## 3 Using the Digital Platform--DNS/SWAT
The sensitivity analysis allows us to assess the reaction of the model to the change of input parameters, allowing us to identify the most important parameters of the model for the analyzed basin. The sensitivity of parameters is measured  
Figure 2: The Raba River basin with topography (DEM), division into sub-basins (calibration and validation points), land use, slopes, and the approximate area of the Wolnica basin with the Dobczye dam reservoir. using t-stat values, where the values are more sensitive to larger absolute values. The \(p\)-values are used to determine the significance of the sensitivity when the parameter becomes significant if the \(p\)-values are close to zero [132]. Most of the parameters selected by the sensitivity analysis are the same for both the upper and lower parts of the Raba basin, but their significance for sediment transport simulation differs (Table 1). In mountainous areas, surface runoff (SURLAG), followed by soil erosion (USLE_K) and the value of saturated conductivity (SOL_K), which is a parameter controlling surface runoff, have the greatest impact on sediment transport simulations. In addition, specific only to the upper Raba are parameters such as the soil evaporation compensation factor (ESCO), and available soil water capacity (SOL_AWC) responsible for the so-called surface response. In contrast, in the sub-mountainous area, groundwater processes (GW_DELAY), and the ratio of soil loss within a specific support practice (e.g., contour tillage, strip tillage) to the corresponding up-and-down cultivation loss (USLE_P) have the greatest impact. The surface runoff (SURLAG) came in third. Characteristic only for this part of the basin area are the parameters responsible for the so-called subsurface response (GW_DELAY, ALPHA_BF), and reservoir parameters (RES_SED, RES_RR, and RES_NSED), which shows the important influence of a dam reservoir on the hydrology of the basin and, consequently, on sediment transport. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Definition** & **t-Stat** & \(p\)**-Value** \\ \hline \multicolumn{4}{c}{mountain area—Upper Raba} \\ \multicolumn{4}{c}{SURLAG.hru} \\ \multicolumn{4}{c}{SURLAG.hru} \\ \multicolumn{4}{c}{USLE\_K(1).sol} \\ \multicolumn{4}{c}{SURLAG.k(1).sol} \\ \multicolumn{4}{c}{SURLAG.k(1).sol} \\ \multicolumn{4}{c}{SURLAG.k(1).sol} \\ \multicolumn{4}{c}{PRF\_BSN.bsn} \\ \multicolumn{4}{c}{Peak rate adjustment factor for sediment routing in the main channel} \\ \multicolumn{4}{c}{CH\_K2.rte} \\ \multicolumn{4}{c}{Effective hydraulic conductivity in the main channel alluvium} \\ \multicolumn{4}{c}{ESCO.hru} \\ \multicolumn{4}{c}{Soil evaporation compensation factor} \\ \multicolumn{4}{c}{\(-2.45\times 10^{-1}\)} \\ \multicolumn{4}{c}{SPEXPbsn} \\ \multicolumn{4}{c}{Exponent parameter for calculating sediment reentrained in channel sediment} \\ \multicolumn{4}{c}{routing} \\ \multicolumn{4}{c}{CH\_COV1.rte} \\ \multicolumn{4}{c}{Channel credibility factor} \\ \multicolumn{4}{c}{CH\_COV2.rte} \\ \multicolumn{4}{c}{Channel cover factor} \\ \multicolumn{4}{c}{ADJ\_PKR.bsn} \\ \multicolumn{4}{c}{Peak rate adjustment factor for sediment routing in the subbasin} \\ \multicolumn{4}{c}{SPCON.bsn} \\ \multicolumn{4}{c}{} \\  
Model calibration for the flow and sediment was carried out in the calculation profiles of Myslenice (upper part) and Proszowki (lower part), and validation for the Stradomka river, which is the right-bank tributary of the Raba. The low frequency of sediment concentration monitoring (12 times a year) was the reason why LOAD ESTimator was used to develop a regression model to reliably estimate loads. Four statistical measures were used to assess the correctness of this process: coefficient of determination (R2) [133], the efficiency of the Nash-Sutcliffe (NSE) [134], the percentage of bias (PBIAS) [15], and Kling's efficiency-Gupta (KGE) [134]. Using the ranges of values for these statistical measures (Table S1), the degree of matching of the simulation results to the observed data was assessed (Table S2).