The spatial and temporal controls of preferential flow (PF) during
infiltration are still not fully understood. As soil moisture sensor
networks allow us to capture infiltration responses in high temporal and
spatial resolution, our study is based on a large-scale sensor network with
135 soil moisture profiles distributed across a complex catchment. The
experimental design covers three major geological regions (slate, marl,
sandstone) and two land covers (forest, grassland) in Luxembourg. We
analyzed the responses of up to 353 rainfall events for each of the 135 soil
moisture profiles. Non-sequential responses (NSRs) within the soil moisture
depth profiles were taken as one indication of bypass flow. For sequential
responses maximum porewater velocities (vmax) were determined from the
observations and compared with velocity estimates of capillary flow. A
measured vmax higher than the capillary prediction was taken as a
further indication of PF. While PF was identified as a common process
during infiltration, it was also temporally and spatially highly variable. We
found a strong dependence of PF on the initial soil water content and the
maximum rainfall intensity. Whereas a high rainfall intensity increased PF
(NSR, vmax) as expected, most geologies and land covers showed the highest PF
under dry initial conditions. Hence, we identified a strong seasonality of
both NSR and vmax dependent on land cover, revealing a lower
occurrence of PF during spring and increased occurrence during summer and
early autumn, probably due to water repellency. We observed the highest
fraction of NSR in forests on clay-rich soils (slate,
marl). vmax ranged from 6 to 80 640 cm d-1 with a median of 120 cm d-1 across all events and soil
moisture profiles. The soils in the marl geology had the highest flow
velocities, independent of land cover, especially between 30 and 50 cm depth,
where the clay content increased. This demonstrates the danger of treating
especially clay soils in the vadose zone as a low-conductive substrate, as
the development of soil structure can dominate over the matrix property of
the texture alone. This confirms that clay content and land cover strongly
influence infiltration and reinforce PF, but seasonal dynamics and flow
initiation also have an important impact on PF.
Introduction
Preferential flow (PF) in soils describes different flow processes with
higher flow velocities than soil matrix flow and heterogeneous flow patterns
(Hendrickx and Flury, 2001). Many studies have shown
that PF is ubiquitous (Jarvis, 2007) and that “PF
is the norm and not the exception” (Weiler, 2017). PF can
affect water distribution in soil (Ritsema et
al., 1996), groundwater recharge (Ireson and
Butler, 2011), root water uptake (Schwärzel et al., 2009) and solute
transport (Larsbo et al., 2014). Since
the early work of Beven and Germann (1982), the importance of
PF pathways such as macropores (created by roots, earthworms), fissures or
cracks has been widely recognized. Most studies focusing on different PF
processes, such as fingered flow (Selker et al.,
1992), macropore flow (Weiler and Naef, 2003)
or funnel flow (Kung, 1990), were carried out
at the point or plot scale (spatial scale smaller than a few meters). Since
PF increases the range of flow velocities in the vadose zone by orders of
magnitudes (Nimmo, 2007), it is essential to include this
process when modeling water and solute transport in soil. Given its
importance, many models now account for PF processes (see Gerke,
2006; Köhne et al., 2009; Steinbrich et al., 2016), but defining
meaningful parameter sets for these models is challenging (Abbaspour
et al., 2004; Arora et al., 2011; Cheng et al., 2017). Furthermore,
Reck et al. (2018) showed that macropore networks and
related parameters such as macropore distance and diameter are not constant
over time. The problem of spatial and temporal variability of PF is also
reflected in the updated paper about PF research by Beven
and Germann (2013). They stated that some fundamental questions are still
not solved. One of the central questions raised by Beven
and Germann (2013) is “When does water flow through macropores in the
soil?”. We know about the importance of PF, but knowledge about the spatial
and temporal properties affecting the distribution of PF across the
landscape is still lacking (Lin
et al., 2006; Wiekenkamp et al., 2016).
Many methods have been developed in the last decades to study and quantify
PF in soils (see, e.g., Allaire et al.,
2009). These methods include using X-ray tomography at the pore to soil core
scale (Larsbo
et al., 2014; Naveed et al., 2016), the analysis of (dye) tracers and
breakthrough curves at the soil core to hillslope scale (Anderson
et al., 2009; Flury et al., 1994; Koestel et al., 2013; Zehe and
Flühler, 2001) or using geophysical methods at the plot to hillslope
scale (Angermann et al., 2017;
Oberdörster et al., 2010). Another way to identify the potential for PF
are measurements that can be related to the number and volume of macropores
or cracks. Watson and Luxmoore (1986) used a
tension infiltrometer to calculate the amount of infiltration that is caused
by pores of a specific equivalent pore size, a method that has been
frequently used (e.g., Buttle and McDonald, 2000). Stewart et al. (2016a, b) measured soil crack structure
and volume and used this information to model soil water infiltration.
Nevertheless, most methods lack either spatial or temporal resolution to
quantify the frequency and properties of PF simultaneously for larger areas
(∼ km2) and longer timescales
(∼ years).
An alternative approach to study PF during infiltration are soil moisture
measurements at high temporal resolution (∼ minutes). While
soil moisture sensors only measure at the point or profile scale, they can
be deployed widely throughout the landscape (Zehe et al., 2014). Soil
moisture sensors can be installed at different depths and are minimally
invasive (Hardie et al.,
2013). So far, soil moisture sensors were used to detect PF by either using
the measured response velocities after a rainfall event (Blume
et al., 2009; Eguchi and Hasegawa, 2008; Germann and Hensel, 2006; Hardie et
al., 2013; Kim et al., 2007) or for analyzing the sequence of their response
with depth (Graham
and Lin, 2011; Lin and Zhou, 2008; Liu and Lin, 2015; Wiekenkamp et al.,
2016). Using these methods most studies found a relationship with
precipitation characteristics (Liu
and Lin, 2015; Wiekenkamp et al., 2016) or initial soil moisture (Blume
et al., 2009; Hardie et al., 2013; Liu and Lin, 2015; Wiekenkamp et al.,
2016).
Even though some of the studies described above show differences in PF
occurrence between soils or landscape properties, most of them do not
rigorously compare contrasting landscape units at the larger scale. Zhao et al. (2012) tested out-of-sequence responses of the soil moisture sensors as an
indication of PF for two contrasting land covers and found much higher
occurrence of PF in the forest sites compared to a cropland. However, since
both sites also had different soils, it could not clearly be attributed to
land cover. Most field experiments studying the effect of soil texture and
land cover on soil water flow measured infiltration characteristics or
hydraulic conductivities of soil cores (Bormann
and Klaassen, 2008; Gonzalez-Sosa et al., 2010; Jarvis et al., 2013;
Zimmermann et al., 2006). In general, higher infiltration rates and
hydraulic conductivities were observed at sites with natural vegetation or
forests. These higher infiltration rates were often attributed to the
presence of macropores, but not connected to the dynamics of PF occurrence
under natural field conditions. Studies linking the spatial and temporal PF
occurrence in high resolution and comparing contrasting landscapes under
natural initial and boundary conditions are still scarce.
