Application of remotely sensed rainfall data in rainfall runoff modelling: a case of Pangani river basin-Tanzania

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dc.contributor.authorKibasa, Patrick
dc.date.accessioned2019-06-22T11:21:03Z
dc.date.accessioned2020-01-07T14:40:58Z
dc.date.available2019-06-22T11:21:03Z
dc.date.available2020-01-07T14:40:58Z
dc.date.issued2011
dc.descriptionAvailable in printen_US
dc.description.abstractUniversity of Dar es Salaam, Department of water resources Engineering, 2011 Rainfall runoff modelling in a river basin is vital for number of hydrologic application including water resources assessment. However, rainfall data from sparse gauging stations are usually inadequate for modelling which is a major concern in Tanzania. This study presents the results of comparison of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall products at daily and monthly scales with ground stations rainfall data; and explores the possibility of using satellite rainfall data for rainfall runoff modelling in Pangani River Basin, Tanzania. Simple statistical analysis was carried out to find the correlation between the ground stations data and TRMM estimates. It was found that TRMM estimates at monthly scale compare reasonably well with ground stations data. Furthermore, time series comparison was done at daily, monthly and annual scales; monthly and annual time series compared reasonably with coefficient of determination of 0.68 and 0.70 respectively. It was also found that areal rainfall comparison at the northern parts of the study area had poor results compared to the rest of areas. On the other hand, rainfall runoff modelling with ground stations data alone and TRMM data set alone was carried out using five Galway Real-Time River Flow Forecasting System models and then outputs combined by Models Outputs Combination Techniques. The results showed that ground stations data performed better during calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% for Simple Average Method, Weight Average Method and Neural Network Method respectively. TRMM data results were 59.8%, 73.5% and 76.8%. However, the study demonstrates TRMM data are adequate and promising in hydrological modelling.en_US
dc.identifier.citationKibasa, P. (2011) Application of remotely sensed rainfall data in rainfall runoff modelling: a case of Pangani river basin-Tanzania. Master dissertation, University of Dar es Salaam. Available at http://41.86.178.3/internetserver3.1.2/detail.aspxen_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/232
dc.language.isoenen_US
dc.publisherUniversity of Dar es Salaamen_US
dc.subjectRain and rainfallen_US
dc.subjectRunoffen_US
dc.subjectMathematical modelsen_US
dc.subjectPangani River (Tanzania)en_US
dc.subjectTanzaniaen_US
dc.titleApplication of remotely sensed rainfall data in rainfall runoff modelling: a case of Pangani river basin-Tanzaniaen_US
dc.typeThesisen_US

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