© nerc all rights reserved groundwater flooding flood1 – interreg iiia project (bgs, bgrm,...
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© NERC All rights reserved
Groundwater floodingFLOOD1 – Interreg IIIA project (BGS, BGRM, Brighton Uni.)Aim to understand processes leading to groundwater flooding in Chalk and
produce predictive models for Somme and Brighton
Somme valley at Abbeville, April 2001(Ministère de l’Ecologie et du Développement Durable)
Flooding of the Patcham area, Brighton November-December 2000
( John Ellis, EA)
www.flood1.info
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Outline
• What specific questions are we trying to answer using models?
• How do we represent the processes of interest?
• With current commonly applied techniques, what can we do well, and where is there room for improvement?
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Groundwater flooding
• Definition:
”A groundwater flood event results from a rise in groundwater level sufficient for the water table to intersect the ground surface and inundate low lying areas”
Flooding from other sources (HA4a), 2006, jba Consulting
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What do we want models to do?
• Explain – why and how
• Predict – where and when
Constraint – need a methodology that can be applied in other regions
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Processes of interest
• Response of water table to rainfall
• Effect of “micro” geology on water table
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What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
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What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
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© NERC All rights reserved
© NERC All rights reserved
What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
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Average recharge (1990-2003)
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1990
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Gro
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dw
ater
lev
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m a
OD
)
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5
10
15
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Mean
mo
nth
ly recharg
e (mm
/day)
Pyecombe Old Rec Mean monthly recharge (mm/day)
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What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
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Average groundwater levels
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Transmissivity distribution
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Modelled water levels
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Difference between model and observed water levels
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BRIGHTON PRESTON PARK
Model
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BRIGHTON ST PETERS CHURCH
Model
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LOWER STANDEAN Model
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NORTH BOTTOM Model
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FAULKNERS BOTTOM Model
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What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
Data
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Envirosmart Probe
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30-Jul 11-Aug 23-Aug 04-Sep 16-Sep 28-Sep 10-Oct 22-Oct 03-Nov 15-Nov 27-Nov 09-Dec 21-Dec 02-Jan 14-Jan 26-Jan
Date
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Rai
nfa
ll (m
m)
0.1 m
0.3 m
0.4 m
0.6 m
0.8 m
1.0 m
1.2 m
1.5 m
Rainfall
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What have we tried
• Using DTM and water table contours
• Distributed recharge model
• Regional groundwater model with simplified geology
• Unsaturated zone model
Data
Boundary conditions
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The way forward for prediction
• Analysis of hydrographs
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The way forward for prediction
• Analysis of hydrographs
Interested in GWL maxima
Water level related to rainfall
Want to predict how high water level may rise during next recharge season
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Predicting groundwater floods1. Calibrate and validate model using historic GWL dataMultiple linear regression model where annual maximum groundwater level (m OD) is a function of previous annual minimum and winter rainfall (mm)
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Predicting groundwater floods1. Calibrate and validate model using historic GWL dataMultiple linear regression model where annual maximum groundwater level (m OD) is a function of previous annual minimum and winter rainfall (mm)
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2. Predictive modelUse Monte Carlo simulation to predict a range of annual maximum groundwater levels, based on a range of possible winter rainfall scenarios, up to nine months ahead
1. Calibrate and validate model using historic GWL dataMultiple linear regression model where annual maximum groundwater level (m OD) is a function of previous annual minimum and winter rainfall (mm)
Predicting groundwater floods
© NERC All rights reserved
1. Calibrate and validate model using historic GWL dataMultiple linear regression model where annual maximum groundwater level (m OD) is a function of previous annual minimum and winter rainfall (mm)
Predicting groundwater floods
2. Predictive modelUse Monte Carlo simulation to predict a range of annual maximum groundwater levels, based on a range of possible winter rainfall scenarios, up to nine months ahead
Frequency Chart
(m OD)
.000
.006
.011
.017
.022
0
138.2
276.5
414.7
553
9.30 10.33 11.37 12.40 13.43
25,000 Trials 24,733 Displayed
Forecast: Predicted Max (m OD)
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