It is widely accepted that spring hydrographs are an effective tool for evaluating the internal structure of karst aquifers because they depict the response of the whole aquifer to recharge events. The spring hydrograph is affected by various factors such as flow regime, geometry, type of recharge, and hydraulic properties of conduit. However, the effect of conduit network geometry received less attention and required more comprehensive research studies. The present study attempted to highlight the impact of the two most frequent patterns of karst conduits (i.e., branchwork and network maze) on the characteristic of the spring hydrograph. Therefore, two conduit patterns, branchwork and network maze, were randomly generated with MATLAB codes. Then, MODFLOW-CFP was used to quantify the effect of conduit pattern, conduit density, and diffuse or concentrated recharge on the spring hydrograph. Results reveal that peak discharge, fast-flow recession coefficient, and the return time to baseflow are mainly affected by conduit network pattern, conduit network density, and recharge, respectively. In contrast, the time to reach peak flow only reacts to recharge conditions. Large variations in conduit density produce tangible changes in the baseflow recession coefficient.

Assessing the Effect of Conduit Pattern and Type of Recharge on the Karst Spring Hydrograph: A Synthetic Modeling Approach

Fiorillo, F
Membro del Collaboration Group
2023-01-01

Abstract

It is widely accepted that spring hydrographs are an effective tool for evaluating the internal structure of karst aquifers because they depict the response of the whole aquifer to recharge events. The spring hydrograph is affected by various factors such as flow regime, geometry, type of recharge, and hydraulic properties of conduit. However, the effect of conduit network geometry received less attention and required more comprehensive research studies. The present study attempted to highlight the impact of the two most frequent patterns of karst conduits (i.e., branchwork and network maze) on the characteristic of the spring hydrograph. Therefore, two conduit patterns, branchwork and network maze, were randomly generated with MATLAB codes. Then, MODFLOW-CFP was used to quantify the effect of conduit pattern, conduit density, and diffuse or concentrated recharge on the spring hydrograph. Results reveal that peak discharge, fast-flow recession coefficient, and the return time to baseflow are mainly affected by conduit network pattern, conduit network density, and recharge, respectively. In contrast, the time to reach peak flow only reacts to recharge conditions. Large variations in conduit density produce tangible changes in the baseflow recession coefficient.
2023
spring hydrograph
conduit patterns
MATLAB code
MODFLOW-CFP
recession coefficient
baseflow
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12070/61400
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