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.comAfrica Climate Conference: Arusha, Tanzania, October 15-18, 2013

Methodology

Recent Climate History

Introduction Agricultural Data

Streamflow data

Meteorological Data

Correlation Analysis

Gerrity Skill Score and Index Selection

Preliminary projections

Conclusions and Future Work

Key References

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.

• 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, de-

trended time series of the agricultural data were used.

1 2 3 4 5 6 7 8 9 10 11 120

1000

2000

3000

4000

5000Koulikoro climatology (upper basin)

Month

Str

eam

flow

m3s

-1

1 2 3 4 5 6 7 8 9 10 11 120

500

1000

1500

2000 Niamey climatology (middle basin)

Month

stre

amfl

ow m

3s-1

0 1 2 3 4 5 6 7 8 9 1011121314150

0.05

0.1

0.15

0.2

0.25TCE frequency for generic baseline

no MDV MDVnumber of TCEs

prob

abil

ity

0 1 2 3 4 5 6 7 8 9 1011121314150

0.1

0.2

0.3

0.4Niamey high flow frequency

wetter driernumber of TCEs

prob

abil

ity

• 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-TimeImprovements 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

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 riskIndex 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 (anti-

correlated risks) concurrently for different populations

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).

1960 1970 1980 1990 2000 2010

-3-2-101234

Standardized detrended millet production

Niger Burkina Faso Mali

year

Stan

dard

dev

iatio

ns

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.

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.

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)

Maps of correlation between the de-trended 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. Bolded values indicate statistical significance.

Correlation Niger Burkina Faso

Mali

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 GHCN (JAS rainfall)

Correlation Niamey December

Niamey January

Niger rice area -0.527 -0.418Niger rice yield -0.223 -0.317Niger rice production

-0.571 -0.534

Correlations with streamflow

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).

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.

GSS values

Niger millet prod.

BF millet prod.

Mali millet yield

Niger rice prod.

GHCN 0.557 0.756

streamflow

0.395 0.352

NOAA PRECL

0.379 0.49

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.

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.

• 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

AcknowledgementsThe 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.

Probability (pij) index threshold exceeded

index threshold not exceeded

Extreme low crop production

0.08 0.02

Not extreme low crop production

0.04 0.87

Contingency table for GHCN Burkina Faso millet production contract.