Constraining groundwater recharge estimates at
the field scale using soil hydrologic modellingand measured root zone soil moisture:

How to deal with the vertical sensitivity ofcosmic-ray measurements
Lena M. Scheiffele*, Matthias Munz*, Till Francke*, Gabriele Baroni+, Sascha E.
Oswald*, Katya Dimitrova Petrova**University of Potsdam, +University of Bologna

Intermediate scale groundwater recharge (GWR)Motivation• Sustainable use of groundwater resources• Required as groundwater model input• Agricultural landscapes as source of pollutants• No direct measurement possible• One option: vadose zone modelling• Calibration on soil moisture• Matching scales of measurements

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Hydrus Workshop, Prague1

Study overview
Research questions
Measured soil
moisture
products

Depth specific* benchmark

Result 1:
Comparison to measured
soil moisture (θSN)*

Hydrus models with optimized
parameter sets (SHPs)

SNSensor
Network(θSN)*

Neutrons

IntegratingModelcalibration
procedure

COSMIC

D86

PC60

Simulatedsoil
moisture

PSO

Result 2:
Comparison ofcumulative bottom flux

CRNS soilmoisture
Corrected
CRNS soilmoisture

Simulatedflux

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Experimental site Katharinentaler Hof
Model setup▪
▪

Cropped agricultural
field (winter wheat)▪

1D Water transport▪

Root water uptake

Sensor network:
volumetric soil
moisture every 10 cm
down to 90 cm▪

150 cm soil column▪

Plough layer (30 cm)▪

Discretization 1 cm▪

Atmospheric boundary▪

Free drainage▪

Van Genuchten▪

3 month spin-up▪

April to August 2013▪

CRNS in center of thefield

Data publication for the field site:
Weber et al. (2021, 2022)
CRNS studies: Baroni et al. (2018),
Scheiffele et al. (2020)

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Measurement principles
Cosmic-ray neutron sensing - CRNS
▪ Neutron probe to measure soil moisture▪ Non-invasively, installed above ground▪ Inverse relationship between neutrons and soil moisture▪ Integral field water content:▪ Depth of several tens of centimeters▪ Horizontal coverage 150 -240 m radius

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Sensitivity of a CRNS probe▪

Higher sensitivity at theclose range and uppersoil layers▪

Sensing depth dependson soil wetness▪

Correcting CRNS for
vertical sensitivity:
Profile shape correction“unweighting” CRNS

Weightingfunctions

θSNθCRNSθPCBaroni et al. (2018),
Scheiffele et al. (2020)

Calibration options

SNPC60

D86
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Cosmic operator in HYDRUS 1D▪ Analytical solution of neutrontransport▪ Able to deal with sensitivity of thesensor▪ Simulating neutron counts above
soil, from simulated soil moisture

Shuttleworth et al. (2013)▪ Available as add on module inHYDRUS
Brunetti et al. (2019)
Neutrons

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Calibration procedure▪ Initial parameter estimates▪ For the plough layer from Rosetta

Initialestimate
depth
0 – 30▪ For bottom layer from evaporation experiment onsoil cores▪ Some parameters fixed▪ Residual water content (thr = 0.082 in both layers)▪ l = 0.5

▪ Parameter ranges (partially based on Rosetta,partially set following literature values)

31 – 150

Parameter bounds
lower

θr
θsαn
Ks
l
θr
θsαn
Ks
l0.082
0.430
0.004
1.5
19.0
0.5
0.069
0.432
0.0064
1.61
10.40
0.5

0.30
0.001
1.1
0.1

0.30
0.001
1.1
0.1

upper
0.082
0.54
0.1
2100
0.5
0.082
0.54
0.1
21000.5▪ PSO as external optimizer (10 000function calls)▪ Maximizing KGE
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Time series simulated soil moisture andsimulated bottom flux
▪ For the benchmark case(SN)▪ KGE 0.91 (θsim vs. θobs))▪ Bottom flux timing
matches independent
observation

