Transcript capacity building to evaluate and adapt to climate change

CAPACITY BUILDING TO EVALUATE AND
ADAPT TO CLIMATE CHANGE-INDUCED
VULNERABILITY TO MALARIA AND
CHOLERA IN THE LAKE VICTORIA REGION
AN OVERVIEW OF PROJECT
PRESENTED BY
SHEM O. WANDIGA
PRINCIPAL INVESTIGATOR
KENYA NATIONAL ACADEMY OF
SCIENCES
P.O. BOX 39450
Tel: 254-02-311714
Fax: 254-02-311715
Email: [email protected]
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Project Team are:
P.Z. Yanda
R. Kangalawe
Rehem Jackton Sigalla
Tanzania
P.P. Mugambi
E. Kirumura
R. Kabumbili
Uganda
S.O. Wandiga
M. Opondo
D.O. Olago
F. Mutua
Faith W. Githui
Kenya
Tim Downs
U.S.A
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Every 30 seconds a child dies of malaria. In the
poorest 20 per cent of the world’s population malaria
takes many more lives than HIV/AIDS. Why then do
we hardly notice malaria stories and programmes in
the media? Asks Angela Dawson of Liverpool
School of Tropical Medicine, U.K.
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Cholera epidemics have been associated with
El Niño rains. The epidemic when it occurs
claims many lives and disrupts societal
functions. How are malaria and cholera
related to climate change? What is the
contribution of socio-economic status of the
affected communities to severity of the
epidemics?
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PROJECT OBJECTIVES
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To develop climate change scenarios using General
Circulation Models (GCM).
To develop a Geographic Information System (GIS)
To analys time series data to link precipitation and
temperature change with incidences of malaria and
cholera in six vulnerable communities in Kenya, Uganda
and Tanzania.
To analys the socio-economic status of communities,
disease rates and the sensitivities of these rates to climate
variables.
To identify communities at highest risk from exposure to
malaria and cholera agents under different climate change
scenarios.
To evaluate existing adaptation capacity of vulnerable
communities.
To work with identified high-risk communities to
generate improved adaptation capacity.
To examine climate and non-climate determinants of
malaria and cholera risk in order to inform policy-making
that builds adaptation capacity.
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Lake Victoria and its catchment
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Lake Victoria
Position:
0o 21’ N and 3o’S
Age:
4000,000 years old
Surface Area:
68,800 Km2
Catchment Area:
184,000 Km2
Lake temperature: 0.5oC warmer than in 1960s.
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The Riparian Communities has Seen rapid population
expansion
Year
Population
1820
23 million
1932
4.6 million
1995
27.7 million
2030
53 million (projected)
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Economic Statistics
Gross economic product US$ 3 to 4 million annually
Average income range US$ 90 to 270 per annum.
Entry into History
Historically famous for being source of the Nile River.
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The Conceptual Framework
Add cc
additional change to  T,  P
Additional Risk Factors affected by CC
T P
Water quality + quantity
Food Security
Science
Changes in Risk Agent
Target population exposure
Risk
Malaria
Risk agent source
Data Integration
GIS
Cholera
Policy + Actions
 Exposure
Risk Management +
Adaptation options
 Source
Existing Data (20)
Demonstrating priority
health risks
KEY: CC=Climate change;  T  P = change in temperature and precipitation; 2o = secondary data
= strong linkage
= hypothetical linkage
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The Conceptual Framework assumes:•
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That the project has a science component that can be
integrated into policy and mitigation.
The science will inform policy by identifying the risk of
the target population and their copying mechanism.
The relationship between precipitation and temperature to
malaria and cholera explored hypothetically.
That the project encompases integrated vulnerability
assessment, adaptation policy and implementation of an
adaptive project.
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Concept preplanning
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2
Strategic
Analysis
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3
Strategic Plan
+
Identification
of options
(Multi-criteria
Decision
Making
Preferred
strategy
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Implementati
on of
Preferred
Management
Options/
Strategy
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Operation +
Management
Evaluation
Monitoring of
performance
Adaptive Feedback
Beginning of Project
T (year)
KEY: - = see workplan (section 5.0) for detailed activities in each stage; t=time in
years.
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Research Questions
1.
What is the possible range of climate variables
(scenarios) indicated by different climate change models in
the Lake Victoria region? (see Figure 4) (Application of
existing models)
2.
What is the relationship of malaria and cholera to
climate variability (temperature and precipitation) in the
Lake Victoria region?
3.
Which target groups are most vulnerable, i.e. how
are sources of vulnerability differentiated within the
population of the Lake Victoria region?
4.
What excess risk could be attributable to climate
change? i.e. What are the possible ranges of malaria and
cholera in the region given the different possible scenarios
and what excess malaria and cholera risk could be
attributable to these climate change scenarios?
5.
What are the existing and coping adaptation
mechanisms and how can these be strengthened at the
community level to cope with the possible range of excess
risk? i.e. What are the possible adaptation strategies at the
community level?
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Justification
Very few studies on climate and water resources impacts on
health have been undertaken in the East African region. This
project will help fill the gap. It will assess vulnerability of
communities to climate variability and change and adaptation
mechanisms that can be incorporated in policy. It will
enhance the capacity of scientists and institutions involved in
the project.
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METHODOLOGY
a)
To characterize baseline temperature and
precipitation variability and apply existing climate
change models to the region in order to estimate possible
perturbations to those conditions.

