Developing Climate Impact Assessments in an Idealized Case Study of Flood/Drought
Index Insurance in the West African Sahel
Asher Siebert, Rutgers University, asherb.siebert@gmail.com, M. Neil Ward, mneilward@gmail.com
Africa Climate Conference: Arusha, Tanzania, October 15-18, 2013
Data from the Food and Agriculture Organization Statistical Database (FAOSTAT)
Data was collected at the national level for Niger, Mali and Burkina Faso from 1961-2011
Data were collected for area harvested, crop yield and production for millet and rice
To correct for the effect of population growth and the coincident rise in agricultural production, detrended time series of the agricultural data were used.
Note the relationship between agricultural
data and the pattern of regional rainfall in
the figure at right. Agricultural production
is here considered a preferable agricultural
variable on which to base a theoretical
index insurance contract as both crop
yields and area harvested can suffer under
adverse climate conditions.
Standardized detrended millet production
4
3
2
1
0
-11960
1970
The rainfall graphic on the right, depicting
a time series of a Sahel rainfall index shows
that there was a wet period in the 1950s
and 1960s, followed by a prolonged drought
in the 1970s, 1980s and early 1990s,
followed by a partial recovery of rainfall
since the mid-1990s: a trend also reflected
in regional streamflow levels (Greene et al.,
2009, Ndiaye et al. 2011).
More general paleoclimate studies of the
region have shown a pronounced tendency
towards strong multi-decadal variability
(Shanahan et al., 2009).
The station data for this analysis are
taken from the Global Historical
Climate Network in the domain 10N to
20N, 20W to 30E. (Ward et al., 2012
and Siebert and Ward 2013).
1990
2000
-3
Burkina Faso
Mali
Streamflow data
Streamflow m3s-1
5000
4000
3000
2000
1000
0
1
2
3
4
5
6 7 8
Month
9 10 11 12
Daily streamflow data from the Niger Basin
Authority for 1950-2009 for three stations:
• Koulikoro, Mali (upper basin)
• Dire, Mali (inland delta)
• Niamey, Niger (middle basin)
The peak flood-month varies from station
to station as depicted in the monthly
climatologies on the left and right.
Niamey climatology (middle basin)
2000
streamflow m3s-1
Koulikoro climatology (upper basin)
index
threshold
exceeded
index
threshold not
exceeded
Extreme low crop
production
0.08
0.02
Not extreme low
crop production
0.04
0.87
GSS
values
Niger
millet
prod.
BF
millet
prod.
GHCN
0.557 0.756
NOAA
PRECL
Mali
millet
yield
Niger
rice
prod.
0.395 0.352
0.379 0.49
Preliminary projections
year
Niger
Probability (pij)
stream
flow
2010
-2
Methodology
Recent Climate History
1980
From a user perspective,
there is an interest in seeing
that the chosen contract
performs well historically
(i.e. that the contract pays
out in years when there was
a shortfall of production).
One way to measure this
more directly than the
correlation is by use of the
Gerrity Skill Score (GSS).
Contingency table for GHCN Burkina
Faso millet production contract.
One can think of the GSS by
means of the following
equation:
GSS = Σpijsij,
where pij, and sij are
respectively the probability
and coefficients of the ijth
elements of a contingency
table (eg. shown at right).
A perfect GSS is 1 and
random guessing gives a
GSS of 0. GSS>0.3 are fairly
good and are shown.
1500
1000
500
0
1 2 3 4 5 6 7 8 9 10 11 12
Month
Thus far, projections of future threshold crossing event (TCE)
frequencies have only been made for the Niger Basin
streamflow. In the grayscale histogram on the right, the
probability of a given number of TCEs (1 in 10 threshold of
exceedance) in a 30 year period is simulated with and without
multi-decadal variability (MDV here simulated by means a lag-1
year autocorrelation of 0.6). Clearly, the MDV expands the range
of outcomes.
Likewise, the colored histogram depicts the effect of imposing
a 20% wetter or drier trend (over 2010-2040) in mean
streamflow on the high flow TCE frequency in Niamey during
the same period. There is a clear enhancement of the TCE
frequency in the case of a wetter climatology and clear
suppression of TCE frequency in the case of a drier climatology.
TCE frequency for generic baseline
0.25
0.2
0.15
0.1
0.05
0
probability
•
•
•
•
Standard deviations
Extreme climate events have significant adverse impacts on many components of
African society, including the agriculture sector. One of the ideas that have been explored to
help address these risks and vulnerabilities is the concept of index insurance. Index insurance
differs from traditional insurance in that payouts are made on the basis of a geophysical index
directly measuring an environmental condition, rather than through a loss claim and
verification procedure. As a consequence of this simpler framework that holds the potential
for a more rapid payout, index insurance is perceived as having practical potential in a
developing world context where traditional property or agriculture insurance markets tend to
be limited or nonexistent. Furthermore, as several climate risks have relatively high levels of
spatial coherence (at least over relatively flat terrain), there is the potential that such projects
may serve a large area.
However, climate related index insurance in Africa has a number of key challenges.
