station
to another.
Volumetric fractions of the topsoil elements
used in Exp1 and Exp2 are listed in Tables S2 and S3, respectively.
Differences in fSOM and fmin values are listed in
Table 6.
The estimated Train differences, fmin
differences, and fSOM differences between Exp1 and control and between Exp2 and
control. Changes in values of Train larger than ±0.5∘C are in bold. Changes in values of volumetric fractions larger
than 0.05 m3 m-3 are in bold.
Differences in estimated Train values for Exp1 and Exp2 with
respect to the control are shown in Table 6. In Exp1, Train
estimates tend to present slightly lower values, with differences down to
-0.3∘C, and the median difference value is -0.1∘C,
with a standard deviation of 0.1 ∘C. In Exp2, Train
differences range from -1.23 to +0.17∘C, and the median
difference value is 0 ∘C, with a standard deviation of
0.4 ∘C. Merging results from Exp1 and Exp2, 80 % of the
statistical distribution of Train differences range between
-0.3 and +0.1 ∘C. The most marked changes in Train
(-0.50 and -1.23∘C) are observed for Exp2 (cases 1 and 4, at
PRG and NBN stations, respectively). They correspond to the largest changes
in fmin (+0.070 and +0.105, respectively). This gives an idea of
the uncertainties on Train related to poorly known soil
heterogeneities and to their impact on the soil characteristics measured in
the field.
Another source of uncertainties is that the topsoil layer, from the soil
surface down to a depth of -0.1 m, may not be completely affected by the
rainfall event or that the instruments positioned at a depth of -5 cm may
not be able to sample mean values relevant for the topsoil layer. In order to
limit this effect, we selected only 13 intense soil-cooling events by
imposing a marked change in VSM10cm during the considered
12 min slot. If the latter condition is ignored, 32 soil-cooling events can
be considered instead of 13, and we checked that similar results are found
(not shown).
A limitation of the method used in this study is that the soil moisture and
soil temperature probes at a depth of 5 cm are not placed at exactly the
same location. We found some examples for which the VSM response to rain does
not match with the drop in topsoil temperature (Figs. S18, S19, S20, S21, and
S22). This limited the number of events for which rainwater temperature could
be estimated.
More research is needed to develop techniques to measure rainwater
temperature. Instruments similar to the rain temperature equipment of Byers
et al. (1949) could be developed. Our results show that using automatic
temperature and volumetric moisture observations in a porous medium of known
thermal properties has potential to estimate Train and possibly
the amount of hailstones in real time.
Does soil cooling matter?
We showed (e.g., Fig. 2) that the temperature of raindrops reaching the soil
surface can impact the soil temperature profile during several hours.
Investigating the impact on longer time periods would require using a LSM
able to activate or deactivate the representation of sensible heat input from
liquid water into the soil. This impact was investigated experimentally by
Wierenga et al. (1975) through an irrigation experiment with cold and warm
water (4.1 and 21.6 ∘C, respectively). They showed that 42 h was
needed before differences in soil temperature at a depth of 0.2 m were
reduced to less than 1 ∘C, and more than 5 d was needed below a depth of
0.5 m. They used quite large irrigation amounts of more than 120 mm.
Figure 7 shows that accumulated rainfall during one event can exceed 120 mm
in Mediterranean climate conditions (M and MM). Such events are not observed
in O and SO conditions, but less intense precipitation events can impact the
surface energy budget, even if this is not obvious in the soil temperature
time series. Figure 7 shows that intense soil-cooling events (step 5 in
Table 2) are associated with rather uniformly distributed increases in topsoil
VSM values. Actually, the statistical distribution of δVSM5cm values tends to shift towards larger values at each
data-sorting step listed in Table 2. This is illustrated by Fig. 9 for steps
2, 3, and 4. For fully documented rainfall events (step 2),
VSM5cm does not increase for about 60 % of the rainfall
events (δVSM5cm≤0 m3 m-3 12 min-1). Two possible reasons are that
(1) rainwater can be intercepted by vegetation and/or litter, especially when
the rain is very slight and/or the soil ground is very dry; and
(2) VSM5cm is close to saturation, so no
VSM5cm increase is observed. We found examples under the
above two situations, shown in Fig. S16 (VSM5cm is relatively
small and the rainwater might be intercepted) and Fig. S17
(VSM5cm is close to saturation). For steps 3 and 4, Fig. 9
shows that only 7 % to 9 % of VSM5cm values do not
increase. About the same proportion is observed for the 122 intense
soil-cooling rains (Fig. 7).
