Introduction
Climate and land use changes are taking place on different spatial and
temporal scales, affecting all environmental compartments. Soil water content
is known to be a major control for evapotranspiration, precipitation–runoff
response, and heat transfer between soil and atmosphere, and it plays an
essential role for climate projections, weather and flood forecasting, water
and soil resources management, agriculture, and water quality control
(Albertson and Kiely, 2001; Betts et al., 1996; Crow et al., 2005; Robinson
et al., 2008; Vereecken et al., 2008; Western et al., 2002). However, the
highly heterogeneous pattern of soil water content leading to complex and
scale-dependent patterns of water, energy, and matter fluxes makes it
challenging to predict terrestrial system responses for both scientists and
policymakers (Jaeger and Seneviratne, 2011; Seneviratne et al., 2010).
Therefore, integrated observations of soil water content and the exchange of
water and heat between the soil, vegetation, and atmosphere are critical to
improving our understanding of the terrestrial system response to changes in
climatic conditions and land management (Dirnbock et al., 2003; Foley et al.,
1998; Hinzman et al., 2005; Refsgaard, 1997; Seneviratne et al., 2010; Guo
and Lin, 2016) and serve as key data in validating remote sensing data
products (e.g., Rötzer et al., 2014; Cosh et al., 2016).
To this end, a network of integrated observation platforms has been
established in the framework of the Terrestrial Environmental Observatories
(TERENO) initiative (funded by Helmholtz Association of German Research
Centers) to investigate the consequences of global change on terrestrial
ecosystems (Bogena et al., 2012; Zacharias et al., 2011). TERENO aims to
collect long-term time series of system states and fluxes using
state-of-the-art monitoring technologies, e.g., to close the local water
balance (Graf et al., 2014) and to investigate of the effects of
deforestation on water, energy, and matter fluxes in an integrative manner
(Bogena et al., 2015). The Rollesbroich grassland experimental catchment is
part of the TERENO observatory Eifel/Lower Rhine Valley. All components of
the water balance (e.g., precipitation, evapotranspiration, runoff, soil
water content) are continuously monitored using state-of-the-art
instrumentation, providing detailed information about the spatial and
temporal variation of the local water cycle for the evaluation of
hydrological models (Bloschl and Sivapalan, 1995; Thompson et al., 2011). In
addition, using water balance data within the context of hydrological
modeling helps to determine measurement errors, to diagnose such errors,
and to avoid misattribution of water balance components (Evett et al., 2012;
Kampf and Burges, 2010; Vasilenko, 2004). Finally, quantification of water
balance components is helpful for understanding the availability of water
resources, the potential of hydrologic extremes such as floods and droughts,
and the interactions between the land surface and the atmosphere
(Flerchinger and Cooley, 2000; Huntington, 2006).
Here, we present a comprehensive hydrological data set recorded in the
Rollesbroich catchment from 1 May 2011 to 31 December 2013. The hydrological
data set includes time series of meteorological forcing (i.e., precipitation,
air temperature, air humidity, radiation components, and wind speed), actual
evapotranspiration, runoff, as well as soil water content data from a
wireless sensor network (SoilNet). In addition, information on soil physical
properties and vegetation (i.e., leaf area index, LAI) useful for the
parameterization of hydrological models is presented.
Methods
Meteorological data
Meteorological data, i.e., precipitation, air temperature, air humidity,
radiation components, and wind speed, were recorded at a micrometeorological
tower located in the center of the almost flat terrain in the southern part
of the Rollesbroich catchment (see Fig. 1). Wind speed was obtained with a
sonic anemometer at 2.6 m above surface (CSAT3, Campbell Scientific, Inc.,
Logan, USA). The H2O concentration was measured using an open-path
infrared gas analyzer (LI7500, LI-COR Inc., Lincoln, NE, USA) at the same
height. Air temperature and humidity (HMP45C, Vaisala Inc., Helsinki,
Finland) were measured at 2.6 m height above the ground surface. Incoming
short- and long-wave radiation were determined using a NR01 four-component net
radiometer (Hukseflux Thermal Sensors, Delft, Netherlands). Data of all
instruments including diagnostic data were recorded with a logger (CR3000,
Campbell Scientific, Logan, UT, USA) at 20 Hz. Precipitation was recorded by
a heated Hellmann-type tipping bucket rain gauge (eco-Tech GmbH, Bonn,
Germany). In July 2013, a weight-based precipitation gauge (Pluvio2, OTT
Hydromet GmbH, Kempten, Germany) was added to the nearby backup climate
station, providing more accurate measurements of all precipitation types.
