Hydrogeology Journal
https://doi.org/10.1007/s10040-022-02471-y

REPORT

Estimation of groundwater recharge rates using soil-water isotope
profiles: a case study of two contrasting dune types on Langeoog
Island, Germany
V. E. A. Post 1,2 & T. Zhou 3 & C. Neukum 1 & P. Koeniger 1 & G. J. Houben 1

1& A. Lamparter & J. Šimůnek

3

Received: 15 September 2021 / Accepted: 21 February 2022
# The Author(s) 2022

Abstract
The creation of artificial dunes for coastal protection may have important consequences for freshwater lenses in coastal aquifers.
The objective of this study was to compare the recharge processes below such a young dune with scant vegetation to an older
dune covered by grass and herbaceous vegetation. To this aim, soil and water samples were collected from the unsaturated zone attwo sites on Langeoog Island in northern Germany, and the soil water was analysed for stable water isotopes and chloride.
Recharge rates were calculated by using a new version of HYDRUS-1D, which was modified to simulate isotope fractionation
during evaporation. Both the model outcomes and the data highlight the importance of fractionation, which is slightly more
pronounced at the older, more vegetated dune. At the newly constructed dune, vegetation dieback seemingly reduces theimportance of transpiration during summer. Recharge occurs year-round, albeit predominantly during the winter months.
Calculated recharge rates are consistent with lysimeter measurements, but are significantly higher than previously reported rates
based on groundwater age data, which is primarily attributed to the absence of dune shrub at the sites investigated here. More data
are needed to establish the importance of soil-water repellency and overland flow. Based on the results, it is proposed that
repeated isotope sampling can yield important insights into the dynamics of recharge processes, including their response to
climate change.
Keywords Groundwater recharge . Barrier islands . Unsaturated zone . Numerical models . Isotopes

Introduction
Barrier islands are highly dynamic and unique environments,
which provide many ecosystems services and form attractive
tourist destinations. Fresh groundwater often is the only water
resource. It is stored as a thin veneer in freshwater lenses
surrounded by more saline water, making groundwater abstraction a delicate trade-off between meeting water demandand preventing salinization. The freshwater lens condition andthe amount of water that can be safely withdrawn from it* G. J. Houben
georg.houben@bgr.de
1

Federal Institute for Geosciences and Natural Resources (BGR),
Stilleweg 2, D-30655 Hannover, Germany

2

Edinsi Groundwater, Nederhorst den Berg, The Netherlands

3

Department of Environmental Sciences, University of California,
Riverside, Riverside, CA, USA

depend strongly on groundwater recharge. Knowing the recharge rate is thus of utmost importance to ensure sustainableuse.
The exposure of barrier islands to sea currents and wave
actions makes their coastlines susceptible to erosion. This, andinundations by storm floods, puts the fresh groundwater at
direct risk (Post and Houben 2017). The erosion threat often
prompts local authorities to take coastal defence measures.
Typical measures include the heightening of existing dunes
or even creating artificial coastal dunes by spraying andbulldozing dredged sand in the form of elongated bodies along
the foredunes (Caljé et al. 2018; Thorenz 2008).
While the creation of artificial dunes and islands is becoming more widespread (Huizer et al. 2016; van Ginkel 2015),
little is known about groundwater recharge processes in thesesystems. The initial scarcity of vegetation limits transpiration
and allows a greater fraction of precipitation to reach the watertable and recharge the freshwater lens. Over the course of
several years, the artificial dunes undergo a change from analmost unvegetated “white dune” type to a “brown dune”

Hydrogeology Journal

type, vegetated by mosses, grass, and low shrubs. Studies in
the coastal dunes in the Netherlands have shown that underbare or poorly vegetated dunes, recharge occurs during thewhole year, with the fraction of rainfall becoming recharge
being lowest in May and June and highest in November andDecember (Stuyfzand 1993). Under densely vegetateddunes, recharge only occurs from August/September toApril, with a soil-moisture deficit developing during theremaining months.
The objective of the present work is to determine groundwater recharge rates and temporal dynamics for two contrasting sites on Langeoog Island in northern Germany: an old,vegetated dune and a recently created, sparsely vegetateddune. To this aim, the study involved soil-water stable isotope(2H, 18O) measurements that were simulated using a new version of the HYDRUS-1D code recently extended by Zhou
et al. (2021) to include isotope fractionation. Soil-moistureisotope measurements have been used successfully to constrain recharge rates in many studies (Koeniger et al. 2016;
Tewolde et al. 2019; Mattei et al. 2020), and models of varying complexity have been applied for this purpose (Barbecot
et al. 2018; Boumaiza et al. 2021). Recharge estimates canalso be based on chloride concentration measurements, but
given that chloride inputs are highly variable in space and timeclose to the coast, e.g., due to sea spray, isotopes form a valuable alternative, albeit with added complexity due to measurement procedures and fractionation effects. The results fromthis work form a demonstration example of the applicationof the new HYDRUS-1D code for recharge estimation moregenerally. They also provide insight into the effects of artificial dune creation on groundwater recharge to freshwater
lenses in coastal areas.