Beyond establishing trust, communicating the risks and achieving the consent of the potential
users, there are a number of technical challenges as well. Among these technical challenges is
the time-varying nature of the climate system itself. As the climate system changes, the
frequency of threshold crossing extreme events (TCEs) would trigger a change in the payout
frequency.
This study explores the sensitivity and viability of such a model index
insurance scheme in the West African Sahel in the context of a changing climate in
the 2010-2040 period. As currently envisioned, the theoretical index insurance
contracts will be conceived as national-scale contracts for Niger, Mali and Burkina
Faso.
Two components:
• a component targeted towards insuring irrigated farmers along the River Niger
against flooding risk
• a component targeted towards insuring rainfed (mostly subsistence) farmers
against drought risk
Index data include:
• Streamflow data from the Niger Basin Authority for the irrigated farmer
component
• A suite of rainfall datasets from gauges and satellite measures for the
subsistence farmer component
• Agricultural data to inform the index insurance models will be from the Food
and Agriculture Organization (FAO), although they may be supplemented by
some regional data.
Monte Carlo simulations will be employed to model TCE frequency under a
range of assumptions about the trend, decadal variability and temporally evolving
shape of the index distributions, consistent with IPCC projections and regional
climate literature. The TCE frequency has direct bearing on the financial
viability/solvency of such an index insurance contract over time in the context of a
changing climate. Two adaptive features of this study could promote financial
viability in light of the changing basis risk:
• The use of temporally evolving thresholds (explored in Siebert and Ward, 2011)
• The framing of a suite of contracts to target flood risk and drought risk (anticorrelated risks) concurrently for different populations
Gerrity Skill Score and Index Selection
Agricultural Data
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
number of TCEs
no MDV
MDV
Niamey high flow frequency
0.4
probability
Introduction
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
number of TCEs
wetter
drier
The contour plot displays the continuous effect of the
combination of both multi-decadal variability and a trend in the
streamflow on TCE frequency. The probability of 10 or more TCE
high flow events in the 2010-2040 period occurs with maximum
trend and MDV.
Meteorological Data
Three rainfall datasets have been
explored in this analysis:
• Global Historical Climate Network
(GHCN – station only)
• Global Precipitation Climatology
Project (GPCP – satellite only, 2.5
deg resolution)
• NOAA Precipitation over land
(NOAA PRECL – station data
interpolated to a 2.5 deg grid)
Conclusions and Future Work
• NOAA PRECL and GHCN rainfall datasets could show some potential as a basis
for contract for rainfed millet farmers
• Streamflow data from the Niger Basin Authority could show some potential as
a basis for contract for irrigated millet farmers in Niger
• The TCE frequency is highly sensitive to model parameters such as trend and
MDV
• Integrate GCM data into future projections
• Model rainfall and index insurance contracts explicitly
• Model time-varying higher order statistical moments
Correlation Analysis
Maps of correlation
between
the
detrended
agricultural
indices and the NOAA
PRECL are shown on the
left.
Tables of correlation
values between the
agricultural indices and
the GHCN data and
streamflow data are
shown on the right.
Correlations with GHCN (JAS rainfall)
Correlation
Niger
Burkina Mali
Faso
Millet area
0.39
0.436
0.273
Millet yield
0.533
0.444
0.244
Millet
production
0.48
0.616
0.493
Correlations with streamflow
Correlation
Niamey
Niamey
December January
Niger rice area
-0.527
Niger rice yield -0.223
-0.418
-0.317
Niger rice
production
-0.534
-0.571
Bolded values indicate statistical significance.
Acknowledgements
The authors would like to gratefully thank the African Centre of Meteorological Applications for
Development (ACMAD), the Niger Basin Authority and Dr. Katiella Mai Moussa for their
respective guidance, contributions and assistance with this research.
Key References
• Greene, A., A. Giannini, and S. Zebiak, 2009: Drought Return Times in the Sahel: A Question of Attribution, Geophysical
Research Letters, 36, L12701.
• Ndiaye, O., M. N. Ward and W. Thaw, 2011: Predictability of Seasonal Sahel Rainfall Using GCMs and Lead-Time
Improvements Through the Use of a Coupled Model, Journal of Climate, 24, 1931-1949.
• M. N. Ward, A. Siebert, and O. Ndiaye 2012. “Decadal‐to‐Multidecadal Variation in Sahel Rainfall since 1950 and Associated
Changes in the Frequency of Threshold--‐Crossing Seaonal Rainfall Totals”, AMMA Conference, Toulouse, France, July 2012.
• Shanahan, T. M., and Coauthors, 2009: Atlantic forcing of persistent drought in West Africa. Science, 324, 377–380.
• Siebert, A. and M. N. Ward, 2011: Future Occurrence of Threshold-Crossing Seasonal Rainfall Totals: Methodology and
Application to Sites in Africa, Journal of Applied Meteorology and Climatology, 50, pp. 560-578.
• Siebert, A. and M. N. Ward, 2013: Exploring the Frequency of Hydroclimate Extremes on the River Niger Using Historical Data
Analysis and Monte Carlo Methods, African Geographical Review, accepted, in press.
• Zhang, X., F. W. Zwiers, and G. Li, 2004: Monte Carlo Experiments on the Detection of Trends in Extreme Values, Journal of
Climate, 17, pp. 1945-1952