Both precipitation gauges were installed at a height of 1 m above ground
surface as recommended by the German Weather Service for sites at an
elevation above 500 m a.s.l. with occasional heavy snowfall. All
meteorological measurements were stored at 10 min intervals.
Map of the Rollesbroich catchment showing locations of the SoilNet
sensor network, locations of the soil samples to determine soil physical and
chemical properties, locations of the soil samples for site-specific
calibration of the CRIM model, the location of the eddy covariance (EC)
station, 2.5 m contour lines, and catchment boundary.
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Actual evapotranspiration
Latent heat flux was obtained by the eddy covariance (EC) technique. The EC
post-processing software TK3.1 (Mauder and Foken, 2011) was used to calculate
latent heat flux from the vertical wind velocity obtained by the sonic
anemometer (CSAT3, Campbell Scientific, Inc., Logan, USA) and water vapor
density obtained by an infrared gas analyzer (LI7500, LI-COR Inc., Lincoln,
NE, USA). The processing and quality assurance of the EC data followed the
corresponding TERENO strategy presented in Mauder et al. (2013). Actual
evapotranspiration was calculated from the latent heat flux using
ETa=LHρw⋅LwaterLwater=10-3⋅(2500.8-2.36⋅T+0.0016⋅T2-0.00006⋅T3),
where ETa is actual evapotranspiration (m s-1), LH is latent
heat flux (W m-2), ρw is water density (kg m-3),
Lwater is latent heat of condensation of water in the temperature
range from -25 to 40 ∘C (J kg-1), and T is air temperature
(∘C).
Runoff
Runoff was measured at the catchment outlet using a gauging station equipped
with a combination of a V-notch weir for low flow measurements and a Parshall
flume to measure normal to high flows. Runoff data of the two weir types were
combined by using V-notch values for water levels below 5 cm, Parshall flume
values for water levels greater than 10 cm and the weighted mean of V-notch
and Parshall flume values for water levels between 5 and 10 cm, where the
water levels refer to those of the V-notch weir.
Soil water content
Soil water content was measured at 87 SoilNet locations within the southern
part of catchment (Fig. 1) using SPADE soil moisture sensors (Qu et al.,
2013; Hübner et al., 2009). The SPADE sensors were installed at 5, 20, and
50 cm depth. Two SPADE sensors were installed in parallel at each depth with
a distance of ∼ 10 cm to increase the sensing volume and to allow
examination of inconsistencies in sensor reading. The measurement frequency
was 15 min.
The SPADE sensor is a ring oscillator. The oscillator frequency is a function
of the dielectric permittivity of the surrounding medium (Qu et al., 2013),
which strongly depends on the water content of the soil because of the high
permittivity of water (εw≈80), as compared to
mineral soil solids (εs≈2 ∼ 9), and air
(εa≈1). The temperature of the soil was also
determined by the SPADE sensor using a digital thermometer (DS18B20) with an
accuracy of ±0.5 ∘C in the range from -10 to 85 ∘C.
The two-step calibration procedure suggested by Jones et al. (2005) was used
to relate sensor reading to soil moisture. In a first step, reference liquids
with a known dielectric permittivity were used to calibrate the following
empirical model (Eq. 3) that relates sensor reading to apparent dielectric
permittivity:
Ka=γ+1α+β/ν,
where Ka is the dielectric permittivity, α, β, and
γ are the fitting parameters, and ν is sensor output (unit, V).