Study area
The island of Langeoog is an elongated sandy barrier island
located at the northern rim of the shallow intertidal Wadden
Sea, off the coast of northern Germany (Fig. 1).
Undifferentiated Holocene and Pleistocene (glacio-)fluvial
sediments form the base of the island, overlain by Holocene
tidal flat deposits of the Wadden Sea (at depths of 10–20 m
below sea level) and, finally, Holocene beach and dune sands(Bungenstock and Schäfer 2009). The island formed around2800–2200 years ago, with the first dunes forming at around
AD 100. Due to constant aeolian sand movement, vegetation
was very scarce, and settlement only began in the thirteenth
century, after some soil had formed (Barckhausen 1969). The
dunes grew considerably between the thirteenth and seventeenth centuries. Catastrophic storm floods, especially
the Christmas flood of 1717, separated the dune belt
into three individual parts (Houben 2018). Since thebeginning of the twentieth century, the dunes wererepeatedly widened and heightened with sand dredged
from the North Sea and stabilized after deposition byplanting beach grass (marram).
Due to generally favourable conditions in the dune area(sandy soils, low transpiration and interception losses by the
predominantly low vegetation), three freshwater lenses
formed below the island. Groundwater extraction in the western lens began in 1909 with three wells located in the western
Kaap dunes, close to the village of Langeoog (Fig. 1). They
were abandoned in 1989. The main well field was started in1938 in the Heerenhus dunes, more to the east of the village
but became fully operational in 1957. While the local population is small (2,000 people), intensive tourism is the main
driver of water demand, using up to three-quarters of the total
extraction of around 350,000 m3 year−1 (Houben et al. 2014).
Demand is highly seasonal, with consumption during the main
tourist season up to eight times higher than in the off-season.
The German barrier islands have been intensively studied
because of the importance of their freshwater lenses for watersupply. Detailed descriptions of their hydrogeology, including
Langeoog, can be found in Costabel et al. 2017; Holt et al.
2021; Houben et al. 2014; Post et al. 2019; Post and Houben
2017; Röper et al. 2012. Several previous studies have attempted
to quantify groundwater recharge on Langeoog Island. Seliger(2002) reported groundwater recharge estimates based on threedifferent calculation procedures. Two of these were based on a
calculation of actual evapotranspiration, either on a daily or annual basis. The third method calculated the percolation rate using
a soil-water balance model, based on inputs of monthly rainfall
and potential evapotranspiration. Depending on the method, the13-year average value calculated by Seliger (2002) ranged from
300 to 563 mm year−1. For individual years, the differences incalculated recharge rates between the methods amounted up to afactor of 3.
Houben et al. (2014) used groundwater ages based ontritium-helium data to calculate recharge rates and found that
rates varied across the island depending on land use and vegetation. The highest recharge rates of 400 mm year−1 or more
were found below the dune valleys (Houben et al. 2014). Agedepth profiles from wells below dune crests yielded recharge
rates lower by a factor of two. In the built-up village and theforested part of Langeoog, groundwater recharge rates
were found to be as low as 100 mm year−1, the former
caused by soil sealing, the latter by leaf interception andtranspiration.
Liebscher (1970) published the results of lysimeter studies
conducted on the nearby island of Norderney, which is locatedsome 20 km west to Langeoog, between 1956 and 1967.
Percolation rates for an unvegetated lysimeter in the dune area
covered with grass ranged between 346 mm (1959, total rainfall of 511 mm) and 939 mm (1966, total rainfall of
1,070 mm). Overall, the reported annual percolation rate was
86% of the annual rainfall sum (Liebscher 1970).

Hydrogeology Journal

Fig. 1 a–b Maps showing the location of Langeoog Island and the soil
sampling sites and weather stations; c–d soil surface at profile sites LB01
(older dune) and LB02 (younger dune), respectively. Photograph in dcourtesy of M. Howahr, OOWV. Base map and data fromOpenStreetMap and OpenStreetMap Foundation (OpenStreetMap 2021)

Hydrogeology Journal

Data and methods

Soil hydraulic properties and wettability

Soil-water samples

Due to the dry conditions, undisturbed soil core samples could
not be taken at the sites of the soil-water profiles because thesand was too loose. Instead, soil hydraulic properties wereobtained from soil cores collected at two sites at the nearby
waterworks (Fig. 1), sampled and analysed during an earlier
project in 2014. Water characteristic curve and unsaturated
hydraulic conductivity were measured in the BGR soil laboratory in Hannover with the evaporation method (Wind 1969)
using a Hyprop device (Meter Group, Munich, Germany).
Eight 250-cm3 samples, collected at 6–11 cm depth, weretaken to the laboratory and saturated with tap water before
the evaporation measurement (630 and 82 data pairs, respectively). The saturated hydraulic conductivity was determinedusing a laboratory permeameter (12 replications; Eijkelkamp,
Giesbeek, The Netherlands) on 250-cm3 soil cores sampled at
50–55-cm depth.
The wettability of the dune sand was measured withthe water-drop-infiltration-time test (WDPT). This test
can be performed in situ or on disturbed samples in thelaboratory. Dekker and Ritsema (1994) introduced theconcept of actual and potential water repellency.
Measurements on moist/wet soil material indicate actual
water repellency (WDPTact), i.e., water repellency of thesoil material at a certain water content (typically determined in situ). As the wettability of soil material is dependent on its water content, the WDPT was also determined on oven-dried (40 °C) samples to measure the potential water repellency (WDPTpot). Potential water repellency is assumed to be the maximum water repellency ofa sample (even though a higher drying temperature might
create higher WDPTpot values in some samples).
In situ measurements were carried out along a transect ofabout 100 m, crossing a dune valley and the corresponding
dune crest (Fig. 1a). Three drops of water (approx. 50 μL)
were placed on a smoothed, bare (unvegetated) spot of dune
sand, and the time it took to infiltrate was recorded(WDPTact). At each location, approximately 100 g of dunesand was sampled, which was dried at 40 °C for 7 days in
the laboratory. After equilibration to room temperature, three
drops of water were placed on the smoothed sample surface,
and the time was recorded for the drop to infiltrate into thesand (WDPTpot). Measured WDPT-values were classified according to Bisdom et al. (1993).

