Statistical modelling of extreme rainfall variability in Tanzania.
| dc.contributor.author | Ngailo, Triphonia Jacob | |
| dc.date.accessioned | 2020-04-19T10:37:03Z | |
| dc.date.available | 2020-04-19T10:37:03Z | |
| dc.date.issued | 2018 | |
| dc.description | Available in print form, East Africana Collection, Dr. Wilbert Chagula Library, Class mark (THS EAF QC926.2.T34N452) | en_US |
| dc.description.abstract | Extreme rainfall events, on various time scales causing floods, often destroy life and property. This work aimed to statistically analyze and predict the pattern of extreme rainfall events for the future climate of Tanzania. A series of daily rainfall data over 54 and 31 years (1961--2014 and 1984--2014) recorded at fourteen different stations is modeled using Extreme Value Theory models. The aim is to determine the best fitting distribution to the extreme daily rainfall. Both stationary and non-stationary processes were considered. The model parameters and the extreme rainfall return periods were estimated using maximum likelihood method. The temporal and spatial variability of rainfall was investigated. All stations show stationary time series except for Dar es Salaam. Finally, the return level estimates for the return periods of 10, 20, 50 and 100 years are determined. The results show that extreme rainfall events has less than 20 years return period in most stations in Tanzania. The occurrence of events in time was modeled using a Non- Homogeneous Poisson Process (NHPP). The model incorporated seasonality and trend covariates in the intensity function to determine the trend on extreme rainfall events of daily rainfall exceeding a prefixed threshold value. The results show a good fit for time--varying intensity of rainfall occurrence process by the first order harmonic Fourier law. Ten experiments were conducted using several physical parameterization schemes to find the best combination that optimize the Weather Research Forecasting model for the study area, for heavy rainfall events. The optimal parameterization schemes is achieved with Kain-Fritsch, Lin and Asymmetric Convection Model 2 (KLA) than any other combinations of physical parameterization schemes. | en_US |
| dc.identifier.citation | Ngailo, T. J. (2018). Statistical modelling of extreme rainfall variability in Tanzania. Doctoral dissertation, University of Dar es Salaam. | en_US |
| dc.identifier.uri | http://41.86.178.5:8080/xmlui/handle/123456789/9582 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Dar es Salaam | en_US |
| dc.subject | Rain and rainfall | en_US |
| dc.subject | Statistics | en_US |
| dc.subject | Mathematical models | en_US |
| dc.subject | Periodicity | en_US |
| dc.subject | Tanzania | en_US |
| dc.title | Statistical modelling of extreme rainfall variability in Tanzania. | en_US |
| dc.type | Thesis | en_US |