HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-1809-2017A site-level comparison of lysimeter and eddy covariance flux measurements of evapotranspirationHirschiMartinmartin.hirschi@env.ethz.chhttps://orcid.org/0000-0001-9154-756XMichelDominikLehnerIreneSeneviratneSonia I.sonia.seneviratne@env.ethz.chhttps://orcid.org/0000-0001-9528-2917Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerlandpresent address: Centre for Environmental and Climate Research (CEC), University of Lund, Lund, SwedenMartin Hirschi (martin.hirschi@env.ethz.ch) and Sonia I. Seneviratne (sonia.seneviratne@env.ethz.ch)28March20172131809182520May201615June201616December20165February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/1809/2017/hess-21-1809-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/1809/2017/hess-21-1809-2017.pdf
Accurate measurements of evapotranspiration are required
for many meteorological, climatological, ecological, and hydrological
research applications and developments. Here we examine and compare two
well-established methods to determine evapotranspiration at the site level:
lysimeter-based measurements (EL) and eddy covariance (EC) flux
measurements (EEC). The analyses are based on parallel measurements
carried out with these two methods at the research catchment Rietholzbach in
northeastern Switzerland, and cover the time period of June 2009 to December 2015.
The measurements are compared on various timescales, and with respect
to a 40-year lysimeter-based evapotranspiration time series. Overall, the
lysimeter and EC measurements agree well, especially on the annual timescale.
On that timescale, the long-term lysimeter measurements also
correspond well with catchment water-balance estimates of
evapotranspiration. This highlights the representativeness of the site-level
lysimeter and EC measurements for the entire catchment despite their
comparatively small source areas and the heterogeneous land use and
topography within the catchment. Furthermore, we identify that lack of
reliable EC measurements using open-path gas analyzers during and following
precipitation events (due to limitations of the measurement technique under
these conditions) significantly contributes to an underestimation of
EEC and to the overall energy balance gap at the site.
Introduction
Evaporation E from land, also termed evapotranspiration, is an essential
contributor to the water and energy balances on continents. It returns about
60 % of the precipitated water on land back to the atmosphere and also
uses up more than 50 % of all net radiation available on land (e.g., Oki
and Kanae, 2006; Trenberth et al., 2009; Jung et al., 2010; Seneviratne et
al., 2010; Wang and Dickinson, 2012). In addition, it is coupled to the
carbon dioxide (CO2) uptake by vegetation (e.g., Farquhar and Sharkey,
1982; Sellers et al., 1996; Ciais et al., 2005; Reichstein et al., 2013),
which implies important links between carbon and water cycles. Furthermore,
evapotranspiration is related to other nutrient cycles such as the nitrogen
cycle (Larcher, 2003).
Approaches to measure or estimate evapotranspiration are diverse and can
include ground observations, remote sensing-based estimates, diagnostic
techniques, as well as modeling and reanalyses (e.g., Seneviratne et al.,
2010; Jiménez et al., 2011; Mueller et al., 2011; Wang and Dickinson,
2012). Despite their relative scarcity, the best-established reference
measurement remains ground observations, which can be for example either
performed with the lysimeter technique commonly used in hydrology (e.g.,
Maidment, 1992; Rana and Katerji, 2000; Seneviratne et al., 2012), or the
eddy covariance (EC) flux measurement technique established in
micrometeorology (e.g., Baldocchi et al., 2001; Aubinet et al., 2012). Both
techniques hold specific intrinsic limitations (see Sect. 2).
Unfortunately, long-term parallel measurements of evapotranspiration with
different techniques are rare. Many studies are limited in terms of number
of methods and/or length of analyzed time period (e.g., Schume et al., 2005;
Kosugi and Katsuyama, 2007; Castellví and Snyder, 2010; Wang and
Dickinson, 2012, and references therein). Reported differences between
lysimeter and EC measurements from these short-term comparisons amount from
a few percent up to 30 %, with EC evapotranspiration being mostly lower
than the lysimeter-based values (e.g., Chávez et al., 2009; Ding et al.,
2010; Gebler et al., 2015). It is also worth mentioning the BEAREX08 field
experiment, within which several methods of determining evapotranspiration
were evaluated at different spatial scales for one vegetation period (Evett
et al., 2012a, b) and substantial differences between EC and lysimetry were
reported (Alfieri et al., 2012). In particular, the impact of surface
heterogeneity (i.e., vegetation density) on the uncertainties of both
lysimeter and EC measurements, as well as the influence of advective fluxes
and energy balance closure deficits on the discrepancy between the two
methods were investigated. Over 35 % of the discrepancy could be
attributed to differences in vegetation, while for the rest imperfect energy
balance closure, advective effects and sensor-related measurement
uncertainties were responsible.
The purpose of the present study is to compare lysimeter- and EC-based
measurements of evapotranspiration in the pre-alpine Rietholzbach catchment,
which is characterized by a unique hydroclimatological record, including
lysimeter measurements since 1976 (Seneviratne et al., 2012). As compared to
numerous previous studies (see above), which were carried out in irrigated
agroecosystems in semi-arid or arid climate, the presented inter-comparison
is based on data of a non-irrigated environment in a temperate humid
climate. In 2009, EC sensors were installed, thus allowing for an extensive,
multi-year comparison between the two techniques (as compared to the
shorter-term comparisons of previous studies). Furthermore, we use the
catchment-wide water balance as an additional constraint for estimated
evapotranspiration on the yearly timescale. Hence, we compare three
approaches to measure or estimate evapotranspiration, which vary both in
temporal resolution (from minutes to a year) and spatial scale (from m2
to km2). This allows us to evaluate (i) the correspondences between the
two well-established local-scale evapotranspiration measurement techniques,
(ii) the representativeness of these local-scale measurements for the
catchment, and (iii) the quality of the EC measurements under the considered
conditions at the site.
Box plots showing the median, interquartile range as well as minimum
and maximum of monthly climatological values (in black, climatological mean
included as black square) with the monthly values for the period of investigation 2009–2015
(in color) for (a) air temperature Tair, (b) precipitation P,
(c) net radiation Rn, (d) catchment runoff QC,
(e) lysimeter evapotranspiration EL, and (f) lysimeter
seepage QL.
Left panel: aerial map of the site (see https://s.geo.admin.ch/6de2dcf3b5
for an interactive online version of the map) and right panel: schematics of
the EC tower (denoted T), lysimeter (L), and radiation (R) measurement setup as
well as the frequency of wind direction and velocity at the site “Büel”
(white rectangle defines the area of the measurement field). The distance
between the tower and the sonic (S) volume equals 1.17 m. The wind sector
obstructed by the tower and the IRGA is highlighted in red and masked for the
analyses (from 310 to 50∘). The identical hatching of the surrounding
grassland and the lysimeter surface indicates that the latter is treated according
to the former (see text for details).
