Rajan Dev Gupta

Land use land cover (LULC) changes are key indicators of environmental transformation, directly influencing hydrological balance, ecosystem services and sustainable land management. Sher River basin, an agro-ecological diverse sub-basin of the Upper Narmada River system in India, is the primary hydrological and socio-economic lifeline for local communities, but no comprehensive study on Sher River basin related to long term LULC dynamics could be tracked in the literature. To fulfil this research gap, long term LULC spatiotemporal change detection and transition patterns analysis with a dual-seasonal focus on the Rabi and Kharif cropping periods over a 23-year period (2001–2023) in Sher River basin are carried out. LULC maps are created using multi-sensor Landsat data (TM, ETM+ , OLI/TIRS) for 2001, 2006, 2011, 2016 and 2023 at an interval of 5 years for both Rabi and Kharif seasons using a supervised classification technique with the maximum likelihood classifier. Five LULC classes, namely, built-up, agricultural, forest, water body and barren land are delineated. All classified maps achieved overall accuracies exceeding 85% with kappa coefficients greater than 0.80. The agricultural land increased significantly in both seasons, more sharply during Rabi (5.86% to 10%) while built-up areas expanded more than fivefold (0.18% to 0.85%) reflecting rapid urbanization during 23 years. Barren land declined noticeably, transitioning mainly into agricultural and urban land uses. Forest cover, after an initial decline, showed recovery post 2016 with a modest increase in the later years due to afforestation initiatives. Water bodies remained relatively stable with minor seasonal variations. This study provides critical insights into seasonal land dynamics, highlighting clear seasonal contrasts in land use behavior between Rabi and Kharif periods. The findings emphasize the need for integrated land use planning to balance agricultural growth, urban expansion and ecological sustainability in sub-humid watersheds.

Glaciers, in general, are sensitive to changes in the climate but Himalayan glaciers, in particular, are highly affected by climate change. Mass balance (MB) of glaciers is one of the important parameters to examine the response of glaciers to climate variability and change. The study of mass balance sensitivity (MBS) due to climate perturbations for glaciers is also important to understand future behavior of the glaciers. For Chhota Shigri Glacier, research on the estimation of long-term annual and seasonal MB and MBS as well as equilibrium-line altitude (ELA) and accumulation area ration (AAR) sensitivity analysis is not reported in detail. Accordingly, the present study carries out a detailed analysis of annual and seasonal MBS from 1953 to 2014 using annual and monthly climate perturbations as well as ELA and AAR sensitivities for the Chhota Shigri Glacier. The long-term annual and seasonal MB of Chhota Shigri Glacier from 1953 to 2014 is reconstructed using distributed temperature-index model by simulating minimal model parameters, namely melt factor, snow, and ice radiations using Monte-Carlo simulation. The mean annual MB of Chhota Shigri was -0.28 ± 0.41 m w.e./year during 1953–2014. The annual MB decreased from − 0.09 ± 0.41 m w.e./year (1953–1968) to − 0.57 ± 0.41 m w.e./year (2000–2014). The estimated MBS of Chhota Shigri Glacier is 0.57 m w.e./°C due to temperature change which is high and can be attributed to the presence of significantly less debris-covered ice in Chhota Shigri Glacier. It is analyzed that ELA and AAR of Chhota Shigri Glacier will change to + 107.7 m a.s.l. and − 15.03% respectively due to increase in temperature by + 1 °C. Further, ~ 38% more precipitation is required to compensate for the change in MB, ELA and AAR which will occur due to + 1 °C temperature rise. The findings of the present study will also support the estimation of future MB of Chhota Shigri Glacier using minimal simulated model parameters for distributed temperature-index model which have been found to produce good results using long term high resolution climate data.

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987–2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25–4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8–3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398–4.016 °C in summer and 0.45–2.24 °C in winter. Getis–Ord G_i* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability.