Local flood forecasting capability for fluvial and estuarine floods: Use of GridStix for constraining uncertainty in predictive models

Lead Research Organisation: Lancaster University
Department Name: Lancaster Environment Centre

Abstract

This project aims to make use of lots of networked GridStix depth sensors to improve predictions of flood inundation and water level elevation at important locations with a view to improving flood warning capabilities. The project involves improving the software that links the sensors and distributed computing resources. This will allow distributed hydraulic routing models to be run, with the possibility of reducing the uncertainty in their predictions by using the sensor information in real-time. Since the GridStix also have on-board computing capabilities there is also a possibility of building a cheap local forecasting system for specific points at risk of flooding. The science questions involved include how best to make the netwroking robust, how best to constrain the uncertainty in flood routing models and improve their predictions, and how best to implement the local flood forecasting models. The research will be implemented on the River Ribble, subject to regular fluvial flooding, and the tidal system of the River Dee Estuary. The research represents a collaboration between Lancaster and Bristol Universities, the Proudman Oceanographic Laboratory and the Environment Agency.