The CloudRoots field experiment was designed to obtain a
comprehensive observational dataset that includes soil, plant, and
atmospheric variables to investigate the interaction between a heterogeneous
land surface and its overlying atmospheric boundary layer at the sub-hourly
and sub-kilometre scale. Our findings demonstrate the need to include
measurements at leaf level to better understand the relations between
stomatal aperture and evapotranspiration (ET) during the growing season at
the diurnal scale. Based on these observations, we obtain accurate
parameters for the mechanistic representation of photosynthesis and stomatal
aperture. Once the new parameters are implemented, the model reproduces the
stomatal leaf conductance and the leaf-level photosynthesis satisfactorily.
At the canopy scale, we find a consistent diurnal pattern on the
contributions of plant transpiration and soil evaporation using different
measurement techniques. From highly resolved vertical profile measurements of carbon dioxide (
Evapotranspiration (ET), the net exchange of water vapour between the land and the atmosphere, remains an elusive process to be measured, quantified, and represented in models because it depends on the interaction of multiple processes that act in a wide range of scales (Katul et al., 2012). ET is a key variable in the exchange of heat, moisture, and carbon dioxide at the surface, and it strongly depends on how radiation and energy are partitioned into latent and sensible heat (Moene and Dam, 2014; Monson and Baldocchi, 2014). The amounts of direct and diffuse radiation reaching the leaves depend on the transfer of radiation that is strongly perturbed by clouds and aerosols and on its subsequent penetration into the canopy. Triggered by ambient light conditions, the stomatal responses coupled to the surface and boundary layer dynamics is the main driver that regulates how the net available radiative energy is partitioned between the turbulent sensible and latent heat fluxes (van Heerwaarden and Teuling, 2014). However, due to the highly non-stationary nature of atmospheric radiation (van Kesteren et al., 2013b) and turbulent nature of the meteorological fluctuations, we still lack a fundamental understanding of the two-way feedback between stomatal control and cloud radiation perturbations across scales and land and atmosphere conditions (Katul et al., 2012; Sikma et al., 2018).
The bidirectional link between surface processes and boundary layer clouds
as described above is what we refer to as the CloudRoots concept, where
boundary layer dynamics and clouds are rooted in or coupled to the surface
and vice versa (Vilà-Guerau de Arellano et al., 2014). The degree of
coupling depends on soil, plant, and weather conditions characterized by the
diurnal variability of wind, temperature, and specific humidity (Sikma et
al., 2018). To fully comprehend this system requires inclusion of all
necessary parameters at the required spatial scales, from the size of the
stomata (10–100
Thanks to their high-quality routine measurement programme (Franz et al.,
2018; Rebmann et al., 2018), ICOS sites lend themselves as anchors for
additional experiments. Here, we describe the CloudRoots campaign near the
agricultural site “Selhausen” (ICOS site DE-RuS) and
the Jülich Observatory for Cloud Evolution – Core Facility (JOYCE,
To this end, we study the following five facets of the diurnal interactions
between the land and the atmosphere: (i) observational validation at leaf
level of the mechanistic model representation of the stomatal aperture and
photosynthesis, (ii) the diurnal variability of
The paper is organized as follows. In Sect. 2 we give a detailed overview of the field experiment with special emphasis on the instrumentation used that serve the overall goals of our CloudRoots concept. The results in Sect. 3 are organized into the five topics outlined below. First, at leaf level, we validate a photosynthesis–conductance mechanistic model that is commonly used in large-eddy simulations (Pedruzo-Bagazgoitia et al., 2017; Sikma et al., 2018) and the global numerical model prediction system ECMWF-IFS (Boussetta et al., 2013). This allows us to assess the need to revisit currently used constants in the mechanistic model representing photosynthesis. This part is completed by comparing leaf transpiration rate with tiller-level measurements of sap flow at different stages of the growing season. Second, and in order to scale up to the canopy level, we analyse the soil and plant partitioning of the net ET and net ecosystem exchange (NEE) based on the inversion of observed high-resolution vertical concentration profiles (Warland and Thurtell, 2000; Santos et al., 2011). Third, in analysing the impact of clouds on ET, we measure the potential effectiveness of diffuse radiation in enhancing ET and NEE (Kanniah et al., 2012). Extending previous work by van Kesteren et al. (2013b), we quantify the time lag between fluctuations in incoming shortwave radiation and ET in the field. These real-world measurements are an essential addition to time lag of plant responses to radiation changes studied in laboratory experiments (Vico et al., 2011). Fourth, we infer the spatial variability of ET around the CloudRoots site using SIF remote sensing observations. Fifth, all of these observations are then integrated into several numerical experiments made by CLASS with special emphasis on the treatment and role of how to include surface heterogeneity and heat and moisture advection to improve the interpretation of the observations. Finally, in the discussion in Sect. 4 we bring together and discuss all CloudRoots methodologies by comparing their daily ET estimates. Conclusions are given in Sect. 5.