Prior to installation, 60 SPADE sensors were calibrated in five reference
liquids that covered a permittivity range from 2.2 to 34.8. The outputs for
the 60 sensors as well as the fitted model are shown in Fig. 2. The root mean
square error (RMSE) between known and predicted dielectric permittivity was
0.0188, and the best fitting parameters of α, β, and γ
were -0.1502, 0.3612, and -0.1599, respectively.
Sensor output of 60 SPADE sensors in five reference liquids (Ka
refers to dielectric permittivity). The fitted “universal” calibration
relationship (Eq. 3) is also presented.
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In a second step, a site-specific calibration between dielectric permittivity
and soil water content was obtained using a complex refraction index model
(CRIM, Eq. 4) as proposed by Birchak et al. (1974):
θ=Ka0.5-1-η⋅Ksolid0.5-η⋅Kair0.5Kwater0.5-Kair0.5,
where η is the porosity of the soil, 1-η is the solid fraction,
Ka is the permittivity of soil, and Kwater, Ksolid,
and Kair are the permittivity of water, solids, and air component
of soil, respectively. In order to estimate the unknown value of
Ksolid and to assess the accuracy of this relationship,
15 undisturbed samples (length = 7.7 cm, diameter = 5 cm) were
taken from the two main soil types in 5, 20, and 50 cm depth. These samples
were first saturated with deionized water and then CS 640-L three-rod TDR probes
with a length of 7.5 cm were inserted in the middle of the sample. These
probes were connected to a TDR100 system (Campbell Scientific, Inc., Logan,
USA) to determine the dielectric permittivity of the soil samples using a
custom-made Matlab algorithm based on the travel time analysis algorithm of
Heimovaara and Bouten (1990). Subsequently, the samples were dried and both
weight and dielectric permittivity of each sample were determined in regular
time intervals. After drying at room temperature, the remaining water was
removed by oven-drying at 105 ∘C for 24 h so that the dry bulk
density, porosity, and the volumetric soil water content could be determined
from the recorded weights.
The measured dielectric permittivity and soil water content and the fitted
relationships are shown in Fig. 3. Because of the large difference in
porosity at the three depths, the mean porosity at each depth was determined
from the calibration samples and used to parameterize three different
relationships. After fitting the solid permittivity for each depth, the
performance of these calibration relationships was judged by the RMSE
(Table 1). It was found that the three relationships performed well with a
RMSE ranging from 0.022 to 0.028 cm3 cm-3.
Parameters and the RMSE of the CRIM model for 5, 20, and 50 cm
depth.
5 cm
20 cm
50 cm
Kwater
78.54
78.54
78.54
Ksolid
2.08
3.78
4.40
Kair
1.00
1.00
1.00
η
0.59
0.49
0.41
RMSE
0.028
0.025
0.022
As already briefly discussed in Qu et al. (2014), we found that sensor output
showed artificially pronounced diurnal variations after the deployment of the
sensor network. This behavior was attributed to a charging capacitor that
affected the first reading of the SPADE sensor. If multiple sensor readings
were made sequentially without turning off the sensor, the stability of the
measurement considerably improved and the temperature dependence of the
measurements disappeared (Qu et al., 2014). To correct these
temperature-dependent oscillations in sensor reading, the acquisition
software of SoilNet was changed temporarily so that two measurements were
saved from 5 September 2012 to 3 March 2013. After 3 March 2013, the software
was updated again so that only the second more accurate measurement was
saved. Figure 4 shows an example of the measured voltage for the first and
second measurements and the soil temperature for a selected sensor (location
053, 5 cm depth). It can be seen that the difference between the two
measurements (Δv) is strongly correlated with soil temperature and
could be fitted with an empirical second-order polynomial function with a
RMSE of 5.18 mV (Fig. 5). Such second-order polynomial functions were
obtained for all sensors individually and subsequently used to correct
measurements made between April 2011 and September 2012. After the
correction, measurements from the closely spaced sensors at a single
measurement location agreed well with each other with a RMSE that varied from
0.010 to 0.035 cm3 cm-3. The uncorrected and corrected voltage as
well as the associated soil water content of one representative sensor is
plotted in Fig. 6. It is clear that the corrected measurements before
September 2012 better match the expected soil water content after
September 2012. On average, the corrected soil water content was
0.07 cm3 cm-3 lower than the uncorrected values. The plausibility
of the corrected soil water content values is further supported by the fact
that the increase in soil water content in the winter of 2012 disappeared
after the temperature correction. The corrected soil water content is now
relatively constant in winter and the maximum of the soil water content
corresponds well with the porosity determined from the soil samples (both
0.59 cm3 cm-3).
