Workshop held on 1st of April in Vientnane, Laos. Participants from national institurions (agriculture, education, planning) where joining presentations on the overview of climate variability in the Greater Mekong Sub-Region, using crop modeling and land use change analysis.

1. Overview of climate variability
and climate change
Eitzinger Anton, Giang Linh, Lefroy Rod
Laderach Peter, Carmona Stephania
Overview of climate variability and likely climate change impacts on
agriculture across the Greater Mekong Sub-region (GMS)
1 April, 2014, Vientiane, Laos

3. • Cross-cutting, multi-disciplinary team who believe that better
decisions can be made with the power of information
• Supporting functions within CIAT, and global research
leadership in specific themes
Decision and Policy Analysis

4. • Focussed on delivering research outcomes in:
o Climate change (CRP7)
o Ecosystem Services (CRP5)
o Linking Farmers to Markets (CRP2)
• Through expert, disciplinary groups in:
o Modelling
o Gender analysis
o Impact and Strategic Studies
o Policy Analysis
o Knowledge Management
o Big Data
Decision and Policy Analysis

5. Our focus
• Providing information and climate data for Agriculture
• Climate Change impact assessment
– For Food security & cash crops, entire value chains
• Vulnerability of communities
– Perception of risks, adaptive capacity, gender differences
• Social & economic constraints for adaptation
• Adaptation & mitigation strategies
• Cost & benefit of strategies
• Supply chain inclusive adaptation framework
• Work for/with national policy institutions!
• Mitigation through carbon insetting
• Triple-win of adaptation, mitigation and food security …
whilst conserving biodiversity
• Bring to implementation of CSA (climate smart agriculture)
practices

6. Climate science … many questions
and uncertain answers!
1. What is the evidence and observed changes in
the climate system and how reliable are climate
models and scenarios?
2. How to use climate models & future predictions
for Agriculture and modeling?
3. How can we adapt agriculture systems to
unknown future conditions?

7. The atmosphere and ocean
have warmed, the amounts
of snow and ice have
diminished, sea level has
risen, and the
concentrations of
greenhouse gases have
increased.
IPCC, 2013: Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A.
Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

8. Each of the last three decades has been successively warmer at the Earth’s surface
than any preceding decade since 1850. In the Northern Hemisphere, 1983–2012 was
likely the warmest 30-year period of the last 1400 years (medium confidence).
Ocean warming dominates the increase in energy stored in the climate system,
accounting for more than 90% of the energy accumulated between 1971 and 2010
(high confidence). It is virtually certain that the upper ocean (0−700 m) warmed from
1971 to 2010.
IPCC AR5 report – observed changes in the climate system
Over the last two decades, the Greenland and Antarctic ice sheets have been losing mass, glaciers
have continued to shrink almost worldwide, and Arctic sea ice and Northern Hemisphere spring snow
cover have continued to decrease in extent (high confidence)
The rate of sea level rise since the mid-19th century has been larger than the mean rate during the
previous two millennia (high confidence). Over the period 1901–2010, global mean sea level rose by
0.19 [0.17 to 0.21] m.
The atmospheric concentrations of carbon dioxide (CO2), methane, and nitrous oxide have increased
to levels unprecedented in at least the last 800,000 years. CO2 concentrations have increased by 40%
since pre-industrial times, primarily from fossil fuel emissions and secondarily from net land use
change emissions. The ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide,
causing ocean acidification.
IPCC, 2013

9. Drivers of Climate Change Total radiative forcing is positive,
and has led to an uptake of energy
by the climate system. The largest
contribution to total radiative
forcing is caused by the increase in
the atmospheric concentration of
CO2 since 1750.
Climate models have improved since the AR4.
Models reproduce observed continental-scale
surface temperature patterns and trends over many
decades, including the more rapid warming since
the mid-20th century and the cooling immediately
following large volcanic eruptions.
(very high confidence).
This evidence for human influence has grown since
AR4. It is extremely likely that human influence has
been the dominant cause of the observed warming
since the mid-20th century.
Changes in the global water cycle in response to the
warming over the 21st century will not be uniform.
The contrast in precipitation between wet and dry
regions and between wet and dry seasons will
increase, although there may be regional exceptions.
IPCC, 2013

