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A data-driven approach for modelling karst spring discharge using transfer function noise models

Rudolph, Max Gustav (författare)
Collenteur, Raoul Alexander (författare)
Kavousi, Alireza (författare)
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Giese, Markus, 1985 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för geovetenskaper,Department of Earth Sciences
Wöhling, Thomas (författare)
Birk, Steffen (författare)
Hartmann, Andreas (författare)
Reimann, Thomas (författare)
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 (creator_code:org_t)
2023
2023
Engelska.
Ingår i: Environmental Earth Sciences. - 1866-6280 .- 1866-6299. ; 82
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Karst aquifers are important sources of fresh water on a global scale. The hydrological modelling of karst spring discharge, however, still poses a challenge. In this study we apply a transfer function noise (TFN) model in combination with a bucket-type recharge model to simulate karst spring discharge. The application of the noise model for the residual series has the advantage that it is more consistent with assumptions for optimization such as homoscedasticity and independence. In an earlier hydrological modeling study, named Karst Modeling Challenge (KMC; Jeannin et al., J Hydrol 600:126–508, 2021), several modelling approaches were compared for the Milandre Karst System in Switzerland. This serves as a benchmark and we apply the TFN model to KMC data, subsequently comparing the results to other models. Using different data-model-combinations, the most promising data-model-combination is identified in a three-step least-squares calibration. To quantify uncertainty, the Bayesian approach of Markov-chain Monte Carlo (MCMC) sampling is subsequently used with uniform priors for the previously identified best data-model combination. The MCMC maximum likelihood solution is used to simulate spring discharge for a previously unseen testing period, indicating a superior performance compared to all other models in the KMC. It is found that the model gives a physically feasible representation of the system, which is supported by field measurements. While the TFN model simulated rising limbs and flood recession especially well, medium and baseflow conditions were not represented as accurately. The TFN approach poses a well-performing data-driven alternative to other approaches that should be considered in future studies.

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Oceanografi, hydrologi och vattenresurser (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Oceanography, Hydrology and Water Resources (hsv//eng)

Nyckelord

Modelling
Karst
Uncertainty Quantification
Transfer Functions
Spring discharge

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

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