Catastrophic Ice-Debris Flow in the Rishiganga River, Chamoli, Uttarakhand (India)

A catastrophic flood occurred on 7 February 2021 around 10:30 AM (local time) in the Rishiganga River, which has been attributed to a rockslide in the upper reach of the Raunthi River. The Resourcesat 2 LISS IV (8 February 2021) and CNES Airbus satellite imagery (9 February 2021) clearly show the location of displaced materials. The solar radiation observed was higher than normal by 10% and 25% on 6 and 7 February 2021, respectively, however, the temperature shows up to 34% changes. These conditions are responsible for the sudden change in instability in glacier blocks causing deadly rock-ice slides that led to the collapse of the hanging glacier as a wedge failure. The displaced materials mixed with ice, snow, and debris caused catastrophic floods downstream within no time that destroyed critical infrastructure and killed human lives. The hydrodynamic modelling (HEC-RAS software) shows mean flow velocity up to 22.4 ± 8.6 m/s with an average depth of 16.3 ± 6.5 m that caused deadly devastation in the source region and along the rivers due to the flow of water in the valley

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya

The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to Ghuttu highway corridors in the Garhwal Himalaya. A landslide inventory has been prepared through field surveys and LISS-IV and Landsat 8 satellite images. The datasets of 85 landslides were categorised into training and test sets. In this study 11 landslide conditioning variables were used that are; altitude, slope angle, aspect, plan curvature, topographic wetness index, normalised difference vegetation index (NDVI), land use, soil texture, distance to rivers, distance to faults, and distance to the road. The result produced using MaxEnt and SVM model were subsequently validated using receiver operating characteristics curve (ROC) with test sets of landslide dataset. Both the models have good prediction capabilities. MaxEnt has ROC value of 0.78 while SVM has the highest prediction rate of 0.85

A review of artificial intelligence methods for predicting gravity dam seepage, challenges and way-out

Seepage is the phenomenon of water infiltrating through a gravity dam's foundation, causing erosion and weakening the dam's construction over time. If not properly managed, this can eventually lead to the dam's catastrophic failure, posing a significant danger to public safety and the environment. As a result, precise seepage prediction in gravity dams is essential for ensuring their safety and stability. This review paper looks at the use of artificial intelligence (AI) techniques for predicting seepage in gravity dams, as well as the challenges and possible solutions. The paper identifies and suggests potential solutions to the challenges connected with using AI for seepage prediction, such as data quality and model interpretability. The paper also covers future research paths, such as the creation of advanced machine learning algorithms and the improvement of data collection and processing. Overall, this review gives insight on the current state of the art in using AI to predict gravity dam seepage and recommends methods to improve the accuracy and reliability of such models. HIGHLIGHTS AI methods for predicting gravity dam seepage reviewed, with challenges and solutions.; The review provides an overview of using AI for seepage prediction in gravity dams.; AI challenges addressed with suggested solutions for improved seepage prediction.; Standardizing data collection and improving quality reduces errors in prediction models.; Insights for dam safety practitioners, improving seepage.

Land Use Dynamics and Impact on Regional Climate Post-Tehri Dam in the Bhilangana Basin, Garhwal Himalaya

Land use and land cover (LULC) changes are a dynamic process determined by natural factors as well as the degree of human interaction in spatial and temporal perspectives. The present study focuses on analysing the LULC changes in the Bhilangana basin post-Tehri dam construction in the Garhwal Himalaya. Landsat series satellite images were used for three time periods to quantify spatial and temporal changes in the LULC using unsupervised classification techniques. The calculations of the areal coverage and change detection were carried out using the ArcGIS 10.3 software. The study finds that LULC changes were observed in the area surrounding the Tehri reservoir. The area under forest cover decreased by 54.71 km2, which is −5.7% of the geographical area, followed by agricultural land by 6.06 km2 (−0.4%) and scrubland and grass cover by 4.23 km2 (−0.28%) during the decade 2000 to 2010. Gradually, due to compensatory afforestation, forest cover increased by 5.65% in the period 2010–2020. A significant relationship with climatic variability is also established with LULC change in the region. The presence of a large water surface at a high altitude modified the albedo and air temperature and increased the atmospheric humidity and precipitation pattern. This study would be vital in understanding the climatic variability in the Himalayas and its impact on the community, environment and climate

Decadal terminus position changes and ice thickness measurement of Menthosa Glacier in Lahaul region of North-Western Himalaya

