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The DOI for this manuscript is doi: 10.1175/BAMS-D-15-00277.1
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Wolf, B., C. Chwala, B. Fersch, J. Garvelmann, W. Junkermann, M. Zeeman, A.
Angerer, B. Adler, C. Beck, C. Brosy, P. Brugger, S. Emeis, M. Dannenmann, F.
De Roo, E. Diaz-Pines, E. Haas, M. Hagen, I. Hajnsek, J. Jacobeit, T. Jadghuber,
N. Kalthoff, R. Kiese, H. Kunstmann, O. Kosak, R. Krieg, C. Malchow, M. Mauder,
R. Merz, C. Notarnicola, A. Philipp, W. Reif, S. Reineke, T. Rödiger, N. Ruehr, K.
Schäfer, M. Schrön, A. Senatore, H. Shupe, I. Voelksch, C. Wanninger, S.
Zacharias, and H. Schmid, 2016: The ScaleX campaign: scale-crossing land-
surface and boundary layer processes in the TERENO-preAlpine observatory.
Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-15-00277.1, in press.
© 2016 American Meteorological Society
The ScaleX campaign: scale-crossing land-surface and boundary layer
processes in the TERENO-preAlpine observatory
B. Wolf1, C. Chwala1, B. Fersch1, J. Garvelmann1, W. Junkermann1, M. J. Zeeman1, A.
Angerer3, B. Adler2, C. Beck4, C. Brosy1, P. Brugger1, S. Emeis1, M. Dannenmann1, F. De Roo1,
E. Diaz-Pines1, E. Haas1, M. Hagen11, I. Hajnsek7,9, J. Jacobeit4, T. Jagdhuber7, N. Kalthoff2, R.
Kiese1, H. Kunstmann1,4, O. Kosak3, R. Krieg6, C. Malchow1, M. Mauder1, R. Merz6, C.
Notarnicola8, A. Philipp4, W. Reif3, S. Reineke1, T. Rödiger6, N. Ruehr1, K. Schäfer1, M.
Schrön5, A. Senatore10, H. Shupe1, I. Völksch1, C. Wanninger3, S. Zacharias5, and H. P.
1 Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-
2 Institute of Meteorology and Climate Research (IMK-TRO), Karlsruhe Institute of Technology (KIT), 76021 Karlsruhe,
3 Institute for Software & Systems Engineering (ISSE), University of Augsburg, Germany
4 Institute of Geography (IGUA), University of Augsburg, Germany
5 Department Monitoring & Exploration Technologies, Helmholtz-Centre for Environmental Research (UFZ), 04318 Leipzig,
6 Department Catchment Hydrology, Helmholtz-Centre for Environmental Research (UFZ), 06120 Halle/Saale, Germany
7 Department of Radar Concepts, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
8 Institute for Applied Remote Sensing, European Academy of Bolzano (EURAC), 39100 Bolzano, Italy
9 Institute of Environmental Engineering, ETH Zürich, Switzerland
10 Department of Civil and Chemical Engineering, University of Calabria, Rende/Cosenza, Italy
11 Institute of Atmospheric Physics, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
Corresponding author: Benjamin Wolf
Institute of Meteorology and Climate Research (IMK-IFU)
82467 Garmisch-Partenkirchen, Germany
Manuscript (non-LaTeX) Click here to download Manuscript (non-LaTeX)
Augmenting long-term ecosystem-atmosphere observations with multidisciplinary intensive
campaigns aims at closing gaps in spatial and temporal scales of observation for energy- and
biogeochemical cycling, and at stimulating collaborative research.
ScaleX is a collaborative measurement campaign, co-located with a long-term
environmental observatory of the German TERENO (TERrestrial ENvironmental
Observatories) network in mountainous terrain of the Bavarian Prealps, Germany. The aims
of both TERENO and ScaleX include the measurement and modeling of land-surface
atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the
recognition that long-term intensive observational research over years or decades must be
based on well-proven, mostly automated measurement systems, concentrated on a small
number of locations. In contrast, short-term intensive campaigns offer the opportunity to
assess spatial distributions and gradients by concentrated instrument deployments, and by
mobile sensors (ground/airborne) to obtain transects and three-dimensional patterns of
atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are
ideal proving grounds for innovative instruments, methods and techniques to measure
quantities that cannot (yet) be automated or deployed over long time-periods. ScaleX is
distinctive in its design that combines the benefits of a long-term environmental monitoring
approach (TERENO) with the versatility and innovative power of a series of intensive
campaigns, to bridge across a wide span of spatial and temporal scales. This contribution
presents the concept and first data products of ScaleX-2015. The second installment of
ScaleX is set for the summer 2016 and periodic further ScaleX campaigns are planned
throughout the life-time of TERENO. This paper calls for collaboration in future ScaleX
campaigns, or by using our data in modeling studies. It is also an invitation to emulate the
ScaleX concept at other long-term observatories.
ScaleX is an intensive interdisciplinary observation campaign in a region of complex
topography and land-use/land-cover variations in Southern Germany. It explores the
question how well measured and modeled components of biogeochemical and biophysical
cycles match at the interfaces of soils, vegetation and the atmosphere, and across various
spatial and temporal scales. This type of lead question is not new: scale-integration in
observation and modeling for land surface – atmosphere exchange processes was one of
the principal motivations for past large-scale field programs, such as FIFE (First ISLSCP Field
Experiment, Kansas, USA; e.g., Sellers et al., 1988, 1992), BOREAS (Boreal Ecosystem-
Atmosphere Exchange Study, Canada; e.g., Hall, 1999; Sellers et al., 1995 – and articles in
the same issue), or CME (Carbon in the Mountains Experiment; e.g., Sun et al. 2010; Desai et
al. 2011) to name just three prominent examples. These (and other) field programs have
resulted in numerous publications, have spawned research ideas, and led to new
observation and modeling techniques in ecosystem-atmosphere science. Data from these
programs have served as valuable benchmarks for model development and measurement
inter-comparisons, and have contributed significantly to progress in scale-integration and
matching of observations and modeling. So why should we endeavor on yet other field
campaigns with similar objectives?
This question has many answers. Firstly, despite the progress achieved by past field
campaigns, the mismatch between observations of land-surface processes and their
modeled equivalents is still so large that it constitutes a major source of uncertainty in
climate models (e.g., Best et al., 2015). Secondly, new knowledge in science invariably gives
rise to new questions (Firestein 2012). As we learn more about dominant processes and
feedback relations, we discover patterns of discrepancy and unexplained deviations at
previously disregarded scales that are potentially responsible for long-term trends. Thirdly,
progress in instrumentation and data communications allow us to close gaps in the
temporal and spatial coverage of observations that previous field campaigns were limited
by. Lastly, experience shows that, whenever scientists from various backgrounds work
together, on the same objectives, and on the same field sites, collaboration fosters new
ideas and thinking-outside-the-box that gives rise to new knowledge (Hall 1999; Goring et al.
In our view, these points alone justify a new scale-crossing field campaign such as ScaleX.
However, in a number of ways ScaleX is different from previous field programs. As
presented below, ScaleX is directed at a range of spatial scales that is generally smaller, but
with higher measurement and modeling resolution and more complex topography than
considered in previous land surface – atmosphere processes campaigns.
Yet the most important novelty of ScaleX probably lies in its infrastructural setting and
temporal outlook. The backbone of micrometeorological, hydrological and ecosystem-
atmosphere exchange instrumentation used by ScaleX is formed by the permanent
environmental TERENO-preAlpine observatory (Zacharias et al. 2011), with stations
distributed along an elevation gradient in the pre-Alpine region of Germany (see Section 3).
The ScaleX campaign builds on this research infrastructure with a multitude of additional
instruments and observation platforms (ground based in-situ, remote sensing, and
airborne), to enhance spatial and temporal measurement resolutions, and to complement
the permanent suite of measurements with additional observed variables and processes.
The first campaign (June-July 2015) was run by KIT/IMK-IFU Garmisch-Partenkirchen (the
institute that operates the backbone infrastructure) with collaborating partners from the
region (see list of co-author affiliations). The second campaign, set for June-July 2016,
includes a larger number of national and international partners and collaborators, who are
invited to use the permanent research infrastructure, with data- and power connectivity.
Because TERENO-preAlpine is set to be operated for the next two decades or longer, it will
be possible to re-visit the same sites periodically again in future editions of ScaleX. In our
view this long-term continuity is a valuable opportunity to expand the usual narrow
temporal constraint of intensive measurement campaigns toward time-scales that are
important for land-use change, climate change and ecosystem renewal. The ScaleX concept
can likely serve as a model for similar combinations of long-term backbone observatories
and periodic intensive campaigns in other permanently operated ecosystem-atmosphere
observatories, such as in the AmeriFlux network (Baldocchi 2003; Boden et al. 2013) and the
National Ecological Observatory Network (NEON; Kampe et al. 2010) of the United States, or
the Integrated Carbon Observation System (ICOS; https://icos-ri.eu) in Europe.
