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FLOOD 1
BGS & BRGM
• Groundwater Flooding events 2000/2001– Brighton– Somme Valley
FLOOD 1
BGS & BRGM
• Groundwater Flooding events 2000/2001– Brighton– Somme Valley– East Ilsley (LOCAR)
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Groundwater flooding in the Somme Valley
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Patcham and Moulsecomb
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Groundwater Flooding in the Pang Valley
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Groundwater Flooding in the Pang Valley
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Compton to East Ilsley
Looking east towards Compton
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FLOOD 1 Objectives
• To understand the hydraulic behaviour of water flow in the unsaturated zone which leads to triggering of groundwater flood events.
• To develop unsaturated zone monitoring techniques, including non-intrusive ones such as Magnetic Resonance Sounding (MRS), to reduce cost and environmental; impact, and to improve areal representation of the data.
• To produce more appropriate methodologies and tools for forecasting groundwater flood events capable of operating within a much longer timescale than is currently possible (i.e. days and weeks rather than hours.
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The Somme model
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20 km
Bohain-
Chauny
Gauchy
Soissons
Tergnier
Villers-CotteretsLes Andelys
Gaillon
Gisors
AnicheAnzin
Auby
Auln
Cambrai
Le
C
Cuincy
Douchy-les
Flines-Lez-RachesOrchies
Pecquencourt
Beauvais
ChamblyChantilly
ClermontCompiegne
Creil
Lamorlaye
Liancourt
Meru
Mouy
Noyon
Thourotte
Achicourt
Annay
Avion
Barlin
BerckSaint-Pol-sur-Ternoise
Eu
Gournay-en-Bray
Neufchatel-en-Bray
Abbeville
Albert
Amiens Corbie
Doullens
Ham
Montdidier
Peronne
Roye
Magny-en-Vexin
Abbeville
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Distributed model for MARTHEDistributed model for MARTHEGeometry•Network (500 m x 500 m)
about 29500 meshes•Topography and substratum levels•River network
about 1500 meshes
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Forecasting of the Somme discharge at Abbeville with GARDENIA
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GARDENIA
A lumped hydrological model for the simulation of relationships between series of:
• Discharge data of a spring or stream and amounts of rainfall received by the corresponding basin
• The piezometric levels in an aquifer and amounts of rainfall received by the corresponding basin
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RUMAX déficit maximal du sol
RUIPER Hauteur d'équiRuissellement Percolation
THG Temps ½ percolation
Percolation(RECHARGE de la nappe)
PLUIE EFFICACE
Q = SURF * EC + Qo NP = G / S + NB
Écoulement total(DEBIT du cours d'eau)
Niveau Piézom. NPDébit Q
PLUIE ETP
Surface du bassin versant
GARDENIA
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Somme à Abbeville - Modèle GARDENIAMode prévision
0
20
40
60
80
100
120
mai-00 nov-00 mai-01 nov-01 mai-02 nov-02 mai-03 nov-03 mai-04
m3/
s
20 à 10 %50 à 20 %80 à 50 %90 à 80 %> 90 %Débit observéDébit simulé
Début de la prévision
19/01/2004
GARDENIAmode prévision
40 simulated series of discharges using observed rain rates from 1962 to 2001
Dischargeforecasting
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The Patcham flooding
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The Patcham groundwater floods
Time
Gro
undw
ater
lev e
l
Spring flow “on”
Spring flow “off”
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The experimental sites
• Warloy Baillon – The Somme Valley• North Heath Barn – Brighton, South Downs• East Ilsley – River Pang
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The Brighton experimental site
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North Heath Barn 1
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North Heath Barn 2
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EnviroSmartProbe 4 m
Neutron Probe access tube 4 m
Data Logger
PurgeableTensiometers
10 m approx
6 m approx
NOT TO SCALE
Shallow pit with one recording and one storageraingauge
1 m
2 m
3 m
4 m
5 m
EquitensiometersBuried cable to borehole tensiometers
New wooden post and rail fence
Existing fence
The Brighton recharge site
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East Ilsley
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East Ilsley 1East Ilsley No. 1
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(SU) 49960 81142East Ilsley, nr. Compton, Berkshire
BGS
Ground SurfaceTopsoilSubsoilSubsoil and chalkPutty chalkHard chalkPutty chalk with lumpsBlocky chalk with little matrixSoft chalkHarder ?flinty chalk
Soft chalk
Harder (?flinty) chalk
Hard chalk
Hard chalk and flints
Soft chalk
Harder chalk ?with flintsSoft chalk
1.2
3.1
5.3
7.1
27.0
30.0
33.0
35.0
37.0
40.0
Firs
t wat
er s
trike
(30
m)
Stan
ding
wat
er le
vel a
fter f
irst s
trike
(24
m)
Natural infall
Pea Gravel
Bentonite
Pea gravel
Bentonite
Ope
n ho
le
Grout
Fugro Ltd.