A correct estimation of PF occurrence is important for hydrological
predictions (e.g., modeling) and can improve water resource management.
Therefore, the main aim of this study is to identify and compare the
temporal dynamic of PF occurrence by using profiles of soil moisture sensors
in different large-scale spatial units that could potentially be used as
representative units for catchment modeling. Since it can be expected that
rainfall intensity and soil moisture have a strong influence on the
initialization of PF (Beven and Germann, 1982), we will mainly
focus on the temporal controls of initial soil moisture and rainfall. More
specifically, we attempt to answer the following questions. Does PF
occurrence increase with rainfall intensity since higher intensity leads
more frequently to an exceedance of matrix infiltration capacity? Does PF
occur more often under wet conditions since the infiltration capacity is
lower? How is the temporal PF dynamic influenced by spatial factors like
geology/soil type and land cover?
Material and methodsStudy sites
To test our research question we analyzed a dataset of 405 soil moisture
sensors at 45 sites distributed across a complex landscape (varying geology
and land cover) but under similar climatic conditions. The monitoring
network is located in the Attert catchment in the Grand Duchy of Luxembourg.
The climate is temperate semi-oceanic with a mean annual rainfall of 845 mm (Pfister et al., 2006), mean monthly
temperatures between 0 ∘C (January) and 17 ∘C (July) and
only very few days per year with snow coverage (Wrede et al., 2015). Elevation ranges between
265 and 480 m a.s.l. and the catchment covers three major geologies
(Colbach and Maquil, 2003). The northwestern part of the
catchment is located at the southern edge of the Ardennes and the geology
here is dominated by Devonian Slate bedrock covered by periglacial slope
deposits mixed with eolian loess (Juilleret et
al., 2011; Moragues-Quiroga et al., 2017). The southern part of the
catchment is dominated by sedimentary rocks of the Paris Basin (Wrede et al., 2015) with Jurassic Luxembourg
Sandstone at the southern catchment border and Triassic Sandy Marls in the
central part of the catchment (Fig. 1). The slate region has agriculturally
used plateaus between steep forested slopes (∼15–25∘). The sandstone hillslopes are mostly forested with
grasslands only present on the footslopes (Juilleret
et al., 2012; Martínez-Carreras et al., 2012). The land cover in the
Luxembourgian part of the marl region is mainly characterized by
agricultural sites (30 %) and grasslands (41 %, mainly pasture) with
gentle slopes (∼3∘).
Map of the Attert catchment in Luxembourg with the three main
geologies and the locations of the 45 soil moisture monitoring sites.
Site information of the six defined landscape units. Additional
textural information can be found in the Supplement (Table S1). Texture
denoted with ∗ was estimated with a field test by feel. Date format is mm/yyyy.
Soil types in the slate geology are Haplic Cambisols (Ruptic, Endosketelic,
Siltic) (IUSS Working Group WRB, 2006) with a main texture of
silty clay loam (Table 1). Texture was determined by sedimentation analysis
following DIN ISO 11277 (2002) from randomly distributed samples taken mostly in
the upper 30 cm. Coarse particle fraction (>2 mm) was much
higher than in the other geologies and is estimated between 10 % and up
to 50 % volume fraction in the Bw horizon and increases with depth.
Layers of weathered rock (C horizon) are found usually below 50 cm.
Weathered slate rocks are mostly embedded slope parallel due to solifluction
of the soil layers during the last ice age (Juilleret et
al., 2011). In the Luxembourg Sandstone, Colluvic Arenosols dominate in the
valley bottom and Podzols (IUSS Working Group WRB, 2006) with a
sandy loam texture on the slopes and plateaus. The depth to the unweathered
bedrock is more than 2 m (Sprenger et al., 2015) with
banded Bt horizons deeper than 1 m. The soils of the marl geology have a
more diverse texture (Wrede et al., 2015) but
are often showing a clay rich layer (>50 % clay) starting
between 20 and 50 cm depth. Therefore, Stagnosols (IUSS Working
Group WRB, 2006) are very common in this region. Sandy horizons can be found
as well, whereas topsoils mostly exhibit a loamy texture (Table 1). The soils show
high macroporosity documented by the excavation of horizontal soil profiles
and counting of pores >2 mm Ø.
The instrumentation at each site includes air temperature, groundwater table elevation and rainfall measurements and three
soil moisture profiles separated by 5–20 m. A soil moisture profile
consists of three volumetric soil moisture (θ) sensors at 10, 30 and 50 cm depth below the
surface. In total 135 soil moisture profiles at 45 different sites were
distributed across the catchment (Fig. 1). The time series used in this
study start between March 2012 (first installed profiles) and October 2013
(last installed profiles) and end in February 2017 (Table 1). The soil moisture sensors (5TE
capacitance sensors, METER Group Inc., USA) measured at 5 min temporal
resolution. These sensors measure with a 70 MHz frequency and have a sample
volume of around 300–715 mL (Cobos, 2015; Vaz
et al., 2013), although other studies found smaller sampling volumes in
wetter soils for other sensors of similar type (Blonquist et
al., 2005). Due to sensor defects, 43 sensors were replaced with SMT100
(TRUEBNER GmbH, Neustadt, Germany) and 9 sensors with GS3 sensors (METER Environment, USA) in 2016. Sensors were installed horizontally
with minimum disturbance from a 30 cm diameter hole drilled with a power
auger. Each sensor was installed slightly shifted in the horizontal
direction to the one above, to be unaffected by potential flow path changes
by the sensor above. Furthermore, sensor cables were laid downwards in the
hole first and led up on the opposite wall to prevent artificial PF along
the cables leading to the sensors. In each of the three main geologies, the
sensor sites were situated in two different land cover classes, forest and
grassland. The selected forest sites were dominated by European beech
(Fagus sylvatica) with occurrence of oak (Quercus robur, Quercus petraea) and common hornbeam (Carpinus betulus). Furthermore, rainfall
was measured with one tipping bucket (Davis Instruments, USA, 0.2 mm
resolution, collection area 214 cm2) at each grassland site
and five randomly placed tipping buckets at each forest site to account, to
at least some degree, for the spatial variability of throughfall. We defined
six different landscape units distinguishing the three main geological
formations and the two land covers (forest, grassland) to test our research
questions. The number of soil moisture profiles for the different land cover
and geological classes are summarized in Table 1. Additional information and
specific site properties are shown in Appendix A.
Data analysisEvent classificationRainfall events
A full workflow of the data analysis is depicted in Fig. 2 showing the
number of excluded events due to different quality criteria. Rainfall (P)
events were defined using the rainfall data with 5 min resolution
individually for each site. For the forest sites the mean of all five
tipping buckets for every 5 min time step was calculated to obtain
average throughfall for each site. Forest tipping buckets that measured no
rainfall over one hour were excluded (assuming they were clogged), when at
least three other buckets observed rainfall during the same timeframe. If
the rainfall data contained more than one missing value in a 2 h
period, it was excluded from further analysis. Following the approach of Graham and Lin (2011)
and Wiekenkamp et al. (2016), a rainfall event was defined as rainfall with a minimum amount of 1 mm. The end was defined as the last monitored response of a rain gauge
followed by a specific time period without rain (te). The procedure of
determining this time period is described below.