In short, the general idea of ScaleX is to introduce a concept that combines the objectives of
long-term ecosystem research with those of intensive campaigns; to expand the scale and
resolution of observations; to stimulate collaborative, interdisciplinary research and
The purpose of the present article is to provide some background on the rationale,
organization and specific research goals of ScaleX (Section 2); to briefly introduce the
TERENO-preAlpine observatory with its principal site and the long-term backbone
observation program (Sections 3 and 4); to give an overview of the instrumentation
deployed during ScaleX-2015 (Section 5); and to present examples of derived data products
(Section 6). Lastly, but most importantly, this article hopes to attract interested research
groups as collaborating partners in future campaigns of ScaleX (Section 7).
In the biogeochemical and biophysical cycles that shape our world, terrestrial and aquatic
ecosystems are the most important brokers for energy and matter exchanges between the
atmosphere, oceans and continents. They provide natural resources, are mediators of
climate change, and contribute to water availability and soil conservation. Terrestrial
ecosystems in particular are extremely variable over a wide range of scales both in space
and time, and yet they form the most direct foundation for the majority of food production,
water- and air-quality that humanity depends on. Processes, such as the flows of energy,
water, oxygen (O), carbon (C), nitrogen (N), and other essential trace substances in and
between ecosystems and their environment indicate the vibrance and variability of
ecosystems, and underline the inter-dependency of supporting, provisioning and regulating
services that ecosystems provide (e.g., Reid et al., 2005).
In terrestrial ecosystems, important exchange fluxes occur at the interfaces of the Earth-
system compartments atmosphere, biosphere, pedosphere, and hydrosphere that each act
as reservoirs and sites of transformation in biogeochemical and energy cycling. Given their
different nature, chemical and physical transformation and transport processes within these
compartments act on vastly different temporal and spatial scales (temporally from fractions
of a second for turbulence and biochemical light-responses, to decades or longer for climate
trends and soil development; spatially from soil microbes to hydrological catchments or
landscape units; e.g., Ehleringer and Field 1993), and interactions between them are
typically characterized by highly non-linear feedback dynamics. Thus, no single natural scale
of study exists that can adequately represent the manifold interplay of ecosystem-
atmosphere processes (e.g., Levin, 1992). Scaling errors typically arise from inconsistencies
or nonlinear behavior when observations or models at one scale are transferred or
aggregated to another, or when model or measurement resolutions filter out temporal or
spatial interactions (e.g., Mahrt 1987; Bünzli and Schmid 1998; Schmid and Lloyd 1999).
From this perspective, any activity aiming to understand interactions between Earth-system
compartments requires a scale-integrative observation strategy and needs to go beyond
simply assigning aggregated measured values to a larger spatial or temporal domain
(Osmond et al. 2004; May 1999; Caldwell et al. 1993).
One pertinent example for which a scale-integrative observation approach is considered to
be essential is the observation of the energy balance at the land surface. The turbulent
components of the relevant exchange fluxes (i.e., sensible and latent heat fluxes) are
commonly determined by the eddy-covariance (EC) method. In typical deployments, EC
measurements capture turbulent surface-atmosphere interactions on spatial scales of a few
hundred meters or less, and over time scales of an hour or less (e.g., Baldocchi 2003).
However, microscale atmospheric processes (e.g., Orlanski, 1975) can be influenced by
circulation patterns at scales of up to several 10s of kilometers, persisting for hours (sub-
meso- to mesoscale; e.g., Emeis 2015). This kind of scale-interaction is now widely
recognized as a principal cause for the so-called energy balance closure problem (Mauder et
al. 2010): in most energy balance observations worldwide, the turbulent components are
seen to underestimate the sum of their radiative and conductive counterparts by 10-20%
(e.g., Stoy et al. 2013), likely due to unaccounted for sub-meso- and mesoscale contributions
to sensible and latent heat transport.
Scale related complications are of particular concern in complex and fragmented landscapes
such as mountain regions, where high spatial variability of land use and topography typically
entail abrupt changes in available energy, precipitation, soil moisture, vegetation, or soils
(Beniston 2006; Poulos et al. 2012). Thus, ecosystem research in complex environments
especially demands scale-integrative approaches for observations and modeling.
The ScaleX-2015 campaign was motivated by far-reaching research questions and topics,
including (1) how do mesoscale structures in the atmospheric boundary layer (ABL)
influence EC-derived surface fluxes; (2) interaction of trace-gas plumes from strong
(anthropogenic) point sources with natural background fluxes; (3) development of
instrumentation and methods to use unmanned aerial vehicles (UAV) for ABL
characterization of scalars and turbulence; (4) how do patterns of precipitation relate to soil
moisture and runoff over different temporal and spatial scales.
Examples of observations in ScaleX-2015 motivated by these questions are presented in
3. The TERENO-preAlpine Observatory
TERENO (TERrestrial ENvironmental Observatories) is a German network of observatories
investigating the ecological and climatic impact of global environmental change on
terrestrial systems (Zacharias et al. 2011). The TERENO-preAlpine observatory is located in
the Bavarian foothills of the Alps (i.e., the Bavarian Prealps), with elevations from 450 m up
to 2000 m above sea level (a.s.l.) roughly to the west of an axis between Munich, Germany,
and Innsbruck, Austria (Fig. 1). At its core is an extensively instrumented site cluster in the
catchments of the rivers Ammer (709 km2) and Rott (55 km2). With dairy farming as the
dominant land use in the valleys of this region, the preAlpine observatory includes the
grassland sites Fendt, Rottenbuch and Graswang (www.europe-fluxdata.eu station codes:
DE-Fen, DE-RbW, and DE-Gwg) at elevations of 595, 769 and 864 m a.s.l., respectively (Fig.
The climate change sensitivity of mountain regions, such as the TERENO-preAlpine
observatory, is seen to be amplified compared to global averages (Böhm et al. 2001;
Smiatek et al. 2009; Calanca 2007), with expected strong consequences in the regional
thermal and precipitation regimes, C- and N-dynamics, and thus nutrient cycling and
ecosystem functioning (Mills et al. 2014). To study the impact of climate change on
ecosystem functioning and services, regional circulation and precipitation patterns, the
continuously operated backbone infrastructure of TERENO-preAlpine includes ecosystem-
atmosphere flux stations along an elevation gradient, micrometeorology and boundary layer
sounding systems, and a hydro-meteorological mesoscale network with precipitation gauge
transects and a rain radar (Fig. 1, right). The ScaleX campaign 2015 focused primarily on DE-
Fen, which is described in detail in the next section.
4. The DE-Fen site and its permanent backbone instrumentation
DE-Fen is located at the head of a small tributary stream to the river Rott (Fig. 2). The land
use at the bottom of this shallow valley is dominated by grassland, sometimes with small
patches of cropland, mostly maize. Three dairy farms are located within a distance of less
than 1 km to the south and west of the site. To the west, a plateau parallels the valley
approx. 100 to 130 m above its floor. The plateau’s shoulder is covered predominately with
mixed forest. About 5 km south-west of the site the German Weather Service (DWD)
operates its Meteorological Observatory Hohenpeissenberg (MOHP, 988 m a.s.l.). The
northern rim of the Alps lies approx. 30 km to the south.
The permanent backbone instrumentation at DE-Fen includes a micrometeorology station,
hydro-meteorological installations, and a lysimeter cluster containing the principal local soil
types for the measurement of biosphere-atmosphere-hydrosphere exchange processes
(specifics of instrumentation are given in Table 1). The core micrometeorology
instrumentation is an EC-system (for momentum, CO2, water vapor, and heat exchange
fluxes), a multi-component surface radiation balance system (including direct/diffuse
incoming shortwave radiation), photosynthetically active radiation (PAR), soil heat-flux
plates and profiles of soil temperatures and soil moisture, as well as other standard
meteorological instruments. This array of in-situ instruments (Fig. 2) is augmented by a
ceilometer for the determination of boundary layer height.
To quantify the grassland water balance at high temporal resolution (30 minutes) the
lysimeter cluster (Fig. 2 and Table 1) contains 18 weighable large (1.0 m2, 1.4 m height)
grassland-soil monoliths, equipped with soil temperature and moisture sensors. Over each
monolith, soil-atmosphere exchange fluxes of CO2, CH4 and N2O are determined through
sequential sampling by an automated static chamber system in conjunction with a quantum
cascade laser absorption spectrometer.