This article is structured as follows: the methods and data employed in this
study are presented in Sect. 2. Section 3 shows the resulting evapotranspiration
estimates by the different techniques and on different timescales. Section 4
discusses the results, and the summary and conclusions of this study are provided in Sect. 5.
Methods and dataSite description and catchment characteristics
The measurements considered in this study were conducted at the
hydrometeorological research catchment Rietholzbach in northeastern
Switzerland (47.38∘ N, 8.99∘ E; 795 m a.s.l.; see
Seneviratne et al., 2012 for an overview of the site). The hilly, pre-alpine
catchment (elevation range: 682–950 m a.s.l.) drains an area of 3.31 km2
and is a headwater catchment of the Thur river. The region is characterized
by a temperate humid climate with a mean air temperature Tair of
7.1 ∘C and ample precipitation P with a mean annual sum of
1438 mm yr-1 (data basis 1976–2015, Fig. 1a and b). Net
radiation Rn exhibits a clear seasonal pattern with on average
105 W m-2 in summer and -7 W m-2 in winter (data basis 2000–2015,
Fig. 1c). Predominantly weak winds (77 % below 2 m s-1) blow along the
east–west orientation of the valley (Fig. 2). Catchment runoff QC is
strongly related to subsurface storage (Teuling et al., 2010a) and shows an
annual mean of 104 L s-1, which corresponds to 991 mm yr-1. It
displays the lowest values in summer and its peak value during snowmelt in March
(data basis 1976–2015, Fig. 1d). The conglomerate
Nagelfluh, the main parent rock type, originates from the Würm
glaciation. The soil type and depth exhibit a high spatial variability.
Overall, shallow Regosols dominate on steep slopes, deeper Cambisols are
found in flatter areas, and gley soils are located in the vicinity of small
creeks. Land use has undergone no major changes since the start of the
measurements in late 1975 and is highly related to the topography. On slopes
and along creeks, in about one-fourth of the area, forest dominates.
Otherwise the area is used as grassland and partially as pasture. The
catchment is only sparsely populated.
Most measurements considered in this article are conducted at the site
“Büel”, which is located in a grassland area next to the valley bottom
in the upper part of the Rietholzbach catchment (see
Fig. 2 and https://s.geo.admin.ch/6de2dcf3b5). The ongoing measurements include
standard meteorological and hydrological variables such as air temperature,
precipitation, air humidity, radiation, soil moisture, runoff, and groundwater
level. Evapotranspiration measurements are provided by a lysimeter and
an eddy flux tower (see Fig. 2 for an overview on
the setup of these measurements). Further details on the relevant
instrumentation for this study are given in the following sections.
Seneviratne et al. (2012) provided an overview of the characteristics of the
catchment and of measurements at the site. For more general information
about the catchment we also refer to http://www.iac.ethz.ch/url/rietholzbach.
Lysimeter measurements
Lysimetry is a well-established technique to measure evapotranspiration
(e.g., Maidment, 1992; Rana and Katerji, 2000; Goss and Ehlers, 2009;
Meissner et al., 2010; see also Seneviratne et al., 2012, for a recent
overview). Lysimeters are vessels containing a soil column in near-natural
condition. Weighing lysimeters allow for the quantitative measurement of water
changes within the soil column and thus, in combination with precipitation
and lysimeter seepage measurements, also the estimation of evapotranspiration.
Formally, the lysimeter evapotranspiration EL (mm) within a given time
interval Δt (here 1 h) is estimated from the initial weight Wt
minus the final weight Wt+1 (both in kg), the precipitation P (mm),
and lysimeter seepage QL (mm) at the vessel bottom:
EL=Wt-Wt+1ρwπr2+P-QL,
where ρw stands for the density of water (kg m-3) and r (m)
for the radius of the lysimeter. For the comparison with eddy covariance
measurements, we derive a parallel time series “EL0” in which
EL is set to 0 during hours with precipitation (P≥ 0.1 mm,
15.4 % of data), as no reliable data are available from the EC measurements
in those cases (see Sect. 2.3).
The Rietholzbach lysimeter has a surface area of 3.1 m2 (radius of
1 m) and a total depth of 2.5 m including a gravel filter layer at the
bottom. This size of vessel ensures a higher quality of the measurements
(see Seneviratne et al., 2012, and references therein). The lysimeter weight
is measured with three load cells and a resolution of 100 g, which
corresponds to a water column of approximately 0.03 mm. The surface is
covered by grass of similar species composition and treated according to the
surrounding grassland (same cutting scheme, but synthetic fertilization
instead of slurry; see also Fig. 2). At its
installation at the site “Büel” in late 1975, the lysimeter was
backfilled with a typical gleyic Cambisol. Seepage at the lysimeter lower
boundary is measured by a tipping bucket with a volume of 50 mL, i.e., with
a resolution of 0.02 mm (Gurtz et al., 2003). Following the recommendations
of the World Meteorological Organization (WMO, 2008), precipitation data
were not derived from the lysimeter measurements but were taken from a
standard tipping bucket (see Sect. 2.5).
A key requirement for the accurate estimation of local evapotranspiration is
the representativeness of the lysimeter for the surrounding area in terms of
soil conditions, vegetation composition, and treatment. Major drawbacks are
the existence of the vessel and its specific design (Allen et al., 2011;
WMO, 2008). At the investigated site, this implies the main following limitations:
The lateral water transport is not contributing to the lysimeter water
storage dynamics. This point is assessed as being relatively negligible, as
the lysimeter is located in a flat area close to the valley bottom and
surrounded by a slight and uniform slope. Thus, potential lateral inflow and
outflow to the investigated soil volume are assumed to be equal.
There is no connection to the groundwater. This may become potentially
important under drought conditions (e.g., Rana and Katerji, 2000; Seneviratne
et al., 2012) even for a grass-covered lysimeter in a temperate humid climate.
Drainage occurs by gravitation only as soil suction is not artificially
reproduced within the vessel.
Time periods with snow cover have to be analyzed with special care as snow
drift induced by wind as well as snow bridges to the surrounding can falsify
the weight measurement.
Despite these issues, Seneviratne et al. (2012) show that the Rietholzbach
lysimeter seepage and catchment runoff display very similar monthly
dynamics, which suggests to a first approximation, that the lysimeter is
well representative for the entire catchment despite the scale discrepancy
and mentioned limitations. The largest discrepancies between lysimeter
seepage and catchment runoff are found in March, most likely linked to a
higher spatial variability of hydrological processes in that month, due to
snowmelt and the onset of the growing season.
Lysimeter data analyzed in this study cover the time period 1 June 2009
to 31 December 2015 (start being restricted by the availability of the EC
measurements; see Sect. 2.3). In addition, we refer to the climatological
lysimeter time series dating back to 1976 (see Sect. 2.5).