Workflow for the estimation of spatial and temporal PF occurrence
from soil moisture data with the number of included and excluded events. Event
numbers refer to the sum of the events on a profile base (since this number is
the resulting number of data points used for each analysis).
Dividing soil water dynamics into single events based on P input is always a
trade-off: on the one hand, short rainfall events do not allow for a clear
separation of the infiltration signals from different input pulses. On the
other hand, long rainfall that is grouped into one event can result in too
much information from several consecutive rain input pulses that are merged
into one rainfall event. Hence, different rainfall regimes require different
threshold values, i.e., hours without rainfall (te) for the
identification of event endings. The sensitivity of te to the number of
rainfall events and their characteristics in our case was investigated by
testing different values of te: 3, 6, 12 and 24 consecutive hours
without rain.
For each P event total rainfall amount (Psum), the maximum P intensity in
a 5 min time step (Pmax) and the event average rainfall intensity of the
entire event (Pint) was determined. Events that were not plausible were
excluded by using a threshold method for event P amount (Psum>100 mm), average event intensity (Pint>15 mm h-1) and maximum P intensity in a 5 min time step (Pmax>80 mm h-1). These implausible events were observed to
happen during the reconnecting of the loggers following a logger error (no
power etc.) or clogging and release of the clogged water. To exclude
snowfall or frozen soil conditions, events with a mean air temperature below
0 ∘C during the event were not included in the analysis. By
applying the quality criteria for rainfall events using te=12 h,
1392 of 32 025 rain events (sum of profile rainfall events) were excluded
because of the threshold criteria and 426 because the mean temperature was
below 0 ∘C during the event.
The rainfall event separation method is sensitive to the required number of
consecutive hours without rain (te) between the events. Table 2 shows
te values with the resulting number of events, mean event duration,
rainfall amount (Psum) and event average rainfall intensity
(Pint). Shorter te results in more events and decreasing mean event
duration. Mean Pint is gradually decreasing with longer te due to
longer event durations while mean Psum is increasing. We considered
te=12 h to be sufficient to ensure event separation yielding an
appropriate event length and to avoid possible superimposition of soil water
flow signals from different input pulses. Therefore, the following analyses
are performed with the event definition based on te=12 h. This
results in total rainfall event numbers between 144 and 353 per profile.
54.2 % of all analyzed rainfall events had sums lower than 5 mm and 77.7 % lower than 10 mm. The distribution of rainfall intensities
(Pint) shows that 69.2 % of all events had a Pint<0.4 mm h-1. The density distributions show slightly higher Pmax for
grassland sites but no difference among the geologies (Fig. 3).
Rainfall event characteristics over all 135 profiles depending on
minimum hours without rain (te) required between consecutive rainfall events.
hours without rain (te) 361224Sum of profile rainfall events45 68139 01830 20718 546Mean duration (h)11.318.733.876.0Mean Psum (mm)5.46.48.111.9Mean Pint (mm h-1)0.880.650.480.33
Density distribution of maximum rainfall intensity for the six
landscape units.
Soil moisture and infiltration events
Signal spikes (strong increase in soil moisture within a 5 min time step
and a decrease to the initial value) in the measured soil moisture time
series were removed and data were visually checked for plausibility and
long-term consistency. In addition, sensor readings were validated against
those of the other sensors at the same depth for each site. No site-specific
calibration of the soil moisture sensors was conducted and soil moisture
values were obtained by the sensor-internal θ-permittivity
relationship following Topp et al. (1980). For the 5TE
sensors the manufacturer gives an absolute sensor accuracy of volumetric
water content of ±3 vol % (DecagonDevices, 2016). For a
relative change of 1 vol % a maximum sensor-to-sensor difference of
±0.25 vol % can be found in the very dry range (θ∼10 vol %)
(Rosenbaum et al., 2010).
Since Rosenbaum et al. (2011, 2012) showed that temperature effects on the sensors and on soil
dielectric properties can cancel each other out, permittivity was not
corrected for soil temperature. Furthermore, electrical conductivity effects
of soil water on permittivity were neglected as bulk electrical conductivity
was low (<0.1 dS m-1) for most profiles. Although some marl
profiles show higher bulk electrical conductivities, results of soil water
content change should not be affected since these profiles do not reveal
fast bulk electrical conductivity fluctuations on the event scale.
For each defined rainfall event the soil moisture time series of all sensors
in a profile was checked for their response. Infiltration events were
defined as a θ increase of ≥1 vol % of at least one sensor in
the soil profile. This threshold was chosen to avoid diurnal fluctuation,
caused by, e.g., soil temperature, being classified as infiltration events (Graham
and Lin, 2011; Wiekenkamp et al., 2016). If a soil moisture event was
identified, the timing of the first response of every sensor was determined. The
first response is defined as the point in time when the θ change is
higher than the instrument noise (Lin and Zhou, 2008) that was found
to be 0.4 vol % for the 5TE sensors (Rosenbaum
et al., 2010; Wiekenkamp et al., 2016). Linear interpolation was used to
calculate the time between two 5 min readings to increase the temporal
resolution. The soil moisture response was tracked for up to 48 h after
the end of a rainfall event or until the time a new rainfall event starts.
The chosen rainfall event separation based on te=12 h already
avoids superimposition of consecutive rainfall input signals on the soil
water content. However, to have clearly separated soil water flow events
that are uninfluenced by a new rainfall event for at least 24 h, both
consecutive infiltration events were excluded if the second rainfall event
occurred within 24 h after the first rainfall event end. In the case of
a response later than 24 h we assumed that the following infiltration
event is likely to be triggered by the new rainfall event (Hardie et al.,
2013). Only if more than 99 % of the data points for all profile sensors
during an infiltration event were usable were they considered for further
analysis (termed completeness criterion). Furthermore, infiltration events
that showed an increase in soil moisture but were caused by an oscillating
signal (not more than four different θ values during one event) were
excluded (termed consistency criterion).
From the total of 30 207 rainfall events, 15 645 could be used for the
analysis of the soil moisture, since they allowed for a clear separation of
soil water flow by more than 24 h without a new rainfall input; 7395 of these
events did not meet the quality criteria of completeness and consistency of
the soil moisture time series; hence, 8250 infiltration events (the sum of soil
moisture event observations at all 135 profiles) could be used for the
analysis. Changing the completeness criterion from 99 % usable soil
moisture data points during an event to, e.g., 95 % only slightly
affects the number of infiltration events (e.g., 8353 events usable in the
analysis). This is due to the fact that most exclusions result from long-term failure of one sensor of a profile that leads to a complete exclusion
of the entire profile. A diagram showing the portion of active (all quality
criteria met) profiles on a daily basis can be found in the Supplement
(Fig. S1).
