*3.1. Model Calibration and Validation for Streamflow*

The observed streamflow from 2004 to 2015 was used for model calibration and validation. The streamflow-related parameters for calibration were referred to the previous study [30]. The parameters include curve number (CN2), plant uptake compensation factor (EPCO), surface runoff lag time (SURLAG), baseflow alpha factor (ALPHA\_BF), effective hydraulic conductivity in main channel alluvium (CH\_K2), and Manning's "*n*" value for the main channel (CH\_N2). In addition, we included Manning's "*n*" value for the tributary channel (CH\_N1) and effective hydraulic conductivity in tributary channel alluvium (CH\_K1) as they are also sensitive in this study. In order to differentiate the characteristics of the parameters in various land use, slope, and soil, some parameters (i.e., CN2, ALPHA\_BF, CH\_K1, CH\_K2, CH\_N1, CH\_N2, CH\_K1) were individually calibrated for specific land use, slop and soil. Table 5 shows the calibrated ranges and fitted parameter values for daily streamflow simulation. The model did satisfactory and good performance for the calibration and validation, respectively (Figure 7), indicating the fitted streamflow parameters in model could well reflect the runoff characteristics of the Chenyulan watershed.


<sup>1</sup> FRST, RNGE, AGRL denote forest, grassland, agricultural land, respectively; three groups of sub-basin by slope are downstream (sub-basin no. 1–3, 5–7, 9–11, 13–15), sub-basin mean slope that greater than 60% (sub-basin no. 4, 8, 12, 16, 18, 19) and head stream (sub-basin no. 17, 20–23).

**Figure 7.** Streamflow calibration and validation results.

## *3.2. Comparison of SWAT 2016 and Modified SWAT Models*

After calibrating the daily streamflow, we compared the uncalibrated simulated sediment yields from SWAT 2016, SWAT-TUSLE, and SWAT-Twn to quantify the impacts of using TUSLE and landslide volume equation on sediment yields at HRU and watershed levels (Tables 6 and 7). It should be noted that we only used the sediment yield data during the streamflow calibration period because the fitted parameter values during validation period are different than those during calibration period. However, the range of parameter values are the same for both calibration and validation periods, and the simulation results can reflect the model uncertainty. Thus, we used the simulated sediment yield data during the calibration period (2004–2009) with calibrated fitted streamflow-related parameters and default sediment-related parameters to demonstrate the difference driven by different models.


**Table 6.** Sediment yields at hydrologic response unit (HRU) level.


**Table 7.** Sediment yields at watershed level.

The major differences between SWAT 2016 and the two other modified SWAT (SWAT-TUSLE and SWAT-Twn) are the LS factor, which has more influence in steep slope areas (slope > 9%), and the C factor, which was calculated by NDVI resulting various C factor values for different land uses. There were 77.12% and 80.29% of urban and agricultural lands located in areas with steeper slope (slope > 9%). Therefore, with unchanged C factor for urban, sediment yield from urban had decreased by approximate 60% due to the modified LS factors in TUSLE (Table 6). However, sediment yields from agricultural lands did not change significantly by modified SWAT models. It was because the C factor calculated by NDVI is doubled than the SWAT default value (Table 3), compensating the decrease in sediment yields by modified LS factor in TUSLE. Besides urban and agricultural lands, significant changes in sediment yields from forest, grassland and landslide were found. Although LS factors could influence the sediment yields, the C factor of forest and grassland which were changed from 0.001–0.003 to 0.2, played an important role in increases in sediment yields.

In the SWAT-Twn model, the landslide volume estimation is activated when the daily precipitation reaches over 350 mm. It should be noted that landslide volume estimation is only applied to the landslide area, not other land uses. It is obvious that sediment yields from landslide area increased significantly when the landslide volume estimation was activated in SWAT-Twn. Moreover, since forest is the main land use occupying 74.46% of the watershed and the NDVI-calculated C factor of forest is greater than SWAT default C factor, the annual sediment yields from the watershed were increased by 59.9% and 65.7% by SWAT-TUSLE and SWAT-Twn, respectively (Table 7). The increase of 5.8% of sediment yield by SWAT-Twn was due to landslide volume estimation at the landslide areas. It shows that landslide volume estimation should be considered as the major contribution to sediment yields.