10. IPCC Global-scale assessment of recent observed changes, human
contribution to the changes, and projected further changes
IPCC, 2013

11. Observed ocean and surface temperature anomaly
• Annual average
• Decadal average
• Contribution to change
IPCC, 2013

12. Representative Concentration Pathways (RCPs)
… former Emission Scenarios (SRES)
concentrations of the full suite of greenhouse gases and aerosols
and chemically active gases, as well as land use/land cover
RCP 8.5
(high emissions)
RCP 6.0
RCP 4.5
RCP 2.6
(low emissions)
IPCC, 2013

13. Climate Models & Future predictions
IPCC, 2013

14. AR 5 projected regional changes:
Southeast Asia
“Reduced precipitation in Indonesia during Jul-
Oct. due to the pattern of Indian Ocean
warming; increased rainfall extremes of landfall
cyclones on the coasts of the South China Sea,
Gulf of Thailand, and Andaman Sea.”
IPCC, 2013

15. How to use climate models & future
predictions for Agriculture and modeling?

16. To know the uncertainty of
the data is important!
We don’t know… What are the
conditions in 30, 50, 100 years?
The different emission scenarios are
not important ... by 2030 the
difference between the
concentration pathways is minimal.
Understand variability and precise
forecasting is important!
2030
For agriculture:
IPCC, 2013

17. Climate variability
• There is still uncertainty on climate models
when it comes to variability
• Historical observations of weather and climate
can help to understand better variability
• We need a better forecasting for Agriculture

18. http://ccafs.cgiar.org

19. http://ccafs-climate.org

20. Statistical downscaling of climate models
• Use anomalies and discard baselines in GCMs
– Climate baseline: WorldClim
– Used in the majority of studies
– Takes original GCM timeseries
– Calculates averages over a baseline and future periods (i.e.
2020s, 2050s)
– Compute anomalies
– Spline interpolation of anomalies
– Sum anomalies to WorldClim

21. http://www.worldclim.org
Worldclim stations worldwide
47,554 precipitation
24,542 tmean
14,835 tmax y tmin
Sources:
•GHCN
•FAOCLIM
•WMO
•CIAT
•R-Hydronet
•Redes nacionales
for GMS

22. • Bio1 = Annual mean temperature
• Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp))
• Bio3 = Isothermality (Bio2/Bio7) (* 100)
• Bio4 = Temperature seasonality (standard deviation *100)
• Bio5 = Maximum temperature of warmest month
• Bio6 = Minimum temperature of coldest month
• Bio7 = Temperature Annual Range (Bio5 – Bi06)
• Bio8 = Mean Temperature of Wettest Quarter
• Bio9 = Mean Temperature of Driest Quarter
• Bio10 = Mean Temperature of Warmest Quarter
• Bio11 = Mean Temperature of Coldest Quarter
• Bio12 = Annual Precipitation
• Bio13 = Precipitation of Wettest Month
• Bio14 = Precipitation of Driest Month
• Bio15 = Precipitation Seasonality (Coefficient of Variation)
• Bio16 = Precipitation of Wettest Quarter
• Bio17 = Precipitation of Driest Quarter
• Bio18 = Precipitation of Warmest Quarter
• Bio19 = Precipitation of Coldest Quarter
Changes from 24 climate models using climate clusters for GMS
* X current annual mean temperature, X current annual rainfall, source http://worldclim.org
x x
x
x
x
x
x
x
x
x

23. Historical climate in GMS
Presentation: Linh Giang

24. • Recent studies show the emergence of general
trends in the climate of the GMS.
• Average daily temperatures across Southeast Asia
have increased
• Precipitation patterns are quite complex across
Southeast Asia.
• In the Greater Mekong region from 1961 to 1998,
although the number of extreme rainfall events
decreased, the amount of rain falling during these
events increased (Manton et al 2001).
OVERVIEW

25. CRU TS 3.10.01
The CRU TS 3.10.01 Climate dataset has been produced by the
Climatic Research Unit (CRU) of University of East Anglia.
The database comprises 5583 station records of which 4842 have
enough data for the 1961-1990 period to calculate estimate the
average temperatures for this period.

