Space technology assistsSpace technology assistsMongolia herders & farmersMongolia herders & farmers

to reduce disaster riskto reduce disaster risk

By PhD. N.Togtokh, PhD. R.Oyun, MongoliaBy PhD. N.Togtokh, PhD. R.Oyun, Mongolia

UNITED NATIONS OFFICE AT VIENNA15th United Nations/International Astronautical Federation Workshop on“Space Education and Capacity Building for Sustainable Development”

ContentContent

1. Global change affects the livelihoods of rural population2. Disaster risk reduction: “Ideas-Actions-Achievements”3. Space technology assists the herder & farmers 4. Community participation5. Indigenous knowledge of the herders used

to predict risk of coming winter and spring6. Partnership and Capacity building7. Examples on use of satellite remote sensing for DRR8. e-Soum - new challenge for space technology

1. Global change affects1. Global change affectsthe livelihoods of rural populationthe livelihoods of rural population

• Global change affects Mongolia:– Climate warming is twice as rapidly

as the global average. – The epicenter of atmospheric pressure

changes located in Mongolia

• The faster the climate changes, the greater the risk of disaster:

– Increase of variability and extremes– Steppe grasslands are vulnerable

to such changes, – Pastoral animal husbandry and

arable farming is at great risk,– 1999-2003 severe droughts, dzud

disasters caused :

• Increase in poverty: – 36 per cent poverty nationally,– 43 per cent in the rural population

Agriculture GDP

10%

15%

20%

25%

30%

35%

40%

1998

1999

2000

2001

2002

2003

Animal husbandry is national wealthAnimal husbandry is national wealth

• Since 1999 in Mongolia consequent drought and dzud disaster have affected natural pasture resources and caused mass loss of animals.

• Mongolia had lost one third of its livestock• Poverty incident in rural area has reached to 43%.

Risk of animal husbandryRisk of animal husbandry

Assessment of Dzud disaster of 2000Assessment of Dzud disaster of 2000

with use of NOAA AVHRR data

Crop is a strategic food productCrop is a strategic food product

2. DRR: 2. DRR: ““Ideas Ideas -- Actions Actions –– AchievementsAchievements””

• “Lessons Learnt” case studies:– Dzud of 1999-2000– Drought of 2002– Hazardous windstorm of 2001 and 2002– Early warning and emergency management system – Disaster management information system

• Risk studies and assessments:– Impact assessment of climate change – Livelihoods of herders and farmers – Policy recommendations

• “Ideas - Actions - Achievements” pilots:– Methodology to assess hazard, vulnerability and risk– Risk prediction for coming winter and spring– Introducing liquid gas to rural herders and small restaurants– Restocking the herders with high productive sire– Carpet knitting with “male” wool of camel

• “Risk manager” information system development:– Integrated database for rural county– Community information center at rural county– Partnership for communication and information sharing– Integrated information processing techniques and software– www.agronet.mn Internet web site

3. Space technology assists the herder & farmers3. Space technology assists the herder & farmers

• Communication:– Data network with VSAT– Mobile phone

• Positioning of ground true data provided by the herders, farmers:

– Notes on local weather– Vegetation biomass– Animal bodyweight– Air pressure, temperature– Hazards, extreme events

• Positioning and mapping of data provided by the local government:

– Wells– Livestock location by winter stand– Crop fields

• Mapping of local environment:– Cloud, weather, climatic norms– Pasture vegetation– Snow coverage – Extreme temperature– Soil moisture

Information and Computer Center,

MNE

Institute of Meteorology and

Hydrology

NOAA AVHRR data

Weather forecast

JEMR Consulting LLC.

Meteorological Society, NGO AgroSoft LLC.