Soil samples were collected from two sites adjacent to thePirolatal on the northern edge of the dune belt (Fig. 1) on
25 June 2019. Profile LB01 (old dune) is located on theflank of a natural dune vegetated with grasses and herbaceous vegetation (Fig. 1d) at an elevation of 8.46 m above
mean sea level (amsl) as determined by differential global
positioning system (GPS) measurements. Profile LB02
(young dune) is located on top of an artificial white dune
(elevation 9.25 m amsl) with scant vegetation (Fig. 1e).
The sands there were deposited for the first time in 2008
and again in 2015.
The soil samples were obtained by hand augering usinga Dutch auger system, and collected in laminated coffee
bags (WEBER Packaging, Güglingen, Germany) that weresealed using a Ziplock system as well as by heat-sealing.
They were transported the next day to the laboratory,
where they were stored refrigerated until extraction.
Chloride concentrations was analyzed by ion chromatography (Dionex ICS-3000) after aqueous extraction fromoven-dried (105 °C for 24 h) soil samples according to
the procedures in the EN 15934:2012–11 standard. For
stable isotope analysis the soil water was extracted usinga modified cryogenic vacuum extraction method (Koeniger
et al. 2011). For each soil sample, three subsamples weretaken for chemical and isotope analysis.
Stable isotope analyses were conducted at the stable isotope laboratory of BGR in Hannover (Germany) using a
Picarro L2120-i water vapour analyser connected to a vaporizer and autosampler system. Isotope values are given in thestandard delta notation per mil (‰) versus VSMOW. The data
sets were corrected for machine drift during the run and normalized to the VSMOW/SLAP scale by assigning a value of0‰ and –55.5‰ (δ18O) and 0‰ and –428‰ (δ2H) to
VSMOW and SLAP, respectively. External analytical reproducibility for the laser spectrometer, defined as the standard
deviation of a control standard during all runs, was better than
0.5 and 1.5‰ after soil-water extraction (2-sigma standard
deviation) for δ 18O and δ2H, respectively (Koeniger et al.
2011).
Soil-moisture contents were determined gravimetrically byoven-drying at 105 °C as well as by weighing the original andcryogenically dried sample weights. The gravimetric soilwater content was converted to a volumetric soil-water content using a dry bulk density of 1.6 g cm−3 for quartz sand witha porosity of 0.4. Grain size analyses were conducted using a
Camsizer laser particle sizer (Retsch, Germany) after sieving
the samples to remove the fraction <63 μm (constituting only
0.2% by weight on average for all samples) and >2 mm (absent in all samples).

Numerical modelling
The processes in the unsaturated zone at sites LB01 and LB02
were simulated using a modified version of the HYDRUS-1D
software package that is able to simulate isotope fractionationby evaporation (Zhou et al. 2021). The model considers waterflow with snow hydrology, evaporation, root water uptake,Hydrogeology Journaland heat and solute transport. Variably saturated water flow
was described using the Richards equation:


∂θ
∂
∂h
¼
K ðhÞ þ K ðhÞ −S
ð1Þ
∂t
∂z
∂z
where θ is the volumetric water content, z is the depth below
the soil surface (L), t is time, K(h) is the hydraulic conductivity(L T−1), which is a function of the pressure head (h) (L), and S(T−1) is a sink term representing root water uptake.
The water retention characteristic curve θ(h) and the hydraulic conductivity function K(h) are described as suggested
by van Genuchten (1980) and Mualem (1976):8θs −θr
<
h< 0
θr þ
m
θðhÞ ¼
ð2Þ
ð1 þ jαhjn Þ
h≥0
:
θs

K ð hÞ ¼ K shi2−m1−ðαhÞn−1 ½1 þ ðαhÞn m½1 þ ðαhÞn  2

ð3Þwhere θs and θr are the saturated and residual water contents,
respectively, α (L−1), n, and m (= 1 – 1/n) are empiricalparameters, and Ks is the saturated hydraulic conductivity(LT−1). These parameters were determined by fitting Eqs.
(2) and (3) to the measured soil hydraulic property data.
The site 1 data were therefore used to parameterise the van
Genuchten-Mualem model in HYDRUS-1D. An uncertainty
analysis was conducted to explore the sensitivity of the yearlyrecharge rates to the parameter uncertainty. This involved running 100 realisations of each model (i.e., LB01 or LB02) inwhich θs, θr, α and Ks were randomly sampled from a normal
distribution (see Table S1 in the electronic supplementary material (ESM1).
Because winter precipitation is sometimes in the form ofsnow, snow cover was modelled using the simplified approach implemented in HYDRUS-1D (Šimůnek et al. 2008).
This approach assumes that precipitation is in the form ofsnow when air temperatures are below zero °C and, consequently, does not infiltrate immediately into the soil duringfreezing periods. Snowmelt is simulated to be proportional
to air temperature above freezing, using a snow melting constant of 0.43 cm day−1 K−1 (i.e. the HYDRUS-1D default).
Root water uptake was calculated using the Feddes et al.
(1978) water-uptake-reduction model using the parametersprovided by Voortman (2018) for a similar dune vegetationsite in the Netherlands, which has comparable climatic andsoil conditions as the present study site. For profile LB01
(natural dune, vegetated), the leaf area index (LAI) was set
to 1, which is at the lower range of values for stands of dune
grass and herbaceous vegetation reported of a similar dunearea in Belgium (Willaert et al. 2005). The LAI for profile
LB02 (artificial dune, scant vegetation) was treated in a

special manner discussed later in the article. Based on observations made during augering, roots were assumed to be present in the upper 40 cm of the soil profiles. The root distributionfunction in HYDRUS-1D [b′(x)] was assumed to decrease
exponentially with depth to reflect the fact that root density
is highest just below the land surface.
For water flow simulations, an “atmospheric boundary
condition with surface runoff” and a “free drainage” boundary
condition (BC) were used for the upper and lower
boundaries, respectively. The upper BC involves alternatively Neumann and Dirichlet boundary conditions,depending on conditions in the soil. Within a prescribedpressure head interval, the Neumann BC is used. Oncethe limit of this interval is reached, the Dirichlet BC isused. The “free drainage” BC is a zero-pressure-headgradient-boundary condition. No immobile water regions
were considered in the simulations.
HYDRUS-1D solves the advection-dispersion equation forsolute transport. The dispersivity was optimised to fit the measured isotope values. Cauchy and Neumann BCs were used
for solute transport at the top and bottom boundaries of the soil
profile, respectively. Different to the standard HYDRUS-1D
code, the Cauchy BC at the top was modified to consider
fractionation by evaporation, as described by Zhou et al.
(2021). The isotopic composition (δ2H, δ18O) of the evaporation flux is calculated according to the Craig-Gordon model(δE, CG) as
h
i 

