Experimental watersheds have a long tradition as research sites in hydrology
and have been used since the late nineteenth and early twentieth centuries.
The University of Natural Resources and Life Sciences Vienna (BOKU) recently
extended its experimental research forest site “Rosalia” with an area of 950 ha towards the creation of a full ecological-hydrological experimental
watershed. The overall objective is to implement a multi-scale,
multi-disciplinary observation system that facilitates the study of water,
energy and solute transport processes in the soil–plant–atmosphere
continuum. This article describes the characteristics of the site and the
monitoring network and its instrumentation that has been installed since 2015, as well as
the datasets. The network includes four discharge gauging stations and
seven rain gauges along with observations of air and water temperature,
relative humidity, and electrical conductivity. In four profiles, soil water
content and temperature are recorded at different depths. In addition, since
2018, nitrate, TOC and turbidity have been monitored at one gauging station.
In 2019, a programme to collect isotopic data in precipitation and discharge
was initiated. All data collected since 2015, including, in total, 56 high-resolution time series (with 10 min sampling intervals), are provided to the
scientific community on a publicly accessible repository. The datasets are
available at 10.5281/zenodo.3997140
(Fürst et al., 2020).
Introduction
Environmentally oriented water management depends on understanding
hydrological processes and their dominant controls at different spatial and
temporal scales. To investigate hydrological processes and their complex
interactions with the environment, long-term measurements from
multi-disciplinary hydrological observatories are required (Schumann et
al., 2010; Blöschl et al., 2016). As the earliest hydrological
observatories, experimental watersheds have been used as far back as the
late nineteenth and early twentieth centuries (USGS Reynolds Creek; Seyfried et al., 2018). Given these long-term
datasets, changes in the hydrological cycle, such as those resulting from
climate warming, can be investigated in these watersheds
(Bogena et al., 2018).
In recent decades, there has been growing recognition that hydrology (and
its related disciplines) cannot be treated in isolation. Rather,
hydrological processes driven by meteorological conditions are also strongly
controlled by complex feedback mechanisms with biotic and abiotic systems
(Porporato and Rodriguez-Iturbe, 2002). Therefore, hydrological
experimental watersheds have gradually transitioned into multi-disciplinary
experimental watersheds. A prominent example for this is the “Critical Zone
Observatories” research project, which was initiated in 2007 by the US
National Science Foundation (Anderson et al., 2018) and has
been succeeded by the Critical Zone Collaborative Network (CZN) in 2021.
Understanding processes based on research conducted at individual catchments
is limited to the physio-geographic conditions at the particular location.
In an effort to understand hydrological processes based on a wider spectrum
of boundary conditions, networks of multi-disciplinary hydrological
observatories have been established in recent decades. Examples of such
networks are the German “TERrestrial ENvironmental Observatory network”
(TERENO) (Zacharias et al., 2011), the “International Network
for Alpine Research Catchment Hydrology” (Bernhardt et al., 2015),
the “US National Science Foundation's National Ecological Observatory
Network” (NEON) (Kampe et al., 2010), and the “Euro-Mediterranean
Network of Experimental and Representative Basins” (ERB) as part of UNESCO
FRIEND (Flow Regimes from International Experimental and Network Data)
(Holzmann, 2018).
A prominent example of an observatory network is the “Long-Term Ecosystem
Research” (LTER) initiative, which aims to better understand the structure
and functioning of complex ecosystems and their long-term response to
environmental, societal and economic pressures at different spatial scales
(LTER Network Office, 2020). The LTER was initiated in 1980 with six US
catchments and has since expanded to other continents, comprising different
ecosystem types, climates and pressures. The LTER was further developed
into the “Long Term Socio-economic and Ecosystem Research” (LTSER) platform
to emphasise the importance of the human dimension and to explicitly
consider the socio-economic system in multi-disciplinary ecosystem research
(Haberl et al., 2006). The European LTER is the “European Long-Term
Ecosystem, Critical Zone and Socio-Ecological Systems Research
Infrastructure” (eLTER RI), which was established in 2003 by the European
Commission as part of the “European Strategy Forum on Research Infrastructures”
(ESFRI) (ESFRI, 2020).
These networks of observatories make it possible to address some open
research questions in hydrology that were recently formulated
(Blöschl et al., 2019). The most challenging questions regarding
catchment hydrology relate to the effect of small-scale variability in the
upscaling of model parameters and processes (e.g., hydraulic conductivity
and porosity of soils, soil water movement), the transfer of model
parameters to other (especially ungauged) catchments, and the derivation of
flow paths and residence times of water and solutes in the subsurface at
different scales. Overcoming these challenges requires the existing networks
of observatories to be complemented in their instrumentation and
observational capacities, harmonising temporal and spatial frequencies and
continuously monitoring natural tracers such as ions, metals and stable
isotope ratios such as 2H/1H, 18O/16O and
15N/14N in precipitation, discharge and the subsurface. The
Plynlimon research catchment in the UK (Neal et al., 2011; Cosby and
Emmett, 2020) and the Krycklan catchment study in Sweden
(Laudon et al., 2013) are good examples of such research
catchments, with long term tracer and hydro-geochemical data available.
