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This paper documents the development of PixelSWAT, a Graphical User interface (GUI) python toolbox developed with the motive of creating gridded watershed and stream features to run the Soil and Water Assessment Tool (SWAT) in a distributed discretization scheme thus allowing optimum utilization of gridded weather datasets. Additionally, the tool also aims to automate the preparation of SWAT weather input files from Network Common Data (NetCDF) files for any SWAT user along with the option to interpolate the weather files for each grid. A case study was conducted in the Mago basin of Tawang, Arunachal Pradesh, using gridded weather datasets for hydrological simulation. Three SWAT models were prepared – a conventional SWAT model; a 500 m and a 1000 m gridded watershed PixelSWAT models. Statistical indices Nash Sutcliffe (NSE), Coefficient of Determination (R2) and Percent Bias (PBIAS) showed that the PixelSWAT projects performed marginally better than the conventional model and also incorporated the weather data more meaningfully.

This study presented detailed analysis of glacier surface extent in the Alaknanda river basin, Western Himalaya, using Landsat series data, land surface temperature and digital elevation model (DEM). The clean glaciers were delineated using automatic glacier extraction index (AGEI) and the debris-covered glaciers were extracted by utilizing slope and land surface temperature. The generated glaciers map was compared and cross-checked with Randolph Glacier Inventory (RGI v5.0) and sharp google earth image. The reduction in glacier surface area was observed by 367.364 sq. km from 1994 (1150.471 sq. km) to 2015 (783.107 sq. km) at the shrinkage rate of 1.45% per annum. The shrinkage rate was higher from 2006 to 2015 (2.43% per annum) than from 1994 to 2006 (0.78% per annum). The glacier count expanded from 314 (1994) to 360 (2015) due to the fragmentation of individual glaciers. The elevation range of 5000–5500 m above m.s.l. hosted maximum number of glaciers and glacial coverage. The debris-covered glaciers showed deglaciation at the rate of 0.59% per annum from 1994 to 2015. The total area occupied by glacier extent was about 25% of the total basin in the year 1994, 23% in the year 2006 and 17% in the year 2015. HIGHLIGHTS Mapped clean glaciers using automatic glacier extraction index.; Mapped debris-covered glaciers by intersecting slope and LST layers.; Detected shrinkage in glacier surface area during the analysis period.; Found higher shrinkage rate in the recent period than the older period.; Observed increase in count of glaciers due to fragmentation.;

Climate change-induced glacier recession has sparked a dynamic transformation of glaciers in high-mountain areas worldwide, resulting in genesis, expansion, and dissipation of glacial lakes that pose a potential threat to downstream communities, underscoring the need for regular monitoring. This study incorporates the automated glacier extraction index to improve debris-free glacier mapping accuracy by reducing water and shadow area categorization errors using multi-temporal Landsat-5 Thermal Mapper, Landsat-8 Operational Land Imager, and digital elevation model (Shuttle Radar Topography Mission) to obtain precise glacier extent. Waterbodies were delineated using thresholding techniques applied to water indices: normalized difference water index, modified normalized difference water index, normalized difference turbidity index, and slope information. The study attempted to map and analyze the temporal variations of glaciers and glacier lakes in the glaciated sub-basins of Arunachal Pradesh: Dibang Basin, Mago Basin, and Subansiri Basin. Glaciers receded at a shrinkage rate of 1.08% per year (1988–2017) in Mago Basin, 1.01% per year (2013–2022) in Subansiri Basin, and 1.42% per year (1995–2021) in Dibang Basin. Glacier lakes showed an increasing trend in number and area due to glacier melt, expanding at rates of 3.31% in the Mago Basin, 0.48% in the Subansiri Basin, and 0.68% in the Dibang Basin per year. Findings suggest that global climate change is likely the primary factor driving glacier changes in these basins. HIGHLIGHTS Studied temporal variations of glacier surface area and glacier lakes associated with selected glaciated basins of Eastern Himalaya, namely Mago Basin (1988–2017), Subansiri Basin (2013–2022), and Dibang Basin (1995–2021).; The study observed and discussed the glacier dynamics of unexplored basins in Eastern Himalayan, which exhibit distinct characteristics compared to the extensively studied Western Himalayan Basins.;

With the country's economy directly dependent on food grain production, accurate estimation of the net irrigation requirement (NIR) of crops is essential for maximizing production. The daily IMD precipitation (0.25° × 0.25°) and temperature (1° × 1°) gridded datasets (1971–2010) were considered for the present scenario. Two satellite-based crop coefficient layers were prepared for the study: (i) kc_EEFlux, the evaporative fraction layer from earth engine evapotranspiration flux (EEFlux) and (ii) kc_FEWS, the ratio of the famine early warning system network (FEWSNET) monthly actual evapotranspiration (ET) (ETa) to ET0_FEWSNET layers. The NIR was estimated for three different cropping pixels derived from the land-use land cover (LULC) map of India, namely, Kharif-only, Rabi-only, and double/triple pixels using ‘ET0 and NIR toolbar’. The National Aeronautics and Space Administration (NASA)'s Earth Exchange Global Daily Downscaled Projections dataset were employed for projecting the future scenario. Results showed discrepancies in the extreme values of FAO56-Penman–Monteith (PM) and Hargreaves–Samani's estimated reference ET (ET0) but followed a similar trend in all months (R2 = 0.88). Results also suggested that FEWSNET offered more reliable estimates of NIR over EEFlux. The projected ensemble NIR indicated a probable negative relative change during the Kharif season (RCP4.5: from −14.47 to −7.25%; RCP8.5: from −11.45 to −5.59%) while a probable positive relative change during the Rabi season (RCP4.5: 3.30–5.21%; RCP8.5: 2.34–8.83%). HIGHLIGHTS Two satellite-based actual crop coefficient (kc_act) layers were prepared.; Net irrigation requirement (NIR) was estimated for Kharif-only, Rabi-only, and double/triple-cropped pixels.; kc_act prepared using the famine early warning system network dataset, reported better estimates.; NASA's NEX-GDDP dataset was used for evaluating future scenario.; Findings indicated higher NIR during the Rabi season compared to the Kharif season.;