Evapotranspiration is calculated in hourly time steps according to
Eq. (1) and taking into account the weight changes due to management activities.
Missing values in lysimeter weight change Wt-Wt+1 (< 0.1 %
of data) are filled by a linear interpolation as the gaps were
short and no precipitation occurred. For lysimeter seepage QL missing
values (< 0.1 % of data) are filled manually preserving the actual
seepage pattern. Evapotranspiration is defined here as an upward flux, i.e.,
comprising positive values only, as the lysimeter accuracy does not allow one to
resolve dew formation. Yet, Eq. (1) can result in negative EL
values, because the measurements entering the calculation are based on
instruments with differing resolutions, and because they can be biased due
to sensor uncertainty. The latter is in particular the case for the
precipitation measurements, which are often biased due to an undercatch
(e.g., Sevruk, 1982; Adam and Lettenmaier, 2003). Days comprising
negative EL are thus treated as described in Jaun (2003) to eliminate such
negative values, being consistent with the scheme used for the climatological
data series dating back to 1976 (see Sect. 2.5). It takes into account the
amount and predominant sign of EL during such days. The method mainly
affects the winter period, when it leads to a reduction of the overall
amount of positive EL as compared to, e.g., simply setting all negative
EL values to 0 (not shown), as the occurrence of negative EL is
increased during this season. Based on measured values, a threshold for
maximum realistic EL of 0.2 mm h-1 during nighttime (global
radiation Rsd< 10 W m-2) and periods with snow cover
(albedo α> 0.5) is applied. In addition, a limitation
of EL is defined as a function of Rsd. The subsequent gap filling is
conducted in two steps: (i) missing nighttime values (0.7 % of data) are set
to 0, and (ii) for missing daytime values (0.8 % of data) a linear
regression with global radiation Rsd (R2: 0.67) is applied.
Eddy covariance measurements
The eddy covariance method estimates the vertical mass flux of water
vapor (EEC) exchanged by an ecosystem based on fast measurements of
vertical wind velocity w (m s-1) and specific humidity q (kg m-3),
respectively, on their turbulent fluctuations (denoted by a prime):
EEC=w′q′‾.EEC has been measured at the Rietholzbach catchment since late May 2009. The
measurements are conducted on a 9 m flux tower, installed at the site
“Büel” (see Fig. 2 and https://s.geo.admin.ch/6de2dcf3b5) and equipped on three levels with an
ultrasonic anemometer thermometer (sensor type CSAT3, Campbell Scientific
Inc., USA; hereafter referred to as “sonic”). On the bottom and top
levels, an open-path CO2/H2O infrared gas analyzer (sensor type Li-7500,
LI-COR Biosciences, USA; hereafter referred to as “IRGA”)
complete the setup. The instruments are operated at 10 Hz and data are saved
with a CR3000 data logger (Campbell Scientific Inc., USA). The present study
is based on data obtained from the sensors at the lowest level (2 m above
ground), as their source area is smallest and closer to the lysimeter (see
Fig. 2), and therefore, they experience more
homogeneous physical environmental conditions. However, up to 10 % of the
measurements are potentially affected by obstacles (trees and a farmhouse)
in the area, whereas only 1 % of the measurements within the main wind
direction (i.e., from west, see Fig. 2) are
potentially influenced (Peter, 2011, based on the footprint model of Kljun
et al., 2004). Note that this level is well above the vegetation height
(mostly below 15 cm, maximum 40 cm), and clear of the roughness sublayer
(estimated three times the canopy height; see Kaimal and Finnigan, 1994;
Foken, 2008). In this study we consider data from the time period 1 June 2009 until 31 December 2015.
To enable the comparison with the lysimeter estimates, the statistics were
calculated on an hourly time step following the methodology described in,
e.g., Lee et al. (2004) or Aubinet et al. (2012). This includes a time lag
correction of w and q by maximization of their covariance, the application of
the planar fit method after Wilczak et al. (2001) for the coordinate
rotation, spectral correction (Moore, 1986), conversion of the buoyancy flux
into the sensible heat flux (Schotanus et al., 1983), and correction of
density fluctuations (Webb et al., 1980). As open-path IRGAs are not
reliably measuring when water is accruing on the optical elements,
λEEC is explicitly set to 0 during hours with precipitation
(P≥ 0.1 mm, 15.4 % of the data).
Figure 2 displays the location of the tower with
respect to the lysimeter and the radiation measurements, together with the
frequency of wind direction and velocity at the location. The horizontal
separation distance between sonic volume and IRGA volume amounts to 0.2 m
both laterally and longitudinally, with the IRGA being situated west of the
sonic (not shown). EC data are masked when the tower and the IRGA are in the
upwind direction of the sonic volume (i.e., from 310 to 50∘, red
sector) in order to avoid impacts on the measured turbulent fluxes. This is
the case for 10.6 % of the data.
Evapotranspiration EEC is related to the surface energy balance as follows:
Rn-G=H+λEEC,
where Rn refers to the net radiation, G is the surface soil heat flux
(see below), H stands for the sensible heat flux, and λEEC stands
for the latent heat flux, where λ (J kg-1) is the latent heat
of vaporization. Details on the measurements of Rn and G are given in
Sect. 2.5. The storage of energy between the surface and the measurement
height is neglected in the analyzed measurements as these are performed at
2 m above short grassland (vegetation height mostly below 15 cm, maximum
40 cm). However, it is not negligible for tall vegetation (e.g., Foken et al.,
2006). In addition, effects of diurnal storage changes can be averaged out
when considering daily instead of hourly energy balances (e.g., Leuning et
al., 2012; Anderson and Wang, 2014).
Advective fluxes are also not considered in Eq. (3). This effect can
contribute to a non-closure of the surface energy balance (e.g., Leuning et
al., 2012, see also below). The vertical component of advection of latent
and sensible heat (see e.g., Paw U et al., 2000; Casso-Torralba et al.,
2008) can be assessed at the site following the notation of Lee (1998),
which is based on the average vertical gradient of moisture or temperature
multiplied by the mean vertical wind speed at a specific level. For a
quantitative estimate of the horizontal component of advection, measurements
are not available at the site and its surroundings. Possible reasons for
horizontal advection include slope drainage in complex terrain and
heterogeneous land cover (e.g., Katul et al., 2006). Concerning the first
reason (slope drainage), the wind from the south-facing valley slope is
masked in all analyses as it includes the tower (see above).
Concerning the impact of surface heterogeneity, we estimate the potential
effect on the energy balance closure by separating the analyses into three
wind sectors (i.e., east, west, and south wind directions). While the west
sector (i.e., the main wind direction) features homogeneous land cover and
horizontal advection should thus not be relevant, the east sector is
potentially impacted by a small street and a farmhouse (see Fig. 2).