The University of Natural Resources and Life Sciences in Vienna (BOKU) has a
long tradition and extensive experience in operating multi-disciplinary
experimental sites. BOKU has been using these sites for research purposes
to monitor environmental changes and climate change impacts, to develop new
monitoring techniques, and to train students in applied research. One of
BOKU's sites is the experimental research forest “Rosalia”, with an area of
950 ha that was established in 1875 to facilitate research and education,
mainly in forestry disciplines (Fig. 1). Several
forest dieback studies were conducted in the 1980s. In 2013, a 222 ha
watershed within the Rosalia forest site was established as an
eco-hydrological experimental watershed, and this Rosalia watershed became
part of the Austrian LTER-CWN (Research Infrastructure for Carbon, Water and
Nitrogen) initiative.
Map of the watersheds and the monitoring network (DEM source: Land
Niederösterreich – http://data.noe.gv.at, last access: 29 June 2021).
The overall objective is to implement a multi-scale, multi-disciplinary
observatory that facilitates the study of water, energy and solute
transport processes in the soil–plant–atmosphere continuum. Research
emphasis is put on deriving effective parameters for scales on which models
simulate flow and transport processes (e.g. hillslope, catchment) by
upscaling point measurements. A distinctive feature of the current
monitoring setup is the continuous measurement of tracers in precipitation
and discharge of selected creeks within the catchment, which allows us to derive
travel time distributions for sub-catchments and investigate flow pathways
in detail. Because BOKU has the right of access for educational and research
purposes, large-scale controlled experiments can be undertaken. For example,
rain-out shelters were used in parts of the forest by
Netherer et al. (2015) to investigate drought impacts
on bark beetle attacks on Norway spruce, while Schwen et
al. (2015) and Leitner et al. (2017) used rain-out shelters
to investigate soil water repellency and short-term organic nitrogen fluxes
under a changing climate. Besides such local experiments, the monitoring
network established in 2015 enables researchers to investigate the
impacts on the large-scale forest ecosystem and its services by providing
the necessary baseline data. Investigating the transition of the forest
ecosystem from its actual state into a pristine, unmanaged natural forest is
among future research plans.
The objective of this article is to present the monitoring network and the
recorded data of the Rosalia watershed and to make them available to the
scientific community.
Description of the watershed
The Rosalia watershed is part of the Rosalia mountains (German:
Rosaliengebirge) that belong to the eastern foothills of the Alps on the
state border between Lower Austria and Burgenland in Austria
(Fig. 1). Terrain heights range from 385 to 725 m a.s.l., and the watershed is characterised by steep slopes (96 % of the
area is steeper than 10 %, and 55 % is steeper than 30 %). From 1990 to
2017, annual precipitation was between 560 and 1100 mm (average 790 mm,
standard deviation 128 mm), and the mean annual air temperature was between
5.5 and 10 ∘C (average 8.2 ∘C, standard deviation 1.2 ∘C) (data:
https://deims.org/dataset/839e7779-f6ee-4b81-b4d8-924177b9c562, last access: 29 June 2021).
Precipitation is not equally distributed throughout the year. Frequently in
summer, heavy thunderstorms occur, causing floods that destroy forest roads,
road culverts and other infrastructure (Fig. 2).
Destructions of forest roads due to a storm on 29 June 2009
(Photo: Josef Gasch).
Crystalline rocks dominate in the Rosalia mountains, but coarse-grained
gneiss, sericitic schist, phyllite and dolomite are also encountered.
However, only coarse-grained gneiss with occasionally embedded dark or white
mica schist is found in the actual catchment area of the hydrological
research site.
The soils are predominantly cambisols that can be classified into four
categories (Fig. 3). The source materials for the
recent soil formation are often remnants of tertiary soils that were
modified by frost action and landslides during the ice age. The cambisols on
steep slopes (slope > 40 %, category 1 in Fig. 3) cover 5 %
of the area. They are podzolic cambisols with more than 40 % coarse grain.
These sites are characterised by poor water holding capacity and loss of
organic material due to gravitational transport and wind erosion. The
characteristic species for these sites are beech with white woodrush (Luzula albida) associated with pine (Pinus sylvestris) and European larch (Larix decidua) above 500 m a.s.l., while below 500 m a.s.l. they are associated
with oak (Quercus petraea). Cambisols on plains and moderate slopes
(category 2 in Fig. 3, 68 % of the area) contain 30 %–50 % coarse grain
and have a medium water holding capacity. The characteristic species is
beech with woodruff (Galium odoratum). At higher elevations and cool north
slopes, beech, spruce and fir (abieti-fagetum) are found. Cambisol and
planosol on plains and moderate slopes (category 3 in Fig. 3, 22 % of
the area) are characterised by periodic water stagnation. They are typically
on concave land forms and have good water capacity and nutrient sustenance.
There is a risk of wind throw due to possible root dieback in long wet
periods. Forest associations are the same as for category 2. Cambisol and
fluvisol on valley slopes and bottoms (category 4, 5 % of the area) are
characterised by varying contents of coarse material and profile thickness
but always have good nutrient and water supply. Where valleys form a flat
bottom, fluvisols are the basis for plant growth. The dominant tree species
on the slopes are ash and sycamore (aceri-fraxinetum), while ash and black
alder (pruno-fraxinetum) dominate the valley floors.
Soil map with the four main soil categories, watershed divides and
discharge gauges.
Forest management is undertaken by the Austrian Federal Forests
(OeBf; Österreichische Bundesforste) owned by the Republic of Austria.