The heart of hydro-meteorological measurements at DE-Fen is a wireless sensor network
(nicknamed SoilNetFen, following Bogena 2010) which covers an area of approx. 400 m x
330 m in the footprint of the EC station. SoilNetFen measures soil moisture, soil
temperature, and matrix potential every 15 minutes at 5, 20, and 50 cm depth at 55
locations (Fig. 2 and Table 1). A Cosmic Ray Neutron Sensor (CRNS; Zreda et al. 2008)
monitors the field-integrated variations of the soil water content. SoilNetFen is augmented
by three discharge gauges, five groundwater wells and one precipitation gauge (Fig. 2 and
5. Additional Instrumentation and Measurements during ScaleX
To extend spatial and temporal scales of observation beyond the range covered by the
permanent backbone setup at DE-Fen, the measurement program was complemented
during ScaleX-2015 by a combination of additional measurement locations, remote sensing
instruments, and airborne platforms (visit www.scalex.imk-ifu.kit.edu for illustrations).
Instruments that could not be operated in a continuous mode were integrated by means of
intensive observation periods. Specifics of all instruments or installations mentioned in this
section are summarized in Table 1.
Boundary layer remote sensing was conducted by three high-resolution scanning Doppler-
LIDAR (light detection and ranging) systems for vertical profiles of wind and turbulence
(1000 m max.), as well as by a radio acoustic-sounding system (RASS; Emeis et al., 2009) to
determine vertical profiles of wind and temperature (560 m max.). Resulting data products
include the characterization of the turbulence and thermal structure in the boundary layer,
as well as the detection of low level jets. In addition, a ground-based scanning
microwaveradiometer was operated to obtain integrated water vapor (IWV), liquid water
path (LWP), and temperature and humidity profiles.
The remote sensing measurements were complemented by airborne observations. A swarm
of unmanned aerial vehicles (UAV) was jointly operated by the IMK-IFU, ISSE and IGUA in
different experiments and coordinated flight patterns (four copters, three fixed-wing), each
equipped with temperature, humidity and pressure sensors. Due to legal provisions, the
maximum ascent of the copters above ground level was limited to 150 m. In addition to the
UAVs, the microlight aircraft D-MIFU (see Junkermann 2001; Junkermann et al. 2011;
Metzger et al. 2013) was deployed to provide wind, temperature, moisture, turbulent fluxes
and radiation measurements at a larger spatial extent of about 12 km by 12 km around DE-
Fen, from 50 m up to 2.5 km above ground level (a.g.l.).
To explore the spatial- and temporal variability of precipitation, rain gauges were installed
at 5 locations within the SoilNetFen area and at 17 additional locations in the Rott
catchment. This gauge network, as well as a micro rain radar (MRR) and two disdrometers,
augmented and provided ground-truth for the DWD C-band radar at MOHP and the TERENO
X-band rain radar (Fig. 1). Furthermore, the chemistry and isotopic composition of
precipitation, surface- and subsurface water was tracked by water samples taken both
manually and automatically throughout the campaign (using a cavity ring down, CRD,
spectrometer; Table 1). To link the soil moisture measurements at the point and catchment
scales, Mobile CRNS (TERENO Rover), air-borne (synthetic aperture radar; F-SAR) sensors
were used, and linked to satellite derived data (RADARSAT 2).
Greenhouse gas (GHG) flux measurements at the lysimeter cluster were complemented by a
large static chamber (Schäfer et al. 2012, in conjunction with a trace gas analyzer) for CH4
flux measurements on a patch of grassland that is frequently flooded, and by atmospheric
CH4 concentration measurements. To evaluate the regional CH4 sink- or source strength,
profiles of atmospheric CH4 concentrations (using a CRD) were determined on a tower at
heights of 1, 5 and 10 m above ground, and up- and downwind of a dairy farm (using an
open-path methane analyzer; range ~100 m), along with wind speed and direction (wind
6. Some first data products
The activities in ScaleX-2015 were organized along the overarching research questions of
land-surface-atmosphere interactions in the atmospheric boundary layer discussed in
Section 2. Here, a selection of first data products is presented, to illustrate the range of
intensive observations conducted in the ScaleX campaigns.
a. High resolution ABL motion structure by a LIDAR cluster
Standard observations of biosphere-atmosphere exchange (e.g., using the EC method)
assume horizontal homogeneity of the turbulence structure and generally ignore the
contributions of ABL-scale or mesoscale motions on exchange fluxes. In fragmented
landscapes with topography and mixed land use, secondary circulations can develop that
affect the validity of standard exchange observations. To account for the effects of such
non-local motions on turbulent exchange near the surface is difficult, but their exclusion
introduces bias in long-term fluxes (Mahrt 1987, 2010).
In ScaleX-2015 a cluster of three Doppler boundary layer LIDARs was used, in conjunction
with a network of sonic anemometers to characterize the motion structure over the entire
ABL continuously, and at high temporal and spatial resolution, over the duration of the
campaign. The Doppler LIDARs (Table 1) recorded three-dimensional wind vectors (u, v, and
w) in a vertical scanning profile arrangement that served as a virtual tower up to
approximately 1000 m above the surface (Fig. 3, left panel).
The observations revealed flow features over a range of time and length scales. The right
panel of Fig. 3 illustrates a representative day (1 July 2015): The development of thermally
driven activity in the ABL at around 07:00 UTC (08:00 local standard time) was visible first as
a change of wind direction and vertical wind speed, starting at the surface and rising rapidly.
Daytime flow was dominated by northerly to easterly wind throughout the boundary layer.
In addition, the daytime boundary layer was characterized by typical convective motion
features over scales of several minutes and vertical extents of several hundred meters. After
sunset, the wind direction shifted to the east and a low-level easterly jet formed around
19:00 UTC between 200 and 500 m a.g.l., but decayed in magnitude around 23:30 UTC as
shown by the horizontal wind speed. Nighttime wind direction above 200 m stayed mostly
southeasterly to easterly, in contrast to layers below 200 m, which showed low wind speed,
but directional shear up to 180 degrees, even below the low-level jet.
On this particular day, more than 82% of the recorded nocturnal half-hourly EC observations
of CO2 and heat exchange were rejected, based on standard quality control criteria including
stationarity, turbulence characteristics and signal noise (Mauder et al. 2013). The remaining
nocturnal surface flux observations coincided with the presence of the low-level jet after
sunset. Between 9:00 and 19:00 UTC no data were rejected or flagged. This nighttime bias
of missing turbulence data underlines the difficulty of obtaining nighttime trace-gas flux and
transport information, discussed in the next Section. High resolution ABL motion data, such
as those presented here, are anticipated to be valuable to evaluate (e.g.) Large Eddy
Simulation (LES) models for the assessment of typically unresolved non-local contributions
to surface fluxes.
b. Variability of methane concentration in the nocturnal boundary layer (NBL)
Methane (CH4) is an important GHG of predominantly biogenic origin, with ecosystems
acting either as net sources or sinks. Wetlands and water-logged soils emit CH4 due to
activity of methanogenic microbes, while upland and well-aerated soils are usually net sinks
for atmospheric CH4, because of the predominance of CH4-oxidizing microbes. Although
methane sensors fast enough for eddy-covariance are available, CH4 fluctuations often
range near the limit of sensitivity and EC-signals tend to be noisy (e.g., Hommeltenberg et al.
2014). A convenient alternative method is the static chamber method (see Pihlatie et al.
2013 for a review). This method determines surface exchange fluxes over a well-defined
area of ground (commonly < 1 m2), by measuring trace gas accumulation or depletion over a
given time, referenced to the chamber volume. Because variability in soils (e.g., moisture
and substrate availability) typically ranges down to similar spatial scales as the chamber
dimensions, the scaling up from chambers to a scale comparable to an EC-flux footprint
(e.g., Schmid 2002), or to the resolution at which ecosystem exchange models are
commonly run, is a formidable problem (e.g., Pihlatie et al. 2010). An additional
confounding problem is posed by larger scale spatial heterogeneity, e.g., in mixed-land use
areas with upland grassland or crops, patches of wetlands, and pastures or barns with
methane-producing cattle. Particularly in night-time stable conditions, plumes of methane
enriched air (for example) can be transported over considerable distances with very little
mixing: if such a transient plume increases the local atmospheric CH4 concentration, the
higher CH4 supply may lead to extra stimulation of the methane consuming microbes in the
soil. The resulting increased uptake rate needs to be quantified and considered when
calibrating and validating biogeochemical models designed to simulate CH4 exchange based
on local soil properties and soil environmental conditions. Transient plumes may also affect
night-time EC-measurements of methane: shallow CH4 plumes may lead to spurious vertical
gradients that, in the presence of weak turbulence, introduce a contribution to the EC-flux
signal that has no linkage to surface sources or sinks in the flux footprint (Finnigan 2004).
Alternately, larger-scale spatially averaged trace gas fluxes (e.g., at the scale of a model grid-
cell) can in theory be derived by the boundary layer budget method (Denmead et al. 1996;
Emeis 2008), an inverse method, where surface fluxes are the residual result of
concentration changes and transport terms observed and modeled over a hypothetical box
bounded by the height of the ABL.
