Browsing by Author "Sajeev, A."

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A Non-stationary Hydrologic Drought Index Using Large-Scale Climate Indices as Covariates
(Springer Science and Business Media Deutschland GmbH, 2023) Sajeev, A.; Kundapura, S.
The dry and wet periods can be analyzed based on different drought indices. Most existing drought studies are based on stationary assumptions, and environmental changes are not considered. This study proposes a non-stationary streamflow-based drought index, incorporating large-scale climate indices to study hydrological drought for 45Â years. Climate indices are used as covariates for building the non-stationary model fitted to streamflow. Correlation analysis is carried out to determine the best covariates for the streamflow in the Netravati River basin in India. The Southern Oscillation Index (SOI) exhibited a significant influence on streamflow at all time scales. The non-stationary model is compared with the stationary model, and the best model is chosen based on the Akaike information criterion (AIC). Under statistical measures, non-stationary models performed better than stationary ones at all time scales. The generalized additive model for location, scale, and shape (GAMLSS) is used for non-stationary modeling. The models are developed for short-term (3 and 6Â months) and long-term (12 and 24Â months) droughts. The influence of climate variables on drought classes is analyzed, and more severe drought is observed under the non-stationary scenario. The deficiency in streamflow was more than 60% in the basin in 1987 and 2002. The non-stationary drought index detected more severe drought events than the stationary index under short-term scales. Hydrological drought properties such as drought severity, duration, and peak are calculated under stationary and non-stationary scenarios, and a noticeable difference is observed. Compared to stationary models, the non-stationary model yields more logical and satisfactory findings because it effectively takes into account non-stationarities in the streamflow caused by climate change. Â© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Bivariate Drought Characterization of Two Contrasting Climatic Regions in India Using Copula
(American Society of Civil Engineers (ASCE), 2021) Sajeev, A.; Deb Barma, S.; Mahesha, A.; Shiau, J.-T.
This study aims to construct the multiple time-scale joint distributions of drought duration and severity using two-dimensional copulas and compare the drought characteristics in India's two contrasting climate regions: the arid Rajasthan and humid, tropical Kerala. The drought occurrences were defined by the standardized precipitation index (SPI) with a threshold below -0.8 at time scales of 3, 6, 12, and 24 months for 1900-2016. Significant correlations were noted between the drought severity and drought duration in both regions. The Clayton copula gave a better fit than other copulas for modeling the dependence among the observed drought duration and severity. The results indicate that the probability of short-term droughts (SPI-3 and SPI-6) is more significant than those of long-term droughts (SPI-12 and SPI-24) for an identical drought event in both regions. Also, the probability of severe drought events with greater duration and severity for long-term droughts (SPI-12 and SPI-24) is higher in Kerala than that in western Rajasthan. For all the time-scale SPIs, the conditional probability of drought severity for a given duration exceeding a threshold showed an increasing trend in both regions. Furthermore, the conditional probability of the drought duration given the severity for short-term droughts is greater than that of the long-term droughts for the same drought event. For short-term droughts, the conditional return period of an identical drought event is lower in Kerala than in western Rajasthan. In contrast, the conditional return period of long-term droughts is lower in western Rajasthan. Additionally, copula-based nonexceedance conditional distributions for the major crops were established based on rainfall. © 2020 American Society of Civil Engineers.
Comparative evaluation of meteorological and hydrological drought using stationary and non-stationary indices in a semi-arid river basin in India
(Springer Science and Business Media B.V., 2024) Sajeev, A.; Kundapura, S.
Few researchers have incorporated climate change in drought indices calculations and conducted comparative analyses of meteorological and hydrological droughts using non-stationary indices. The primary objective of this research is to develop non-stationary indices for assessing meteorological and hydrological droughts in the Shetrunji River basin in India. The climate oscillations are used as covariates to create non-stationary models by applying the generalized additive model in location, scale, and shape 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 drought. A significant correlation is observed between meteorological and hydrological drought at all analyzed 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. 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. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Temporal Assessment of Meteorological Drought Events Using Stationary and Nonstationary Drought Indices for Two Climate Regions in India
(American Society of Civil Engineers (ASCE), 2023) Sajeev, A.; Kundapura, S.
This study attempts to build nonstationary indices for assessing meteorological drought in two different climate zones in India: the arid Saurashtra and Kutch and humid-tropical Coastal Karnataka. Time and climate indices are considered as covariates to develop nonstationary 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 nonstationary 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 analyzed, and more rainfall-deficient years are detected in Saurashtra and Kutch regions than in Coastal Karnataka. The nonstationary index performed better in capturing drought properties in statistical analysis over both the study areas at all time scales. The nonstationary drought index shows better consistency with historical drought and flood events than the stationary index. Cooccurrence 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. The impacts of rainfall and drought on the yield of major crops in study areas are also analyzed. The yield loss rate of bajra significantly correlates with the nonstationary standardized precipitation index (NSPI) in Saurashtra and Kutch, whereas rice yield has no significant correlation with the index in Coastal Karnataka. This new aspect of drought analysis provides feasible results in both arid and humid regions in a changing environment. © 2023 American Society of Civil Engineers.