Friday 29 Sep 2017: Radar rainfall forecasting in sewer flood modelling to support decision-making in sewer network operations
Arshan Iqbal - University of Exeter
Flood forecasting techniques are growing in popularity as useful ways of dealing with flooding. Particularly, they could be used by mitigation bodies in operational settings to quantify risk and choose interventions using the lead time created in the forecasting process to tackle flooding before it occurs. Forecasting techniques are alternative solutions to infrastructure developments that are designed to reduce flooding. This is because they are less costly to operate due to little infrastructure involved and are intended for real-time applications, which supports operational management. Therefore, a STREAM EngD project had been setup with Northumbrian Water to predict sewer flooding using radar Quantitative Precipitation Forecasts (QPFs) with a maximum lead time of 6 hours. In this four-year project, Arshan has developed techniques to improve the accuracy of radar QPFs, which would produce accurate hydrological flood forecasts. Uncertainties inherent in radar forecasts are quantified using a probabilistic approach and this has been improved using Bayesian methods, which will be discussed. Finally, a sewer flood forecasting technique is described using the spatial structure of radar rainfall over a sewer catchment. The advantages of this approach include faster predictions of simulated flood variables. Overall, this STREAM EngD project has shown that radar rainfall data could be used as part of real-time sewer flood warning systems that are able to inform decision-making in sewer operations.
Arshan Iqbal is a STREAM EngD Research Engineer working on a project titled ‘Effective use of short range weather forecasting in sewer network operations’ at University of Exeter. The project is sponsored by Northumbrian Water who would benefit from the research outcomes. He had gained a first class BSc Natural Sciences degree from the University of Birmingham in 2011 and started the EngD project in 2013. His background is predominantly pure and applied mathematics with programming. Research interests include radar rainfall forecasting, sewer modelling and forecasting, Bayesian applications and geostatistics in hydrology. During the project, Arshan had been part of several international collaboration schemes including work with National Taiwan University (Taipei, Taiwan), Taiwan Typhoon and Flood Research Institute (Taipei, Taiwan), Indian Institute of Science (Bangalore, India) and New Mexico State University (New Mexico, US).