CSIR - National Institute for Data Science and AI

On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties

by Arya V. B., Sajani Surendran and Kavirajan Rajendran

Abstract
Association of higher (lower) rainfall with lower (higher) Aerosol Optical Depth (AOD) is consistent with the understanding that increased washout (build-up) and shorter (longer) life-time of aerosols occur in wetter (drier) conditions. Given the life-time of aerosols, it is imperative to examine how aerosols impact active/break (wetter/drier than normal) spells, the prominent intraseasonal variability (ISV) of Indian summer monsoon (ISM), through their composite analysis using recent satellite observations of aerosols and cloud properties, circulation and rainfall. Dust aerosols can act as CCN and participate efficiently in cloud processes during active phase. During breaks, build-up of desert dust transported by prevalent circulation, is associated with lower cloud effective radius implying aerosols’ indirect effect where they can inhibit cloud growth in the presence of reduced moisture and decrease precipitation efficiency/rainfall. Correspondingly, correlation albeit small, between intraseasonal anomalies of AOD and rainfall is negative, when AOD leads rainfall by 3–5 days implying that indirect aerosols impact is effective during breaks, though it is not the dominant responsible factor. During breaks, lower shortwave flux at top of atmosphere hints at dust-induced semi-direct effect. As breaks are permanent features of ISM, incorporation of dust-induced feedbacks in models, is essential for improved ISV simulation and ISM prediction.

Citation: Arya V. B., Sajani Surendran and Kavirajan Rajendran. On the build-up of dust aerosols and possible indirect effect during Indian summer monsoon break spells using recent satellite observations of aerosols and cloud properties. J. Earth Syst. Sci., 130, 42 (2021).

Source: https://doi.org/10.1007/s12040-020-01526-6

Mapping seismic hazards in the Himalayas

by Imtiyaz A. Parvez and Sanjeev Ekka

The Himalayas rank among the world’s most seismically active regions, as evidenced by major historical earthquakes, including the 1905 Kangra (Mw 7.8), 1934 Bihar–Nepal (Mw 8.2), 1950 Assam (Mw 8.4), 2005 Kashmir (Mw 7.6), and 2015 Gorkha (Mw 7.8) events. These earthquakes underscore the ongoing tectonic activity along the Himalayan arc and have caused substantial human and economic losses. Seismic hazard assessment is a critical tool for mitigating such impacts, informing preparedness efforts, risk reduction strategies, and policy formulation. Significant advancements have been made in recent decades in assessing seismic hazard and risk across the Himalayas. This article reviews key developments in probabilistic and deterministic seismic hazard studies, integrating both historical and instrumental earthquake data. Notably, peak ground accelerations (PGA) in certain Himalayan regions surpass 0.6 g, with areas near the 1897 and 1950 Assam earthquake epicenters recording extreme PGA values of 1.2–1.3 g. These zones also exhibit high ground velocities and permanent displacements, reflecting the severe ground-shaking potential of large Himalayan earthquakes. The article further explores how local geological conditions and source characteristics influence ground motion, emphasizing their role in microzonation studies. Despite progress, critical research gaps remain, particularly the need for high-resolution seismic hazard mapping across the Himalayan arc to enhance risk reduction and resilience-building efforts.

Source: https://doi.org/10.1007/s43538-025-00626-5 

Leaf area index estimation over the Kosi watershed in central Himalaya from remote sensing data using machine/deep learning models and validation against in situ measurements

by V. Rakesh, V. Arun Kumar & K. V. Ramesh

In this study, we present machine learning model support vector machine (SVM) and deep learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), long- and short-term time series network (LSTNet), and temporal convolution network (TCN) to estimate leaf area index (LAI) utilizing Sentinel-2 multispectral imagery within the Kosi watershed located in the central Himalayan region is based on various vegetation indices. In situ LAI data were collected through a random sampling method using a plant canopy analyzer LI-COR over a continuous field survey period of 25 days for model training and validation. The current study evaluated four variants of the LSTM model, with the bidirectional LSTM emerging as the most effective in estimating LAI. The LAI derived using the Rendvi index was found to be superior compared to those obtained from other vegetation indices. The bidirectional LSTM (BiLSTM) model outperformed the SVM model in LAI retrieval based on all accuracy metrics computed. Specifically, the R² and mean absolute percentage error (MAPE) values for the BiLSTM model were 0.99 and 0.29, respectively, while the SVM model achieved corresponding values of 0.93 and 0.56. Additionally, the performance of the BiLSTM in predicting LAI surpassed that of other deep learning models such as GRU (R² = 0.99, MAPE = 0.82), LSTNet (R² = 0.99, MAPE = 0.74), and TCN (R² = 0.99, MAPE = 0.39). We compared the performance of the BiLSTM with the SVM model and the moderate-resolution imaging spectroradiometer (MODIS) LAI product by validating against in situ LAI measurements. The LAI retrieved using the BiLSTM model (R² = 0.99) demonstrated superior agreement with in situ measurements compared to the MODIS LAI product (R² = 0.0003). Furthermore, seasonal changes in vegetation and interannual variations in LAI over the Kosi watershed were more accurately depicted in the high-resolution LAI retrieved with BiLSTM from Sentinel-2 compared to the MODIS LAI product.