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Towards a methodology for testing models as hypotheses in the inexact sciences.

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This study introduces a new method for testing hydrological models by analyzing observational data and event mass balance. It addresses epistemic uncertainties in hydrological data to pragmatically evaluate model performance before simulations.

Keywords:
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Area of Science:

  • Environmental science
  • Hydrology

Background:

  • Hydrology is treated as an inexact science with significant epistemic uncertainties.
  • Testing hydrological models requires robust methods to account for data limitations.

Purpose of the Study:

  • To propose a novel method for developing limits of acceptability for testing hydrological models.
  • To address epistemic uncertainties in observational data within hydrological modeling.

Main Methods:

  • The approach analyzes available observations and considers event mass balance for rainfall-runoff events.
  • It focuses on identifying and accounting for epistemic uncertainties in input data.

Main Results:

  • Many events exhibit epistemic uncertainties in input data, leading to unsatisfied mass balance.
  • The proposed method pragmatically incorporates these uncertainties before model execution.

Conclusions:

  • The developed approach provides a framework for evaluating hydrological models under epistemic uncertainty.
  • This method may be applicable to other environmental science fields with similar modeling challenges.