http://117.252.14.250:8080/jspui/handle/123456789/11 Sun, 24 May 2026 04:56:40 GMT 2026-05-24T04:56:40Z http://117.252.14.250:8080/jspui/handle/123456789/8030 Title: Ice-flux divergence and strain rates reveal compressive-flow hotspots on Gangotri glacier Authors: Islam, Anikul; Swarnkar, Somil; Varade, Divyesh; Sinha, Rajiv Abstract: Observation of glacier dynamics are critical for assessing hydro-climate processes in the high altitude regions of the world. This study investigated critical glacier ice parameters such as the ice flux divergence (IFD), surface mass balance (SMB) and the spatial pattern of ice surface deformation through logarithmic strain rate of the Gangotri glacier in the upper Bhagirathi basin using fully distributed models based purely on remote sensing data. The primary input for the investigations are based on the ice-thickness change rate, glacier velocity and the digital elevation model (DEM). The SMB referred to as the sum of the vertical and lateral changes of the glacier was observed to be 0.97 m of ice equivalent(m i.e.) yr 1 , indicating significance mass loss over the study period. The results derive several alignments with other published studies, and reveal key insights on the internal glacier processes through critical parameters such as highly variable longitudinal and shear strain rate indicated by standard deviations exceeding 0.025 yr 1 and 0.013 yr 1 , respectively. These highly variable and negative strain rates indicate significant compressive deformation of the glacier in certain regions of the ablation zone, resulting in ice cliffs and large crevasses that were observed in other studies. Thu, 01 Jan 2026 00:00:00 GMT http://117.252.14.250:8080/jspui/handle/123456789/8030 2026-01-01T00:00:00Z http://117.252.14.250:8080/jspui/handle/123456789/8029 Title: Assessing Glacier Dynamics and Debris-Cover-Controlled Ablation in Svalbard: Insights from Gåsbreen (2003 2023) Authors: Patel, Lavkush Kumar; Głowacki, Oskar; Jain, Vineet; Moskalik, Mateusz Abstract: The Svalbard Archipelago is a highly glaciated region, making it particularly sensitive to progressive climate shifts. This sensitivity is evident in the major changes observed in glacier dynamics. For this reason, both experimental observations and long-term monitoring of glaciers' mass loss in Svalbard are necessary and urgent. Responding to these needs, this study presents a quantitative analysis of melt rate and mass balance of Gåsbreen – a debris-covered land-based glacier located in Hornsund fjord, Spitsbergen. We combined a direct observation on the glacier with satellite data spanning the period 2003–2023 to quantify glacier dynamics and the role of debris cover on glacier ablation. Results covering the study period (2003–2023) indicate a shrinking glacier area (1.47 ±0.07 km 2 ) and a notable retreat of the lower terminus (280 ± 45 m at a rate of 13.3 ma melting doubled in recent years (2016–2023: 0.78 ± 0.07 km 2 a 1 1 ). Notably the rate of ). The surface elevation changes across the glacier ranged within ±2 m a 1 , indicating moderate spatial variability between the ablation and accumulation zones. The computed total ice flux was ~9.9 × 10 5 m yr 1 m 3 yr 1 , corresponding to a mean emergence velocity of 0.09 . Overall, Gåsbreen exhibited a slight positive average mass balance of 0.13 m w.e. a 1 , suggesting that accumulation marginally exceeded ablation during the study period. Approximately 10% of the glacier area was covered by debris during the study period, with a noticeable increase in debris accumulation observed during 2016–2023. Importantly, analysis of ablation rates revealed varying thinning rates across different debris thickness categories, with a significant reduction (25–30%) in ablation rates observed over thicker debris. The study also highlights the projected increase in debris cover and its implications for glacier morphology including the formation of supraglacial ponds and ice cliffs, which are anticipated to contribute significantly to total mass loss. This quantitative assessment provides new insights into the complex interplay between debris thickness and glacier response to the warming climate. Thu, 01 Jan 2026 00:00:00 GMT http://117.252.14.250:8080/jspui/handle/123456789/8029 2026-01-01T00:00:00Z http://117.252.14.250:8080/jspui/handle/123456789/7962 Title: Large-scale modeling of solar water pumps using machine learning Authors: Zuffinetti, Guillaume; Meunier, Simon; Hudelot, Celine; John