Debris flows caused by glacial lake outburst floods (GLOFs) are common hazards in mountain environments. The risk posed by glacial lake outburst hazards is particularly evaluated where the lower reaches of catchments are populated. A potentially dangerous lake has been identified adjacent to the Uchitel Glacier in Northern Tien Shan. This lake formed between 1988 and 1994 on the site of a retreated glacier in the upper part of the Aksay Valley. In this study we consider the possibility of an outburst of this pro-glacial lake in the future. The study involved bathymetry mapping of the lake, detailed profile sections of the valley, flow rate measurements on the Aksay river, and monitoring of the lake development using satellite images. Modelling of secondary debris flow inundation heights and hazard footprints has been undertaken. The outburst of this lake could cause powerful debris flows posing a threat to permanent residents living downstream, in the Ala-Archa Valley. Monitoring of the lake over the past ten years suggests certain changes in the runoff to the subsurface, and an increase in lake depth is observed. Glacial lakes with subsurface drainage are considered to be the most hazardous type as the knowledge of drainage channels functioning is still very limited and, thus, the timing of an outburst is hard to predict. Development of monitoring approaches to support forecasting of these hazards is of paramount importance to safety in mountain territories globally.

Tree-ring-width chronologies for 33 samples of Picea abies (L.) Karst. were developed, and a relationship between tree growth and hydrometeorological features was established and analyzed. Precipitation, temperature, and discharge records were extrapolated to understand past climate trends to evaluate the accuracy of global climate models (GCMs). Using Machine Learning (ML) approaches, hydrometeorological records were reconstructed/extrapolated back to 1886. An increase in the mean annual temperature (Tmean_a) increased the mean annual discharge (Dmean_a) via glacier melting; however, no temporal trends in annual precipitation were detected. For these reconstructed climate data, root-mean-square error (RMSE), Taylor diagrams, and Kling–Gupta efficiency (KGE) were used to evaluate and assess the robustness of GCMs. The CORDEX REMO models indicated the best performance for simulating precipitation and temperature over northern Tien Shan; these models replicated historical Tmen_a and P_a quite well (KGE = 0.24 and KGE = 0.24, respectively). Moreover, the multi-model ensembles with selected GCMs and bias correction can significantly increase the performance of climate models, especially for mountains region where small-scale orographic effects abound.