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\begin{document}

\title{High resolution in-situ stable isotope measurements reveal atmospheric
vapour dynamics above contrasting urban vegetation}



\author[1]{Ann-Marie Ring}%
\author[1]{Dörthe Tetzlaff}%
\author[2]{Maren Dubbert}%
\author[1]{Dubbert D}%
\author[3]{Chris Soulsby}%
\affil[1]{Leibniz-Institut fur Gewasserokologie und Binnenfischerei im Forschungsverbund Berlin eV}%
\affil[2]{Leibniz-Zentrum fur Agrarlandschaftsforschung (ZALF) e V}%
\affil[3]{University of Aberdeen School of Geosciences}%


\vspace{-1em}



  
  \date{June 04, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Quantitative knowledge about ecohydrological partitioning across the
critical zone in different types of urban green space is important to
balance sustainable water needs in cities during future challenges of
increasing urbanization and climate warming. We monitored stable water
isotopes in liquid precipitation and atmospheric water vapour (\selectlanguage{greek}δ
\selectlanguage{english}\textsubscript{v}) using \emph{in-situ} cavity ring-down spectroscopy
(CRDS) over a two-month period in an urban green space area in Berlin,
Germany. Our aim was to better understand the origins of atmospheric
moisture and its link to water partitioning under contrasting urban
vegetation. \selectlanguage{greek}δ \selectlanguage{english}\textsubscript{v} was monitored at multiple heights (0.15,
2 and 10 m) in grassland and forest plots. The isotopic composition of \selectlanguage{greek}δ
\selectlanguage{english}\textsubscript{v} above both land uses was highly dynamic and positively
correlated with that of rainfall indicating the changing sources of
atmospheric moisture. Further, the isotopic composition of \selectlanguage{greek}δ
\selectlanguage{english}\textsubscript{v} was similar across most heights of the 10 m profiles
and between the two plots indicating limited aerodynamic mixing. Only
the surface at \textasciitilde{}0.15 m height above the grassland, \selectlanguage{greek}δ
\selectlanguage{english}\textsubscript{v} showed significant differences, with more enriched
values indicative of evaporative fractionation immediately after
rainfall events. Further, disequilibrium between \selectlanguage{greek}δ \selectlanguage{english}\textsubscript{v} and
precipitation composition was evident during and right after rainfall
events with more positive values (i.e. vapour more enriched than
precipitation) in summer and negative values in winter, which probably
results from higher evapotranspiration and more convective precipitation
events in summer. Our work showed that it is technically feasible to
produce continuous, longer-term data on \selectlanguage{greek}δ \selectlanguage{english}\textsubscript{v} isotope
composition in urban areas from \emph{in-situ} monitoring using CRDS,
providing novel insights into water cycling and partitioning across the
critical zone of an urban green space. Such data has the potential to
better constrain the isotopic interface between the atmosphere and the
land surface and to improve ecohydrological models that can resolve
evapotranspiration fluxes.%
\end{abstract}%



\sloppy


\textbf{High resolution \emph{in-situ} stable isotope measurements
reveal atmospheric vapour dynamics above contrasting urban vegetation}

Ring\textsuperscript{1,2}, Tetzlaff\textsuperscript{1,2,3}, Dubbert
M\textsuperscript{4}, Dubbert D\textsuperscript{1,4},
Soulsby\textsuperscript{3}

\textsuperscript{1} Department of Ecohydrology, Leibniz Institute of
Freshwater Ecology and Inland Fisheries, Berlin, Germany.

\textsuperscript{2} Department of Geography, Humboldt University Berlin,
Berlin, Germany.

\textsuperscript{3} Northern Rivers Institute, School of Geosciences,
University of Aberdeen, UK.

\textsuperscript{4} Research Area 1 ``Landscape Processes'', Leibniz
Institute for Agricultural and Landscape Research, M\selectlanguage{ngerman}üncheberg, Germany

Corresponding author: Ann-Marie Ring (ann-marie.ring@igb-berlin.de);
Doerthe Tetzlaff (doerthe.tetzlaff@igb-berlin.de)

\emph{Novelty and International Appeal Statement:}

Quantitative knowledge about ecohydrological partitioning in different
types of urban green space is important. We present novel
high-resolution isotope data of atmospheric vapour (\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v})
measured continuously over several month in an urban green space. To our
knowledge such data have not been measured in an urban green space
setting with the goal to increase understanding evapotranspiration from
different urban vegetation cover. We also detected occasional
dis-equilibrium between vapour and precipitation isotopes.

\emph{Graphical Abstract Image:}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\emph{Graphical Abstract Text:}

High resolution \emph{in-situ} stable isotope measurements reveal
atmospheric vapour dynamics above contrasting urban vegetation

Ann-Marie Ring, D\selectlanguage{ngerman}örthe Tetzlaff, Maren Dubbert, David Dubbert, Chris
Soulsby

\emph{In-situ} measurements of urban atmospheric water isotopes
(\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}) at different elevations produce reliable
high-resolution data. Urban atmospheric vapour is influenced by varying
drivers depending on the type of green space. \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above
grassland and tree stands was similar at 10 m height, but near-surface
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above indicated higher evaporation and vapour
enrichment over grass. We detected occasional dis-equilibrium between
vapour and precipitation isotopes.

