Glacial Lake Outburst Flood (GLOF) Events and Water Response in A Patagonian Fjord

Recession of Gya Glacier and the 2014 glacial lake outburst flood in the Trans-Himalayan region of Ladakh, India

This study assessed spatiotemporal changes at Gya Glacier, the associated development of a proglacial lake, and reconstructed the 2014 outburst flood that struck Gya Village in the Trans-Himalayan region of Ladakh, India. This study analyzed and for the first time modeled a Glacial Lake Outburst Flood (GLOF) event in the Trans-Himalayan region of Ladakh. Glacier and glacial lakes changes were quantified using remote sensing data supplemented with field observations. Glacier ice-thickness and glacier-bed overdeepenings were modeled using a shear-stress based model, GlabTop (Glacier-bed Topography). The reconstruction of the 2014 GLOF and the potential hazard assessment of Gya Lake were carried out using the hydrodynamic model HEC-RAS; results were validated against ground-collected data. Temporal evaluation of satellite data revealed a 45.6% loss in the total glacier area between 1969 and 2019. The earliest snow-free image available for the region shows that a proglacial lake existed as early as 1969 with an area of 3.06 ha. The lake has expanded to ~11 ha in 2019. Results from the GlabTop model suggest that the lake could grow further up to 12 ha in the future. Field-based geomorphic indicators suggest that the 2014 GLOF event resulted from a piping failure of the frontal moraine destroying numerous agricultural fields, some buildings, downstream infrastructure, and eroded natural channel embankments. The reconstruction of the event revealed that 25% of the lake waters drained out with a peak discharge of 470 m³s^-1, inundating an area of ~4 km² around Gya Village. However, a complete breaching of the terminal moraine could result in an event that would be 5.5 times larger than the 2014 GLOF. Therefore, this study could be useful not only in planning disaster-resilient infrastructure around proglacial lake environments in the cold-arid Ladakh but also in framing mitigation plans to reduce risk for vulnerable downstream communities.

Modeling of Extreme Hydrological Events in the Baksan River Basin, the Central Caucasus, Russia

High mountain areas are prone to extreme hydrological events, and their study is especially important in the context of ongoing intensive deglaciation. In this research, a model “chain” consisting of a hydrodynamic model and a runoff formation model is adopted to simulate a glacier lake outburst flood (GLOF) from Bashkara Lake (the Central Caucasus, Russia) and its effect on downstream. In addition to an actual GLOF event that occurred on 1 September 2017 and led to casualties and significant destruction in the Adylsu and Baksan Rivers valleys, possible scenarios for the re-outburst of the lake are considered. The hydrographs of the outburst and the downstream movement of the flood wave along the Adylsu River valley are estimated using STREAM_2D two-dimensional hydrodynamic model. The water discharges in the entire river network of the Baksan River are assessed using the ECOMAG (ECOlogical Model for Applied Geophysics) runoff formation model. The output flood hydrograph from the hydrodynamic model is set as additional input into the Baksan River runoff formation model in the upper reaches of the Adylsu River. As a result of the simulations, estimates for the contribution of GLOFs and precipitation to an increase in peak discharge along the Baksan River were obtained. The actual outburst flood contributed 45% and precipitation 30% to the peak flow in the Baksan River at the mouth of the Adylsu River (10 km from the outburst site). In Tyrnyauz (40 km from the outburst site), the contributions of the outburst flood and precipitation were equal and, in Zayukovo (70 km from the outburst site), the outburst flood contributed only 20% to the peak flow, whereas precipitation contributed 44%. Similar calculations were made for future potential re-outburst flood, taking into account climatic changes with an increase in air temperatures of 2 °C, an increase in precipitation of 10% in winter and a decrease of 10% in summer. The maximum discharge of the re-outburst flood in the Adylsu River mouth, according to model estimations, will be approximately three times less than the discharge of the actual outburst on 1 September 2017 and can contribute up to 18% of the peak discharge in the Baksan River at the confluence.

Effect of Extreme Climate Events on Lake Ecosystems

The Earth is facing a major change in climate due to ongoing global warming [...]

Maker Buoy Variants for Water Level Monitoring and Tracking Drifting Objects in Remote Areas of Greenland

Meltwater runoff from the Greenland Ice Sheet changes water levels in glacial lakes and can lead to glacial lake outburst flooding (GLOF) events that threaten lives and property. Icebergs produced at Greenland's marine terminating glaciers drift into Baffin Bay and the North Atlantic, where they can threaten shipping and offshore installations. Thus, monitoring glacial lake water levels and the drift of icebergs can enhance safety and aid in the scientific studies of glacial hydrology and iceberg-ocean interactions. The Maker Buoy was originally designed as a low-cost and open source sensor to monitor surface ocean currents. The open source framework, low-cost components, rugged construction and affordable satellite data transmission capabilities make it easy to customize for environmental monitoring in remote areas and under harsh conditions. Here, we present two such Maker Buoy variants that were developed to monitor water level in an ice-infested glacial lake in southern Greenland and to track drifting icebergs and moorings in the Vaigat Strait (Northwest Greenland). We describe the construction of each design variant, methods to access data in the field without an internet connection, and deployments in Greenland in summer 2019. The successful deployments of each Maker Buoy variant suggest that they may also be useful in operational iceberg management strategies and in GLOF monitoring programs.