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

Lead Research Organisation: National Oceanography Centre
Department Name: Proudman Oceanographic Laboratory


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.


10 25 50
Description The methodology has later been demonstrated in an application in Nepal where, following local training, the forecasting toolbox has been successfully adopted by Dept. of Hydrology and Meteorology, Government of Nepal and applied to all the basins in Nepal where real-time data is available. As part of the Nepalese operational flood early warning systems for the 2016 and 2017 monsoon periods it has been used to provide forecasts with a maximum lead time of between 2 & 8 hours.
First Year Of Impact 2016
Sector Environment
Impact Types Societal,Policy & public services