The CloudRoots field campaign was carried out at the Terrestrial
Environmental Observatory (TERENO) Selhausen, which is located in the
southern part of the lower Rhine embayment in western Germany (50
The test field covered 9.8 ha and was surrounded by other croplands (Ney and
Graf, 2018). As Fig. 1 shows, these cultivated areas are mainly comprised of winter
wheat, winter barley, sugar beet, rapeseed, maize, potatoes, and peas,
whereby the various field sizes and locations of crops has led to
small-scale heterogeneity in the vegetation cover. An agricultural road,
mainly used by farm machinery, passes by the northern edge of the field. The
next inhabited settlement is located 500 m to the west (Fig. 1a). There are
two lignite open-cast mines in the wider surrounding of the study site,
located 6 km northeast (extension of 4400 ha with a maximum depth of 470 m b.g.l.) and 6 km west (extension of 1400 ha with a maximum depth of 200 m b.g.l.). In general, the land surface at the study site is flat and has a
slope less than 4
The local climate is classified as temperate maritime with an annual mean
air temperature of 10.3
The field campaign covered the main growing phases (booting, heading, and
maturity stages) of winter wheat. During the observation period, we did three
intensive observation periods (IOP). During these IOPs the following
complementary instruments and measurements were added: microlysimeters,
leaf-level measurements, SIF measurements on canopy and regional scale, and vertical profiles of state variables and
Campaign-specific measurement setup and temporal developments from May to June 2018, including three intensive operation periods (IOP).
Meteorological and biometric conditions during the intensive
operation periods on 7 May (IOP 1), 15 June (IOP 2), and 28 June 2018 (IOP 3). Global radiation, water vapour–pressure deficit (VPD),
photosynthetically active radiation (PAR), and soil water content (SWC) are
daily averages. The meteorological variables were measured at the height
The weather situation during all three IOPs was mainly characterized by an
anticyclonic pressure pattern over central Europe (IOP 1 and IOP 2),
extending up to northern Europe during IOP 3, which led to high 2 m temperatures up to 24 to 26
The persistent high-pressure weather conditions resulted in a drought during
the entire observation period. Ongoing dryness led to a reduction in the
soil water content at 20 cm depth (Table 1) from 27 vol % during IOP 1 to
15 vol % at IOP 3. Maturity occurred 14 d earlier than in previous
years. The leaf area index (LAI) ranged from 4.5 m
List of symbols, description, units, and the representative scale.
Table 2 summarizes all the variables measured and modelled during CloudRoots, together with specific nomenclature and information on units and scales.
For direct measurements of soil evaporation (
Soil respiration (
Leaf gas exchange was measured using a Li-Cor LI-6400XT portable
photosynthesis system with a 6400-02B LED light source. Leaf-level
measurements included instantaneous stomatal conductance to water vapour
(
Sap flow in wheat tillers was measured with the heat balance method (Sakuratani 1981; Baker and van Bavel, 1987). A total of 24 tillers were selected at random, diameters measured with an electronic calliper, and SGA3-type sap-flow sensors installed at the lowest possible internodes following the procedure recommended by the manufacturer (Dynamax, 2007, 2017). Sensors were connected with electrically shielded wire to AM 16/32 multiplexers controlled and scanned by CR1000 data loggers (Campbell Scientific, Logan, Utah, USA). Energy supply to the stem heaters was carefully regulated to the highest permissible level in order to obtain a strong heat signal. We employed the dual voltage regulators (Dynamax AVRDC) which were parts of wired measurement, control, and extension units assembled and tested by the heat balance sensor manufacturer (Flow32 1K A and B models, Dynamax Inc., Houston, Texas, USA). Data were processed according to the calculation procedure of Dynamax (2007) with adaptations to wheat (Langensiepen et al., 2014) to obtain reliable data on the convective stem heat flow generated by sap flow. Here we take the evolution of the tiller densities from 480 tillers per square metre (IOP 1 and IOP 2) to 370 tillers per square metre (IOP 3) into account.
