Assessment of methods for infilling missing streamflow records: the case study of little Ruaha catchment, Rufiji basin, Tanzania.
| dc.contributor.author | Kamwaga, Stanislaus Salvatory | |
| dc.date.accessioned | 2020-03-13T12:24:46Z | |
| dc.date.available | 2020-03-13T12:24:46Z | |
| dc.date.issued | 2015 | |
| dc.description | Available in print form, East Africana Collection, Dr. Wilbert Chagula Library, Class mark (THS EAF GB1369.K35) | en_US |
| dc.description.abstract | In order to plan and design water resources and engineering projects, complete and reliable hydrological datasets are required. Presence of missing data can severely compromise data quality and utility. The Rufiji water Basin office has kept a database of daily streamflow records from 1950s to-date for at least 87 gauging stations. While majority of records are complete, certain stations are experiencing large gaps of up to 19 consecutive years. This study therefore made a contribution by appraising rainfall-runoff modelling, recession model, empirical and regression based methods in little rRuaha river, a sub-catchment of the great Ruaha river sub-basin in Rufiji river basin. The methods employed included linear regression rainfall-runoff modelling using HBV-Light, flow duration matching, drainage-area ration and recession model. With the exception of rainfall-runoff relationship and recession model, all other methods relied upon data transfer from donor stations (upstream & downstream station(s)) for infilling downstream/upstream station. Data quality checks were performed and performances of infilling methods were evaluated based on performance criteria namely Nash-Sutcliffe efficiency coefficient (NSE) during calibration and validation periods. Overall, the results indicated that the flow duration matching and multiple linear regression methods performed better than other methods. These results have potential for wide application in other basins of Tanzania for hydrological analysis and water resource management, where missing data is common. I future, it is recommended to explorer other infilling methods such as stochastic and neural network. | en_US |
| dc.identifier.citation | Kamwaga, S.S.(2015) Assessment of methods for infilling missing streamflow records: the case study of little Ruaha catchment, Rufiji basin, Tanzania. Master dissertation, University of Dar es Salaam. Dar es Salaam. | en_US |
| dc.identifier.uri | http://41.86.178.5:8080/xmlui/handle/123456789/7771 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Dar es Salaam | en_US |
| dc.subject | Streamflow | en_US |
| dc.subject | Mathematical models | en_US |
| dc.subject | Stream measurements | en_US |
| dc.subject | Water supply | en_US |
| dc.subject | Tanzania | en_US |
| dc.title | Assessment of methods for infilling missing streamflow records: the case study of little Ruaha catchment, Rufiji basin, Tanzania. | en_US |
| dc.type | Thesis | en_US |