SPATIO-TEMPORAL ANALYSIS OF RAINFALL PATTERN AND URBANIZATION: UNDERSTANDING THE RISE OF URBAN FLOODING ALONG THE YAMUNA RIVER IN DELHI

Abstract

A growing number of cities throughout the world are at risk from urban flooding, a serious and increasingly common effect of climate change. The increasing susceptibility of metropolitan areas to floods is a result of the ongoing changes in our global climate, including rising temperatures, unpredictable weather patterns, and an increase in precipitation occurrences. The growing urban population further compounds this vulnerability, intensifying the strain on vital resources such as food, water, and energy. This heightened demand has led to the proliferation of settlements along riverbanks, particularly among economically disadvantaged urban communities known as the "urban poor." Constrained by financial limitations and limited access to piped water, these populations often rely on rivers for their daily necessities. However, this proximity to water bodies places them at heightened risk during flood events, exposing them to both the immediate dangers of natural disasters and the long-term health hazards posed by contaminated floodwaters. By understanding its underlying causes, dynamics, and impacts, stakeholders can develop effective strategies and interventions to mitigate the adverse consequences and enhance the resilience of urban communities facing this multifaceted challenge. Given that flood severity is directly correlated with increasing precipitation and land use land cover change, the study has been conducted comprehensively on the assessment of available precipitation data, procured for eleven AWS (Automatic Rainfall Stations) in Delhi for a period of 13 years from 2010 to 2022, focusing on the monsoon season spanning from June to September. The Gini index is calculated as a statistical measure to assess the uniformity or non-uniformity in the distribution of a rainfall over the study area. Rainfall trend analysis involves the examination of historical rainfall data to identify patterns or trends in precipitation over time i.e., to determine whether there was a consistent and significant increase or decrease in the amount of rainfall during study period. The semi-variogram is a geostatistical tool used to assess the spatial dependence of rainfall across different locations to analyse how monitoring stations influenced one another regarding rainfall. The land use land cover change analysation has been done to check the urbanized area for the year 1993, 2003, 2013 and 2023 and how its impacting on flooding. The flood model developed is used to identify the significant flood hotspots in the study area. During flood this flood model will help in the disaster management organizations to build efficient pathways for developing resilience mechanisms to ensure optimum protection for the communities at risk and urban planners, policymakers, and public health professionals to mitigate the impacts of climate change on urban health risks.