https://dspace.iiti.ac.in:8080/jspui/handle/123456789/9538 Tue, 12 May 2026 17:12:10 GMT 2026-05-12T17:12:10Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17741 Title: Snow and glacier melt contributions and SLA-ELA relationship in himalayan basins Authors: Vinze, Parul Abstract: Snow and glacier meltwater are critical hydrological components in the glacierized and snow-covered basins of the Himalaya-Karakoram (HK), yet their accurate quantification remains challenging due to limited in-situ observations in this remote and rugged terrain. This thesis aims to advance the understanding of meltwater dynamics, improve modelling approaches, and enhance glacier monitoring techniques using remote sensing to better assess hydrological responses of glacierized basins to climatic variability in the region. The thesis focuses on three glacierized basins situated in distinct climatic regimes of HK. In the western Himalaya, the Snowmelt Runoff Model (SRM) was applied in the Chandra-Bhaga Basin, using a data-rich reference catchment of Chhota Shigri Glacier to constrain key parameters from extensive field observations, while the remaining parameters were calibrated against observed discharge. Daily discharge simulations for 2003–2018 indicated that flow was primarily controlled by summer temperature in the Chhota Shigri Catchment and by summer SCA at the basin scale. Although parameters calibrated in the reference catchment produced good results at the catchment scale, their direct application to the basin scale resulted in substantial overestimation of discharge, indicating that the parameters are not transferable even within the same basin. In the central Himalaya, the long-term melt contributions and their climatic controls in the Gangotri Glacier System (GGS) were examined over 1980–2020, by applying a high-resolution glaciohydrological model Spatial Processes in Hydrology (SPHY) forced with Indian Monsoon Data Assimilation and Analysis reanalysis data. Wed, 07 Jan 2026 00:00:00 GMT https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17741 2026-01-07T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17563 Title: Assessment of global wetlands using a conceptual framework of entropy and climate extremes Authors: Kumar, Nakka Naveen Abstract: Wetlands are highly sensitive ecosystems whose stability is strongly influenced by rainfall variability and extremes. While previous studies have examined wetland shrinkage and precipitation extremes separately, this study uniquely integrates entropy-based metrics, Standardized Variability Index using Apportionment Entropy (SVIAE) and Marginal Entropy (SVIME), with 12 Standardized extreme precipitation indices to assess hydroclimatic risk across 2,490 Ramsar wetlands worldwide. Using high-resolution precipitation datasets and CMIP6 climate projections, we analysed historical (1951 - 2024) and future (2025 - 2100) scenarios under SSP 245 and SSP 585 pathways. Results reveal a clear rise in monthly rainfall variability (SVIAE), especially under SSP 585, in areas including Africa, South Asia, and West Asia; in contrast, yearly variability (SVIME) remains stable, masking critical intra-annual instability. Extreme indices (e.g., R95pTOT, Rx5) show significant intensification under SSP 585, with more than 40% of wetlands falling into high-risk zones for unpredictability and rainfall intensity. Arid wetlands, despite low rainfall, face increasing flash-flood risks due to more intense and erratic rainfall events. These findings emphasize that increasing rainfall does not guarantee stability; rather, the combination of variability and extremes amplifies wetland vulnerability. This study provides a novel, integrated framework for identifying climate-sensitive wetlands and guiding adaptive conservation planning. Keywords: Ramsar wetlands, Entropy, Rainfall variability, Precipitation extremes, Climate projections. Mon, 02 Jun 2025 00:00:00 GMT https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17563 2025-06-02T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17562 Title: Enhancing CMIP6 climate predictions through machine learning Authors: Maheep Dev Arun Abstract: This study examines how machine learning (ML) approaches can be applied to enhance the performance of CMIP6 multi-model ensembles (MME) for climate projections across ten vulnerable locations in India. The research evaluates traditional MME methods (simple mean) alongside ML models—Long Short-Term Memory (LSTM), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) to predict precipitation (PCP), maximum and minimum temperature (TMAX and TMIN) under both scenarios SSP245 and SSP585. Key findings include performance improvement of ML models consistently outperforming traditional MME, with LSTM achieving the highest R2 values (e.g., 0.85 for precipitation in Location 3 under SSP245) and reduced RMSE and MAE. SVR and ANN also showed significant improvements, particularly in capturing extreme events and seasonal trends. Temperature Projections show that all methods performed well for temperature variables, with minor variations, as temperature trends exhibit less variability over time. Trend Analysis shows that the MME-mean revealed statistically significant increasing trends in all locations, while LSTM displayed high variability, and ANN provided more stable projections. SVR was less reliable for long-term trend detection. Entropy Analysis: Variability indices (SVIAE and SVIME) indicated that SVR and MME-mean exhibited higher variability, whereas LSTM and ANN produced more consistent results, especially at annual scales. The study concludes that ML-augmented ensembles, particularly LSTM, enhance the accuracy of climate projections, offering valuable insights for climate resilience planning in vulnerable regions. However, traditional MME remains robust for consensus-based trend analysis. These findings contribute to optimizing climate model ensembles for improved decision-making in adaptation strategies. Keywords: Climate Change, Extreme Events, Machine Learning, Climate Variability Mon, 02 Jun 2025 00:00:00 GMT https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17562 2025-06-02T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17561 Title: Understanding the flood-generating mechanisms in a monsoon-dominated river basin Authors: Shukla, Suyash Abstract: This study examines changes in flood magnitude and timing across twelve stream gauging stations in the Narmada River Basin from 1972 to 2024. Flood events were identified using the Annual Maximum Series (AMS) and Peaks-Over-Threshold (POT) approaches. Flood frequency analysis employed best-fit probability distributions, with Weibull and Gamma selected for AMS based on the Akaike Information Criterion (AIC). Circular statistics were used to assess flood timing, while magnitudes were categorized into small, moderate, and large floods based on return periods. Notably, large floods were predominantly concentrated in the Sandia catchment under both AMS and POT approaches. Trend analysis using the Modified Mann-Kendall test revealed a significant decline in peak streamflow at most stations, particularly along the mainstem Narmada in the AMS dataset. The POT approach showed a shift toward delayed flood timing at Sandia, Mohgaon, and Garudeshwar. The mean flood timing across the basin typically occurred from early to mid-August. Hydro-meteorological analysis of major flood events indicated that floods were driven by extreme precipitation, catchment wetness, or a combination of both. A catchment-based assessment of sensitivity to antecedent precipitation buildup (APB) revealed that flood magnitudes in upper and upper- mid reaches of catchments were notably correlated with APB. These findings highlight evolving flood dynamics in the Narmada basin and emphasize the need for incorporating catchment-scale processes and antecedent conditions into regional flood management strategies . Keywords: Flood frequency analysis, Annual Maximum Series, Peaks Over Threshold, Circular Statistics, Trend Analysis, Flood generation mechanisms, Narmada River Basin. Sun, 01 Jun 2025 00:00:00 GMT https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17561 2025-06-01T00:00:00Z