Geographical & Environmental Modelling

Dr Nigel Trodd

Coventry University

In this lecture you will:

identify reasons for geographical modelling

define a ‘(geo-)information envelope’ using data quality parameters

exemplify the process of geographical modelling

apply a terrain-based model to analyse a natural hazard

ModelsModels

Why Model ?

There is a growing demand for models that can simulate and predict environmental processes.

A range of computer models have been developed that simulate processes (e.g. debris flow, pollutant dispersal, flooding) at scales between 1:50 000 and 1:500 000.

Results of these models provide important information for decision makers and planners - allowing better implementation of appropriate land management measures.

‘I’m a celebrity get me …a risk map’

In the past one map

might exist

qualitative

select & combine digital geospatial information

quantitative– statistical model– process model

Modern risk assessment

In the past one map

might exist

qualitative

co n ceptu al mo d elling q u an titative mo d elling ap p lication

m o d elling

Developing a model ?

risk = f {hazard, vulnerability }

co n ceptu al mo d elling q u an titative mo d elling ap p lication

m o d elling

co n ceptu al mo d elling

p aram eterise p o p u late o p erate valid ate

q u an titative mo d elling ap p lication

m o d elling

Quantitative modelling

co n ceptu al mo d elling

what info

do we need?

p aram eterise

how do we

get it?

p o p u late

how does the

model perform?

o p erate

is the model

accurate,precise

& robust?

valid ate

q u an titative mo d elling ap p lication

m o d elling

What (geospatial) info do we need?

the ‘information envelope’ identify variables of interest

which ones? prioritise the mission-critical data requirements for

effective decision-making how?

– sensitivity analysis we need some criteria

- data quality e.g. thematic, positional & temporal character

Elements of spatial data quality

Accuracyprecisioncoverage

Case study: regional soil erosion

It is suggested that soil erosion reaches its maximum in areas with an effective mean annual precipitation of 300mm.

This largely affects semi-arid and semi-humid regions.

The problem of soil erosion in these areas is also compounded by the need for water conservation, and the ecological sensitivity of the environment - removal of natural vegetation for cultivation can have a major effect.

Mediterranean regions experience some of the highest erosion rates

Map of desertification hazard

Major causes: human activity

Factors Factors influencing influencing soil erosionsoil erosion

Factors Influencing Soil Erosion Rate

RainfallRun-offWindSoilSlopePlant CoverConservation Measures

Erosivity

Erodibility

Protection

Modelling Modelling soil erosionsoil erosion

Model pre-requisites

the model should be based on the concepts of the erosion process

the model should reliably simulate the distribution character of the erosion process

the model should be validated under a range of natural conditions

the scale at which the model operates should match the spatial resolution of the EO data and DEM.

Physically-Based Models

Physically based models are based on the knowledge of the fundamental erosion processes and incorporate laws of conservation of mass and energy.

Most of them use a statement of the conservation of matter at it moves in time and space, and can be applied to soil erosion on a small segment of a hill slope.

Empirical Models A simple empirical model can be of the

type:

Qs = aQwb

Qs =sediment discharge Qw = water discharge

This is a simple model that does not explain why the erosion takes place.

In order to do this, more complex models expressing the relationship between soil loss and a number of variables can be constructed.

Physically-Based– CREAMS - Chemicals,

Runoff and Erosion from Agricultural Management Systems

– WEPP - Water Erosion Prediction Project

– GUSS - Griffith University Erosion Sedimentation Systems

– EUROSEM - European Soil Erosion Model

Empirical– USLE - Universal Soil

Loss Equation– RUSLE - Revised USLE– SLEMSA -Soil Loss

Estimator for SA– Morgan, Morgan and

Finney Method

Soil Erosion Models

The The Universal Universal Soil Loss Soil Loss EquationEquation

The USLE

The USLE, developed by W. Wischmeier and D. Smith (1978), has been the most widely accepted and utilised soil loss equation for over 30 years.

Designed as a method to predict average annual soil loss caused by sheet and rill erosion.

it can estimate long - term annual soil loss and guide conservationists on proper cropping, management, and conservation practices,

it should not be applied to a specific year or a specific storm.

The USLE is mature technology and enhancements to it are limited by the simple equation structure.

The USLE

A = R.K.L.S.C.P

A = average annual soil loss in t/a (tons per acre)

R = rainfall erosivity index

K = soil erodibility factor

LS = topographic factor (L is for slope length & S is for slope)

C = crop management factor

P = conservation practice factor

Remote Remote sensing & sensing &

GISGIS

The role of RS and GIS

Modelling environmental processes such as soil erosion requires the spatial and temporal assessment of process controlling variables for the entire area under investigation.

EO and GIS technologies enable the extraction of information from imagery and DEM’s, and to process vast amounts of data for this purpose.

USLE Factor Derivation

R Climate Data

K Field work or soil maps

LS DEM

C Remote Sensing

P Remote Sensing or Field Observation

Source: Mongkolsawat et al. (1994)

Source: Scilands GmbH

In this lecture you have:

identified reasons for modelling terrain

defined a ‘(geo-)information envelope’

exemplified the process of terrain modelling to analyse natural hazards