Tensiometer
SPHs from soilcores

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Hydrus Workshop, Prague
8Results 1 – simulated soil moisture over depth
KGE

SN
0.91

COSMIC
-1.28

D86
0.48

PC60
0.71

▪ The SN option as benchmarkcase▪ The PC60 is presenting thesm in the profile very well

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Hydrus Workshop, Prague
9Results 2 cumulative bottom fluxes▪ Cumulative sums similar▪ Sensor network and CRNS options with clear
response to strong precipitation▪ Dampened behaviour of PC60
▪ Next steps:▪ Longer simulation period – how does the curve(cummulative flux) develop further?
▪ And how does it look for the main recharge period?

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Hydrus Workshop, Prague
10Limitations and outlook▪ Value of the current study: bring insights on how
using different soil moisture products can benefit
potential groundwater recharge estimates.

▪ In agricultural fields, maintaining a SN (soil sensornetwork) is challenging due to soil management.
While CRNS can stay.
▪ Short calibration period▪ Is one big event is sufficient to calibrate long term
behaviour also in winter precipitation?

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Thank you for your attention!
Questions?
Acknowledgement@EGU 2023 - Fri, 14:45–14:55, Oral at HS2.2.5

Funding by DFG FOR-2694 “COSMIC SENSE”
References▪

Baroni, G., Scheiffele, L. M., Schrön, M., Ingwersen, J., and Oswald,
S. E.: Uncertainty, sensitivity and improvements in soil moisture
estimation with cosmic-ray neutron sensing, Journal of Hydrology,
564, 873–887, https://doi.org/10.1016/j.jhydrol.2018.07.053, 2018.
▪ Brunetti, G., Šimůnek, J., Bogena, H., Baatz, R., Huisman, J. A.,
Dahlke, H., and Vereecken, H.: On the Information Content ofCosmic-Ray Neutron Data in the Inverse Estimation of Soil Hydraulic
Properties, Vadose Zone Journal, 18,
https://doi.org/10.2136/vzj2018.06.0123, 2019.
▪ Scheiffele, L. M., Baroni, G., Franz, T. E., Jakobi, J., and Oswald, S.
E.: A profile shape correction to reduce the vertical sensitivity ofcosmic-ray neutron sensing of soil moisture, Vadose Zone Journal,
19, e20083, https://doi.org/10.1002/vzj2.20083, 2020.
▪ Shuttleworth, J., Rosolem, R., Zreda, M., and Franz, T.: The
COsmic-ray Soil Moisture Interaction Code (COSMIC) for use in data
assimilation, Hydrol. Earth Syst. Sci., 17, 3205–3217,
https://doi.org/10.5194/hess-17-3205-2013, 2013.

▪

Weber, T. K. D., Ingwersen, J., Högy, P., Poyda, A., Wizemann, H.-D.,Demyan, M. S., Bohm, K., Eshonkulov, R., Gayler, S., Kremer, P.,
Nkwain, Y. F., Troost, C., Witte, I., Cadisch, G., Müller, T., Andreas
Fangmeier, Wullmeyer, V., and Streck, T.: Regional climate change
observational data FOR 1695, BonaRes Data Centre (Leibniz Centrefor Agricultural Landscape Research (ZALF)), University of Hoheneheim,2021.
▪ Weber, T. K. D., Ingwersen, J., Högy, P., Poyda, A., Wizemann, H.-D.,Demyan, M. S., Bohm, K., Eshonkulov, R., Gayler, S., Kremer, P., Laub,M., Nkwain, Y. F., Troost, C., Witte, I., Reichenau, T., Berger, T., Cadisch,G., Müller, T., Fangmeier, A., Wulfmeyer, V., and Streck, T.: Multi-site,
multi-crop measurements in the soil–vegetation–atmosphere continuum:
a comprehensive dataset from two climatically contrasting regions insouthwestern Germany for the period 2009–2018, Earth System
Science Data, 14, 1153–1181, https://doi.org/10.5194/essd-14-11532022, 2022.
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