Relationship between dielectric permittivity and soil water
content for the Rollesbroich test site and the derived Ka-θ
models (CRIM).
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Time series of the first and second voltage measured after
software update as well as the respective voltage differences (ΔV)
and the associated temperatures.
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Soil physical properties
Soil cores were taken at all locations where soil water content sensors were
installed (length of 100 cm, diameter of 8 cm; Carl Hamm GmbH, Essen, Germany).
Then, soil samples were taken from three pedological horizons (0–10, 10–20,
and 20–40 cm) within the soil cores (see Fig. 1), which resulted in a total
of 282 soil samples. Sand, silt, and clay fractions were determined using a
combination of wet sieving (sand fractions) and sedimentation (silt and clay
fraction) following ISO-11277 (2009). Organic carbon (C) and total nitrogen
(N) content were determined by a combination of standard laboratory analyses
(ISO-10694, 1995) and mid-infrared spectroscopy (MIRS) using partial least
square regression (PLSR) (Bornemann et al., 2008). Mid-infrared spectra were
recorded in diffuse reflectance mode by a Tensor 27 (Bruker, Billerica,
United States), extended with a high throughput device (HTS-XT) equipped with
a liquid N2-cooled mercury–cadmium–telluride detector. The OPUS 6.0
software (Bruker) was used for spectrum analyses and evolution of prediction
models for C and total N. Models were validated through cross-
(leave-one-out) and test-set validation (10 %). Cross validation yielded
coefficients of determination of 0.99 (for both C and total N).
Second-order polynomial function fitted to the relationship
between soil temperature and the difference between the first and second
voltage measurements.
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Leaf area index
The agricultural management of the different fields in the Rollesbroich
catchment is very similar. Heterogeneity of the grass cover is mainly caused
by different mowing times, which typically vary only by a few days.
Therefore, we assume that the grass cover is homogeneous on the long-term in
the Rollesbroich catchment. The effective leaf area index (LAIeff)
that contributes to actual evapotranspiration was computed from grass height,
h, using the following equations (Allen et al., 2006; Rochette et al.,
1991):
LAI=24⋅h,LAIeff=LAI0.3⋅LAI+1.2.
Time series of the uncorrected (red) and corrected (black) voltage
and soil water content (SWC) measurements of one representative sensor.
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Average grass height in the Rollesbroich catchment was determined weekly by
measuring grass height at five representative locations in the catchment.
Time series of LAI were also derived from RapidEye images using the
normalized difference vegetation index (NDVI) approach (Myneni et al.,
1997). The NDVI is calculated from infrared (NIR) and red-edge spectral
bands. The NDVI of a plant with high LAI has a high ratio between NIR and
red reflectance that can be detected by RapidEye. The LAI was computed from
the NDVI values using a radiative transfer model (Myneni et al., 1997).
Detailed information about the procedure can be found in Ali et al. (2013).
Data management and quality control
The distributed spatial data infrastructure TEODOOR
(http://www.tereno.net) was developed to handle, describe, exchange,
and publish all monitored environmental data of the TERENO project (Kunkel et
al., 2013). Each institution hosting an observatory maintains its local data
infrastructure. The observatories are connected via OGC-compliant
web services, while the TERENO Data Discovery Portal (DDP) as a central
application enables data searching, visualization and download. According to
the TERENO data policy it is obligate that each data set is described by
standardized metadata elements, like ISO19115, OGC SensorML, or NetCDF's
CF Conventions.