Various soil moisture and rainfall characteristics were determined for each
event. Initial volumetric water content (θini) was defined as
the water content before the rainfall event starts. Furthermore, change of
θini to the peak water content (Δθmax) of
every event and sensor response was calculated. We grouped soil moisture
into dry and wet initial conditions using θ quartiles of each
profile. Additionally, rainfall amounts and intensities were calculated for
the time before the first soil moisture sensor response (Δθ=0.4 vol %) of any profile (rPsum, rPint, rPmax). This was done
since our infiltration event classification described in the next section is
partly based on the first sensor response and later rainfall input is not
further influencing the classification.
Soil moisture sensor response by infiltration events
For all soil moisture profiles and rainfall events which met the described
quality criteria, the sequence of the first sensor response was classified
similarly to Liu
and Lin (2015) into
not classifiable (NC): none of the sensors in the profile showed a response
(≥1 vol %) or only a 10 cm sensor response was observed;
non-sequential response (NSR): events where the first response did not progress
in a sequence starting from the surface (e.g., the 30 cm sensor showed a
response before the 10 cm sensor); and
sequential response (SR): the sensors in the profile showed a response in
the sequence from the uppermost sensor downwards (e.g., 10 to 30 to 50 cm
or 10 to 30 cm).
The potential for using these different infiltration responses (SR, NSR) and
related parameters as a proxy for PF is described in the following
sections. All statistical analysis were performed using Dunn's rank sum test
(Dinno, 2017).
Additionally, we estimated how often PF should have to be observed based on the
classical assumption that rainfall intensity exceeded matrix infiltration
capacity (Beven and Germann, 1982). We used matrix-saturated
hydraulic conductivity (Kmat) as the minimum infiltration capacity and
tested how often maximum 5 min rainfall intensity exceeded this threshold
(Pmax>Kmat; the measurements of Kmat are described at the end of the “Sequential response” section). Furthermore, comparison of maximum water content change
during an event (Δθmax) between the infiltration
response types can give information on PF processes by showing differing
water content depth distributions and can help to estimate the relevance of
the different flow processes in terms of transported water quantity.
Non-sequential response (NSR)
The NSR classification indicates non-uniform flow that can be a result of
various PF processes (e.g., bypass flow); hence, it is taken as a proxy for
PF. NSR could also be a result of subsurface lateral flow or groundwater rise
before the vertically downward progressing wetting front reaches that depth
(Lin and Zhou, 2008). But even in
these cases, such responses describe water flow that shows either
non-uniform flow or surroundings that infiltrate water faster than the
profile. Both can be seen as an indication of PF. None of the profiles
showed a permanent water table smaller than 50 cm below ground level;
nevertheless, some profiles are influenced by groundwater fluctuations and
are temporarily waterlogged at 50 cm, especially during winter. The length of
the time series is adequate for detecting patterns of NSR, as Liu
and Lin (2015) showed in their analysis that overall sensor response
patterns show stable results using >3 years of soil moisture
data. The occurrence frequency of NSR was analyzed with respect to initial soil
moisture and rainfall characteristics for the landscape units. All NSR analyses
were done with pre-response rainfall characteristics (rPsum,
rPint, rPmax). Calculated portions of NSR for the landscape units,
geologies or land covers for different rPmax, θini or months
are always calculated as the sum of NSR responses of the indicated class
divided by the total number of infiltration events in the same class.
Sequential response (SR)
A sequential response of the sensors in the profile does not necessarily
mean that no PF occurred. To get an estimate for the frequency of SR events
showing PF, one method is to compare soil matrix (capillary) flow velocities
to measured in situ flow velocities (Germann
and Hensel, 2006; Wiekenkamp et al., 2016). A measured flow velocity that is
faster than the soil matrix flow velocity can be expected to be influenced
by PF. Matrix flow velocity can either be obtained by modeling or with
measurements. To determine the in situ flow velocities, we used the approach
of Germann and Hensel (2006), where the maximum
porewater velocity (vmax) is determined from the first responses of two
sensors (often called the wetting front velocity). The upper sensor allows for
the definition of a clear starting time of the water flow. Hence, vertical
maximum porewater velocities were calculated from the SR for two distinct
flow depths: 10 to 30 and 30 to 50 cm. It is important to note that
vmax represents only the fastest flow components in the sphere of
influence around the soil moisture sensor (Hardie
et al., 2011).
To model matrix flow velocity (vmat), the 1-D steady-state flow equation
according to Darcy's law for unsaturated conditions was used
(Hillel, 1998):
q=-Kψm∂H/∂z,
with q being the vertical volume flux (cm d-1), K the hydraulic
conductivity (cm d-1), ψm the matric potential
(cm), H the hydraulic potential (–) and z the depth (cm). For the vertical 1-D
case, matrix flow velocity (or piston flow velocity) can be calculated by
dividing the volume flux by the volumetric water content θ (–)
(Gerke, 2006):
vmat=q/θ.
The hydraulic gradient was calculated between two sensors using the matric
and gravitation potential (H=ψm+ψg). The maximum
gradient between the θ peak of the upper sensor and θini of the lower sensor is calculated to obtain maximum vmat.
This is a conservative approach since steady-state assumptions are used to
calculate flow velocity. To obtain the matric potential, the van
Genuchten retention curves (van Genuchten,
1980) were parameterized using the parameter sets of Sprenger et al. (2016) (Supplement Table S2).
The van Genuchten parameters of Sprenger et
al. (2016) do not need further corrections to match θ with
absolute values of, e.g., soil core data since these parameters were
calibrated for a shorter period of the same dataset. For those 10 sites
where no parameters were determined by Sprenger et al. (2016), we simply used the
mean for the respective geology. Although these retention parameters were
inversely fitted and should therefore account for fast flow components, they
more closely represent matrix flow due to the single-domain Richards
equation and the unimodal nature of the van Genuchten retention function
that was used (Durner, 1994). In addition, the fit on a daily basis
does not allow for fast processes other than matrix flow. A geometric mean
hydraulic conductivity was calculated between two sensors located at
different depths (Zhu, 2008) to obtain the effective unsaturated hydraulic
conductivity of the vertical layered soil profile. To again provide a
conservative estimation of PF and rather overestimate vmat, the
moisture content used to calculate this unsaturated hydraulic conductivity
was the maximum event water content, determined for both sensor depths
individually. The mean of these two maximum event water contents was also
used to calculate the matrix flow velocity (vmat) from the volume flux
(q) (Eq. 2). Events that showed an upward hydraulic gradient based on this
calculation were excluded from further comparisons.