However, nighttime application of direct or inverse trace-gas flux estimates is a challenge,
because very little is known about spatial and temporal CH4 variability above the surface in
the NBL, and observations are difficult and rare. In ScaleX a new approach was explored to
assess plumes and gradients of methane in the NBL that may make such observations more
accessible in future. Measurements of atmospheric CH4 concentration were performed by
pumping ambient air through a sampling tube (Teflon®, outer diameter: 1/8”, 3.2 mm) to a
CRD-spectrometer. To extend nighttime vertical CH4 profiles to beyond the 10 m tower at
DE-Fen (location H in Fig. 2), the end of a sampling line (70 m length) was mounted to the
hexacopter F550 and periodically raised to heights of 10, 25, and 50 m a.g.l. Data are
reported as one minute averages for all heights.
Observations from 21 July 2015 (Fig. 4) showed that atmospheric CH4 concentrations at all
measurement heights increased well above background concentration (1.9 ppm,
determined as the average ABL concentration in well-mixed daytime conditions). The lower
sampling heights exhibited strong variations, whereas fluctuations were much reduced
above the 10 m tower height. Fig. 4 also show considerable negative vertical CH4
concentration gradients which start to decrease in the second half of the night, indicating
slow vertical mixing in the NBL. These findings suggest shallow advection from areas with
strong CH4 sources, because the local grassland soils were sinks for atmospheric CH4.
Concurrent wind directions point to dairy barns nearby as the likely culprit. Observations
from other nights confirmed this night-time advection to be a regular occurrence. The
observations also show that the measured values by the hexacopter method agree well with
the tower measurements at 10 m. They indicate that, using UAVs to carry trace gas intake
lines to heights beyond the reach of common instrument masts, is promising as a low-cost
and flexible way to explore the GHG, or other trace gas structure in the nocturnal boundary
c. Use of UAVs and microlight aircraft for three-dimensional boundary layer
One of the biggest challenges for projections of regional climate or ecosystem-atmosphere
interactions is to get the hydrometeorology right (Clark et al. 2015). Without knowledge of
how much water is transpired or evaporated in a region, we cannot predict how much CO2
the plants will assimilate. The amount of water vapor that is transported from one meso-
scale model grid-cell to another is crucial information for predictions of when and where
that water will fall as rain. In complex terrain, it is important to know on which side of a
ridge rain falls, to infer whether vegetation will be water stressed, or whether a river might
flood. However, the evaluation of regional scale hydro-meteorological models by
observation is challenged by (i) unresolved spatial variability of atmospheric temperature
and humidity, and (ii) a lack of adequate experimental tools to determine the balances of
water and heat over model grid cells or model sub-domains (Lorenz and Kunstmann 2012).
In ScaleX an attempt is made to tackle this problem by combining the hydrometeorological
in-situ observations of TERENO-preAlpine with a suite of remote sensing techniques
(ground-based and airborne), instrumented UAVs, and a microlight aircraft, to capture the
three-dimensional variability of atmospheric state variables in the ABL at high resolution.
ScaleX-2015 included a proof-of-concept campaign to coordinate the flight patterns of a
swarm of UAVs and the microlight aircraft.
The microlight aircraft D-MIFU was used to assess 3D distributions of winds, air
temperature, dewpoint, latent and sensitive heat fluxes, surface temperature, radiation
balance and aerosol size distributions. Flights included horizontal tracks as well as vertical
“spiral staircase” profiles from about 50 m up to 2000 m a.g.l. directly above and at the
vertices of a 12 km by 12 km rectangle around DE-Fen. At a radius of about 500 m around
the DE-Fen EC-station, small UAVs were operated to determine the small-scale spatial and
temporal variability of the thermal structure in the ABL.
Battery operated UAVs are constrained by flight duration, horizontal distance (~300 m) and
maximum ascent, while aircraft are limited by the lowest legally possible flight level (50 m).
To capture the thermal structure in the boundary layer over DE-Fen, several vertical profiles
of air temperature were determined with the hexacopter, one fixed wing UAV and the D-
MIFU microlight on 15 July (Fig. 5), each set within about 15-30 minutes. Though the
measurements were not taken at exactly the same time and location, the temperature
measurements of all three systems mostly agreed within 0.5 °C for the overlapping heights.
Therefore, the aerial vehicles complemented each other to obtain a seamless
representation of the vertical structure from the ground up to the free troposphere.
Together with the use of the hexacopter in trace gas measurements, these first results are
encouraging for the use of lightweight UAVs as an emerging technology in atmospheric
boundary layer research. Battery operated UAVs have no exhaust, and very little heat
emissions, and can be programmed to perform complex flight patterns or (for copters) hold
a given position even in convectively turbulent conditions. The 2015 campaign also
established that a swarm of 3 copters and 3 fixed-wing UAVs can be deployed together, to
perform complex coordinated sensing patterns in a small boundary layer volume (ca. 300 m
wide and high; not shown), e.g., to perform in-situ measurements at exactly the same time
and height at different locations. The UAVs used here are lightweight (below 2.5 kg) and
thus the instrument payload is very limited (currently temperature, humidity, pressure, and
wind velocity; see Table 1). With progressive developments in sensor miniaturization, rapid
expansion of further research applications of UAVs can be expected in future campaigns.
d. Soil moisture and precipitation patterns at a range of scales
Precipitation and soil moisture are the fundamental hydrologic quantities required for a
more profound understanding of runoff- and flood generation, but they are also essential
for plant-physiological and biogeochemical processes (Ruehr et al. 2014; Yao et al. 2010;
Clough et al. 2004). At the same time, the measurement of precipitation and soil moisture
beyond the point scale is one of the most critical challenges in hydrological sciences.
Therefore, a major focus within the ScaleX campaign concerned the characterization of the
spatial and temporal variability of rainfall and soil moisture at DE-Fen and within the Rott
For precipitation, this objective is accomplished using weather radar data and a dense
network of rain gauges at 22 locations (Fig. 6) in the Rott catchment region. The average
distance between gauges was 250 m at DE-Fen and 2.5 km in the catchment. To handle
random errors in the rain gauge data, each of the 22 locations was equipped with a set of
three tipping bucket rain gauges (Krajewski et al. 2003). With this level of redundancy
spurious outliers and instrumental errors could be identified and the faulty sensor excluded
from estimates of precipitation, resulting in quality controlled and mostly gap-free
precipitation time series.
Though the average distance between the rain gauge sites is only 2.5 km, local convective
events may remain concealed. To cover the whole target region with high spatial resolution,
data from the polarimetric C-band weather radar at MOHP (see Fig. 1) are used. Fig. 6 shows
an example of the high spatial variability of hourly precipitation during a convective event.
While the rain gauge and radar data is in good agreement at the gauge locations, the gauges
alone cannot resolve the spatial variability at an hourly scale. The combination of radar and
gauges facilitates validation and adjustment of the radar field at the gauge locations, and
correcting it for inherent radar errors. The corrected radar field can then serve as additional
high resolution rainfall information to be used in hydrological modeling.
Soil moisture patterns were identified based on SoilNetFen measurements (Fig. 2).
Individual point-measurements of soil volumetric water content (VWC) at 5, 20 and 50 cm
depth were interpolated to maps for each depth, using a simple inverse distance weighting
scheme (Pebesma 2004). Fig. 7 a-c to illustrate resulting moisture fields for July 15, 2015.
ScaleX-2015 included a first comparison of soil moisture distributions determined by the
SoilNetFen capacitance-based sensors and by the TERENO Rover, a mobile cosmic-ray
neutron sensor system mounted on a pick-up truck. CRNS is a relatively new technique to
estimate spatially integrated soil moisture, introduced by Zreda et al. (2008), but based on
theory largely from the 1950s. Primary cosmic rays enter Earth from galactic origins mainly
as protons. Collisions with nuclei in the atmosphere, and later in soils, generate cascades of
neutrons with decreasing levels of energy (secondary cosmic rays). In the words of Zreda et
al. (2008), “soil moisture content on a horizontal scale of hectometers and at depths of
decimeters can be inferred from measurements of low-energy cosmic-ray neutrons that are
generated within soil, moderated mainly by hydrogen atoms, and diffused back to the
atmosphere. These neutrons are sensitive to water content changes, but largely insensitive
to variations in soil chemistry, and their intensity above the surface is inversely correlated
with hydrogen content of the soil”. However, according to Köhli et al. (2015), the spatial
sensitivity of the sensor decreases sharply with distance, and the effective measurement
depth depends on soil type and moisture content, typically ranging from 10 to 40 cm.
Stationary CRNS are commonly used for monitoring of soil moisture variations over time,
while the mobile CRNS TERENO Rover can detect spatial variations along transect paths,
filtered by a footprint size on the order of several hundred meters diameter (Zreda et al.
