Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non‐existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite‐based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite‐based rainfall products with relatively high spatial and temporal resolutions and quasi‐global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10‐day) and monthly time‐scales. The evaluation was done by comparing the satellite products with rain‐gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time‐scales, while TAMSAT performed better at the daily time‐scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.

In Somalia, challenges related to energy access is influenced by both weather and climate extremes and associated conflict. The objective of this article is to gain an improved understanding of these risks and challenges, which are faced by the most vulnerable populations in the country. In particular, cooking energy-related challenges faced by households affected by weather and climate extremes and conflicts include protection risks, malnutrition, health risks, environmental degradation and heightened tension and conflict between social groups. Interventions to address these issues should focus on both fuel supply and fuel demand as well as on improving the livelihoods of affected populations. In the aftermath of an extreme weather event it is recommended that assessments of the energy needs of all affected populations, including both hosts and Internally Displaced People (IDPs), be conducted. Post-disaster support should include the promotion of energy-efficient technologies for cooking as well as alternative sources of fuel where available, including non-wood based renewable energy. The implementation of a field inventory to assess the status of natural resources in areas vulnerable to climate impacts could help to determine woody biomass trends and enable the development of ecosystem restoration plans. These could include provisions for the establishment of woodlots and agro-forestry, thus building resilience to environmental degradation while maintaining woody biomass resources in and around displacement camps. Interventions should also be designed jointly with partners, and activities should be conflict-sensitive to ensure an enhanced state of resiliency and preparedness among vulnerable populations.

SDI is a well-known concept in Africa, many countries are on the way to having a formal SDI strategy Certain countries, such as Somalia, are starting the process of nation building after years of war. These countries stand to leapfrog other African countries by implementing current SDI best practices. The FAO‐SWALIM project is in the unique position to be able to assist Somali authorities in providing some of the building blocks for SDI development, even though SWALIM does not have the legal mandate to do so. This paper highlights what SWALIM can currently contribute and what significant work (and resources) are still required for a Somalia National SDI.

Multi-temporal very high-resolution satellite images and field work have been used for quantifying the tree cutting rate over a 5 years period in a very arid tiger bush area of North Eastern Somalia with intensive charcoal production activities. By applying both a classical area frame sampling approach with visual interpretation of the samples and a semi-automatic tree detection algorithm, it was possible to create baseline tree density layers for the 2 years of observation and to quantify the tree cutting rates for the period from 2001 to 2006. An average annual tree loss of −2.8%, coupled with the total absence of regrowth during the 5 years study period, confirm the tremendous ecological impacts of charcoal driven tree cutting on tiger bush vegetation. Analysis of the results evidences spatial and temporal patterns in the cutting locations and poses the basis for a better understanding of the ecological and human dimensions of deforestation in the fragile rangeland environment of Northern Somalia.

Prosopis spp is a fast and aggressive invader threatening many arid and semi-arid areas globally. The species is native to the American dry zones and was introduced in Somaliland for dune stabilization and fuel wood production in the 1970’s and 1980’s. Its deep rooting system is capable of tapping into the ground water table thereby reducing its reliance on infrequent rainfalls and near-surface water. The competitive advantage of Prosopis is further fuelled by the hybridization of the many introduced sub species that made the plant capable of adapting to the new environment and replacing endemic species. This study aimed to test the mapping accuracy achievable with Landsat 8 data acquired during the wet and the dry seasons within a Random Forest (RF) classifier, using both pixel- and object-based approaches. Maps are produced for the Hargeisa area (Somaliland), where reference data was collected during the dry season of 2015. Results were assessed through a 10-fold cross-validation procedure. In our study, the highest overall accuracy (74%) was achieved when applying a pixel-based classification using a combination of the wet and dry season Earth observation data. Object-based mapping were less reliable due to the limitations in spatial resolution of the Landsat data (15–30 m) and problems in finding an appropriate segmentation scale.