Journal of Geotechnical Studies

Analysis of Glacial Lake Outburst Flood Susceptibility in the Saraswati River Basin, Uttarakhand, India, Using GIS and HEC-HMS Modeling

Vanshika Yadav — 2026-05-25

Glacial lake outburst floods (GLOFs) are among the most destructive natural hazards in mountain environments. Rising temperatures have accelerated glacier retreat, leading to the growth of unstable glacial lakes, particularly in the Himalayan region. Sudden failure of these lakes can generate intense downstream flooding, severe erosion, and damage to settlements and infrastructure. The present study evaluates flood susceptibility in the Saraswati River Basin, Chamoli District, Uttarakhand, India, through the combined use of geographic information systems (GIS) and HEC-HMS hydrological modeling. Digital elevation model data were applied to delineate the watershed, generate drainage characteristics, and assess terrain influence on runoff behavior. A storm event dated 16 June 2013 was selected for rainfall-runoff simulation. The model employed the Initial and Constant Loss method, Clark Unit Hydrograph transform, and the recession baseflow approach. The computed watershed area was 590.59 km². Results showed total rainfall of 246 mm, excess rainfall of 215.10 mm, and a peak discharge of 3692.2 m³/s at 13:00 h. The study indicates that steep slopes, limited infiltration, and rapid flow concentration make the basin highly prone to flash flooding. If a GLOF event coincides with extreme rainfall, hazard intensity may significantly increase. The methodology demonstrates the usefulness of GIS-based hydrological tools for preliminary risk evaluation in remote mountainous catchments.

Change Detection using NDVI in Some Selected Villages of Pendurthi Mandal as an Aid to Augment Crop Production, Visakhapatnam District, Andhra Pradesh, India: A Geospatial Study

Dr. Usha Chirala — 2026-06-23

With global warming and the overexploitation of resources driven by population growth and other challenges, several research projects are underway worldwide to restore balance between current resource use and consumption. In the Visakhapatnam district, Andhra Pradesh, India, in Pendurthi mandal, a few villages have been selected as exemplary models to promote sustainable green initiatives that boost crop production and provide adequate fodder for livestock using NDVI. NDVI of the study area was calculated in ArcGIS 10.0 using Sentinel 2015 and Sentinel 2025 data. Change detection is used to present the study's findings, which can aid in planning and construction of sustainable villages to boost vegetation in the study area. NDVI plays a crucial role in distinguishing healthy vegetation from areas lacking vitality, as it reflects the unique spectral characteristics of thriving plants. This technology enables effective monitoring of plant growth and health, including stressed or damaged areas. Its applications are vast, encompassing crop monitoring, vegetation health assessment, precision agriculture, land-use and land-cover mapping, and ecosystem health monitoring. The selected village communities are currently undergoing considerable human-driven changes. The details from this study can serve as a foundation for planning and developing sustainable village practices.

Predictive Analytics for Environmental and Oceanographic Systems

Raut. A — 2026-07-01

Fishing zone prediction is a deal because it helps fishers make a living and plan their trips better. Old ways of finding fishing spots use fixed rules, but they do not work well because ocean conditions keep changing. These changes affect where fish gather. This paper talks about a way to predict where fish will be. It uses data to forecast and map high-density fishing zones every week. The study checks two methods: the baseline ARIMA model and the Sequential Environmental Gradient Network (SEGN) model. The new workflow has four steps: Cleaning Data, Training Models, Predicting fishing zones, and Checking Errors. The results show SEGN works better; ARIMA is still useful for simple predictions. The study also finds that finding the balance between being precise and catching all the fish is tricky. Next steps include using ocean data, improving predictions, and helping fishers make better decisions. The goal is to support fishers and make their operations better. The study aims to make fishing more efficient and effective. It wants to help fishers catch fish while being sustainable. Fishing zone prediction can make a difference in their lives.