26. Climate grids are constructed for nine climate variables
for the period 1901-2009
- Temperature,
- Diurnal temperature range,
- Daily minimum temperature,
- Maximum temperatures,
- Precipitation,
- Wet-day frequency,
- Frost-day frequency,
- Vapor pressure, and
- Cloud cover.
CRU TS 3.10.01

27. 842 points in GMS were
collected from CRU TS 3.10.01 which
covers from 1901 to 2009, globally at
0.5 degree spatial resolution on land
area, including:
• Precipitation
• Mean temperature
• Minimum temperature
• Maximum temperature

28. • Mean temperature
increased by between 1.8 ˚C
and 2 ˚C.
• Maximum temperature rose
by between 1.7˚C and 2.2˚C.
• Minimum temperature grew
by between 1.6˚C and 2.2˚C.

29. The region has seen more hot
days and warm nights and
fewer cool days and nights.

30. • Total annual rainfall
will increase by 5-25%
across the northern
part of the Mekong
region in the next few
decades.
• Heavier storms during
the wet season will
account for the
regional increase
because drier dry
seasons are predicted
(TKK & SEA START RC
2009).

31. The trends in rainfall had
the range of highly
variable

32. Conclusion
• In spite of a few station in South-East Asia , CRU
data is useful to get overview of the climate
change in the long time,
• The highest temperature in research area
concentrate in the south, and recorded the
significant increase in South of Cambodia and
South-East of Thailand,
• The shoreline area receive the large amount of
precipitation (Especially in Middle of Vietnam,
Myanmar, and Thailand)

33. Ecocrop-modeling

34. Why crop modeling in climate change?
… assessing the impact of climate change on
productivity and climate-suitability of crops and
production systems … and understand the limiting
factors
… using well-established empirical and mechanistic
models such as Ecocrop, Maxent, DSSAT, …..
that allow for the incorporation of spatial data and
fine-tuned biophysical data
How?

35. outline
• What is Ecocrop?
• FAO Ecocrop plant database
• Suitability modeling with Ecocrop
• Modeling Ecocrop with DIVA GIS
• Calibrating ecological ranges (using literature)
• Projecting suitability into the future

36. • The database was developed 1992 by the Land and Water
Development Division of FAO (AGLL) as a tool to identify
plant species for given environments and uses, and as an
information system contributing to a Land Use Planning
concept.
• In October 2000 Ecocrop went on-line under its own URL
www.ecocrop.fao.org. The database now held information on
more than 2000 species.
• In 2001 Hijmans developed the basic mechanistic model (also
named EcoCrop) to calculate crop suitability index using FAO
Ecocrop database in DIVA GIS.
• In 2011, CIAT (Ramirez-Villegas et al.) further developed the
model, providing calibration and evaluation procedures.

37. • http://ecocrop.fao.org

38. • Common bean

39. • database held information on more than 2000
species

40. Suitability modeling with Ecocrop
EcoCrop, originally by Hijman et al. (2001), was further developed, providing calibration and
evaluation procedures (Ramirez-Villegas et al. 2011).
It evaluates on monthly basis if there
are adequate climatic conditions
within a growing season for
temperature and precipitation…
…and calculates the climatic suitability of the
resulting interaction between rainfall and
temperature…
How does it work?

