Search

The Sediment Transport and Erosion Prediction (STEP) model is methodically described in this paper. The STEP model was implemented at a 3.47 km × 3.47 km grid resolution in the Kalisapi sub-watershed, Central Java, Indonesia. It was calibrated and validated in the period of 2003–2008 and 2010–2014 respectively. The result shows that the STEP model functions satisfactorily at monthly and annual time window in the calibration and validation periods. In both periods, the STEP model is able to produce fruitful annual sediment transport rates with R2 values of 0.71 and 0.9 in the calibration and validation period respectively. Moreover, the STEP model exhibits more equitable spatial variability than that of the widely known USLE model. Correspondingly, it can be surmised that the STEP model is capable of simulating spatial and temporal variability of sediment transport from a watershed at monthly and annual time scales. • STEP model is developed to simulate soil erosion, deposition and sediment transport processes in time and space. • STEP model has been successfully implemented, calibrated and validated in the Kalisapi sub-watershed, Indonesia. • Calibrating STEP model to fit the observation is straightforward as it only needs to modify two model parameters. • STEP model source code and input data for the study area are freely available in the Bitbucket repository service. [ABSTRACT FROM AUTHOR]

The prediction and control of river sediment yield (SY) are critical but challenging tasks. Erosion and sediment transfer in river catchments are controlled by different processes, whose relative importance varies in space and time. We thus put forward that SY can be estimated more efficiently by using explicitly the information contained in the similarity within groups. To test this hypothesis, we developed a novel Bayesian hierarchical model, applied it to a sample of heterogeneous river catchments and compared its fixed-effects and mixed-effects performance incorporating different group levels, namely gauges, rivers, basins and clusters of catchments. With a parsimonious linear model consisting of four variables (specific and extreme discharge, elevation and retention coefficient), we achieved good performance criteria in the calibration (NSE: 0.79–0.85) and in the cross-validation for temporal and spatial prediction (NSE: 0.71 and 0.72, respectively). These results support the promising potential of this technique. • We develop a Bayesian hierarchical framework to model river sediment yield (BaHSYM). • For spatial predictions, we combine BaHSYM with clustering of catchments. • Robust predictions can be achieved with a parsimonious linear model. • The mixed-effects model performs largely better than ordinary linear regression. [ABSTRACT FROM AUTHOR]