Faculty Publications
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Item Spatiotemporal distribution of aerosols over the Indian subcontinent and its dependence on prevailing meteorological conditions(Springer Netherlands rbk@louisiana.edu, 2019) Nizar, S.; Dodamani, B.M.The prevailing meteorological conditions that influence the advection and diffusion of the atmosphere govern the distribution of atmospheric particles from its sources. The present study explores the spatiotemporal distribution of atmospheric aerosols over the Indian subcontinent (5°–40° N, 65°–100° E) and its dependence on the prevailing meteorological conditions. Eleven years (2002–2012) of Aerosol Optical Depth (AOD) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with meteorological parameters extracted from reanalysis data are analysed at monthly timescales. Wind speed, wind divergence and planetary boundary layer height (PBLH) are studied as parameters for advection and diffusion of atmospheric aerosols. The result shows higher aerosol loading during the monsoon season with increased spatial variability. Wind speed and divergence correlate with AOD values both over land (R = 0.75) and ocean (R = 0.82) with increased aerosol loading at higher wind speeds, which are converging in nature. Owing to the varied climatology of the Indian subcontinent, land and ocean areas were classified into subregions. Analysis was carried out over these subregions to infer the influence of meteorological conditions on aerosol loading. Results are indicative of a distinct characteristic in the prevailing meteorological conditions that influence the distribution of certain aerosol types. Further, the PBLH was analysed as an indicator of atmospheric diffusion to infer its importance in aerosol distribution. The results indicate that PBLH explains almost 30 to 90% of the total variance in AOD over the subregions which is particularly evident during the winter and pre-monsoon seasons. © 2019, Springer Nature B.V.Item Trend analysis of rainfall, rainy days and drought: a case study of Ghataprabha River Basin, India(Springer Science and Business Media Deutschland GmbH, 2020) Pathak, A.A.; Dodamani, B.M.Drought is a recurring natural hazard, which has the potential to alter the ecological conditions of a region. A deficit in rainfall and a decrease in the number of rainy days induce the meteorological drought. The present study considered the nonparametric Mann–Kendall to investigate annual and seasonal rainfall (rainy-day) trend and meteorological drought trends over the Ghataprabha River Basin, India. A significant number of moderate and severe droughts were observed over the study period, and the eastern portion of the basin possessed the highest number of drought frequency (20–35 No.) in all the time scales of SPI. Results of trend analysis revealed that the stations having significant negative SPI trends are increasing with the SPI time scale, which could lead to the droughts of higher duration and severity. From the study, it was also noted that the negative trend of SPI was moving from the western portion of the basin to the eastern side, as the SPI time scale increases. Comparison between rainfall trend and rainy-day trend with SPI trend revealed high (ranging from 0.91 to 0.97) and moderate (0.67–0.7) correlation, respectively. This indicates that the rainfall trend will capture the SPI trends effectively. The findings of this work could be useful for a better understanding of regional drought trends and also establish effective water resources management policies over the basin. © 2020, Springer Nature Switzerland AG.Item Identifying Rice Crop Flooding Patterns Using Sentinel-1 SAR Data(Springer, 2022) Keerthana, N.; Salma, S.; Dodamani, B.M.In India, the majority of the population relies heavily on rice as it is their primary source of nutrition. Rice crop yield productivity depends on seasonal variations and mainly depends on hydrological conditions. Long-term water clogging in rice fields for an extended period causes crop flooding and reduces production in terms of quality and quantity. This study deals with the identification of rice crop fields and their flooding due to surface irrigation using Sentinel-1 SAR data. The identification of rice fields was attempted by classifying the image data using a random forest algorithm. For crop flooding analysis, the temporal backscatter of the corresponding fields has been extracted from SAR data and local thresholding is used. The temporal analysis of the SAR backscattering showed a similar tendency in terms of crop growth. The overall accuracy of rice crop classification for VH and VV is 97.30% and 92.24% with RMSE errors of 0.0143 and 0.0145, respectively, obtained at the peak stage of the crop. From the crop flooding analysis, it is observed that crop fields have been flooded at the growth stage due to surface irrigation and rainfall. We identified crop flooding even at the crop mature stage. In the analysis, it has been observed that the flooding is not due to irrigation water but is due to the precipitation water. © 2022, Indian Society of Remote Sensing.Item Identifying Municipal Solid Waste Dumping Site Location Using AHP and GIS Techniques: A Case Study of Coimbatore District, India(Springer, 