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Hydraulic niche descriptors of final instar larvae of nine Drusus species (Trichoptera) were studied in small, spring-fed, first-order headwaters located in the Mühlviertel (Upper Austria), Koralpe (Carinthia, Austria), and in the Austrian and Italian Alps. The species investigated covered all three clades of Drusinae: the shredder clade (Drusus franzi, D. alpinus), the grazer clade (D. biguttatus, D. chauvinianus, D. dudor, D. monticola), and the filtering carnivore clade (D. chrysotus, D. katagelastos, D. muelleri). Flow velocity was measured at front center of 68 larvae, head upstream, on the top of mineral substrate particles at water depths of 10–30 mm, using a tripod-stabilized Micro propeller meter (propeller diameter = 10 mm). Each data series consisted of a sampled measurement lasting 30 s (measuring interval = 1 s). In total, 2040 single velocity measurements were taken. Instantaneous flow velocities and drag at the sites of the 68 larvae varied from 0 to 0.93 m s−1 and 0 to 8346 *10−6 N, respectively. Flow velocities and drag between the three clades were highly significantly different (p < 0.001); mean velocity (+ 95% confidence limits) for the three clades were 0.09 + 0.00 m s−1 for the shredder, 0.25 + 0.00 m s−1 for the grazer, and 0.31 + 0.01 m s−1 for the filtering carnivore clade; the corresponding data for drag were (85 + 18)*10−6 N, (422 + 61)*10−6 N and (1125 + 83)*10−6 N, respectively. Adhesive friction ranged from (41.07 + 53.03)*10−6 N in D. franzi to (255.24 + 216.87)*10−6 N in D. chrysotus. Except in D. franzi and D. dudor adhesive friction was always well below drag force, indicating that submerged weight alone was not sufficient to stabilize the larvae in their hydraulic environment. Reynolds numbers varied between 0 in D. franzi and D. alpinus, and 12,634 in D. katagelastos, with 7% of the total in the laminar (R < 500), 30% in the transitional (R = 500–2000), and 61% in the fully turbulent stage (R > 2000). Froude numbers (Fr) varied from 0 to 2.97. The two Drusus species of the shredder clade and three out of four species of the grazer clade were exposed to subcritical Fr < 1, one species of the grazer clade and two out of three species of the filtering clade to supercritical Froude numbers >1. [ABSTRACT FROM AUTHOR]

Glacial lake formations are currently being observed in the majority of glacierized mountains in the world. Given the ongoing climate change and population increase, studying glacier ice thickness and bed topography is a necessity for understanding the erosive power of glacier activity in the past and lake formation in the future. This study uses the available information to predict potential sites for future lake formation in the Upper Rhône catchment located in the Southwestern Swiss Alps. The study integrates the latest available glacier outlines and high-quality digital elevation models (DEMs) into the Volume and Topography Automation (VOLTA) model to estimate ice thickness within the extent of the glaciers. Unlike the previous ice thickness models, VOLTA calculates ice thickness distribution based on automatically-derived centerlines, while optimizing the model by including the valley side drag parameter in the force equation. In this study, a total ice volume of 37.17 ± 12.26 km3 (1σ) was estimated for the Upper Rhône catchment. The comparison of VOLTA performance indicates a stronger relationship between measured and predicted bedrock, confirming the less variability in VOLTA's results (r2 ≈ 0.92) than Glacier Bed Topography (GlabTop) (r2 ≈ 0.82). Overall, the mean percentage of ice thickness error for all measured profiles in the Upper Rhône catchment is around ±22%, of which 28 out of 42 glaciers are underestimated. By incorporating the vertical accuracy of free-ice DEM, we could identify 171 overdeepenings. Among them, 100 sites have a high potential for future lake formation based on four morphological criteria. The visual evaluation of deglaciated areas also supports the robustness of the presented methodology, as 11 water bodies were already formed within the predicted overdeepenings. In the wake of changing global climate, such results highlight the importance of combined datasets and parameters for projecting the future glacial landscapes. The timely information on future glacial lake formation can equip planners with essential knowledge, not only for managing water resources and hazards, but also for understanding glacier dynamics, catchment ecology, and landscape evolution of high-mountain regions. [ABSTRACT FROM AUTHOR]