Vertical profiles
The receiver of a displaced-beam laser scintillometer, hereafter referred to
as DBLS (SLS-20, Scintec, Rottenburg, Germany), was placed 9 m southeast
of the EC station (Fig. 1). The scintillometer measurements height was
1.95 m a.g.l. The path length towards the instrument transmitter was 86.8 m. It was pointed along a transect from northwest to southeast. The DBLS measures the
scintillation intensity of two displaced laser beams (wavelength of 670 nm
and separation distance of
The added value of DBLS fluxes over the traditional EC method is that they converge to statistically stable flux estimates at much shorter flux averaging times of 1 min or less, while the EC technique typically requires flux averaging times of 10 to 30 min (Hartogensis et al 2002; van Kesteren et al., 2013b). The essence behind this is that the flux estimate is based on structure parameters that are defined in the inertial range of the turbulent spectrum. As such, the flux estimates rely on a limited range of the turbulent scales that contribute to the flux rather than all of them as is the case with the EC method.
We also adopted the combination technique introduced by van Kesteren et al. (2013a, b) to obtain fluxes of
A continuously running EC system was operated in the middle of the field
(Fig. 1), comprising a three-dimensional sonic anemometer (Model CSAT-3,
Campbell Scientific, Inc., Logan, Utah, USA) and an open-path infrared gas
analyser (Model LI-7500, Li-Cor, Inc., Biosciences, Lincoln, Nebraska, USA).
The sensors height was 2.34 m a.g.l. Raw data were sampled in 20 Hz mode
and fluxes and averages were calculated as 30 min block averages
using the TK3.11 software package developed at the University of Bayreuth,
including corrections and quality control as given in Mauder and Foken (2011).
Missing values in the calculated turbulent fluxes were filled with the
marginal distribution sampling (MDS) method following Reichstein et al. (2005) which is implemented in the REddyProc software package (Wutzler et
al., 2018). The station also included measurements of all components of the
radiation budget (NR01, Hukseflux, Delft, the Netherlands), PAR (LI-190R,
Li-Cor Inc. Biosciences, Lincoln, Nebraska, USA, and BF5, Delta-T Devices,
Cambridge, UK), air temperature (
A second mobile EC station with instruments heights of 1.93 m a.g.l. was
deployed in the immediate vicinity of the continuously monitoring station
during the measurement campaign. The system comprised an IRGASON EC system
(SN1185 Irgason EC150, Campbell Scientific, Inc., Logan, Utah, USA; PTB101B
pressure sensor, Vaisala Inc., Helsinki, Finland) with an additional LI-7500
sensor (same manufacturer). Here, fluxes were processed with the LiCor
EddyPro v6.2.2 software. Radiation (CM11 for global and CG2 for long-wave
radiation, Kipp & Zonen B.V., Delft, Netherlands), ground heat flux (
A field spectroscopy system was used (FLOX, JB Hyperspectral Devices UG,
Düsseldorf, Germany) for canopy-level measurements of reflectance and
SIF. FLOX is constructed for high temporal frequency acquisition of
continuous top-of-canopy optical properties with a focus on sun-induced
chlorophyll fluorescence. The system is equipped with two spectrometers: an
Ocean Optics FLAME S, covering the full range of Visible and Near-Infrared
(VIS-NIR) and an Ocean Optics QEPro, with a high spectral resolution (Full
Width at Half Maximum, FWHM, of 0.3 nm) in the 650–800 nm range of the
fluorescence emission. The optical input of each spectrometer is split
between two fibre-optic cables that lead to a cosine receptor that measures
solar irradiance and a bare fibre bundle that measures the target-reflected
radiance. Spectrometers are housed in a Peltier thermally regulated box to
keep the internal temperature lower than 25
An airborne high-performance imaging spectrometer (HyPlant) was used for
regional-level measurements of the same quantities. Several flight lines
over the 15 km
Sun-induced fluorescence (
The JOYCE remote sensing facility (Löhnert et al., 2015) (located at a distance of 5 km from the test site) provided continuous information about boundary layer and cloud characteristics. Specifically, microwave and lidar measurements were used to compare the CLASS model results (see Sect. 2.4) with the inferred boundary layer depth. This comparison was completed by vertical profiles measured by the routine radio soundings at Essen (station ID EDZE/10410 at a distance of 75 km).
