2.5 Introduction to quantitative climate risk analysis - Ghana

Climate risk analysis for identifying and weighing adaptation
strategies in Ghana
Dr. Jascha Lehmann, Dr. Abel Chemura & Lisa Murken
Working group: Adaptation in agricultural systems

Climate risk analysis for identifying and weighing
adaptation strategies in Ghana
2Chemura, Lehmann & Murken, Potsdam Institute for Climate Impact Research (PIK)
Rationale: Climate change adaptation and NDC investment planning should be risk-informed,
science-based
11.09.2018:
Kick-off
workshop
September 2018
– March 2019:
Study
completion
15.03.2019:
Validation
workshop
Uptake of
results for
policy and
planning

The study
• PIK Team: Paula Aschenbrenner, Abel Chemura, Christoph Gornott, Fred
Hattermann, Hagen Koch, Jascha Lehmann, Stefan Liersch, Lisa Murken,
Felicitas Röhrig, Bernhard Schauberger, Amsalu W. Yalew
• Special thanks to all contributors from GIZ, MoFA, EPA and all interview
partners
• Available Format:
 In-depth study: ~ 70 pages
 Policy brief: 5 pages
 Factsheet: 2 pages
 Upcoming: climate risk profile

Stakeholder engagement throughout the study
Participation
Consultation
Validation
Kick-off workshop: discussion with key stakeholders
from the Ghanaian government
20 key informants interviewed: NGOs,
development actors, private sector,
academia
Cross-checking of adaptation selection;
validation workshop

GFZ
AWI
PIK
PIK
PIK
Einstein Tower
Great Refractor
Helmert Tower
Michelson Hall
Süring Hall
Library
GFZ
AIP - Astrophysical Institute Potsdam
AWI - Alfred-Wegener-Institute for Polar- and Marine Research
GFZ - German Research Centre for Geosciences
PIK - Potsdam Institute for Climate Impact Research
AIP
“Trefoil”
PIK
Telegrafenberg: Where the science happens

Chemura, Lehmann & Murken, Potsdam Institute for Climate Impact Research (PIK)
• 636 processors (CPU)
(in total: 5088 processor cores) with scalar
frequencies of up to 3.4 GHz,
• 20 TB RAM in total,
• a set of graphical coprocessors (GPU),
• non-blocking infiniband 56Gbps high
performance network,
• direct water-cooled processors and
memory, to heat office building(s)
• eight times performance /
capacity of previous system
• extendible system design
Our new super computer

PIK‘s Research Departments
7
Earth System Analysis:
Oceans, Atmosphere and Biosphere
in Past, Present and Future
Stefan Rahmstorf
Wolfgang Lucht
Climate Resilience:
Climate Impacts and Adaptation
Hermann Lotze-Campen
N.N.
Transformation Pathways:
Climate Risks and Sustainable
Development
Elmar Kriegler (acting)
Katja Frieler (acting)
Complexity Science:
Machine Learning, Nonlinear
Methods and Decision Strategies
Jürgen Kurths
Anders Levermann

ENZYCLICAL LAUDATO SI`
Published 18 June 2015
Servizio Fotografico, © L'Osservatore Romano

2018 – A year of extremes
Rekord-Sturm, Irma, August 2017
Record Hurricane Michael, October
Drought and heat
in Europa, Juli
Multiyear drought in South Africa
Record-floods in the Horn of Africa, MayForest fires in California, August

WEATHER CONDITIONS ARE CONNECTED WORLDWIDE
Trade winds
Jetstream
Jetstream

THE GLOBAL MEAN TEMPERATURE CONTINUES TO INCREASE
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
1,2
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Data source: GISS Surface Temperature Analysis (GISTEMP), Land-Ocean Temperature Index, http://data.giss.nasa.gov/gistemp/
TemperaturAnomalie
Year
El Niño
Pinatubo
La Niña
GISTEMP-Data in 12-month running mean until
incl. July 2018
Base period:
1951 – 1980

Climate change can bring about conditions exceeding
human thermoregulatory capacity
12
Mora et al., 2017, Nature Climate Change