0
0
δE;CG ¼ RHs ∙α*i ∙δlsi −RHa ∙δa −RHs ∙ε* −εk = RHs −RHa þ εk =103

ð4Þ
where δlsi and δa (‰) are the isotopic composition of the soil
water (ls = liquid/soil), and the atmospheric vapour, respec0
tively, RHs is the relative humidity at the soil surface [−], RHa
is the relative humidity of the atmosphere [−] normzalized to
are the equilibrium
the soil surface temperature, α*i and αtotalifractionation factor [−] and the total fractionation factor [−],
respectively, and ε∗ and εk are the equilibrium and kinetic
fractionation enrichment (‰), respectively. Details about thecalculation procedure and model parameters can be foundin Zhou et al. (2021). Isotope fractionation is implemented for the first (upper) model cell. Root water uptake does
not cause fractionation; hence, any water that is lost byplant transpiration does not lead to isotopic enrichment of
the soil moisture.

Weather data
Daily precipitation data measured by the Deutscher
Wetterdienst (DWD) from Langeoog (station number
02861) for the period between 1941 and 1994 were
downloaded from the climate data centre (DWD 2021).

Hydrogeology Journal

Daily precipitation, wind velocity, air humidity, mean air temperature, minimum air temperature, maximum air temperature, and sunshine duration are available from 1947 onward
for the barrier island Norderney (station number 3631). The
DWD data also include the potential evapotranspiration (ETo)according to Penman-Monteith from 1991 onward, whichwere also used in the numerical model. The island’s watersupply company (Oldenburgisch-Ostfriesischer
Wasserverband, OOWV) operates a weather station in thedune area on Langeoog (see Fig. 1 for location). The measured
daily precipitation and temperature were used as input in the
numerical models, with missing data substituted by the DWD
data from Norderney.
Precipitation samples for isotope analysis were collected at the OOWV weather station (Fig. 1) as biweekly tomonthly cumulated samples using a precipitation isotope
sampler (PALMEX RS1) (Gröning et al. 2012) from
July 2012 and June 2019. This period was chosen as the
HYDRUS-1D model simulation period. The delta valuesof the atmospheric water vapour (δ2Ha, δ18Oa) were determined by assuming they are in equilibrium with theprevailing rainfall values.

Lysimeter data
The former municipal water supply company of Langeoog
operated a lysimeter on the island of Langeoog from
July 1963 to October 1970 and from November 1974 toApril 1977. In total, data for 118 months are available, including the summer of 1976, which was particularly hot and dry.
The archived data for the lysimeter were supplied by the current water supplier (OOWV) in paper form. The unvegetatedlysimeter had a surface area of 1 m2, and the inferred depth
was 1.45 m. It was located in the dunes near the old well field,
situated west of the village (Fig. 1). The soil had a porosity
ranging between 0.39 and 0.43, a coefficient of uniformity(d60/d10) in a narrow range between 1.1 and 1.3, and a hydraulic conductivity range of 2.9 ∙ 10−5–4.0 ∙ 110−5 m s−1. These
parameters indicate a well-sorted sediment, typical for dunesands. The method of determining the hydraulic conductivity
was not documented.
Monthly sums of precipitation data were also reported aspart of the lysimeter dataset; however, these showed significant differences compared with the monthly sums of the
DWD data. There is a good correlation between the monthly
sums for the first series of lysimeter data (July 1963 to October1970), with the lysimeter values generally being higher by
15%. This may reflect spatial variability of the rainfall or adifference in instruments used. For the second series of lysimeter data (November 1974 to April 1977), there is a very poorcorrelation, suggesting a problem with the reported monthly
sums for the lysimeter. This part of the data series was therefore not considered. ET o data (required for modelling

purposes) prior to 1991 were not available, nor were windspeed data required to calculate ETo available for the period
July 1963 to October 1970. Instead, the vapour pressure deficit (VPD) was determined from temperature and relative humidity data, which were available for the entire measurement
period. Linear regression was then made between ETo and
VPD for the 1991 and beyond data, and this regression relationship was used to calculate ET o based on VPD for
July 1963 to October 1970.

Results
Figures 2 and 3 show the measurements for both soil profiles
from June 2019. These will be discussed in detail in the following subsections.

Soil-moisture content, isotope values, and chloride
concentrations
Soil-moisture contents show only minor variability andare close to the residual water content θr (discussed in
more detail further on). The lowest values were measured in the upper 40 cm of profile LB02. There is agood correspondence between the oven-dried and cryogenically extracted values, providing confidence that thecryogenic extraction method provided representative water samples for the isotope analysis. The large error bars
in Fig. 2a are due to the measurement of triplicate samples, and the high variability indicates that the isotope
measurements of these samples potentially have to beignored because of incomplete extraction.
The isotope delta values of profile LB01 show only little
variability below a depth of 1 m. Above this level, the samples
are more enriched in the heavier isotopes. The variations are
somewhat larger in the profile of profile LB02 (especially forδ2H), and strong enrichment of the topmost sample is evident
from the high delta values. These samples also have very highchloride values. The sample between the 30 and 40 cm depthof profile LB02 had a chloride concentration of 1,110 mg L−1
and is not shown in the graph. A clear chloride concentration
gradient can be seen from the soil surface down to a depth of
80 cm in profile LB01.
The scatter plot in Fig. 4a reinforces the correlation between δ18O and chloride that is visible in the soil profiles,
especially for profile LB01. In the scatter plot of δ2H versus
δ18O (Fig. 4b), most of the data points straddle along the local
meteoric water line (LWML, δ2H = 7.23 × δ18O + 4.56, R2= 0.97), except for the most enriched samples, which arelocated towards the right of the LMWL, which is evidence
for evaporation.