The Chemistry Land-surface Atmosphere Soil Slab (CLASS,
Leaf-level photosynthesis was modelled using the representation of
photosynthetic biochemistry, as included in CLASS (Vilà-Guerau de
Arellano et al., 2015), which was originally developed by Goudriaan (1986)
and further adapted to meteorological applications by Jacobs and de Bruin (1997). As this model describes the relationship between stomatal
conductance (
Available field measurements were used for improving the model settings at
the leaf level. The parameters representing the initial value of the
light use efficiency (
The fundamental assumption of the mixed-layer model is that under convective
conditions the atmospheric boundary layer (ABL) dynamics lead to profiles of
the meteorological state variables that are uniform (well-mixed) with
height. As a result, these state variables are governed by horizontally
averaged 0-dimensional slab equations: one equation for the evolution
through time of the slab variable and another for the difference between the
residual layer (in the morning transition) and the free tropospheric values
and the slab value, i.e. the jump at the interface between residual layer and
ABL. The ABL dynamics are governed by the mixed-layer equations of potential
temperature (heat), specific humidity (moisture),
A key feature of the model is its representation of the sub-daily
variability of the land–atmosphere interactions (van Heerwaarden et al.,
2010; Vilà-Guerau de Arellano et al., 2015). The net ecosystem exchange
is calculated as a result of the assimilation of
This section is structured following the five facets of the diurnal interactions between the land and the atmosphere outlined in the introduction.
Parameters representing the maximum leaf-level photosynthesis rate
(
We combine leaf-level and sap flow measurements of tiller assimilation and
transpiration with leaf-level assimilation modelled by CLASS,
Our leaf-level measurements revealed clear diurnal patterns in
Diurnal changes in photosynthetically active
radiation (PAR) and vapour pressure deficit (VPD) measured for
One of the main aims in CloudRoots is to improve the mechanistic modelling
of photosynthesis and stomatal aperture. To this end, we calibrate the
constants of the
Figure 6 shows a comparison of the model results of
Sap flow measured using the heat balance method for 7 June 2018 (non-IOP day).
Moving from leaf to canopy scale, we analyse the detailed profiles of micrometeorology and carbon dioxide collected using the elevator and infer vertical assimilation profiles and the diurnal variability in the surface contributions to ET and NEE.
Measurements of leaf-level photosynthesis (
Measured leaf-level photosynthesis (
Figure 7 shows selected 30 min mean profiles of
Selected (08:00, 13:00 and 18:30 UTC) 30 min mean profiles of
the
The highest temperatures appeared near the canopy top (Fig. 7d, e, j and
l). In the late morning of IOP 2, the temperature reached a distinct
maximum just below the canopy top (Fig. 7j). This phenomenon has been
reported in previous studies (Ney and Graf, 2018) and is caused by the
changing solar incidence angle. A low angle of incidence in the morning and
afternoon limited the heating to an area just below the canopy surface.