\emph{Keywords:} ecohydrology, urban green spaces, in-situ monitoring,
atmospheric vapour isotopes, equilibrium assumption, cities

\emph{Main Findings}

\begin{itemize}
\tightlist
\item
  \emph{In-situ} measurements of urban atmospheric water isotopes
  (\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}) at different elevations produce reliable
  high-resolution data.
\item
  Urban atmospheric vapour is influenced by varying drivers depending on
  the type of green space.
\item
  \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above grassland and tree stands was similar at 10 m
  height, but near-surface \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above indicated higher
  evaporation and vapour enrichment over grass.
\item
  We detected occasional dis-equilibrium between vapour and
  precipitation isotopes.
\end{itemize}

\textbf{Abstract}

Quantitative knowledge about ecohydrological partitioning across the
critical zone in different types of urban green space is important to
balance sustainable water needs in cities during future challenges of
increasing urbanization and climate warming. We monitored stable water
isotopes in liquid precipitation and atmospheric water vapour
(\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}) using \emph{in-situ} cavity ring-down spectroscopy
(CRDS) over a two-month period in an urban green space area in Berlin,
Germany. Our aim was to better understand the origins of atmospheric
moisture and its link to water partitioning under contrasting urban
vegetation. \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was monitored at multiple heights (0.15,
2 and 10 m) in grassland and forest plots. The isotopic composition of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above both land uses was highly dynamic and
positively correlated with that of rainfall indicating the changing
sources of atmospheric moisture. Further, the isotopic composition of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was similar across most heights of the 10 m profiles
and between the two plots indicating limited aerodynamic mixing. Only
the surface at \textasciitilde{}0.15 m height above the grassland,
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} showed significant differences, with more enriched
values indicative of evaporative fractionation immediately after
rainfall events. Further, disequilibrium between \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and
precipitation composition was evident during and right after rainfall
events with more positive values (i.e. vapour more enriched than
precipitation) in summer and negative values in winter, which probably
results from higher evapotranspiration and more convective precipitation
events in summer. Our work showed that it is technically feasible to
produce continuous, longer-term data on \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} isotope
composition in urban areas from \emph{in-situ} monitoring using CRDS,
providing novel insights into water cycling and partitioning across the
critical zone of an urban green space. Such data has the potential to
better constrain the isotopic interface between the atmosphere and the
land surface and to improve ecohydrological models that can resolve
evapotranspiration fluxes.

\section*{1. Introduction}

{\label{introduction}}

Urban green spaces mediate trade-offs between ``green'' and ``blue''
water fluxes with potential for high evapotranspiration (ET) rates. They
potentially mitigate the Urban Heat Island (UHI) effect, but on the
other hand might reduce groundwater recharge and stream flow generation
. Understanding, quantifying and optimizing this partitioning across
urban critical zones is increasingly important in the face of increased
urban growth and climatic warming. In addition, wider benefits of urban
green spaces -- or green infrastructure -- are increasingly recognized;
these include the potential to enhance infiltration and ameliorate urban
storm runoff to increase local biodiversity , to provide social
functions through improved health for local residents and to improve
water security in terms of sufficient provision of good water quality .
Consequently, as one component of an evidence base for wider urban
planning, the trade-offs between higher ET rates and groundwater
recharge , as well as the linked uncertainties, are an increased focus
for research (e.g. BMUB )\emph{.}

Water stable isotopes have proved valuable tools that can help resolve
the partitioning of incoming precipitation into different components of
ET fluxes or to constrain biosphere-atmosphere feedbacks between
atmospheric vapor and ET, and thus have high potential to contribute to
a scientific evidence-base for managing urban green spaces . Water
isotopes have also been shown to be a useful tracer to understand
processes and linkages across the critical zone and the
soil-plant-atmosphere continuum in different geographic regions although
critical zone studies in urban areas are still relatively rare . Use of
isotopes includes tracking the effects of evaporation in isotopic
fractionation and in identifying the effects of seasonality of water
sources for different vegetation types . Numerous isotope studies have
used soil water or river water isotopes to assess evaporative effects ,
whilst others have related the composition of xylem water to potential
sources of root water uptake . However, studies using high-resolution
data to investigate how evaporation and/or transpiration affect the
isotopic composition of atmospheric vapour (\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}) at the
surface boundary layer are especially rare for urban areas (e.g. Gorski
et al., ).

The onset of relatively inexpensive cavity ring-down spectroscopes
(CRDS) has revolutionized the field of isotope studies allowing
efficient tracing of isotopic transformations across the atmospheric
water cycle , quantifying ecohydrological interactions and the origin of
atmospheric moisture (i.e. evaporation or condensation; Gao et al., ).
Recent developments in using \emph{in-situ} measurements of stable water
isotopes are making use of non-destructive online monitoring techniques
and are increasingly advanced . In terms of analyzing
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}, grab samples or refrigerated traps for offline
analysis in the laboratory were already used in the 1990s with rapidly
accelerating progress in recent years . Today, CRDS techniques have been
shown to be useful for measuring \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} at continuously
high-resolution and thus, enabling real-time analysis of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} which can give more novel insights than precipitation
alone . For example, the technique has been successfully deployed for
monitoring sub-tropical sub-cloud raindrop evaporation ; for testing
vapour equilibrium assumption for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O cellulose
estimates ; diurnal and intra-seasonal variations in evaporative signals
at different heights above the Greenland ice sheet ; and to characterise
variation in \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and their controlling factors during
extreme precipitation events . To date, however, hardly
any\emph{in-situ} studies have assessed \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} dynamics in
the urban atmospheric boundary.

Previous isotopic studies have reported contrasting ecohydrological
partitioning under different land use types in urban green spaces . A
study in Scotland assessed land use influences on isotopic variability
revealing that urbanisation, intensive agriculture and responsive soils
caused rapid cycling of precipitation to stream water . Others found
higher ET and older groundwater recharge beneath urban trees, but more
marked soil evaporative losses under grassland . By integrating simple
modelling and observational water isotope data, Stevenson et al.
quantified the heterogeneities in urban ecohydrological partitioning and
found that median ET increased from grassland, to evergreen shrub, to
larger deciduous forest through to larger conifer trees, with
groundwater recharge behaving contrary. Mixing models applied to
different Berlin green spaces showed that trees were more dependent on
deeper, older sub-soil and groundwater sources, whereas grass very
probably recycled shallow, younger soil water in transpiration . Such
isotopic information of water fluxes through the critical zone can be
used in ecohydrological models that can resolve ET into its component
parts. However, to do this, the isotopic gradient at the
atmospheric-land interface is usually defined in models assuming
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} is in equilibrium with current or recent rainfall .
Despite now being logistically possible, monitoring
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}\emph{in-situ} at different heights and above
vegetation canopies is still rare. Braden-Behrens et al. demonstrated
the value of direct\emph{in-situ} eddy covariance measurements of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} in the surface boundary layer. Despite standard model
assumptions of an equilibrium between \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and
precipitation, \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} can be out of equilibrium with local
water sources and can show gradual depletion with altitude .
High-resolution\emph{in-situ} monitoring of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} allows
testing of such equilibrium assumptions, but so far, very few studies
have tested this with \emph{in-situ} ambient data .