Rainfall is highly seasonal in the region. The
rainfall and temperature will be for specific season (s).

Climate data (rainfall and temperature) covering
the period 1961 to the present will be collected from
meteorological stations in the Lake Victoria basin.
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Statistical downscaling capacity is available and
shall be used. Some regional climate models are also
available in the region.
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A statistical analysis will be run of the time series
data in order to estimate the probability distribution
functions for temperatures and precipitation for each
decade (baseline variability for 1960-70, 1970-80, 198090, 1990-2000). Descriptive statistics will be determined
(mean, mode, median, standard deviation). Baseline is
the state before any climate change variability is
imposed.
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
Downscale the outputs of the climate models and
scenarios of changes in extreme (the El-Nino years and
La Niña years) and to assign probabilities to extreme
events.
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Apply the common IPCC method of running
ensemble of GCM projections with a set of scenarios.
The ensemble models that have been recently used in
the regional studies include the Canadian CCM,
UKMO, GFDL Ver 2 and 3; and Japanese climate
model.

Use some regional climate models that are
available. (e.g Ssemazi, et.al.)
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Selected climate change models will be used to
estimate possible changes to baseline conditions i.e.
perturbations to temperature and precipitation.
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Use a range of increasing and decreasing
synthesis of extreme seasonal rainfall and temperature
scenarios, and some of the observed extremes and
trends.
The possible envelope of future climate scenarios will
be drawn paying attention to extremes and if possible
the relative probability of different scenarios
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+20%
P,
CC Models
M,
Scenario Envelope
+10CT
c
Now
- 10%P

T (yr)
Where :
P = historical changes in precipitation
M = historical changes in malaria
C = historical changes in cholera
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b) To improve our understanding of the dynamic
relationships between changes in temperature and
precipitation, and changes in malaria and cholera risk.

Historical data on malaria and cholera incidences will
be obtained from medical centres in the Lake basin region.

GIS layers will be constructed to provide the spatial
distribution of key variables; temperature (T), precipitation
(P); malaria (M) and cholera (C) incidences. These maps
will allow us to select the candidate pilot sites – those sites
demonstrating high variability in climate and disease
indicators.
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The temporal relationship between the variables will
be explored by comparing time series data of P and T for
each station and M and C incidence for the areas
surrounding each station. Daily values will be plotted and
compared in order to capture the signal coupling between
climate and disease data.
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
For malaria, data on vector habitat, numbers and
distribution changes will be collected and incorporated into a
GIS layer. This will allow us to explore the relationship
between these vector-related parameters, climate parameters
and disease indicators.

For cholera, data on location of water bodies (streams,
rivers, lakes) and wetlands (swamps) will be plotted on a GIS
layer using existing maps and remotely sensed imagery as
appropriate. Relevant cholera habitat indicators such as water,
temperature and quality (alkalinity, algal blooms) will also be
plotted on a GIS layer. Existing hydrological data on river
flooding will be collected and plotted on a GIS layer. This is
because river flooding is also a risk factor for cholera.
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c) In order to understand the sources of vulnerability,
the following information will be gathered:
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time -activity pattern of exposure to risk agents
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socio -economic household risk factors
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access to health facilities
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access to water, water quality and stagnant
water bodies
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disease awareness, perceptions and attitudes
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malaria and cholera incidences per member of
family
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treatment and fatality from risk agents, etc.
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GIS layers of temperature, precipitation, malaria,
cholera incidences will be constructed for various
community groups.
 Potential risk communities will be identified both
through statistical data correlation and GIS
maps.
 The identified communities will be engaged in the
project through group participatory approaches.