Previous studies have shown that the presence of such a pronounced
temperature maximum has the potential to increase thermal stability within
the canopy and thus inhibit the vertical turbulent exchange of sensible heat
(Gryning et al., 2001; Ney and Graf, 2018; Sikma et al., 2020). It can be
assumed that the sensible heat flux within the dense plant stand was largely
determined by the entire canopy. In other words, during the day, mixing near
the soil surface was impeded by stable temperature stratification, while in
the evening cooling expanded upwards from the soil surface (Fig. 7f). In
general, the processes described above were more pronounced during IOP 2
with its greater canopy height than with the lower canopy during IOP 1. The
vertical wind profile showed consistently low wind speeds within the dense
canopy (
The detailed profile observations presented in the previous section enable
us to calculate height-resolved estimates of gross primary production
Figure 8b shows that the entire canopy contributes to the photosynthetic
activity but with maximum
Source partitioning results for
Figure 9 shows the measured fluxes of latent heat, NEE, and soil respiration,
as well as their partitioning based on the inversion of vertical
high-resolution concentration profiles into the soil evaporation and plant transpiration
and the
Variations in
One of the main aims of CloudRoots was to obtain observational evidence of
the effects of clouds on the
IOP 2 (15 June 2018) time series of
We find that the observed
The influence of VPD on
The short interval fluxes (1 min) of the double-beam laser
scintillometer (DBLS) technique enable us to study the vegetation response
to rapid radiation perturbations due to changes in cloud cover. The goal
here is to illustrate this potential by discussing selected time series
under changing cloud conditions during IOP 2. The morning of IOP 2 was
characterized by rapidly changing cloud conditions due to the overpass of a
shallow cumulus cloud deck. A breakdown of the 1 min DBLS sensible heat
flux in terms of contributions from turbulent exchange (
IOP 2 (15 June 2018) time-series of
First of all, the 1 min DBLS fluxes of
Next we examine how soon the fluxes of
Studying spatial and seasonal variabilities in ET during plant growth was
one of the key goals of CloudRoots. To this end, we analysed SIF
observations measured on time and on space. The top-of-canopy measurements
of SIF were carried out in two ways: (i) diurnal courses from a single
representative location were recorded from a stationary FLOX system, and
(ii) mobile measurements covering several locations within a field were
recorded from a FLOX system that was housed in a backpack. To ensure
reproducible measurements, the two fibre optics of the system were attached
to a gimbal and were placed with a movable tripod 2 m above ground. Diurnal
curves were acquired on 7 May, 4 June, and 14 June (only morning hours due to
cloudy conditions in afternoon); mobile measurements (with change of
measurement locations during the day) on 6 and 26 June. As SIF
measurements should be performed under clear-sky conditions only, records
affected by clouds were carefully removed. Aerial maps of SIF were acquired
with the high-resolution imaging spectrometer HyPlant. Figure 13a shows the
aerial map of
Diurnal changes in photosynthetic activity are clearly visible in
It is difficult to directly quantify spatial variations in the ET flux with
the currently available in-situ equipment due to the necessity of installing
a large number of measurement stations. Recently, some promising concepts
have been published that exploit the relationship between SIF and plant
water relations (Damm et al., 2018; Jonard et al., 2020). Following these
concepts, we studied the connections between ET to regional
measurements of SIF in two steps, which were recorded on this scale by the airborne
sensor HyPlant (see Fig. 13a). First, to obtain an estimation of the spatial
variability ET at CloudRoots, we used the 15 km
Estimated
For the estimation of
Spatial variability of evapotranspiration inferred from combining
To integrate and improve the interpretation of our observations, we used
CLASS to model the cloudless day 7 May 2018 (IOP 1). Our specific aims,
related to the scales and processes under study, are (i) at leaf level to
make use of the new constants in the mechanistic
Relation between evapotranspiration (ET) and fluorescence
Comparison of the model and observed results of 7 May 2018:
Our explanation of the improved comparison between the observations and the
CLASS results using the aggregated sensible heat flux is the following: in a
heterogeneous landscape such as the location of CloudRoots (Fig. 1a), each
surface type contributes its own latent and sensible heat fluxes. It is the
landscape aggregate of heat fluxes (named regionally and shown with triangles
in Fig. 16a and introduced in Fig. 1b) and more specifically the sensible
heat flux that governs the boundary layer evolution in terms of height,
potential temperature, specific humidity, and atmospheric constituents. Only
by using this higher
CLASS, besides solving the diurnal variability of the boundary layer
dynamics and the state variables, offers the possibility of adding a
large-scale contribution that represents the advection of heat and/or
moisture (see Vilà-Guerau de Arellano et al., 2015). We have performed a
sensitivity analysis to determine the role played by heat advection for the
surface fluxes and the boundary layer development. In the specific case that
is modelled on 7 May, we relate this advection of heat or moisture to the
diurnal evolution of
Summary of midday evapotranspiration collected using different
instrumental techniques during
CloudRoots offers an integrated methodology that combines field experiments
across spatial scales (from leaf to landscape) closely linked to the
modelling of the diurnal variability of the soil–plant–atmosphere continuum.
