Sujata Biswas

This study utilizes Geographic Information System (GIS) based multi-criteria decision analysis to delineate avalanche susceptibility zones in the Upper Alaknanda River Basin of the Western Indian Himalaya. The Analytical Hierarchy Process (AHP) was applied to rigorously assess the relative contribution of eleven critical parameters influencing avalanche initiation and propagation. The selected terrain and environmental factors include slope, aspect, elevation, curvature, topographic roughness index (TRI), topographic wetness index (TWI), Length Slope (LS) factor, land use/land cover, soil, maximum temperature, and Wind Exposition Index (WEI). The terrain-derived parameters were generated from Shuttle Radar Topography Mission (SRTM) data. The Landsat 8 satellite imagery was utilized to evaluate the land use and snowcover information. The database was further complemented with ancillary soil and climatic datasets. A pairwise comparison matrix was constructed to derive weights for each parameter based on expert opinion. The weights were subsequently integrated within the AHP framework to produce an avalanche susceptibility map. The resulting classification reveals that 10% of area under low susceptibility, 56% under moderate susceptibility, 34% under high susceptibility and negligible portion of the basin falls under very low and very high susceptibility. This spatial delineation provides an essential indication base for identifying the most vulnerable zones thereby supporting early-warning systems along with informed land-use planning and hazard mitigation strategies. Furthermore, the susceptibility map can assist in designing structural interventions and implementing controlled avalanche releases to reduce natural hazard risks at selected sites.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson’s lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

River stage prediction is indispensably a challenging task in flood-prone river basins to disseminate accurate early warning in advance. In this study, multivariate wavelet-based long short-term memory (WLSTM) models have been developed to predict river stage at six gauging stations of the Teesta River basin in India for 1, 3, and 5-day lead time, the comparison of which has been done with long short-term memory (LSTM) models. Various combinations of wavelet decomposed components were utilized to form different sub-series that were fed as input in WLSTM models. In terms of statistical indicators, both the models yielded exceptionally good results, but the root-mean-square error values of the WLSTM model for 1- and 3-day lead time were minimal compared to the LSTM model. However, the accuracy of the LSTM model in longer lead time prediction is noticeable. Specifically, the WLSTM model predicted the peak stage values more precisely compared to the LSTM model, indicating the potential of wavelet analysis to capture the variations and periodicities of the data by removing the noise. Though the WLSTM model marginally outperformed the LSTM model in prediction accuracy, the results highlight both models as feasible alternatives for longer lead time water level prediction.

Droughts frequently occurring in India have significant societal, economic, and environmental effects. The lack of direct measurements of groundwater in location and time hinders quantitative methods to analyse the intricate nature of groundwater drought. This work used the datasets derived from the Gravity and Climate Experiment (GRACE and GRACE-FO) and Global Land Data Assimilation System (GLDAS) to extensively analyse Groundwater Storage changes in the Lower Gangetic Basin (LGB) using unique hydrological parameters between the years 2003 and 2022. The analysis highlights that the GRACE-derived terrestrial water storage anomaly in the LGB decreased significantly (-12.12 mm/yr), and the amount of Groundwater Storage Anomaly (GWSA) decreased similarly (-10.80 mm/yr), while in the GRACE-FO period, a positive trend has been noticed in TWSA (33.96 mm/yr) and GWSA (64.8 mm/yr) respectively. A drought indicator called the GRACE-derived groundwater drought index (GGDI) has been computed for the entire LGB region. A traditional drought study viz. Standardised Precipitation Index (SPI) was performed over LGB to justify the results of the GGDI. The results from GGDI study effectively matched the periods of significant drought occurrences with the 12-month SPI time series. From the GGDI, this study examined groundwater drought’s spatial distribution, temporal evolution, and trend (Modified Mann Kendall trend) aspects. According to research findings, the LGB experienced three major drought periods between 2009–2010, 2019 (moderate), and 2015–2016 (severe). The study offers reliable quantitative data on the evolution of GRACE-derived groundwater drought, which may add a new perspective to additional drought research in the densely populated study area, which depends majorly on agriculture, livestock and less skilled water-intensive industries such as leather and textile industries in a sub-tropical climate. This paradigm incorporates changes in groundwater resources caused by human activities and climate change, paving the way for measuring progress towards sustainable use and water security.