The TERENO Rover was repeatedly employed at DE-Fen during ScaleX-2015, and also along
tracks throughout the Rott catchment. The lower right panel of Fig. 7 shows VWC derived
from 127 data points observed by the CRNS along the Rover tracks at DE-Fen (transect
velocity of approx. 2 km h-1). Because the southern part of the SoilNetFen area could not be
accessed with the vehicle, data are missing there. Considering the large footprint of CRNS,
the gradient of dry to moist conditions from west to east in SoilNetFen, is captured well by
the Rover, both qualitatively and quantitatively. The “eye” structures of apparent high VWC
in the plot are likely artifacts of the basic interpolation method used in these preliminary
e. Runoff generation mechanisms and storage interactions
Surface water – groundwater interactions are complex (Sophocleous 2002) and crucial for
the functioning of riparian ecosystems (Kalbus et al. 2006; Jones and Holmes 1996) and the
hyporheic zone (Sophocleous 2002; Kalbus et al. 2006; Jones and Holmes 1996). Because
groundwater is usually depleted in heavier stable isotopes compared to surface water
bodies (Uhlenbrook et al. 2002; Tetzlaff et al. 2009; Coplen et al. 2000; Hinkle et al. 2001),
the stable isotope abundances of oxygen-18 and deuterium in water have been used widely
as natural tracers to explore hydrological processes and interactions between surface- and
As DE-Fen is located at the bottom of a shallow valley, the hydrodynamic gradient is weak
and it can be expected that groundwater – surface water interactions are an important
mechanism in the study area. So, not surprisingly, hydrochemical analysis and groundwater
level measurements indicate the existence of exchanges between groundwater and surface
water (not shown here). However, the detailed mechanisms of runoff generation and
storage system interactions are not satisfyingly understood in this region.
To explore runoff generation dynamics and the connections of stream water to the local
aquifer system, the water isotopic composition was analyzed during the ScaleX campaign
(Table 1) by automatically drawing stream water samples every 6 hours at the outlet of the
headwater catchment (Fig. 2, location D). In addition, groundwater was manually sampled
bi-weekly at the same location, and batch samples of precipitation were collected weekly
close to the EC-station.
Figure 8 shows an overview of observed precipitation, stream discharge, groundwater table
variations and the δ18O isotopic composition of water from these three hydrological
compartments in the Rott catchment over the ScaleX-2015 period. The top panel
demonstrates that the relatively strong rainfall events in the first half of the campaign were
closely traced by peaks in discharge. The middle panel indicates that precipitation water
tends to exhibit higher δ18O values than groundwater (which varies only very little). In
contrast, the stream water isotope signature is evidently reacting rapidly to inputs from
precipitation (with its higher isotope signature). Despite different sampling frequencies, the
isotopic enrichment of stream water after strong rainfall events suggests that infiltration or
drainage of excess water was the dominant runoff mechanism during rainfall events. During
the recession of stream flow, the contribution of groundwater to runoff increased, resulting
in very similar isotopic composition of stream water and groundwater in the low flow period
observed during the comparatively dry second half of the campaign.
The data in Fig. 8 illustrate the usefulness of continuous time series of isotopic water
signatures to identify response times of flow regimes, recharge of water storage bodies and
mixing processes. Such data, in conjunction with regional scale hydrometeorological and soil
moisture information presented above (Fig. 6 and 7), form a valuable test-bed for model
evaluation and as ground-truth for satellite based estimates of the land surface water
balance. To this end, and to integrate local observations of soil moisture over the region,
airborne and satellite remote sensing methods are used. During ScaleX-2015, an airborne
synthetic aperture radar mission (F-SAR, fully polarimetric L-band) was conducted by DLR
(German Aerospace) over the ScaleX area, and a space-borne SAR (RADARSAT 2) scene was
acquired for the entire TERENO-preAlpine region.
7. Discussion and outlook
Integrated observation programs of ecosystem – atmosphere interactions are always
intensive in instrumentation and labor. To conserve costs, long-term observatory operations
are commonly based on well-proven, mostly automated measurement systems,
concentrated on a small number of locations. Such systems constitute the long-term
backbone to build understanding of interactions and feedbacks between the atmosphere
(from turbulence to climatic scales) and ecosystems (from photosynthesis to the life-cycle of
vegetation). In contrast, short-term intensive campaigns are useful to pursue specific
research goals with an all-out and focused effort. Past examples of intensive campaigns
have shown them to be fertile spawning-grounds for collaboration and research innovation.
The ScaleX concept combines the benefits of both long-term monitoring and short-term
intensive approaches. It uses an integrated TERENO long-term observatory site, with its
backbone infrastructure, logistics and long-term expertise, as the staging area for repeated
short intensive campaigns. The continuity of the backbone measurements and the broad
spectrum of campaign observations complement each other.
In the coming months and years, more comprehensive and interdisciplinary analyses of
ScaleX-2015 data are anticipated, beyond the few examples presented here, and these will
likely lead to new insights on scale interactions of ecosystem – atmosphere processes in
complex terrain. In such analyses, modeling activities from single process models to fully
coupled regional climate models or Large Eddy Simulation systems will play important roles
as scale-integrators, and to pinpoint process interrelations and feedback mechanisms that
are reflected in the data. Vice-versa, enhanced ScaleX and TERENO data products will likely
serve for model performance evaluations at a wide range of scales and applications. For
example, Hingerl et al. (2016) used energy balance measurements from TERENO-preAlpine
for the evaluation and analysis of the distribution of water- and energy fluxes over the Rott
catchment, computed by GEOtop, a distributed water- and energy-balance model for
complex terrain (http://www.geotop.org).
An important aspect of the ScaleX concept is that the same study area will be re-visited by
recurrent campaigns. After its inception in 2015, the second installment of ScaleX is set for
the summer of 2016 and periodic further ScaleX campaigns are planned throughout the life-
time of TERENO. Data from these campaigns are stored in an on-line ScaleX cloud that is
freely available to all partners collaborating in measurements, modeling or data-mining. For
access to the cloud, visit the ScaleX web site (www.scalex.imk-ifu.kit.edu), or contact the
As we progress, we hope that ScaleX will become more integrated in terms of in-situ
observations, remote sensing and modeling, and that the spectrum of observations
continues to grow towards a more comprehensive modeling test-bed for processes at the
interface of soils, vegetation and the atmosphere. To follow up and go beyond the specific
research questions of ScaleX-2015 (see Section 2), we invite expertise on specific topics for
future ScaleX campaigns, including (i) the contribution of advective terms to EC flux
measurements; (ii) simulation of farm-animal related emissions to derive CH4 source
strengths at catchment scale; (iii) atmospheric transport modeling to link point sources to
background emission, and NBL concentration profiles of trace gases; (iv) miniaturisation of
trace gas sensors for UAVs; (v) space-borne and airborne remote sensing estimates of soil
moisture and precipitation, land cover, elevation and biomass productivity; (vi) advanced,
coupled soil-vegetation-atmosphere exchange modeling. Over time, the group of scientists
and institutions that participate in ScaleX is expected to evolve as well as the topical foci,
and thus, this paper is an invitation to collaborate in future ScaleX campaigns, and to
emulate the ScaleX concept at other long-term observatory sites.
Research at KIT/IMK-IFU for TERENO-preAlpine and ScaleX is funded, in part, by the
Helmholtz Association and its program ATMO (Atmosphere and Climate), through grants
from the German Federal Ministry of Education and Research (BMBF). The TERENO activities
of ATMO are integrated in the German climate research initiative REKLIM. MM, CM, FDR
and MZ were supported by the Helmholtz Young Investigator Group “Capturing all relevant
scales of biosphere-atmosphere exchange – the enigmatic energy balance closure problem”,
and by KIT. We want to thank Dr. Jörg Seltmann and Dr. Michael Frech (both of DWD) for
assistance with the C-band weather radar data, Dr. Christoph Münkel (of Vaisala) for
support with the ceilometer data processing, and the IMK-IFU technical staff and numerous
student field assistants for their engaged work in the field at all hours. We are indebted to
Mr. Anton Jungwirth, farmer at Fendt, for providing access to the field site DE-Fen, and for
his great flexibility and tolerance during the ScaleX campaign.
Baldocchi, D., 2003: Assessing the eddy covariance technique for evaluating carbon dioxide
exchange rates of ecosystems: past, present and future. Glob. Chang. Biol., 9, 479–492,
Beniston, M., 2006: Mountain weather and climate: A general overview and a focus on
climatic change in the Alps. Hydrobiologia, 562, 3–16, doi:10.1007/s10750-005-1802-0.
Best, M. J., and Coauthors, 2015: The plumbing of land surface models: benchmarking
model performance. J. Hydrometeorol., 16, 1425–1442, doi:10.1175/JHM-D-14-0158.1.
Boden, T. A., M. Krassovski, and B. Yang, 2013: Geoscientific Instrumentation Methods and
Data Systems The AmeriFlux data activity and data system: an evolving collection of
data management techniques, tools, products and services. Geosci. Instrum. Method.