41. What happens when Ecocrop model runs?
1
2
3
4
5
6
7
8
9
10
11
12
1 kilometer grid cells
(climate environments)
The suitability of a location (grid cell) for a crop
is evaluated for each of the 12 potential
growing seasons.
Growing season
0 24 100 80

42. For temperature suitability
Ktmp: absolute temperature that will kill the plant
Tmin: minimum average temperature at which the plant will grow
Topmin: minimum average temperature at which the plant will grow optimally
Topmax: maximum average temperature at which the plant will grow optimally
Tmax: maximum average temperature at which the plant will cease to grow
For rainfall suitability
Rmin: minimum rainfall (mm) during the growing season
Ropmin: optimal minimum rainfall (mm) during the growing season
Ropmax: optimal maximum rainfall (mm) during the growing season
Rmax: maximum rainfall (mm) during the growing season
Length of the growing season
Gmin: minimun days of growing season
Gmax: maximum days of growing season

43. • Growing season: xx days (average of Gmin/Gmax)
• Temperature suitability (between 0 – 100%)
• Rainfall suitability (between 0 – 100%)
• Total suitability = TempSUIT * RainSUIT
If the average minimum temperature in one of these months is 4C or less above Ktmp, it is
assumed that, on average, KTMP will be reached on one day of the month, and the crop will die.
The temperature suitability of that month is thus 0%. If this is not the case, the temperature
suitability is evaluated for that month using the other temperature parameters.
The overall temperature suitability of a grid cell for a crop, for any growing season, is the lowest
suitability score for any of the consecutive number of months needed to complete the growing
season
The evaluation for rainfall is similar as for temperature, except that there is no “killing” rainfall and
there is one evaluation for the total growing period (the number of months defined by Gmin and
Gmax) and not for each month.
The output is the highest suitability score (percentage) for a growing season starting in any month
of the year.

44. Results from GMS study

46. Not available = natural (forest, wetland, …), protected, water, bare, urban areas
Needs change = land mixed with pastoralism (forest, herbaceous, wetlands, …)
Available = Agriculture (commercial, subsidized, irrigated, …)
Land use change at risk
for agriculture

48. www.ciat.cgiar.org Science to cultivate change
Use and Interpretation of EcoCrop
• Purely Climatic Suitability:
• Does not include soils
• Does not include pests and diseases
• Rainfall does not equal available water:
• Irrigation
• Soil water management (SOM, mulch, etc.)
• Topography and soil type affect drainage
• Phenology: Different requirements at different
stages of growth (especially for perennials)
• What is “most suitable” not necessarily the best to
grow – markets, labour, farming system, etc.

49. Other approaches for crop modeling

50. • Maximum entropy methods are very general ways to predict probability
distributions given constraints on their moments
• Predict species’ distributions based on environmental covariates
What is Entropy Maximization?
• You can think of Maxent as having two parts: a constraint
• component and an entropy component
• The output is a probability distribution that sums to 1
• For species distributions this gives the relative probability of observing
the species in each cell
• Cells with environmental variables close to the means of the presence
locations have high probabilities
MaxEnt model

51. B
51
Input: Crop evidence (GPS points)
19 bioclimatic variables of current (worldclim) & future climate
Output:
Probability of distribution of coffee (0 to 1)
MaxEnt model

52. Bioclimatic variables for suitability modeling
• Bio1 = Annual mean temperature
• Bio2 = Mean diurnal range (Mean of monthly (max temp - min temp))
• Bio3 = Isothermality (Bio2/Bio7) (* 100)
• Bio4 = Temperature seasonality (standard deviation *100)
• Bio5 = Maximum temperature of warmest month
• Bio6 = Minimum temperature of coldest month
• Bio7 = Temperature Annual Range (Bio5 – Bi06)
• Bio8 = Mean Temperature of Wettest Quarter
• Bio9 = Mean Temperature of Driest Quarter
• Bio10 = Mean Temperature of Warmest Quarter
• Bio11 = Mean Temperature of Coldest Quarter
• Bio12 = Annual Precipitation
• Bio13 = Precipitation of Wettest Month
• Bio14 = Precipitation of Driest Month
• Bio15 = Precipitation Seasonality (Coefficient of Variation)
• Bio16 = Precipitation of Wettest Quarter
• Bio17 = Precipitation of Driest Quarter
• Bio18 = Precipitation of Warmest Quarter
• Bio19 = Precipitation of Coldest Quarter
derived from monthly temperature & precipitation

