Over the last decade in Ethiopia, Central and Western regions have experienced climatic greening as seen by increasing vegetation associated with rising rainfall trends. However, some areas in North and East Ethiopia have shown anomalous vegetation degradation despite significant rainfall gains. This unfavorable condition has impacted over 2% of Ethiopia's land. Meanwhile, climatic greening affected more than 3% of the country. Analyzing long-term trends in satellite-observed rainfall and vegetation can identify locations sensitive to climate variability or human impacts for further investigation.

6. Rainfall data = 10-day rainfall from NOAA-CPC FEWSNET (RFE 2.0); spatial resolution: 8km, period: 2001-2010 10-day composite aggregated into annual rainfall (mm/year) Normalised Difference Vegetation Index (NDVI) 10-day NDVI composite derived from SPOT Vegetation GVT4 Africa spatial resolution: 1km, period 2001-2010 Max-NDVI derived for each year Data aggregated for a calendar year (January to December) Analysis performed at pixel level and summarised for administrative regions: administrative divisions level-2 integrated with the level-3 for Sudan and the DR of Congo Earth Observation data

7. A standardised rainfall / max-NDVI anomalies were calculated as the deviation from the long-term mean normalised by the temporal standard deviation (SD) for the same period. Zn anomaly (i,j) = ( Zn (i,j) – Zmean (2001-2010) (i,j) ) / Zσ (2001-2010) (i,j) Zn – annual rainfall (mm/year) or max-NDVI for the year n (from 2001 to 2010) Zmean – long-term mean annual rainfall/max-NDVI for the period 2001 - 2010 Zσ – long-term standard deviation of annual rainfall / max-NDVI over the period 2001 - 2010 i, j – pixel index Interpretation of rainfall anomalies (RA): dry condition: moderately dry if -1.0 < RA < -1.49 severely dry if -1.5 < RA < -1.99 extremely dry if RA ≤ -2.0 wet condition: moderately wet if 1.0 < RA < 1.49 severely wet if 1.5 < RA < 1.99 extremely wet if RA ≥ 2.0 Methods: standardised anomalies

8. Methods: long-term trends A linear least squares regression was used to examine temporal trends in a) annual rainfall/max-NDVI and b) standardised rainfall/max-NDVI anomalies A t-test was applied to determine whether the negative or positive slope of the trend line was statistically significant : t-test = m (i,j) / se(m) (i,j) m – estimated slope coefficient se(m) – standard error values for the coefficient m (i, j) – pixels

9. Long-term rainfall trend (2001 – 2010)

10. Long-term rainfall trend (2001 – 2010) Sub-Saharan Africa (759 admin. regions) Positive trend (%) Number of polygons % of the total no of polygons 0.1_10 116 15.3 10_20 40 5.3 20_30 49 6.5 30_40 28 3.7 40_50 30 4.0 >50 124 16.3 Sub-Saharan Africa (759 admin. regions) Negative trend (%) Number of polygons % of the total no of polygons 0.1_10 82 10.8 10_20 24 3.2 20_30 17 2.2 30_40 4 0.5 40_50 9 1.2 >50 14 1.8

11. Standardised Rainfall Anomaly (2001 - 2010)

12. Long-term trend in max-NDVI (2001 – 2010)

13. Max-NDVI anomalies (2001 – 2010)

15. NDVI vs. RFE anomaly trends

16. SUMMARY: Over the last decade, Central and Western Ethiopia has experienced climatic greening. The resulting vegetation improvement is associated with an increasing long-term trend in rainfall. However, in some locations of North and Eastern Ethiopia, the vegetation condition has deteriorated despite a significant increase in rainfall, resulting in anomalous degradation. This condition has been largely unfavourable for vegetation development and crop production. Overall climatic greening has affected more than 3% (36,400 km 2 ) of Ethiopia, whereas anomalous degradation distressed almost 2% (more than 20,400 km 2 ) of the country. NDVI vs. RFE anomaly trends – Ethiopia

17. NDVI vs. RFE anomaly trends – Ethiopia % of the area % of the area

19. Thank You!