To directly measure matrix flow velocity we assumed that saturated matrix
hydraulic conductivity at the surface is an appropriate threshold for
dividing flow into matrix flow and PF (Wiekenkamp et al.,
2016). Tension infiltrometer measurements were used to obtain saturated
matrix hydraulic conductivity in the field. The tension infiltrometer used
in this study is a special type called a “hood infiltrometer”. The advantage
of the hood infiltrometer is that it can be placed directly on the soil
surface without need for any contact material (Schwärzel and Punzel, 2007). The derivation
of matrix-saturated hydraulic conductivity (Kmat) from measured
infiltration rates accounts for the 3-D nature of flow using
the solution of Wooding (1968) (steady-state
infiltration from a circular source). Measurements were carried out either
in the direct vicinity of our sensor sites or within the same geology and
land cover class (Appendix A). All values of matrix surface hydraulic
conductivity consist of at least three measurement locations (median),
except for two sites where the infiltration rate was too high and the hood
could not be filled. Hood infiltrometer measurements were not available for
grassland sites in the sandstone, and hence observed flow velocities of this
landscape unit were not compared with measured matrix flow velocities. In
total measurements from 66 locations were used to determine Kmat for the
different landscape units. For every measurement location infiltration rates
with at least three tensions between 0.4 and 5.9 hPa were recorded to be able
to fit an exponential function to calculate surface hydraulic conductivity
at a tension of 6 hPa (Gardner, 1958). At this tension,
pores with a diameter ≥0.5 mm are excluded from flow and measured
hydraulic conductivities represent matrix infiltration capacities (Jarvis,
2007; Schwärzel and Punzel, 2007). Due to the high macroporosity at many
forest locations pressure in the hood was difficult to adjust and
measurements could only be conducted for maximum tensions of 1–3 hPa. Hence,
for some sites matrix-saturated hydraulic conductivity is just an
extrapolation of the Gardner fit to a tension of 6 hPa.
ResultsInfiltration events
The number and proportions of classified infiltration event responses (NC,
SR, NSR) of the six defined landscape units are shown in Table 3. The absolute
number of events in a certain landscape unit and response class, which were
included in the different analysis, can be found in the Supplement (Table S3). Between 63.2 % and 79.5 % of the infiltration events per
landscape units were not classifiable (NC) in their infiltration response,
with the marl forest sites having the lowest amount of NC. 49.6 % of all
NC events resulted from events with a Psum of 3 mm or less. Approximately
a third of all infiltration events showed a change in soil moisture deeper
than 10 cm. Most classifiable infiltration events were of type SR. Under
sandstone forest sites they accounted for 24.6 %, whereas under marl
grassland sites they accounted for only 13.6 % of all events. Within the
group of SR, 47.4 % were observed at a depth of 30 cm, whereas sequential
flow to sensors at 50 cm depth was found for 52.6 % of the SR. NSR events
occurred in 5.3 % to 16.1 % of all events depending on the landscape
unit. The slate and marl forest regions showed the highest proportion (13.3 % and 16.1 %, respectively). In total 48.7 % of the NSR events showed
a response in 30 cm first and 23.9 % in 50 cm; 27.4 % of the NSR events
reacted in 10 cm first and then in 50 cm without a 30 cm reaction
in between. The NSR variability between the single profiles within a landscape
unit was found to be high (Table 3). The site-internal variability of NSR
(profiles within the same sites) measured as the median standard deviation
was highest in marl (forest: 7.5 %, grassland 6.4 %), followed by slate
(forest: 4.2 %, grassland 6.1 %) and sandstone (forest: 1.9 %,
grassland 3.0 %).
Number of events, infiltration responses and standard deviation
(SD) of the six landscape units, showing a not classifiable response (NC),
sequential response (SR) and non-sequential response (NSR).
Slate Marl Sandstone ForestGrasslandForestGrasslandForestGrasslandNo. of infiltration events297511217338521871698NC (%)65.075.063.279.570.172.8SR (%)21.718.320.713.624.621.9NSR (%)13.36.716.16.95.35.3Min.–max. NSR of single profiles (%)0–46.20–22.70–37.60–17.40–31.80–15.6SD NSR (variability between single9.47.511.85.48.64.8profiles) (%)Pmax>Kmat (%)0.91.80.013.80.2no Kmatmeasurement
To estimate how often PF should have to be observed based on the classical
assumption that rainfall intensity exceeded matrix infiltration capacity in
the different landscape units, we calculated the portion of rainfall events
with a Pmax exceeding Kmat. With the exception of marl grassland
(13.8 % Pmax>Kmat), all other landscape units only
showed an exceedance rate lower than 2 % (Table 3).
To test how much P characteristics and θini influence the
different response behaviors, we calculated the median of each parameter for
all infiltration events of a certain response type and their corresponding
depth (Table 4). We included pre-response P characteristics (rP) to show their
differences between NSR and SR events. High Psum mainly affect the depth of
the soil moisture front during SR. In addition, Pmax also
increases with depth of response, which could partly be due to a
correlation of Pmax and Psum (Spearman R=0.54). SR events show
similar median θini values for both infiltration depths, which
suggests no effect of θini on the flow depth. The
rPsum is similar for SR and NSR 30 and 50 cm events, while rPmax is higher
for NSR events. NSR10–50, with a response in 10 cm first followed by a 50 cm
reaction, shows a different pattern than the other NSR reactions with the
lowest rP intensities, but the highest θini and rPsum. In
contrast to SR the median θini of the NSR events is lower and also
decreases with increasing depth of the first response (30, 50 cm), which
indicates that this infiltration response type is sensitive to dry soil
moisture conditions.
Rainfall characteristics of the different infiltration types and
their corresponding depths (median values of all profiles and events).
Sequential response (SR) with maximum response depth (cm) and non-sequential
response (NSR) with depth of first out-of-sequence response (cm). Rainfall
variables were calculated for the entire event (P) and also for the time
prior the first (out-of-sequence) sensor response (rP).
To estimate the relevance of the different response types in terms of the
transported water quantity through the soil, the maximum change in water
content for every event (Δθmax) has been taken as a
proxy which can further indicate differences in response properties. The
patterns of Δθmax in each geology were compared among
response type and depth. Figure 4 shows violin plots with Δθmax at the two individual depths during SR. For SR the plots include all
events that show a response at the respective depth, independent of the
maximum response depth. For NSR 30 and 50 cm events only Δθmax of the first response depth was considered at the respective
depth. For NSR10–50 only the water content change in 50 cm (first
out-of-sequence reaction) was taken into account. Observed median Δθmax values range between 1.8 vol % and 4.3 vol %. For the SR
events, a significant decrease in Δθmax with depth was
observed for slate and sandstone sites. Marl sites did not show this damping
of the water content signal with depth and exhibited a significant increase
in Δθmax at 50 cm depth (SR). For the NSR events no damping
of Δθmax with depth was observed. In contrast, NSR in
sandstone and marls both had higher Δθmax at 50 cm
depth compared to 30 cm. Furthermore, for all geologies Δθmax at NSR 50 cm was similar or even stronger than for NC/SR 10 cm or SR 30 cm
responses.
Violin plots of maximum volumetric soil moisture change (Δθmax) per depth for the three geologies and differentiated by
infiltration response. Δθmax at 10 cm could result from
a NC response (10 cm only) or a SR that ends at a deeper sensor (30 or 50 cm).
Horizontal lines in the plot indicate the median Δθmax.
Same letters symbolize no significant difference between the response
classes of the same geology (Dunn test, two-sided, Benjamini–Hochberg
correction, p>0.025).
