Department of Civil Engineering
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Item Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory(Elsevier, 2020-11) Guntu, RavikumarPrecipitation regionalization deals with an investigation of the seasonality and its temporal variability and is useful for a wide variety of applications in hydro-meteorology. The d homogeneous regions can be used as a basis for transforming the information from gauged to ungauged sites and can reduce the uncertainty in estimating the seasonal characteristics of precipitation across India. Despite several studies stressing the importance of seasonality and temporal variability to the environment, there is a lack of studies on accounting for temporal variability in regionalization. Precipitation regionalization must account for both the precipitation magnitude and its temporal variability at multiple time-scales to extract the seasonality of a region representing coherent local and inter-annual variability. Therefore, in this study, we propose a framework for precipitation regionalization, considering both precipitation magnitude and its temporal variability. High resolution (0.25° × 0.25°) gridded daily precipitation time series over the period 1901–2013 from Indian Meteorological Department (IMD) was used for the evaluation of the framework. First, the historical daily time series was transformed into multiple time scales, i.e., annual, seasonal, and monthly time scales. Entropy-based standardized variability index was used to measure the inter-annual variability of precipitation at each time scale. Regionalization of grid points was performed using self-organizing maps, an artificial neural network. Ten distinct regions were identified that can be tied back to two general categories, such as climate characteristics and physical characteristics. Coupling of the self-organizing map with standardized variability index reveals unique seasonal distribution of precipitation for each region. The temporal evolution of clusters unravels a new emerging pattern across Central India. Consideration of temporal variability plays an insignificant role in the shape, size and stability of south-central India, south-eastern coastlines, and Konkan Coast. Intriguingly, separate Rain-belt and Rain-shadow Western Himalayas are formed due to the difference in topography and seasonal characteristics of precipitation. The temporal evolution of clusters unravels a significant change in the occurrence of the 50th percentile monsoon after the 1940s across the north-western region; a significant increase in the 50th percentile monsoon after the 1940s across western India, and decrease in the 50th percentile monsoon after the 1980s in the north-central Region.Item Disentangling increasing compound extremes at regional scale during Indian summer monsoon(Springer, 2021-08) Guntu, RavikumarCompound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951 to 2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unravelled that CDHE (CWCE) frequency has increased (decreased) by 1–3 events per decade for the recent period (1977–2019) relative to the base period (1951–1976). Overall, the increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.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 Compound dry and hot extremes: a review and future research pathways for India(Elsevier, 2024-05) Guntu, RavikumarCompound Dry and Hot Extremes (CDHEs) is gaining attention compared to individual dry or hot extremes, due to their amplified impacts on both the population and ecosystems in India. This underscores the importance of transitioning from studying individual extremes to adopting a compound perspective. Despite this, investigation of CDHEs during the Indian summer monsoon remain limited, and a comprehensive review of methodologies for the investigation of CDHE is absent. This review systematically synthesizes recent literature, covering concepts of CDHE with illustrative examples, including identification, characterization, drivers, and prediction. It illustrates three widely used methods for the identification of CDHEs along with their advantages and disadvantages. Furthermore, it describes concepts with illustrative examples to investigate the characteristics (frequency, spatial extent, timing, duration, severity, and likelihood), explores drivers using event coincidence analysis and a complexity-based framework, and discusses the strengths and weaknesses of a logistic regression model for predicting the occurrence of CDHE. In light of the growing significance of CDHEs, we suggest future directions for Indian CDHE research, including an improved characterization of CDHEs across multiple temporal and spatial scales, a deep understanding of the physical mechanism, a robust evaluation of climate models, attribution and projection, and a comprehensive impact assessment. CDHEs are the new normal, and there is an urgent need to advance research on CDHEs in vulnerable regions like India to combat and mitigate their effects.