Data Syst. Discuss, 3, 59–85, doi:10.5194/gid-3-59-2013.
Bogena, H., 2010: Potential of wireless sensor networks for measuring soil water content
variability. Vadose Zo. Journal2, 9, 1002–1013, doi:10.2136/vzj2009.0173.
Böhm, R., I. Auer, M. Brunetti, M. Maugeri, T. Nanni, and W. Schöner, 2001: Regional
temperature variability in the european Alps : 1760 – 1998 from homogenized
instrumental time series. Int. J. Climatol., 21, 1779–1801, doi:10.1002/joc.689.
Bünzli, D., and H. P. Schmid, 1998: The influence of surface texture on regionally aggregated
evaporation and energy partitioning. J. Atmos. Sci., 55, 961–972, doi:10.1175/1520-
Calanca, P., 2007: Climate change and drought occurrence in the Alpine region: How severe
are becoming the extremes? Glob. Planet. Change, 57, 151–160,
Caldwell, M. M., P. A. Matson, C. Wessman, and J. Gamon, 1993: Prospects for scaling.
Scaling Physiological Processes: Leaf to Globe, J.R. Ehleringer and C.B. Field, Eds.,
Academic Press, San Diego, 223–230.
Clark, M. P., and Coauthors, 2015: Improving the representation of hydrologic processes in
Earth System Models. Water Resour. Res., 51, 5929–5956,
Clough, T. J., F. M. Kelliher, R. R. Sherlock, and C. D. Ford, 2004: Lime and Soil Moisture
Effects on Nitrous Oxide Emissions from a Urine Patch. Soil Sci. Soc. Am. J., 68, 1600–
Coplen, T., A. Herczeg, and C. Barnes, 2000: Isotope Engineering—Using Stable Isotopes of
the Water Molecule to Solve Practical Problems. Environ. Tracers Subsurf. Hydrol., 79–
Denmead, O. T., M. R. Raupach, F. X. Dunin, H. A. Cleugh, and R. Leuning, 1996: Boundary
layer budgets for regional estimates of scalar fluxes. Glob. Chang. Biol., 2, 255–264,
Desai, A. R., and Coauthors, 2011: Seasonal pattern of regional carbon balance in the central
Rocky Mountains from surface and airborne measurements. J. Geophys. Res.
Biogeosciences, 116, 1–17, doi:10.1029/2011JG001655.
Ehleringer, J. R., and C. B. Field, eds., 1993: Scaling physiological processes: leaf to globe.
Emeis, S., 2008: Examples for the determination of turbulent (sub-synoptic) fluxes with
inverse methods. Meteorol. Zeitschrift, 17, 3–11, doi:10.1127/0941-2948/2008/0265.
——, 2015: Observational techniques to assist the coupling of CWE/CFD models and meso-
scale meteorological models. J. Wind Eng. Ind. Aerodyn., 144, 24–30,
——, K. Schäfer, and C. Münkel, 2009: Observation of the structure of the urban boundary
layer with different ceilometers and validation by RASS data. Meteorol. Zeitschrift, 18,
Finnigan, J. J., 2004: The footprint concept in complex terrain. Agric. For. Meteorol., 127,
Firestein, S., 2012: Ignorance: How it drives science. Oxford University Press, USA, 208 pp.
Goring, S. J., and Coauthors, 2014: Improving the culture of interdisciplinary collaboration in
ecology by expanding measures of success. Front. Ecol. Environ., 12, 39–47,
Hall, F. G., 1999: Introduction to special section: BOREAS in 1999: Experiment and science
overview. J. Geophys. Res., 104, 27627–27639, doi:10.1029/1999JD901026.
Hingerl, L., H. Kunstmann, S. Wagner, M. Mauder, J. Bliefernicht, and R. Rigon, 2016:
Spatiotemporal variability of water and energy fluxes - A case study for a mesoscale
catchment in pre-alpine environment. Hydrol. Process., accepted.
Hinkle, S. R., J. H. Duff, F. J. Triska, A. Laenen, E. B. Gates, K. E. Bencala, D. A. Wentz, and S.
R. Silva, 2001: Linking hyporheic flow and nitrogen cycling near the Willamette River - A
large river in Oregon, USA. J. Hydrol., 244, 157–180, doi:10.1016/S0022-
Hommeltenberg, J., M. Mauder, M. Drösler, K. Heidbach, P. Werle, and H. P. Schmid, 2014:
Ecosystem scale methane fluxes in a natural temperate bog-pine forest in southern
Germany. Agric. For. Meteorol., 198-199, 273–284,
Jones, J. B., and R. M. Holmes, 1996: Surface-subsurface interactions in stream ecosystems.
Trends Ecol. Evol., 11, 239–242, doi:10.1016/0169-5347(96)10013-6.
Junkermann, W., 2001: An ultralight aircraft as platform for research in the lower
troposphere: System performance and first results from radiation transfer studies in
stratiform aerosol layers and broken cloud conditions. J. Atmos. Ocean. Technol., 18,
——, R. Hagemann, and B. Vogel, 2011: Nucleation in the Karlsruhe plume during the
COPS/TRACKS-Lagrange experiment. Q. J. R. Meteorol. Soc., 137, 267–274,
Kalbus, E., F. Reinstorf, and M. Schirmer, 2006: Measuring methods for groundwater -
surface water interactions: a review. Hydrol. Earth Syst. Sci., 10, 873–887,
Kampe, T., B. R. Johnson, M. Kuester, and M. Keller, 2010: NEON: the first continental-scale
ecological observatory with airborne remote sensing of vegetation canopy
biochemistry and structure. J. Appl. Remote Sens., 4, 043510, doi:10.1117/1.3361375.
Köhli, M., M. Schrön, M. Zreda, U. Schmidt, P. Dietrich, and S. Zacharias, 2015: Footprint
characteristics revised for field-scale soil moisture minitoring with cosmic-ray neutrons.
Water Resour. Res., 51, 5772–5790, doi:10.1002/2014WR016259.
Krajewski, W. F., G. J. Ciach, and E. Habib, 2003: An analysis of small-scale rainfall variability
in different climatic regimes. Hydrol. Sci. J., 48, 151–162,
Levin, S. A., 1992: The problem of pattern and scale in ecology. Ecology, 73, 1943–1967,
Lorenz, C., and H. Kunstmann, 2012: The hydrological cycle in three state-of-the-art
reanalyses: Intercomparison and performance analysis. J. Hydrometeorol., 13, 1397–
Mahrt, L., 1987: Grid-Averaged Surface Fluxes. Mon. Weather Rev., 115, 1550–1560,
Mahrt, L., 2010: Computing turbulent fluxes near the surface: Needed improvements. Agric.
For. Meteorol., 150, 501–509, doi:10.1016/j.agrformet.2010.01.015.
Mauder, M., R. L. Desjardins, E. Pattey, and D. Worth, 2010: An Attempt to Close the
Daytime Surface Energy Balance Using Spatially-Averaged Flux Measurements.
Boundary-Layer Meteorol., 136, 175–191, doi:10.1007/s10546-010-9497-9.
——, M. Cuntz, C. Drüe, A. Graf, C. Rebmann, H. P. Schmid, M. Schmidt, and R. Steinbrecher,
2013: A strategy for quality and uncertainty assessment of long-term eddy-covariance
measurements. Agric. For. Meteorol., 169, 122–135,
May, R., 1999: Unanswered questions in ecology. Philos. Trans. R. Soc. Lond. B. Biol. Sci.,
354, 1951–1959, doi:10.1098/rstb.1999.0534.
Metzger, S., and Coauthors, 2013: Spatially explicit regionalization of airborne flux
measurements using environmental response functions. Biogeosciences, 10, 2193–
Mills, R. T. E., K. S. Gavazov, T. Spiegelberger, D. Johnson, and A. Buttler, 2014: Diminished
soil functions occur under simulated climate change in a sup-alpine pasture, but
heterotrophic temperature sensitivity indicates microbial resilience. Sci. Total Environ.,
473-474, 465–472, doi:10.1016/j.scitotenv.2013.12.071.
Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Am.
Meteorol. Soc., 56, 527–530.
Osmond, B., and Coauthors, 2004: Changing the way we think about global change research:
Scaling up in experimental ecosystem science. Glob. Chang. Biol., 10, 393–407,
Pebesma, E. J., 2004: Multivariable geostatistics in S: The gstat package. Comput. Geosci.,
30, 683–691, doi:10.1016/j.cargo.2004.03.012.
Pihlatie, M., and Coauthors, 2013: Comparison of static chambers to measure CH4 emissions
from soils. Agric. For. Meteorol., 171-172, 124–136,
Pihlatie, M. K., and Coauthors, 2010: Greenhouse gas fluxes in a drained peatland forest
during spring frost-thaw event. Biogeosciences, 7, 1715–1727, doi:10.5194/bg-7-1715-
Poulos, M. J., J. L. Pierce, A. N. Flores, and S. G. Benner, 2012: Hillslope asymmetry maps
reveal widespread, multi-scale organization. Geophys. Res. Lett., 39, 1–6,
Reid, W. V, and Coauthors, 2005: Millenium Ecosystem Assessment: Ecosystems and Well-
being: Synthesis. Island Press, Washington, DC, 155 pp.
