The study investigated the impacts of tropical cyclone (TC) Fantala (11 th to 27 th April, 2016) to the coastal areas of Tanzania, Zanzibar in particular. Daily reanalysis data consisting of wind speed, sea level pressure (SLP), sea surface temperatures (SSTs) anomaly, and relative humidity from the National Centres for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) were used to analyze the variation in strength of Fantala as it was approaching the Tanzania coastal line. In addition observed rainfall from Tanzania Meteorological Authority (TMA) at Zanzibar office, Global Forecasting System (GFS) rainfall estimates and satellite images were used to visualize the impacts of tropical cyclone Fantala to Zanzibar. The results revealed that, TC Fantala was associated with deepening/decreasing in SLP (from 1012-1010 mb) around the northwestern Madagascar and coastal Tanzania, whereas the mean SSTs was greater than 28˚C and an SSTs anomaly ranged from 0 to 2.3˚C. The vertical wind shear which ridged at Mozambican Channel and over northeastern Madagascar was high enough (12-15 ms −1) to support the intensifying of Fantala. The thermodynamic and dynamic conditions of Fantala influenced heavy rainfall of greater than 170 mm over most stations in Zanzibar. Moreover, Fantala disrupted the temporal variability of 2016 March to May (MAM) seasonal rainfall. Besides, more than 420 people were homeless, at least 3330 houses were destroyed, and about 2 people died. As for mainland Tanzania Fantala resulted in a death of 12 people in Kilimanjaro and Arusha, more than 315 houses were washed away by flooding leading to 13,933 people being homeless. Conclusively the study calls for an extensive research work based on examining and forecasting the TCs rainfall impacts and their contribution during the two rainfall seasons of OND and MAM in Tanzania.

This article examines the off season rainfall in northern coast Tanzania (NCT) including Zanzibar which occurred in January and February 2020 (JF). Like the JF rainfalls of 2001, 2004, 2010, 2016 and 2018, the JF (2020) rainfall was more unique in damages including loss of lives, properties and infrastructures. The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001, 2004, 2010, 2016, 2018 and 2020 over NCT and Zanzibar. These datasets include monthly mean u, v wind at 850, 700, 500, and 200 mb; SSTs, mean sea level pressure (MSLP) anomalies, Dipole Mode Index (DMI), and monthly rainfall from NCT and Zanzibar stations. Datasets were processed and calculated into long term, seasonal, and monthly averages, indeed, Precipitation Index (PI) was calculated. Correlation analysis between the rainfall (December to January), SST, DMI and 850 mb wind vectors; and long-term percentage contribution of investigated parameters was calculated. Results revealed significant positive and negative correlations between JF rainfall, SSTs and DMI. Moreover, JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%, while January and February, 2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas, with highest January long-term rainfall contribution of 356% in Zanzibar and 526% over NCT. The DJF (2019/20) had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%, while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas. JF, 2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged. Conclusively, more research works on understanding the dynamics of wet and dry JF seasons should be conducted.

This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen's slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.

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Climate change and variability have been inducing a broad spectrum of impacts on the environment and natural resources including groundwater resources. The study aimed at assessing the influence of weather, climate variability, and changes on the quality of groundwater resources in Zanzibar. The study used the climate datasets including rainfall (RF), Maximum and Minimum Temperature (T max and T min ), the records acquired from Tanzania Meteorological Authority (TMA) Zanzibar office for 30 and 10 (2010-2019) years periods. Also, the Zanzibar Water Authority (ZAWA) monthly records of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Ground Water Temperature (GWT) were used. Interpolation techniques were used for controlling outliers and missing datasets. Indeed, correlation, trend, and time series analyses were used to show the relationship between climate and water quality parameters. However, simple statistical analyses including mean, percentage changes, and contributions to the annual and seasonal mean were calculated. Moreover, t and paired t-tests were used to show the significant changes in the mean of the variables for two defined periods of 2011-2015 and 2016-2020 at p ≤ 0.05. Results revealed that seasonal variability of groundwater quality from March to May (MAM) has shown a significant change in trends ranging from 0.1 to 2.8 mm/L/yr, 0.1 to 2.8 μS/cm/yr, and 0.1 to 2.0˚C/yr for TDS, EC, and GWT, respectively. The changes in climate parameters were 0.1 to 2.4 mm/yr, 0.2 to 1.3˚C/yr and 0.1 to 2.5˚C/yr in RF, T max , and T min , respectively. From October to December (OND) changes in groundwater parameters ranged from 0.2 to 2.5 mm/L/yr