USING SOIL INFORMATION IN NATURAL

DISASTER ANALYSIS: VALUE FOR MONEY

Victor Jetten

Dept. of Earth Systems Analysis, ITC

Duration Hrs.

96

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USING SOIL INFORMATION IN NATURAL DISASTER

ANALYSIS: VALUE FOR MONEY

What is a disaster, concept of risk.

a framework for Disaster Management Research

Role of soil science in Disaster Management:

as a source primary data for hazard analysis,

2 examples from flash flood and land slide modelling

as a disappearing resource: what is the risk associated with

soil degradation

Concluding remarks

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A DEFINITION OF DISASTERS

“ A serious disruption of the functioning of a community or a

society, causing widespread human, material, economic

or environmental damage which exceed the ability of the

affected community to cope using its own resources.”

(EEA, 2005)

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NATURAL HAZARDS AND DISASTERS

“ A serious disruption of the functioning of a community or a

society, causing widespread human, material, economic

or environmental damage which exceed the ability of the

affected community to cope using its own resources.”

(EEA, 2005)

serious

disruption

ability to

cope

causing widespread

damage

RISK

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RISK IS OVERLAP HAZARD & VULNERABILITY / VALUE

Natural

Hazards Society

DISASTER

Frequency

Triggers

Dynamics

Location

Inventory

Elements at risk

Vulnerability

Value

Cost

Awareness

Coping

strategies

RISK

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Disaster management research framework

Cause

Effect

Response

Hazards Landslides, floods, earthquakes Geophysical and climate driven

Risk analysis Vulnerability, urbanization, direct and indirect risk

Disaster mitigation Damage assessment, planning, adaptation, mitigation, protection, awareness

Hazards Desertification and erosion Climate and land use change

Risk Analysis On-site and off-site effects, rural risk, food security

Sustainable Land Management Soil and water conservation, adaptation, awareness

Rapid disasters Land degradation

Where does soil science come in ?

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EXAMPLE 1: STORM RUNOFF CAPE VERDE

Juan Sanchez (PhD), Chris Mannaerts,

Jaques Tavares, Isaurinda Baptista

Semi arid with 4 months

of rainfall

Cyclone storms on steep

terrain causing flashfloods

Many conservation

measures (mainly to

prevent erosion)

Predict storm runoff with

“classical” event based

runoff modelling

Where is runoff

generated?

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HOW TO USE THE SOIL INFORMATION AVAILABLE?

1. Course scale soil map (ASG1)

2. More detailed land suitability map based providing texture

information (ASG2)

3. Field measurements – Ordinary Kriging (OK)

4. Combination: Kriging with External Drift combined with

texture units (KED)

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Leads to 4 datasets of soil parameters, all ‘legitimate’

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DISCHARGE AND SPATIAL PATTERNS OF RUNOFF

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Monitoring point

center of

catchment

Outlet at

coastal plain

CONCLUSIONS CAPE VERDE

Equifinality problem, all datasets

can be calibrated to give reasonable

results.

Datasets with large map units need

larger calibration adjustments.

Possible solutions:

More (detailed) soil data?

Monte Carlo simulation?

Ask farmers what happens

during a heavy rainstorm:

map the effects!

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EXAMPLE 2: LANDSLIDE HAZARD AND RISK

Two important factors determine the hazard and risk:

Triggers and source areas:

Groundwater fluctuations, earthquakes.

Predictive modelling is data driven (statistics) or

deterministic: groundwater on steep slopes.

Flow dynamics and runout

distance modelling:

Reach, velocity and kinetic

energy are determined by:

mass & density, moisture

content, viscosity & friction,

terrain geometry.

