Improvement of the KarstMod modeling platform for a better assessment of karst groundwater resources

Abstract

Abstract. We propose an updated version of KarstMod, an adjustable platform dedicated to lumped parameter rainfall-discharge modeling of karst aquifers. KarstMod provides a modular, user-friendly modeling environment for educational, research and operational purposes. It also includes numerical tools for time series analysis, model evaluation and sensitivity analysis. The modularity of the platform facilitates common operations related to lumped parameter rainfall-discharge modeling, such as (i) set up and parameter estimation of a relevant model structure, and (ii) evaluation of internal consistency, parameter sensitivity and hydrograph characteristics. The updated version now includes (i) external routines to better consider the input data and their related uncertainties, i.e. evapotranspiration and solid precipitation, (ii) enlargement of multi-objective calibration possibilities, allowing more flexibility in terms of objective functions as well as observation type and (iii) additional tools for model performance evaluation including further performance criteria and tools for model errors representation.

Karst water resources in a changing world: Review of solute transport modelling approaches

“…Nonetheless, the characterization methods for the data acquisition and/or interpretation techniques in karst aquifers is rather appropriate for the continuum (Darcy) scale applications where the effective parameters can be readily used for the entire model domain (e.g., Faulkner et al, 2009;Huntoon, 1995;Kresic & Stevanovic, 2009;Zhang et al, 2021). For this reason, when the direct measurements of transport parameters (i.e., dispersivity, mass transfer coefficient) are not practical and/or their values might not be representative in resembling the process of interest, model parameters are frequently estimated manuscript submitted to Review of Geophysics via model calibration (e.g., Bittner et al, 2020;Cinkus et al, 2023;Dewaide et al, 2016;Sivelle et al, 2023;Çallı et al, 2023a).…”