For the latent heat flux λEEC the same data constraints are
applied as for lysimeter evapotranspiration EL, i.e., during nighttime
conditions, periods with snow cover, and limitation by Rsd (see
Sect. 2.2). Under the present generally humid climate conditions at Rietholzbach,
net radiation Rn is the main driver and limiting factor for λEEC
(Teuling et al., 2010b; Seneviratne et al., 2012). Thus, gaps in
the λEEC time series (31.1 % of data) are filled by a linear
regression (R2: 0.90) of these two variables. However, it should be
noted that the simple regression with radiation could lead to errors when
evapotranspiration is constrained by soil moisture (e.g., Seneviratne et al.,
2010). Overall, the relation between the λEEC and the lysimeter
time series is not changed by the gap filling (not shown).
As commonly observed with using EC data (e.g., Twine et al., 2000; Wilson et
al., 2002; Franssen et al., 2010), the energy balance is not closed at the
investigated site (see Sect. 3.2), i.e., the available energy (Rn -G) is
generally higher than the sum of the turbulent fluxes (H+λEEC).
This known issue of the EC method is extensively discussed in the
literature (e.g., Mahrt, 1998; Foken, 2008; Aubinet et al., 2012; Leuning et
al., 2012). It is important to address this issue also in light of the use
of EC data for model validation (e.g., Jaeger et al., 2009).
Several approaches can be used to force close the energy balance. Here we
apply three different simple approaches to enforce the energy balance
closure on an hourly basis, assigning the gap to
both H and EEC according to the Bowen ratio β(EEC_BOWEN)
sensible heat-flux only (EEC_H)
latent heat-flux only (EEC_E).
Due to weak turbulent conditions, small turbulent fluxes, and a poor
definition of the Bowen ratio during nighttime, the approaches are only
applied to daytime values (Rsd≥ 10 W m-2). Approach (i)
(EEC_BOWEN) is a commonly used assumption in the
literature (e.g., Twine et al., 2000; Jaeger et al., 2009; Jung et al.,
2010). It assumes that the Bowen ratio is correctly measured by the EC
method so that λEEC and H can be adjusted to balance
Eq. (3). Approaches (ii) and (iii) represent two extreme assumptions but they are useful
as they indicate the entire range of possible energy balance options (given
that Rn and G are correctly estimated and no other fluxes (e.g.,
advection) or storage terms are of importance; see also Sect. 4.2). For
comparison, approach (i) is also applied on daily timescale (i.e., based on
daily aggregated fluxes).
Statistical properties of the catchment water balance for the
hydrological years (i.e., October–September) 1976/1977 to 2014/2015 and the
absolute values for the hydrological years 2009/2010 to 2014/2015. P denotes
precipitation, QC catchment runoff, QL lysimeter seepage,
EC catchment evapotranspiration, and EL lysimeter
evapotranspiration. Units in mm yr-1 (except for EC/P and
EL/P, which are dimensionless).
a from Seneviratne et al. (2012), based on calendar-year
(January–December) values. b For P (and EL/P) the statistics based
on undercatch-corrected and uncorrected (in brackets) P are provided (see
Sect. 2.4).
Catchment water-balance measurements
The catchment water balance integrates its components over the entire
catchment area over longer time periods. While we focus here on the
comparison of the lysimeter and EC evapotranspiration measurements, such
catchment water-balance estimates provide an additional reference to
evaluate the local-scale techniques. Using this approach, the
evapotranspiration EC is estimated as the difference between
precipitation P and catchment runoff QC (all in millimeters):
EC=P-QC.
This approach implies that the change in catchment storage over the given
time interval is 0. This assumption generally only holds for long-term
averages (≥ 1 year). Although year-to-year variations in storage cannot
be fully excluded (e.g., Seneviratne et al., 2012), it can be assumed to
yield a reasonable estimate for hydrological years (October to September in
Switzerland). In addition, catchment precipitation is estimated here using
one precipitation gauge only (thus assumed to be spatially representative).
This approach also assumes that all water is leaving the catchment through
the discharge gauge at the catchment outlet (see below). Previous analyses
suggest that both conditions are reasonably met for the study catchment
(Gurtz et al., 2003; Seneviratne et al., 2012).
Precipitation data are taken from the standard tipping bucket (for details
see Section 2.5). As the catchment evapotranspiration EC is known to
suffer from unrealistic negative values during winter (Lehner et al., 2010),
related to high precipitation undercatch during snowfall (up to 60 %, see
Gurtz et al., 2003), the precipitation data entering Eq. (4) is
corrected for undercatch (based on Gurtz et al. 2003, Table 1 therein).
Catchment runoff (QC) is captured at the catchment outlet at the gauge
“Rietholz–Mosnang”. This gauge is operated by the FOEN (Federal Office for
the Environment, Hydrology Division, Berne, Switzerland). More information
on the gauge is available on http://www.hydrodaten.admin.ch/en/2414.html
(last access: 21 February 2017).
Additional measurements at the site “Büel”
Precipitation is measured by a standard tipping bucket installed at 1.5 m.
Data from parallel measurements with a standard tipping bucket at 0 m and a
weighing pluviometer at 1.5 m at the same measurement site are used for
quality assessment and gap filling. For remaining gaps, a regression with
data from nearby meteorological stations operated by MeteoSwiss is applied.
Net radiation is derived from separate measurements of all four components
of the radiation balance (CM21 and CG4, Kipp & Zonen, NL, all ventilated)
at a height of 2 m (see Fig. 2).
Soil heat flux is captured with three heat-flux plates (HFP01, Hukseflux,
NL) installed at 0.05 m below ground and situated within a 1 m periphery,
which are averaged for the analysis. Surface soil heat flux G is determined
following Fuchs and Tanner (1968) by calculating the change in heat storage
above the sensors. This estimation is performed using the average of three
soil temperature sensors (107T, Campbell Scientific Inc., USA) installed at
the same locations as the heat-flux plates, as well as soil moisture
(TRIME-IT, IMKO GmbH, D) and soil density measurements (Mittelbach et al.,
2012). The ensemble of the three different locations for soil heat flux and
soil temperature is used to account for the spatial heterogeneity of the
soil matrix and thus to obtain surface soil heat-flux data, which are
spatially representative for the footprint domain of all other measured
components of the energy balance.
Note that soil heat flux was not measured during a 4-month period from
July to October 2014 due to a logger failure. This leads to some gaps in the
following comparisons due to the fact that the energy gap corrections (i.e.,
EEC_BOWEN and EEC_E, see Sect. 2.3)
cannot be applied for this time period. Apart from this period, the soil
heat flux and net radiation time series only hold few and short gaps
(< 0.1 % of data), which are filled by a linear interpolation.
