This paper reports the findings of the first initiative of developing a year-round streamflow forecasting system using the 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. In addition, the researchers adopted a phased approach of developing the streamflow forecasting system, using HBV as a hydrological model, which resulted in 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 few parameters were reduced to improve model efficiency. As a measure to demonstrate model performance, in addition to the Nash–Sutcliffe Efficiency (NSE) parameter used for model calibration and verification, several other error functions and graphical displays were used. The model performance values, as measured by NSE for calibration and verification periods, are 0.85 and 0.82 for Ruvu Roadbridge (1H8A), and 0.80 and 0.82 for Kidunda (1H3), respectively, and all are classified as “Very Good”. In addition, the PBIAS of less than ±5% in calibration indicates excellent water balance simulation. Furthermore, the forecast’s performance in this study is evidenced by an annual forecast R2 of 0.933, with operational meteorological forecasts improving to 0.962 with “perfect” precipitation; dry season performance with R2 of 0.964, demonstrating high skill in baseflow-dominated periods; and the PBIAS for forecasts of 0.866, indicating a slight systematic under-forecasting correctable by a ~15% precipitation adjustment. Although the Ruvu catchment has been characterized by this study as a data-scarce catchment, the results of the operational hydrological forecasting system vary with season and quality of forecast meteorological data, and the model is already launched for operational use. As evidenced by these study findings, the journey from data scarcity to operational forecast provision in the Ruvu catchment demonstrates that the principal barriers are fundamentally institutional and capacity-related. 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 regarding changes in the catchment characteristics and model performance during its life cycle, as the performance of the developed model is only valid under the condition that it was calibrated and validated.
Ndomba et al. (Thu,) studied this question.