Mountain‐block recharge (MBR) is the subsurface inflow of groundwater to lowland aquifers from adjacent mountains. MBR can be a major component of recharge but remains difficult to characterize and quantify due to limited hydrogeologic, climatic, and other data in the mountain block and at the mountain front. The number of MBR‐related studies has increased dramatically in the 15 years since the last review of the topic was conducted by Wilson and Guan (2004), generating important advancements. We review this recent body of literature, summarize current understanding of factors controlling MBR, and provide recommendations for future research priorities. Prior to 2004, most MBR studies were performed in the southwestern United States. Since then, numerous studies have detected and quantified MBR in basins around the world, typically estimating MBR to be 5–50% of basin‐fill aquifer recharge. Theoretical studies using generic numerical modeling domains have revealed fundamental hydrogeologic and topographic controls on the amount of MBR and where it originates within the mountain block. Several mountain‐focused hydrogeologic studies have confirmed the widespread existence of mountain bedrock aquifers hosting considerable groundwater flow and, in some cases, identified the occurrence of interbasin flow leaving headwater catchments in the subsurface—both of which are required for MBR to occur. Future MBR research should focus on the collection of high‐priority data (e.g., subsurface data near the mountain front and within the mountain block) and the development of sophisticated coupled models calibrated to multiple data types to best constrain MBR and predict how it may change in response to climate warming.

Watersheds have served as one of our most basic units of organization in hydrology for over 300 years (Dooge, 1988, https://doi.org/10.1080/02626668809491223; McDonnell, 2017, https://doi.org/10.1038/ngeo2964; Perrault, 1674, https://www.abebooks.com/first-edition/lorigine-fontaines-Perrault-Pierre-Petit-Imprimeur/21599664536/bd). With growing interest in groundwater‐surface water interactions and subsurface flow paths, hydrologists are increasingly looking deeper. But the dialog between surface water hydrologists and groundwater hydrologists is still embryonic, and many basic questions are yet to be posed, let alone answered. One key question is: where is the bottom of a watershed? Knowing where to draw the bottom boundary has not yet been fully addressed in the literature, and how to define the watershed “bottom” is a fraught question. There is large variability across physical and conceptual models regarding how to implement a watershed bottom, and what counts as “deep” varies markedly in different communities. In this commentary, we seek to initiate a dialog on existing approaches to defining the bottom of the watershed. We briefly review the current literature describing how different communities typically frame the answer of just how deep we should look and identify situations where deep flow paths are key to developing realistic conceptual models of watershed systems. We then review the common conceptual approaches used to delineate the watershed lower boundary. Finally, we highlight opportunities to trigger this potential research area at the interface of catchment hydrology and hydrogeology.

Abstract:Climate change threatens water resources in snowmelt-dependent regions by altering the fraction of snow and rain and spurring an earlier snowmelt season. The bulk of hydrological research has focused on forecasting response in streamflow volumes and timing to a shrinking snowpack; however, the degree to which subsurface storage offsets the loss of snow storage in various alpine geologic settings, i.e. the hydrogeologic buffering capacity, is still largely unknown. We address this research need by assessing the affects of climate change on storage and runoff generation for two distinct hydrogeologic settings present in alpine systems: a low storage granitic and a greater storage volcanic hillslope. We use a physically based integrated hydrologic model fully coupled to a land surface model to run a base scenario and then three progressive warming scenarios, and account for the shifts in each component of the water budget. For hillslopes with greater water retention, the larger storage volcanic hillslope buffered streamflow volumes and timing, but at the cost of greater reductions in groundwater storage relative to the low storage granite hillslope. We found that the results were highly sensitive to the unsaturated zone retention parameters, which in the case of alpine systems can be a mix of matrix or fracture flow. The presence of fractures and thus less retention in the unsaturated zone significantly decreased the reduction in recharge and runoff for the volcanic hillslope in climate warming scenarios. This approach highlights the importance of incorporating physically based subsurface flow in to alpine hydrology models, and our findings provide ways forward to arrive at a conceptual model that is both consistent with geology and hydrologic principles.

Mountain block systems are critical to water resources and have been heavily studied and modeled in recent decades. However, due to lack of field data, there is little consistency in how models represent the mountain block subsurface. While there is a large body of research on subsurface heterogeneity, few studies have evaluated the effect that common conceptual choices modelers make in mountainous systems have on simulated hydrology. Here we simulate the hydrology of a semi-idealized headwater catchment using six common conceptual models of the mountain block subsurface. These scenarios include multiple representations of hydraulic conductivity decaying with depth, changes in soil depth with topography, and anisotropy. We evaluate flow paths, discharge, and water tables to quantify the impact of subsurface conceptualization on hydrologic behavior in three dimensions. Our results show that adding higher conductivity layers in the shallow subsurface concentrates flow paths near the surface and increases average saturated flow path velocities. Increasing heterogeneity by adding additional layers or introducing anisotropy increases the variance in the relationship between the age and length of saturated flow paths. Discharge behavior is most sensitive to heterogeneity in the shallow subsurface layers. Water tables are less sensitive to layering than they are to the overall conductivity in the domain. Anisotropy restricts flow path depths and controls discharge from storage but has little effect on governing runoff. Differences in the response of discharge, water table depth, and residence time distribution to subsurface representation highlight the need to consider model applications when determining the level of complexity that is needed. Plain Language Summary Computer models are a commonly used tool to understand and predict the behavior of mountainous watersheds. However, modeling groundwater is uniquely challenging in these systems because groundwater flow is strongly influenced by the geometry of the bedrock, yet observations of depth to bedrock and measurements of hydraulic properties with depth are infrequent. Modelers commonly rely on simplifying assumptions to represent the geologic layering in mountainous watersheds. However, the impact of these assumptions on simulated hydrology is rarely evaluated. Our study compares six commonly used representations of the subsurface to explore how basic choices in model construction influence the resulting hydrology. The six models vary in complexity from no layering at all to more complex variations in soil depth. We compare the behavior of our six cases based on groundwater levels, streamflow, and groundwater flow pathways. We show that streamflow changes the most due to changes in the model layers closest to the surface. The speed of groundwater flow pathways is more sensitive to the properties of layers at all depths. Differences in how streamflow and groundwater flow pathways respond to various representations highlight the fact that good model performance with r...