2022) Aishwarya, V.; Salma, S.; Dodamani, B.M.Increased municipal solid waste generation in urban areas is a result of fast population growth and urbanization. Dumping or landfilling in unsuitable areas becomes the biggest concern for solid waste management authorities. The present dump yard at Vellalore, Coimbatore district, affect nearby settlements with a foul stench and flying ashes due to strong winds. The study’s main goal was to provide alternative landfilling sites in the Coimbatore district using GIS and analytic hierarchy process (AHP) techniques. Nine criteria were considered. These were population density, slope, geology, geomorphology, land use/land cover, and proximity to road, river, railway, and airport. Weighted overlay, a spatial analyst tool that reclassifies raster maps and a final suitability map, is generated. According to the findings, the possible landfill zones were found in the northeastern region of Coimbatore. Hence, the environmentally suitable sites can be selected by using remote sensing and GIS techniques. © 2022, Indian Society of Remote Sensing.Item ANFIS-based soft computing models for forecasting effective drought index over an arid region of India(IWA Publishing, 2023) Kikon, A.; Dodamani, B.M.; Deb Barma, S.D.; Naganna, S.R.Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neuro-fuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSO-ANFIS show better performance results with R2 ¼ 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R2 ¼ 0.78. The results are presented suitably with the aid of scatter plots, Taylor’s diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model. © 2023 The Authors.Item Trend Analysis of Rainfall and Meteorological Drought Indices over India During 1958–2017(Springer Nature, 2023) Kikon, A.; Dodamani, B.M.Rainfall plays a very vital role and its deficit causes a huge impact on the environment. Understanding the pattern of rainfall and drought trends has become increasingly crucial in many regions due to climate change. In this study, using the rainfall data from 1958 to 2017 for thirty-four meteorological subdivisions of India, trend analysis is performed for annual and seasonal rainfall. Along with the rainfall trend analysis, the study is also performed for meteorological drought indices, i.e., Effective Drought Index (EDI), Standardized Precipitation Index-9 (SPI-9), and Standardized Precipitation Index-12 (SPI-12). The results obtained from the Mann–Kendall test show that the rainfall patterns in the area under investigation are changing over time. As evidenced by the decrease in rainfall, the study region has been experiencing a lack of water supply in numerous subdivisions. The drought frequency for the meteorological drought indices has also been investigated, and it has been observed that the region is experiencing drought from extremely dry conditions to normal dry conditions. The findings in this study will help us to better comprehend the changes in rainfall and drought severity over the study region. This study may also benefit effective disaster management and preparedness strategies for this catastrophe, which is wreaking havoc on the environment. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.Item Nonlinear analysis of groundwater levels: investigating trends and the impact of El Niño on groundwater drought in a southern region of India(Springer Science and Business Media Deutschland GmbH, 2025) Poojitha, K.; Dodamani, B.M.The expansion of groundwater irrigation and the cultivation of water-intensive sugarcane, combined with low rainfall, have exacerbated groundwater depletion and intensified droughts in the semi-arid Upper Krishna basin, India. This study employs an iterative singular spectrum analysis (iterative SSA) approach to impute missing groundwater level data from 25 monitoring wells. Cross-validation results show that iterative SSA effectively preserves the overall data structure when missing data was random, achieving good performance metrics with NSE > 0.79, R2 > 0.8 and RMSE < 0.88 under optimal parameters (L = 12 and k = 5). The reconstructed groundwater levels were then used to identify nonlinear trends with a 180-month smoothing SSA window and to investigate the impact of strong El Niño events on groundwater drought through cross-wavelet transform (XWT) and wavelet coherence (WTC) analyses between 1983 and 2017. The nonlinear trends revealed short-term deviations in groundwater levels during 1991–2000, 2002–2003, and 2015–2017. These deviations were corroborated by significant cross-wavelet power and high wavelet coherence between the Niño 3.4 SST Index and groundwater drought, particularly under low rainfall conditions, indicating stress on the region’s groundwater system. Although the study effectively captures the nonlinear nature of groundwater levels and the influence of climate variability on drought, the complexity of the groundwater system in the region persists due to physical water scarcity and high groundwater extraction for irrigation. This study highlights the importance of imputing missing data and applying nonlinear trend and wavelet analyses to detect short-term deviations caused by severe droughts, driven by strong El Niño events and high irrigation demands. © The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences 2025.