Small wetlands in Kenya and Tanzania cover about 12 million ha and are increasingly converted for agricultural production. There is a need to provide guidelines for their future protection or use, requiring their systematic classification and characterisation. Fifty-one wetlands were inventoried in 2008 in four contrasting sites, covering a surveyed total area of 484 km 2 . Each wetland was subdivided into sub-units of 0.5-458 ha based on the predominant land use. The biophysical and socio-economic attributes of the resulting 157 wetland sub-units were determined. The wetland sub-units were categorized using multivariate analyses into five major cluster groups. The main wetland categories comprised: (1) narrow permanently flooded inland valleys that are largely unused; (2) wide permanently flooded inland valleys and highlands floodplains under extensive use; (3) large inland valleys and lowland floodplains with seasonal flooding under medium use intensity; (4) completely drained wide inland valleys and highlands floodplains under intensive food crop production; and (5) narrow drained inland valleys under permanent horticultural production. The wetland types were associated with specific vegetation forms and soil attributes.Electronic supplementary material The online version of this article (

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Many water-related illnesses show an increase during the wet season. This is often due to fecal contamination from runoff, yet, it is unknown whether seasonal changes in water availability may also play a role in increased illness via changes in the type of primary water source used by households. Very little is known about the dynamic aspects of access to water and changes in source type across seasons, particularly in semi-arid regions with annual water scarcity. The research questions in this study were: (1) To what degree do households in Uganda (UG) and Tanzania (TZ) change primary water source type between wet and dry seasons?; and (2) How might seasonal changes relate to water quality and health? Using spatial survey data from 92 households each in UG and TZ this study found that, from wet to dry season, 26% (UG) and 9% (TZ) of households switched from a source with higher risk of contamination to a source with lower risk. By comparison, only 20% (UG) and 0% (TZ) of households switched from a source with lower risk of contamination to a source with higher risk of contamination. This research suggests that one pathway through which water-related disease prevalence may differ across seasons is the use of water sources with higher risk contamination, and that households with access to sources with lower risks of contamination sometimes choose to use more contaminated sources.

The dynamic nature and inaccessibility of wetland ecosystems restricts in situ data collection and promote the use of various remote sensing platforms. This is because of their ability to record large areas in comparatively short time periods and map physically unreachable areas. Sensors in the optical and microwave range of the electromagnetic spectrum play a critical role in wetlands detection and delineation, as they complement each other in data collection. This study examined the potential of optical and microwave remote sensing in detecting the diversity of small wetlands (<500 ha) in the semi-arid and sub humid parts of Laikipia and Pangani plains and the humid parts of Mt. Kenya and Usambara highlands in Kenya and Tanzania, respectively. An intensive field survey was conducted to supplement the remotely sensed data. Decision tree, supervised and unsupervised classification techniques, facilitated the detection of floodplains and inland valley wetlands within the study sites. The results reveal that although optical and microwave data work effectively in the detection of wetlands the latter would be more effective in larger wetlands than those in the scope of this study.

Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place * Corresponding author. D. N. Kuria et al. 34 between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.

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Small wetlands in East Africa are increasingly converted into sites for agricultural production. The resulting changes in land use and cropping systems will impact on the wetlands' vegetation. We characterized the plant communities in four wetlands of Kenya and Tanzania, each comprising four types of land use differentiated by the degree of anthropogenic disturbance (cropland, fallow, grazing land and unused). Since no syntaxonomical scheme was available as a reference, a first classification of vegetation units and the identification of diagnostic species is proposed. We collected 207 relevés in the representative wetlands in relation to the current land uses. The plant communities were determined using a modified TWINSPAN classification. For each vegetation unit, diagnostic species were selected according to their fidelity index (phi coefficient). Floristic relationships between vegetation units were surveyed by nMDS ordination analyses. We identified 15 plant communities and selected 147 diagnostic species. The communities were differentiated into (1) semi-natural wetland vegetation (associated with less disturbed environments), (2) grassland and fallow vegetation, and (3) weed communities (associated with eu-hemerobic, drained and cultivated cropland). While the semi-natural vegetation was distinctly matched with unused fields, the differential matching of the other plant communities with land use types was less clear. According to the floristic similarity, the weed communities associated with cropland tended to be aggregated in the nMDS configuration while the semi-natural vegetation was dispersed. The results of the ordination did not differ when involving all species or only the selected diagnostic ones. As the plant communities described are rankless syntaxa, the establishment of a comprehensive syntaxonomic classification for African wetlands will require further vegetation surveys as well as their comparison with published data.Keywords: flood plain; inland valley; land use; modified TWINSPAN; papyrus swamp. Nomenclature: African Plant Database (CJB & SANBI 2010).Abbreviations: nMDS = non-metric multidimensional scaling; SWEA = Small Wetlands in East Africa; TWINSPAN = Two-way indicator species analysis.

The public health and well-being of people in many rural communities in developing countries suffer due to poor water resources management and undesirable agricultural practices. This study was conducted in a pastoral community in northern Tanzania. The objective was to identify the most reliable water source in terms of quality and access from three main water sources: surface water, shallow wells, and deep wells. The Water Quality Index (WQI) was used to assess the overall water quality and was determined to be 1,876, 875 and 157, respectively, for surface water, shallow wells, and deep wells (&lt;50 – excellent, &gt;300 – poor). A Water Poverty Index (WPI) tool was used to quantify five factors that limit access to water: (1) seasonal availability, (2) distance to water sources, (3) cost of purchasing water, (4) preference, and (5) water quality. WPI scores indicated that surface water has the highest score followed by shallow wells; deep wells had the lowest score. In conclusion, in terms of access and quantity, deep wells and shallow wells were the least reliable, and surface water although highly contaminated, is the most reliable. Improving water quality and access of existing water resources is critical to improving the well-being of rural populations.