2. Thesis and Dissertations

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    Assessment of Meteorological and Hydrological Droughts Using Stationary and Non-Stationary Indices for two Contrasting Climate Regions in India
    (National Institute of Technology Karnataka, Surathkal, 2024) SAJEEV, ARYA; KUNDAPURA, SUBRAHMANYA
    Only a few researchers have incorporated climate change in drought indices calculations. This research attempts to build non-stationary indices for assessing meteorological drought in two different climate zones of India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop non-stationary models using the Generalized Additive Model in Location, Scale, and Shape (GAMLSS) for the period, 1951-2004. A comparative study has been conducted to assess the statistical performance of stationary and non-stationary models on various time scales (3-,6-,12- and 24- months). The best model is selected to conduct copula-based bivariate drought analysis. For this purpose, drought properties such as drought severity, duration, and peak are calculated. The annual and seasonal rainfall departures are also analysed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The non-stationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The non-stationary drought index shows better consistency with historical drought and flood events than the stationary index. The impact of rainfall and drought on the yield of major crops in study areas is also analysed. The yield loss rate of bajra significantly correlates with Non-stationary Standardized Precipitation Index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. Co-occurrence and joint return periods are calculated and compared with univariate return periods. A significant difference is observed between bivariate and univariate return periods, and more risk is detected in Saurashtra and Kutch than in Coastal Karnataka. Drought forecasting is crucial in water resource management and agricultural planning, particularly in regions vulnerable to water scarcity. Hence, the efficacy of various time-series forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Feed-forward Neural Network (FNN), Recurrent Neural Network (RNN), as well as hybrid combinations such as ARIMA-FNN, ARIMA-RNN, FNN-ARIMA, and RNN-ARIMA, for predicting drought indices at different time scales (3, 6, 12, and 24 months) is performed in Saurashtra and Kutch. The effectiveness of the models is evaluated through Correlation Coefficient (CC), R-squared (R2), Mean ii Square Error (MSE), Mean Absolute Error (MAE), and Relative Absolute Error (RAE). FNN exhibits superior performance as a standalone model across all time scales considered, and scale 24 was the best-performing time scale with a Correlation Coefficient of 0.874 and R2 of 0.911. However, further improvements in forecast accuracy are observed at all time scales when incorporating ARIMA as a post-processing step in the hybrid FNN-ARIMA model. Notably, FNN-ARIMA emerges as the top-performing model among all evaluated approaches, demonstrating its effectiveness in capturing the complex temporal dynamics of drought phenomena. This research emphasizes the significance of hybrid forecasting techniques, especially the combination of neural networks with traditional time-series models, in enhancing drought prediction accuracy. The findings contribute to the advancement of forecasting methodologies for better-informed decision-making in water resource management and agricultural sectors, thereby aiding in mitigating the impacts of drought events on vulnerable regions. Comparative analyses of meteorological and hydrological droughts using non-stationary indices have not been explored yet. The other objective of this research is to develop non-stationary indices for assessing meteorological and hydrological droughts in the Shetrunji River basin in Saurashtra region, India, from 1971 to 2015. The statistical performance of stationary and non-stationary models has been compared across various time scales (3-,6-,12- and 24- months), and the results indicate that non-stationary models more effectively capture meteorological and hydrological drought events than stationary models. The drought and flood events detected by non-stationary indices are compared with historical episodes to assess the robustness of the indices. The results are also compared with drought events obtained from rainfall and streamflow departures. The annual and seasonal departures in rainfall and streamflow show the highest deficiency of rainfall and streamflow in 1987. The probability of different drought classes is calculated, and a higher likelihood of severe to extreme dry conditions is observed compared to very wet and extreme wet conditions in the basin. Investigation has been conducted on the impact of meteorological drought on hydrological drought and a correlation analysis between both types of droughts. A significant correlation is observed between meteorological and hydrological drought at iii all analysed time scales. Meteorological drought impacts surface water resources with a one-month lag at all time scales, with the highest response rate obtained at 6-month scale (91.13%). The study also examines the impact of drought on yield loss in Kharif (Bajra) and Rabi (Wheat) crops. Bajra and wheat yield loss rates strongly correlate with non-stationary drought indices, with a more significant effect of drought on bajra yield than wheat during major drought events. The hydrological drought analysis in the humid Netravathi River basin is also conducted using stationary and non-stationary indices. This drought analysis provides feasible results in both arid and humid regions in a changing environment. This novel dimension of drought studies provides practical insights into semi-arid regions in a changing environment. The findings can be utilized by various sectors, including drought management, agricultural planners, and policymakers, to reduce crop loss due to drought.
