Indian Institute of Technology Gandhinagar

Intensive agricultural production in the Indo-Gangetic Plain has led to widespread overapplication of fertilizers, particularly in sugarcane-based systems, resulting in elevated nitrate concentrations in groundwater. In the Hindon River Basin, the Central Ground Water Board (CGWB) has reported significant nitrate contamination, which poses a threat to both agricultural sustainability and drinking water security. Addressing this degradation while maintaining productivity requires quantitative tools to evaluate management interventions. This study employs the SWAT+ model to evaluate the impact of reduced fertilizer application and alternative irrigation practices on groundwater nitrate leaching and sugarcane yield in the sugarcane-dominated Hindon Basin. The model's ability to represent basin hydrology and crop growth was evaluated through calibration and validation using observed streamflow at two gauging locations, and sugarcane yield data from three districts. The model demonstrated satisfactory performance, with streamflow calibration and validation producing average KGE values of 0.74 and 0.73, respectively, and an average percent bias (PBIAS) of 12% and +9%. Crop yield simulations yielded average KGE values of 0.76 and 0.83 during calibration and validation, respectively, with PBIAS values of -4% and -6%. These results confirm the model's reliability for management-oriented assessments. Four management scenarios were simulated against a baseline that reflected current farmers' practices, as identified through field surveys. Scenarios included fertilizer reductions of 15% and 30%, implemented under both furrow and drip irrigation systems. Groundwater quality responses were evaluated using annual average nitrate percolation below the root zone for leaching, while sugarcane yield was used to assess productivity trade-offs. Across all alternative scenarios, nitrate percolation decreased by 46% to 68% relative to the baseline. Changes in sugarcane yield were minimal, remaining within 1-2% of current practices. Drip irrigation demonstrated greater nitrate reduction compared to furrow irrigation at the same fertilizer levels, highlighting the importance of irrigation efficiency in mitigating nutrient loss. These findings suggest that moderate decreases in fertilizer use, combined with drip irrigation, can significantly reduce groundwater nitrate contamination in the Indo-Gangetic Plain without compromising yields.

Declustering of earthquake catalogs is a fundamental preprocessing step in seismicity analysis and probabilistic seismic hazard assessment (PSHA), as it aims to separate background, approximately Poissonian seismicity from dependent events such as foreshocks and aftershocks. The choice of declustering method can significantly influence estimated seismicity rates, b-values, spatial source models, and ultimately seismic hazard results. Despite its widespread use, there is no consensus on the most reliable declustering approach, and different algorithms often produce substantially different background catalogs for the same dataset. This study presents a systematic comparison of commonly used declustering techniques, including the window-based methods of Gardner and Knopoff, Uhrhammer, and Grünthal; the interaction-based Reasenberg algorithm; the nearest-neighbor clustering method of Zaliapin; and Epidemic-Type Aftershock Sequence (ETAS) based stochastic declustering. All methods are applied to the same regional earthquake catalog with consistent magnitude completeness and spatial coverage to ensure a fair comparison. The resulting declustered catalogs are evaluated in terms of the fraction of events classified as background, their temporal and spatial distributions, and their impact on magnitude-frequency relationships. To assess the reliability of each declustering approach, we use the ETAS model as a reference framework. The comparison reveals pronounced method-dependent variability, particularly at short inter-event times and distances, with window-based methods generally removing a larger proportion of clustered events and interaction-based methods showing sensitivity to user-defined parameters. The Zaliapin method offers a data-driven alternative but may be influenced by spatial heterogeneity, while ETAS-based stochastic declustering provides a probabilistic and internally consistent representation of seismicity at the cost of higher computational and data-quality requirements. The results highlight the need for careful method selection and uncertainty-aware declustering in seismic hazard applications and demonstrate the value of ETAS-based diagnostics as an objective benchmark for evaluating declustering performance.

