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

Max Gustav Rudolph1, Raoul Alexander Collenteur2,3, Alireza Kavousi1

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Summary
This summary is machine-generated.

A new transfer function noise (TFN) model effectively simulates karst spring discharge, outperforming other methods in a benchmark study. This data-driven approach offers a promising alternative for hydrological modeling of complex karst systems.

Keywords:
KarstModellingSpring dischargeTransfer functionsUncertainty quantification

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Area of Science:

  • Hydrology
  • Environmental Science
  • Water Resource Management

Background:

  • Karst aquifers are critical global freshwater sources.
  • Accurate hydrological modeling of karst spring discharge remains a significant challenge.
  • Previous studies, like the Karst Modeling Challenge (KMC), have explored various modeling approaches.

Purpose of the Study:

  • To apply and evaluate a transfer function noise (TFN) model combined with a bucket-type recharge model for simulating karst spring discharge.
  • To compare the TFN model's performance against other models using KMC benchmark data.
  • To quantify the uncertainty of the TFN model using Bayesian methods.

Main Methods:

  • A transfer function noise (TFN) model was coupled with a bucket-type recharge model.
  • A three-step least-squares calibration identified the optimal data-model combination.
  • Markov-chain Monte Carlo (MCMC) sampling was employed for uncertainty quantification.
  • The model was validated on an unseen testing period.

Main Results:

  • The TFN model demonstrated superior performance compared to all other models in the KMC benchmark.
  • The model provided a physically plausible representation of the karst system, supported by field data.
  • The TFN model excelled at simulating rising limbs and flood recession, though medium and baseflow conditions were less accurately represented.

Conclusions:

  • The TFN model is a well-performing, data-driven alternative for karst spring discharge modeling.
  • This approach should be considered in future hydrological studies of karst systems.
  • Further refinement may improve the representation of baseflow and medium flow conditions.