Estimation of monthly snowmelt contribution to runoff using gridded meteorological data in SWAT model for Upper Alaknanda River Basin, India.

The purpose of hydrologic modeling of a watershed is to gain valuable information about the processes occurring within watershed. With increasing temperature of the earth atmosphere, the snow fed mountainous river basins are going to get impacted severely. Lack of adequate weather station limits the scope of researches in these mountainous basins which are critical source of water resource for the country. However, improvement of satellite-based weather products has been able to nullify this barrier to great extent. In this study, a semi distributed hydrologic model of Upper Alaknanda river basin has been developed using gridded meteorological input data sourced from India Meteorological Department (IMD), National Aeronautics and Space Administration (NASA) Power, and The SWAT (Soil and water Assessment Tool) model. The calibration and validation of the model reflected satisfactory performance with the validation period (2013-2017) showing better match between simulated and observed flow than calibration period (2005-2012). The values of Nash-Sutcliffe efficiency, coefficient of determination, and Percent of bias for calibration period are 0.65, 0.67, and 14% respectively. Adoption of semi distributed approach for modeling enables to analyze the basin while preserving the heterogeneous nature of the basin. The spatiotemporal evaluation of snowmelt reveals that highest snowmelt was generated during month of April which also causes highest snowmelt contribution to runoff for April (59.76 %). The outcomes of this study reveals that satellite-based meteorological product can be adopted satisfactorily with SWAT model for estimation of snowmelt in upper Himalayan regions which gives a new direction of research in SWAT diaspora.

Analyzing Joshimath's sinking: causes, consequences, and future prospects with remote sensing techniques.

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022-2023 using a PSInSAR-based approach, varies from - 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas - ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016-2017, 2018-2019, and 2020-2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022-2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022-2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022-2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to - 13.13 mm/year (2022-2023). Towards the south-east, the town experienced rapid land subsidence, - 13.13 mm/year to - 5 mm/year (2022-2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.