BOKU has the right of access for educational and research purposes. OeBf
manages the forest sustainably, balancing the protection of the environment,
the needs of society and economic success. The management of the forest is
characterised by long production cycles of 100 to 140 years. The main
species of the forest are the broadleaved beech (Fagus sylvatica) and the
coniferous Norway spruce (Picea abies). The forest is at different
development stages ranging from clear cut areas to mature forest stands.
Natural regeneration is preferred to planting, and fertilisation is almost
never done. Timber harvesting is usually done with harvesters and
forwarders, and cable cranes are used on steep slopes. Management and timber
transport are supported by a dense network of forest roads (50 m per
hectare), suitable for heavy timber trucks. Main threats to the forest are
snow break, wind throw and bark beetles, the latter affecting mainly
coniferous tree species.
The main advantages of Rosalia as a research site are as follows:
The watershed is part of the larger 950 ha forest site used by BOKU, and
therefore a large amount of watershed information already exists, including
soil maps, high-resolution DEMs (digital elevation models), maps on forest
growth and productivity, detailed topographic maps, etc.
There is a well-established cooperation between BOKU and the owners of the
forest, the Austrian Federal Forests, which facilitates even large-scale
experiments with durations of several years.
Rosalia can be reached from Vienna within less than an hour, making
maintenance cost-effective.
BOKU has an educational centre right at the border of the watershed with
seminar rooms, basic laboratory facilities and accommodation for up to 40 persons. Resident staff at the educational centre can assist in urgent
situations, such as a storm or power failure.
Network of measurement sites
A network of stations (Fig. 1,
Tables 1 and 2) has been set up to collect hydro-meteorological data: at four gauging stations,
river discharge, water and air temperature, relative humidity, and electrical
conductivity of water are monitored. The locations were selected to cover
nested sub-watersheds of 9, 27, 146 and 222 ha. At one of
these sites (Q4, 146 ha), water quality (NO3-N, TOC, turbidity) is
monitored with a S::can multi::lyser™ spectrometer probe. Here, also
stream water samples are taken for analysing stable isotopes of oxygen
(δ18O) and hydrogen (δ2H). Precipitation is
measured by seven rain gauges at different altitudes. At two of these
locations, K1 and Q4, precipitation is additionally collected for the
analysis of δ18O and δ2H. At four locations, soil
profiles were equipped with sensors measuring soil water content (SWC), electrical
conductivity of soil water, and soil temperature at four and three depths,
respectively.
List of sites, sensors and observed variables.
SiteSensorsObserved variablesQ1 Mittereckgraben 559.87 m a.s.l Watershed 9 haConductivity and temperature sensor Ponsel C4EElectrical conductivity and water temperatureRain gauge RG1 (Adcon tipping bucket)10 min rain depthAir temperature and humidity sensor TR1 (Adcon)Air temperature Relative humidity1-ft H-flume with two ultrasonic distance sensors (Baumer)Water level in H-flume DischargeTipping bucket, 1 L per tip (for discharge < 0.02 L s-1)Small dischargeQ1S0 Soil water profileFour HydraProbe soil sensors (Stevens) at Q1S0 Sensor depths: 10, 20, 40, 60 cmSoil water content Soil temperature Electrical conductivity of soil waterQ2 Grasriegelgraben 550.06 m a.s.l Watershed 27 haConductivity and temperature sensor Ponsel C4EElectrical conductivity Water temperatureRain gauge RG1 (tipping bucket)10 min rain depthAir temperature and humidity sensor TR1Air temperature Relative humidity1-ft H-flume with two ultrasonic distance sensorsWater level in H-flume DischargeQ2S0 Soil water profileFour HydraProbe soil sensors at Q2S0 Sensor depths: 10, 20, 40, 60 cmFor parameters, see aboveQ2S1 Q2S2 Soil water profilesThree HydraProbe soil sensors at Q2S1 and Q2S2 Sensor depths: 10, 20, 40 cmFor parameters, see aboveQ3 Weir Grasriegelgraben 410 m a.s.l Watershed 222 haDepth sensor Keller PR46XWater level at weir DischargeRain gauge RG1 (tipping bucket)10 min rain depthAir temperature and humidity sensor TR1Air temperature Relative humidityQ4 Grasriegelgraben 415 m a.s.l Watershed 146 ha2-ft H-flume with two ultrasonic distance sensors (Baumer)Water level in H-flume DischargeRain gauge RG1 (tipping bucket)10 min rain depthAir temperature and humidity sensor TR1Air temperature Relative humidityS::can conductivity and temperature sensor condu:lyserElectrical conductivity Water temperatureS::can multi::lyser spectrometer probeTOC, NO3-N, turbidityPalmex rain samplerPrecipitation isotopes (δ18O, δ2H)Teledyne ISCO full-size portable sampler 6712River water isotopes (δ18O, δ2H)K1 Heuberg 640 m a.s.lOTT Pluvio2 L – weighing rain gauge10 min rain depthAir temperature and humidity sensor TR1Air temperature Relative humidityPalmex – rain samplerPrecipitation isotopes (δ18O, δ2H)K2 Mehlbeerleiten 385 m a.s.lOTT Pluvio2 L – weighing rain gauge10 min rain depthAir temperature and humidity sensor TR1Air temperature Relative humidityK3 Krieriegel 655 m a.s.lOTT Pluvio2 L – weighing rain gauge10 min rain depth
Specifications of sensors. Last access date of all URLs: 29 June 2021.