All measurements mentioned in Sect. 2 are ongoing. The descriptions refer
to the instrumentation for the data since (at least) June 2009. The
climatological data series (since 1976) are generally based on varying
sensors (types), but have been homogenized over time. More details on the
climatological record and respective instrumentation since 1976 can be found
in Seneviratne et al. (2012) and in a German-language report (Gurtz et al., 2006).
ResultsClimate conditions in 2009–2015 compared with long-term climatology
We first assess how the measurements in the study period compare with the
long-term climatology to evaluate if the study period from 1 June 2009 to
31 December 2015 is representative for the mean climatological conditions at
the site. Figure 1 displays the average monthly
meteorological conditions during the study period compared to the long-term
climatology with respect to air temperature Tair, precipitation P, net
radiation Rn, catchment runoff QC, lysimeter evapotranspiration EL,
and lysimeter seepage QL. The long-term climatological values
are derived over the time period 1976–2015, with the exception of net
radiation, which has only been measured since 2000 at the site.
Temperature (Fig. 1a) during the study period
ranges on average in the upper part of the distribution based on the
climatological data series. This is consistent with the recorded long-term
increasing temperature trend in Rietholzbach (Seneviratne et al., 2012) and
in Switzerland (OcCC, 2008). The variability within the 7 years is
similar to the climatology. Precipitation of the study period
(Fig. 1b) shows high variability with extreme
values in November 2011 (0 mm) and December 2011 (275 mm). Overall, the
precipitation data shows that the spring season is often drier and the
summer season often wetter in the 7 considered years compared to the
long-term climatology. Absolute values and variability for Rn
(Fig. 1c) are close to the long-term average.
Catchment runoff and lysimeter seepage (Fig. 1d and f) show high variability within the 7 years and compared to the
climatological values. QC and QL show a similar behavior (see also
Seneviratne et al., 2012). Lysimeter evapotranspiration EL often shows
higher summer values (i.e., mostly for June and August) in 2009–2015
compared to the climatology.
Catchment water balance for the hydrological years (i.e., October–September)
1976/1977 until 2014/2015. EC denotes catchment evapotranspiration,
EL lysimeter evapotranspiration, QC catchment runoff,
QL lysimeter seepage, and P precipitation (corrected according to
Gurtz et al. (2003), Table 1 therein). The lines show the ratio EC/P
(dark gray) respectively EL/P (blue).
Mean precipitation P of a hydrological year sums up to 1598 mm yr-1
(respectively 1446 mm yr-1 when undercatch is not corrected), whereof
about 37 % are evaporated as EC and about 63 % leave the catchment
as runoff QC (Table 1). All catchment water-balance components show a
high year-to-year variability, which is highest for QC with respect to
the mean. However, none of the components displays a significant trend over
the entire time period (see also Seneviratne et al., 2012, for trends in
calendar-year values over the time period 1976–2007).
Figure 3 displays the catchment water balance for
the hydrological years since 1976/1977. The comparison with the
lysimeter-based evapotranspiration EL suggests that in the long-term
mean the E/P ratio agrees well with the catchment water-balance approach. The
discrepancies between EC and EL on a year-to-year basis are likely
due to non-negligible year-to-year variations in terrestrial water storage
(soil moisture, groundwater, snow). In fact, the non-equality of (EL+QL)
vs. P indicates annual storage variations at the lysimeter, while
for EC the change in catchment storage over the given time interval is
assumed to be 0 (see Sect. 2.4).
The hydrological year 2009/2010 is one of the wettest hydrological years in
terms of precipitation (+14.5 % resp. +18.6 % compared to the
average, undercatch-corrected, and uncorrected values), yet the partitioning
of P into QC and evapotranspiration (EC and EL) is still close to
the long-term average (Table 1). The pattern of the hydrological year 2010/2011,
in contrast, is different. Precipitation is lower than average
(-9.8 % resp. -6.9 %) for that year, but evapotranspiration (EC and
EL) is up to 1.25 times the average, whereas runoff and seepage
(QC and QL) display the lowest values of the entire period
(-28.8 and -34.2 % respectively compared to the average). It should be
noted that the spring 2011 was very dry (e.g., Wolf et al., 2013; Wetter et
al., 2014; Whan et al., 2015), which can partly explain these features. Both
hydrological years 2009/2010 and 2010/2011 display amongst the highest
evapotranspiration values since the beginning of the measurements (EC
and EL). The year 2011/12 appears rather normal on the catchment scale,
but the lysimeter again shows high EL (+24.8 % compared to the
average) and low QL (-18.2 %). This is followed by a wetter
year 2012/2013 with higher P (+10.8 % resp. +10.4 %), QC (+23.8 %)
and QL (+16.6 %), but lower EC and EL (-11.1 %
resp. -3.9 %). The years 2013/2014 and 2014/2015 finally show again lower than
average precipitation (up to -10.5 %). For the catchment, this resulted in
below normal QC for both years (-12.4 and -14.1 %) and close to
normal EC (+4.2 and -2.7 %). The lysimeter on the other hand
shows for 2013/2014 slightly enhanced QL (+6.0 %) while EL is
close to normal (-4.8 %). For 2014/2015 it experienced a pronounced drying
with high EL (+14.7 %) and low QL (-16.8 %), related to the
hot and dry summer of that year (e.g., Scherrer et al., 2016). Overall, the
study period covers the climatological variability well, including both
years with rather extreme conditions as well as years close to average
conditions (see Table 1).
(a) Sum of turbulent fluxes (i.e., sum of sensible heat flux H
and latent heat flux λEEC; H+λEEC) vs. the
available energy (i.e., net radiation Rn minus surface soil heat flux G;Rn-G) and (b) mean daily pattern of the energy balance
components. Graphs are based on measured hourly values (i.e., excluding gap-filled data,
and masked for precipitation and wind directions affected by the tower) for
the time period 1 June 2009–31 December 2015.
Energy balance closure
The energy balance closure as evaluated from the ordinary least-squares
regression between the hourly estimates of the turbulent fluxes
(H+λEEC) and the available energy (Rn-G) reaches
values of 0.77 for the slope and 18.94 W m-2 for the intercept
(R2: 0.94, Fig. 4a; note ideal closure is
represented by an intercept of 0 and slope of 1). The ratio of the
total amount of the turbulent heat fluxes to available energy amounts to
101.9 %, indicating a surplus of turbulent energy. This is due to mostly
slight positive nighttime values of the sum of the turbulent fluxes while
available energy displays negative values during night (see
Fig. 4b). Ignoring nighttime values (Rsd< 10 W m-2),
the closure ratio amounts to 86.4 %; i.e., the
sum of the turbulent fluxes H+λEEC is generally lower than the
available energy Rn-G. The regression between the daytime hourly
estimates reveals a slope of 0.80 and intercept of 10.63 W m-2
(R2: 0.94). All these values are in the range of values reported in
literature (e.g., Wilson et al., 2002). Note that the analyses presented
here are based on measured data only (i.e., excluding gap-filled data) and
masked for precipitation and for wind directions impacted by the tower (see
also Sect. 4.2).
