The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones

Increasing climate variability and extreme weather conditions along with declining trends in both rainfall and temperature represent major risk factors affecting agricultural production and food security in many regions of the world. The rangelands of Ethiopia, Kenya, and Somalia in the East African Horn remain one of the world's most food insecure regions, yet have substantially increasing human populations predominantly dependent on pastoralist and agro-pastoralist livelihoods. We identify regions where substantial rainfall decrease between two periods interrupted by the 1998 El Nino event (1981–2012) in the East African Horn is coupled with human population density increases. Vegetation in this region is characterized by a variable mosaic of land covers, generally dominated by grasslands necessary for agro-pastoralism, interspersed by woody vegetation. Recent assessments indicate that vegetation degradation is occurring, adversely impacting fragile ecosystems and human livelihoods. Using AVHRR and MODIS vegetation products from 1981 to 2012, we observe changes in vegetation patterns and productivity over the last decade across the East African Horn. We observe vegetation browning trends in areas experiencing reduced main-growing season precipitation; these areas are also concurrently experiencing increasing population pressures. We also found that the drying precipitation patterns only partially statistically explain the vegetation browning trends, indicating that other factors such as population pressures and land use changes might be responsible for the observed declining vegetation condition. Furthermore, we show that the general vegetation browning trends persist even during years with normal rainfall conditions such as 2012, pointing to potential long-term degradation of rangelands on which approximately 10 million people depend. These findings may have implications for current and future regional food security monitoring and forecasting as well as for mitigation and adaptation strategies in a region where population is expected to continue increasing against a backdrop of drying climate trends and increased climatic variability.     

El Niño–Southern Oscillation diversity and Southern Africa teleconnections during Austral Summer

Climate Dynamics - A wide range of sea surface temperature (SST) expressions have been observed during the El Niño–Southern Oscillation events of 1950–2010, which have occurred...     

Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments

Abstract

A novel approach is presented for combining spatial and temporal detail from newly available TRMM-based data sets to derive hourly rainfall intensities at 1-km spatial resolution for hydrological modelling applications. Time series of rainfall intensities derived from 3-hourly 0.25° TRMM 3B42 data are merged with a 1-km gridded rainfall climatology based on TRMM 2B31 data to account for the sub-grid spatial distribution of rainfall intensities within coarse-scale 0.25° grid cells. The method is implemented for two dryland catchments in Tunisia and Senegal, and validated against gauge data. The outcomes of the validation show that the spatially disaggregated and intensity corrected TRMM time series more closely approximate ground-based measurements than non-corrected data. The method introduced here enables the generation of rainfall intensity time series with realistic temporal and spatial detail for dynamic modelling of runoff and infiltration processes that are especially important to water resource management in arid regions.

Editor D. Koutsoyiannis

Citation Tarnavsky, E., Mulligan, M. and Husak, G., 2012. Spatial disaggregation and intensity correction of TRMM-based rainfall time series for hydrological applications in dryland catchments. Hydrological Sciences Journal, 57 (2), 248–264.     

Developing seasonal rainfall scenarios for food security early warning

Rainfed agriculture in Sub-Saharan Africa accounts for 95 % of the local cereal production, impacting hundreds of millions of people. Early identification of poor rainfall conditions is a critical indicator of food security. As such, monitoring accumulated seasonal rainfall gives an important mid-season estimate of final accumulated totals. However, characterizing the remaining uncertainty in a season has largely been ignored by the food security community. This paper presents a new technique describing rainfall conditions over the duration of a crop-growing cycle by combining estimated rainfall-to-date with potential scenarios for the remaining season based on available satellite rainfall estimates, the common tool for rainfall analysis in Africa. The limited historical record provided by satellite rainfall estimates using previous seasons provides only a coarse view of likely seasonal totals. To combat this, scenarios developed by bootstrapping dekadal data to create synthetic seasons allow for a finer understanding of potential seasonal accumulations. Updating this throughout the season shows a narrowing envelope of seasonal totals, converging on the final seasonal result. The resulting scenarios inform the expectations for the final seasonal rainfall accumulation, allowing analysts to quantify and visualize the uncertainty in seasonal totals. Giving decision makers a tool for understanding the likelihood of specific rainfall amounts provides additional time to enact and mobilize efforts to reduce the impact of agricultural drought.     

Using high resolution satellite imagery to estimate cropped area in Guatemala and Haiti

Guatemala and Haiti are two of the most food insecure nations in the Western Hemisphere. Measurements of food availability and access are instrumental in developing targeted hunger reduction strategies yet no estimates of cropped area (a critical input in the calculation of food production) at either a national or sub-national-level exist. The purpose of this research is to produce estimates of cropped area for Guatemala and Haiti using an area frame sampling approach and very high resolution (∼1 m) satellite imagery. Related research has combined livelihood data with topographic information to construct cropped area estimates in other settings using generalized additive models. We expand this approach with the inclusion of specific population variables in place of the livelihood data. We produce estimates of cropped area for the two countries and sub-national units and our results highlight the significance and complexity of incorporating explicit population characteristics into models of cropped area.