SensorVariableRangeResolutionAccuracyAdcon RG1 tipping bucket rain gauge http://www.adcon.atPrecipitation (mm)0–200 mm/h0.2 mm< 50 mm h-1± 1 % 50–100 mm h-1± 3 % 100–200 mm h-1± 5 %Ott Pluvio2 weighing rain gauge https://www.ott.comPrecipitation (mm)12–1800 mm h-10.01 mm min-1±0.05 mmAdcon TR1 air temperature and humidity http://www.adcon.atAir temperature (∘C) Relative humidity (% RH)-40 to +60 ∘C 0–100 % RH±0.1 ∘C±0.1 ∘C ± 1 % RH at 0 % RH–90 % RH ±2 % RH at 90 % RH–100 % RHUGT – 1-ft H-flume https://www.ugt-online.deDischarge (L s-1) 0.02–55 L s-12 %–5 %UGT – 2-ft H-flume https://www.ugt-online.deDischarge (L s-1) 0.04–315 L s-12 %–5 %Keller PR-46X water level https://keller-druck.comWater level (m)0–1 m< 1 mm±0.55 mmPonsel C4E water temperature and electricalconductivity https://en.aqualabo.frWater temperature (∘C) Electrical conductivity (µS cm-1)0–50 ∘C 0–2000 µS cm-10.01 ∘C < 0.1 µS cm-1±0.5 ∘C ±1 % of the full ranges::can condu::lyser™ watertemperature and electrical conductivity https://www.s-can.atWater temperature (∘C) Electrical conductivity (µS cm-1)-20–130 ∘C 0–500 000 µS cm-1< 0.1 ∘C 1 µS cm-1Not specified ±1 % of valueStevens HydraProbe II https://www.stevenswater.comSoil water content (cm3 cm-3) Electrical conductivity (dS m-1) Soil temperature (∘C)Dry to saturated 0–20 dS m-1-10 to +65 ∘C±3 % ±2 % or ±0.2 dS m-1±0.6 ∘CData acquisition
Although the observed variables have different temporal characteristics, it
was decided to record all in situ measurements (except stable water isotope data) at
synchronous 10 min intervals to simplify data storage and organisation. For
this purpose, a UHF radio telemetry network (ADCON telemetry by OTT Hydromet
GmbH) was implemented, enabling data acquisition, storage and management by
a web-accessible database management system (DBMS). At each monitoring site,
different sensors are connected to a remote telemetry unit (RTU). Within the
network, several RTUs store and transmit data to a base station (located at
the education centre building) and receive control commands from the base
station. Apart from physically connecting the sensors to the RTU and
providing a power supply (solar or external), all setup, parameterisation,
etc. are done remotely via a web interface to the base station.
The DBMS addVANTAGE Pro, which is connected to the base station via an
internet link, is the main interface for administrators, regular users and
the public. It is ADCON's universal data visualisation, processing and
distribution platform. It is fully web-based, runs on a reliable PostgreSQL
database engine and is fully scalable from a single user version for five
RTUs to servers with thousands of clients and thousands of RTUs. The addVANTAGE
Pro interface was configured to provide intuitive diagnostic displays of the measured
hydro-meteorological variables, as well as of hardware state and broadcasting
parameters. Pre-defined conditions, such as power failure or exceedance of
certain thresholds in the data, can trigger e-mail alerts to site
administrators to enable timely remediation of issues, avoiding or reducing
gaps in the records.
Stable water isotope data are not automatically uploaded to the DBMS, but
samples are collected on-site and picked up manually by university staff for
analysis in the laboratory. Precipitation samples are collected bi-weekly
with totalisators with plans to refine the sampling interval to daily, while
streamflow samples are collected as daily grab samples using an autosampler.
Description of sitesDischarge gauges
The sites for discharge measurements were selected to collect data for
nested sub-catchments of different sizes. It was possible to find locations
just at culverts of forest access roads, which has several advantages: (i) the sites are accessible by car, which is important for cost-effective
maintenance; (ii) they have a defined sub-catchment outlet; and (iii) the H
flume devices could be mounted directly on culverts, which meant that the
road embankments could be used to fully capture even larger flows.
H-flume devices were selected to measure discharge as they cover a wide
range of flow rates and most sediments are flushed through due to their
horizontal bottom (Morgenschweis, 2010). For sites Q1 and Q2 with a
watershed size of 9 and 27 ha, respectively, the 1-foot H-flumes can measure
discharge from 0.02 up to 55 L s-1, in which the upper limit corresponds
to an approximately 5-year flood discharge (at the 27 ha site). Site Q4,
with a watershed of 146 ha, is equipped with a 2-foot H flume
(Fig. 4). Water level at the H-flumes is measured
by pairs of ultrasonic distance sensors. One of these sensors measures the
depth to the water level, and the second measures a fixed reference distance.
With the ratio of known reference distance to measured distance, the depth
to water level is corrected for the dependence of the speed of sound on air
temperature and relative humidity. Although H-flumes are comparatively
insensitive to sediment accumulation, we developed a compressed-air flushing
system to keep the outflow section and the water level reference point free
of sediments and debris. Site Q3 (222 ha) was already constructed in the
1980s using a Thomson weir (Thomson, 1859). The water level at Q3 is
measured by a capacitive pressure transmitter.