Non-sequential response (NSR)
The fraction of NSR events in dependence of θini and P
characteristics was analyzed to reveal the spatial and temporal patterns and
possible controls of PF. Pmax and θini of each profile are
only weakly correlated (median profile Spearman R: -0.19). An increase in
NSR with increasing rPmax was observed (Fig. 5). Especially forested sites
in the slate and marl region showed a strong increase in NSR above a threshold
of rPmax=10 mm h-1. This pattern was only weakly pronounced for
the grassland sites. More NSR with higher rPmax in the forests was also
found when using maximum rainfall intensity for the whole event (P) instead
of the pre-response characteristics (rP).
NSR vs. rPmax. The numbers above the bars (n) indicate the number of
events per class.
Figure 6 shows the portion of NSR response for the six different landscape
depending on individual θini quartiles for every profile to
account for the differences in absolute θini values among
landscape units. We observed that the drier the forested sites were, the
higher the measured NSR occurrence was. Especially slate and marl sites showed
a strong increase in NSR occurrence (up to ∼25 % of events)
for the driest θini quartile. At slate grassland sites observed
NSR occurrence was not responding to drier conditions in the same way as for
the forested sites. The fraction of NSR events at the marl grassland sites did
not change with initial conditions and at sandstone grassland sites NSR
occurrence increased only under wetter conditions.
Relationship of NSR with θini for each landscape
unit. Every point represents % NSR for all events which fall in the four
different quartiles of initial soil moisture (the plotting position of
θini value represents a quartile median). Number of events
observed in the different classes can be found in the Supplement (Table S4).
Monthly mean rPmax and θini(a) and
fraction of NSR events for the two land covers (b). The solid lines
represent the forest and the dotted lines the grassland response. The shaded
areas in the lower diagram show the standard deviation between the single
years for each month. For the number of events observed in the individual month
over the total time period and for individual years, see the Supplement
(Table S5).
To test for a seasonal effect on the NSR occurrence we also analyzed the
frequency of NSR on a monthly basis. Since land cover seems to play an
important role for NSR occurrence (Figs. 5 and 6) the NSR portion for all
infiltration events of the two land covers was calculated separately.
Forests show a distinct seasonal dynamics (Fig. 7): from March to June NSR
showed a constant value slightly higher than 5 % which increases to 13 %–20 % from July until October and decreases again towards winter. In the same
time period θini dropped to its lowest annual values and
rPmax also had its maximum in the summer months. For grasslands this dynamic
was less pronounced, with the highest value in September.
Sequential responses (SR) and flow velocitiesEstimating PF by comparison with modeled and measured matrix flow
To identify PF from SR, we further compared measured maximum porewater
velocities (vmax) against measured (hood infiltrometer, Kmat) and
modeled matrix flow velocities (vmat). Table 5 indicates the percentage
of observed vmax that exceeds either the measured infiltrometer or
modeled values. Both comparisons indicate that observed water flow is in
most of the cases faster than water that is flowing in the soil matrix only.
Between 72.9 % and 89.0 % of the observed SR responses are faster than the
modeled matrix flow velocities. The median difference in flow velocity for
the events with vmax>vmat is 114 cm d-1.
The model matches the exceedance obtained by the hood infiltrometer,
except for marl and sandstone forest sites, with an exceedance rate of the
infiltrometer being only 48.7 % and 44.0 %, respectively. This is due to
the high surface Kmat values that were measured with the hood
infiltrometer for these two landscape units. The high conductive parameters
of these two landscape units were not distinct higher in the set of
hydraulic parameters used for modeling.
Percentage of event with measured vmax exceeding the
infiltrometer (Kmat) or modeled matrix flow velocities (vmat).
InfiltrometerModeledForestSlate80.078.0Marl48.788.1Sandstone44.077.2GrasslandSlate74.172.9Marl79.287.7Sandstone–89.0Observed maximum porewater velocities
Since the vmax observed from soil moisture responses (SR) exceeded the
modeled or measured matrix values most of the time we examined vmax in
more detail. The measured vmax ranged from 6 to 80 640 cm d-1 with
a median of 120 cm d-1. Only a weak correlation was found between
vmax of the shallow versus the deeper depths (10–30 to 30–50 cm; Spearman-R: 0.36). Median observed vmax values per group ranged
between 72 cm d-1 for forested sandstone sites (for the shallow
depth 10–30 cm) and 274 cm d-1 for forested marl sites (for the
depth 30–50 cm) (Fig. 8). Comparing vmax for all landscape units the
marl soils showed more variable flow velocities and higher median values,
especially between 30 and 50 cm soil depth. Slate soils do not show a
significant difference between the two depths or the land covers. Sandstone
exhibited highest flow velocities under grassland sites. Forested sandstone
soils had a significant lower SR flow velocity than all other soils.
Violin plot of observed vmax for the six landscape units
(colors) and two depths (10–30, 30–50 cm). Same letters below the plots
symbolize no significant difference (p<0.025, Dunn test, two-sided,
Benjamini–Hochberg correction).
Measured SR maximum porewater velocities (vmax) in relation to
θini and Pmax. Color contours indicate 2-D kernel density
estimation (2-D KDE). The points show single event values.
To further evaluate the variability of vmax with respect to θini and Pmax for all observed events, 2-D kernel density
estimations (KDEs) (Venables and Ripley, 2002) are shown in Fig. 9, with higher KDE values indicating more events. There is no clear
relationship of vmax with θini or Pmax, and
high maximum porewater velocities can be found over the full range of
θini and Pmax.
Median vmax vs. Pmax. The numbers above the bars indicate
the number of events included in the analysis (n) and the standard
deviation (SD).
Analyzing the median response of vmax to θini and
Pmax for the different landscape units, we can see an increase in median
vmax for high Pmax for most landscape units (Fig. 10). Furthermore,
the median vmax is increasing under dry conditions for marl
independent of land cover and for slate grassland (Fig. 11). The other
landscape units do not show a clear pattern between vmax and θini.
Relationship of median vmax with θini for
each landscape unit. Each point represents median vmax for all events
which fall in the four different quartiles of initial soil moisture (the
plotting position of θini value represents a quartile median).
Number of events observed in the different classes can be found in the
Supplement (Table S6).
Although the relationship of vmax with Pmax and θini
is not as clear as with NSR, the seasonal dynamics of median vmax shows
an increase during the summer months, with the highest flow velocities during
times with low θini and high Pmax. In contrast to NSR,
grasslands showed a stronger increase than forests with a maximum between
June and August and a median vmax between 225 and 325 cm d-1. For
forests a weaker increase in the time between July and August and a stable
median vmax of around 200 cm d-1 were seen. The number of observed
events furthermore indicates that most SR events are not observed during the
times of high vmax, but rather during the wet winter month.
DiscussionGeneral relevance of PF
PF as either non-uniform flow (NSR) or as fast sequential flow was observed in
all landscape units and under all event conditions (Pmax, θini). The importance of PF during infiltration was highlighted by the
fact that observed SR flow velocity (vmax) was most of the time faster
than pure soil matrix flow and depended on the landscape unit NSR accounted for
18 %–44 % of the responses deeper than 10 cm. The variability of response
types within the landscape units and even within some sites was high, which
highlights soil heterogeneity on such larger scales and shows the influence
of small-scale soil properties on soil water flow. However, despite the strong variability we found that PF occurrence was dependent on some spatial and temporal factors which are discussed in Sects. 4.4 and 4.5.
