DEVELOPMENT OF STRATEGIES FOR MITIGATION URBAN FLOODING

Abstract

This study focuses on the analysis of historical land use and land cover (LULC) changes in Delhi, aiming to predict future alterations and assess their hydrological impacts. The research is conducted in three primary phases: analyzing past LULC changes, detecting climate change trends, and mapping flood susceptibility to identify the best Low-Impact Development (LID) controls. The goal is to reduce flood risk by implementing suitable LID strategies in the context of LULC changes and climate change. The first phase involves a spatial-temporal analysis of LULC images using techniques to interpret patterns and changes over time. Spatial analysis examines LULC patterns within a specific area, identifying trends and relationships. Temporal analysis studies changes in LULC over time, revealing the drivers and impacts of these changes. Classified images from 2000, 2005, 2010, 2015, and 2020 demonstrate significant increases in built-up areas and reductions in vegetation and barren land. This data helps predict future LULC changes and their potential impacts on the study area's hydrological regime.A confusion matrix evaluates the precision of classified images, showing the number of true positives, true negatives, false positives, and false negatives. For example, the confusion matrix for the LISS III image for 2020 indicates an overall accuracy of 66% and a Kappa coefficient of 0.57, suggesting moderate performance. These metrics ensure the reliability of the LULC classification and subsequent analyses. LULC changes from 2000 to 2020 are quantified, revealing a significant increase in built-up areas, a decrease in barren land, and a reduction in vegetation. Built-up areas have grown 1.6 times in the last two decades, while vegetation cover has reduced by nearly half since 2000. These changes highlight the urbanization trends in Delhi and their potential impact on the environment. The Mann-Kendall test detects trends in climate data, revealing significant changes in annual rainfall at a 0.05 significance level. Three out of four stations in Delhi show an increasing trend, while one indicates a downward trend. The Standardized Anomaly Index vi (SAI) further identifies anomalies in rainfall data from 1970 to 2020, indicating normal and wet years compared to dry years.An Intensity-Duration-Frequency (IDF) curve is developed for Delhi using maximum rainfall data from 1971 to 2020. The curve illustrates the relationship between rainfall intensity, duration, and frequency, crucial for stormwater management and drainage system design. The IDF curve shows maximum rainfall intensities for different return periods, helping assess flood risk and design appropriate flood mitigation measures. Landsat 8 satellite data and the NDVI and MNDWI indices are used to investigate the Urban Heat Island (UHI) effect and flood susceptibility. The NDVI map shows vegetation cover, while the MNDWI map highlights water bodies. Land Surface Temperature maps, derived from thermal image analysis, reveal temperature variations across Delhi, helping identify UHI areas. The Urban Heat Island Index (UHII) further confirms the presence of UHI in the study area.LID techniques are suggested at two scales: intermediate and catchment. Intermediate-scale LID focuses on neighborhood or sub-watershed levels, implementing techniques like rain gardens, green roofs, infiltration trenches, permeable pavement, bioretention systems, and swales. Catchment-scale LID encompasses entire watersheds, integrating LID principles into land use and development policies. LULC maps and Wetness Index calculations help select suitable sites for LID implementation.LULC significantly impacts the selection of LID techniques. Urban areas with high impervious surfaces benefit from techniques that reduce runoff, such as green roofs and rain gardens. A Digital Elevation Model (DEM) helps derive the Wetness Index, indicating hydrological and ecological conditions. The Topographic Wetness Index (TWI) is calculated using flow accumulation and slope, identifying areas with high and low wetness values for LID site selection.LID performance is assessed by the percentage reduction in flow depth and volume for each technique. The study shows that LID practices like swales, bioretention, green gardens, and permeable pavement significantly reduce stormwater volume and flow depth. Swales achieve the highest reduction, followed by bioretention, green gardens, and permeable pavement. This study provides a comprehensive analysis of historical and future LULC changes, climate change trends, and flood susceptibility in Delhi. By identifying suitable LID techniques and their performance, the research offers valuable insights for sustainable urban water management. Implementing appropriate LID controls can significantly reduce flood risk, mitigate the impacts of urbanization, and improve water quality in Delhi.