Agriculture Risk Study Center, NGO

Local Community Information and Training

Center

National Agency for Disaster Management

Disaster management

Local climate, weather study

Integrated Information Processing

Database, www.agronet.mn

Agriculture Bank Incomnet LLC.VSAT datanet

Local branch of Agriculture Bank

Local disaster management staff, professional service units, rescue team

Local Meteorological

Station

Telephone

Local FM radio

NOAA Intelsat

Color Printer

Public Bulletin Board Local

community, herders

Weather Forecast and Hazard Warning Information Flow Ulaanbaatar and Erdenedalai county (Dundgobi province)

e-mail

Information to the herders

Meteo. station: Tsagaa

Field measurement dweather, soil moistu

crop observation

Gatsuurt Crop far

AgroSoft: Database, DEM , W eb masteringJEMR Consulting: Integrated information processing

National Rem ote

Sensing Center

Agriculture Risk Study Center: Coordination partnership,

contracting, information service

Exchange V irtual: Cloud> ICC L3

Im age processingsystem

3D M ap 1: NDVI

3D M ap 2: RT

3D M ap 3: W10

Secretary PC

Im age processingsystem

County database:

DEM,Statistics

D igitizing

Database

www.agronet.m nWeb Master

Meteorological Society

Ground data: formal, informal

Portable field equipment: Barom eter, thermom eter,

anim om eter

National Agency for Meteorology, Hydrology

& Environmental Monitoring

Meteorological database

Gatsuurt Company Headquarter

e-m ail

Secretary PC Printed maps

Company post

Printed maps

Agro

M anager

Institute of Meteorology, Hydrology:

Weather forecast: weekly, monthly

Synoptic engineer

Information flow: Ulaanbaatar – Tsagaannuur crop farm

Information to the farmers

4. Community participation in disaster risk reduction4. Community participation in disaster risk reduction

• Monthly and seasonal weather prediction around the living area:

– Indigenous knowledge of nomads on astrology and nature has been accumulated over thousands of years and passed from generation to generation

• Local climate, weatherand hazard monitoring:

– Observation and notes– Use of barometers, thermometers

• Interpretation of satellite images, verification of class maps:

– Name of local land represent their specifics and in some cases match well with natural color composite of images

– Easy interpretation of images and verification of classification results with good knowledge on their native land

Discussion with local government and community of Erdenedalai county

The herders notes on local weather

5. Indigenous knowledge of the herders used5. Indigenous knowledge of the herders usedto predict risk of coming winter and springto predict risk of coming winter and spring

• Herders’ observation:– Every year in full moon night of middle month

of the autumn the herders D.Bor, S.Dashdorj and D.Tavanjin from Gobi-Ugtaal and Tsagaandelger counties of Dundgobi province claim up the Bagazalaa mountain

– They observe the moon, stars, airflow and morning sun rise;

• They determine:– What is the most risk of coming winter and

spring? – Is there any threatening hazards expected

(snowfalls, extreme cold, epidemic diseases, etc.)?

• Predicting time: – 6-7 months a head, from October of current

year to March-April of the next year.

• Observing area:– Visible area with approximate radius of 100

km around the mountain.

Herders’ observation at Bagazalaa mountain

Participatory risk prediction Participatory risk prediction andand decision makingdecision making

• Next morning discussion is important to conclude risk prediction, formulate management tactics, make decision.

• The herders introduce their prediction

• Risk study team introduce:– Official weather prediction for coming 6

months– The results of vegetation index, pasture

capacity and animal bodyweight monitoring

– The results of evaluation of previous year risk prediction

– What lessons was learnt from past winter, spring and summer

• The local government and the herders make decision on:

– Animal and pasture capacity is sufficient for predicted winter and spring condition?

– If no? • Amount of additional fodder for vulnerable

part of the herds• Where is the new location to move herds?

