Faculty Publications
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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 Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis(American Society of Civil Engineers (ASCE), 2021) Pathak, A.A.; Dodamani, B.M.Groundwater is a major resource of freshwater that provides additional resilience to agricultural drought during rainfall deficit and also helps in understanding the nature of the hydrological drought risk of an area. This study investigated the response of groundwater drought to meteorological drought and local aquifer properties by considering monthly groundwater levels of a tropical river basin in India. Further, bivariate frequency analysis was carried out for groundwater drought to develop severity-duration-frequency curves by considering the copula function. Long-term monthly groundwater levels were procured, and cluster analysis was performed on groundwater observations to classify the wells. Standardized Groundwater level Index (SGI) was used to evaluate groundwater drought for each cluster, and the same was compared with the meteorological drought of different association periods. The cluster analysis conveyed that wells can be grouped into three clusters optimally. Based on the comparison of groundwater drought with meteorological drought, it was inferred that SGI is well harmonized with the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) in humid and semiarid regions, respectively. Analysis of hydraulic diffusivity with the autocorrelation structure of SGI emphasizes the crucial role of aquifer characteristics in local groundwater droughts. The results of joint and conditional return periods obtained from bivariate frequency analysis conveyed that high severity and high-duration droughts were more frequent in the well of Clusters 1 as well as Cluster 3 and comparatively less for the well of Cluster 2. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies by considering conjunctive use of surface and groundwater. It also provides a framework to evaluate groundwater drought risk in other parts of the world. © 2021 American Society of Civil Engineers.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.