Precipitation variability in tropical high mountains is a fundamental yet poorly understood factor influencing local climatic expression and a variety of environmental processes, including glacier behavior and water resources. Precipitation type, diurnality, frequency, and amount influence hydrological runoff, surface albedo, and soil moisture, whereas cloud cover associated with precipitation events reduces solar irradiance at the surface. Considerable uncertainty remains in the multiscale atmospheric processes influencing precipitation patterns and their associated regional variability in the tropical Andes—particularly related to precipitation phase, timing, and vertical structure. Using data from a variety of sources—including new citizen science precipitation stations; new high-elevation comprehensive precipitation monitoring stations at Chacaltaya, Bolivia, and the Quelccaya Ice Cap, Peru; and a vertically pointing Micro Rain Radar—this article synthesizes findings from interdisciplinary research activities in the Cordillera Real of Bolivia and the Cordillera Vilcanota of Peru related to the following two research questions: (1) How do the temporal patterns, moisture source regions, and El Niño-Southern Oscillation relationships with precipitation occurrence vary? (2) What is the vertical structure (e.g., reflectivity, Doppler velocity, melting layer heights) of tropical Andean precipitation and how does it evolve temporally? Results indicate that much of the heavy precipitation occurs at night, is stratiform rather than convective in structure, and is associated with Amazonian moisture influx from the north and northwest. Improving scientific understanding of tropical Andean precipitation is of considerable importance to assessing climate variability and change, glacier behavior, hydrology, agriculture, ecosystems, and paleoclimatic reconstructions.

Tropical high‐Andean wetlands, locally known as ‘bofedales’, are key ecosystems sustaining biodiversity, carbon sequestration, water provision and livestock farming. Bofedales' contribution to dry season baseflows and sustaining water quality is crucial for downstream water security. The sensitivity of bofedales to climatic and anthropogenic disturbances is therefore of growing concern for watershed management. This study aims to understand seasonal water storage and release characteristics of bofedales by combining remote sensing analysis and ground‐based monitoring for the wet and dry seasons of late 2019 to early 2021, using the glacierised Vilcanota‐Urubamba basin (Southern Peru) as a case study. A network of five ultrasound loggers was installed to obtain discharge and water table data from bofedal sites across two headwater catchments. The seasonal extent of bofedales was mapped by applying a supervised machine learning model using Random Forest on imagery from Sentinel‐2 and NASADEM. We identified high seasonal variability in bofedal area with a total of 3.5% and 10.6% of each catchment area, respectively, at the end of the dry season (2020), which increased to 15.1% and 16.9%, respectively, at the end of the following wet season (2021). The hydrological observations and bofedal maps were combined into a hydrological conceptual model to estimate the storage and release characteristics of the bofedales, and their contribution to runoff at the catchment scale. Estimated lag times between 1 and 32 days indicate a prolonged bofedal flow contribution throughout the dry season (about 74% of total flow). Thus, our results suggest that bofedales provide substantial contribution to dry season baseflow, water flow regulation and storage. These findings highlight the importance of including bofedales in local water management strategies and adaptation interventions including nature‐based solutions that seek to support long‐term water security in seasonally dry and rapidly changing Andean catchments.

This study used the first detailed radar measurements of the vertical structure of precipitation obtained in the central Andes of southern Peru and Bolivia to investigate the diurnal cycle and vertical structure of precipitation and melting-layer heights in the tropical Andes. Vertically pointing 24.1-GHz Micro Rain Radars in Cusco, Peru (3350 m MSL, August 2014–February 2015), and La Paz, Bolivia (3440 m MSL, October 2015–February 2017), provided continuous 1-min profiles of reflectivity and Doppler velocity. The time–height data enabled the determination of precipitation timing, melting-layer heights, and the identification of convective and stratiform precipitation features. Rawinsonde data, hourly observations of meteorological variables, and satellite and reanalysis data provided additional insight into the characteristics of these precipitation events. The radar data revealed a diurnal cycle with frequent precipitation and higher rain rates in the afternoon and overnight. Short periods with strong convective cells occurred in several storms. Longer-duration events with stratiform precipitation structures were more common at night than in the afternoon. Backward air trajectories confirmed previous work indicating an Amazon basin origin of storm moisture. For the entire dataset, median melting-layer heights were above the altitude of nearby glacier termini approximately 17% of the time in Cusco and 30% of the time in La Paz, indicating that some precipitation was falling as rain rather than snow on nearby glacier surfaces. During the 2015–16 El Niño, almost half of storms in La Paz had melting layers above 5000 m MSL.

In the context of expected future melt reductions in the high‐Andes, the buffering capacity of non‐glacial stores, and especially of high‐altitude bofedal wetlands, is of increasing importance. Isotope signatures potentially indicative of water undergoing evaporation on transit through bofedales have been found in the tropics, but end‐member uncertainty has so far prevented streamflow separation using this signal. We undertook a stable isotope sampling campaign over the 2022 wet‐dry season transition in a 53.6 km², 16% glacierized catchment in southern Peru with a bofedal coverage of 11%. Diurnal proglacial hydrographs and remote sensing were used to interpret seasonal snowmelt dynamics and identify the dry periods when glacial melt and bofedal contributions are assessed to be the two principal components of streamflow. Following the final wet season precipitation event, a rapid ~3 week transition occurs in the main river from a stable isotope signature consistent with dynamic rainfall/snowmelt contributions to one of ice‐melt. In both wet and dry seasons, the main river and tributary streams show evaporative enrichment suggesting ongoing supply from water transiting bofedales. A two‐component mixing model using lc‐excess during the dry season shows the bofedal source contribution varies from 9% to 20% [±9–10%], indicating that streamflow is greatly augmented by the presence of glaciers at these headwater scales. However, applying these proportions to river discharge shows a sustained bofedal contribution of around 0.09 m³/s during the dry season study window whereas the flux of glacial water halves from 0.73 to 0.36 m³/s over this timeframe. The results highlight the important role of bofedales and the connected groundwater system in buffering seasonal declines in streamflow months into the dry season, and suggests the hydrological functioning of bofedales as part of this wider system should be considered when exploring the effectiveness of potential options to sustain baseflows in a post‐glacial future.

Accurate glacier mapping is crucial for assessing future water security in Andean ecosystems. Traditional accuracy assessment may be biased due to overlooking spatial autocorrelation during map validation. In recent years, spatial cross-validation (CV) strategies have been proposed in environmental and ecological modeling to reduce bias in predictive accuracy. In this study, we demonstrate the influence of spatial autocorrelation on the accuracy assessment of glacier surface predictive models. This is achieved by comparing the performance of several widely used machine learning algorithms including the gradient-boosting machines (GBM), k-nearest neighbors (KNN), random forest (RF), and logistic regression (LR) for mapping nine main Peruvian glacier regions. Spatial and non-spatial cross-validation methods were used to evaluate the model’s classification errors in terms of the Matthews correlation coefficient. Performance differences of up to 18% were found between bias-reduced (spatial) and overoptimistic (non-spatial) cross-validation results. Regarding only spatial CV, the k-nearest neighbors were the overall best model across Huallanca (0.90), Huayhuasha (0.78), Huaytapallana (0.96), Raura (0.93), Urubamba (0.96), Vilcabamba (0.93), and Vilcanota (0.92) regions, consistently demonstrating the highest performance followed by logistic regression at Blanca (0.95) and Central (0.97) regions. Our validation approach, accounting for spatial characteristics, provides valuable insights for glacier mapping studies and future efforts on glacier retreat monitoring. Incorporating this approach improves the reliability of glacier mapping, guiding future national-level initiatives.

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.