This document describes a dzud risk modelling methodology using multi-criteria decision analysis in a geographic information system. It lists available spatial datasets on weather, climate, vegetation, livestock, and pasture conditions. A ranking and weighting process assigns weights to each criteria based on its importance. A weighted overlay analysis combines the spatial datasets using the assigned weights to produce risk maps showing very high, high, moderate, low and very low risk of dzud conditions occurring. The expected results are forecast maps of dzud risk to help decision makers plan responses to protect livestock and pasturelands.

1. УС ЦАГ УУР, ОРЧНЫ СУДАЛГАА, МЭДЭЭЛЛИЙН
ХҮРЭЭЛЭНГИЙН 50 ЖИЛИЙН ОЙД
Ажлын баг: Н.Элбэгжаргал, Б.Эрдэнэцэцэг, Б.Бархас, М.Должинсүрэн

2. Information and Research Institute of Meteorology, Hydrology
and Environment
GROUP MEMBERS: B.ERDENETSETSEG, B.NANDINTSETSEG,
N.ELBEGJARGAL, B.BARKHAS, AND M.DOLJINSUREN
n_elbegjargal@yahoo.com

3. Content
 Data list
 Available data list
 Flowchart
 Methodology
Multi Criteria Decision Analysis
 Ranking and Weight
 Weighted Overlay
 Dzud Risk Modelling
 Expected Results
 End users

4. Available data list
1
Weather forecast
Air temperature
2 Precipitation
3
Agricultural forecast
Snow hight
4 Snow density
5 Anomal precipitation
6 Anomal temperature
7 Biomass
8 Livestock density
9 Pasture carrying capacity
10 Summer condition
11
Summer: number of days +30c > Winter: number of
days -30c >
12
Remote Sensing
data
Biomass/NOAA
13 Snow cover days/MODIS
14 Snow cover/MODIS
15 Snow depht/AMSR
16 Drought index/MODIS

5. Drought and white dzud frequency map
(based on ground observation data 1973-2009)
Drought frequency
White dzud frequency

6. Monthly air temperature and precipitation
anomaly maps, 2015
Air tem, Jul 2015 Air tem, Aug 2015
Precipitation, Aug 2015Precipitation, Jul 2015

7. Accumulated rainfall and it’s anomaly

8. Summer condition, 2015
Summer pasture condition 10 July 2015

9. 2015-2016 winter-spring season pasture carrying
capacity, %
largely ungrazed-40% of the
total territory
Slightly ungrazed -20 %
Lightly overgrazed -30 %
Moderately overgrazed -5 %
Heavily overgrazed -5 %
• About 60% of total territory could have ungrazed conditions (slightly to largely),
acting as pasture reserves available for a certain numbert of livestock migrating
from other locations.
• >40% of total territory had overgrazed conditions (lightly to heavely), which
showed the need to plan and regulate the movement of livestock and provision of

10. Biomass map, cn/ha: 2014 and 2015
(1500 sites)
Aug 2015
Aug 2014

11. Snow depth, (Nov 2015, Feb 2016)

12. Snow cover by Satellite data

13. Drought map

14. Livestock number, 1918-2015
Source: NSO

15. Criteria for zud assessment (Government
decision №286, 2015)
White zud:
• Pasture snow density is
>0.25 g/sm3 in all natural
zones
• 10 days and monthly air tem
is below more than norm
>3.0 C
• Snow average depth >25 sm
in high mountain and forest
steppe,
• >22sm in steppe
• >12 sm in the gobi region.
White semi-zud:
• Pasture snow density is 0.20-0.24 g/sm3
in all natural zones
• Snow average depth >16 sm in high
mountain
• forest steppe, >11 sm in steppe, and >5 sm
in the gobi region.

16. With Spatial datasets
ID Data group Data
1 Air temperature
2 Precipitation
3 Snow hight
4 Snow density
5 Anomal precipitation
6 Anomal temperature
7 Biomass
8 Livestock density
9 Pasture carrying capacity
10 Summer condition
11 Summer: number of days +30c > Winter: number of days -30c >
12 Biomass/NOAA
13 Snow cover days/MODIS
14 Snow cover/MODIS
15 Snow depht/AMSR
16 Drought index/MODIS
Weather forecast
Agricultural forecast
Remote Sensing data

17. Overview System

18. Method
1. Ranking and weight analysis
• Rank
• Numerator
• Weight
2. Weighted Overlay analysis
• Overlay

19. Ranking and weight
Weighting criteria
Criteria
𝑁𝑢𝑚𝑒𝑟𝑎𝑡𝑜𝑟 =
𝑘=1
𝑛
n − 𝑟𝑘 + 1
𝑊𝑖
n − 𝑟𝑘 + 1
𝑘=1
𝑛
n − 𝑟𝑘 + 1
𝑊𝑖=1
Reference: GIS and Multicriteria Decision Analysis
By Jacek Malczewski, 1999
id Layers name Rank Numerator Weights 0-100 scale
1 Summer condition 3 9 0.09 9
2 Pasture carrying capacity 2 10 0.10 10
3 Livestock density 3 9 0.09 9
4 1500-site/biomass 2 10 0.10 10
5 Anomal precipitation 5 7 0.07 7
6 Anomal temperature 5 7 0.07 7
7 Snow depth 1 11 0.11 11
8 Snow cover/MODIS 3 9 0.09 9
9 Drought index/MODIS 4 8 0.08 8
10 Air temperature forecast 2 10 0.10 10
11 Precipitation forecast 1 11 0.11 11
11 101 1.00 100

20. Criteria Weight
id Layers name Rank Numerator Weights 0-100 scale
1 Summer condition 3 9 0.09 9
2 Pasture carrying capacity 2 10 0.10 10
3 Livestock density 3 9 0.09 9
4 1500-site/biomass 2 10 0.10 10
5 Anomal precipitation 5 7 0.07 7
6 Anomal temperature 5 7 0.07 7
7 Snow depth 1 11 0.11 11
8 Snow cover/MODIS 3 9 0.09 9
9 Drought index/MODIS 4 8 0.08 8
10 Air temperature forecast 2 10 0.10 10
11 Precipitation forecast 1 11 0.11 11
11 101 1.00 100

21. Weighted Overlay
Weighting criteria
Weighted
Overlay
Overlay
id Layers name Rank Numerator Weights 0-100 scale
1 Summer condition 3 9 0.09 9
2 Pasture carrying capacity 2 10 0.10 10
3 Livestock density 3 9 0.09 9
4 1500-site/biomass 2 10 0.10 10
5 Anomal precipitation 5 7 0.07 7
6 Anomal temperature 5 7 0.07 7
7 Snow depth 1 11 0.11 11
8 Snow cover/MODIS 3 9 0.09 9
9 Drought index/MODIS 4 8 0.08 8
10 Air temperature forecast 2 10 0.10 10
11 Precipitation forecast 1 11 0.11 11
11 101 1.00 100

22. Dzud risk Modelling in ArcMAP

23. Result maps

24. ForecastCriteria Class Risk value Risk class
-1> 5 Very high risk
4 High risk
0 3 Moderate risk
2 Low risk
1< 1 Very low risk
Air temperature

25. Forecast

26. Anomaly precipitation

27. Anomaly Temperature

28. Livestock density

29. 1500-site biomass

30. Summer condition

31. Pasture carrying capacity

32. Snow density

33. Snow depth

34. Biomass/NOAA

35. Drought index/MODIS

36. Snow cover/MODIS

38. Dead livestock