Validation of PSIAC Model for Sediment Yields Estimation in Ungauged Catchments of Tanzania

Abstract

The main objective of this paper is to report on preliminary validation results of the newly applied sediment yields estimation model in Tanzania, the Pacific Southwest Inter-Agency Committee (PSIAC). This is a follow-up research on the call to customize simple and/or multi-processes sediment yields estimation models such as PSIAC in the region. The PSIAC approach is based on a sediment yield classification scheme employing individual drainage basin characteristics: surface geology, soils, climate, runoff, topography, ground cover, land use, upland erosion, channel erosion, and sediment transport. In this study, PSIAC model is built from readily available environmental variables sourced from Government ministries/agencies and public domain global spatial data. The sediment classification exercise was verified with field observations. The set up model was then validated by 31 small dams’ siltation surveys and previous sedimentation study findings. PSIAC model performance for major part of central Tanzania was good during calibration (BIAS = 7.88%) and validation (BIAS = 18.12%). Another observation was that uncalibrated model performs fairly well, though performance improves with calibration. The extension of the uncalibrated PSIAC model to 3 selected large basins of Tanzania, with drainage areas size up to 223,000 km2, registered a satisfactory performance in one of them with fair performance in the rest. For large basins, the performance seems to correlate with general ground slope. The higher the slope, the better the performance. It is, however, not apparent from this study on the threshold drainage area and slope requirements for better performance of the model. Notwithstanding, the PSIAC model has improved previous sediment yields estimates based on simple regressive models. Finally, the paper proposes two main further research works: use of high resolution geospatial data and additional validation dams siltation data even beyond the central part of Tanzania, and carries out rigorous study on spatial scale model application limitations.