Secondary data will be first collected from pilot
communities to identify data gaps; these gaps will be
filled by primary data from representative samples of the
pilot populations. This information will be used to
identify the target risk groups within the pilot
populations, for example, by age, occupation or gender.

Socio-economic surveys by means of participatory
approaches, focused group discussions, household
interviews, field surveys and key informant interviews
will be used to collect the relevant data.

Both primary and secondary data will be used to
identify vulnerable (risk) groups.
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
The previous information about climate and disease
relationships will be used together with the following socioeconomic data to better understand sources of vulnerability
and adaptation capacities:
Awareness, perceptions and attitudes of
stakeholders from local institutions and communities to
climate-related human health problems especially malaria
and cholera.
Anecdotal data on historical coping mechanisms,
adaptation strategies and traditional knowledge of pilot
communities.
Socio-economic characteristics of pilot
communities (household resource endowments, poverty
levels). This will be obtained from Household Welfare
Monitoring Studies, Census and Statistical Bureaus and
participatory interviews).
Data on water sources (wells, rivers, springs),
water availability, use and management strategies at
household level and hygiene practices.
The daily time activity patterns of the pilot
communities.
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d)
To estimate the excess risk of malaria and cholera that
may be attributable to future climate change.

Characterize baseline temperature and precipitation
variability. These will be statistically correlated to incidences of
malaria and cholera. We shall then apply climate change models
described in section (a) to estimate possible perturbations to
these conditions. Historical data on malaria and cholera
incidences will be correlated with climate (T & P) data to
evaluate the temporal relationship between the two sets of data.
We shall use the following tools and models:
GIS software as spatial-temporal database data
integration
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Statistical analysis
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MAGICC SCENGEN
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ACRU model
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SYSTAT PACKAGE/CLIMLAB 2000
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COSMIC
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GCM & models
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Statistical and multiple regressions models.
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Through statistical and multiple regression models, we shall
correlate other factor such as land-use change and socioeconomic factors.

Combining results from the analyses in (a) (baseline
variability and future climate change scenario) and (b)
(relationship between climate and disease), it will be
possible to estimate the change in incidence of malaria and
cholera attributable to climate change. In other words it will
be possible to say by how much the incidence of malaria
and/or cholera may change with a given change in T and/or
P.
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a)
done:
To represent future scenarios the following will be

Use either statistical downscaling techniques and/or
dynamical downscaling with regional climate models; average
daily precipitation and maximum and minimum temperature
over the baseline period 1961 to 1990 at spatial resolution of
0.5 x 0.5 degree 50km sq to identify climate variables.

Using the regional data sensitivity scenarios of climate
change we shall develop the scenarios for annual uniform
warming of 2degree C, 4degree C and 5degree C and no
change in 2010 and 2050’s; the same warming with 5%, 10%
and 20% increase in precipitation in 2010 and 2050’s will be
analyzed. We shall use climate scenarios developed by IPCC
SRES. We will not develop new scenarios models.

Statistical and multiple regression analysis will be used
to correlate future diseases scenarios with changes in social,
economic and land-use conditions.

To integrate data and knowledge types in a geographic
information system to inform local and national policy
making, and enhance stakeholder awareness.

Using the results from the GIS layers obtained in (b) it
will be possible to represent future disease scenarios for
presentation to policy makers.
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f)
To work with risk groups in the pilot
communities to assess existing adaptation and coping
strategies for malaria and cholera, and to identify
alternative strategies that can accommodate the possible
changes in risk.

In each pilot community, a working group
representing the target risk groups will be formed and
together evaluate the alternative adaptation and coping
strategies for malaria and cholera. Multi-criteria decision
making will be used to compare the different alternatives
and select preferred adaptation strategies.

Design Strategic Action Plans (SAPs) for the
preferred adaptation strategies.

Monitor disease incidences, hospital/clinic visits
and diagnosis, household welfare indices and other socioeconomic data gathered at pre and post application of
adaptation strategies to evaluate responses.
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g)
To implement priority strategies to prevent and
mitigate existing and potential risks, monitor the
performance of the strategies, and adapt to changing
conditions.

Meetings will be held with the national and regional
government and local communities in order to present SAPs
and mobilize resources to implement them.

Apply a “practice and attitude” pre-survey to a sample
of the pilot population before implementation of SAPs.

Implement SAPs in each pilot community.

Monitor and evaluate performance of the strategies.
Future incidences of malaria and cholera will be used as
indicators of the effectiveness of the preferred strategies.

Carry a post implementation “practice and attitude”
survey.

Present performance results to the policy makers and
local communities and modify and or adapt accordingly.
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