**5. Conclusions**

The Chenyulan watershed has suffered from serious landslide and debris flow induced by heavy rainfall and typhoons. In this study, we integrated the TUSLE and landslide volume estimation into the SWAT model as SWAT-Twn. By evaluating the simulated sediment yields from different land uses, the importance of topographic (LS) factor and NDVI-calculated weighted C factor were identified and landslide volume estimation should be taken into concern. The examination of five different sediment transport methods revealed some important issues. First, the level of sensitivity of sediment-related parameters is different for those sediment transport methods, and parameters (i.e., CH\_COV1, CH\_BNK\_D50, CH\_BED\_D50) that are estimated on each level, are suggested to be calibrated by spatial and slope conditions. Second, it is more accurate to investigate the channel vegetation (CH\_COV1) and measure the particle sizes of channel bank and bed sediment (CH\_BNK\_D50 and CH\_BED\_D50). The calibrated parameter values by SWAT-CUP for different sediment transport methods may be misleading. Third, the particle size distribution assumed by SWAT is suggested to be an option that can be edited by users. Furthermore, the calculation of fall velocity is suggested to not be only limited for median particle size as it would be biased for channels of wide range of particle sizes. Last but not the least, like the streamflow simulation in SWAT and SWAT-CUP, an option for the user to compare and plot the sediment simulation in logarithmic scale would provide more insights into sediment calibration. In sum, the SWAT-Twn model performed better than SWAT 2016 and SWAT-TUSLE, as TUSLE calculated less sediment at steep area, resulting reasonable sediment export simulation at low flow condition and landslide volume estimation reflected the real situation. Additional improvements in SWAT and SWAT-CUP need to be made to better predict the sediment yields and loads at mountainous watersheds.

**Author Contributions:** Conceptualization, C.-M.L. and L.-C.C.; Formal analysis, C.-M.L.; Funding acquisition, L.-C.C.; Methodology, C.-M.L.; Resources, L.-C.C.; Software, C.-M.L.; Supervision, L.-C.C.; Validation, L.-C.C.; Visualization, C.-M.L.; Writing—original draft, C.-M.L. and L.-C.C.; Writing—review & editing, L.-C.C.

**Funding:** This research was funded by the Ministry of Science and Technology, Taiwan (MOST 107-2625-M-239-001).

**Acknowledgments:** We thank Wen-Cheng Liu from National United University, and Yung-Chieh Wang and Chia-Jeng Chen from National Chung Hsing University for constructive comments that improved the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