Here, we conducted a ``proof of concept'' study to assess the changing
isotopic composition of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} over a 2.5 months period in
an urban green space with contrasting landcover. We deployed a laser
spectrometer in the field for continuous \emph{in-situ} monitoring of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} in the urban surface boundary layer. Our overarching
research question was whether we can generate data with
\emph{in-situ}real-time sequential monitoring that increases our
understanding of origins of atmospheric moisture and its link to water
partitioning by contrasting urban vegetation. Our specific objectives
were to:

\begin{enumerate}
\tightlist
\item
  investigate dynamics in \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} within two contrasting
  urban vegetation types to understand what types of landcover enhance
  moisture fluxes back to the atmosphere.
\item
  investigate these changes in relation to related ecohydrological
  dynamics of soil moisture storage, sap flow rates and biomass
  accumulation.
\item
  assess the extent of equilibrium between vapour and precipitation.
\end{enumerate}

Based on these assessments, we discuss the future value, challenges and
potential in gaining and processing such high-resolution data to improve
understanding of ET partitioning at different heights in the atmosphere
above different types of landcover in urban green spaces, which would be
important for increased process understanding across urban critical
zones.

\section*{2. Study Site}

{\label{study-site}}

The experiment was carried out in the SE of Berlin, Germany (Fig. 1).
Berlin is located on the flat North European Plain where the topography
and geology are dominated by deposits from the Pleistocene glaciation .
The climate is continental temperate with long-term (1981--2010) mean
annual rainfall of 577-602 mm ranging between stations and mean annual
air temperatures of 9.4--10.2 \selectlanguage{ngerman}°C . Berlin covers 891 km², with a
population of 3.66 million (, 2020; Fig. 1B). The majority of the city
is covered by residential areas and streets (\textasciitilde{}59\%), but
there are large amounts of green and blue spaces: vegetation covers
\textasciitilde{}34\% (forests, parks, agriculture) plus
\textasciitilde{}7\% surface waters .

Our study site is located at the grounds of the Leibniz-Institute of
Freshwater Ecology and Inland Fisheries (IGB), roughly 220 m north of
Lake Müggelsee (Berlin's largest lake) (Fig. 1B). The geology is
characterized by sand and gravel deposits of the Berlin-Warsaw glacial
spillway, (, SenUVK online, 2007). The surrounding district (Fig. 1B) is
characterized by residential areas and roads (38 \%), forest (40 \%),
water bodies (12 \%) and public green space (0.06 \%; ). The study site
is a park-like space with older trees (\textasciitilde{}30-100 years
old) surrounded by brick buildings of former
19\textsuperscript{th}century water works and extensive rough grassland
above subsurface slow sand filter systems, which were used for drinking
water treatment until the beginning of the 1990s (, online). Within a
100 m radius of the study site center (Fig 1C), premises are covered by
buildings (10 \%), different types of non-irrigated urban green spaces,
including grassland (49 \%), shrubs (8 \%) and trees (17 \%); and
streets, semi-permeable or sealed pathways and parking spaces (16 \%).

The experiment focused on two small areas; one tree dominated, the other
grassland dominated and 16~m apart (Fig 1C). The grassland site was
covered by grass (e.g. \emph{Lolium perenne} , \emph{Arrhenatherum
elatius} ) and herbs (e.g. \emph{Trifolium pratense, Achillea
millefolium} ) of 30-50 cm height, mowed twice a year and can be
referred to as an urban meadow . The tree site was dominated by black
locust, lime, oak, birch and maple trees. We selected one dominant maple
tree (\emph{Acer platanoides} ) with a stem diameter of 550 mm (August
2021) and height of \textasciitilde{}16 m. In other studies, \emph{Acer
platanoides} has been shown to have a high drought tolerance and can
maintain low leaf gas exchange rates .

The soils reflect anthropogenic impacts, such as partly backfilled
ground after construction work. They are classified as Anthrosols
(SenUVK, , 2017, online), which consist of debris, sandy materials and a
shallow humus layer from extensive gardening.

\section*{3. Data and Methods}

{\label{data-and-methods}}

\textbf{3.1. Monitoring}

The study period was from August 20\textsuperscript{th} to November
3\textsuperscript{rd} 2021. Climate data (air temperature, precipitation
amount, wind speed and direction, relative humidity, air pressure,
global radiation) were available from the rooftop of IGB
\textasciitilde{}300 m away. Additionally on site, precipitation
(tipping bucket raingauge, 0.2 mm/tip, precision ±3\% of total rainfall;
AeroCone® Rain Collector, Davis Instruments, Hayward, USA) was recorded
with a CR800 Datalogger (Campbell Scientific, Inc. Logan, USA) logging
every 15 min. Temperature was recorded (every 5~min) with BetaTherm
100K6A1IA Thermistors T107 (Campbell Scientific, Inc. Logan, USA;
tolerance ±0.2°C (over 0°--50°C)), with a CR300 Datalogger (Campbell
Scientific, Inc. Logan, USA). Precipitation and temperature data were
verified against available data from the German Weather Service (DWD) of
the ``Berlin-Marzahn'' station (Station ID: 420), \textasciitilde{}12 km
north of the study site.

Precipitation for stable water isotope analysis was collected using a
HDPE deposition sampler (100 cm\textsuperscript{2} opening;
Umwelt-Geräte-Technik GmbH, Müncheberg, Germany). Overall, 32 daily and
15 bulk (interval \textasciitilde{}weekly) samples with precipitation
\textgreater{}1 mm (to limit evaporation effects) were collected between
July and November 2021. Further, daily precipitation samples were
collected \textasciitilde{}350 m away from the study site with an
autosampler (ISCO 3700, Teledyne Isco, Lincoln, USA) at a 24 hours
interval. All autosampler bottles were filled with a paraffin oil layer
\textgreater{} 0.5 cm in thickness (after IAEA/GNIP, 2014) to avoid
evaporative effects. Additionally, groundwater samples were taken weekly
with a submersible pump (COMET-Pumpen Systemtechnik GmbH \& Co. KG,
Pfaffschwende, Germany) from a well on IGB grounds \textasciitilde{}300
m away from the site.