Shell 0 - 5.65 m, rock bit 5.65 - 40 m
3 - 8 February 2005
250 mm to 5.65 m, 200 mm to 40 m
Ground level
Log of Borehole:
Project:
NGR:Location:
Project Manager:
Drilled By:
Drill Method:
Drill Date:
Hole Size:
Datum:
Sheet: 1 of 1
BGSWallingfordOX10 8BB
FLOOD1 ProjectFIELD DESCRIPTION WELL COMPLETION DETAILS
Dep
th0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Sym
bol Description
Dep
th
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East Ilsley 2East Ilsley No.2
(SU) 4996 8114East Ilsley, nr. Compton, Berkshire
BGS
Ground SurfaceChalk
Hard bandHard band
Hard band
11.0
18.8
40.5
Firs
t mea
sure
d w
ater
leve
l afte
r dril
ling
(24
hrs
late
r)
Ope
n ho
le
Pea gravel
Bentonite
Grout
Fugro Ltd.
Shell 0 - 5.86 m, wireline coring 5.86 - 40.5 m
9 - 14 February 2005
250 mm to 5.86 m, 143 mm to 40.5 m
Ground level
Log of Borehole:
Project:
NGR:Location:
Project Manager:
Drilled By:
Drill Method:
Drill Date:
Hole Size:
Datum:
Sheet: 1 of 1
BGSWallingfordOX10 8BB
FLOOD1 ProjectFIELD DESCRIPTION WELL COMPLETION DETAILS
Dep
th0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
Sym
bol Description
Dep
th
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The jacking tensiometers
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The jacking tensiometers
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Magnetic Resonance Sounding
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Hydrograph response
• Brighton• Warloy Baillon• East Ilsley
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Comparative hydrograph characteristics
• How similar or dissimilar are the groundwater level (GWL) hydrographs from the Somme, Brighton and the Pang?
• How similar or dissimilar are the responses of GWLs in the three catchments to rainfall?
• What do the GWL hydrographs mean in the context of groundwater flooding?
• What are the controls on the form of the hydrographs?
• Not looking at specific rainfall / flooding events
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Comparative hydrograph characteristics
• Sites in Hallue and Pang show slower & longer responses to rainfall events
• Sites in Hallue show the longest GWL memory -those in Brighton have the shortest memory
• These observations are consistent with the relative lengths of the groundwater flooding events
• A variety of geological & hydrogeological factors control the form of the hydrographs
• Semi-quantitative relationships between these factors and the hydrographs could be established and hence Chalk throughout the Paris and London Basins could potentially be zoned or categorised in terms of the potential risk of groundwater flooding
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Predicting groundwater floods
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)
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)
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
0
2
4
6
8
10
12
14
25-May-
79
6-Sep-82
19-Dec-85
2-Apr-89
15-Jul-92
28-Oct-95
9-Feb-99
24-May-
02
5-Sep-05
Date
GW
Ls (m
OD
)
Monthly GWLsAnnual minimaAnnual maximaPredicted maxima
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Predictive models• Use historic cumulative rainfall distribution
and model or use simple assumptions related to expected rainfall as a percentage of average rainfall to predict subsequent groundwater level maxima
• In both cases, trigger levels can be defined to set in train other procedures
• Problems with setting trigger levels – how to relate levels to flooding events
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Envisaged use• First in a tiered series of actions of
increasing complexity (cost)• This initial step is simple, cheap and
formalises intuition of local staff• Subsequent steps may include increased
monitoring and monthly groundwater level modelling (stochastic, lumped parameter, neural network models)
• Tiered approach may enable better management of scarce resources and help with public awareness
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Data from Monitoring sites
• Jacking Tensiometers– Brighton – East Ilsley
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