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.

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.

Abstract. In the earth and environmental sciences, many fundamental processes are explained through conceptual illustrations-a powerful medium for scientific communication. The processes depicted are generally highly complex, spatially and temporally variable, subject to high degrees of uncertainty, and non-linearly impacted by anthropogenic actions. Conceptual illustrations necessarily simplify these processes, but also often suffer from a preventable lack of visual clarity, and/or are based on implicit assumptions that are mismatched to key conclusions in published literature. In this Innovative Viewpoint paper, we highlight considerations of conceptual and visual clarity relevant to illustrations in earth and environmental sciences. Using the water cycle as an example, we examine a range of conceptual illustrations of this process to assess what ideas they convey. An exploratory survey of 32 water cycle diagrams shows that they tend to depict generalized, well-defined processes. Anthropogenic influences are included and/or implied in only half the diagrams, and none depict uncertainty in any form. The concept of the water cycle conveyed by these diagrams is therefore not quite the same as the concept of the water cycle as understood by hydrologists. This mismatch may negatively impact decision-making related to water resources management, because the parties involved may unknowingly hold significantly different conceptual models of the processes at work. Other concepts in the earth and environmental sciences may be susceptible to similar issues. Our analysis highlights the importance of carefully assessing the assumptions and simplifying choices inherent in the process of translating a concept into an illustration. We conclude with an example of how these issues can be remedied by presenting a modified water cycle diagram designed to address common misconceptions associated with dryland systems, account for uncertainty in fluxes, and include key anthropogenic effects. A general list of best practices, many of which were used to develop this diagram, is included to help increase awareness among environmental researchers of strategies for increasing the conceptual and visual clarity of illustrations.

Anisotropic fast-marching algorithms are computationally efficient tools for generating realistic maps of karst conduit networks, constrained by both the spatial extent and the orientation of karstifiable geologic units. Existing models to generate conduit network maps are limited either by high computational requirements (for chemistry-based models) or by their inability to incorporate the effects of elevation and orientation gradients (for isotropic fast-marching models). The new anisotropic fast-marching approach described here provides a significant improvement, though it imitates rather than reproduces actual speleogenetic processes. It can rapidly generate a stochastic ensemble of plausible networks from basic geologic information, which can also be used as input to karst-appropriate flow models. This paper introduces an open-source, easy-to-use implementation through the Python package pyKasso, then describes its application to a well-mapped geologically complex long-term study site: the Gottesacker alpine karst system (Germany/Austria). Groundwater flow in this system is exceptionally well understood from speleological investigations and tracer tests. Conduit formation primarily occurs at the base of the karst aquifer, following plunging synclines. Although previous attempts to reproduce the conduit network at this site yielded implausible network maps, pyKasso quickly generated networks faithful to the known conduit system. However, the model was only able to generate these realistic networks when the inlet-outlet connections of the system were correctly assigned, highlighting the importance of pairing modeling efforts with field tracer tests. Therefore, a model ensemble method is also presented, to optimize field efforts by identifying the most informative tracer tests to perform.