local flood forecasting for local flood risk areas keith beven, david leedal, peter young and paul...
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Local flood forecasting for local flood risk areas
Keith Beven, David Leedal, Peter Young and Paul Smith
Lancaster Environment Centre
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Local flood forecasting
• NFFS : primarily provides forecasts for gauging stations (some hydrodynamic models that make predictions but of unproven accuracy away from gauging stations)
• Some moves towards probabilistic forecasts (SC080030) but few models with data assimilation capabilities
• Warnings based on extrapolations from gauging stations (baseline LOS 2 hr lead time, cannot always be met in small catchments such as Boscastle)
• Extend lead time using QPF - STEPS (but still high uncertainty)
• Or provide local forecasts for local flood risk areas?
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Rules of Flood Forecasting
The Rules1. The event of greatest interest is the NEXT event when a warning
might (or might not) need to be issued.
2. The next event is likely to be different from all previous events (in rainfall pattern; radar anomalies; NWP errors; antecedent conditions; runoff generation; rating curve etc)
3. Allowing for uncertainty means being right more often in terms of bracketing when POD warning thresholds are crossed and allows better assessment of risk of false alarms (NB. this is a GOOD thing in communicating risk).
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Rules of Flood Forecasting
The Rules4. Mass balance is not necessarily helpful in forecasting damaging
floods
• Rainfall estimates for the next event will be wrong• Rating curve for extreme events will probably be wrong – or at least introduce significant heteroscedasticity into errors• Levels are measured; level thresholds used in warning - Why not use levels directly in forecasting?
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Local Flood Forecasting
Data Based Mechanistic (DBM) Modelling Approach• Simple nonlinearity + transfer function model within stochastic
data assimilation framework– See Young (Phil Trans Roy Soc Lond, 2002)– Romanowicz et al. (WRR2006, AWR, 2008); – Leedal et al. (FloodRisk2008) – Beven et al. (FloodRisk2008 – emulation of hydraulic models)
First implemented for SRPB on River Nith for Dumfries in Scotland (with uncertainty and data assimilation and tidal influences) in 1991 (5 hour natural lag; 6 hour lead time required). State Dependent Nonlinearity in FRMRC1
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Predicted Water Level
DBM Forecasting Methodology
Rainfall to level model
Effective Rainfall Nonlinearity
(yt ,ut )
yt
ut
rt
t
InstantaneousEffect
Quick Pathway
Slow Pathway
(“Baseflow”)
Effective Rainfall
Rainfall
Noise
Nonlinear Input Transform
Linear Transfer Function
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Identification of State Dependent Nonlinearity
• Find first estimate of transfer function
• Use in inverse mode to identify gains on inputs
• Rank in order of some state or exogenous variable and filter (level or flow as index of antecedent state)
• Use resulting non-parametric gains to provide inputs to reidentify transfer function
• Check for parametric function to represent nonlinearity (power law / RBF / ….)
• Iterate if necessary
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DBM Identification of nonlinearity
River Eden
(a) Rainfall to Level at Temple Sowerby
(b) Rainfall to level at Greenholme
(c) Levels to Level at Sheepmount
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Adaptive Forecasting
Rainfall to level model
With data assimilation
Effective Rainfall Nonlinearity
(yt ,ut )
yt
rt
t
InstantaneousEffect
Quick Pathway
Slow Pathway
(“Baseflow”)
Effective Rainfall
Rainfall Predicted Water Level
Noise
Ot Observed Water Level
{Ot – yt}
Update gain gt using weighted innovation
gt
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DBM Forecasting: Data Assimilation
• Assimilate Observed Data to produce the best deterministic forecast – Start forecasting from ‘best estimate’ of current
hydrological states• State Space form of DBM model
– Kalman Filter– State dependent variances– Optimise variance parameters on f-step ahead forecast
(presuming future precipitation known)– Use expected value of predictive distribution as a
deterministic forecast.
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River Eden Sensor NetworkFunded by FRMRC2 to
(a) Test HD model predictions and
(b) Test local flood forecasting
Stead McAlpin site
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Probabilistic Level Forecasting for Eden-EA Gauges using raingauge inputs
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River Eden - January 2005 event
Upstream at Appleby
Emergency Centre at Carlisle
Public responseat Carlisle
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Probabilistic Level Forecasting for Eden-EA Gauges using reduced network
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Probabilistic Level Forecasting for Eden-EA Gauges using reduced network
6 hour ahead forecasts at Sheepmount
Aug 2004
Calibration
4 m
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Probabilistic Level Forecasting for Eden-EA Gauges using reduced network
7 m6 hour ahead forecasts at Sheepmount
Jan 2005
Prediction
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Local Forecasting on the River Caldew• Stead McAlpin Factory – flooded in Jan 2005 (almost in 2009 &
2010)
River Caldew and installed level sensor
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Local Forecasting on the River Caldew• Stead McAlpin Factory: Calibration (November 2009)• 2hr ahead forecasts for local level with upstream raingauge input
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Local Forecasting on the River Caldew
• Stead McAlpin Factory: Validation (Nov 2010)
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Emulating distributed flood inundation predictions
Identified nonlinearities for selected sites as a function of input stage
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Emulating distributed flood inundation predictions
HEC-RAS model v emulated water levels –
calibration event
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Emulating distributed flood inundation predictions
HEC-RAS model v emulated water levels –
validation event
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Emulating distributed flood inundation predictions
HEC-RAS model v emulated water levels
reproduction of input-output level hysteresis
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Summary• The next event will be different so data assimilation should
be used wherever possible• DBM approach using rainfall-level (or level-level) forecasts
based on local level sensor• Still requires an input signal – make use of EA gauges?• Are there ways of making these local models self-
calibrating as soon as river starts to go up and down?• Can also be used to emulate hydrodynamic models for
forecasting purposes (at least for simple routing) – but cannot be more accurate than original model (unless data assimilation becomes possible….)