Ruehr, N. K., B. E. Law, D. Quandt, and M. Williams, 2014: Effects of heat and drought on
carbon and water dynamics in a regenerating semi-arid pine forest: A combined
experimental and modeling approach. Biogeosciences, 11, 4139–4156, doi:10.5194/bg-
Schäfer, K., J. Böttcher, D. Weymann, C. von der Heide, and W. H. M. Duijnisveld, 2012:
Evaluation of a Closed Tunnel for Field-Scale Measurements of Nitrous Oxide Fluxes
from an Unfertilized Grassland Soil. J. Environ. Qual., 41, 1383,
Schmid, H. P., 2002: Footprint modeling for vegetation atmosphere exchange studies: a
review and perspective. Agric. For. Meteorol., 113, 159–183, doi:10.1016/S0168-
——, and C. R. Lloyd, 1999: Spatial representativeness and the location bias of flux
footprints over inhomogeneous areas. Agric. For. Meteorol., 93, 195–209,
Sellers, P. J., F. G. Hall, G. Asrar, D. E. Strebel, and R. E. Murphy, 1988: The first ISLSCP Field
Experiment (FIFE). Bull. Am. Meteorol. Soc., 69, 22–27, doi:10.1007/BF00138905.
——, ——, ——, ——, and ——, 1992: An Overview of the First International Satellite Land
Surface Climatology Project (ISLSCP) Field Experiment ( FIFE ). J. Geophys. Res., 97,
——, and Coauthors, 1995: The Boreal Ecosystem-Atmosphere Study (BOREAS): An
Overview and Early Results form the 1994 Field Year. Bull. Am. Meteorol. Soc., 76,
Smiatek, G., H. Kunstmann, R. Knoche, and A. Marx, 2009: Precipitation and temperature
statistics in high-resolution regional climate models: Evaluation for the European Alps.
J. Geophys. Res., 114, 1–16, doi:D19107\r10.1029/2008jd011353.
Sophocleous, M., 2002: Interactions between groundwater and surface water: The state of
the science. Hydrogeol. J., 10, 52–67, doi:10.1007/s10040-001-0170-8.
Stoy, P. C., and Coauthors, 2013: A data-driven analysis of energy balance closure across
FLUXNET research sites: The role of landscape scale heterogeneity. Agric. For.
Meteorol., 171-172, 137–152, doi:10.1016/j.agrformet.2012.11.004.
Sun, J., and Coauthors, 2010: A multiscale and multidisciplinary investigation of ecosystem-
atmosphere CO2 exchange over the rocky mountains of colorado. Bull. Am. Meteorol.
Soc., 91, 209–230, doi:10.1175/2009BAMS2733.1.
Tetzlaff, D., J. Seibert, K. J. McGuire, H. Laudon, D. A. Burns, S. M. Dunn, and C. Soulsby,
2009: How does landscape strucutre influcence catchment transit tiem across different
geomorphic provinces. Hydrol. Process., 23, 945–953, doi:10.1002/hyp.
Uhlenbrook, S., M. Frey, C. Leibundgut, and P. Maloszewski, 2002: Hydrograph separations
in a mesoscale mountainous basin at event and seasonal timescales. Water Resour.
Res., 38, 31–1 – 31–14, doi:10.1029/2001WR000938.
Yao, Z., X. Wu, B. Wolf, M. Dannenmann, K. Butterbach-Bahl, N. Brueggemann, W. W. Chen,
and X. H. Zheng, 2010: Soil-atmosphere exchange potential of NO and N2O in different
land use types of Inner Mongolia as affected by soil temperature, soil moisture, freeze-
thaw, and drying-wetting events. J. Geophys. Res., 115, doi:doi:10.1029/2009JD013528.
Zacharias, S., and Coauthors, 2011: A Network of Terrestrial Environmental Observatories in
Germany. Vadose Zo. J., 10, 955, doi:10.2136/vzj2010.0139.
Zreda, M., D. Desilets, T. P. A. Ferre, and R. L. Scott, 2008: Measuring soil moisture content
non-invasively at intermediate spatial scale using cosmic-ray neutrons. Geophys. Res.
Lett., 35, L21402, doi:10.1029/2008GL035655.
Table 1: Permanent and campaign instrumentation at DE-Fen site, available during ScaleX-2015 (1 June to 31 July). Deployment dates are given
for intermittent measurements only. See Fig. 1 and 2 for deployment locations.
PERMANENT (principal components of TERENO-preAlpine backbone instrumentation at DE-Fen site)
Instrument / Installation
(number, if multiple)
EC Flux station
(+ supporting micromet)
CO2, latent heat and sensible heat fluxes (supporting micromet including: short
and long wave radiation components, PAR, soil heat flux, soil moisture)
CSAT-3 (a); LI-7500 (b)
aerosol backscatter for ABL-height estimation (15 min resolution)
(55 locations, 3 depths)
soil volumetric water content (capacitance/frequency domain technology), soil
water potential, soil temperature (15 min resolution)
Cosmic ray neutron sensor
field scale top soil water content
Discharge gauges (2)
river discharge (Thomson V-notch weir)
(by IMK-IFU) (f)
Groundwater wells (5)
(by IMK-IFU) (f)
radar reflectivity and precipitation
Lysimeter cluster with
static dark chamber
groundwater recharge, evapotranspiration, CO2, CH4 and N2O fluxes
Science Lysimeter (i);
Dual Laser Trace Gas
ADDITIONAL ScaleX-2015 Instrumentation
Radio acoustic sounding
wind & temperature profile, vertical velocity variance, range: 20 - 560 m, 10
482 MHz RASS (k)
Doppler LIDAR (3)
3-D wind and turbulence profile, range up to 1000 m in 18 m increments, 1 to
Stream Line (l)
3 min means
Passive microwave and
Temperature and humidity profiles, integrated water vapor (IWV) and liquid
water path (LWP) and cloud base temperature
payload sensors for: relative humidity, air temperature, air pressure;
deployment dates: 24, 25 and 30 June, 1, 10, 15, 16 20, 21, 23 and 30 July
Hexacopter F550 (n)
Quadrocopter swarm (3)
payload sensors for: relative humidity, air temperature, air pressure;
deployment dates: 30 June, 1 July, 6 August
Fixed wing UAVs (3)
payload sensors for: relative humidity, air temperature, air pressure, wind;
deployment dates: 30 June, 1 and 15 July, 6 August
(by IGUA) (f)
Microlight aircraft D-MIFU
Temperature, dewpoint and aerosol profiles, turbulent fluxes, radiation (UV-
IR); deployment dates: 5, 12, 25 and 26 June, 4, 7, 10, 15, 16 and 22 July
(for/by IMK-IFU) (f)
Rain gauges (groups of 3)
precipitation amount; 5 groups at DE-Fen, 17 groups within Rott catchment
Rain Collector (o)
DWD C-band radar
Spatial information on precipitation amount and hydrometeor types
Dual-Pole Doppler C-
band weather radar (by
Micro rain radar
vertical profiles of rain rate, drop size distribution
drop size distribution, rain rate
Parsivel (g); and LNM (p)
Cavity ring down (CRD)
isotopic composition (18O-H2O and 2H-H2O) of precipitation, groundwater and
soil water content; vehicle-based CRNS
top soil water content; (one overflight during ScaleX-2015)
L-band SAR (by DLR) (f)
CH4 soil flux; static chamber principle (dimensions: 10 m x 2.60 m, max. height
0.61 m); deployment dates: 9, 16, 25, 26 and 30 June, 10, 14, 20, 21, 23, 28
and 30 July
(by IMK-IFU) (f)
Trace Gas Analyzer
CH4 and H2O concentrations
Fast Methane Analyzer (r)
Wind sensor network
Wind and turbulence (profile at 1(s), 5(s), 10 (a) m, location H in Figure 2; two
stations (s) (t) at 3 m height, locations A and K in Figure 2)
CSAT-3 (a); WindSonic (s);
CH4, N2O and CO2 concentrations
Open path methane
Line averaged methane mixing ratios
Gas Finder 2 (u)
Manufacturers: (a): Campbell Scientific, Logan UT (USA); (b): LI-COR, Lincoln, NE (USA); (c): Vaisala, Helsinki (Finnland); (d): TRUEBNER Instruments, Neustadt (Germany);
(e): Hydroinnova LLC, Albuquerque, NM (USA); (f): in-house or custom built; (g): OTT Hydromet, Kempten (Germany); (h): Selex ES GmbH, Neuss (Germany); (i): UMS,
Munich (Germany); (j): Aerodyne Research, Billerica, MA (USA); (k): METEK GmbH, Elmshorn (Germany); (l): Halo Photonics, Worcestershire (UK); (m): Radiometer Physics
GmbH, Meckenheim (Germany); (n): DJI, Beijing (China); (o): Davis Instruments, Haward, CA (USA); (p): Thies Clima, Göttingen (Germany); (q): Picarro Inc., Santa Clara, CA
(USA); (r): Los Gatos Research, San Jose, CA (USA); (s): Gill Instruments, Lymington, UK; (t): RM Young, Traverse City, MI (USA); (u): Boreal Laser Inc., Edmonton, AB
(Canada); ; (v): rOsewhite Multicopter, Mauerstetten (Germany); (w): distributed by iRC-Electronic, Wehringen (Germany)
Fig. 1. Location of the TERENO-preAlpine observatory between Innsbruck (Austria) and
Munich (Germany) (left). The map on the right shows the southern Ammer catchment (black
boundary) and the northern Rott catchment (grey boundary), with the three principal sites
(black rectangles), precipitation gauges (red dots), X-band rain radar (red triangle) and the
meteorological observatory MOHP (black asterisk). See text for details. The red square
indicates the ScaleX-2015 study area presented in Fig. 2. Color bars show elevation in m
a.s.l. The maps were produced using Copernicus data and information funded by the
European Union – EU-DEM layers (uploaded 10/08/2003) and the ATKIS stream network.
