Snowmelt drives a large portion of streamflow in many mountain areas of the world. However, the water pathways since snow melts until water reaches the streams, and its associated transit time is still largely unknown. Such processes are important for drawing conclusions about the hydrological role of the upstream snowpack after melting. This work analyzes for first time the influence of snowmelt on spring streamflow in years of different snow accumulation and duration, in an alpine catchment of the central Spanish Pyrenees. A multi-approach research was performed, by combining the analysis of climatic, snow, streamflow, piezometric levels, water temperature, electrical conductivity and isotopic (δ 18O) data. Results show that snow played a preeminent role on the hydrological response of the catchment during spring. Liquid precipitation during the melting period also determined the shape of the spring hydrographs. When snow cover disappeared from the catchment, soil water storage and streamflow showed a sharp decline. Consequently, streamflow electrical conductivity, temperature and δ 18O showed a marked tipping point towards higher values. The fast hydrological response of the catchment to snow and meteorological fluctuations, as well as the marked diel fluctuations of streamflow δ 18O during the melting period, strongly suggests soil storage was small, leading to short meltwater transit times. As a consequence of this hydrological behavior, independently of the amount of snow accumulated and of melting date, summer streamflow remained always low, with small runoff peaks driven by rainfall events. The expected reduction of snow accumulation and duration in the area in a next future will bring an earlier snowmelt and rise of stream water temperature. However, given the low storage capacity of the catchment and the contribution of rainfall events to spring runoff, the annual water balance and the runoff seasonality of the catchment would not change drastically.

This research analyses the snow depth distribution in canopy gaps across two plots in Central Pyrenees, to improve understanding of snow–forest and topography interactions. Snow depth maps, forest structure–canopy gap (FSCG) characteristics and topographic variables were generated by applying Structure from Motion algorithms (SfM) to images acquired from Unmanned Aerial Vehicles (UAVs). Six flights were conducted under different snowpack conditions in 2021, 2022 and 2023. Firstly, the snow depth database was analyzed in terms of the ratio between the radius of the canopy gap and the maximum height of the surrounding trees ( r/ h), in order to classify the gaps as small-size, medium-size, large-size or open areas at both sites independently. Then the Kendall correlation coefficients between the snow depth, FSCG and topographic variables were computed, and a Random Forest (RF) model for each survey day was implemented, to determine the influence of these variables for explaining snow depth patterns. The results demonstrate the high reliability of the UAV SfM photogrammetry approach for measuring snowpack dynamics at fine scale in canopy gaps and open areas. At site 1, the larger the r/ h observed, the greater was the snow depth obtained. This pattern was not evident at site 2, which presented high variability related to the survey dates and categories, highlighting the relevance of topography for determining optimum snow accumulation in forested areas. Slope systematically exhibited a negative and significant correlation with snow depth, and was consistently the highest-ranked variable for explaining snow distribution at both sites according to the RF models. Distance to the Canopy Edge also presented high influence, especially at site 1. The findings suggest differences in the main drivers throughout each site and survey of the topographic and FSCG variables are needed to understand snow depth distribution over heterogeneous mountain forest domains.

The Colombian Andean Mountains host the headwaters of the main basins of the country. However, the interactions between high-mountain ecosystems and the isotopic composition of water in this region has been poorly studied. Here we present and analyze the first set of stable isotopes data collected in the Central Andes of Colombia. Stable isotopic composition of stream water and precipitation was determined for a period between 2017 and 2018 in the Upper Claro River Basin. The driving factors influencing the spatial and temporal variability of δ 2H, δ 18O and d-excess were identified and compared to daily air temperature and precipitation at seven meteorological stations. A Local Meteoric Water Line was defined as δ²H = 8.13 δ 18O + 12.5, R 2=0.98. δ 2H, δ 18O and d-excess values of precipitation were more negative during the rainy season and changes were more related to precipitation events and amounts than to temperature. An altitude effect of -0.11 ‰ / 100 m and -0.18 ‰ / 100 m was estimated for stream water and precipitation, respectively, where the latter showed a non-linear behavior. The data set was compared to stations of the Global Network of Isotopes in Precipitation (GNIP) database in Colombia and a back-trajectory analysis of air masses was conducted to compare with d-excess. δ 18O weighted means changed with respect to the position of the Central Andes and the altitudinal range 2,100 to 3,100 m a.s.l.. High d-excess can be attributed to moisture recycling enhanced by the local ecosystems and the travel of precipitable water from the Amazon basin across the northern Andes. The results showed a high range of variation due to the differences in elevation, seasonality and atmospheric circulation patterns across the year. The present study contributes to fill the gap of spatial and temporal isotopic composition data in the northern Andes as well as to the implementation of the first “National Network for Isotopes” in Colombia.

This study updates information on the evolution of glacier shrinkage in Cocuy-Güican mountains since the maximum glacier extent of the Little Ice Age (LIA), and presents the first mass balance data of Ritacuba glacier since 2009, that is compared to the available mass balance for the Conejeras Glacier (Los Nevados National Park). This study also discusses the hydrological significance of Colombian glaciers which is still largely unknown because of the very limited information available. Glaciers in Cocuy-Güican covered 13.2 km2 in 2019 that compared to the 127.8 km2 during the maximum LIA represents a shrinkage of 89.7%. Glacier cover observations in 1955, 1994, 2010 and 2019, reveal that the rate of ice loss was the largest from 1994 to 2010 (0.59 km2 yr-1) and was then more than halved from 2010 to 2019 (0.34 km2 yr-1). This slowdown in glacier retreat is in line with a moderate negative mass balance measured for 2009-2019, with an accumulated loss of 1,766 mm w.e. The progressive confinement of glaciers to higher elevation and optimal topographic context together with a lack of recent marked climatic anomalies, could explain that Cocuy-Güican glaciers have temporally reached near equilibrium state condition. This is in stark contrast with Conejeras glacier where 47,000 mm w.e. has been lost in the same period. The available data on runoff and isotopic traces of streamflows and precipitation suggest a primary control of precipitation on the hydrological variability of the high elevated sites, compared to glacier melt water.

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.