Glacier ice-thickness measurement and distribution is one of the essential variables to assess present status of glacier-water equivalent and its volumetric reserve as well as to model the future glacier dynamics under the climate changing scenario. Yet, substantial gaps in ice thickness information exist for the Himalayan glaciers. The present study provides a long-term assessment (1965–2016) of recessional and area change patterns, as well as the detailed field-based (2016–2017) Ground Penetrating Radar(GPR), derived ice-thickness measurement of the Menthosa Glacier, Lahaul Himalaya. Additionally, the study examines whether the modelled ice thickness from remote sensing data is consistent with the field-based GPR measurement and how can it be improved. The extensive field surveys coupled with the multi-temporal high (Corona KH-4A) to medium resolution (Landsat Enhanced Thematic Mapper+ (ETM+)/Operational Land Imager (OLI), Sentinel 2A-Multispectral Instrument (MSI)) remote sensing data and cross-sectional GPR surveyed profile measurements have been used to examine past half a century (1965–2016) glacier fluctuation and the recent ice-thickness estimations, respectively. The results show that the Menthosa Glacier receded by 301.5 ± 19.2 m during the past half a century (1965–2016) with an average annual retreat of 5.9 ± 0.4 m a−1, whereas glacier lost 0.09 km2 ice in the frontal section. Field measurement over the past one decade (2006–2017) also conforms to a continuous recessional pattern and substantial glacier degeneration particularly the extensive surface lowering and significant appearance of ice-cliffs in the ablation and lateral zones over this period. The GPR measurements (2017) show the minimum glacier ice thickness of 24 meters at 4691 m a.s.l. (in the lower part of ablation area) and maximum glacier ice thickness of 55 meters measured at 4758 m a.s.l. (in the upper left-side tributary part of ablation area). Moreover, the modelled ice thickness derived from remotely sensed data is having Root Mean Square Error (RMSE) between 38 to 72 ± 10 m as compared with GPR measured ice thickness

Water budgeting in conservation agriculture-based sub-surface drip irrigation using HYDRUS-2D in rice under annual rotation with wheat in Western Indo-Gangetic Plains

Rapidly depleting groundwater in western Indo-Gangetic Plains (IGP) is a major threat to food security in South Asia. Conventional tillage-based and flood irrigated puddled transplanted rice (PTR) is a major contributor to faster depleting aquifers. Urgent actions are therefore warranted to develop alternate productive, profitable, water and N-use efficient rice production practices for rice-wheat (RW) cropping system. Conservation agriculture (CA) based direct-seeded rice (DSR) has been advocated as a potential alternative to PTR. Further, bundling CA with precision water and N management using sub-surface drip irrigation (SSD) has demonstrated significant benefits over CA-based flood irrigation (FI). However, for more efficient use of water, water budgeting is needed which is a challenging task as it requires expensive tools, and time, and efforts. Information about complete water budgeting in high water demanding crops like rice grown under CA-based SSD, FI, and PTR are not available. We deployed HYDRUS-2D model for estimating water budgeting of rice under CA+ (CA-based SSD), CA-based FI, and PTR-based systems. The objective of our study was to calibrate and validate the HYDRUS-2D model to simulate water dynamics in rice grown under CA-based SSD and FI compared to PTR and to design water and N- use efficient production practices for rice cultivation in western IGP. Five treatments comprised of PTR+FI with 120 kg N ha−1 (PTR), zero-till direct-seeded rice (ZTDSR)+FI without N (ZT-N0), ZTDSR+FI with 100% of N recommended dose (ZT-N100), ZTDSR+SSD without N (SSD-N0), and ZTDSR+SSD with 100% of N-recommended dose (SSD-N100) were compared. The result showed that the HYDRUS-2D model satisfactorily simulated the soil moisture content with low root mean square error (RMSE) (0.014–0.028), high coefficient of determination (74–92%), and model efficiency (59–87%) during the simulation period (80 days: 35–114 days after sowing). The highest grain yield (7.18 t ha−1) was observed in the PTR treatment, which was statistically similar to SSD-N100 (6.54 t ha−1) and significantly higher than ZT-N100. During the simulation period, PTR plots received 131.7 cm of water (rainfall + irrigation) which was 27.3% and 50.1% higher than ZT-N100 and SSD-N100 plots, respectively. Out of the cumulative water applied, PTR transpired only 18.4% of applied water, compared to 24% in ZT-N100 and 36.3% in SSD-N100. Interestingly, SSD-N100 plots recorded 20.6% and 23.5% less evaporative loss and 45.0% and 66.0% less water loss by deep drainage than ZT-N100 and PTR, respectively. Thus, conversion to CA+ system with 100% N-recommended dose saved 50.1% and 31.3% of water, and consequently attained 2.0 and 1.45-times higher biomass water use efficiency than PTR and ZT-N100, respectively. Based on the results, CA-based SSD could be recommended for precise utilization of water and to curtails the unproductive water loss components such as evaporation and deep drainage