Developing an Operational Streamflow Forecasting System in Data Scarce Catchments in Developing Countries: A Case of Ruvu Catchment in Tanzania

This paper reports the preliminary findings of the first initiative of developing a year-round streamflow forecasting system using HBV hydrologic model in a data scarce Ruvu catchment in Tanzania. Considering the importance of the Ruvu catchment as the main source of water to the fast-growing mega city of Dar es Salaam, the researchers in this study made the most of the available data and their joint previous application experience of the modelling framework for the purpose of setting up a reliable operational model. Besides, the researchers adopted a phased approach of developing the streamflow forecasting system, using HBV as hydrological model, which resulted into a simplified model structure with minimized complexity. For instance, the snow routine was removed as it is not relevant to the study area, and a number of parameters was reduced to improve model efficiency. As a measure to improve confidence in model predictions, various error functions were used for hydrological model calibration and validation. Although, Ruvu catchment has been characterized by this study as data scarce catchment, the results of the hydrological forecasting system range from good to very good and varying with season and quality of forecast meteorological data, and the model is already launched for operational use and the forecasts are published online. The authors suggest that any future forecasting initiative should put much emphasis on both the understanding of the modelling framework to be used and adequate data collection and analysis, in a synergetic manner with all relevant agencies. And it is also recommended to be vigilant to changes of the catchment characteristics and model performance during its life cycle as the performance of the developed model is only valid to the condition it was calibrated and validated.