\par\null

For isotope analysis of the liquid water samples at the IGB laboratory,
samples were filtered (0.2 um cellulose acetate) and decanted into 1.5
ml glass vials (LLG LABWARE). They were analysed by cavity ring-down
spectroscopy (CRDS) with a L2130-i Isotopic Water Analyser (PICARRO,
INC., Santa Clara, CA) using four standards for a linear correction
function and which were referenced against three primary standards of
the International Atomic Energy Agency (IAEA) for calibration (VSMOW2
(Vienna Standard Mean Ocean Water 2), GRESP (Greenland Summit
Precipitation) and SLAP2 (Standard Light Antarctic Precipitation 2)).
Liquid samples were injected six times and the first three injections
discarded. To screen for interference from organics, the ChemCorrect
software (Picarro, Inc.) was applied and contaminated samples discarded.
After quality-checking and averaging multiple analyses for each sample,
the results were expressed in \selectlanguage{greek}δ-\selectlanguage{english}notation with Vienna Standard Mean Ocean
Water (VSMOW). Analytical precision was 0.05 %0 standard deviation (SD)
for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and 0.14 %0 SD for \selectlanguage{greek}δ\selectlanguage{english}D.

Stable isotopes of atmospheric water vapour (\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v)} were
measured \emph{in-situ} at the tree-dominated and grassland sites,
respectively at 0.15 m, 2 m and 10 m height to capture the effects of
vegetation heterogeneity and potential turbulence within an urban
surface boundary layer. To monitor the elevation profile above the
grassland, a 10 m flag mast with \textasciitilde{} 100 cm long
perpendicular poles at required sample points was set up (Fig. 2). At
the tree site, we measured directly at the trunk within the canopy of
the maple tree. The measurement campaign started on 20.08.2021 above the
grassland and on 03.09.2021 in the tree canopy.

We performed \emph{in-situ} real-time sequential measurements of water
vapour via CRDS (Picarro L2130-i, Picarro Inc., Santa Clara, CA, USA)
placed in a box between the sampling sites. Air inlets and CRDS were
connected with polytetrafluoroethylene (PTFE) tubing (1.6 mm x 3.2 mm).
We used PET bottles covered with aluminum foil to prevent the inlets
from rain and sun exposure. Each tube inlet (Fig. 2) was sampled for 20
min in resolution of seconds. Then sampling was switched automatically
to the next one; resulting in a 2-hourly resolution for each inlet. We
only used the data when a measurement showed stable values (i.e. ranges
of 2 %0 for \textsuperscript{2}H and 0.3 %0 for \textsuperscript{18}O).
The first 5 min of data after switching inlets were always discarded to
avoid memory effects. Prior the vapour entering the CRDS unit, a
preceding sub-micron particulate filter was connected to prevent liquid
water from entering by creating a low dew point by lowering the air
pressure. The sample flow rate was at 0.04 L min\textsuperscript{-1}.
Water vapour concentrations were always above 6000 ppm (this is where
the concentration dependent deviation becomes low and thus measurement
precision is not compromised).

To allow for later conversion of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} measurements into
liquid water isotope values, temperature probes were installed at all
heights near the tube inlets at both sites with BetaTherm 100K6A1IA
Thermistors T107 (Campbell Scientific, Inc. Logan, USA; tolerance \selectlanguage{ngerman}±0.2°C
(over 0°--50°C)), with a CR300 Datalogger (Campbell Scientific, Inc.
Logan, USA) logging mean values every 5~min from secondly-resoluted
data. To avoid tube condensation, heating cables (ILLw.CT/Qx, Quintex
GmbH, Lauda-Königshofen, Germany) were installed and wrapped with the
tube in insulation material. The cables were controlled via an automatic
multi socket (Gembird 235 EG-PMS2, Gembird Software Ltd., Almere, The
Netherlands) to prevent overheating in summer. To minimise condensation
effects, the measurements were checked daily. The PTFE-tubes were
flushed weekly or if required for 10 minutes per probe to remove any
water . Data was discarded when condensation inside the system was
identified in the respective tube.

By combining \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and temperature data from each inlet we
derived the values for all heights of temperature dependent equilibrium
fractionation from vapour to liquid with the correction formulated by
Majoube :

\(\alpha=exp\frac{a\left(\frac{10^{6}}{T_{k}^{2}}\right)+b\ \left(\frac{10^{3}}{T_{k}}\right)+c}{1000}\)(1)

where \selectlanguage{greek}\emph{α} \selectlanguage{english}is the isotopic fractionation
factor,\emph{T\textsubscript{k}} is the temperature (in K), and \emph{a}
,\emph{b} , and \emph{c} are empirical parameters that vary depending on
the isotopologue. All values of isotopic compositions are given in
liquid phase and relative to Vienna Standard Mean Ocean Water (VSMOW).

To investigate the local evaporative effects, the line-conditioned
excess (short lc-excess) (see ) was calculated. The lc-excess describes
the deviation of the sample from the local meteoric water line (LMWL):

lc-excess = \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H - \emph{a} \selectlanguage{ngerman}· \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O
-\emph{b} (2)

where \emph{a} is the slope and \emph{b} the intercept of the weighted
isotopic composition of the local precipitation. The LMWL was calculated
by amount-weighted least square regression from daily precipitation
isotopes measured at IGB from July until November 2021.

In order to assure stable values to offset variability in the field,
stability of the CRDS was tested in the lab before installing the setup
outside. During the sampling campaign, we calibrated once a week (cf.
calibration periods ) with two standards. Stored in sealed glass
containers, the standards were connected to the CRDS for two-point
calibrations (liquid values: light: \textsuperscript{2}H -109.91
%0/\textsuperscript{18}O -17.86 %0; medium: \textsuperscript{2}H -56.7
%0/\textsuperscript{18}O -7.68 %0). We used measured water vapour
concentrations and added linear regressions of temperature dependency
slopes to correct for isotopic offsets and vapour concentration
dependency (resembling the approach by Schmidt et al. ).