53. Coffee suitability - Maxent Results Nicaragua

54. B
Results
Variable Adjusted
R2
R2 due to
variable
% of total
variability
Present
mean
Change by 2050s
Locations with decreasing suitability (n=89.8 % of all observations)
BIO 14 – Precipitación del mes más seco 0.0817 0.0817 24.8 24.49 mm -3.27 mm
BIO 04 – Estacionalidad de temperatura 0.1776 0.0959 29.1 0.83 0.166
BIO 12 – Precipitación anual 0.2057 0.0281 8.5 2462.35 mm -24.31 mm
BIO 11 - Temperatura media del cuarto más frío 0.2633 0.0576 17.5 20.11 ºC 1.86 ºC
BIO 19 - Precipitación del cuarto más frío 0.2993 0.0155 4.7 169.13 mm -7.08 mm
BIO 05 - Temperatura máxima del mes más cálido 0.3198 0.0102 3.1 28.45 ºC 2.30 ºC
BIO 13 - Precipitación del mes más húmedo 0.2838 0.0205 6.2 450.27 mm 10.72 mm
Otros - - 6.2
Coffee suitability - Maxent Results Nicaragua

55. Decision Support System for Agro technology Transfer (DSSAT)
+

56. Decision support system modelling (for benchmark sites)
Agronomic management
Expert & farmer survey
Integrated crop-soil modeling
160 LDSF sample sites
Baseline
domains
Impact
2030 A1b
Experimental
[n] cultivars
[n] fertilizer application
[n] seasons
Application domains
Analysis of biophysical systems and simulating crop yield in relation to management factors. Combine these
models with field observations that allow adjustment of the models in the course of the growing season .
Future
24 GCM
A1B (IPCC)
Current
worldClim
Validation with
available station data
Daily weather generator
MarkSIM
Weather
station data
(daily)
Climate data
yield
soil management

57. Statistical negative and positive outliers of predicted yield change by 2020

58. 58
Areas where the production systems of crops can be
adapted
Adaptation-Spots
Focus on adaptation of production system
Areas where crop is no longer an option
Hot-Spots
Focus on livelihood diversification
New areas where crop production can be established
Pressure-Spots
Migration of agriculture – Risk of deforestation!
Identifying Impact-Hot-Spots and select sites for socio-economic analysis

59. 59
• Beans as most important income (sell 70% of harvest)
• Climate variability (intense rain, drought), missing labor & credits,
high input costs, … forces them to changes
• Increasing livestock displace crops into hillside areas
• Half of farmer rent their land
• Distance to market is far
• Mostly no road access in rainy season
• They buy inputs/sell produce from/to farm-stores
(they call them: Coyotes)
Result: Sample-site 1 - Texistepeque (Las Mesas), Santa Ana ,El Salvador
Message 2: Adaptation Strategies must be fine-tuned at each site!
Las Mesas
Altitude: 667 m
(about 2188 feet)
Hot-spot -141 kg/ha
For 2020:
• 35 mm less rain (current 1605mm)
• mean temperature increase 1.1 C
For 2050:
• 73mm less rain ( -5%)
• mean temperature increase 2.3 C
• hottest day up to 35 C (+ 2.6 C)
• coolest night up to 17.7 C (+ 1.8 C)
Hot-spot

60. 60
Message 3: There can be winners if they adapt quickly!
Result: Sample-site 2 – Valle de Jamastran, Danlí, Honduras Adaptation-spot
Jamastran
Altitude: 783 m
(about 2568 feet)
Adaptation-spot -
115 kg/ha
• Active communities with already advanced agronomic
management of maize-bean crops
• Favorable soil conditions and management
• Long-term technical assistance / training
• Irrigation schemes (e.g. 50 mz of 17 bean producers)
• Diversification options (vegetables, livestock)
• Market channels through processing industries
• Advanced infrastructure (electricity, roads)
• Need to optimize water use efficiency
• Credit problems
For 2020:
• 41 mm less rain (current 1094 mm)
• mean temperature increase 1.1 C
For 2050:
• 80 mm less rain ( -7%)
• mean temperature increase 2.4 C
• hottest day up to 34.2 C (+ 2.6 C)
• coolest night up to 17 C (+ 2.1 C)