Statistical distribution of δVSM5cm for
104 178 fully documented rainfall events (a), 5714 marked rainfall
events (b), and 1577 marked rainfall events affecting topsoil
temperature (c) defined in Table 2.
Assessing the impact of neglecting precipitation-induced sensible heat in the
soil temperature simulations of a LSM is a key issue. Developing LSMs able to
represent the sensible heat input from liquid water into the soil is needed,
as well as a way to diagnose rainwater temperature from atmospheric model
simulations (Feiccabrino et al., 2015). Soil temperature and soil moisture
simulations from the ISBA LSM are presented in the Supplement (Figs. S24 and
S25). It is shown that ISBA simulations are not able to represent the
response of soil variables to intense soil-cooling precipitation events. Now,
the ISBA model has no representation of heat exchanges due to water mass
movement. This process needs to be introduced in ISBA. We think that data
from a fully instrumented site including direct measurements of rain water
temperature are needed to completely address this issue and to validate the
upgraded model version. Such an experiment would give insights to understand
when, where, and why soil cooling occurs or not and would be valuable to help
model development. In particular, the precipitation-induced sensible heat
flux is not limited to intense precipitation, and the impact of this process
on the surface energy budget needs to be investigated in all conditions.
Attempts were made in a few studies to simulate the precipitation-induced
sensible heat (e.g., Emanuel et al., 2008; Wang et al., 2016), but, in
general, it was assumed that Train was equal to Twb.
This study shows that Train can be much lower than
Twb during severe convective events and confirms the findings of
Byers et al. (1949). Since severe convective events associated with the
intense soil-cooling events observed in this study tend to become more and
more frequent in relation to climate change (Feng et al., 2016), soil-cooling
effects may play a role in the response of the Earth system to climate
change. Moreover, rainwater temperature estimates from observation networks
or from atmospheric model simulations could be beneficial for a number of
applications such as urban heat island monitoring (e.g., Jelinkova et al.,
2015), drinking water quality monitoring (e.g., Chubaka et al., 2018), the
estimation of the emission rates of greenhouse gases by soils (e.g., Gagnon
et al., 2018), or the quantification of soil erosion (e.g., Sachs and Sarah,
2017).
Conclusions
In situ rain temperature measurements are rare. We used the soil moisture and
soil temperature observations from the SMOSMANIA network over 9 years in
southern France to assess the cooling effects on soils of rainfall events.
The rainwater temperature was estimated using observed changes in topsoil
volumetric soil moisture and soil temperature in response to the rainfall
event. We found that most (72 %) marked rainfall events did not impact
the T5cm change range more than ±1∘C. On the other
hand, about 2 % of marked rainfall events triggered intense soil cooling
with drops in T5cm (ΔT5cm)≤-1.5∘C in 12 min. Such intense soil-cooling rains were mainly
observed under Mediterranean climate conditions, in summer, at daytime. The
average frequency of the occurrence of such events for the 12 Mediterranean
stations was once a year. Among all these intense soil-cooling rains, the
minimum observed value of ΔT5cm was -6.5∘C
in 12 min. Rain temperature estimates were obtained for 13 cases. They were
generally lower than the ambient air temperatures, wet-bulb temperatures, and
T5cm values (with mean differences of -5.1, -3.8, and
-11.1∘C, respectively). In five cases, rain temperature estimates
were much cooler than air temperature, by at least -5 and down to
-17.5∘C, likely in relation to hailstones melting just before
reaching the surface or melting at the surface of the soil. More research is
needed to develop measurement techniques for rainwater temperature and
perform such measurements in contrasting climate conditions.
Data availability
The soil moisture (temperature) observations are available
to the research community through the International Soil Moisture Network
website (https://ismn.geo.tuwien.ac.at/, ISMN, 2018).