We also monitored sap velocities and stem circumference of the maple
tree. Two sap flow sensors (SFM-4, Umwelt-Ger\selectlanguage{ngerman}äte-Technik GmbH,
Müncheberg, Germany; ±0.1 cm/hr heat velocity precision) were installed
at breast height (1.3 m) at the north and south side of the tree stem.
The sap flow sensors work according to the heat ratio method by Marshall
. Daily reference crop evapotranspiration (ET\textsubscript{0}) was
estimated using the FAO Penman--Monteith method with ``R''-Package
``Evapotranspiration'' . To investigate dynamics during the growing
season, both daily mean sap velocity {[}cm h\textsuperscript{-1}{]} and
ET\textsubscript{0} were then normalized (to
sapvelocity\textsubscript{norm} and ET\textsubscript{norm},
respectively) by feature scaling. One dendrometer (DR Radius
Dendrometer, Ecomatik, Dachau, Ger170; accuracy max. ± 4.5\% of the
measured value (stable offset)) was also installed to measure stem
diameter dynamics at high temporal resolution. Sap velocity and stem
increments were logged as 15 min intervals using a CR300 Datalogger
(Campbell Scientific, Inc. Logan, USA). Throughfall amount was sampled
manually at a height of 30 cm above ground using four rain gauges (Rain
gauge kit, S. Brannan \& Sons, Cleator Moor, UK) which were installed 1
m and 3 m, respectively, north and south of the tree's stem.

Volumetric soil water content and soil temperature were measured at both
sites (Fig. 2) by soil moisture temperature probes (SMT-100,
Umwelt-Geräte-Technik GmbH, Müncheberg, Germany) in the upper soil at 6
cm depth. Recording took place with a CR800 Datalogger (Campbell
Scientific, Inc. Logan, USA) with a 15 min frequency and a precision of
±3 \% for volumetric soil water content and ±0.2~\selectlanguage{english}\textsuperscript{*}C
for soil temperature. Groundwater level in one well was monitored with
an automatic datalogger (groundwater level probe) at an interval of 15
min (see location in Fig. 1C).

\textbf{3.2 Testing differences, correlations and the equilibrium
assumption}

In order to analyse differences and correlations of the data, we
performed several statistical tests. Each dataset was tested for
normality using Shapiro-Wilk . If normally distributed, we performed
simple \emph{t} \selectlanguage{english}-statistics to test for significant differences. If data
were skewed or non-normal, we performed non-parametric alternatives:
Wilcoxon signed-rank test for two groups and Kruskal-Wallis test by
ranks for more than two groups . To test correlations of non-linear data
we applied Spearman's rho statistic .

Due to the nature of the high-resolution dataset of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v},
we could also test the equilibrium assumption of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and
precipitation for the sampling period using the following equation:

\(R_{\text{atm}}=\ R_{v}-R_{p}\)(3)

where \emph{R\textsubscript{v}} and \emph{R\textsubscript{p}} are the
liquid Majoube-corrected isotope ratios of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and liquid
isotope ratios of precipitation and \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} is the
difference in isotope ratios of water vapour and precipitation in
atmosphere. These laboratory standards are also relative to VSMOW.
If\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} = 0, a perfect equilibrium between
precipitation and \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} isotope ratios prevails. We used
the daily mean of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} at 2 m height for the tree site and
grassland site separately to compare both types of
landcover.\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} was calculated for days when
precipitation occurred.

\section*{4. Results}

{\label{results}}

\textbf{Hydroclimate}

Figure 3 shows the variability in hydroclimatic variables during the
study period in the context of a longer 6-month period. Summer and
autumn precipitation were close to the seasonal average; with a large
convectional event in July and numerous smaller events in August and
September when monitoring was ongoing. Air temperatures reflected
radiation, and decreased from July until December, though they were also
near-average in the monitoring period. Relative humidity was lower in
summer, when temperatures were highest and declined thereafter. Wind
speeds were low (\textless{}1 m/s) through most of the sampling.

\textbf{4.2 Ecohydrological dynamics}

Throughfall (\textgreater{} 1 mm; not shown) only occurred during three
major precipitation events and varied between the sampling points
underneath the canopy as follows: 2.8 - 5.5 mm on 15.09., 9-15 mm on
29.09 and 2.5-4.8 mm on 24.10. Soil moisture in the upper soils rapidly
responded to rainfall at both sites, though quickly dried until more
persistent rewetting towards November (Fig 4b). The tree site soil was
generally more responsive to wetting particularly following heavy
rainfall on September 29\textsuperscript{th}. The average groundwater
level (Fig 4b) was around 2.3 m b.g.l. and varied only by 2cm as it was
primarily controlled by the water levels in the lake.

Daily mean sap velocity ranged from 0-9.2 cm
h\textsuperscript{-1}(0-0.92 m h\textsuperscript{-1}; Fig. 4c-d). The
southern side of the tree showed higher values but similar dynamics
until October. Towards the end of the growing period, the northern side
had slightly higher sap velocities. Cumulative increments of stem size
showed progressive growth between the end of August and November.
Normalised for mean sap velocity, sapvelocity\textsubscript{norm} showed
the same ranges as mean ET\textsubscript{norm} (i.e. also normalized
against the mean) from August until mid-October, implying there was no
limit on the trees meeting atmospheric moisture demand. As leaf
senescence and fall progresses in the middle of October,
ET\textsubscript{norm} exceeded sapvelocity\textsubscript{norm}.

\textbf{Stable water isotope dynamics}

Figure 5 shows that stable water isotopes in precipitation were highly
variable and showing more negative values (i.e. high depletion in
heavier isotopes) for events in late August and early November.
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} at the tree and grassland sites (both exemplary shown
for 2 m height) was influenced by depleted rainfall inputs. For example,
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} at the grassland sites (prior to tree monitoring
commencing) showed particularly high depletion in response to depleted
rainfall at the beginning of September (Fig 5a). Summary statistics of
measured stable water isotope values and ranges of precipitation,
groundwater and atmospheric vapour \selectlanguage{greek}δ\selectlanguage{english}v (liquid values) are given in Table
1.

