Glacier retreat results in the formation and expansion, and sometimes outburst, of moraine‐dammed lakes worldwide. Sudden outburst floods from such lakes have caused enormous damage to settlements and infrastructure located downstream. Such lakes located in the Himalayan region are highly prone to outburst floods due to climatic conditions and geotectonic settings. In this study, multi‐temporal Landsat images from 2002–2014, digital elevation models (DEMs), geomorphic analysis and modelling were used to assess the changes in glacial lakes and the outburst susceptibility of moraine‐dammed lakes in the Chandra–Bhaga basin of the north‐western Indian Himalaya. An inventory of lakes was developed using satellite data, thematic maps and ground‐based investigations for the Chandra–Bhaga basin. The total area of all glacial lakes (size >5000 m2) increased by 47% from 2002 to 2014, with a pronounced increase of 57% for moraine‐dammed lakes. Sixteen moraine‐dammed lakes were identified and assessed for outburst susceptibility using the analytic hierarchy process (AHP). Forty‐one reported glacial lake outburst flood (GLOF) events from moraine‐dammed lakes in Himalayan regions were analysed, culminating in the identification of 11 critical factors for assessing outburst susceptibility using the AHP, including those related to the lake area and change, surrounding terrain characteristics, dam geometry, regional seismicity and rainfall history. The past three GLOF events in the Himalayan region were used to validate the method and to classify moraine‐dammed lakes as having very high, high, medium or low outburst susceptibility. Eight lakes classified as very high and high outburst susceptibility should be further investigated in detail. The proposed AHP‐based approach is suitable for first‐order identification of critical lakes for prioritising future detailed investigation and monitoring of moraine‐dammed glacial lakes in the Himalayan region. Copyright © 2017 John Wiley & Sons, Ltd.

Climatic change-induced glacier recession has been accompanied by formation and growth of proglacial lakes in the Himalayan region, which pose an emerging significant threat to the downstream communities/ settlements in the form of outburst floods. To understand spatiotemporal evolution patterns, sources and driving mechanism of formation and expansion of glacial lakes, a temporal inventory of glacial lakes (area > 2000 m 2 ) in Chandra basin has been developed from 2000 to 2014 using IRS LISS-III images. From 2000 to 2014, the total number of glacial lakes in Chandra basin increased from 28 to 46 and area expanded from 1.91 § 0.24 km 2 to 3.26 § 0.24 km 2 . Glacier recession and increased glacier melt runoff due to climate warming led to the formation and expansion of glacial lakes in space vacated by glacier recession. The increase in number and area of ice-dammed lakes at higher elevations confirms the continued glacier retreat in the basin. Lakes in contact or in the proximity of the mother glacier exhibit higher growth and formation rate. The accelerated growth of glacial lakes has resulted in increased hazard and damage potential of glacial lake outburst floods in Chandra basin. Seven potentially dangerous lakes are identified and analysed qualitatively for outburst probability.

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Landslide susceptibility mapping is a crucial step in comprehensive landslide risk management. The purpose of the present study is to analyze the landslide susceptibility of Mandi district, Himachal Pradesh, India, based on optimum feature selection and hybrid integration of the Shannon entropy (SE) model with random forest (RF) and support vector machine (SVM) models. An inventory of 1723 rainfall-induced landslides was generated and randomly selected for training (1199; 70%) and validation (524; 30%) purposes. A set of 14 relevant factors was selected and checked for multicollinearity. These factors were first ranked using Information Gain and Chi-square feature ranking algorithms. Furthermore, Wilcoxon Signed Rank Test and One-Sample T-Test were applied to check their statistical significance. An optimum subset of 11 landslide causative factors was then used for generating landslide susceptibility maps (LSM) using hybrid SE-RF and SE-SVM models. These LSM’s were validated and compared using receiver operating characteristic (ROC) curves and performance matrices. The SE-RF performed better with training and validation accuracies of 96.93% and 88.94%, respectively, compared with the SE-SVM model with training and validation accuracies of 94.05% and 82.4%, respectively. The prediction matrices also confirmed that the SE-RF model is better and is recommended for the landslide susceptibility analysis of similar mountainous regions worldwide.

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