Hourly energy balance closure is also compared with daily closure for days
where maximally five of the hourly values were gap-filled, which leaves
462 days of valid EC observations (Fig. S1 in the Supplement). The energy balance
closure slightly improves on daily timescales: regression slopes increase
from 0.76 to 0.84, and R2 from 0.95 to 0.97. The increase of the energy
balance closure from hourly to daily timescale hints at an effect of
diurnal storage variations on hourly timescale, which tend to get averaged
out on daily timescale (see Sect. 2.3).
As mentioned in Sect. 2.3, advective fluxes are not accounted for in the
energy balance Eq. (3) and can contribute to the imbalance between the
turbulent fluxes and the measured available energy. The estimated amount of
vertical advection reveals that the magnitude of vertical advection of
latent and sensible heat is small (on average at most around -0.1 W m-2
respectively 0.05 W m-2 at noon; see Fig. S2) compared
to the respective average turbulent fluxes (less than 1 %). For horizontal
advection, the potential effect on the energy balance closure is estimated
by separating the closure analyses into three wind sectors (i.e., east, west,
and south wind directions; note that sector north is completely masked due
to the presence of the tower in that sector). We focus on daytime here in
order to rule out biasing due to the differing distribution of nighttime
fluxes among the wind sectors, which are typically small yet can have
opposite directions. The results of these analyses reveal that the energy
balance closure is rather independent of the wind direction (see
Fig. S3). The slope and R2 of the regression analyses
are similar for all three wind sectors. This also holds for the daytime
ratio of the total amount of the turbulent heat fluxes to available energy,
which amounts to 86.5, 86.8, and 82.1 %, respectively, for the east,
west, and south sectors. This robustness in the energy balance closure
independent of the wind direction (in light of spatial homogeneity in the
west sector and the small street and the farmhouse in the east sector; see
Sect. 2.3) indicates that horizontal advection is not of great importance
at the site.
The mean daily patterns of the energy balance components
(Fig. 4b) show that during nighttime H and λEEC often
are of similar small magnitude but opposite sign, resulting
in slight positive nighttime values of the sum of the turbulent fluxes and
an energy balance closure gap equivalent to about the amount Rn-G. The
zero crossing of Rn and H occurs at around 07:00 CET when
λEEC starts to increase as well. G is delayed by about 2 h. All fluxes
have their peak value around 13:00 CET. In the afternoon Rn and H change sign
again after 18:00 CET, followed by G. λEEC reaches the nighttime
values at around 20:00 CET. Available energy is larger than the turbulent fluxes
throughout the day. The energy balance closure gap displays a pronounced
daily cycle. During nighttime the closure gap is almost constant at around
25 W m-2 and the largest closure gap is found around noon. The overall
daily cycle of the energy closure gap is approximately symmetric around the
noon peak, and generally increases with higher fluxes.
Figure S4 displays the daily cycles of surface and 5 cm soil
heat fluxes, as well as of soil temperature (see Sect. 2.5). For the
latter two, the average based on the three heat-flux plates and the three
soil temperature sensors respectively are shown, while the range is based on
the data from the three individual sensor locations (and displays the
minimum and maximum values, respectively). For the surface soil heat flux,
the estimate calculated from the averaged heat-flux plates and temperature
sensors is displayed, along with a minimum and maximum estimate based on the
individual sensor locations. The effect of the correction based on Fuchs and
Tanner (1968) is clearly visible and leads to a shift of the daily cycle of
the surface soil heat flux vs. the 5 cm soil heat flux, and to an
enhancement of the daily amplitude. The range of the surface soil heat
fluxes amounts to 6.7 W m-2 on the average. Especially during
nighttime, this amount is substantial compared to the available energy of
around -25 W m-2. These results illustrate the spatial heterogeneity of
the surface soil heat-flux footprint and underline the importance of
employing a set of several soil heat-flux sensors in order to obtain spatial
representativeness of the data.
Monthly values of the different evapotranspiration estimates (with
EL denoting lysimeter evapotranspiration, EL0 lysimeter
evapotranspiration with values set to 0 during hours with precipitation,
and EEC_BOWEN EC-based evapotranspiration corrected according to
the Bowen ratio) for the time period June 2009 to December 2015. The black bars
indicate the range based on EEC_H and EEC_E (i.e.,
EEC corrected by assigning the energy balance closure gap to sensible
heat-flux only and to latent heat-flux only; see Sect. 2.3). Note that from
July to October 2014 an energy gap correction is not possible due to missing
soil heat flux (see Sect. 2.5) and thus EEC_BOWEN and EEC_E
are not available.
Monthly relative differences between lysimeter evapotranspiration
EL0 and EC-based evapotranspiration EEC, i.e.,
(EEC-EL0)/EL0
vs. the absolute values of EL0. Different seasons are displayed
in different colors. The points indicate EEC_BOWEN (EEC corrected
according to the Bowen ratio) and the black bars indicate the range based on
EEC_H and EEC_E (EEC corrected by assigning the
energy balance closure gap to sensible heat-flux only and to latent heat-flux only). Note that the July to October 2014 values with missing EEC_BOWEN
and EEC_E (see Sect. 2.5) are omitted.
Comparison of the different evapotranspiration estimates
In the following we compare the evapotranspiration estimates on different
timescales, from yearly down to hourly timescales. The lysimeter values EL
and EL0 are used as reference. The analysis is based on the
period 1 June 2009 to 31 December 2015, respectively on the 6
hydrological years therein (i.e., 2009/2010 to 2014/2015). For consistency
with EL (see Sect. 2.2), also only the upward fluxes are considered
for EEC (i.e., thus based on (neutral to) unstable conditions only).
This may result in sums of EEC_H to become higher than
the sums of EEC_BOWEN (depending on the distribution of
the negative hourly fluxes; see Figs. 5 and 6). Note that the absolute sum of
negative EEC amounts to 2.3 % of positive EEC.
Table 2 summarizes the evapotranspiration values of the different methods
for the hydrological years 2009/2010 to 2014/2015. The lysimeter estimates (EL)
range between 537 and 704 mm yr-1. Setting
evapotranspiration to 0 during precipitation events (EL0) allows for
a comparison with the estimates of the eddy covariance method and reduces
the values remarkably to a range of 521 to 672 mm yr-1 (by
up to -8 and -5 % on the average). Except for EEC_E, the eddy
covariance estimates show mostly lower values than the lysimeter estimates.
