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BGS & BRGM

• Groundwater Flooding events 2000/2001– Brighton– Somme Valley

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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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