Gauging site Q4 with 2-ft H-flume, spectrometer device and ISCO
autosampler.
Sites Q1, Q2 and Q4 are additionally equipped with sensors for electrical
conductivity, water temperature, air temperature and relative humidity. At
sites Q1 and Q2, Ponsel C4E sensors (four electrodes) were installed to
measure water temperature and conductivity as they have an SDI-12 interface
and low power consumption. They work electronically reliably, but the
measured conductivities are sensitive to biofilms on the sensor, and the
internal firmware requires more than an hour to achieve a stable reading
after turning on or after cleaning. Furthermore, the measured conductivity
tends to show an offset compared to manual measurements conducted
approximately bi-weekly. Nevertheless, the recorded curves show plausible
dynamics, e.g., during storm events. Currently, alternative sensors are being
tested to replace the C4E devices. At site Q4, a different type of sensor
(s::can condu::lyser™) is used, which, after more than a year
of operation, recorded reliable and stable data.
Rain gauges
Sites K1, K2 and K3 are equipped with OTT Pluvio2 weighing rain gauges. Antifreeze fluid is added during the frost period
so that continuous measurements are possible. At the discharge sites Q1 to
Q4, tipping bucket rain gauges are installed. They require more maintenance
than weighing rain gauges because the funnel is easily blocked by deposition
of leaves, pollen, dust or insects, and they are inoperable during frost.
Records from November to April must therefore be carefully checked using air
temperature records and comparing the data with the records from the
weighing rain gauges. Furthermore, it was not possible to place all rain
gauges in the forest in such a way that no negative wind influences occur.
Particularly, the recommendation that the height of nearby objects, such as
trees, should not exceed the distance from the gauge to the objects
(WMO, 2008) had to be disregarded for Q1 and Q2. In particular, the
rain gauge at Q1 is directly affected by the interception of the trees
above.
Soil water
Stevens® HydraProbe® soil sensors (Stevens Water
Monitoring Systems, Inc., Portland, OR, USA) were installed to
simultaneously measure soil moisture, temperature and salinity (Stevens
Water Monitoring Systems, 2015). The sensors deliver a standard data packet
of six variables, including three variables characterising the dielectric
properties of the soil and the resulting values of soil water content,
temperature and bulk electrical conductivity. The sensor-internal
calculation of soil water content refers to the general calibration function
published by Seyfried et al. (2005). In total, four soil
profiles were equipped with HydraProbes. In two of the profiles, the sensors
were installed at depths of 10, 20, 40 and 60 cm below the surface
(Fig. 5); in the others, the sensors were
installed at 10, 20 and 40 cm depth. Soil profile Q1S0 is located
approximately 20 m upslope of gauge Q1. Soil profiles Q2S0, Q2S1 and Q2S2
form a transect up the slope line at 16, 30 and 45 m distance from Q2. This
design supports a transect of soil water parameters measured along the slope
line (Fig. 1).
HydraProbe sensors installed at site Q2S0.
Water quality
Since 2018, the water quality parameters NO3-N, TOC and turbidity have
been monitored with a spectrometer probe, s::can multi::lyser™, at site
Q4. In June 2019, two rain totalisators (Palmex Ltd., Croatia) specifically
designed to minimise isotope fractionation were installed to collect
precipitation samples for isotope analysis at meteorological station K1 and
discharge site Q4. At the same site, a Teledyne ISCO full-size portable
autosampler with a capacity of 24 1 L bottles (model no: 6712) was
installed to collect water samples for the laboratory analysis of δ18O and δ2H. A daily sampling interval with 500 mL of
water per sample was chosen to cover long-term changes in base flow and
allow for daily snapshot information in case of events. The amount of water
ensures a statistically sound sample size, while the sampling interval is
short enough to enable the investigation of runoff events and is long enough
that the autosampler can be left in the field for 24 d without
maintenance. The suction tube leading from the H-flume to the autosampler is
occasionally affected by frost. The frozen water inside the tube prevents
the autosampler pump from collecting water samples. Since the installation
of the system, this has happened only rarely (less than 20 d), and we
plan on further measures to mitigate freezing issues arising from small
amounts of residual water in the tube that the pump cannot fully flush out.
A potential evaporation issue arises from the fact that the autosampler is
not a cooled field sampler and the sample bottles are open to the
sampler's internal atmosphere. Hence, we manually collected streamflow grab
samples in closed high density polyethylene (HDPE) bottles each time the field site was visited and
measured their isotope ratio within a few days. These values were then
compared to those of the sampling bottle which was standing the longest in
the field. Preliminary results indicated no major evaporation enrichment
problem with a mean difference in δ18O of
0.11 ‰ for more than a year of data (measurement
uncertainty of 0.1 ‰). Nonetheless, occasionally larger
deviations up to 0.4 ‰ were observed. To minimise
possible evaporation effects we adapted the sampling bottles according to a
recent publication (von Freyberg et al., 2020) by
placing a 100 mm syringe (without the needle) into the opening of the
sampling bottle which effectively reduced the area open to atmosphere to a 2 mm diameter opening (the tip of the syringe body).
