We investigate the relationships among the mass-weighted mean diameter ( D_m ), normalized intercept parameter ( N_w ), and rainfall rate (R) using five disdrometers and GPM Dual-frequency Precipitation Radar (DPR) observations over the northeastern Indian subcontinent. By extending the conventional D_m – N_w framework to include rainfall rate, we directly link rainfall microphysics to rainfall intensity. Rainfall with 1.5 ≤ D_m < 2.5 mm exhibits a bimodal structure in the D_m –R– N_w relationship, consisting of Type A rainfall ( N_w ≥ 36 dB) with high rainfall rates and Type B rainfall ( N_w < 36 dB) with lower rainfall rates. Type A rainfall is more frequent during the monsoon season and over orographic regions. The DPR product shows limited variability in N_w and rarely reproduces high- N_w rainfall, leading to systematic underestimation of rainfall intensity. These results highlight the importance of representing N_w variability for improving satellite-borne precipitation radar retrievals.
This study presents continuous, real-time monitoring of the atmospheric boundary layer (ABL) dynamics over Dibrugarh, an easternmost location of the state of Assam in Northeast India, using a Ceilometer Lidar CL31. A distinct diurnal cycle and strong seasonal variability in ABL height (ABLH) are observed. The ABL reaches its maximum height ( 1750 m and 1450 m) during the pre-monsoon (March–May) and monsoon (June–September) seasons, while it remains shallower ( 925 m and 1025 m) during the post-monsoon (October–November) and winter (December–February) periods. Surface sensible heat flux and latent heat flux significantly influence ABL growth during the warmer months. The diurnal evolution of the lifting condensation level (LCL) is also analysed to investigate the ABL interaction with cloud formation and development. It is observed that the LCL generally lies above the ABL. However, the ABL often exceeds the LCL during afternoon hours in the pre-monsoon and monsoon seasons, suggesting favourable conditions for cumulus cloud formation. Ceilometer-derived ABLH are further compared with European Centre for Medium-Range Weather Forecasts version 5 (ERA5) reanalysis data. The ERA5 is observed to capture the diurnal and seasonal evolution of ABL but is underestimating the Ceilometer retrieved ABL with a mean bias error (MBE) (root mean square error, RMSE) values ranging − 5.5 m – − 183.6 m ( 97.2 m – 316.7 m). Seasonally, the highest correlation coefficient of 0.98 is observed in the monsoon with MBE (RMSE) − 53 m (87.6 m). However, the highest MBE and RMSE, despite a good correlation (0.97), in pre monsoon suggests a discrepancy in reanalysis data, mainly arising from the differences in retrieval methods, where Ceilometer uses gradient method while ERA5 uses the Bulk Richardson method. The present observation of real-time diurnal ABL cycle will be helpful in explaining the diurnal evolution of atmospheric composition specially the aerosols and trace gases measured over the study location using ground-based observations as well as those simulated using climate models.
Watersheds constitute integrated hydrological and geomorphological units and provide an effective framework for land and water resource management, particularly in erosion-prone and data-scarce mountainous regions. This study applies Remote Sensing (RS) and Geographic Information System (GIS) techniques to evaluate morphometric characteristics and land use/land cover (LULC) dynamics of the Kamlang River watershed for sub-watershed prioritization. Linear, areal, and shape morphometric parameters, along with LULC indicators related to erosion susceptibility, were computed for eighty sub-watersheds. A compound parameter (Cp) was derived for each sub-watershed, where lower Cp values represent higher erosion risk and management priority. Morphometric analysis identified sub-watersheds SW64 and SW65 as high-priority zones due to unfavorable drainage density, stream frequency, and basin shape parameters, indicating enhanced runoff and erosion potential. LULC-based prioritization classified a larger set of sub-watersheds (including SW1, SW6, SW8, and SW12) under high priority, primarily owing to the dominance of erosion-prone land use classes such as barren land, sparse vegetation, and exposed surfaces. Integrated prioritization of morphometric and LULC parameters revealed that sub-watershed SW65 consistently ranked as high priority across both approaches, highlighting its critical vulnerability driven by combined terrain configuration and land use pressures. The study demonstrates the effectiveness of integrating morphometric and LULC analyses for objective sub-watershed prioritization and provides a scientifically robust basis for identifying critical erosion hotspots. The findings underscore the urgent need for targeted soil and water conservation measures in sub-watershed SW65 to support sustainable watershed management and land-use planning in mountainous river basins.
RUSLE and MMF models were employed to estimate soil loss using geospatial technology in the Dhemaji district of Assam, India, and to compare the results obtained from both models. The MMF model estimates soil erosion in two phases—water and sediment—by comparing soil detachment and transport capacity. The results of this study reveal clear differences in the spatial distribution of areas susceptible to erosion. RUSLE model identifies the high LS factor areas as erosion-prone irrespective of its land use. Whereas MMF showed variation in soil loss for low slope gradient considering soil type and land use parameters. The rate of soil loss from the study area is estimated at 59 t/ha/yr and 52.11t/ha/yr by MMF and RUSLE model respectively. In RUSLE, 55.43
The North Eastern Region (NER) of India is one of the most seismically active zones, where earthquake-induced landslides pose a major threat to the settlements, infrastructure, and livelihoods. Objectives: The objectives of this research are to assess the co-seismic landslide susceptibility in the Imphal area of Manipur, Northeast India, by incorporating the effect of earthquake-induced ground motion into the slope stability analysis using the modified Newmark Sliding Block Model. Background: The critical gap in landslide susceptibility research in the Imphal region of NER is the lack of earthquake triggering in physically based slope stability modelling. Methods: The Peak Ground Acceleration (PGA) data for the Mw 6.7 Imphal earthquake (January 4, 2016) were collected from the USGS database and converted to Peak Horizontal Acceleration (PHA) at the ground surface using the non-linear site response relationship for National Earthquake Hazards Reduction Program (NEHRP) site class D conditions. A Deterministic Seismic Hazard Approach (DSHA) was considered to simulate the scenario-based ground motion input to the slope stability analysis model. The critical factor of safety (FSc) was computed by integrating the effect of seismic acceleration and terrain parameters, particularly slope gradient, into the slope stability analysis using the modified Newmark model. Findings: This study highlights a scenario-based co-seismic landslide vulnerability; the M 6.7 Imphal earthquake shows PGA ranging from 0.1 to 0.24 g with highest intensities near the epicenter, and ~ 70.2% of the area is moderately susceptible, 18.3% low, and 11.5% highly susceptible, showcasing priority areas for hazard mitigation during future M ≥6.7 earthquake events. Novelty: The novelty of this research is that it uses a physically based co-seismic landslide susceptibility analysis that incorporates the role of seismic forces in the analysis of slope instability, as opposed to the conventional approaches that are based on the terrain parameters alone. Keywords: Co-seismic, Landslide Susceptibility, Modified Newmark Model, Earthquake, Imphal