Where will be less snowfall and less cold?• If above 2 measures are not appropriate, so

to slaughter animals and sale on the market, and restocking again next spring

6. Partnership and Capacity building6. Partnership and Capacity building

• Series of workshops on community information center, disaster risk reduction and training on use of thematic maps and computer conducted with support of public and private stakeholders:

– National Agency for Disaster Management, – National IT Park– National Remote Sensing Center– Mongolia Development Gateway NGO– MIDAS/MONITA NGO

• Local government of 15 counties encouraged participation of rural communities in activities towards disaster risk reduction

Workshop at Erdenedalai county

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y = 250,01x2 - 36,533x + 1,9194R2 = 0,9582

0,400,60

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0,05 0,07 0,09 0,11 0,13 0,15

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7. Pasture vegetation monitoring, 20047. Pasture vegetation monitoring, 2004Vegetation map produced at the county meteorological station with ground data

High correlation between biomass and vegetation index

Biomass, c/hVegetation indexPasture, affected by locust Pasture for winter

precipitation

Weekly vegetation index map, June-July

July - August

August - September precipitation

Weekly vegetation index map, June-July

July - August

August - September

May-October weekly vegetation index maps

Pasture vegetation monitoring, 2005Pasture vegetation monitoring, 2005

Steppe vegetation, 2005.08.20

y = -552.89x2 + 241.26x - 24.511R2 = 0.9974

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.050 0.070 0.090 0.110 0.130 0.150 0.170 0.190 0.210 0.230

Vegetation index

Bio

mas

s, c

/h

Gobi vegetation, 2005.08.20

y = -68.701x2 + 37.005x - 1.9224R2 = 0.9583

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.050 0.070 0.090 0.110 0.130 0.150 0.170 0.190 0.210 0.230

Vegetation index

Bio

mas

s, c

/h

Vegetation index and Biomas sÝðä ýí ýä àë àé ñóì , 2005.06.24

y = 7.6251x + 0.0341R2 = 0.7246

00.1

0.20.3

0.40.50.6

0.70.8

0.91

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14

Vegetation index

Bio

mas

s

2005.10.02-08

Relationships between vegetation index and biomass

Animal bodyweight monitoring, 2004Animal bodyweight monitoring, 2004--20052005

Sheep bodyweight and green mass, 2005.08.15-09.20

y = -0.9916x2 + 7.8634x - 8.3423R2 = 0.7616

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5Monthly increase in sheep bodyweight, kg

Gre

en m

ass,

c/h

Winter is coming …

Crop field monitoring, 2005Crop field monitoring, 2005

Climatic norm and microClimatic norm and micro--climatic class mapsclimatic class maps

-24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14-24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14

January climatic norm: Air temperature Micro-climatic class map

Micro-climatic class parameters

Ulaanbaatar city, Nalaikh district - 14.2 ± 1.168 ± 41861 ± 1835

- 16.2 ± 0.988 ± 41711 ± 1894

- 18.6 ± 0.826 ± 31520 ± 1463

- 19.9 ± 0.714 ± 31416 ± 1362

- 23.5 ± 0.862 ± 21387 ± 1261

January airtemperature norm, oC

Slope angle, oAltitude, mClass number

- 14.2 ± 1.168 ± 41861 ± 1835

- 16.2 ± 0.988 ± 41711 ± 1894

- 18.6 ± 0.826 ± 31520 ± 1463

- 19.9 ± 0.714 ± 31416 ± 1362

- 23.5 ± 0.862 ± 21387 ± 1261

January airtemperature norm, oC

Slope angle, oAltitude, mClass number

Information and Communication Technology Authority of Mongolia

supports e-Soum

National Petroleum company NIC/Petrovis supports e-Soum

Signing of MoU to collaborate towards establishing e-Kara Korum

as model for e-Soum

Event for launching e-Soum web site and partnership for e-Kara Korum

8. 8. ee--SoumSoum initiative is new challenge for space technologyinitiative is new challenge for space technology

• Mongolia’s IT community and Risk study team have initiated ‘e-Soum’ project proposal:

– to establish a sustainable system that enable the monitoring, assessment, prediction and warning of changes both natural and human-made environments, structures and systems with use of advanced information and communication technology.

– to assist people in understanding, evaluating and coping with complex systems, sharing knowledge to create new ones, and actively participating in management and decision-making processes in order to achieve sustainability

– Meanwhile more than 50 organizations are supporting e-Soum