Intrinsic vulnerability assessment in karst areas: A numerical modeling approach

Abstract

[1] The main objective of this study was to quantify the intrinsic vulnerability of karst springs by numerical modeling. A global approach is used, modeling the discharge of a karst spring. This approach includes the hydrological dynamics of karst systems and is applicable to complex karst settings, where structural and hydraulic characteristics cannot be spatially resolved with sufficient accuracy. A basis model and four extended versions were set up to determine the individual characteristics of the present karst system and to include different flow processes that could affect the vulnerability of the system. All these model setups consider, besides recharge (soil and epikarst system), the conduit and the diffuse flow system as the main characteristics of the karst aquifer. The extended setups additionally account for surface runoff, an intermediate flow system, exchange flow between the conduit and the diffuse system and seasonal variation in the water storage capacity of the recharge system. Potential use of the calibrated models to quantify the intrinsic vulnerability of karst springs is discussed on the basis of (1) the temporally changing contributions of the conduit and diffuse flow systems to spring discharge, and (2) modeled breakthrough curves resulting from a standardized contaminant input into the karst system. The modeling approach complements vulnerability mapping methods by addressing temporal and quantitative aspects of vulnerability.Citation: Butscher, C., and P. Huggenberger (2008), Intrinsic vulnerability assessment in karst areas: A numerical modeling approach, Water Resour. Res., 44, W03408,

Model-aided quantification of dissolved carbon and nitrogen release after windthrow disturbance in an Austrian karst system

“…While many studies identify N and DOC as source of contamination in karst systems (Einsiedl et al, 2005;Jost et al, 2010;Katz et al, 2001Katz et al, , 2004Tissier et al, 2013) or provide static vulnerability maps (Andreo et al, 2008;Doerfliger et al, 1999), only very few studies use models to quantify the temporal behaviour of a contamination through the systems (Butscher and Huggenberger, 2008). Some studies use N and DOC to better understand karst processes (Charlier et al, 2012;Mahler and Garner, 2009;Pinault et al, 2001) or for advanced karst model calibration (Hartmann et al, 2013b(Hartmann et al, , 2014b, but to our knowledge there are no applications of such approaches to quantify the drainage processes of N and DOC, and particularly so after strong impacts on ecosystems (e.g.…”

Groundwater management of a highly dynamic karst by assessing baseflow and quickflow with a rainfall-discharge model (Dardennes springs, SE France)

“…Other structures also exist: Hartmann et al (2012) tested two lower reservoirs in series, while Arfib and Charlier (2016) added a third reservoir. These models were used to discuss the regional karst groundwater resources (Bakalowicz, 2005;Fleury et al, 2009;Ladouche et al, 2014), as a tool for the identification and quantification of flow (Fleury et al, 2007), to assess the vulnerability (Butscher and Huggenberger, 2008), to estimate the groundwater balance (Jukić and Denić-Jukić, 2009), or for flood hazard (Fleury et al, 2013). The main advantage of lumped models is that they can run even if the observed discharge time series is not complete, which is one of the main limitations of time series analysis.…”

Modeling the Temporal Variability of Karst Groundwater Vulnerability, with Implications for Climate Change

“…The numerical modeling approach chosen here has recently been proposed by the authors (13). It considers karst groundwater vulnerability to be a dynamic response to variable precipitation and evaporation conditions.…”

“…The quantification is based on (1) the vulnerability index VI, representing varying relative contributions of the conduit and diffuse flow system to spring discharge, and (2) the modeled vulnerability concentration C V in spring water resulting from a standardized contaminant input into the system. VI is calculated to quantify the "conduit flow vulnerability" to short-lived contaminants (e.g., fecal bacteria), whereas C V is calculated to quantify the "diffuse flow vulnerability" to persistent contaminants such as pesticides (13,16).…”

“…The conduit and diffuse systems both contribute to the spring discharge proportional to their water volume (linear outflow), with a higher outflow coefficient (shorter residence time) in the conduit system. The model is calibrated by applying a numerical nonlinear regression algorithm to minimize deviations between measured and calculated spring discharge (13).…”