MacAllister, Donald; Krishan, Gopali; Lutton, Evelyne; Bhattacharya, Prosun; Kitanidis, Peter K.; MacDonald, Alan M. Abstract: Photovoltaic Groundwater Pumping Systems (PVGWPSs) have experienced growing interest, particularly in two key regions. In Africa, they offer a means to improve water availability for millions. In northern India, they could help decarbonize the agricultural sector. However, large-scale deployment must be approached carefully to avoid risks such as groundwater overextraction or widespread unmet irrigation demand. To support informed deployment, a large-scale, physics-based, dynamic PVGWPS model is introduced, that simulates pumping ca pacities of PVGWPS. Given the computational intensity of this model, machine learning-based emulators are explored to replicate its results more efficiently without significant loss in accuracy. The emulator operates in two stages. First, it predicts whether the motor-pump will stop due to water level dropping below the operational threshold. Among the models tested, the Gradient Boosting Classifier model performed best. Second, when no stoppage is predicted, the emulator estimates the pumping capacity of the PVGWPS. Among the models tested for this second task, the Random Forest Regressor gave the most accurate results. Applied to datasets from Africa and the Indo-Gangetic Basin within India, the emulator achieved high accuracy (R 2 ≥ 0.99, NRMSE ≤ 5 %) while reducing computation time by more than a factor of 1500. The emulators thus offer high computational speed and sufficient accuracy to open the way to addressing large-scale dispatch problems, such as the optimal positioning and pre-sizing of PVGWPSs at regional, national, or even continental scales while considering a large number of possible climate scenarios. Coupled with sustainability analyses (not explored in this study), they could serve as powerful upstream decision-support tools for PVGWPSs planning, complementing more detailed, site-specific analyses . Thu, 01 Jan 2026 00:00:00 GMT http://117.252.14.250:8080/jspui/handle/123456789/7962 2026-01-01T00:00:00Z http://117.252.14.250:8080/jspui/handle/123456789/7961 Title: Analysing flood resilience in the anthropocene: Integrated insights from a multi-scalar extreme event in the himalayas Authors: Pathania, Ashish; Raaj, Saran; Krishan, Gopal; Lapworth, Dan; Brauns, Bentje; MacDonald, Alan; Gupta, Vivek; John MacAllister, Donald Abstract: Extreme precipitation events are intensifying globally due to climate change, often leading to unprecedented f lood risks and challenges in water resource management. In August 2023, Punjab, India, experienced cata strophic flooding, impacting approximately 12,000 villages and resulting in 65 reported fatalities. The flood highlighted the need to understand better the multifaceted drivers of such extreme events, especially in regions dependent on major dams. This study analyzes the hydrometeorological drivers, dam operations, hydrological responses, and socioeconomic impacts associated with the event. A detailed spatiotemporal meteorological analysis demonstrates that heavy and very heavy rainfall events in July significantly elevated antecedent soil moisture levels, heightening the region's flood susceptibility in August despite seasonal rainfall deficits. Using HEC-RAS hydrodynamic modeling coupled with high-resolution demographic data, the study demonstrates the Pong Dam's critical role in mitigating flood impacts, reducing population exposure by approximately 80 % compared to unregulated conditions. A deterministic population exposure assessment was carried out using 2011 village-level census data. The results showed disproportionate impacts on vulnerable groups, with flood exposure rising from August 15–17 by ~49 % among children, ~46 % among women, and ~47 % among non-working populations. Genetic Algorithm-based optimization with a piecewise penalty function improved the balance between flood mitigation and water conservation. It underscores the importance of integrating real-time hydrological data to enable adaptive reservoir management. The study recommends that policymakers prioritize advanced flood forecasting systems incorporating soil moisture data, high-resolution rainfall forecasts, and demographic vulnerability indices, while addressing the critical operational challenges of data availability, monitoring density, and institutional capacity. Mon, 01 Dec 2025 00:00:00 GMT http://117.252.14.250:8080/jspui/handle/123456789/7961 2025-12-01T00:00:00Z