Hydrogeology Journal
Fig. 2 Depth distribution for
profile LB01 of a the volumetricwater content (θ), b the stable
isotope delta values of the soil
moisture, c the chloride
concentration of the soil moisture,and d the median grain size

Soil physical parameters
The median grain size of both profiles varies within a narrow
range, which is generally between 170 and 190 μm for LB01
and between 190 and 210 μm for LB02 (Figs. 2 and 3).
For all samples, the grain size fractions 112–200 μm
and 200–355 μm combined make up 90% by weightof the total, indicating a uniform sediment. The particle
size distribution curves for all soil samples are shown in
Fig. S1 in ESM1.
The van Genuchten-Mualem model parameters (Eqs. 2 and
3) were determined by an unconstrained inverse parameter
estimation to the 630 data points from the Hyprop measurements; however, the resulting value of n was higher than 5,
and this caused frequent crashes of the HYDRUS-1D model.
Trial runs demonstrated that values of n = 4.8 resulted in stablemodel runs, so parameter estimation was subsequently
Fig. 3 Depth distribution for
profile LB02 of a the volumetricwater content (θ), b the stable
isotope delta values of the soil
moisture, c the chloride
concentration of the soil moisture,and (d the median grain size. The
sample between 30 and 40 cm
depth has a chloride concentrationof 1,110 mg L−1 and is not visible

conducted using this value. The resulting parameters
are shown in Fig. 5. The two sets of data that were fitted
were the samples at site 1 and the surface samples at site
2, located near the Langeoog waterworks. All samples
come from the Heerenhus dunes and are thus of similar
origin and lithology. The site 2 samples at the 50–55 cm
depth interval are very similar to the surface samples andwere not separately fitted.
The measured saturated hydraulic conductivity varied bymore than one order of magnitude (between 1.8 ∙ 10−5 and2.2 ∙ 10−4 m/s, or 156 and 1,963 cm/day), with the lower
conductivity values occurring in the uppermost part of theunsaturated zone. Only the four samples that were analysedusing a tension-infiltrometer yielded values that were in a pF
range that allowed constraining the K(h) relation. The resulting value of l = 4,030 is extremely high compared to therecommended value of 0.5 (Mualem (1976)). Although thisHydrogeology Journal
Fig. 4 a δ18O versus the chloride
concentration and b δ2H versus
δ18O of the two soil-moistureprofiles. The rainfall data points
included in b were analysed insamples collected at the OOWV
weather station from July 2012
and June 2019has been reported for other datasets as well (Yates et al. 1992),
the value was not used in the subsequent HYDRUS-1D simulations because it resulted in extremely long runtimes.
Moreover, trial simulations showed that the parameter set
based on the site 2 data resulted in much poorer model fits
to the isotope data than the site 1 data.
The dune sand on Langeoog Island shows severe to extreme water repellency when dry, as indicated by high
WDPTpot values (Fig. 6). The in-situ measurement showed
lower WDPTact times compared to WDPTpot times. Water
repellency was not at its maximum at the time of measurement; however, actual water repellency was observed at thedune crest. This is not only due to potentially lower watercontents of the sand at these locations, as WDPTpot times also
increased at these locations. A slight trend can be seen that thedune crest is more water repellent compared to the dune
valley.
Fig. 5 a Soil-water retention
curves and b relationship between
K(h) and the log soil matric potential pF. The blue curve wasfitted to the data points of site 1,
the orange curve to the surface
sample at site 2. The corresponding van Genuchten-Mualem parameter values are provided next
to the curves. The green-shaded
areas indicate the range of retention and K(h)/pF relationships
considered in the uncertainty
analysis

Model results
The simulated isotope profiles are compared to the measurements in Fig. 7b–e. The fit between the model that considers
fractionation and the data is especially good in the deeper partof the soil profiles for both sites LB01 and LB02. Because thevan Genuchten-Mualem parameters were determined independently from the soil hydraulic property measurements(Fig. 5), the only parameter used to obtain the fit in Fig. 7
was the dispersivity. The adjusted value was 4 cm for LB01
and 2 cm for LB02, which is within the reported range of otherstudies (2–20 cm) with comparable transport distances (80–200 cm; Stumpp et al. 2012; Vanderborght and Vereecken2007).
The animated version of Fig. 7 (see ESM2) shows theseasonal dynamics of the isotope profiles. The animation
starts 250 days before the soil samples were taken at a moment

Hydrogeology Journal
Fig. 6 Potential and actual water
repellency as determined with the
WDPT test on samples from atransect across a sand dune (seethe location in Fig. 1). Repellencyclasses according to Bisdom et al.
(1993)

when the delta values are highest in the top part of the profile
due to evaporation during the summer of 2018. The water withthese high delta values gets pushed down as rainfall increases
Fig. 7 a Rainfall (P), potential
evapotranspiration (ET), andrainfall δ2H values versus time as
input to the model (δ18O values
not shown). Measured andsimulated δ2H values for b profile
LB01 and c profile LB02.
Measured and simulated δ18O
values for d profile LB01 and e
profile LB02

during the winter. The animation clearly shows how the
downward migration accelerates during periods of high recharge. While the bulk of the enriched soil moisture exits