Close to the autosampler, open precipitation samples are collected
approximately bi-weekly with a totalisator station (Palmex Ltd., Croatia)
which is suitable for isotope sampling (Gröning et
al., 2012). The sample bottle is inside a plastic pipe and thus protected
from direct sunlight. The tube that connects the sample bottle to the funnel
outlet has a small diameter and extends to the bottom of the sample bottle
to limit air exchange. Since the collected rainfall at Q4 is not affected by
interception, the samples did not undergo canopy-induced changes in the
isotopic ratio that can influence the results of hydrologic models
(Stockinger et al., 2015). Additionally, a Palmex totalisator
station was installed at K1 to consider elevation effects on isotope ratios
and sampled approximately bi-weekly until September 2020. Since September 2020, the totalisator has been emptied daily during work days (Monday to Friday)
by staff of the BOKU education centre.
Both δ18O and δ2H are analysed using laser spectroscopy
(Picarro L2140-i, Picarro Inc., Santa Clara, CA, USA) in the isotope
laboratory at BOKU. A calibration with laboratory reference material
calibrated against the Vienna Standard Mean Ocean Water and Standard Light
Antarctic Precipitation scale was used. All values are given in delta
notation, and the precision of the instrument (1σ) was better than
0.1 ‰ and 0.5 ‰ for δ18O
and δ2H.
Data
All time series data are recorded, stored and routinely visualised using
addVANTAGE Pro. For comprehensive analysis, data are regularly exported into
the frequently used and freely available time series management system HEC
DSS and the management software HEC DSSVue (Hydrologic Engineering
Center, 2010). HEC DSSVue has powerful visualisation features and provides a
convenient graphical editor for the time series. During editing, obvious
artefacts such as spikes generated during maintenance, occasional
obstructions of flumes during storms and similar disturbances are removed
from the raw data. The data cleaning is specific to the variables and is
therefore discussed in detail in the respective sections below. For even
more flexible and automated processing, as well as for publication, the HEC
DSS database was converted into a simple SQLite database (Hipp
et al., 2019), which provides efficient and simple access from different
software tools, including Python and R (Müller et al.,
2018).
As the implementation of the instruments started in spring 2015, the
earliest time series are from sites Q1 and Q2 and start in May 2015. Until
September 2015, rain gauges K1 and K2, soil water profiles Q1S0 and Q2S0,
and stream gauge Q3 were also added and are delivering data. Soil water profiles
Q2S1 and Q2S2 were added in April 2016 and rain gauge K3 and stream gauge Q4
in summer 2018. For the majority of the data, more than 4 years of
records are currently available (spring 2021). Out of the 5 years of
records available at the time of publication, only the years 2018 and 2019
are presented in the graphs below to maintain readability.
Discharge data
Raw discharge data at the H-flume gauges Q1, Q2 and Q4 needed careful
inspection and editing. First, spikes in the hydrographs (one or two
consecutive values significantly exceeding the value before and after the
spike) were attributed to random events such as a leave under the ultrasonic
depth sensor and were automatically replaced by linear interpolation. Next,
visually detected implausible discharges were replaced by linear
interpolation when reliably possible or were deleted otherwise. As an example,
occasionally during very low flow (water level less than 2 cm in the flume),
single leaves can temporarily (a few hours) get stuck at the narrow outlet
of the flume and cause the water level to rise a few millimetres. Such
events are clearly visible as plateau-shaped parts of the hydrograph and can
be safely replaced by linear interpolation. At these gauges, the
measurements have never been disturbed by freezing.
At the weir Q3, two issues required editing. (1) During very low flow, leaves
and grass can occasionally get stuck at the weir crest, causing the water
level to rise. These events can be detected in the images transmitted daily
by a surveillance camera and visually in the hydrograph. Such artefacts are
replaced by linear interpolation. (2) During longer frost periods, the
stilling basin may be covered by ice, and therefore the discharge is no
longer described by the weir formula. These situations can be detected by
visual inspection of the hydrograph and comparison with the temperature.
These parts of the records have been deleted.
Discharge is characterised by its wide range of values
(Table 3). At Q3 (watershed outlet with 222 ha), low
flows in summer and autumn are frequently less than 3 L s-1, while peak
flows of more than 500 L s-1 have occurred twice since 2015. Specific
discharge does not vary significantly between the four watersheds and
typically ranges from 1 to 2 L s-1 km-2 during low to medium
flows and up to 30 L s-1 km-2 during peak flows (calculated from
daily means).
Statistics of discharge records and of missing data.
SiteTime periodMin dischargeMax dischargeMean dischargePercent(L s-1)(L s-1)(L s-1)missingQ11 Jun 2015–31 Dec 20190.058.110.273.3Q21 Jun 2015–31 Dec 20190.2412.640.810.9Q31 Sep 2015–31 Dec 20191.75582.347.556.8Q41 Jul 2018–31 Dec 20191.35309.684.231.1
In the hydrographs for the period 2018 to 2019
(Fig. 6) it can be seen that the base flow is
greater in spring and early summer than in autumn and winter and that sharp
runoff peaks occur after rainfall events. The zoomed-in hydrographs for
July/August 2018 (Fig. 7) illustrate
characteristic diurnal fluctuations of discharge during no-rain periods in
the vegetation period (see section “Applications” for more details).
Discharge hydrographs at gauges Q1 to Q4 for the years 2018–2019
(Q in log scale).