After testing Spearman\selectlanguage{ngerman}´s rank correlation between \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and
soil moisture at both sites for different heights, only the grassland
site indicated a significant positive correlation of soil moisture with
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} (0.3 at 0.15 m; 0.24 at 2 m;
0.22 at 10 m), but none for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O. The tree site showed
no significant correlations between \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and soil moisture
for either isotope.

Lc-excess in precipitation increased from late summer to late autumn,
though variability remained high especially in October. The lc-excess
was generally negative at both sites until mid-September (Fig. 5b),
reflecting high energy for evaporation. From Mid-September until
November, lc-excess of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was generally positive but
more variable. Spearman rank correlation coefficients between
precipitation and \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} were 0.55 for \selectlanguage{greek}δ\selectlanguage{ngerman}²H and 0.43 for
lc-excess indicating positive correlations.

The amount-weighted LMWL of the sampling period (July -- November 2021)
(Fig. 6) was \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H = 7.71 \selectlanguage{ngerman}± 0.11 *
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O + 7.42 \selectlanguage{ngerman}± 1.12 (\emph{R²} = 0.987). The dual
isotope plot (Fig 6) clearly shows that precipitation isotopes were
characterized by large ranges (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H -145.2 %0 to -10.2
%0; \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O -19.3~%0 to -1.5 %0), groundwater in comparison
showed little variation with mean isotopic signatures (Tab. 1). Beneath
the tree canopy, \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was more homogenous across the
elevation profile than above grass. Grassland showed a higher variance
of ambient vapour within the elevation profile, with a tendency of
near-surface air (0.15 m height) to be more enriched in heavier isotopes
in comparison to the tree-site surface air, but attenuating with height
(Tab. 1). Overall, the boxplots and median values of grassland
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} showed a slightly higher range compared to the tree
site (Fig 6, Tab 1). The Kruskal-Wallis-Test showed significant
differences of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} (p-values \textless{} 0.05) between
ground-level (0.15 m) and higher elevations (2~m, 10 m) at the
grassland, while there were no significant differences (p-values
\textgreater{} 0.05) between different heights underneath the tree
canopy and also no significant differences between both sites. The
Wilcoxon signed-rank test indicated a p-value of 0.0507 (\selectlanguage{greek}α\selectlanguage{english}=0.05) between
the sites for 0.15 m height showing they were significantly different.

We also investigated higher resolution dynamics of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}during precipitation events, and we display here the
event on the afternoon of 15.09.2021 (where 6.6 mm fell between 15:00 --
18:30; Fig. 7). Both sites showed enriched \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} values at
night corresponding to the signature of precipitation, with uniform
distribution at different heights. The next day, \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}above
grassland reflected clear evaporative losses by more enriched values
just above the surface, where windspeed and soil moisture was higher,
which was also observed during the other events. In the tree canopy, no
differences with height occurred. During the event, soil water content
underneath the tree was 4.2 \% and increased to 5.6 \% 24 h after the
rainfall; whereas the grassland was wetter increasing from 5.0 \% to 8.7
\% (see also Fig. 4). A common aspect for all the precipitation events
was a close link between precipitation isotopic signature and
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}.

\textbf{4.4 Equilibrium between vapour and precipitation}

The difference in isotope ratios of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and precipitation
in the atmosphere\selectlanguage{greek}\emph{, ΔR\textsubscript{atm,}}\selectlanguage{english} generally differed from
zero (i.e. the equilibrium) at both sites, with higher deviation from
the equilibrium between mid-August and mid-September (Fig. 8). Responses
of \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} after precipitation events showed
heterogenous patterns, reflecting varying differences from the
equilibrium throughout the monitoring period. In August, grassland
showed only positive \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} , depicting more
enriched \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} compared to precipitation. On an event
basis, grassland and tree sites differed slightly
in\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} and the timeseries
of\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H showed similar patterns.

Boxplots showed higher \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} values at the
grassland site (though note that grassland included more data, Fig. 9).
T-tests did not show significant differences between grassland and tree
site for \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H (p=0.055)
but showed significance for
\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english}\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O (p=0.012), again
showing a difference between the two vegetation types in their variation
from equilibrium. Median\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} values of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H were 2.95 %0 and 0.51 %0 for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O
at the grassland, and 0.51 %0 for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H and 0.36 %0 for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O at the tree site. Maximum and minimum
\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} values at the grassland ranged between -30
and 46 %0 and between -3.7 and 7.7 %0 for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O, respectively. At the tree site they varied
between -31 and 26 %0 and -4 and 2.7 %0 for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{2}H and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O, respectively. Thus, the difference from
equilibrium of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} with precipitation was greater at the
grassland than at the tree stand. The higher and more abundant positive
values at grassland reflect a higher isotopic enrichment of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}.

\section*{5. Discussion}

{\label{discussion}}

\section*{5.1 Vapour stable water isotopes in different urban
vegetation}

{\label{vapour-stable-water-isotopes-in-different-urban-vegetation}}

This study showed that extended (i.e. \textgreater{}2 months) periods,
continuous sequential \emph{in-situ} monitoring of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}produces reliable ad novel data in urban green space
environments with a temperate climate. Our distributed network of inlet
ports sampling the atmospheric boundary at different heights above
contrasting urban green space vegetation produced reliable
high-resolution data with a 2-hourly resolution for each inlet. However,
there is no doubt that the method is very labour intensive and requires
almost daily maintenance including checking the correct operation of the
CRDS, ventilation systems and pumps. Detailed, biweekly data checks of
the different inlets are also necessary to detect condensation in the
tubes or other unwanted memory effects in the CRDS. In particular, the
\emph{in-situ} setup requires a secured environment for the CRDS and
vapour tubing (e.g. a locked and fenced box and securing pipes adapted
to the study site). Overall, however, \emph{in-situ} monitoring of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} needs less regular maintenance than \emph{in-situ}
soil water monitoring due to greater condensation issues from deep soil
compared to atmospheric vapour (cf. ).