Climatic conditions are changing
Annual mean temperature in Ghana
Increased warming
of around +4 °C
Stabilized warming
at > +1 °C
+1 °C
+2 °C

Number of hot extremes will increase
Low-emission
scenario
High-emission
scenario

Regional specific changes in rainfall
Low-emission
scenario
High-emission
scenario
2090

Duration of rainy season(s) changes
High-emission
scenario

Changing water availability for agricultural production
Annual mean discharge at the Volta River basin
Individual model results project extremes in both
directions
 Conditions may become either very dry or very wet

Climate impacts on crop production
Key questions:
- What is the long term impact of climate change on crop yields?
- What is the share of climate-attributable crop yield variability?
- What is the risk for crop failures today and in the future?
- What would this mean for the entire food production and availability?

Climate impacts on crop production: Key Findings
Potsdam Institute for Climate Impact Research (PIK)
• Process-based modelling
• Maize
• Statistical modelling
• Maize
• Suitability modelling
• Cassava, groundnuts, sorghum and maize

A methodological framework
21
Suitability models
Statistical models
• Produce growth, yield and economic outcomes from a set
of biophysical conditions and management practices.
• Produce suitability maps based on the observed relationships
between current production and environmental variables.
Process-based modelling
• Use linear and non-linear effects of radiation, temperature, and
precipitation to account for the dependence of primary
production on climate variables.

Key Findings
• Process-based modelling
• Maize
• Statistical modelling
• Maize
• cassava, groundnuts, sorghum
and maize

Process based simulation of maize yields
•Nkwata, West Akim and Akatsi
•Better explanation of year to year maize yield variability
3 Districts

• Overall losses in maize yield
by 2050 (3 – 5%)
• Impact not uniform across the
country with some areas
projected to have maize yield
gains under climate change.
Process based simulation of maize yields

Statistical modelling of maize yields in Ghana
• 56% of the year-to-year maize yield
variability can be explained by
weather factors.
• The weather effect is significant.
• Results are sensitive to choice of
parameters, spatial aggregation and
input data.

Crop suitability modelling
• Rainfall factors influence the potential
for maize, groundnut and cassava
production.
• Temperature-based factors are most
important in determining sorghum
suitability.

CassavaGroundnuts
• RCP2.6: 9.4% to become less suitable and 1.4%
will become more suitable (net loss of 8.1%).
• RCP8.5: 14% to become less suitable and 10.4%
will become more suitable (net loss 4.4%).
• RCP2.6: 6.7% to become less suitable under while
7.3% will become more suitable (net gain of 0.5%).
• RCP8.5: 10.1% will become less suitable and 12.3%
more suitable (net gain of 2.2%).

MaizeSorghum
• RCP2.6: 35.5% to become less suitable and
increase for 9.6% (net loss of 25.9%).
• RCP8.5: 42.7% to become less suitable and
19.1% will become more suitable (net loss
23.6%).
• RCP2.6: Decrease for 3.9% of the country and
increase for 2.9% (net loss of 1%).
• RCP8.5: Decrease for 9.3% and increase for
4.4% of the country (net loss 5%).

Impacts of climate change are
site- and crop-specific,
• bioclimatic factors that
influence crop viability &
• the specific characteristics of
the crops.
Increase
Stable
Decrease

• All model projections generally agree on
direction & magnitude of change.
• Maize projected to have the greatest net
losses in area suitable under climate change.

Projected decrease in areas suitable for 4 crops with
increase where 2/4 crops are suitable.

Scenarios Cassava Groundnut Maize Sorghum
RCP26
Cassava ꟷ 52540 107128 37870
Groundnut -7 ꟷ 64481 127598
Maize -6 -14 ꟷ 88363
Sorghum -5 7 0 ꟷ
RCP85
Cassava ꟷ 48105 118045 19024
Groundnut -2 ꟷ 62776 136971
Maize 5 -1 ꟷ 76095
Sorghum -8 4 -5 ꟷ

Key Findings
• What is the long term impact of climate change on crop yields?
• Depends on crop and area/region.
• Overall impact is decreased crop yields for sorghum, maize and
cassava with potential increases in groundnuts.
• What is the share of climate-attributable crop yield variability?
• 56% for maize.
• Rainfall factors are more important for cassava, groundnuts and maize
while temperature is a key factor for sorghum.

