9 research outputs found
Nature and dynamics of climate variability in the uganda cattle corridor
The study was conducted in the districts of Nakaseke and Nakasongola stratified into four farming systems of crop dominancy, pastoralists, mixed crop and livestock and fishing. The study was guided by two research questions: (1) how do community residents perceive climate change/variability? (2) What is the trend and nature of climate variability and how does it compare with people’s perceptions? Ninety eight percent (98%) of the respondents reported that the routine patterns of weather and climate had changed in the last 5 to 10 years and it has become less predictable with sunshine hours being extended and rainfall amounts being reduced. This compared well with the analyzed secondary data. Over 78% respondents perceived climate change and variability to be caused by tree cutting other than the known scientific reasons like increase in industrial fumes or increased fossil fuel use. Climate data showed that over the period 1961 to 2010 the number of dry spells within a rainfall season had increased with the most significant increase observed in the first rainfall season of March to May as compared to the season of September to November. The first dry season of June/July to August is short while the second dry season of December to February is long during the study period. The two rainfall seasons of March to May and September to November seem to be merging into one major season from May to November. Temperature data shows a significant increasing trend in mean annual temperatures with the most increase observed in the mean annual minimum temperatures than the maximum temperatures.Key words: Climate variability, community perceptions, Uganda cattle corridor, dry spells
Patterns and Perceptions of Climate Change in a Biodiversity Conservation Hotspot
Quantifying local people's perceptions to climate change, and their assessments of which changes matter, is fundamental to addressing the dual challenge of land conservation and poverty alleviation in densely populated tropical regions To develop appropriate policies and responses, it will be important not only to anticipate the nature of expected changes, but also how they are perceived, interpreted and adapted to by local residents. The Albertine Rift region in East Africa is one of the world's most threatened biodiversity hotspots due to dense smallholder agriculture, high levels of land and resource pressures, and habitat loss and conversion. Results of three separate household surveys conducted in the vicinity of Kibale National Park during the late 2000s indicate that farmers are concerned with variable precipitation. Many survey respondents reported that conditions are drier and rainfall timing is becoming less predictable. Analysis of daily rainfall data for the climate normal period 1981 to 2010 indicates that total rainfall both within and across seasons has not changed significantly, although the timing and transitions of seasons has been highly variable. Results of rainfall data analysis also indicate significant changes in the intra-seasonal rainfall distribution, including longer dry periods within rainy seasons, which may contribute to the perceived decrease in rainfall and can compromise food security. Our results highlight the need for fine-scale climate information to assist agro-ecological communities in developing effective adaptive management
Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda
A methodology has been applied to investigate
the spatial variability and trends existent in a mid-twentieth
century climatic time series (for the period 1943–1977)
recorded by 58 climatic stations in the Albert–Victoria water
management area in Uganda. Data were subjected to quality
checks before further processing. In the present work, temporal
trends were analyzed using Mann–Kendall and linear
regression methods. Heterogeneity of monthly rainfall was
investigated using the precipitation concentration index
(PCI). Results revealed that 53 % of stations have positive
trends where 25 % are statistically significant and 45 % of
stations have negative trends with 23 % being statistically
significant. Very strong trends at 99 % significance level
were revealed at 12 stations. Positive trends in January,
February, and November at 40 stations were observed. The
highest rainfall was recorded in April, while January, June,
and July had the lowest rainfall. Spatial analysis results
showed that stations close to Lake Victoria recorded high
amounts of rainfall. Average annual coefficient of variability
was 19 %, signifying low variability. Rainfall distribution is
bimodal with maximums experienced in March–April–May
and September–October–November seasons of the year.
Analysis also revealed that PCI values showed a moderate
to seasonal rainfall distribution. Spectral analysis of the time components reveals the existence of a major period around
3, 6, and 10 years. The 6- and 10-year period is a characteristic
of September–October–November, March–April–
May, and annual time series.http://link.springer.com/journal/704hb201