To frame the discussion and link all our observations at the various scales
and modelling efforts, we present in Fig. 17 all the different estimates of
ET obtained during the three IOPs, averaged between 09:00 and 14:00 UTC in
order to avoid the morning and afternoon transitions. Plotted alongside the
ET estimates, we showed the leaf-level measurement of
In comparing ET from the three IOPs, we find significant differences in
magnitude from different techniques. In general, the highest values of ET
are observed during IOP 1. The three IOPs were characterized by differences
in the stages of growth, from very active vegetation to senescent vegetation, and
influenced by a range of weather conditions: IOP 1 cloudless, IOP 2
scattered and thick clouds, and IOP 3 shallow cumuli. It is surprising that
the decay in the vegetation activity as quantified by the measurements of
leaf conductivity (Fig. 3d, e, f) is less evident in differentiating
IOP 3 (senescent stage) from the more active vegetation at IOP 1 and 2.
Furthermore, we observed, moving from IOP 1 to IOP 3, a much stronger decline
in
Several conclusions can be drawn from this intercomparison of ET
observations using different techniques. Firstly, we might expect that the
EC and scintillometer measurements, both with larger footprint and the inclusion
of the soil evaporation contribution, show a net total ET that is similar to
or higher than that obtained by the sap flow measurements. Secondly, we
observed a far more pronounced response in declining
Although the contribution of soil evaporation is small compared to plant transpiration due to the high vegetation cover, we need to stress that EC and scintillometer observations are similar to or smaller than the ET observed or inferred from the other techniques (Fig. 17). This highlights the difficulty of estimating ET due to the need to include and quantify the contributions of the four fundamental processes: soil evaporation, upscaled leaf transpiration, evaporation related to the sap flow and the two non-local processes, entrainment of dry air, and horizontal advection of heat and moisture. Here, the modelling of ET, taking into account for and integrating all these processes, enables us to discriminate among these processes and calculate the budget of ET as a function of these local and non-local contributions. In that respect, the CLASS model is a tool capable of efficiently combining observations and model results that integrate surface and boundary layer dynamics. The averaged modelled ET is at the higher range of the ET observed estimations during IOP 1.
With respect to the differences between the 1 min and 30 min series measured by the scintillometer, their median is very similar in the three IOPs. However, differences become larger at smaller timescales due to the non-steadiness of ET under the presence of clouds. Here, the 1 min flux calculated from the scintillometer can capture the rapid and large fluctuations by clouds (Fig. 12) and the maximum values in particular. In order to obtain more definitive conclusions on how ET varies under cloud conditions, we need to analyse other situations characterized by different diurnal cloud cycles in more detail and systematically relate ET to key cloud characteristics such as the cloud optimal depth to determine how cloud thickness influences ET and the timescale of the cloud passage.
Regarding the quantification of the different processes contributing to ET,
Fig. 9 illustrates the need to continue to test analytical techniques to
identify the individual contributions of soil and plants to determine the
diurnal ET budget. A possibly useful tracer would be the stable isotopic
composition of water vapour and carbon dioxide (Lee et al., 2009; Griffis
2013) and, combined with isotope signals, for modelling the surface and
boundary layer dynamics with the carbon and water exchanges. To further
discriminate between soil and plant sources and sinks under unsteady
conditions due to radiation and dynamic perturbations by cloud shading,
these high-frequency stable isotope measurements should go beyond the
typical average time of eddy covariance (30 min). As van Kesteren et
al. (2013) showed and is further corroborated in this work, the
scintillometer technique combined with high-frequency observations of
Finally, the integration of all processes in the CLASS model shows the
challenges in interpreting the measurements taken at the sub-kilometre
scales and adequately representing the surface turbulent fluxes. Although
the measurements indicate that the day selected for the modelling displayed
a very homogeneous boundary layer depth over an area with a radius of 100 km
Our main findings, organized from the smaller to the larger scales observed and modelled, are summarized as follows:
At leaf scale, we find that stomatal conductance and gross primary production decrease
in line with the increasing senescence of the plant. The tiller-level
measurements of the sap flow are virtually constant throughout the growing
period. Underlying causes need to be further investigated under controlled
conditions. The successful modelling of the leaf stomatal conductance and
the photosynthesis assimilations required the relevant constants used in the
mechanistic model (
At canopy scale, the high-frequency vertical profiles – measured in and above the
canopy – of wind speed, potential temperature, specific humidity, and carbon
dioxide prove to be very valuable in obtaining profiles of gross primary
production in the canopy and as a function of height. By inverting these
observed profiles, we obtain an estimate of the contributions of soils and
plants to the net evapotranspiration and
Under cloud conditions, we show that the perturbation by clouds of direct and diffuse
radiation create large fluctuations in evapotranspiration and the
At landscape and boundary layer integrated scales, the modelled sensible heat flux correlates better with the
area-weighted average flux than the local flux estimates. The area-weighted
flux integrates in a simple manner a composite of bare soil and vegetated
surfaces at regional scale (kilometres). This aggregate regional flux is
representative of an area that is larger than the CloudRoots site (100 m
The comparison of all the ET measurements at the various scales show that there are still large differences in observing ET among the different observing techniques, the modelling of ET and their relation to stomatal aperture during the entire growing season. These ET observations do not show a clear pattern related to the scale at which they were measured.
