Preprints
https://doi.org/10.5194/hess-2022-261
https://doi.org/10.5194/hess-2022-261
02 Aug 2022
 | 02 Aug 2022
Status: a revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

From soil water monitoring data to vadose zone water fluxes: a comprehensive example of reverse hydrology

Marleen Ambrosia Schübl, Giuseppe Brunetti, Gabriele Fuchs, and Christine Stumpp

Abstract. Groundwater recharge is a key component of the hydrological cycle, yet its direct measurement is complex and often difficult to achieve. An alternative is its inverse estimation through a combination of numerical models and transient observations from distributed soil water monitoring stations. However, an often neglected aspect of this approach is the effect of model predictive uncertainty on simulated water fluxes. In this study, we made use of long-term soil water content measurements at 14 locations from the Austrian soil water monitoring program to quantify and compare local, potential groundwater recharge rates and their temporal variability. Observations were coupled with a Bayesian probabilistic framework to calibrate the model HYDRUS-1D and assess the effect of model predictive uncertainty on long-term simulated recharge fluxes. Estimated annual potential recharge rates ranged from 44 mm a-1 to 1319 mm a-1 with a relative uncertainty (95 % interquantile range/median) in the estimation between 1–39 %. Recharge rates decreased longitudinally, with high rates and lower seasonality at western sites and low rates with high seasonality and extended periods without recharge at the southeastern and eastern sites of Austria. Higher recharge rates and lower actual evapotranspiration were related to sandy soils; however, climatic factors had a stronger influence on estimated potential groundwater recharge than soil properties, underscoring the vulnerability of groundwater recharge to the effects of climate change.

Marleen Ambrosia Schübl et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-261', Ty P. A. Ferre, 04 Aug 2022
    • AC1: 'Reply on RC1', Marleen Schübl, 05 Dec 2022
  • RC2: 'Comment on hess-2022-261', Anonymous Referee #2, 26 Aug 2022
    • AC2: 'Reply on RC2', Marleen Schübl, 05 Dec 2022
  • RC3: 'Comment on hess-2022-261', Anonymous Referee #3, 16 Nov 2022
    • AC3: 'Reply on RC3', Marleen Schübl, 05 Dec 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-261', Ty P. A. Ferre, 04 Aug 2022
    • AC1: 'Reply on RC1', Marleen Schübl, 05 Dec 2022
  • RC2: 'Comment on hess-2022-261', Anonymous Referee #2, 26 Aug 2022
    • AC2: 'Reply on RC2', Marleen Schübl, 05 Dec 2022
  • RC3: 'Comment on hess-2022-261', Anonymous Referee #3, 16 Nov 2022
    • AC3: 'Reply on RC3', Marleen Schübl, 05 Dec 2022

Marleen Ambrosia Schübl et al.

Marleen Ambrosia Schübl et al.

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Short summary
Estimating groundwater recharge through the unsaturated zone is a difficult task and fundamentally associated with uncertainties. One of the few methods available is inverse modeling based on soil water measurements. In this study, we used a Nested Sampling algorithm within a Bayesian probabilistic framework to assess model uncertainties at 14 sites in Austria. Further, we analyzed simulated recharge rates to identify factors influencing groundwater recharge rates and their temporal variability.