Flood-hazard risk classification and mapping for urban catchment under different climate change scenarios: A case study of Hyderabad city

Abstract

Climate change and urbanization are two major ongoing process worldwide and in past few years their impact on ground started becoming visible as increased events of intense rainfall and urban floods. Rapidly growing urban areas are liable to greater risk of climate change. Fine-scale rainfall forms the most important input to achieve greater accuracy in urban flood model simulation results. The major concern with modeling urban floods, particularly in developing countries like India is the unavailability of fine-scale rainfall data. This explains the need for developing a methodology to disaggregate large scale precipitation data into a fine-scale interval of time. A statistical rainfall disaggregation method is employed to scale down IDF parameters under climate change conditions. Synthetic rainfall hyetographs are developed from scaled IDF values and used as input to simulate the 1D2D urban flood model. In this study, we used the combination of flow velocity (V), inundation depth (D), and the product (D × V) framed to quantify the flood-hazard for part of Hyderabad, India. The flood vulnerability produced by the hydraulic model is analysed and matched with the 2016 flooding event. The methodology implemented here produced acceptable results and hence evidences to be a reliable approach in an urban setting.