The modelling and scale integration of this comprehensive observational dataset enables us to study the carbon and water exchange at leaf, canopy, and landscape levels. It also allows us to quantify how horizontal advection of heat within the mixed layer influences the surface fluxes and the growth of the atmospheric boundary layer. We show, for instance, that the horizontal advection of heat leads to deeper boundary layer depths. This numerical experiment thus paves the way to more complete modelling studies, for instance using large-eddy simulation numerical experiments, on how the surface and the overlaying atmosphere interact on sub-diurnal and sub-kilometre scales.
For the construction of the light and radiation response curves in Fig. 10,
the data were divided into bins of PAR and
Management activities on the test site over the winter wheat cultivation cycle before, during, and after the observation period of the CloudRoots campaign.
Initial and boundary conditions prescribed in CLASS to reproduce IOP 1 (7 May 2018).
Continued.
All CloudRoots observations are archived at
The supplement related to this article is available online at:
JVGdA designed the CloudRoots study and approach. OH and AG designed and coordinated the CloudRoots field experiment. The individual measurements were gathered by OH (scintillometer); HB (stomatal aperture/photosynthesis); AK, AG, and PN (microlysimeters and elevator profiles); ML (sap flow); MM and DE (SIF); and TR, GA, NB, and YR (stable carbon and water vapour isotopes). HdB integrated the observations to be connected with the modelling efforts. GdG, GM, HdB, and JVGdA performed the numerical experiments with CLASS. PN and JVGdA wrote the paper with key contributions from all the authors: OH, HdB, KvD, DE, GdG, AK, ML, MM, GMG, AFM, UR, TR, and GA.
The authors declare that they have no conflict of interest.
The authors wish to thank Bernhard Pospichal and Tobias Marke for contributing measurements on boundary layer properties. These data were provided by the Jülich Observatory for Cloud Evolution (JOYCE-CF), a core facility funded by DFG via grant DFG LO 901/7-1. Hubert Hüging and Huu Thuy Nguyen from the INRES Crop Science Group of Bonn University for operating the sap-flow equipment.
This study was financed by the Deutsche Forschungsgemeinschaft (DFG)
Collaborative Research Centre 32 (TR32) “Patterns in
Soil-Vegetation-Atmosphere System”. The contribution of AG and deployment
of the profile elevator, microlysimeter and part of the isotope measurements
were financed by the German Federal Ministry of Education and Research
(BMBF) within the framework of the project “IDAS-GHG“ (Grant: FKZ
01LN1313A). Airborne acquisition and data analysis with the HyPlant sensor
were financed by the European Space Agency (ESA) in the frame of the
FLEXSense campaign (ESA Contract No. ESA RFP/3-15477/18/NL/NA). The
contribution of PN to the analysis were financed by the European Space
Agency (ESA) within the project PhotoProxy (Grant. ESA
RFP/3-15506/18/NL/NF). The ancillary hardware and its maintenance were
supported by TERENO (
This paper was edited by Ivonne Trebs and reviewed by Dennis Baldocchi and one anonymous referee.