Hydrogeology Journal

through the bottom model boundary during the winter monthsof 2019, slightly elevated delta values remain visible in themodel as well as the data at a depth just below 4 m. The peakthat is the result of precipitation in January 2019 with highdelta values remains preserved, albeit attenuated, at a depth of
3 m. As the top part of the profile starts drying out fromMay 2019 onward, the delta values there start to increase byevaporative enrichment, eventually leading to the profile asobserved during the fieldwork. The high values persist near
the land surface as long as there is no net downward flux toflush out the enriched soil moisture.
For both models, the upper parts of the measured isotope
data could not be fitted without considering fractionation inboth models (Fig. 7). This was expected because the samples
were taken in summer, and the preceding months had been
unusually dry and warm. Interestingly, the model without
fractionation did not fit the data points in the deeper part ofprofile LB01, but it did so for profile LB02. No matter whatparameter combinations were attempted, models with LAI =1 systematically overpredicted the measured delta values inthe deeper part of the profile at LB02 if fractionation wasconsidered. The animated version of Fig. 7 makes it clear thatthe bulges and troughs in the measured delta values between 2
and 4.5 m depths formed during October 2018 andMarch 2019, i.e., during the winter months. From this, it
was inferred that direct evaporation during this period is apparently negligible at LB02. In order to mimic this behaviour,the model was run with LAI = 10−6 between April and
September and LAI = 100 from October to March. These
parameter values have no physical meaning, but were merely
chosen to partition potential evapotranspiration into transpiration (so that fractionation is effectively switched off) in autumn and winter and into evaporation in spring and summer.
The result is shown as the solid line in Fig. 7b. The

Fig. 8 a Monthly rainfall andpotential evapotranspiration(ETo) versus time and b side byside comparison of monthly
recharge rates (R) for sites LB01
and LB02

interpretation in terms of physical processes will be discussed
further on in the article.
The model’s ability to match the observed isotope patterns
provides confidence that the simulated water fluxes are accurate. The temporal variability of the calculated recharge rates
is shown in Fig. 8. The summer (May–October) and winter(November–April) sums are listed in Table 1. Recharge rates
tend to be highest in winter, but recharge occurs throughout
the year, even during the driest summer months. The differences between sites LB01 and LB02 are small in winter, but
the rates are higher for the less vegetated LB02 profile than for
LB01 in summer. The yearly sums are included in Table 2 forcomparison with other published studies.
The results of the uncertainty analysis are summarised in
Figs. S2 and S3 and Table S2 in ESM1. It can be seen that thestandard deviations for the yearly recharge rates are small,ranging from 9 to 17 mm year − 1 for LB01 to 5 to
13 mm year−1 for LB02. This demonstrates that the calculated
annual recharge rates are not very sensitive to the soil physicalparameters. On the other hand, the shape of the isotope profiles is sensitive to the soil physical parameters, as shown by
Fig. S3 in ESM1.

Lysimeter
Just like the modelling results, the 1963–1970 lysimeter data
were split into 6-month winter and summer periods. Table 3
shows that percolation amounted to between 29 and 53% of
precipitation during summer, while in winter, this ranges between 85 and 93%. Similar to the HYDRUS-1D modelling
results for the soil profiles, the data show that recharge is not
restricted to the cooler winter months.
As a further validation of the HYDRUS-1D model, thesame soil hydraulic parameters were used to calculate theHydrogeology Journal
Table 1 Six-month sums for
precipitation (P), potential
evapotranspiration (ETo),recharge (R), and the ratio of
recharge and precipitation forsites LB01 and LB02. The values
are grouped by winter andsummer values

PeriodP (mm)

Nov 2012–Apr 2013
Nov 2013–Apr 2014
Nov 2014–Apr 2015
Nov 2015–Apr 2016
Nov 2016–Apr 2017
Nov 2017–Apr 2018
Nov 2018–Apr 2019
SummerMay 2013–Oct 2013
May 2014–Oct 2014
May 2015–Oct 2015
May 2016–Oct 2016
May 2017–Oct 2017
May 2018–Oct 2018

2013
2014
2015
2016
2017
2018
Mean

798
585
923
729
1,126
733
816

ETo (mm)

605
696
658
645
660
703
661

LB02

R (mm)R/P

R (mm)

R/P

287
364
406
542
316
544
355

135
159
168
172
164
161
178

285
317
283
389
256
441
279

100%
87%
70%
72%
81%
81%
79%

285
312
279
385
248
435
274

99%
86%
69%
71%
79%
80%
77%

492
285
409
346
652
318

462
538
478
488
487
553

229
129
185
136
363
184

46%
45%
45%
39%
56%
58%

253
154
219
164
394
206

51%
54%
54%
47%
60%
65%

Table 2 Yearly rainfall (P), potential evapotranspiration (ET o),recharge (R), and the ratio of recharge and precipitation for sites LB01
and LB02
P (mm)

LB01

Winterreported percolation rates from the lysimeter. As the lysimeter was unvegetated, root water uptake was not considered. The results in Fig. 9 show that there is a very
good agreement up until the summer of 1968. After thispoint in time, the modelled values become systematicallyhigher than the reported values. Not enough information
about the experimental setup is available to identify thecause for this deviation. Nevertheless, the good fit during
the first 5 years provides additional confirmation that therates calculated by the model are representative of thedune sand on the island.
Figure 10 compares the summer and winter sums forthe 1963–1970 lysimeter data and the 2012–2019 model
results. It is clear that the data points follow similar
trends. It is important to bear in mind that the lysimeterYear

ETo (mm)

LB01

LB02

R (mm)R/P

R (mm)

R/P

555
326
607
431
723
517
527

70%
56%
66%
59%
64%
70%
64%

577
349
635
454
745
536
549

72%
60%
69%
62%
66%
73%
67%

was only 1.45 m deep, while the modelled soil profiles
had a length of 4.5 m. A comparison on a monthly basis,
therefore, looks much less favourable because infiltrated
water needs much longer to travel across the 4.5-m thanacross the 1.45-m soil column. The similar trends of the
6-month sums provide confidence about the accuracy ofthese recharge rates.

