Hydrological Modeling in Karst Environments

Karst regions are underlain by soluble rocks, notably limestones and dolomites, whose dissolution creates highly heterogeneous subsurface networks of conduits, fractures and voids. These structures generate dual-flow regimes in which rapid conduit flow coexists with slower diffuse flow through the rock matrix and epikarst. Hydrological modelling in such settings must reconcile this complexity by combining lumped and distributed approaches, or by employing hybrid schemes that capture both global water balances and detailed flow pathways. Models range from simple rainfall–discharge tools to sophisticated coupled surface–subsurface frameworks capable of simulating sinkhole dynamics, conduit routing and matrix storage. Calibration and validation often draw on spring discharge time series, tracer tests and remote-sensing data, enabling models to resolve seasonal recharge patterns, extreme events and longer-term climatic influences. Advances in machine learning have introduced data-driven components for rainfall–runoff transformations, while process-based models continue to benefit from improved representation of evapotranspiration, soil moisture and fault permeability. Such developments enhance our ability to manage water resources under increasing pressure from urbanisation, agriculture and climate change, offering insights into aquifer resilience, flood risk mitigation and sustainable extraction strategies.

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Hydrological Modeling in Karst Environments publication trend

The graph below shows the total number of articles in hydrological modeling in karst environments across all publications each year (not limited to Nature Index journals).

Technical terms

Karst aquifer: A groundwater system in soluble rock characterised by conduits, fractures and matrix porosity.

Epikarst: The shallow, weathered layer of karst that stores and transmits water to deeper conduits.

Conduit flow: Rapid, channelised groundwater movement through enlarged voids.

Diffuse flow: Slow movement of water through the rock matrix and smaller fractures.

Lumped parameter model: A simplified representation that aggregates hydrological processes into bulk storage compartments.

Distributed model: A spatially explicit approach that resolves variability in parameters and processes across the catchment.

Artificial neural network (ANN): A machine learning model that learns input–output relationships from data without explicit process equations.

SWAT-MODFLOW: A coupled modelling system integrating a watershed model (SWAT) with a groundwater flow simulator (MODFLOW) to capture surface–subsurface interactions.

References

1. Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions. Hydrology and Earth System Sciences (2023).
2. Hydrogeological modelling of a coastal karst aquifer using an integrated SWAT-MODFLOW approach. Environmental Modelling & Software (2025).
3. Assessing the long-term trend of spring discharge in a climate change hotspot area. The Science of The Total Environment (2024).