Monitored \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} data fluctuated around the LMWL in equal
distribution over the entire study period indicating no dominance of
non-equilibrium fractionation , but disequilibrium occurred at shorter
time scales. We found limited difference between the two vegetation
covers reflecting a generally well mixed boundary. \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} of
grassland showed a slightly higher temporal variability and also higher
variance along the height profile compared to the tree site. The only
significant difference was that the surface air (at 0.15 m height) above
the grassland was more enriched, though this was rapidly attenuated with
height. An \emph{in-situ} study by Griffis et al. found similar effects
of surface evaporation enriching surface boundary layer water vapour and
atmospheric loss of light vapour fraction above grassland through the
underlying process of kinetic fractionation during evaporation , while
tree canopy protects from such loss.

At the event scale, \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} showed clear isotopic responses
after rain. The response timing was dependent on the time of day being
more marked around noon when radiation input is elevated. This is due to
the fact that \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} at hourly timescale is controlled by
airmass advection which increases with higher solar radiation . At
seasonal scale, lc-excess was low in summer and higher in autumn
reflecting higher ET in warmer months. Griffis et al. explained the
seasonal amplitude of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} to be driven by Rayleigh
processes that are strongly modulated by evaporation and entrainment,
i.e. inflow of an air parcel to another.

\section*{5.2 Insights into high-resolution urban
ecohydrology}

{\label{insights-into-high-resolution-urban-ecohydrology}}

Our results indicate that the urban grassland surface is contributing to
the atmospheric moisture affecting water partitioning with the main
drivers being high surface evaporation and/or high transpiration of the
grass, high surface temperatures as well as low atmospheric mixing. The
measurements from beneath canopy give useful insights for turbulent
mixing parameterisation of urban canopy layer vertical transport, but
direct transpiration imprint could not be measured.

Additional insights into the processes controlling isotopic composition
of urban green space \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} were leveraged by having
simultaneous ecohydrological monitoring, i.e. measurements of soil
moisture, throughfall, sap flux and tree diameter. At both sites, the
overall low top soil moisture increased in response to precipitation and
then decreased with time reflecting drainage and evaporative losses that
contribute -- at least in the grass plot -- to increased evaporation
that affects the isotopic signal at 0.15 m. Potential normalized
ET\textsubscript{norm} did not exceed total sap
flux\textsubscript{norm}of the maple tree during the phase of active
leaves indicating no drought stress of the tree. Additionally,
dendrometer data revealed normal stem growth for late summer and autumn.
Considering the low top soil moisture and constant sap flux, our results
match with the findings of Kuhlemann et al., from another urban green
space site in Berlin in that urban tree transpiration rates show a
certain resilience to drought (which is of course highly dependent on
tree ages and species). Further, the investigated tree stands were a
group of trees which probably makes a difference compared to individual
urban trees in another study that showed considerably higher sap flux
densities .

Interestingly, despite interception evaporation and transpiration from
the urban tree canopy after events, there was no imprint on
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} captured at 10 m compared to lower heights (cf. .
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} variabilities did not correlate with certain
ecohydrological parameters throughout the whole sampling period; though
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} responded to changes in potential ET during the
warmer period until the end of September. A storm on Oct.
21\textsuperscript{st} led to a quick decrease of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}signatures that day.

These insights on high-resolution dynamics of ecohydrological fluxes and
partitioning can contribute to improved strategies of urban green space
management in the future. However, there is great potential for more
detailed monitoring of urban canopy ET by more distributed networks in
canopies, e.g. it would be interesting to measure at least 5 m over an
urban canopy or higher similar to Braden-Behrens et al. and compare this
with corresponding measurements above an urban grassland.

\section*{5.3 Testing the assumption of equilibrium between vapour and
precipitation}

{\label{testing-the-assumption-of-equilibrium-between-vapour-and-precipitation}}

The equilibrium assumption between the isotopic signatures of
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and precipitation was not always held during our
field campaign. Previous studies showed that the equilibrium assumption
is more robust at subseasonal, longer time scales than for individual
rain events. Lee et al. found that during rain events, vapour in the
surface layer developed in general a state of equilibrium with the
falling raindrops. In our study, this assumption was not robust at the
subseasonal scale and did not confirm an establishment of an
equilibrium.

\selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} (i.e. difference in isotope ratios of water
vapour and precipitation in atmosphere) was greater and showed more
positive values in summer reflecting that vapour was more enriched than
precipitation during summer. Potential reasons for these results are (as
discussed by ): raindrops formed at high elevation , precipitation came
from convective events with big raindrops or high tree transpiration
rates from deeper sources prevented vapour from equilibrium with
precipitation. Additionally, high transpiration rates lead to isotopic
enrichment of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} and could generate higher deviation
from \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} with precipitation.

Testing the equilibrium assumption is especially important for areas
with a distinct microclimate like cities as previous studies showed that
equilibrium estimates can be biased . Further, different regions of the
World show diverging results for \selectlanguage{greek}\emph{ΔR\textsubscript{atm}}\selectlanguage{english} depending
on climate, altitude and latitude. E.g. Mercer et al. showed that the
equilibrium assumption does not hold in continental mountain
environments. By going beyond the standard assumption of equilibrium in
urban ecohydrology, we can improve simple mixing models, complex
process-based, isotope-aided ecohydrological models like
EcH\textsubscript{2}O-iso , estimations in keeling plot and the Craig
and Gordon approach (cf. ).

\section*{6. Conclusion}

{\label{conclusion}}

We monitored stable water isotopes in liquid precipitation and
atmospheric water vapour (\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}) using \emph{in-situ}cavity
ring-down spectroscopy (CRDS) over a two-month period in an urban green
space area in Berlin, Germany. \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was monitored at
multiple heights (0.15, 2 and 10 m) in different vegetation: grassland
and forest plots. Our distributed sampling network of inlet ports
produced novel, reliable high-resolution data with a 2-hourly resolution
for each inlet.