61. Conclusions crop models
• Ecocrop, when there is a lack on
crop information, for global or
regional assessment
• Maxent, perennial crops with
presence only data (coordinates)
available
• DSSAT, only for few crops (beans,
maize, …), high data input demand
and calibrated field experiments are
necessary
• We need to communicate
uncertainty of model predictions
Empirical
models
Mechanistic
models

62. Land use change analysis

63. Questions on land use change
• Where does Forest remain?
• Forest loss?
• Forest gain?
• Forest was converted to agriculture?
• Forest was converted to plantations?
• Forest was converted to other?
• Intensification of Agriculture on Non-Forest?
• Agriculture to other use?

64. • Data resolution 30m
• Forest change calculated by Hansen
– Tree-cover 2000
– Loss
– Gain
– Loss year

65. Forest change in square kilometer in the
Greater Mekong Sub-region
• From 2000 to 2012
• Includes tree-cover > 50%

66. Forest loss/gain in square kilometer in the
Greater Mekong Sub-region
• From 2000 to 2012
– Treecover
– Loss
– Gain
– Loss per year

67. • GFC tree-cover > 50%
year 2000
• GFC tree-cover > 50%
year 2000, loss & gain
year 2012

68. • Data resolution 250m
• 16 day timesteps
• Vegetation index NDVI (0-100)
• derived results:
– NDVI total change between 2000 to 2012
– NDVI inter-annual change (sd)

69. • A time-series of NDVI observations can be used to examine the dynamics of
the growing season or monitor phenomena such as droughts.
• The Normalized Difference Vegetation Index (NDVI) data set is available on a
16 day. The product is derived from bands 1 and 2 of the MODerate-
resolution Imaging Spectroradiometer on board NASA's Terra satellite.
A time-series analysis of Land Use

70. Methodology…
Download
data
• More than 300 images of NDVI 250m MODIS sensor
were downloaded from the period 2000-2013
Image
Filtering
• NDVI scenes was first filtered to eliminate high and
low values (poor quality data) using Quality
Assessment Science Data Sets (QASDS)
Noise
Removal
• Applying the approach of Fourier interpolation
algorithm, to separate the noise spectrum from the
signal spectrum of the data set frequency domain

71. 2004 – 2012

72. • MODIS NDVI
year 2000
• MODIS NDVI
year 2012

73. • MODIS NDVI year 2000
> 0.5 > 0.75
• NDVI by 2012
-10% +10%

75. • loss • gain

76. • GFC-Landsat (a) vs. MODIS (b)
(a)
(b)

77. Inter annual change of NDVI from standard deviation (sd)

78. Using Modis NDVI layer & GFC
Modis NDVI change 2000 to 2012 = A
Modis NDVI inter-annual change(std) 2000 to 2012 = F
Forest 2000 and Non-Forest 2000 from GFC
• Forest remains
– Forest 2000 [A = 0, F = 0]
• Forest converted to Agriculture?
– Forest 2000 [A -1, F +1]
• Forest converted but not agriculture?
– Forest 2000 [A -1, F = 0]
• Intensification of Agriculture on Non-Forest?
– Non-Forest 2000 [F+1]
• Agriculture to other use?
– Non-Forest 2000 [F-1]

80. Overview of climate variability
and climate change
Eitzinger Anton, Giang Linh, Lefroy Rod
Laderach Peter, Carmona Stephania
Overview of climate variability and likely climate change impacts on
agriculture across the Greater Mekong Sub-region (GMS)
1 April, 2014, Vientiane, Laos
Thank you!