PF is important not only in terms of its occurrence frequency, but is also
relevant for the quantity of transported water as indicated by the observed
water content changes (Δθmax). Especially during NSR the
Δθmax is higher than Δθmax for
SR at the same depth, which implies fast flow of large amounts of water into
deeper zones. Furthermore, the marl sites with their high velocities at 50 cm depth also showed the strongest Δθmax increase at
this depth, unlike the other geologies. Similar observations were made by Hardie et
al. (2013), who found higher Δθmax at greater depth
during NSR or events with high vmax, and Eguchi and
Hasegawa (2008) calculated that high amounts (16 % to 27 %) of the total
annual drainage were produced by PF.
Observed non-uniform flow (NSR)
In our study, occurrence of NSR for single soil moisture profiles (0 %–46.2 %)
was similar to other studies. Liu
and Lin (2015) found profile NSR occurrence varying between <1 % and
72.4 % for single years, Graham and Lin (2011)
found 18 % to 54 % for a 3-year period and Wiekenkamp et al. (2016) found 7 %–51 % also using a 3-year time series. However, we
found a lower average NSR occurrence (mean of the profiles within one landscape
unit) of 5.9 %–14.6 % for the landscape units in our study (data not shown)
compared to 26 % in the Shale Hills catchment of Graham and Lin (2011).
Until now, most studies on NSR events from soil moisture time series focused on
a relatively similar substrate (shale), land cover (forest) and a temperate
climate (Graham
and Lin, 2011; Lin and Zhou, 2008; Liu and Lin, 2015; Wiekenkamp et al.,
2016). The slate forest of our study is the landscape unit most comparable
to the studies cited above. It shows a comparable range of NSR occurrence
(0 %–46.2 % for a single profile). As our experimental design targeted not
one but six different landscape units, we were able to compare responses
observed in the shale forest to other environments. Sandstone grassland
showed a maximum NSR at a single profile of only 15.6 % of the events. Soil
profiles under forest on clayey soils (slate and marl) had a higher
occurrence of NSR (based on the landscape units) and a higher maximum NSR
occurrence for single profiles within these landscape units compared to
sandstone or grassland sites. Zhao et al. (2012) also found that a difference in land cover (forest vs. cropland) and soil
characteristics affects NSR occurrence. They found lower values with 5.8 %–32.4 % NSR in the croplands compared to the nearby Shale Hills forest, but
as the geology differs between the sites, the lower NSR cannot be unequivocally
attributed to land cover.
Observed maximum porewater velocities
Maximum porewater velocities (vmax) in this study (6–80 640 cm d-1) are in the same range as observed in other studies; however, we
measured slightly lower median vmax (120 cm d-1) than other
studies (e.g.,
Germann and Hensel, 2006; Hardie et al., 2013; Nimmo, 2007). In addition,
studies that measured vmax in single sprinkling experiments in the slate
forest region of the Attert catchment observed a vmax of 864–19 000 cm d-1 using GPR and TDR during a hillslope irrigation experiment with
an intensity of 30.8 mm h-1 (Angermann et al., 2017). Jackisch et al. (2017) observed vertical
transport velocities of bromide in the range of 2732 cm d-1 with
sprinkling intensities of 30 and 50 mm h-1. The highest vmax of the slate forest landscape unit measured in our study was
with 14662 cm d-1 in a similar range.
Most of the studies mentioned above are sprinkling experiments which apply
high P intensities (>10 mm h-1) and high Psum and thus
do not provide information on the response to low-intensity events that make
up a large portion of the annual rainfall events (see Fig. 3). In his
review, Jarvis (2007) found
that solute transport studies were either carried out at (near-)saturated conditions or with high irrigation rates (>10 mm h-1). Langhans et
al. (2011) found an increase in infiltration capacity with higher rainfall
intensity, probably due to the initiation of more macropore flow. This could
be an explanation for the higher velocities found by high-intensity
sprinkling experiments. Therefore, a reason for the partly lower vmax
observed in this study might be that we are also accounting for low P intensity
events due to our focus on natural rainfall events. This assumption is
supported by the fact that Hardie et
al. (2013) measured a vmax of 24–960 cm d-1 under natural
rainfall conditions, which is more in the range of most velocities observed
in our study. In summary, it is remarkable that no clear differences in flow
velocities between different soil types could be identified (neither in our
study nor across all previous studies). Instead, all soil types showed a
similarly large range of velocities (100–105 cm d-1).
Furthermore, one can see orders of magnitude difference in vmax between
different events, but not among the landscape units. A clear reduction of
maximum porewater velocity with decreasing θ (dry soils) as
predicted by conventional unsaturated hydraulic conductivity relationships
(e.g., van Genuchten, 1980) was not observed
under field conditions. In contrast, higher flow velocities during the driest
conditions were observed for most profiles in our study.
Temporal controls of PF
We found that both, a low initial soil moisture (θini) and a
high maximum rainfall intensity (Pmax) affect the occurrence of PF. This
results in a higher occurrence of PF during summer time. Increased PF
(NSR, vmax) during low θini is in contrast to the classical
assumption of PF, which should be initiated more often under wet initial
conditions with a lower infiltrability. Furthermore, the mismatch of
measured PF occurrence (NSR, fast vmax) compared to the prediction based on
Pmax exceeding Kmat indicates that initiation processes such as
hydrophobicity/water repellency, local microtopographic depressions or
channeling of water by vegetation could be the reason of the frequent
occurrence of PF (Blume
et al., 2008; Doerr et al., 2000; Schwärzel et al., 2012; Weiler and
Naef, 2003). Locally, these processes can lead to higher water contents and
thereby pressures at the soil surface close to atmospheric pressure which in
turn trigger PF. The higher probability of NSR under dryer conditions and with
higher P intensities was also found by Wiekenkamp et al. (2016), Hardie et al. (2013) and Liu
and Lin (2015). Also, Hardie
et al. (2011) found faster flow velocities under dry conditions, which they
concluded was due to hydrophobicity and resulting finger flow, and Blume et al. (2009) found
the response time of soil moisture and thereby flow velocity to be much faster
during summer time. However, Buttle and Turcotte (1999) did not find a relationship of PF with initial soil water content, but
with throughfall intensity.
Monthly meanPmax and θini(a),
monthly median vmax for the two land covers (b) and number
of observed vmax values (SR that reached either 30 or 50 cm) for
each land cover and month (c). The solid lines represent the
forest and the dotted lines the grassland response.