We have shown that the isotopic composition of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} above
both land uses was highly dynamic and positively correlated with that of
rainfall indicating the changing sources of atmospheric moisture. The
isotopic composition of \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} was similar across most
heights of the 10 m profiles and between the two plots indicating
limited aerodynamic mixing. Only the surface at \textasciitilde{}0.15 m
height above the grassland showed significant differences in
\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}, with more enriched values indicative of evaporative
fractionation immediately after rainfall events.

We combined this isotope monitoring with hydroclimatic monitoring and
measurements of sap flow, stem size, soil moisture, throughfall. At both
sites, the overall low top soil moisture increased in response to
precipitation and then decreased after the events reflecting drainage
and evaporative losses. Potential normalized ET\textsubscript{norm} did
not exceed total sap flux\textsubscript{norm} of the maple tree during
the phase of active leaves indicating no drought stress on the tree.
Dendrometer data revealed normal stem growth for late summer and autumn.
Despite interception evaporation and transpiration from the tree canopy
after events, there was no imprint on \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} captured at 10
m compared to lower heights. Our results indicate occasional
dis-equilibrium between water vapour and precipitation isotopes.

Our set up provided novel insights into high-resolution dynamics of
water cycling and partitioning in across the Critical Zone of an urban
green space can contribute to improved urban planning strategies
providing new evidence-base. Such data has the potential to better
constrain the isotopic interface between the atmosphere and the land
surface. Importantly, it can be incorporated into tracer-aided
ecohydrological models that can resolve evapotranspiration fluxes and
improve these estimations.

However, more research is needed to upscale these findings to canopy and
city scale. More detailed monitoring of urban canopy ET by more
distributed networks in and above canopies will benefit further
investigations.

\section*{Acknowledgements}

{\label{acknowledgements}}

This study was funded through the German Research Foundation (DFG) as
part of the Research Training Group ``Urban Water Interfaces'' (UWI;
GRK2032/2) and the Einstein Foundation as part of the ``Modelling
surface and groundwater with isotopes in urban catchments'' (MOSAIC)
project. Funding for DT was also received through the Einstein Research
Unit ``Climate and Water under Change'' from the Einstein Foundation
Berlin and Berlin University Alliance (grant no. ERU-2020-609) and the
project BiNatur (BMBF No. 16LW0156). We also acknowledge the BMBF
(funding code 033W034A), which supported the stable isotope laboratory
and in-situ laser analyser. Contributions from CS have also been
supported by the Leverhulme Trust through the ISO-LAND project (grant
no. RPG 2018 375). We thank all colleagues involved in the
ecohydrological monitoring and daily precipitation and groundwater
sampling, but in particular are grateful to Jan Christopher, Jonas
Freym\selectlanguage{ngerman}üller and Jessica Landgraf.



\section*{Figures}

{\label{figures}}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\emph{\textbf{Fig. 1}. Location of Berlin within Germany and map of
Berlin (A, B); and the study site at IGB Berlin (C) with the two
sampling sites (75 m radius from center) grassland with flagmast and
tree site with Acer platanoides; and installations of atmospheric water
vapour in-situ measurements, sap flow and soil moisture measurements and
precipitation sampler. (Basemap: Google Satellite)}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 2.} \emph{Conceptual diagram of the general in-situ
isotope measuring setup. Numbers refer to different tube inlets for
vapour (1 refers to a reference inlet at the CRDS).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\selectlanguage{ngerman}\emph{\textbf{Figure 3.} Time series of daily climate data during the
study period (July -- November 2021) (precipitation {[}mm{]}, air
temperature {[}°C{]}, relative humidity {[}\%{]}, windspeed {[}m/s{]},
global radiation {[}W/m²{]}) with periods before and after CRDS
measurements shaded in grey and the in detail investigated event marked
with a grey bar (see Figure 7).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 4.} \emph{a) Precipitation {[}mm{]} (focus precipitation
event marked). b)} \emph{Soil moisture (VWC
{[}\%*h\textsuperscript{-1}{]}) and groundwater levels (measured every 2
weeks). c) Daily mean sap velocities measured at the maple tree and
daily stem size variation as cumulative increments (measured for the
stem-radius). d) Daily normalized sap velocity
(sapvelocity\textsubscript{norm}) and normalized Evapotranspiration
(ET\textsubscript{norm}).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\emph{\textbf{Figure 5.} Precipitation amount, daily stable water
isotope ratios of precipitation and hourly} \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} \selectlanguage{ngerman}\emph{of
the tree site and grassland (both at 2 m height) for (a) ²H
of}\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} \emph{and (b) lc-excess of} \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}

\emph{(focus precipitation event marked).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\emph{\textbf{Figure 6.} Dual isotope plot of} \selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v}

\emph{(shown as daily mean) of the different heights and vegetation
types as well as precipitation (daily) and groundwater (weekly). Data
shown here were sampled between 04.09. and 03.11.2021, when sampling was
active on both sites. Boxplots show the sample distribution of the data
sets.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\selectlanguage{ngerman}\emph{\textbf{Figure 7.} Responses of ²H δ\textsubscript{v} to the
precipitation event on 15.09.2021 at the two sites.}\selectlanguage{english}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image10/image10}

\end{center}
\end{figure}

\textbf{Figure 8.} \selectlanguage{greek}\emph{Daily ΔR\textsubscript{atm} of
δ\textsuperscript{18}O and δ\textsuperscript{2}H at 2 m height for
grassland (green) and tree site (brown) based on occurring precipitation
events (daily amounts).}\selectlanguage{english}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image11/image11}

\end{center}
\end{figure}

\textbf{Figure 9.} \selectlanguage{greek}\emph{ΔR\textsubscript{atm} distributions of
δ\textsuperscript{18}O and δ\textsuperscript{2}H of the grassland and
tree site at 2 m height during the sampling period.}\selectlanguage{english}



\textbf{Tables}

\textbf{Table 1.} \emph{Summary statistics of measured stable water
isotope values of precipitation, groundwater and atmospheric
vapour}\selectlanguage{greek}δ\selectlanguage{english}\textsubscript{v} \emph{(liquid values).}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image12/image12}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