Key Findings
• What is the risk for crop failures today and in the future?
• The risk is spatially explicit, overall increase in risk of crop from the
transitional zones to the north.
• What would this mean for the entire food production and availability?
• Although at national levels percentage impacts appear smaller, the most
significant effects will come through changed geography of crop potential.

Some discussion points
• 43 NDC target districts: Good but……
• 56% variability explained by weather factors.
• Individual and multiple crop suitability changes: Impact hotspots.
• National average yield losses are less useful for impact assessments.
• Changes in geography of crop suitability, what does it mean for farmers and
government?
• ……not withstanding uncertainties in data and modelling approaches.

36
TRANSLATING IMPACTS INTO ACTION

Adaptation environment: potential for adaptation
Barriers and potential solutions to adaptation in Ghana
Lack of awareness
and understanding
of climate risk
Demonstration of
adaptation benefits;
Capacity building for
adaptation
Difficulty to access
inputs and finance
Provision of inputs; access
to markets and credit
Insecurity of tenure
and limited land
availability
Mapping of tenure rights;
participatory tenure
reform
Adaptation design:
1. Combinations of adaptation strategies
2. Seize local and indigenous knowledge
3. Importance of local ownership:
participatory consultations
4. Upscaling of strategies

Five adaptation measures selected
• Crop insurance
• Post-harvest management
• Irrigation
• Rainwater harvesting
• Improved crop varieties
Selection criteria:
1) Interest amongst stakeholders in Ghana,
relevance for Ghanaian context
2) Link with existing policy documents
3) Climate impact analysis
4) Suitability for analysis within impact
models
5) Suitability for economic analysis

Multi-criteria analysis: adaptation assessment criteria
Assessment criteria:
1) Risk mitigation vs. risk transfer
2) Risk mitigation potential
3) Cost-effectiveness and average costs
4) No-regret vs. risk-specific
5) Upscaling potential
6) Development co-benefits
7) Stakeholder interest/local ownership
8) Implementation level: Institution-led vs. Autonomous
PIK Models
Literature,
Interviews

Adaptation evaluation using impact models
• Process based modelling

Process-based adaptation evaluation
• At national level, all
adaptation measures
work well with
varieties being more
promising.

Process-based adaptation evaluation
• The response of maize yield to adaptation measures
depends on the measure and the district.
• Late sowing will have a negative effect on maize yields
in West Akim, similarly with increasing manure under
RCP2.6 in Nkwata.
• Nkwata will have the greatest yield responses of the
various adaptation measures.

Suitability modelling adaptation evaluation
Crop Scenario
2 weeks
shift
4 weeks
shift
10%
Organic C 2W+10% 4W+10%OC
Cassava
RCP2.6 -2.15 0.43 0.29 -0.43 0.57
RCP8.5 5.45 5.31 4.30 4.30 6.31
Groundnuts
RCP2.6 -3.59 -0.86 -0.14 -0.14 -1.58
RCP8.5 -0.14 -1.29 -0.14 -0.43 -2.30
Maize
RCP2.6 2.15 0.86 -0.29 0.57 0.57
RCP8.5 2.30 1.29 0.14 0.57 0.00
Sorghum
RCP2.6 2.01 -0.29 -0.72 -0.43 -1.22
RCP8.5 4.30 1.58 1.29 1.87 2.58

Some discussion points
• Planning for adaptation at national levels is potentially disastrous for some
areas.
• Response of crops to adaptation measures depends on the crop and the
region.
• Investing in crop improvement programs important for building maize
resilient systems…….adoption of a key element.
• Current crop production manuals and handbooks may need to be revised in
light of climate change realities

Economic assessment of adaptation options
Economic assessment approach:
• Ex-ante evaluation, based on different cost-scenarios
• Net value of production approach
• Area- and yield impact scenarios, differentiating between the nature of climate impact
• Static comparison of current baseline (2010) with projected impacts in 2050
• Sensitivity analysis: only average results are presented here
Irrigation: Most scenarios too costly, all risk-specific
Post-harvest management: Cost-effective, mostly no-regret
Crop insurance: Current premiums too low to cover increasing damages
Summary of key findings:

The cost of climate change for crop production
Crop/Scenario
Ghana Northern Region Savelugu-Nanton District
BAS Y-CC A-CC BAS Y-CC A-CC BAS Y-CC A-CC
Maize 544 518 531 59 56 57 5 5 5
Sorghum 135 129 132 53 50 52 2 2 2
Groundnuts 347 330 338 152 145 148 19 18 18
Net values of crop production under climate change (million $)
Scenarios:
• BAS = Baseline
• Y-CC = Climate change impact on yield
• A-CC = Climate change impact on area suitability
Costs of climate change from the three cereal crops
in Ghana in 2050: up to 51 million $ (in the Y-CC
case) and about 25 million $ (in the A-CC case)

Economic analysis - PHM
545
518
574
566
558
541
490
500
510
520
530
540
550
560
570
580
BAS CC PHM-NO PHM-MIN PHM-MID PHM-MAX
Net value of maize production in Ghana under climate change
and PHM as adaptation (in million $)
Key message:
PHM is cost-effective under
all scenarios and mostly a
no-regret option
BAS = Baseline
CC = Climate change (yield impact)
PHM NO = No cost post-harvest
management
PHM MIN = minimum costs
PHM MID = medium costs
PHM MAX = maximum costs

Economic analysis - irrigation
545
518 531 529 520
504
351
518
483
507
469
524 524
0
100
200
300
400
500
600
BAS Y-CC IRR-NO IRR-1MinIRR-1MaxIRR-2MinIRR-2MaxIRR-3MinIRR-3MaxIRR-4MinIRR-4MaxIRR-5MinIRR-5Max
Net value of maize production in Ghana under climate
change and irrigation (in million $)
Key message:
Irrigation is only cost-
effective under some
scenarios and never a no-
regret option

Overall performance of adaptation measures
Criteria: Crop insurance Post-harvest
management
Irrigation RWH Improved varieties
1 Risk-transfer Risk reduction Risk reduction Risk reduction Risk reduction
2 Institution-led All levels All levels All levels Institution-led
3 Risk-specific No-regret (Risk-specific) (Risk-specific) (Risk-specific)
4 No risk mitigation High (Medium) (Medium) High
5 High costs Cost-effective High costs (Low costs) High costs
6 High High Medium (Medium) (Medium)
7 Medium High (Low-medium) High Medium
8 High High High High High
1) Risk mitigation vs. risk transfer
2) Implementation level - Institution-led vs. autonomous
3) No-regret vs. risk-specific
4) Risk mitigation potential
5) Cost-effectiveness and average costs
6) Upscaling potential
7) Development co-benefits
8) Stakeholder interest

Main recommendations
- Post-harvest management: cost-efficient and no-regret strategy, recommended for wider
implementation
- Crop insurance: needed to transfer risk, trade-off between premium affordability and
financial sustainability
- Rainwater harvesting and small-scale irrigation: limited in scale, useful for income
diversification
- Improved crop varieties: mostly costly, need for adaptation to local context
- Irrigation: costly under all scenarios, mostly sufficient precipitation levels across Ghana
National
Subnational
Local
Based on biophysical, economic and social assessment:

51
Thank you.
Abel.Chemura@pik-potsdam.de
Lisa.Murken@pik-potsdam.de
Jascha.Lehmann@pik-potsdam.de
Christoph.Gornott@pik-potsdam.de

WP3: A Methodological Framework
• Produce growth, yield and
economic outcomes from a set of
biophysical conditions and
management practices.
Process-based modelling with APSIM (2006 – 2016)

Statistical models: AMPLIFY
Use linear and non-linear effects of radiation, temperature, and precipitation to
account for the dependence of primary crop production on climate variables.
=

Suitability models (2012-2016)
Produce suitability maps based on the
observed relationships between current
production and climatic variables.
Maize Sorghum Groundnut Cassava
Suitability: Ability of an area to meet set
production target with current methods
and at costs

Interaction between temperature and precipitation
Impact of changing climate conditions on crop yields in Western Africa
Sultan et al. 2013

2.5 Introduction to quantitative climate risk analysis - Ghana

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