The observation data sets are processed and assessed within TEODOOR in three
different ways (for details see Devaraju et al., 2015). In the first data
processing type, the imported data undergo automated quality checks (e.g.,
minimum/maximum thresholds) and subsequently are published after visual
inspection by experts. For example, data from weather stations and river
gauges are processed in this way. Secondly, more complex data can be
externally processed and assessed by the principal investigators and
subsequently imported into TEODOOR, e.g., EC data. The third
data processing type also involves automatic data import, but in this case
the data quality assessment is executed using an external evaluation method
developed by the responsible scientist, e.g., wireless sensor network data.
Subsequently, the flagging information is updated by TEODOOR after the
quality assessment has been completed.
Characterization of data quality is done by three descriptors, which are
stored together with each observation: data uncertainty, data processing
levels, and data quality flags. The observed data values remain unchanged in
any case. Data uncertainty arises from the observation process itself and is
mainly determined by the accuracy of the sensors used. Data processing levels
indicate the status of data handling. For instance, (unpublished) raw data
are
termed “level 1” and “level 2” refers to data subjected to quality control,
whereas the higher levels refer to derived data products. The flagging scheme
consists of two tiers: the first tier includes generic flags, e.g., “good”,
“unevaluated”, “suspicious”, or “bad”. The second tier is
use-case-specific and indicates either the result of individual quality
tests, e.g., failed gradient checks, or background events affecting data
values, e.g., icing events. In the following section, we present in more
detail how data were checked for plausibility to derive the data sets used in
this study.
Daily averages of air temperature, relative humidity, wind speed,
and incoming short- and long-wave radiation measured at the eddy covariance
station from 1 May 2011 to 31 December 2013.
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Meteorological data and latent heat flux
Meteorological data were checked for quality by a multi-step quality control
including the use of diagnostic information provided by the instruments, the
application of site specific plausibility limits, visual inspections of the
data series, and cross-checks with data from the nearby backup weather
station. The quality control of the latent heat flux was in accordance with
the standardized method for the processing and quality assessment of EC data as suggested by Mauder et al. (2013). This scheme includes
site-specific plausibility limits and the application of a spike removal
algorithm based on median absolute deviation of raw measurements. Processed
half-hourly fluxes and statistics were checked using three different flags
(high, moderate, and low) based on tests of integral turbulence and
stationarity (Foken and Wichura, 1996). As a result of this quality check,
48.5 % of all possible 30 min observations of latent heat flux were
marked as good-quality data, 12.2 % were marked as moderate quality,
35.4 % were marked as low-quality data (35.4 %), while 3.9 % were
marked as missing data because of power failure and sensor maintenance. In
this study, only data of high and moderate quality were used. A more detailed
description of the treatment of EC data can be found elsewhere
(Gebler et al., 2015; Post et al., 2015).
Runoff
As a first quality check, time series of the V-notch weir and a Parshall
flume were compared for consistency. In addition, both runoff time series
were visually inspected for inexplicable outliers (e.g., runoff peak without
preceding rainfall event) and sensor failures. As outlined above, unreliable
data were identified and appropriate flags were set.
Soil water content
Measurements of soil moisture outside the physical plausibility range (0.05
to 0.85 cm3 cm-3) were identified and flagged (please note that
we used a conservative upper limit that can also be used for organic soils).
Subsequently, unreliable measurements were identified by analyzing the first
derivative of the soil water content time series. In case an increase larger
than 2 times the standard deviations of the preceding 24 h was observed,
this measurement was flagged as an unreliable measurement. In addition, the
whole data set was visually inspected to verify the results of the automatic
flagging procedures.
Descriptive statistics of soil properties determined from 273 soil
samples taken in the Rollesbroich catchment.
Clay
Sand
Silt
Bulk
Carbon
Porosity
%
%
%
density
content
(cm3 cm-3)
(g cm-3)
(g kg-1)
5 cm
mean
18.99
19.90
61.10
0.94
54.47
0.65
SD
2.00
3.82
3.79
0.12
15.82
0.05
20 cm
mean
18.03
20.76
61.20
1.28
34.08
0.52
SD
1.99
4.03
3.46
0.15
16.84
0.05
50 cm
mean
16.50
22.00
61.50
1.52
11.22
0.43
SD
2.40
5.68
4.53
0.16
6.01
0.06
ANOVA results of LAI determination with grass height and RapidEye
from May to December 2011.