Fig. 2. (left panel) The ScaleX study area centered around DE-Fen (black square) with
topographic features (colors encode elevation in m a.s.l.), catchment boundaries (Rott in
grey and Ammer in black) and MOHP. Streams and lakes (blue) are shown for the Rott
catchment only. (right panel) Map (approx. 1000 m x 1000 m) of land cover (see map),
installations, water ways and roads at DE-Fen with additions of SoilNetFen nodes (black
crosses), precipitation gauges (red dots), groundwater wells (brown triangles) and discharge
weirs (brown dots). The marks A to K represent the locations of, A: the remote sensing hub,
B: CRNS, C: EC station, D: automatic stream water sampler, E: RASS, F: big chamber, G:
lysimeter cluster, H: 10 m tower, I: 3D-Doppler LIDARs, J: nearby farm, and K: open path
methane analyzer. Sonic anemometers at locations A, K, and a profile at H constituted the
wind sensor network (see Table 1). Abbreviations are explained in Table 1 and in the text.
The maps were produced using EU-DEM, Corine Land Cover 2006 (The European Topic
Centre on Spatial Information and Analysis, uploaded 8 April 2014, permalink SH04UZP80M)
and OpenStreetMap information (www.geofabrik.de, downloaded Jan 2015).
Fig. 3. (left panel) Schematic illustration of the 3D Doppler LIDAR setup used to observe a
vertical profile of 3-D wind vectors (virtual tower). The schematic is superimposed on a
terrain representation of the DE-Fen site. (right panel) Profiles of vertical and horizontal
wind speed and wind direction at the ScaleX virtual tower on 1 July 2015. Positive (negative)
vertical wind speed indicates upward (downward) motion. Vertical axes are in m a.g.l.
Fig. 4. CH4 concentrations (± std. dev., 1 minute means) measured in 1 and 10 m height on
the tower, and by the hexacopter at 10, 25 and 50 m on 21 July 2015. To improve legibility
of the data at 25 and 50 m, lines were added to connect the measurements at these levels.
Local standard time is UTC+1.
Fig. 5. (left panel) First 300 m of vertical air temperature (Ta) profiles determined by the
hexacopter (blue colors), fixed wing UAV (yellow and orange) and D-MIFU (grey) on 15 July
2015 (start times given in UTC, local standard time is UTC+1). The right panel shows profile
flight tracks of D-MIFU (white), fixed-wing (yellow) and hexacopter (blue).
Fig. 6. Example for the high spatial variability of hourly rainfall in the region of the Rott
catchment (1600 UTC 27 June 2015) recorded by the rain gauge network (color of filled
circles) and the DWD C-band weather radar (colored map). The color scale is given in mm of
Fig. 7. Volumetric water content (VWC) for 1 July 2015, derived from SoilNetFen at the DE-
Fen site for different depths and from the CRNS Rover. Gray lines represent roads and
tracks, the Rott creek is printed in blue. The SoilNetFen profiles are marked by white
crosses. The southern part of the SoilNetFen area was not accessible to the Rover. The “eye”
structures in some regions of the maps are likely artifacts from the simple distance-
weighting interpolation method used.
Fig. 8. Rainfall intensity and stream discharge measured at the location of the automatic
water sampler (A), isotopic composition of precipitation, stream water and groundwater (B),
and groundwater level (C) during the ScaleX campaign 2015. Gaps in streamwater isotopic
composition were caused by instrumental failure.
Fig. 1. Location of the TERENO-preAlpine observatory between Innsbruck (Austria) and Munich
(Germany) (left). The map on the right shows the southern Ammer catchment (black boundary) and
the northern Rott catchment (grey boundary), with the three principal sites (black rectangles),
precipitation gauges (red dots), X-band rain radar (red triangle) and the meteorological observatory
MOHP (black asterisk). See text for details. The red square indicates the ScaleX-2015 study area
presented in Fig. 2. Color bars show elevation in m a.s.l. The maps were produced using Copernicus
data and information funded by the European Union – EU-DEM layers (uploaded 10/08/2003) and
the ATKIS stream network.
Fig. 2. (left panel) The ScaleX study area centered around DE-Fen (black square) with topographic
features (colors encode elevation in m a.s.l.), catchment boundaries (Rott in grey and Ammer in
black) and MOHP. Streams and lakes (blue) are shown for the Rott catchment only. (right panel) Map
(approx. 1000 m x 1000 m) of land cover (see map), installations, water ways and roads at DE-Fen
with additions of SoilNetFen nodes (black crosses), precipitation gauges (red dots), groundwater
wells (brown triangles) and discharge weirs (brown dots). The marks A to K represent the locations
of, A: the remote sensing hub, B: CRNS, C: EC station, D: automatic stream water sampler, E: RASS, F:
big chamber, G: lysimeter cluster, H: 10 m tower, I: 3D-Doppler LIDARs, J: nearby farm, and K: open
path methane analyzer. Sonic anemometers at locations A, K, and a profile at H constituted the wind
sensor network (see Table 1). Abbreviations are explained in Table 1 and in the text. The maps were
produced using EU-DEM, Corine Land Cover 2006 (The European Topic Centre on Spatial Information
and Analysis, uploaded 8 April 2014, permalink SH04UZP80M) and OpenStreetMap information
(www.geofabrik.de, downloaded Jan 2015).
Fig. 3. (left panel) Schematic illustration of the 3D Doppler LIDAR setup used to observe a vertical
profile of 3-D wind vectors (virtual tower). The schematic is superimposed on a terrain
representation of the DE-Fen site. (right panel) Profiles of vertical and horizontal wind speed and
wind direction at the ScaleX virtual tower on 1 July 2015. Positive (negative) vertical wind speed
indicates upward (downward) motion. Vertical axes are in m a.g.l.
Fig. 4. CH4 concentrations (± std. dev., 1 minute means) measured in 1 and 10 m height on the
tower, and by the hexacopter at 10, 25 and 50 m on 21 July 2015. To improve legibility of the data
at 25 and 50 m, lines were added to connect the measurements at these levels. Local standard
time is UTC+1.
Fig. 5. (left panel) First 300 m of vertical air temperature (Ta) profiles determined by the hexacopter
(blue colors), fixed wing UAV (yellow and orange) and D-MIFU (grey) on 15 July 2015 (start times
given in UTC, local standard time is UTC+1). The right panel shows profile flight tracks of D-MIFU
(white), fixed-wing (yellow) and hexacopter (blue).
Fig. 6. Example for the high spatial variability of hourly rainfall in the region of the Rott catchment
(1600 UTC 27 June 2015) recorded by the rain gauge network (color of filled circles) and the DWD
C-band weather radar (colored map). The color scale is given in mm of hourly precipitation.
Fig. 7. Volumetric water content (VWC) for 1 July 2015, derived from SoilNetFen at the DE-Fen site for
different depths and from the CRNS Rover. Gray lines represent roads and tracks, the Rott creek is
printed in blue. The SoilNetFen profiles are marked by white crosses. The southern part of the
SoilNetFen area was not accessible to the Rover. The “eye” structures in some regions of the maps
are likely artifacts from the simple distance-weighting interpolation method used.
Fig. 8. Rainfall intensity and stream discharge measured at the location of the automatic water
sampler (A), isotopic composition of precipitation, stream water and groundwater (B), and
groundwater level (C) during the ScaleX campaign 2015. Gaps in streamwater isotopic composition
were caused by instrumental failure.