The manuscript presents the results of an aggradation experiment performed in a laboratory channel with supercritical flow. The channel was fed with a stationary sediment load exceeding the transport capacity of the flow in the initial condition, thus inducing sediment aggradation and an increase of the bed slope. The experiment is part of a laboratory campaign mimicking sediment overloading in mountain rivers, a process that can determine increased hydraulic risk levels at key spots. A crucial issue in measuring sediment aggradation is the definition and determination of the bed elevation, this issue being quite relevant in experiments with a relatively large transport capacity, where a thick bed-load layer exists and hinders the possibility to determine with confidence a reference bed elevation. The determination of the bed elevation, in turn, impacts the quantification of a number of properties, including the initial sediment transport capacity of the flow, temporal scales of the aggradation process, water depth and Froude number. The manuscript presents a sensitivity analysis of the results to two extreme definitions for the bed elevation: the first one locates the bed at the upper edge of the bed-load layer, while the second one at the lower edge of the bed-load layer where the particles do not move. The presentation of the two alternatives is focused on the experimental methods they use, consistently with the intent of the special issue. Furthermore, it is demonstrated that the definition of the bed elevation also has a major impact on numerical models of the process. The experimental results have been reproduced numerically, demonstrating that the calibration parameters returning a best fit are also impacted significantly by how the bed is defined. The preferred definition for analyzing an experimental campaign is locating the bed below the bed-load layer.

The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The M5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the M5 model, the non-stationary rainfall and runoff signals were decomposed into sub-signals by the wavelet transform (WT). By this technique, the available trends of the main raw signals were eliminated. Finally, the models were developed by multilinear regressions. The model using all four factors had the best performance (DC = 0.93, RMSE = 0.03, ME = 0.05 and RE = 0.15).