SOUFRIERE HILLS,,MONTSERRAT

Modelling Uncertainty in Spatial
D t AN
Data:
NaturaltlHHazards
d E
ExamplelAndrew Lovett
University
y of East Anglia,
g , UK
email: a.lovett@uea.ac.uk
R. Herd, 2003

University of East Anglia
Anglia, Norwich
Norwich, UK
U i
University
it C
CampusCathedralCastle

Website: www.uea.ac.uk

UEA has a large multidisciplinary SchoolS
off Environmental
Sciences. GIS is used in a range of application contexts,
including economics
economics, catchment management,
management natural hazards
hazards,
public health and environmental decision making. We use
specialist 3D visualization facilities as part of our research.

Elumens DomeMulti-Projector
Multi Projector Display

An Introductory Quote“ Uncertainties and errors are intrinsic to spatial
data and need to be addressed properly, not swept
under the carpet
p of fancy
yggraphics
pdisplays”
p yBurrough and McDonnell (1998, p.220)• Much terrain modelling has been driven by data availability• More attention need to be given to ‘fitness for purpose’• This is particularly relevant for natural hazard management• One example is lahars (a type of volcanic mudflow)

Assessing Uncertainty
Evaluating the uncertainty in different inputs and how
this may propagate through modelling operations iscentral to evaluatingg the reliability
y of final results.
Two complementary methodologies• Uncertainty analysis – propagation of error• SensitivityS
iti it analysis
l i – apportion
ti output
t t variationi ti tot
sources
These techniques utilise simulation procedures andcan help
p assess whether results meet the qquality
yrequirement for the application.

Montserrat Study AreaBelham Valley

NovemberNb 2004
2004,
Tourist bus strandedImpact of Lahars in the Belham Valley~3 m2006

1996

GGroundd GPS Survey
S

Used tocreatePrimaryyDEM

CCreatingtia SecondarySdDEM

Photos used to add details of channels and vegetation
gterraces.

Flow Modellingg Methodologygy
5 or 10 m
cells

Construction
methodResolution
P

Spatialdependency

stdv of error
PP

Stdv = 0.1,0.5 or 1 mPDEM
Error'noise' field

PerturbedPt b d
DEM

Cost Distance

Cost Direction

Flow modelling based onelevation change

Flow algorithm

P

Start point

PEnd point

Least-cost
flow path

Probability
surfaceDEM +Random Error

DEM

Least Cost Path

8.2 6.1 5.2000

5.1 4.4 3.4111

8.8 7.0 3.6000

Probable
Paths

8658 1 5.8
8.1
5 8 4.8
48

0

0

0

000

543

4.9 4.2 3.6

1

101

1.66

974

9.0 6.7 3.5

00

100.33

7.8 6.0 5.0000

5.4 4.4 3.3

111

9.1 7.2 4.2000

Flow Modellingg Methodologygy
5 or 10 mcells

Construction
methodResolution
P

Spatialdependency

stdv of error
PP

Stdv = 0.1,0.5 or 1 m

PDEM
Error
'noise' field

Perturbed
Pt b dDEM

Cost Distance

Cost Direction

Flow modelling based onelevation change

Flow algorithm

P

Start point

PEnd point

Least-cost
flow path

Probability
surfaceImpact of DEM Interpolation Method

Rasterised
TIN

SplineTopogrid

I
Impactt off DEM C
ConstructionttiM
Methodth dImpact of Changing Cell Resolution

Stdv = 0.5 m

SSecondary
DEM more robust to resolution changeImpact of Changes in Error Parameter

Incorporating spatial dependency inerrors had relatively little additional
iimpact.
tIImplications
li ti• In this application, DEM construction method and elevation error havethe most significant impacts on model output.
• Primary 10 m DEM, stdv = 0.5, 45 % of area covered by some flow
• Secondaryy 10 m DEM,, stdv = 0.5,, 16 % covered• Primary DEM not ‘fit for purpose’ in this study area
• Results highlight ‘ key’ locations for monitoring terrain changes• Spatial dependency of DEM errors doesn’t merit further investigation• Adding ‘noise’ does not compensate for inadequate DEM creation.

CConclusion
l i
There is no substitute for constructing a DEM thatis informed by the terrain and evaluated as fit for
the application to which it is putput.

Forthcoming Publication
Darnell, A.R., Lovett, A.A., Barclay, J. and Herd, R. (in press)
An application-driven approach to terrain model construction,I tInternationaltil Journal
Jl off GeographicalGhi l IInformation
fti Science.
S i