Precipitation data
For quality control, rainfall data recorded by tipping bucket devices (Q1 to
Q4) are compared to records of the weighing rain gauges and to corresponding
hydrographs. They are deleted if the funnel appears to have been (partially)
blocked. Also, records for the winter season from November to February are
excluded due to tipping bucket issues with freezing. Anomalies observed
during field maintenance visits (one to two per month) are also considered.
The three weighing rain gauges have provided gap-free records since the time
of installation up to now, with a resolution of 0.1 mm. For most rainfall
events between March and October, consistent and plausible data were
acquired by up to seven rain gauges in total, providing a high-resolution
rainfall pattern for a small area of 222 ha and being spread over different
altitudes from 385 to 655 m a.s.l (Table 4,
Fig. 8).
Diurnal fluctuations of flow for July/August 2018 (peak flows are
cut off: Q1 and Q2 at 0.8 L s-1; Q3 and Q4 at 10 L s-1).
Statistics of precipitation data (statistics are calculated only if
there are no missing values in the interval). NA – not available
SiteTimePercentMax dailyAnnual precipitation periodmissingprecip. (mm)(mm) 2016201720182019K126 Aug 2015–31 Dec 2019069.2975676877759K226 Aug 2015–31 Dec 2019060.8949682906739K31 Aug 2018–31 Dec 2019084.1NANANA737Q11 Jun 2015–31 Dec 20192656.6NANANANAQ21 Jun 2015–31 Dec 20192963.0NANANANAQ31 Sep 2015–31 Dec 20193148.6NANANANAQ41 Jul 2018–31 Dec 20192327.0NANANANA
Daily rainfall at the weighing rain gauges for 2018 to 2019.
In this densely forested watershed, it was not possible to place all rain
gauges at sites without interception or rain-shading. However, the rainfall
depths at the seven sites are very similar for events that cover the entire
catchment. Gauge Q1 is affected by interception, which amounts to typically
less than 2 mm per event (compared to weighing rain gauges K1 and K2), but
monthly precipitation at Q1 is on average only 75 % of the mean of K1 and
K2. At Q2, monthly precipitation is on average 87 % of the mean of K1 and
K2. (K1 is close to the highest elevation of the watershed, K2 at the lowest; see Fig. 1 and Table 1.) Therefore, the data from all rain gauges are useful for analysing storm
events as interception reduces rainfall depths by only a small percentage.
For water balance investigations of periods longer than a week, however,
only the gauges not affected by interception should be used.
Soil water data
With 14 HydraProbe sensors installed, and each measuring six variables, 84
soil-water-related time series at 10 min resolution are recorded, resulting
in a large volume of data. In the data repository, only soil water content
(SWC) and soil temperature are provided. Apart from an initial power supply
problem at Q2S2, these sensors worked without any problem or data loss and
required no maintenance. Figure 9 illustrates daily
SWC in four depths at profile Q2S0, together with daily rainfall data. It is
important to mention that the installation of the sensors requires digging a
trench, which causes considerable local disturbance of the soil. Despite
careful refilling, local infiltration paths could be influenced, and data do
not necessarily reflect natural conditions for some time after installation.
During the first few months after installation, for example, deeper probes
reacted faster to rainfall than those close to the surface
(Fig. 10). This can be attributed to artificial
flow paths along the walls of the trench and the cables, or to effects
arising from interrupted and destroyed natural macropores like wormholes.
However, direct effects due to installation practically disappeared after
the first season.
Daily soil water content and corresponding daily rainfall and
log-discharge at site Q2S0 for 2018 to 2019.
Detail of daily soil water content at site Q2S1: deeper sensors
reacted faster to rainfall on 12 May 2016.
Electrical conductivity and temperature of runoff
At discharge sites Q1, Q2 and Q4, water temperature and electrical
conductivity are measured. Due to the risk of damage by frost, the sensors
are removed during the frost period from December to March at sites Q1 and Q2.
Besides frost, conductivity records at sites Q1 and Q2 are additionally
negatively influenced by the sensor problems described in Sect. 3.2.
Regular conductivity measurements with a portable device showed that the
conductivity of base flow is stable at sites Q1 and Q2 (typically approx.
120 µS cm-1) so that the recorded conductivity series are still
informative for the separation of base flow and direct runoff events
despite conductivity offsets in the records.
Isotopic data
At discharge site Q4, river and precipitation samples have been collected
since June and October 2019, respectively (Fig. 11). The precipitation data are collected as bi-weekly bulk samples and are
compared to the daily river water grab samples. The comparison shows the
response of the discharge to the precipitation input tracer signal
(Fig. 11). Furthermore, the precipitation and
river water isotopes vary seasonally, with larger values in summer and lower
values in winter months. This seasonality originated from contributions of
precipitation to discharge, and isotope ratios in precipitation seasonally
vary due to changes in temperature, sources of vapour for cloud formation
and different rain-out histories (Feng et al., 2009). Apart from
this, there are some preliminary indications of different flow paths, such
as base flow (relatively stable δ18O isotope values around -10 ‰), interflow (moderate increases or decreases in
isotopes, for example, at the beginning of August 2019), and faster flow
(sharp peaks), suggesting dynamic runoff processes and transit times in the
Rosalia watershed, which will be analysed in the future.
Precipitation and river water δ18O isotopes at site
Q4.