Source of variation
SS
df
MS
F
P value
F critical
Variability between group
0.311
1
0.311
0.717
0.411
2.145
Variability within group
6.074
14
0.434
Total
6.385
15
Data sets
Hydrometeorological data
Temporal dynamics of the most important meteorological data (i.e., air
temperature, air humidity, radiation components, wind speed, precipitation,
actual evapotranspiration, and runoff) and water balance components (i.e.,
precipitation, actual evapotranspiration, runoff, and soil water content)
from 1 May 2011 to 31 December 2013 are plotted in Figs. 7 and 8. The air
temperature, relative humidity, shortwave radiation, and evapotranspiration
showed a clear annual pattern. The highest runoff amounts occurred during the
winter seasons due to high precipitation amounts and low evapotranspiration
rates, as well as overland flow due to saturation excess (Fig. 8). Generally,
soil water content showed a strong dependence on precipitation events
especially at 5 and 20 cm depth. Quick increases in soil water content can
be observed after rainfall events, which were followed by a slow recession
during periods without precipitation. A detailed analysis of the spatial
variability of the soil moisture has been presented by Qu et al. (2014).
In addition, there seems to be a rising trend in the soil moisture signal in
the first year, which might be due to slow soil recompaction after
installation (see, e.g., Poltoradnev et al., 2014) or a temporal drift in the
electronics of the SPADE sensors. Unfortunately, the exact reasons are
unclear. The trend in the average soil moisture content could be corrected
using independent cosmic-ray soil moisture measurements (see Baatz et al.,
2014), but this is not possible for the individual sensors. Since any
correction of individual sensors is going to be subjective because of a lack
of appropriate reference measurements, we have decided to refrain from doing
further data corrections at this stage. We are convinced that the data are
still useful despite possible (minor) sensor drifts, as also demonstrated by
a range of recent publications relying on this data (e.g., Qu et al., 2014,
2015; Baatz et al., 2014, 2015).
Water balance closure
The water balance can be written as
P=R+ETa+ΔS,
where P is precipitation, R is runoff, ETa is actual
evapotranspiration, and ΔS is the storage term. Because of the
relatively low hydraulic conductivity (10-9 to 10-7 m s-1)
of the aquifer bedrock (HK100, 2009), we neglected deep percolation. Mean
average annual precipitation was partitioned into about 57 % actual
evapotranspiration and 50 % runoff. The residual of the balance was
within 7 % of precipitation for the whole time period as shown in Fig. 8.
This residual is related to measurement uncertainty and soil water storage
depletion. As presented by Graf et al. (2014), soil water storage can be
derived from the measured soil water content, θ, as follows:
St=∫θx,y,z,tdxdydzdt≈∑i=1Nciθi,t+ε,
where t is time, z is depth, the integral refers to the 3-D domain as
defined by the catchment boundary. The discrete version of storage is
expressed by the right-hand side of the equation, where i is the number of
sensors, and ci is the empirical coefficient representing the 3-D domain
which is well represented by the sensor. The residual of ε is
the storage affected by vegetation or groundwater and not represented well by
the sensor. The changes in ΔS are expected to correspond well with
the residual of the major water budget component, P-ETa-R
(Eq. 7). Thus, the time derivative of soil water storage should be linearly
related to this residual, although measurement errors within the other water
balance components and the unaccounted storage terms (e.g., vegetation
storage) will deteriorate this relation. Our results show that there is a
linear relationship between the derivative of soil water content and the
water balance residual (Fig. 9). The R2 is 0.60, which means that
60 % of the residual of the water balance is explained by soil water
storage changes in the catchment.