Byron Quan Luna,

Cees van Westen,

Haydar Yussif Hussin

DEBRIS FLOWS MODELLING

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RAMMS model simulation of debris flows near Barcelonnette (Alps)

Volume of cannot be explained by source area volumes, but is increased

by entrainment. Soil volume available for erosion needs to be known.

bridge Ubaye

river

0

2

4

6

8

10

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0 21 44 67 88 111 134 158 184 207 231 252 272 295

Chainage line (m)

Maxim

um

velo

cit

y (

m)

RAMMS program DAN 3D program

SOIL DEPTH AS A MAJOR PARAMETER

Estimate with different methods of interpolation from

field data

From electrical resistivity measurements

ITC: Phd Muhammad Shafique 14

WHAT SOIL DATA DO WE HAVE AVAILABLE…

Web Portals, compilation of exiting soil information:

JRC – European Soil Data centre (ESDAC)

USDA – National Resources Conservation Service

ASRIS – Australian Soil Resource Information System

ISRIC

National databases

GlobalSoilMap.net: focus on digital soil mapping of

soil properties, compilation of national datasets, but

reprocessed in identical fashion (100 m gridcells,

KED, pedotransfer functions etc.)

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REMAKRS “SOIL AS A DATA SOURCE”

Soil surface properties becoming more and more

available as a valuable data source. Mapping properties

instead of soil types

Soil unit boundaries used indirectly, e.g. geostats.

Can be combined with pedotransfer functions

Sub-soil data less well known but very important:

Needed in hazard analysis involving groundwater

Needed in landslide analysis (source areas and entrainment)

Needed in earthquake damage modelling (wave propagation) not

shown here

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SOIL DEGRADATION AS A DISASTER

Cause

Effect

Response

Hazards Landslides, floods, earthquakes Geophysical and climate driven

Risk analysis Vulnerability, urbanization, direct and indirect risk

Disaster mitigation Damage assessment, planning, adaptation, mitigation, protection, awareness

Hazards Desertification and erosion Climate and land use change

Risk Analysis On-site and off-site effects, rural risk, food security

Sustainable Land Management Soil and water conservation, adaptation, awareness

Rapid disasters Land degradation

HAZARD ANALYSIS

Analyzing and predicting the (bio-physical)

effects of soil degradation is well known:

Erosion

Drought

Compaction, crusting

Salinisation

Pollution

Effects on crop growth or biodiversity

Modelling, remote sensing analysis etc.

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Risk Analysis

SLM

Hazards

SUSTAINABLE LAND MANAGEMENT

We know very well how to do conservation

and mitigate land degradation, technically and

socially

An example is the WOCAT system (www.wocat.org): World Overview of Conservation Approaches and Technologies

The FP6 project as an example of

stakeholder based research

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Hazards

Risk Analysis

SLM

RISK CONCEPT APPLIED TO SOILS

In spite of stakeholder based approach,

large scale SLM remains problematic

When there is a direct economic gain in SLM: e.g.

more water = more yield, or when there is subsidy

There is little notion of “more/better soil” = more yield

No immediate risk apparent and soil is still

considered an infinite resource (at least in politics)

RISK = Hazard * (Vulnerability + Value)

Main question: how much value does the soil have?

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Hazards

Risk Analysis

SLM

COST OF LAND DEGRADATION

For example Barry, Olsen and Campbell (2003):

Data from 7 countries suggest damages are annually in the

order of 4-7% of GDP, while the response is in the order of

0.1% GDP or less

Incompleteness of data, mostly addressing direct on-site

problems

Off-site problems not taken into account

Strong links with poverty, environmental policies are crucial

Data is sufficient to open dialogs with governments

Need for a comprehensive approach

There will be no useful soil conservation policy

unless we put a price tag on soils, and the

ecosystem functions of soils

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SOILS IN DISASTER MANAGEMENT: VALUE FOR MONEY

Soil data is imperative for natural hazard analysis

Trend towards mapping soil properties is very good,

sufficient data for spatial analysis is becoming available

Focus on the surface but more data needed on subsoil

properties (especially a simple parameter as soil depth)

To limit soil degradation we have to seriously design a

comprehensive methodology for the economic value of

soils

Considering on-site functions and off-site functions

Mapping the extent of this value, like any other resource

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THANK YOU

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Yanan, Loess Plateau China, 1999 Yanan, Loess Plateau China, 2010