*4.2. Calibration with Di*ff*erent Sedmient Transport Methods*

Based on the sensitivity analysis (Table 8), the calibrated values are listed in Table 11. For EQN0 and EQN1, the calibrated ranges and fitted values of SPCON, SPEXP and PRF\_BSN are similar. Moreover, ADJ\_PKR shows similar fitted value (0.56–0.63) and calibrated ranges (0.5–1.5) for EQN0, EQN3, and EQN4, indicating the characteristics of peak flow could be well represented in different sediment transport methods. For the simplified Bagnold method (EQN0 and EQN1), channel erodibility is controlled by the channel erodibility factor (CH\_COV1) ranging from 0 to 1. The default value (0) indicates non-erosive channel, while the value of 1 indicates no resistance to erosion. However, CH\_COV1, which is conceptually similar to the soil erodibility factor used in the USLE equation, was not sensitive and thus we used the default value for the simulation with EQN0 and EQN1. For other physics-based methods (EQN2-4), the channel erodibility is calculated by shear stress, and the CH\_COV1 is defined as channel bank vegetation coefficient for estimating critical shear stress [66]. CH\_COV1 is sensitive for EQN2, EQN3, and EQN4, however, the fitted values are quite different. The fitted CH\_COV1 values indicate that the channel vegetation is between sparse trees (CH\_COV1

= 5.40) and dense trees (CH\_COV1 = 19.20), sparse trees, and grassy (CH\_COV1 = 1.97) for EQN2, EQN3, and EQN4, respectively. The smaller the CH\_COV1 value is, the greater the channel erodibility coefficient is [44]. Interestingly, the reflection of CH\_COV1 on the channel erodibility is consistent with the suitability of using the Kodatie model (EQN2) for the stream bed material size ranging from silt to gravel, Molinas and Wu model (EQN3) for primarily sand size particles, and Yang sand and gravel model (EQN4) for primarily sand and gravel size particles.


**Table 10.** Land use distribution (%) in sub-basins.

**Table 11.** Calibrated sediment-related parameters for different sediment transport methods.


<sup>1</sup> unit: μm.

Moreover, due to the lack of the information on channel materials, the calibrated median particle size diameter of channel bank sediment (CH\_BNK\_D50) or bed sediment (CH\_BED\_D50) showed various results among different sediment transport methods. For EQN1 and EQN4, the median size of bank and bed sediments were identified as mostly much greater than large aggregate (500 μm), thus the particle size distribution for D50 (>2000 μm) assumed by SWAT is 15% of sand, 15% of silt, 5% of clay and 65% of gravel [44]. For EQN2, the calibrated CH\_BED\_D50 range is between 250 μm and 2000 μm, indicating the channels are characterized as medium to very coarse sand-bed materials [40]. Similarly, for EQN3, the fitted CH\_BED\_D50 value is between sand (200 μm) and large aggregate (500 μm) [44]. Therefore, for EQN2 and EQN3, the particle size distribution for D50 (50 to 2000 μm) assumed by SWAT is 65% of sand, 15% of silt, 15% of clay and 5% of gravel.

In SWAT, the particle size distribution is usually used for estimating the bank and bed erosion, and the percentage of sediment (sand, silt, clay, gravel, small aggregate and large aggregate) that gets deposited, is calculated by the fall velocity of median size particle. The amount of sediment transported out of the reach is calculated as follows: Amount of total load entering the reach at the beginning of the time period minus the amount of total load deposited in the reach plus the amount of sediment due to bank and bed erosion in the reach. The total load in the reach considered includes bed and suspended load coming from the foregoing reaches, as well as suspended load originating from the soil erosion of the surrounding sub-basin. Thus, further improvements (i.e., the assumption of particle size distribution for bank and bed erosion, the calculation of fall velocity for wide range of particle size) need to be done to calculate more reliable and accurate sediment loads in SWAT.

### *4.3. Selection of a Suitable Sediment Transport Method*

Due to the fact that sediment loads are extremely high during heavy rainfall events, the comparison between measured and simulated data in linear-scaled plot was difficult to identify their difference in sediment loads of low values. Therefore, we applied the logarithmic scale for the measured and simulated sediment data with different sediment transport methods and compared their statistical criteria (R<sup>2</sup> and PBIAS) during 2004–2016 (Figure 11 and Table 12). It should be noted that the simulation results are the same in Figures 9 and 11. The simplified Bagnold method (EQN0 and EQN1) performed better for the low sediment loads with smaller PBIAS values. Although the model has been calibrated for sediment by SWAT-CUP, sediment loads are still overestimated for all sediment transport methods. It is because the SWAT-CUP model tends to adjust the parameters to meet the high observed sediment loads, but results in great PBIAS values. Thus, the SWAT model simulated high sediment loads better than the low sediment loads. Interestingly, when the measured and simulated sediment data are plotted in logarithmic scale, the R<sup>2</sup> values increase and PBIAS values decrease (Table 12). The greater improvement in the statistical criteria values shows the higher degree of overestimation when applying the Molinas and Wu model (EQN3) and Yang sand and gravel model (EQN4). By comparing the sediment load data in linear and logarithmic scales, we can better identify the suitable sediment transport method and suggest that further calibration and validation are needed for log-transformed simulated sediment data in the SWAT-CUP model.

**Figure 11.** Comparison of simulated and measured sediment loads in logarithmic scale.


**Table 12.** Statistical results for simulated sediment loads in linear and logarithmic scales.
