Department of Civil Engineering
Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1927
Browse
3 results
Search Results
Item Spatiotemporal dependence of soil moisture and precipitation over India(Elsevier, 2022-07) Guntu, RavikumarAnthropogenic climate change has impacted almost all phases of the global water cycle. Growing consensus asserts that extreme precipitation events will only rise in the years to come. However, an increase in extreme precipitation events does not necessarily correspond to higher flood risk. Much onus lies on the antecedent conditions before the storm events. Despite the importance of Soil Moisture (SM) – Precipitation (P) dependence in runoff generation, relatively few studies have unraveled the SM – P dependence. Previous studies were constrained by the direct trivial relationship existing between SM and P, and hence there is a need to understand direction and dynamical interdependency. We employed Event Coincidence Analysis (ECA) to identify and quantify the preconditioning of P extremes by soil moisture (SM) anomalies. High precursor coincidence rate (greater than45%) was obtained for traditional flash flood-prone areas over India - Ganga river basin, West-flowing rivers of Kutch and Saurashtra including Luni, inland drainage of Rajasthan and Narmada river basin, indicating the robustness of the approach. The trigger coincidence rate reveals strong SM-P coupling over central India. Our results indicate the applicability of ECA in characterizing the spatiotemporal patterns of SM-P dependence over India.Item Increased likelihood of compound dry and hot extremes in India(Elsevier, 2023-07) Guntu, RavikumarCompound dry and hot extremes (CDHE) are periods of prolonged dry and hot weather. Their joint occurrence typically impacts society and nature stronger compared to the occurrence of the single hazards. Understanding the likelihood, variability and drivers of CDHE is challenging due to the complexity of the climate system involving interactions and feedbacks among atmosphere-land processes. In this study, we first investigate the role of the dependence between precipitation and temperature for the likelihood of CDHEs. We demonstrate that both the dependence strength and its type, i.e. the degree of tail dependence, substantially affect the CDHE likelihood. We then analyze the space-time variation of CDHE characteristics during the Indian Summer Monsoon across India for the period 1961–2014. We find strong negative association and substantial tail dependence between precipitation and temperature in some regions. Event coincidence analysis reveals that low soil moisture preconditioned by dry extremes is responsible for 55–65% of CDHE occurrence. Our analysis of the temporal evolution of CDHE characteristics finds an increasing negative association between precipitation and temperature leading to a 2 to 3-fold rise of CDHE frequency for some regions of India.Item Improving the predictability of compound dry and hot extremes through complexity science(IOP, 2023-11) Guntu, RavikumarCompound dry and hot extremes (CDHE) will have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unraveling the teleconnections of CDHE in India. Previous investigations specifically focused on the teleconnections between precipitation or temperature and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. In this study, we present a framework that combines event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the key drivers of CDHE. To evaluate the effectiveness of the proposed drivers, a logistic regression model is employed. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.