Before calibrating the sediment, these models overestimated the daily sediment load in terms of great positive PBIAS values (Figure 8). However, SWAT-TUSLE and SWAT-Twn performed better than SWAT 2016 in terms of greater R2, NSE and smaller RSR, especially the SWAT-Twn performance had better statistical criteria values (R<sup>2</sup> = 0.74, NSE = 0.66, RSR = 0.58). Therefore, we used SWAT-Twn for further sediment calibration and validation.

**Figure 8.** Simulated daily sediment yield by three models with calibrated streamflow.

#### *3.3. Model Calibration and Validation for Sediment*

The SWAT-Twn model was calibrated and validated for sediment loads with five different sediment transport methods (i.e., EQN0–4). The calibration (2004–2009) and validation (2010–2015) periods for sediment loads were the same as those for streamflow. First, the sensitivity analyses for sediment parameters for different sediment transport methods were examined (Table 8). A total of eight sediment-related parameters were identified as sensitive parameters. Four of these parameters (i.e., SPCON, SPEXP, PRF\_BSN, ADJ\_PKR) are estimated on basin level (\*.bsn), meaning that the parameter values are fixed for the entire watershed; while the rest of parameters (i.e., CH\_COV1, CH\_BNK\_D50, CH\_BED\_D50) are estimated on reach level (\*.rch), which could be varied by spatial and slope conditions.

**Table 8.** The sensitivity analysis of sediment-related parameters (*p*-value < 0.05).


<sup>1</sup> unit: μm; <sup>2</sup> the parameter is sensitive for sub-basins with mean slope < 60%; <sup>3</sup> the parameter is sensitive for sub-basins with mean slope > 60%.

The linear parameter (SPCON), exponent parameter (SPEXP) and peak rate adjustment factor (PRF\_BSN) are only activated for the simplified Bagnold equation (EQN0 and EQN1). The peak rate adjustment factor (ADJ\_PKR) was found to be sensitive to EQN0, EQN3 and EQN4. In order to identify the difference in the reach-level parameters, we separated the watershed by slope of 60% as almost half (49.58%) of the Chenyulan watershed is at a slope greater than 60%.

The channel bank vegetation coefficient for shear stress (CH\_COV1) at the sub-basins with mean slope greater than 60% was sensitive for EQN2, EQN3 and EQN4, indicating that the vegetation at steeper slope areas would have great influence on sediment load compared to that at flatter slope areas. The median particle diameter of channel bank (CH\_BNK\_D50) at steeper slope areas was only sensitive for EQN4, while the median particle diameter of channel bed (CH\_BED\_D50) were sensitive for EQN1, EQN2, and EQN3. It shows that the median particle diameter of channel bank or bed should be measured for increasing the accuracy of sediment simulation with EQN1-4. Although both EQN0 and EQN1 are simplified Bagnold stream power equations, the EQN0 (default in SWAT 2005 version) does not keep track of sediment pools in various particle sizes, while the EQN1 (additional stream power equation in SWAT 2016) has been incorporated with physics-based approach for channel erosion. Moreover, the simulation of channel erosion with EQN0 is not partitioned between stream bank and stream bed. Thus, both CH\_BNK\_D50 and CH\_BED\_D50 are not sensitive for EQN0, while the EQN1 was sensitive with bed erosion (CH\_BED\_D50).

After identifying the sensitive parameters for those five sediment transport methods, the SWAT-Twn was calibrated and validated separately with each sediment transport method (Table 9 and Figure 9). Generally, the simulation results by EQN0 and EQN1 were better than those by other sediment transport methods, in terms of R<sup>2</sup> and NSE greater than 0.5. It indicates that Bagnold equation is more suitable for the Chenyulan watershed. Moreover, the SWAT-Twn with EQN2, EQN3 or EQN4 was found to underestimate for peak flows and overestimate for low flows (Figure 9). Thus, the overestimated low flows at the most flow period resulted in great negative PBIAS values.



<sup>1</sup> Calibration; <sup>2</sup> Validation.

**Figure 9.** Sediment calibration and validation by different sediment transport methods.
