Disaggregation of daily rainfall data into hourly rainfall data using statistical methods for stormwater management in urban areas.

In the recent past, low-lying areas close to riverbanks and urban agglomerations have witnessed frequent floods due to inadequate mapping of flood zones and rapidly growing impervious areas in cities/towns. This inadequacy is due to the use of design storms for run-off estimates, which do not account accurately for antecedent moisture conditions. Furthermore, when using daily rainfall data, capturing accurate run-off estimates becomes challenging as rainfall characteristics such as duration and intensity are not accounted for. These problems can be addressed with long-term hourly rainfall data. However, most gauging stations in India have long-term daily rainfall data and hourly data for the last few years. There are various global methods to overcome this data limitation by disaggregating daily data into hourly data. However, well-established, peer-reviewed research on this process is still in infancy in India. Therefore, a methodology has been developed to disaggregate long-term daily rainfall data into hourly rainfall using statistical and probabilistic principles based on sample hourly data. In this study, the run-off estimates derived from disaggregated data closely match those obtained from actual hourly data with similar characteristics when simulated over the Belagavi city catchment area in Karnataka, India. The methodology developed relies on sample hourly rainfall, making it scalable across various locations. It holds promise for resilient urban stormwater infrastructure planning in the absence of long-term hourly rainfall data. [ABSTRACT FROM AUTHOR]