The use of the HBV model for flood forecasting

dc.contributor.authorSulfab, Salaheldin Agab
dc.date.accessioned2019-06-20T13:12:08Z
dc.date.accessioned2020-01-07T14:40:57Z
dc.date.available2019-06-20T13:12:08Z
dc.date.available2020-01-07T14:40:57Z
dc.date.issued1993
dc.descriptionAvailable in print formen_US
dc.description.abstractThe HBV Model is a conceptual model of hydrological forecasting developed at the Swedish Meteorological and Hydrological Institute (SMHI). The model transforms data of precipitation and estimates of potential evapotranspiration into an estimate of runoff. The model was tested over three catchments from different geographical locations, namely, Kizu (Japan), Bird Creek (U.S.A) and Wollombi (Australia). The data available for these catchments consist of eight years of continuous daily data for the first two catchments anal ten years continuous data for Wollombi catchment. The results of model application gives fair results during both calibration and verification periods. The Rosenbrock procedure was used to optimize the model parameters. The results of stability testing showed no evidence of instability in the optimized parameters. The residuals from model output were tested for evidence of seasonality, non-linearity, such a check is known as diagnostic check . The analysis showed little evidence of seasonality, no evidence of non-linearity. The efficiency of the HBV model was compared with other deterministic catchment models for the same catchments. For Kizu catchment the efficiency was greatly improved, there is slight improvement for Wollombi and Bird Creek catchments.en_US
dc.identifier.citationSulfab, S. A (1993) The use of the HBV model for flood forecasting,masters dissertation, University of Dar es Salaam. Available at (http://41.86.178.3/internetserver3.1.2/detail.aspx )en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/222
dc.language.isoenen_US
dc.publisherUniversity of Dar es Salaamen_US
dc.subjectFlood forecastingen_US
dc.titleThe use of the HBV model for flood forecastingen_US
dc.typeThesisen_US

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