Daily and cumulative time series (1 May 2011 to 31 December 2013) of precipitation (P), runoff (R), actual evapotranspiration
(ETa), and spatial mean soil water content (SWC) at three depths. The
SWC data contains two major gaps due to frozen soil conditions and
maintenance of the SoilNet system. The dashed line indicates the soil
porosity determined from the soil samples (0.59 cm3 cm-3).
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Time derivative of volumetric soil water content (average of all
depth) vs. the water balance residual.
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Soil physical properties
The mean and standard deviation of sand, silt, and clay percentages, organic
carbon content, and bulk density are shown in Table 2. The bulk density
ranged from 0.94 to 1.52 g cm-3 and generally increased with depth.
Porosity ranged from 0.43 to 0.65 cm3 cm-3 and decreased with
depth. The higher spatial variability of porosity of the subsoil is caused by
the higher and more variable stone content at this depth. In addition, former
agriculture land management activities reduced the spatial variability of
porosity of the top soil (plough layer, 20 to 30 cm thick). The measured
soil texture fractions and soil density were used to estimate the spatial
distribution of soil hydraulic properties with the pedotransfer function
Rosetta (Schaap et al., 2001). Figure 10 shows the spatial distribution of
Mualem–van Genuchten (MvG) soil hydraulic parameters (van Genuchten, 1980)
derived with Rosetta at 5 cm depth. Some soil hydraulic parameters show a
distinct pattern. For instance, the MvG parameter α is generally
larger in the northern part than in the southern part of the catchment. Such
information is important for the investigation of controlling factors of
spatial patterns of soil water content.
Spatial distribution of soil hydraulic properties (θs, log10(Ks), log10(α), and n) at 5 cm depth
derived from soil information obtained from the soil cores taken in the
Rollesbroich catchment.
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Leaf area index
The LAI derived from measured grass height agrees well with the LAI obtained
from RapidEye images (Fig. 11). Both LAI time series showed a distinct annual
pattern with the highest values during the summer time. We averaged the
monthly LAI derived from measured grass height and an ANOVA was conducted to
test whether there was a significant difference between the LAI obtained from
grass height and RapidEye in the time period of May to December in 2011. The
results of this ANOVA (Table 3) confirmed that there is no significant
difference between the two methods to determine LAI.
Time series of leaf area index (LAI) computed from measured grass
height and extracted from the RapidEye images of the Rollesbroich catchment.
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Data availability
We presented data from the intensively instrumented hydrological observatory
Rollesbroich providing long-term hydrometeorological data with high spatial
and temporal resolution. Our results showed that the catchment water balance
is reasonably closed by the provided measurements and that 60 % of the
water balance residual could be related to soil water storage changes within
the Rollesbroich catchment. In addition, important soil physical and chemical
properties (e.g., hydraulic properties) have been reported in addition to
catchment-scale information on vegetation. This comprehensive hydrological
data set can be used for the calibration, validation and improvement of
hydrological models, e.g., in hydrological model intercomparison projects
(Breuer et al., 2009; Maxwell et al., 2014; Refsgaard, 1997; Smith et al.,
2004) and for the calibration and validation of remote sensing data products
(Bastiaanssen et al., 1998; Jackson et al., 2010; Le Hegarat-Mascle et al.,
2002; Njoku et al., 2003). All the presented data and data from further
monitoring stations located in the Rollesbroich catchment are freely
available from the TERENO data portal (http://www.tereno.net). In
addition, three persistent identifiers are associated with the data set
described here:
climate/runoff/water quality station: 10.5880/TERENO.2016.001 (Bogena, 2016a)
EC/climate station Rollesbroich: 10.5880/TERENO.2016.004 (Schmidt, 2016)
SoilNet Rollesbroich: 10.5880/TERENO.2016.003 (Bogena, 2016b)
Please note that the NetCDF format is used, which is a standard format for
data exchange. There is free conversion software available to convert the
NetCDF data format in other data formats, e.g., at
http://www.giss.nasa.gov/tools/panoply/. In addition, an
Excel file containing the basic soil information is available at the
“SoilNet Rollesbroich” download site and two Excel files containing the LAI
information presented in Fig. 11 are available at the “EC/Climate station
Rollesbroich” download site.