A better understanding of headwater hydrogeology in the semi-arid Andes is critical because high-elevation basins are considered water towers for the main valleys, where water demand is the highest. Geophysical surveys and a pumping test were carried out to obtain information on aquifer structures and properties. Radioactive and stable isotopes were used to characterize the hydrological functioning of the headwater basins. Low electromagnetic velocities and resistivities reveal the presence of liquid water beneath a rock glacier, which could be the upper limit of a proglacial aquifer. The downstream valley aquifer appears fairly transmissive (3.2 10 -3 m 2s -1) and dominated by old waters (several decades) that are a mix of high-glacierized and low-glacierized basins. Additionally, stable isotopes point out a different signature for high-glacierized and low-glacierized basins, which could result from isotope fractionation. These results indicate that water isotopes could be used to discriminate waters originating from high-glacierized and low-glacierized basins. The study identifies the presence of old waters in a priori low-glacierized basins, which indicates long term storage. This finding is important for understanding late season baseflow and streamflow evolution in the context of climate change. As the contribution of such basins to total streamflow is significant, studies that aim to predict streamflow should not only focus on basins containing large glaciers.

Many agricultural regions around the world rely on melt from mountainous snowpacks for irrigation. As climate change-induced snow droughts intensify, water resource managers will need more efficient and accurate methods to characterize the snowmelt cycle and forecast water availability. Here, we integrate in-situ and remotely-sensed data to assess the relative contributions of groundwater and the current season’s snowmelt to irrigation water supply for water year 2023 (WY, Oct 1 – Sep 30) for a montane headwater catchment in southwestern Montana (423 km 2, elevation ranges between 1465 m and 3270 m). We analyze Sentinel-1 Synthetic Aperture Radar (SAR) data to approximate dates of snowmelt runoff onset at 10 m resolution every twelve days. We find that the watershed’s median date of snowmelt runoff onset in WY 2023 was April 20. To assess relative contributions to streamflow, we compare stable water isotope ratios (δH 2, δO 18) from biweekly stream water samples at low elevations against monthly samples of snow and groundwater. We find that stream water below the highest diversion point is predominantly composed of groundwater. The maximum contribution from snowmelt was measured in May at 22%. Results demonstrate alignment between two disparate approaches for estimating snowpack contribution to stream flow. While our work focuses on a catchment in Montana, the approaches used are potentially applicable globally for agricultural regions that rely on snowmelt for irrigation, particularly in poorly instrumented areas.

Snow is Earth’s most climatically sensitive land cover type. Air temperature and moisture availability are first-order controls on snowfall. Maximum snowfall occurs at temperatures very near 0°C, so even a slight increase in temperature will shift a snowy winter to one with midseason rainfall and melt events. Traditional snow metrics are not able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE, the amount of water represented by the snowpack) is used as a streamflow predictor. Still, it cannot express the effects of midwinter melt events, now expected in warming snow climates. The multiple impacts of a changing snowpack require a suite of climate indicators derived from readily measured or modelled data that serve as proxies for relevant snow-related and climate-driven processes. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic as to be conflated with other variables or, conversely, overly complicated in their interpretation. This paper describes a targeted set of spatially explicit, multi-temporal snow metrics for multiple sectors, stakeholders, and scientists. They include metrics based on satellite data from NASA’s Moderate Resolution Imaging Spectroradiometer, meteorological observations and snow data from ground-based stations, and climate model output. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), snowstorm temperature (ST), At-Risk Snow (ARS), and Frequency of a Warm Winter (FWW).