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    Characterization Of Historical and Future Hydrometeorological Droughts in an Indian Tropical River Basin
    (National Institute of Technology Karnataka, Surathkal, 2020) Pathak, Abhishek A.; Dodamani, B M.
    Drought is acknowledged as a significant natural disaster which leads to food, fodder, and water shortages along with destruction of vital ecological system. Drought is a transient recurring sinister disaster, which originates from the lack of precipitation and further creeps into different subdivisions of hydrological cycle causing adverse effects on agricultural and its allied sector. Combination of these leads to economic losses and several damage to living organisms. Identifying and quantifying drought characteristics of a region is must to understand the behavior of drought and its profound impacts on society, economy, and environment. Along with the historical knowledge, comprehensive overview of future drought projections is a vital step in ensuring future water and food security. The present study focuses on characterizing different hydrometeorological droughts in the historical and future climate of an agrarian Indian river basin. The specific objectives of the study are 1) To investigate annual and seasonal trends of hydro meteorological variables, over the study area. 2) Assessment and comparison of Meteorological, Hydrological and Agricultural drought characteristics with multiple indices 3) To explore the applicability of copulas theory for joint modeling of drought characteristics 4) Characterization of future hydro-climatic droughts. The study was implemented in the Ghataprabha river basin, being one among the potential lands for agriculture in the basin of river Krishna. Firstly, the basin has been categorized in to humid, sub humid and semiarid region based on Aridity Index. Similarly, groundwater well of the study area are grouped in to different clusters using hierarchical and non-hierarchical clustering methods The annual and seasonal trend analysis of different hydrometeorological variables are carried out using Mann-Kendall trend test and the magnitude of the trend was estimated using the Sen’s Slope Estimator. A non-significant decreasing trends in both rainfall and rainy days was observed in semiarid region during monsoon period. Significant increasing trend in mean temperature was observed for all the stations and for all the seasons with the average magnitude of 0.2⁰ C per decade. Along with the mean temperature, annual andseasonal PET trends were also increasing for all the stations but are significant only in semiarid region with the average increase of 3.5mm per decade. The trends in annual streamflow of the basin are decreasing with magnitude of 574.25 cumecs/year, whereas, no significant trends were observed in the reservoir levels. The trend analysis of the groundwater levels of different clusters, revealed that annual water level in the 81% of the wells of cluster 2 and 47% of the total wells of the study area are significantly declining. The hydrometeorological droughts assessment with different indices portrayed significant number of droughts in the past. The RDI and SPI are behaving similarly in all the stations whereas, significant discrepancies was observed between SPI/RDI and SPEI. The hydrological drought assessed with SDI followed similar pattern with SRSI whereas it showed significant divergence with meteorological droughts. Similarly, Agricultural drought derived through VCI followed similar pattern of SPI-6 in comparison with SPI-3. A teleconnection between meteorological drought and groundwater drought was observed along with the crucial role of underlying hydrogeological characteristics. Joint modelling of hydrometeorological drought characteristics and regional bivariate frequency analysis was carried out by employing Archimedean copula. An attempt has also been made to characterize drought in multivariate perspective by developing Standardized Hydro Meteorological drought Index. From the results of bivariate frequency analysis of meteorological drought, it was observed that, droughts of high severity with prolonged duration are frequent in semiarid region compared to humid and sub-humid regions. The joint probability of hydrological drought conveyed drought of smaller duration or severity are more prominent in the basin whereas joint return periods of groundwater drought is high in the well of cluster 2. The developed SHMI considers combined effects of precipitation and streamflow to picturize a near realistic drought scenario of the basin. The future hydrometeorological drought characteristics were assessed by different RCMs. The different bias correction methods were applied to rainfall and temperature to raw RCMs and observed that CNRM-CM5 with LS bias correction method performed better for correcting the rainfall and VS is proved to be superior for correcting the temperature projections. The trend analysis carried out for the future hydrometeorological variable showed significant decreasing trends in annual and post monsoon season whereas temperature trend is increasing significantly with the rise of 0.150 C per decade. The future hydro-meteorological drought characteristics revealed that the basin will experience more number of droughts compared to the past and it can be attributed to decreasing rainfall trend and significant rise in temperature of the basin. In this study, an attempt has been made to characterize future and historical hydrometeorological droughts comprehensively. The outcome of the study will be helpful to design proactive drought mitigation and preparedness strategies for upcoming drought and it also provides a framework to evaluate the drought risks at other parts of the world.