Spatial data
Data interpretation is complemented by a comprehensive amount of spatial
data characterising the site. DEMs at various resolutions are available,
including a 10×10 m DEM (data source: Land Niederösterreich –
http://data.noe.gv.at, last access: 29 June 2021) and a lidar-based DEM at 0.5×0.5 m
(Immitzer, 2009), accessible at https://zenodo.org/record/4601057 (last access: 29 June 2021).
From these DEMs, watershed divides and the drainage network were derived in
GIS. Additionally, a ground survey was performed for the main creeks in
2018. These data are included in the repository in shapefile format.
Applications
The presented data are suitable for studying processes of water flow and
transport in small, forested watersheds. They have been used in academic
teaching and research. The site is regularly used for advanced field courses
in the water management and environmental engineering curriculum. During
these courses, students not only learn about the setup and operation of a
hydrological monitoring network, but they also contribute to the improvement
of knowledge about the watershed by collecting and analysing soil samples or
performing validation measurements of the instruments.
The dataset provided the majority of the database for two master's theses
and a dissertation. Irsigler (2017) applied discharge and electrical
conductivity data in a simple two-end-member mixing model for the separation
of base flow and direct runoff, using an approach described by
Lott and Stewart (2016). Stecher (2021)
investigated a phenomenon that is observed in no-rain periods during the
vegetation period: daily fluctuations of discharge, with peaks at 08:00 CET up
to 40 % higher than the minimum at 17:00 CET, occur consistently at all four
gauging sites. It was hypothesised that this is an effect of forest
transpiration since these diurnal fluctuations are not observed from late
autumn to early spring. By modelling a slope transect at site Q2 with HYDRUS
2D (Simunek et al., 1999), the diurnal fluctuations of discharge are
demonstrated to be caused by the vegetation in the riparian zone within only
a few metres of the creek. Besides the discharge and rainfall records at
site Q2, the model also used soil moisture data at sites Q2S0, Q2S1 and
Q2S2.
Wesemann (2021) investigated the influence of forest roads and skid
trails on runoff during heavy rainfall events in the Rosalia catchment.
Based on the 0.5×0.5 m lidar DEM (Immitzer, 2009), he
reconstructed a historical terrain model without forest roads and buildings,
which allowed the comparison of the runoff from the natural terrain surface
and runoff from the current surface where flow paths are modified by the
forest roads. The physically based rainfall-runoff model RoGeR
(Steinbrich et al., 2016) was set up for the catchment to quantify
the influence of the road network on the runoff behaviour for three flood
events observed at gauge Q3 between 2017 and 2019. Rainfall data from all
seven rain gauges were used to assess the effect of the spatio-temporal
distribution of rainfall on runoff.
Data availability
All time series data were cleaned of the most obvious errors and artefacts
and stored in an easily useable database. In addition, some auxiliary
spatial datasets are made available. The data described above are available
at 10.5281/zenodo.3997140
(Fürst et al., 2020). This repository comprises an SQLite
database file with all the high-resolution time series data, an MS Excel
sheet with the isotopic data and the spatial datasets. Usage of the data is
described by a comprehensive HTML file (generated by an R
Markdown document also included), which includes previews and a full technical description
of the data, including R code chunks to read and visualise them. The data
repository will be updated annually.
Summary
The data presented in this article represent an effort to measure components
of the energy and water cycle in a forested catchment in the eastern
Austrian Alps. The period of record for precipitation, discharge, air and
water temperature, relative humidity, electrical conductivity, and soil water
content started in 2015. Water quality monitoring and sampling for
precipitation and river water δ18O isotopes were added in 2018
and 2019. Measurements use consistent methods to ensure comparability within
the research catchment. The data have proven fit for the purpose of
supporting hydrological and hydro-meteorological process research. Making
the data available to the research and applied hydrology communities has two
main objectives. First, it intends to inform decision-makers in the Rosalia
forest. The record is an important source of baseline data that can be used
to assess the effect of disturbances such as clear-cuts and changing
forestry on hydrological processes. Second, these data are provided to allow
others to also investigate hydrological processes, medium-term patterns and
potential changes in this type of watershed.
Author contributions
JF was involved in field work to collect the data discussed here,
including selection and installation of the instruments, processing, quality
assurance, and quality control. HPN and KS handled
strategic decisions and funding. JG provided advice on site selection
and provided some spatial data. RN handled the selection and
installation of soil sensors. MS and CS set up the isotope
measurement network and maintain it. All the authors contributed to writing
the manuscript.
Competing interests
The authors declare that they have no conflict of interest. Names of
products and companies are only mentioned for better understanding and
traceability; none of the authors are associated with any of the mentioned
companies.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
Over the years, several people have contributed to the implementation of the
Rosalia test site, the operation of the instruments and data
collection and deserve recognition. These include Wisam Almohamed, Matthias
Bernhardt, Laurin Bonell, Reinhard Burgholzer, Roman Eque, Heinz Fassl,
Martin Hackl, Mathew Herrnegger, Freddy Kratzert, Thomas Lehner, Martin
Lichtblau, Johann Karner, Philipp Proksch, Andreas Schwen, Wolfgang Sokol,
Gabriel Stecher and Johannes Wesemann.
Review statement
This paper was edited by Lukas Gudmundsson and reviewed by two anonymous referees.
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