Automatic Monitoring Alarm Method of Dammed Lake Based on Hybrid Segmentation Algorithm.

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system.

Assessment of potential present and future glacial lake outburst flood hazard in the Hunza valley: A case study of Shisper and Mochowar glacier.

In Himalayas, new glacial lake formation and expansion of existing glacial lakes have occurred as a consequence of the increasing temperature and glacier recession. These lakes have the potential to release catastrophic volumes of water and trigger a glacial lake outburst flood (GLOF). GLOFs can cause devastating downstream impacts including loss of lives, damage to infrastructure, and economic loss. The risk associated with GLOFs is evident in the case of the Mochowar and the Shisper glaciers of the Hunza valley in the Karakoram ranges. The present study is divided in two parts: 1) investigation of the recent GLOF event from the Shisper glacier ice-dammed lake on 7th May 2022. 2) identification of an overdeepening site for future lake formation at the Mochowar glacier and its future GLOF susceptibility; We used the Himalayan Glacier Thickness Mapper (HIGTHIM) to calculate the thickness of Mochowar glacier and identify an overdeepening site at its terminus. This site could host a glacial lake of area 0.22 km2 and a mean depth of 58.97 m that can release a potential flood volume leading to cascading effects with the Shisper ice-dammed lake that further increases the GLOF susceptibility. The GLOF susceptibility of this future lake was determined to be high based on a multi-criterion decision analysis. The recent GLOF event of 7th May 2022 occurred from the Shisper glacier ice-dammed lake. We applied a 2D hydrodynamic model for investigating this GLOF episode and estimated a release volume of 6.23 × 106 m3, with a modelled peak discharge of approximately 1505 m3 s-1.

Detecting landslide-dammed lakes on Sentinel-2 imagery and monitoring their spatio-temporal evolution following the Kaikoura earthquake in New Zealand.

Landslide-dammed lakes pose a risk for upriver and downriver communities and infrastructure. The 2016 Kaikoura earthquake affected the northeastern region of the South Island in New Zealand, triggering numerous landslides that dammed river courses leading to the formation of hundreds of dammed lakes. Detecting and monitoring landslide-dammed lakes is important for disaster management. Satellite remote sensing imagery is often complementary to field acquisitions to obtain an overview of large and remote areas and thus can be exploited to monitor landslide-dammed lakes. Yet, the strengths and limitations of freely available multi-temporal satellite imagery for landslide-dammed lake assessment remain largely unexplored. This study aimed at automatically mapping landslide-dammed lakes caused by the 2016 Kaikoura earthquake and monitoring their evolution using time series of Sentinel-2 imagery and the computing capabilities of the Google Earth Engine. Our approach combined dynamic thresholding, change detection, and connected component analysis. Landslide-dammed lakes larger than 300 m2 and located on relatively flat terrain were detected with reasonable accuracy, while lakes located in steeply incised valleys were detected less frequently. Despite the challenging topographical and environmental characteristics of the study area, we were able to detect landslide-dammed lake candidates at a regional scale. Temporal monitoring of the evolution of the landslide-dammed lake area revealed four distinct patterns: 1) constant, 2) increasing, 3) decreasing, and 4) variable. Our approach contributes to the understanding of the utility and limitations of temporal and spatial monitoring of landslide-dammed lakes, their potential cascading hazards and their interactions.

Expansion of Shishper Glacier lake and recent glacier lake outburst flood (GLOF), Gilgit-Baltistan, Pakistan.

Shishper lake is an ice-dammed lake in northern Pakistan that has drained twice within one (1) year. The parameters evaluated in this paper are the lake's area, volume, peak discharge, and its outburst events using various satellite images from November 2018 to June 2020. Based on satellite imagery and empirical approaches, the lake formed in November 2018 and reached a maximum of 0.34 km2 till its first breach that occurred on 22 June 2019. Since June 2019, the lake drained till September 2019. After that, the flow was blocked again, and the lake expanded to an area of 0.27 km2 till its second outburst event that happened on May 29, 2020. Eight cross-sectional profiles of Hassanabad ravine are generated based on peak discharge in the lake's rapid outburst. The results indicate that, the peak discharge for both 2019 and 2020 was more than 4500 m3 s-1. Delineation of downstream Hassanabad ravine shows that more than 1000 buildings and 2000+ population is prone to flood. However, the lake drain twice steadily, but it has a high potential to cause severe damages if it bursts abruptly.