Table 3 Six-month sums for precipitation (P), potential
evapotranspiration (ETo), recharge (R), and the ratio of recharge andprecipitation for the lysimeter. The values are grouped by winter andsummer values
PeriodWinter
Nov 1963–Apr 1964
Nov 1964–Apr 1965
Nov 1965–Apr 1966
Nov 1966–Apr 1967
Nov 1967–Apr 1968
Nov 1968–Apr 1969
SummerMay 1964–Oct 1964
May 1965–Oct 1965
May 1966–Oct 1966
May 1967–Oct 1967
May 1968–Oct 1968
May 1969–Oct 1969
May 1970–Oct 1970

P (mm)ETo (mm)

R (mm

R/P

276
406
488
532
368
348

144
174
162
171
171
135

240
354
452
454
312
295

87%
87%
93%
85%
85%
85%

398
463

475
443

116
247

29%
53%

454
518
674
424
508

408
425
363
401
414

210
238
343
177
159

46%
46%
51%
42%
31%

Hydrogeology Journal
Fig. 9 a Monthly rainfall andpotential evapotranspiration(ETo) versus time and b observed
and modelled cumulative
percolation rates for the lysimeter.
The cumulative values are shownto emphasise the systematicdeviation after the summer of1968 (b)Discussion
The preceding results show that the soil-moisture isotope profiles can be modelled accurately by the HYDRUS-1D code if
fractionation is considered. Over the entire depth of the profiles, the delta values in profile LB01 are somewhat higher
than at LB02. This is somewhat unexpected because the vegetation is more abundant at LB01 than at LB02, and hencetranspiration, which does not cause fractionation, might be
expected to be more important. One reason could be that the
downward displacement at LB01 is slower than at LB02 due
to root water uptake, especially during summer months, sothat there is more opportunity for direct evaporation, andhence fractionation can impart a measurable enrichment of
the soil moisture.
Interestingly, the data of profile LB02 could only be simulated with the model by assuming that the soil moisture did not

Fig. 10 Winter (Nov–Apr) andsummer (May–Oct) sums ofrecharge versus precipitation for
the 1963–1970 lysimeter data as
well as the model-based values
for LB01 and LB02 for the period
2012–2019. An ‘exclamation
mark’ marks the data points representing the summer of 1968 andlater, i.e., the period during which
the lysimeter data started to diverge from the model simulation

experience fractionation during the winter season, but wassignificantly affected by direct evaporation during the summer. The explanation for this is thought to lie in the fact thatduring the unusually dry and warm summer of 2019, a significant part of the vegetation died off (see also Fig. 1e), thusincreasing the importance of direct evaporation over transpiration. By setting the LAI to a very low value in summer, thisbehaviour could be replicated by the model. A comparison of
aerial images from July 2015 (with 407 mm of summer
rainfall, Table 1) and August 2019 clearly shows a scarcer
vegetation cover at LB02 (see ESM1). Voortman et al.
(2015) contended that vegetation dieback is an important process that must be considered in recharge estimates of coastal
dune areas. It is not clear if this effect is equally important
every year. Only a single snapshot in time of the soil-water
isotope profile is currently available for this study area, whichis representative for a period of about 9 months prior toHydrogeology Journal

sampling. Repeated measurements alongside modelling could
help in testing this hypothesis and provide more accurate recharge estimates.
Vegetation dieback is likely to enhance soil-water repellency by exposing the sand surface, which causes the sand to
become extremely dry. Measurements showed significant water repellency of the dune sands, especially on the dune crest(Fig. 6). The difficulty of water to infiltrate would expose it todirect evaporation, which would impart enrichment of theheavier isotopes. Whether or not this contributes to the enrichment of the soil moisture depends on whether the water on thesurface evaporates completely or only partially, and if it infiltrates with the next rain event that wets up the soil sufficiently
so that infiltration can occur again.
To what extent observed soil-water repellency affects therecharge processes could not be ascertained due to a lack ofdata. In water repellent soils, infiltration can only occur when
the ponding height (and thus the water pressure) exceeds theresistance to flow due to water repellency (Bauters et al.
2000). The microtopography of the surface therefore plays
an important role in this. It can lead to the formation of preferential flow with an uneven distribution of soil-moisture contents and downward flow rates. The process is favoured under
initially dry conditions, and when it occurs, water can movevertically downward along the preferential flow paths at greater rates than under wet conditions. Any rainfall that is unable
to infiltrate into the repellent soil may be evaporated (reducing
recharge) or flow down the dune slope (redistributing recharge). Houben et al. (2014) suggested that the inferred difference in recharge rates between dune crests and valleys wasdue to overland flow and accumulation and subsequent
infiltration of water near the dune foot.
Scanlon et al. (2002) advocated using multiple techniques
to enhance the confidence of groundwater recharge estimates.
While the lysimeter data and modelled soil-water profile rates
showed a good resemblance in this study, the calculated recharge rates are higher than values reported in previous studies
on Langeoog Island. For example, the 13-year average thatwas reported by Seliger (2002) was only 224–420 mm year−1,
depending on the calculation method used, which corresponds
to 31–58% of the mean annual precipitation. These estimates,
however, were based on theoretical calculations that involved
numerous assumptions and uncertain parameter values andwere not constrained by any soil or groundwater data.
Houben et al. (2014) used groundwater ages (based on
3 3
H/ He measurements of groundwater samples) in combination with the Vogel (1967) model to determine the spatial
variation of mean annual recharge rates. They found values
between 230 and 400 mm year−1 in the dune area, with thehighest values reported for the dune valleys. These values area lot lower than the annual mean value for profile LB01
(527 mm year−1, Table 2), which is the more representativeof the two sites studied here for the dune area as a whole.