Nomenclature
T5cm (∘C)in situ soil temperature at a depth of 5 cmΔT5cm (∘C 12 min-1)T5cm change every 12 min during a rainfall eventT5cm change range (∘C)maximum minus minimum T5cm during a rainfall event, including 12 min slots just after and before the rainfall eventδT5cm (∘C)T5cm just after the rainfall event minus T5cm just before the rainfall eventVSM5cm (m3 m-3)in situ volumetric soil moisture (VSM) at a depth of 5 cmΔVSM5cm (m3 m-3 12 min-1)VSM5cm change every 12 min during a rainfall eventVSM5cm change range (m3 m-3)maximum minus minimum VSM5cm during a rainfall event, including 12 min slots just after and before the rainfall eventδVSM5cm (m3 m-3)VSM5cm just after the rainfall event minus VSM5cm just before the rainfall eventΔz (m)depth of the topsoil layer (0.1 m in this study)TISBA (∘C)ISBA soil temperature simulations at a depth of 5 cmΔTISBA (∘C 12 min-1)TISBA change every 12 min during a rainfall eventTair (∘C)observed ambient air temperature at 2 mTwb (∘C)ambient wet-bulb temperature at 2 m calculated using the Stull (2011) equationTrain (∘C)estimated rain temperaturePH (W m-2)precipitation-induced sensible heat fluxRnet (W m-2)net radiation fluxRH (dimensionless)in situ air relative humidity at 2 mVSMsat (m3 m-3)VSM at saturation (i.e., the soil porosity)VSM5cmVSMsat (dimensionless)VSM5cm to VSMsat ratio or degree of saturationSOMsoil organic matterfclay, fgravel, fmin, fsand, fsilt, fSOM (m3 m-3)volumetric fractions of clay, gravels, soil minerals, sand, silt, and SOMC5cmt, Cwater, Cmin, CSOM (J m-3 K-1)heat capacity values of the topsoil layer at time t, water, soil minerals, and SOMO, SO, M, MMoceanic, semi-oceanic, Mediterranean, Mediterranean–mountain climate conditionsFor the first eight symbols, the subscript “5 cm” stands for observations made at a depth of 5 cm. In Table 2, marked rainfall events affecting T5cm are defined with T5cm change range ≥1∘C. Intense soil cooling during a marked rainfall event is defined with minimum ΔT5cm≤-1.5∘C in 12 min. In Table 1, the rescaled number of intense soil-cooling events is calculated as NsR=Ns1-fs, where Ns is the number of intense soil-cooling events for one season at one station, and fs is the proportion of missing data for the same season at the same station (see Table S1 and Fig. S2 in the Supplement). The fs values for each season are estimated using the total missing data proportion for all seasons and the scaled seasonal distribution of the fraction of missing data.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-5005-2019-supplement.
Author contributions
SZ and JCC performed the conceptualization; SZ and CM performed the data processing;
SZ, JCC, and CM performed the result analysis; SZ wrote the original draft;
SZ, JCC, and CM reviewed and edited the paper.
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 HyMeX program
(https://www.hymex.org/, last access: April 2019). We thank our
Météo-France colleagues for their support in collecting, checking,
and archiving the SMOSMANIA data: Annick Auffray, Catherine Bienaimé,
Marc Bailleul, Basile Baumann, Laurent Brunier, Jérôme Candiago,
Anne Chaumont, Jacques Couzinier, Mathieu Créau, Pierre Farges,
Hélène Fillancq, Noureddine Fritz, Philippe Gillodes,
Sandrine Girres, Michel Gouverneur, Didier Grimal, Viviane Isler,
Maryvonne Kerdoncuff, Matthieu Lacan, Pierre Lantuejoul, Franck Lavie,
William Maurel, Roland Mazurie, Nicolas Naudet, Dominique Paulais,
Bruno Piguet, Fabienne Simon, Dominique Simonpietri,
Marie-Hélène Théron, and Marie Yardin.
Review statement
This paper was edited by Domenico Cimini and reviewed by three anonymous referees.
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