Browsing by Author "Mpeta, E."
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Item Accuracy of Giovanni and Marksim software packages for generating daily rainfall data in selected bimodal climatic areas in Tanzania(Tanzania Journal of Agricultural Sciences, 2014) Kahimba, F. C.; Tumbo, S. D.; Mpeta, E.; Yonah, I. B.; Timiza, W.; Mbungu, W.Agricultural adaptation to climate change requires accurate, unbiased, and reliable climate data. Availability of observed climatic data is limited because of inadequate weather stations. Rainfall simulation models are important tools for generating rainfall data in areas with limited or no observed data. Various weather generators have been developed that can produce time series of climate data. Verification of the applicability of the generated data is essential in order to determine their accuracy and reliability for use in areas different from those that were used during models development. Marksim and Giovanni weather generators were compared against 10 years of observed data (1998-2007) for their performance in simulating rainfall in four stations within the northern bimodal areas of Tanzania. The observed and generated data were analyzed using climatic dialog of the INSTAT program. Results indicated that during the long rain season (masika) Giovanni predicted well the rainfall amounts, rainy days, and maximum dry spells compared to Marksim model. The Marksim model estimated seasonal lengths much better than the Giovanni model during masika. During short rain season (vuli), Giovanni was much better than Marksim. All the two software packages had better predictions during masika compared to vuli. The Giovanni model estimated probabilities of occurrence of rainfall much better (RMSE = 0.23, MAE = 0.18, and d =0.75) than Marksim (RMSE = 0.28, MAE = 0.23, and d = 0.63). The Marksim model over-predicted the probabilities of occurrence of dry spells greater than seven days (MBE = 0.17) compared to the Giovanni model (MBE = 0.01). In general the Giovanni model was more accurate than the Marksim model in most of the observed weather variables. The web based Giovanni model is better suited to the northern bimodal areas of Tanzania. The Marksim model produced more accurate climatic data when the long-term average climate data are used as input variables. This study recommends the use of rainfall data generated using Giovanni software over Marksim, for areas receiving bimodal rainfall regimes similar to the northern bimodal areas of Tanzania.Item Application of self-organizing-maps technique in downscaling GCMs climate change projections for Same, Tanzania(2010) Tumbo, S. D.; Mpeta, E.; Mbillinyi, B. P.; Kahimba, F. C.; Mahoo, H. F.; Tadross, M.High resolution surface climate variables are required for end-users in climate change impact studies; however, information provided by Global Climate Models (GCMs) has a coarser resolution. Downscaling techniques such as that developed at the University of Cape Town, which is based on Self-Organizing Maps (SOMs) technique, can be used to downscale the coarse-scale GCM climate change projections into finer spatial resolutions; but that must be combined with verification. The SOM downscaling technique was employed to project rainfall and temperature changes for 2046-2065 and 2080-2100 periods for Same, Tanzania. This model was initially verified using downscaled NCEP reanalysis and observed climate data set between 1979 and 2004, and between NCEP reanalysis and GCM controls (1979 - 2000). After verification, the model was then used to downscale climate change projections of four GCMs for 2046-2065 (future-A) and 2080-2100 (future-B) periods. These projections were then used to compute changes in the climate variables by comparing future-A and B to the control period (1961-2000). Verification results indicated that the NCEP downscaled climate data compared well with the observed data. Also, comparison between NCEP downscaled and GCM downscaled showed that all the four GCM models (CGCM, CNRM, IPSL, and ECHAM) compared well with the NCEP downscaled temperature and rainfall data. Future projections (2046-2065) indicated 56 mm and 42 mm increase in seasonal total rainfall amounts for March-April-May (MAM) and October-November-December (OND) (23% and 26% increase), respectively; and a temperature increase of about 2°C for both seasons. Furthermore, it was found that during MAM there will be a decrease in dry spells by 2 days, and an increase in seasonal length by 8 days, while for OND, there will be also 2 days decrease in dry spells, and 40 days increase in the seasonal length. The results for future-B shows a 4°C rise in temperature, and 46.5% and 35.8% increase in rainfall for MAM and OND, respectively. The results imply a better climatic future for the area because of the increase in the amount of rainfall and decrease in dry spells. However, it is suggested that further investigations are required to see if the projected changes will have real positive effects in agricultural production and also identify better agronomic practices that will take advantage of the opportunities.