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Sediment load estimation is essential in many water resources projects. In this study, the capability of two different types of model including SWAT as a process-based model and ANNs as a data-driven model in simulating sediment load were evaluated. The issue of uncertainty in the simulated outputs of the two models which stems from different sources was also investigated. Calibration and uncertainty analysis of SWAT were performed using monthly observed discharge and sediment load values and through the application of SUFI-2 procedure. The issue of uncertainty in the ANN model was also accounted for by training a network several times with different initial weights and bias values as well as randomlyselected training and validation sets, each time a network trained. Trying different input variables to find the best and most efficient network structure, it was found that in the forested watershed of Kasilian, adding average daily rainfall or previous values of discharge dose not change the performance of the ANN model significantly.Comparing the results of SWAT and ANN, it was found that SWAT model has a superior performance in estimating high values of sediment load, whereas ANN model estimated low and medium values more accurately. Moreover, prediction interval for the results of ANN was narrower than that of SWAT which suggests that ANN outputs are with less uncertainty.

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The Agricultural Policy/Environmental eXtender (APEX) model is used to evaluate the impact of different land management strategies associated with water availability, soil and water quality, plant growth, and economics. This article presents APEXSENSUN, an open‐source software package that automates global sensitivity analysis and assists with calibration of the APEX model. APEXSENSUN was developed in R programming language and includes regression‐based, derivative‐based, and variance‐based methods, as well as a regional sensitivity analysis method. Evapotranspiration data measured at a research field located at the United States Department of Agriculture‐Agricultural Research Service Conservation and Production Research Laboratory in Bushland, Texas were utilized to illustrate the main features of APEXSENSUN, which are to identify important parameters and assist with calibration. The results from variance‐based methods were in agreement regarding the ranking of top five sensitive parameters including the soil evaporation‐plant factor, root growth‐soil strength, and the coefficient for adjusting the microbial activity function in the top soil layer. The Fourier amplitude sensitivity testing method required the least number of simulations for calculation of the total‐effect sensitivity indices. The Monte Carlo‐based calibration feature of the package was also tested for calculating the posterior parameter distributions and prediction intervals with a desired level of confidence for output predictions.