The monthly evolution of EL0 (and EL for comparison) displayed in
Fig. 5 displays a pronounced seasonal cycle with
highest values in summer and lowest values in winter. EL0 in spring is
higher than in autumn. The difference of the monthly EC estimates to EL0
(Figs. 5 and 6) exhibits a seasonal cycle as well, with highest
absolute differences in summer when the highest fluxes occur and highest
relative differences in winter. The EC estimates mostly underestimate
(overestimate) EL0 in summer (spring), while there is no clear tendency
during autumn and winter. The EEC_BOWEN estimates based
on the daily force closure (see Sect. 2.3) show a consistent temporal
evolution but a reduction of EC evapotranspiration compared to the one based
on the hourly force closure (see Fig. S5). The
underestimation of the EC estimates in summer based on the hourly
force closure thus becomes slightly larger, while the rather positive EC
bias in winter turns into a predominantly negative bias.
Lysimeter (EL and EL0) and EC (EEC)
evapotranspiration (including EEC corrected according to the Bowen
ratio (EEC_BOWEN), and EEC corrected by assigning the
energy balance closure gap to sensible heat-flux only (EEC_H) and
to latent heat-flux only (EEC_E)) for 6 hydrological years and the
respective 6-year averages. Percentages denote the differences of EEC
and EL to EL0. Note that for 2013/2014 and 2014/2015
an energy gap correction is not possible for a 4-month period due to missing
soil heat flux (see Sect. 2.5) and thus EEC_BOWEN and EEC_E
are not available (denoted as NA in the table). Units in mm yr-1.
Comparison of hourly EC-based evapotranspiration EEC with
lysimeter evapotranspiration EL0 based on measured values (i.e.,
excluding gap-filled data, and masked for precipitation and wind directions
affected by the tower) in the time period 1 June 2009–31 December 2015
(n= 30615 for EEC_H respectively n= 30 002 for
EEC_BOWEN and EEC_E). The comparison is shown separately
for EEC corrected according to (a) the Bowen ratio (EEC_BOWEN),
and EEC corrected by assigning the energy balance closure gap to
(b) sensible heat-flux only (EEC_H) and to (c) latent
heat-flux only (EEC_E).
A similar picture results from the analysis of daily (not shown) and hourly
evapotranspiration values. Regarding the latter,
Fig. 7 displays a scatter plot of hourly values
from our reference lysimeter-based evapotranspiration measurement EL0
and from the different EC evapotranspiration estimates for the raw measured
data (excluding gaps). Overall, all
EC-based estimates appear to underestimate EL0 (slopes of less than 1,
and negative biases except for EEC_E), in particular for
high values. However, the R2 values are similar for the different EC
estimates. In addition, hourly EEC_BOWEN and EL0 is
compared for different meteorological conditions and times of the day (see
Tables S1–S3 in the Supplement). The agreement between EEC_BOWEN
and EL0 (visible in R2 and the relative bias) is worst
during nighttime when evapotranspiration is low and less variable
(Tables S2 and S3). For the same reason, also low vapor pressure
deficit worsens the statistics, as evapotranspiration is also lower in such
conditions. Moreover, the statistics during southern wind directions are
worse than for the other wind sectors (however, based on much less data).
Wind speed, on the other hand, does not seem to have a strong impact on the
agreement between EC and lysimeter evapotranspiration, except for the
increase in relative bias for low wind speed during western wind directions
(Table S1).
DiscussionTemporal/spatial representativeness and differences between measurements
The analysis of the hydrological year-based evapotranspiration values
derived from the catchment water-balance approach EC and from the
lysimeter EL (Fig. 3, Table 1) shows that the
considered period with parallel EC and lysimeter measurements is
representative and covers the climatological variability at the site well
(see also Fig. 1). Despite the scale discrepancy
(catchment vs. local scale), EC and EL overall show a similar
magnitude of the fluxes. In addition, a previous comparison of the lysimeter
seepage and whole-catchment runoff at monthly timescale revealed a high
correlation in these measurements (Seneviratne et al., 2012). Based on this,
we infer that the local-scale lysimeter measurements are representative for
the whole catchment.
The difference between the EC evapotranspiration estimates and the lysimeter
evapotranspiration EL0 displays a seasonal dependency with the highest
absolute differences in summer, with EEC_BOWEN showing
about 10 % lower values during this season (on the monthly timescale). On
the other hand, highest relative differences occur in winter (as shown in
Figs. 5 and 6). Despite these differences, it is important to note that
overall good agreement between the two measurement techniques is achieved.
Limitations/errors in the measurements
In terms of instrumental uncertainty of EC measurements, attention was spent
on the surface heating effect of the IRGA and its influence on the measured
fluxes (e.g., Burba et al., 2008). Reverter et al. (2011) showed that for
evaporation fluxes the impact is much smaller than for CO2. The
instrument at the site is tilted downwards at an angle of 45∘ in
order to minimize the impact on the near-infrared signal by direct solar
radiation, as it faces south, and thus to minimize the surface heating
effect. This orientation of the IRGA also reduces the accumulation of
rainwater on the optical element (see Sect. 2.3). Recently, the accuracy
of the vertical wind component w measured with non-orthogonal sonics and the
temperature measured by sonics have been under discussion as a potential
source of lack of closure. But the different studies disagree on the impact
(Loescher et al., 2005; Mauder et al., 2007; Burns et al., 2012; Kochendorfer
et al., 2012; Frank et al., 2013; Mauder, 2013), and further investigations are thus needed.
In addition, a drawback of the EC method is the commonly observed closure
gap of the energy balance (see Sect. 2.3). The possible underlying reasons
are discussed extensively in literature (e.g., Mahrt, 1998; Foken, 2008;
Franssen et al., 2010; Aubinet et al., 2012; Leuning et al., 2012). Similar
values and daily patterns of the energy balance closure are reported in
numerous studies (e.g., Wilson et al., 2002; Franssen et al., 2010) and are
also consistent with the data presented in this study. The complex
determination of evapotranspiration with the EC method does not allow for a
simple error analysis. The closure gap of the energy balance is taken here
as an appropriate error estimate assuming an error of less than 5 % in the
measurement of the available energy. Thus, the bias in the turbulent fluxes
is assumed to be on the order of 5–15 % with a clear daily cycle and
higher relative (but smaller absolute) errors during nighttime.
Our analysis also points at another contributor to an overall
underestimation of turbulent fluxes by the EC method, namely the lack of
reliable latent heat-flux measurements during and following precipitation
events. Because of its sensitivity to precipitation, the operation of an
open-path IRGA at a rainy site results in a larger fraction of erroneous
data in the EEC time series. The assumption is made that EEC is 0
during hours with precipitation (see Sect. 2.3). But the difference
between EL and EL0, which reaches up to 8 % (with an
underestimation for EL0), shows that this assumption can lead to
substantial underestimation of actual evapotranspiration, e.g., because it
does not always rain during the entire integration period and it takes a
while after a precipitation event until the optical elements are dry again.
