EPSRC Grant: EP/FP202511/1 Probabilistic Forecasting for Local Flooding David Leedal, Paul Smith, Keith Beven and Peter Young Lancaster Environment Centre

Local Flood Forecasting Level sensors are cheap and easily networked GPRS/IP logger Depth sensor fittings £500 £ Total: £ £400 Data…

Local Flood Forecasting Level sensors are quick to installation Work with rainfall or level input Provide a forecast where it’s needed logger tree ladder Dr Paul Smith

Local Flood Forecasting Data Based Mechanistic (DBM) Modelling Approach Simple nonlinearity + transfer function model within stochastic data assimilation framework Identification of State Dependent Nonlinearity directly from data added in FRMRC1 Further development of data assimilation from local sensors in FRMRC2

River Eden Sensor Network Funded by FRMRC2 to (a) Test HD model predictions and (b) Test local flood forecasting Stead McAlpin Local flood forecasting test site:

Local Forecasting at Stead McAlpin (river Caldew nr. Carlisle) Stead McAlpin Factory – flooded in Jan 2005 (almost in 2009 & 2010)

2 hour forecast 4 hour forecast 5 hour forecast Mean forecast Standard error Hourly observations Forecasting Results Calibration: Nov 2009 event Testing: Nov 2010 event

Summary Local flood forecasts might be useful to local stakeholders, even with short to medium forecast lead times FRMRC2 has produced a simple way of providing local flood forecasts with estimates of forecast uncertainty using local level sensors and on-line data assimilation Further work on self-calibrating models is on-going – install-and-leave

EPSRC Grant: EP/FP202511/1 Acknowledgement The research reported in this presentation was conducted as part of the Flood Risk Management Research Consortium with support from the: Engineering and Physical Sciences Research Council Department of Environment, Food and Rural Affairs/Environment Agency Joint Research Programme United Kingdom Water Industry Research Office of Public Works Dublin Northern Ireland Rivers Agency Data were provided by the EA and the Ordnance Survey.