Improving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles

Reconstructing the geologic history of a karst area can advance understanding of the system’s present-day hy- drogeologic functioning and help predict the location of un- explored conduits. This study tests competing hypotheses describing past conditions controlling cave formation in an alpine karst catchment, by comparing an ensemble of mod- eled networks to the observed network map. The catch- ment, the Gottesacker karst system (Germany and Austria), is drained by three major springs and a paleo-spring and in- cludes the partially explored Hölloch cave, which consists of an active section whose formation is well-understood and an inactive section whose formation is the subject of debate. Two hypotheses for the formation of the inactive section are the following: (1) glaciation obscured the three present-day springs, leaving only the paleo-spring, or (2) the lowest of the three major springs (Sägebach) is comparatively young, so its subcatchment previously drained to the paleo-spring. These hypotheses were tested using the pyKasso Python li- brary (built on anisotropic fast-marching methods) to gener- ate two ensembles of networks, one representing each sce- nario. Each ensemble was then compared to the known cave map. The simulated networks generated under hypothesis 2 match the observed cave map more closely than those gener- ated under hypothesis 1. This supports the conclusion that the Sägebach spring is young, and it suggests that the cave likely continues southwards. Finally, this study extends the appli- cability of model ensemble methods from situations where the geologic setting is known but the network is unknown to situations where the network is known but the geologic evo- lution is not. 1 Introductio

Modeling Riparian Restoration Impacts on the Hydrologic Cycle at the Babacomari Ranch, SE Arizona, USA

This paper describes coupling field experiments with surface and groundwater modeling to investigate rangelands of SE Arizona, USA using erosion-control structures to augment shallow and deep aquifer recharge. We collected field data to describe the physical and hydrological properties before and after gabions (caged riprap) were installed in an ephemeral channel. The modular finite-difference flow model is applied to simulate the amount of increase needed to raise groundwater levels. We used the average increase in infiltration measured in the field and projected on site, assuming all infiltration becomes recharge, to estimate how many gabions would be needed to increase recharge in the larger watershed. A watershed model was then applied and calibrated with discharge and 3D terrain measurements, to simulate flow volumes. Findings were coupled to extrapolate simulations and quantify long-term impacts of riparian restoration. Projected scenarios demonstrate how erosion-control structures could impact all components of the annual water budget. Results support the potential of watershed-wide gabion installation to increase total aquifer recharge, with models portraying increased subsurface connectivity and accentuated lateral flow contributions.Walton Family Foundation; Land Change Science (LCS) Program, under the Land Resources Mission Area of the US Geological Survey (USGS); NSF [DBI-0735191, DBI-1265383]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits

Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks.National Science Foundation Graduate Research Fellowship Progra

A model ensemble generator to explore structural uncertainty in karst systems with unmapped conduits

Karst aquifers are characterized by high-conductivity conduits embedded in a low-conductivity fractured matrix, resulting in extreme heterogeneity and variable groundwater flow behavior. The conduit network controls groundwater flow, but is often unmapped, making it difficult to apply numerical models to predict system behavior. This paper presents a multi-model ensemble method to represent structural and conceptual uncertainty inherent in simulation of systems with limited spatial information, and to guide data collection. The study tests the new method by applying it to a well-mapped, geologically complex long-term study site: the Gottesacker alpine karst system (Austria/Germany). The ensemble generation process, linking existing tools, consists of three steps: creating 3D geologic models using GemPy (a Python package), generating multiple conduit networks constrained by the geology using the Stochastic Karst Simulator (a MATLAB script), and, finally, running multiple flow simulations through each network using the Storm Water Management Model (C-based software) to reject nonbehavioral models based on the fit of the simulated spring discharge to the observed discharge. This approach captures a diversity of plausible system configurations and behaviors using minimal initial data. The ensemble can then be used to explore the importance of hydraulic flow parameters, and to guide additional data collection. For the ensemble generated in this study, the network structure was more determinant of flow behavior than the hydraulic parameters, but multiple different structures yielded similar fits to the observed flow behavior. This suggests that while modeling multiple network structures is important, additional types of data are needed to discriminate between networks.National Science Foundation Graduate Research Fellowship ProgramOpen access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]