LIBRARY

Despite the global increase in forest fires in recent years, forestation—the

planting of trees in new areas (afforestation) and previously deforested areas

(reforestation)—remains a major and relatively low-cost approach for sequestering

carbon dioxide. High-resolution satellite data show that the annual incidence

of tree death and corresponding carbon dioxide release from standreplacement

forest fires remains low.

This study aims to explore the impact of climate change on mental health among vulnerable groups, including low-income populations, Indigenous communities, children, women, and the elderly, to highlight their unique vulnerabilities and mental health outcomes. This aligns with the United Nations' Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) and SDG 13 (Climate Action), addressing the need for climate resilience and mental health equity

This study aims to explore the impact of climate change on mental health among vulnerable groups, including low-income populations, Indigenous communities, children, women, and the elderly, to highlight their unique vulnerabilities and mental health outcomes.

The study employed the triangulation approach to investigate the impact of climate variability on selected socioeconomic indicators, particularly food production, health and education, and how much climate variability and its impact on those indicators was realized and understood among the women living in Jambiani community in Zanzibar.

To describe the evolution of atmospheric processes and rainfall forecast in Tanzania, the Advanced

Weather Research and Forecasting (WRF-ARW) model was used. The principal objectives

of this study were 1) the understanding of mesoscale WRF model and adapting the model for Tanzania;

2) to conduct numerical experiments using WRF model with different convective parameterization

schemes (CP’s) and investigate the impact of each scheme on the quality of rainfall forecast;

and 3) the investigation of the capability of WRF model to successfully simulate rainfall

amount during strong downpour. The impact on the quality of rainfall forecast of six CP’s was investigated.

Two rainy seasons, short season “Vuli” from October to December (OND) and long season

“Masika” from March to May (MAM) were targeted

This paper describes how farmers in the South-western Highland of Tanzania predict rainfall using local

environmental indicators and astronomical factors. The perceptions of the local communities on

conventional weather and climate forecasts were also assessed. The study was conducted in Rungwe

and Kilolo districts in Mbeya and Iringa regions respectively. Participatory rural appraisal methods, key

informant interviews and focus group discussions were used in data collection and the collected data

was analyzed using Statistical package for social science. It has been found that plant phenology is

widely used by local communities in both districts in seasonal rainfall forecasting. Early and significant

flowering of Mihemi (Erythrina abyssinica ) and Mikwe (Brachystegia speciformis ) trees from July to

November has been identified to be one of the signals of good rainfall season. The behaviour of

Dudumizi bird has been singled out as one of the best indicator for rainfall. Both Indigenous Knowledge

specialists and TMA experts have predicted 2009/2010 rainfall season to feature normal to above

normal rainfall. Systematic documentation and subsequent integration of indigenous knowledge into

conventional weather forecasting system is recommended as one of the strategy that could help to

improve the accuracy of seasonal rainfall forecasts under a changing climate.

This paper describes how farmers in the South-western Highland of Tanzania predict rainfall using local

environmental indicators and astronomical factors. The perceptions of the local communities on

conventional weather and climate forecasts were also assessed.

This paper describes how farmers in the South-western Highland of Tanzania predict rainfall using local

environmental indicators and astronomical factors. The perceptions of the local communities on

conventional weather and climate forecasts were also assessed. The study was conducted in Rungwe

and Kilolo districts in Mbeya and Iringa regions respectively.