The explanation for this difference might lie in the fact that,
despite the presence of vegetation, the plant water use is lower
at LB01 than in the bulk of the dune area due to differences invegetation type. While shrubs are absent at profile LB01, they
are abundant in other parts of the dune area. Studies in the
Netherlands and Belgium have demonstrated that their presence enhances evapotranspiration (Samson et al. 2005;
Stuyfzand 1993). Moreover, some plants, like marrum, are
known to grow their roots all the way down to the watertable and can take water directly from the saturated zone.
This additional loss of water is not considered in the modelsof this study, which are restricted to the unsaturated zone.
Alternatively, the Houben et al. (2014) rates could be an underestimate, possibly because the flow conditions in the aquifer are more complex than assumed in their application of the
one-dimensional Vogel (1967) model to calculate recharge
based on groundwater age. It should also be recalled that recharge rates based on lysimeter measurements and soil-water
profiles represent point estimates, whereas rates based on
groundwater age data are affected by processes integrated
over a certain area and time span.
The lysimeter data reported by Liebscher (1970) for a duneon the island of Norderney would be expected to be in asimilar range as this study’s estimates. The reported percolation rates are 75% of the annual rainfall (period 1956–1967).
The same percentage was reported by Stuyfzand (1993) basedon a giant (625 m2) lysimeter in the coastal dunes of theNetherlands that was filled with soil that was almost bare,
except for a scant moss cover. Therefore, the percentages
found, based on the HYDRUS-1D model for sites LB01 and
LB02 (Table 2), could well be realistic, and the differenceswith the studies by Houben et al. (2014) are due to different
methodologies or uncaptured processes like direct groundwater use by vegetation or overland flow. Figure 11 summarizes
the results from both lysimeters, the models for both
Langeoog sites and the age data.
The model’s ability to simulate the observed soil-moistureisotope values demonstrates that this method holds a lot ofpromise to improve recharge estimates of coastal dune areas.
While in the current study, the isotope contents were determined by destructive sampling, new technologies make a nondestructive experimental setup possible as well. Using portable laser-based measurement instruments that measure thesoil-water vapour isotope content in-line, a high-resolution
time series of soil-water isotope contents can be derived.
High-frequency sampling of the rainfall isotope content andactual measurements of atmospheric water vapour isotopic
composition could lead to improvements of the model andbetter insight into the temporal dynamics of the soil-waterand recharge processes.
Knowledge of the dynamic nature of groundwater recharge
is especially important given the unknown impact of climatechange. More intense rainfall events may lead to more

Hydrogeology Journal
Fig. 11 Comparison of recharge
rates obtained from theunvegetated lysimeters in
Norderney (N) and Langeoog (L),the HYDRUS-1D models forsites LB01/LB02 and the age
dating by Houben et al. (2014).
Symbols represent individualyears. Legend for bars, see LB02
model

infiltration, but could also enhance overland flow. It may be
anticipated that periods of prolonged drought and warmer
summers will intensify soil-water repellency and vegetation
dieback. Studies in the Netherlands (Witte et al. 2012) haveshown that the proportion of moss and bare soil increases
during summer droughts, increasing recharge in a warmer
climate due to decreased transpiration. Intensified monitoringin combination with modelling is thus needed to establish how
recharge processes will respond to climate change in the
future.

Conclusions
This study investigated the recharge rates and processes below
two contrasting dune types on the Langeoog barrier island innorthern Germany. The recharge is crucial for sustaining the
freshwater lens that is exploited for water supply and determines the hydrology, and thereby the ecology, of the dunearea. The two investigated sites differ in their genesis andvegetation type. The dune of profile LB01 is an older dunethat is covered by grass and herbaceous vegetation, whereas
the dune of profile LB02 is a recent dune that was created toreinforce the island’s coastal protection and is scarcely
vegetated.
Soil samples were collected from the unsaturated zone byhand auguring down to the water table. The grain size andsoil-moisture content of the sand were analysed. Soil-water
samples for stable water isotope analyses were obtained bycryogenic extraction, and chloride concentrations of the soil
water were also measured. The results show that evaporationinduces fractionation of the isotopes in the upper part of the

profile, which was expected, as the samples were taken during
the warm and dry summer of 2019.
Recharge rates were calculated using a new version of
HYDRUS-1D, modified to simulate isotope fractionation during evaporation, and fitting the simulated isotope-depth profiles to the measurements. The model outcomes confirm theimportance of fractionation, and it was found that fractionation effects are more pronounced at the profile with the mostvegetation (LB01). This is attributed to slower downward percolation of the soil moisture as water is lost to root water
uptake, providing more opportunity for direct evaporation in
the upper part of the soil profile. For profile LB02, the model
could be matched to the data by assuming that transpiration
dominates in winter versus direct evaporation in summer. The
inferred absence of transpiration in summer is tentatively attributed to vegetation dieback, but a dataset spanning multiple
years is needed to test this assertion.
The calculated recharge rates are consistent with lysimetermeasurements that were made on Langeoog Island in the past.
Both the model results and the lysimeter data of this study
show that recharge occurs year-round and that, as expected,
the winter months contribute most of the recharge. The recharge rates are significantly higher than the rates reportedby Houben et al. (2014) that were based on groundwater age
data. The discrepancy is attributed to potential methodologicalissues and, predominantly, the role of vegetation. The presence of dune shrub is likely to result in lower recharge rates
than those found at sites LB01 and LB02. Further research isneeded to establish the significance of other processes, likesoil-water repellency and overland flow.
It is expected that the methodology applied in this study
can yield important insights into the dynamical nature of theHydrogeology Journal

recharge processes when measurements are taken repeatedly
and frequently. Such studies are not only needed at the present
study site, but across a range of geographical locations as
recharge processes will respond to climate change in presently
unknown ways.
Supplementary Information The online version contains supplementarymaterial available at https://doi.org/10.1007/s10040-022-02471-y.
Acknowledgements We would like to thank U. Schnückel and M.
Howahr at OOWV for their assistance with obtaining the soil-water samples. Rainfall and temperature data were provided by OOWV. We areindebted to the communities behind the multiple open-source software
packages that we used for this research.
Funding Open Access funding enabled and organized by Projekt DEAL.

Declarations
Conflict of interest The authors declare no conflicting or competinginterests.
Open Access This article is licensed under a Creative Commons

Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are includedin the article's Creative Commons licence, unless indicated otherwise in a
credit line to the material. If material is not included in the article's
Creative Commons licence and your intended use is not permitted bystatutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/.

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