This is an interesting result for the interpretation of the EC measurements,
as EEC is often found to be closer to EL0 than
to EL in this study.
Figure 8 shows the energy balance closure analysis
as calculated by masking precipitation hours (left part) compared with the
same analysis taking into account all hours (i.e., including precipitation
hours, right part). As mentioned in Sect. 3.2, the energy closure amounts
to 86.4 % (for daytime and masked precipitation) for the measured
turbulent fluxes (i.e., no gap correction). For the three applied
force-closing methods (see Sect. 2.3) the gap becomes closed per
definition. When precipitation hours are included in the analysis, the
EC-based energy balance gap becomes larger due to the additional
contribution of available energy during these hours (i.e., 81.5 %
closure). Applying a correction for the missed latent heat flux during
precipitation hours (based on the EL vs. EL0 comparison of Table 2,
i.e., increasing latent heat flux by 5 % on the average) results in a
closure of 84.4 %. This shows that the amount of underestimation of latent
energy during precipitation hours is substantial compared to the overall
energy imbalance, contributing about 15 % to the uncorrected gap
(cf. 81.5 % vs. 84.4 %). Applying in addition the energy gap correction of
the precipitation-masked data (i.e., from Fig. 8,
left part) results in closures of 97.1, 97.5, and 97.7 %,
respectively. Thus, the additional amount of evapotranspiration during
precipitation hours as estimated by the lysimeter corresponds well with the
independent measurements of available energy during these periods. It should
be noted here that the amount of underestimation of evapotranspiration
during precipitation periods scales with the time step used in Eqs. (1) and (2).
Yearly aggregated available energy (Rn-G) vs. sum
of turbulent fluxes (for daytime, time period June 2009 to December 2015,
excluding gap-filled data), with percentages denoting the amount of closure.
(a) Totals calculated from hourly data masked for precipitation. The energy
closure amounts to 86.4 % for the measured turbulent fluxes (i.e., no gap
correction) and the gap becomes per-definition closed for the three applied
corrections (i.e., correction according to the Bowen ratio EC_BOWEN, and
correction by assigning the energy balance closure gap to sensible heat-flux
only EC_H and to latent heat-flux only EC_E; see Sect. 2.3). (b) Totals
calculated by including also precipitation hours. Here the gap is
corrected by applying a correction for missed evapotranspiration during hours
with precipitation (based on the lysimeter evapotranspiration estimates
EL and EL0; denoted EL vs. EL0
correction) plus considering the energy gap correction based on the
precipitation-masked data (see text for details).
The site-specific error in the lysimeter evapotranspiration is discussed in
Seneviratne et al. (2012). An overall measurement uncertainty of 5–10 % is
assumed, whereas higher errors (20 % or more, e.g., Gurtz et al., 2003)
can be assumed during periods of snow cover. In the latter case, also errors
in the precipitation measurement (high undercatch under snowfall, e.g.,
Rasmussen et al., 2012) strongly contribute to errors in EL. While these
errors are large in relative terms during the affected months, they are
nonetheless only affecting EL when it is at very low values (late autumn,
winter, and early spring; see Fig. 1). Hence,
they should neither substantially affect the lysimeter evapotranspiration during
the growing season nor when aggregated on the yearly timescale.
Summary and conclusions
We examined and compared two well-established methods to measure
evapotranspiration at the site level: lysimeter-based measurements (EL),
which are common in hydrological research, and eddy covariance
flux measurements (EEC), which are widely used in micrometeorology. For
the analyses, we employ parallel measurements based on these two methods
carried out at a research catchment in northeastern Switzerland and covering
the time period 1 June 2009 to 31 December 2015. Over this multi-year time
period, the measurements were compared on yearly down to hourly timescales.
Moreover, they are related to a 40-year-long lysimeter evapotranspiration time series.
Overall, the lysimeter and EC measurements agree well, in particular on the
annual timescale. Also, the long-term lysimeter evapotranspiration agrees
well with a catchment-wide estimate of evapotranspiration based on the
catchment water balance over hydrological years (and assuming no changes in
storage). This emphasizes the representativeness of the site-level lysimeter
and EC measurements for the entire catchment despite their comparatively
small source areas. We note, however, that the agreement is closest when the
two time series are processed in the same manner, i.e., setting hourly
evapotranspiration to 0 during hours with precipitation (EL0 for
lysimeter record). The lysimeter measurements actually reveal the occurrence
of non-negligible evapotranspiration fluxes during these periods, which
leads to an underestimation of 5 % on average of the total
evapotranspiration fluxes with this processing. Hence, the lack of reliable
EC measurements from the open-path IRGA immediately following precipitation
events significantly contributes to the overall underestimation of latent
heat flux from EC measurements, at least for humid sites such as
Rietholzbach. Given this issue of underestimation of the EC
evapotranspiration in hours with precipitation, a correction based on
lysimeter estimates for these specific time periods could be possibly
envisaged in future studies for humid sites, in addition to the correction
for the energy balance closure gap. We further note that the difference
between the EC and EL0 lysimeter evapotranspiration shows a seasonal
cycle, but the same pattern on different timescales (monthly to hourly).
In conclusion, our results still highlight remaining uncertainties in the
various methods and techniques available to measure and estimate
evapotranspiration. Nonetheless, the good agreement of the different
methodologies on yearly timescale is an important result in the context of
long-term water-balance studies. In addition, our results emphasize the
value of parallel lysimeter- and EC-based measurements to characterize the
respective errors of these measurement systems.
Data availability
The data basis for the presented analyses is available at 10.5905/ethz-1007-91.
The data consist of the monthly time series for the 1976–2015 time period of
lysimeter evapotranspiration, lysimeter seepage, catchment runoff, air temperature,
precipitation, and net radiation (see also Seneviratne et al., 2012), as well
as the hourly time series for the 2009–2015 time period of lysimeter
evapotranspiration and EC evapotranspiration.
The Supplement related to this article is available online at doi:10.5194/hess-21-1809-2017-supplement.
Irene Lehner implemented the initial analyses for this
study and co-wrote a first version of the manuscript with Sonia I. Seneviratne.
Martin Hirschi and Dominik Michel revised and extended the evaluation and
revised the manuscript together with Sonia I. Seneviratne. Sonia I. Seneviratne
initiated and oversaw the project.
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the Federal Office for the Environment (FOEN/BAFU) for providing
the runoff data, MeteoSwiss for data, as well as ETH Zurich for funding. We also
thank Karl Schroff for his technical support in setting up and maintaining the
measurement site.
Edited by: N. Romano
Reviewed by: S. Consoli, R. Anderson, and one anonymous referee
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