Volatile organic compounds (VOCs) are emitted from various natural (biogenic) and anthropogenic sources. VOCs are important components of photochemical processes with strong significance to atmospheric chemistry and climate change through the formation of ozone and organic aerosols. Time-resolved continuous measurements of ambient isoprene mixing ratios at an urban location in western India were conducted from January to May 2020. The measurement period represents the gradual changes in meteorological parameters from winter to summer, as well as the reductions in anthropogenic emissions from the pre-lockdown phase of COVID-19 to the lockdown period. The day-to-day variations between 0.78-3.25 ppb during January-March and 1.07-2.25 ppb during April-May were associated mainly with the variabilities in night and day data, respectively. Diurnal patterns with higher evening-early morning and daytime concentrations in winter and summer months resemble the features of predominant anthropogenic and biogenic emissions, respectively. The analysis of the ratios of isoprene to aromatic compounds revealed the influence of biogenic sources on diurnal and seasonal variations. The afternoon isoprene/aromatic ratios increased exponentially at higher temperatures (25-42 oC), leading to increasing trends of biogenic contribution during the winter-to-summer transition period. Despite predominant biogenic contributions, reductions in anthropogenic emissions due to the COVID-19 lockdowns could also be a factor for very enhancements of isoprene/xylenes (23.0-30.5 ppb ppb-1), isoprene/ethylbenzene (28.7-37.2 ppb ppb-1), and isoprene/benzene (5.1-9.6 ppb ppb-1) ratios than in winter. The present study shows that there are no significant differences in isoprene mixing ratios between winter and summer seasons. However, tracer-based analysis shows a significant seasonality in the relative apportionment between anthropogenic and biogenic contributions. In addition to relative changes in anthropogenic and biogenic contributions, the trend of the isoprene mixing ratio also reflects the impact of meteorological factors influencing photo-oxidation and dilution.

Climate change is driving a marked intensification of hydrological extremes, including both droughts and floods. When these opposing conditions occur in close succession, known as hydrological whiplash, they generate compounded impacts on ecosystems, infrastructure, and human livelihoods. We analyze hydrological whiplash across India using observed streamflow data and simulations from the validated H08-CaMa-Flood model. The results indicate that nearly 90% of streamflow stations experienced at least one whiplash event, with drought-to-flood transitions being both more common and more abrupt than flood-to-drought shifts. These events are concentrated primarily during the monsoon season, but their occurrence has increased in the non-monsoon months in recent decades, particularly in high-elevation regions. Moreover, we find that whiplash events are becoming more frequent and more intense, while the interval separating dry and wet extremes is shrinking, signaling an escalation of hydrological volatility across the country. Together, these patterns underscore the need for strengthened monitoring, early warning capabilities, and adaptive water management strategies to reduce the growing risks associated with rapid hydrological transitions under a warming climate.

In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN delivers impressive structural similarity and visual appearance after deblurring an image. Like other deep neural network architectures, GANs also suffer from large model size (parameters) and computations. It is not easy to deploy the model on resource constraint devices such as mobile and robotics. With the help of MobileNet based architecture that consists of depthwise separable convolution, we reduce the model size and inference time, without losing the quality of the images. More specifically, we reduce the model size by 3-60x compare to the nearest competitor. The resulting compressed Deblurring cGAN faster than its closest competitors and even qualitative and quantitative results outperform various recently proposed state-of-the-art blind motion deblurring models. We can also use our model for real-time image deblurring tasks. The current experiment on the standard datasets shows the effectiveness of the proposed method.

This book constitutes the proceedings of the 12th International Conference on Big Data and Artificial Intelligence, BDA 2024, held in Hyderabad, India, during December 17–20, 2024. The 16 full papers and 12 short papers presented here were carefully reviewed and selected from 106 submissions. These papers have been categorized under the following topical sections: Image Classification; Graph Analytics; Big Data Analytics; Applications; Data Science; Health-Care Analytics; eLearning; Prediction and Forecasting.