Due to the strong seasonal variation with a PF maximum in summer and early
autumn (Figs. 7 and 12), the most probable explanation is the influence of
water repellency that has frequently been observed on natural surfaces in
summer
(Doerr
et al., 2006; Täumer et al., 2006). Water repellency hinders
infiltration and ensures a pressure buildup at the soil surface until
pressure reaches a positive water entry potential (Bauters et al., 2000). Gimbel et al. (2016) observed that their
clayey and loamy plots developed strong water repellency during a simulated
drought field experiment with a 40-year return period and that infiltration
patterns changed from homogeneous to preferential flow. Also, sandy soils
were found to be strongly affected by water repellency (e.g., Ritsema et al., 1997). Wessolek et al. (2008) found from 1-year
TDR measurements on a pine stand that PF is minor from February to April
since the soil was not water repellent. They found a maximum of PF from May
to September which matches in general with our observations, just that our
observed maximum starts and ends approximately 1 month later. Furthermore, Täumer et al. (2006) observed a
similar seasonal pattern over a 3-year period with a maximum of PF in summer
and early autumn, and Rye and Smettem (2015) also observed a similar seasonality in Australia. That during these dry
and water-repellent conditions the P intensity is highest further supports
the initialization of PF. In general higher P intensities can lead to water
pressures at the soil surface close to the water entry potential (Gjettermann
et al., 1997; Jarvis, 2007; Weiler and Naef, 2003).
Spatial controls of PFClay content
Examining the temporal effects of θini and Pmax between the
landscape units in detail, PF dynamics were not the same throughout all
landscape units in our study. Especially PF occurrence on clayey soils seems to be strongly
influenced by low θini, and a higher clay content enhances NSR occurrence and
vmax. Many studies showed that the clay content increases macroporosity
under dry conditions through shrinkage and the subsequent cracking of the
soil (e.g., Li and
Zhang, 2011; Novák, 1999; Stewart et al., 2016a). Das Gupta et al. (2006)
measured high infiltration capacity for the macropore domain of clay soils
using a tension infiltrometer. The higher macroporosity of the clay soil can
then further enhance the occurrence of PF, initialized by higher Pmax
and hydrophobic condition in summer as observed by (dye) tracers,
infiltration and soil moisture measurements (Dekker
and Ritsema, 1996; Hardie et al., 2011; Jarvis et al., 2008). Liu
and Lin (2015) found clay content to be an important predictor of NSR in the
Shale Hills catchment and we also measured higher NSR in clayey landscape units
(slate, marl). Furthermore, we found high maximum pore water velocities in
the clay rich subsoil of the marl sites. High vmax in the marl topsoil
(lower clay content) is probably more attributed to the high abundance of
biopores observed in the topsoil of this region. The high flow velocities in
the subsoil are in accordance to other studies that showed fastest
velocities due to structure development in unsaturated clay soils (Baram
et al., 2012; Hardie et al., 2011; Tiktak et al., 2012). Probably ponding of
water on top of the clay layer and subsurface initiation of macropore flow
could be a reason of higher flow velocities in the subsoil (Weiler and Naef, 2003). Such a process was
observed in the field by Hardie
et al. (2011). This demonstrates that in the unsaturated zone close to the
surface, clay should not be treated as a low conductivity but rather as a
high conductivity material.
Land cover
The question arises why NSR is much more often observed in forests during summer
compared to grassland and why vmax is higher in grassland. In general,
forests tend to have highly connected macroporosity caused by roots (Alaoui
et al., 2011; Gonzalez-Sosa et al., 2010; Lange et al., 2009). Furthermore,
higher soil organic carbon content in forest can enhance aggregate stability
and hence interaggregate porosity in clayey soil (Lado et
al., 2004; Six et al., 2002). However, the sole presence of a higher
macroporosity in forests does not explain the higher NSR occurrence. That
higher macroporosity results in more NSR could also be caused by more laterally
directed pathways in forests created by roots as observed by Bachmair et al. (2009).
Funneling of rainfall by stemflow (not measured in this study) may support
this mechanism (Schwärzel et al., 2012). In
contrast, the stronger increase in vmax in grasslands during summer
could be an indication of a seasonally changing macroporosity due to high
temporal variation of biopores created by the soil fauna (e.g., earthworms),
as observed in our study region (Schneider et al., 2016). Biopores
such as earthworm burrows were frequently found to enhance vertical PF (Reck
et al., 2018; Weiler and Flühler, 2004; Zehe and Flühler, 2001).
Conclusions
Our results demonstrate that infiltration is strongly controlled by PF
phenomena. As expected a higher maximum rainfall intensity increases the
occurrence of PF, but different from common theory a higher soil moisture
decreases the PF occurrence. However, the here studied landscape units show
a high spatial heterogeneity and high temporal variation with different PF
processes involved, such as more fast PF in grasslands and more non-uniform
flow (NSR) in forest. Clay-rich soils showed to increase both, non-uniform PF
(NSR) and fast PF (high vmax). By systematically comparing the dynamics of
different landscape units we were able to identify that beside the amount of
connected macropores such as cracks (influenced by a high clay content and
low soil moisture) or biotic macropores (roots channels, earthworm borrows),
PF strongly depends on initiation processes (water repellency, rain
intensity). This leads to a strong seasonal dynamics with more non-uniform
flow and highest flow velocities in summer and early autumn due to dry
soils, high rainfall intensities and hydrophobic soil surfaces. Furthermore,
the amounts of transported water are higher during non-uniform flow. This
can have a potential impact on solute transport during summer months and should
be considered in water management.
We were able to show that soil texture is not the main driver of water flow
velocity during infiltration in the vadose zone as we typically assume. We
suggest including dynamic flow processes, dynamic initialization processes and
varying macroporosity in physically based hydrological models rather than
static hydraulic conductivities derived from soil cores or soil maps.
Therefore it needs easily transferable relationships or pedotransfer
functions, which can help to find structure-related PF parameters similar to
retention parameters. More effort is necessary to find or adapt already
existing approaches to measuring and monitoring PF in diverse landscapes. We
further suggest implementing large-scale sensor networks under different
climatic settings, substrates, topographies, and land covers worldwide and
creating standardized approaches for analyzing soil moisture datasets. Our
approach can be expanded by combining it with groundwater response time
series and stable isotope methods to identify and understand flow patterns
in the vadose zone at the landscape scale.
Code and data availability
Data and the analysis code are available from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-23-4869-2019-supplement.
Author contributions
DD prepared the data, developed and performed the analysis strategy and
planned and conducted the fieldwork. MW and TB designed the sensor cluster
setup, were involved in their installation and contributed to the data
analysis strategy. DD prepared the manuscript with contributions from all
the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Linking landscape organisation and hydrological functioning: from hypotheses and observations to concepts, models and understanding (HESS/ESSD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
Special thanks to Britta Kattenstroth, Tobias Vetter, Sibylle Hassler and many other helpers for the installation and maintenance of the
field sites. We thank Conrad Jackisch for partly providing the soil and
infiltration data and Natalie Orlowski for the helpful comments on the
manuscript. We acknowledge the comments of Heye Bogena, Nicholas Jarvis and
two anonymous reviewers which greatly improved this paper.
Financial support
This research has been supported by the German Research Association (DFG; grant no. FOR 1598 –
From Catchments as Organized Systems to Models based on Dynamic Functional
Units – CAOS). The article processing charge was funded by the German Research Foundation (DFG) and the University of Freiburg in the Open Access Publishing funding program.
Review statement
This paper was edited by Hjalmar Laudon and reviewed by Heye Bogena, Nicholas Jarvis, and two anonymous referees.
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