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Item Large-scale atmospheric teleconnections and spatiotemporal variability of extreme rainfall indices across India(Elsevier B.V., 2024) Vinod, D.; Mahesha, A.Identifying trends in hydrometeorological time series during extreme weather events and their interactions with large-scale atmospheric teleconnections is crucial for climate change research. This study evaluates 14 precipitation-based indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) across seven climatic zones of India using gridded daily rainfall data from the India Meteorological Department (IMD) for 120 years (1902–2021) utilised. Trend analysis was carried out using the Mann-Kendall (MK) test, Theil-slope Sen's estimator, Innovative Trend Analysis (ITA), and other statistical tools. Change point detection is established using the Pettitte test and Cumulative Sum algorithm. The relationships between large-scale atmospheric teleconnections and ETCCDI indices are also found, and Multiple Linear Regression (MLR) models are developed between them. The results show significant increasing trends in extreme rainfall indices in India's Ladakh region, located in the arid desert-cold climatic zone. The annual, Southwest Monsoon (SW-Monsoon), Northeast Monsoon (NE-Monsoon), and summer rainfall trends were positive, while winter rainfall had a negative trend across most climatic zones. Significant associations between large-scale atmospheric teleconnections, including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Global Temperature Anomaly (GTA), Southern Oscillation Index (SOI), SST of Niño 3.4 region, Oceanic Niño Index (ONI), and Dipole Mode Index (IOD) and ETCCDI indices were established across multiple climatic zones. Using MLR analysis, this study attempts to establish, for the first time, the relationship between teleconnections and ETCCDI indices across India. Extreme rainfall indices are influenced by climate change during the SW-Monsoon across most of the climatic zones of India. During the previous El Niño event (2014–2016), average annual rainfall decreased by 19.5%, SW-Monsoon rainfall decreased by 25.2%, and NE-Monsoon rainfall decreased by 64.1% in India. The findings may provide valuable insights into mitigation strategies to sustain the adverse effects of extreme weather conditions and enhance climate resilience. © 2023 Elsevier B.V.Item Modeling nonstationary intensity-duration-frequency curves for urban areas of India under changing climate(Elsevier B.V., 2024) Vinod, D.; Mahesha, A.Enhancing stormwater drainage systems is paramount amid evolving climate dynamics, necessitating robust design and continual upgrades to address changing environmental conditions. The present work constructs the nonstationary Intensity-Duration-Frequency (IDF) curves for prominent urban areas of India. It develops 2313 nonstationary Generalized Extreme Value (GEV) models in annual and seasonal timeframes by integrating the influence of local and global climate-informed covariates, including time covariates. The work involves analyzing 1, 2, 3, 4, 6, 12, 24, 36, and 48 hourly maximum rainfall series with return periods of 2, 5, 10, 25, 50, and 100 years. Among the 16 urban areas examined, there's a significant shift from stationary to nonstationary extreme rainfall intensities, marked by a 38.7% increase in shorter duration series with a 5-year return period in New Delhi and Visakhapatnam. AMO, DMI, GTA, and LTA in New Delhi play significant roles. Similarly, in Visakhapatnam, SST in Niño 3.4 and DMI are significant covariates influencing nonstationarity. Recently, in the 2023 monsoon, the 25-year flood wreaked havoc in New Delhi, Rajkot, Surat, and Visakhapatnam. Generating nonstationary IDF curves for the annual and seasonal timeframes offers a comprehensive approach to stormwater design and infrastructure upgradation and effective adaptation strategies across sixteen Indian cities. © 2024 Elsevier B.V.Item Decadal trends and climatic influences on flash droughts and flash floods in Indian cities(Elsevier B.V., 2024) Archana, T.R.; Vinod, D.; Mahesha, A.Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture. This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of ?0.0833 events/year and ?0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies. © 2024 Elsevier B.V.