High-mountain glaciers can be covered with varying degrees of debris. Debris over glaciers (supraglacial debris) significantly alter glacier melt, velocity, ice geometry, and, thus, the overall response of glaciers towards climate change. The accumulated supraglacial debris impedes the automated delineation of glacier extent owing to its similar reflectance properties with surrounding periglacial debris (debris aside the glaciated area). Here, we propose an automated scheme for supraglacial debris mapping using a synergistic approach of deep learning and multisource remote sensing data. A combination of multisource remote sensing data (visible, near-infrared, shortwave infrared, thermal infrared, microwave, elevation, and surface slope) is used as input to a fully connected feed-forward deep neural network (i.e., deep artificial neural network). The presented deep neural network is designed by choosing the optimum number and size of hidden layers using the hit and trial method. The deep neural network is trained over eight sites spread across the Himalayas and tested over three sites in the Karakoram region. Our results show 96.3% accuracy of the model over test data. The robustness of the proposed scheme is tested over 900 km² and 1710 km² of glacierized regions, representing a high degree of landscape heterogeneity. The study provides proof of the concept that deep neural networks can potentially automate the debris-covered glacier mapping using multisource remote sensing data.

Hypsometric analysis describes the elevation distribution across an area of land surface. It is an important tool to assess and compare the geomorphic evolution of various landforms irrespective of the factor that may be responsible for it. The major factors governing the evolution of landscape are tectonics and/or climate and the variation in lithology. The present study takes into consideration the watersheds developed over actively deforming Mohand anticlinal ridge in the frontal part of NW Himalaya. The hypsometric analysis has been used as a morphometric parameter, i.e. hypsometric integral, to deduce its relationship with the area of watersheds. Statistical analysis of these parameters has been carried out by classifying them into different classes based on the natural breaks method. This brings out strong relationships for hypsometric integral classes and area classes with the number of watersheds in respective classes and the total area occupied by respective hypsometric and area classes. It has also been found that stronger relationships exist for watersheds on the southern flank as compared to watersheds of the northern flank. It also highlights the presence of an anomalous watershed on the northern flank that is possibly responsible for the weak statistical relationships on the northern flank. Removal of this anomalous watershed always brings out much stronger relationships for the northern flank. The anomalous watershed has been directly attributed to the difference in geologic structure as it is spatially related to the presence of the Bhimgoda Back Thrust (BBT) present in the area. The results are awe inspiring and very promising as they indicate some statistically strong relationships among the hypsometric integral and area of watersheds that are not apparent in the spatial distribution of these parameters, especially in actively deforming areas.

Neotectonism in the Sub-Himalayan belt is not new. Moreover, the word ‘Sub-Himalaya’ is almost synonymous with ‘neotectonic activity’. In the present paper, we report some of the most convincing geomorphic and structural evidences of neotectonic activity from the Sub-Himalayan belt in the Nahan Salient. The geomorphic evidences mainly include the four geomorphic surfaces identified from the transverse topographic profiles drawn parallel to the Himalayan front. These surfaces are commonly covered with terrace deposits that are tilted as well as faulted at a number of places. A number of faults, directly observable in the field, are normal in nature and they are oriented at high angles to the Himalayan Frontal Thrust (HFT). These faults are similar to the E-W extension in southern Tibet in response to the oblique convergence of India at ~N20oE in the NW Himalaya. They are attributable to the kinematics of neotectonic compression along the HFT, the frontal ramp-oblique ramp-frontal ramp geometry of the thrust fault and related adjustments.

The Afghanistan earthquake of 21 June 2022 ruptured a ~10 km-long fault segment in the North Waziristan–Bannu fault system (NWBFS) located towards the north of the Katawaz Basin. The earthquake was shallow and reportedly caused widespread devastation. In this article, we investigated the long-term, i.e., geological and geomorphological, evidence of deformation along the earthquake segment. For comparison, we also studied the short-term space geodetic and remote sensing results documenting a visible offset between the fault traces. Focusing on the fault modelling and on the published results, it is thus clear that the earthquake rupture did not reach the surface; instead, it stopped in the shallow sub-surface at ~1 km depth. Moreover, the InSAR analyses show some technical issues, such as coherence loss, etc., likely due to severe ground-shaking leaving some gaps in the results; geological and geomorphological evidence complemented this information. As an outcome of this research, we confirmed that InSAR results could generally capture the overall fault geometry at depth, even in cases of blind faulting, whereas the detailed geometry of the tectonic structure, in this case with a right stepping en-echelon pattern, could be successfully captured by combining it with geological and geomorphological approaches and optical remote sensing observations. Accordingly, the right stepping fault generates a restraining bend in the dominantly left-lateral shear zone. Therefore, such fault stepovers are capable of localizing strain and could act as loci for seismic ruptures, bearing strong implications for the seismic hazard assessment of the region, as well as of other strike-slip fault zones.