土石流是山區常見的自然地質災害，常發生於溪溝或陡峭邊坡上。其主要成分為泥、砂、礫與巨石，這些顆粒與水混合後，在重力主導、水流輔助下形成高濃度、具黏滯性的流體。土石流具有突發性強、流速快、破壞力大的特性，一旦發生，往往伴隨大量土砂的挾帶與沉積，造成河道下切、堆積與改道，嚴重時更可能威脅居民生命財產安全。過去已有眾多研究針對土石流進行現地監測與室內模擬實驗，探討如坡度、粒徑分布等因素對流動與堆積行為的影響。然而，在室內實驗中對「堆積區底床性質」對堆積型態的影響尚少有深入探討。因此，本文分別透過兩項實驗——「土石流堆積實驗」與「動床水槽掏刷實驗」——以探討不同底床條件對於堆積與沖刷過程的影響。
在土石流堆積實驗中，分別設定兩種入流方式：鋪砂供水與瞬間釋放，並搭配四種底床條件（乾燥與飽和的可侵蝕底床與光滑與粗糙條件下的不可侵蝕）進行測試。實驗條件控制為單一標準砂，平均粒徑（0.07 cm）與固定坡度（15°），並以正射攝影方式記錄堆積物流動過程，透過標定座標轉換建立各組試驗之數值高程模型，可看出瞬間釋放在可侵蝕乾砂底床條件下為尖頭狀堆積；鋪砂供水在可侵蝕乾砂底床條件下為分岔狀堆積，其餘皆為前端橢圓狀堆積型態。在動床水槽掏刷實驗中，採用穩定入流條件與低坡度渠道設置，模擬水流由不可侵蝕段進入可侵蝕底床段之作用。
研究中提出水砂面線（Sand–Liquid Interface）的擷取方法，進行不同時間序列下之水砂交界面變化分析，以觀察其週期性演化特徵，推測其週期性為115 s。此外，透過高速攝影機拍攝發生底床掏刷過程，並利用 PIVlab 進行影像速度場分析，分析底床發生掏刷時所伴隨水中砂漩渦狀流場與砂體遷移機制。

This study comprises two different types of experiments: debris flow deposition experiments and scour flume experiments.In the debris flow deposition experiments, experiments were conducted in a channel with a 15° slope and a horizontal deposition area,using two supply methods: instant release (I) and continuous sediment supply (II).Four different bed conditions were set up: dry erodible bed (ED), saturated erodible bed (ES), rigid plastic bed (RP), and rigid sand-coated bed (RS).Orthophotography and a CMOS high-speed camera combined with structured light scanning were used to record the flow and deposition process and to establish a digital elevation model (DEM).The results show that in the Type I test, the ED condition produced the smallest deposition area, with the deposit width narrowing along the flow direction,while both the ES and RS conditions formed elliptical-shaped alluvial fans.In the Type II test, the ED condition, affected by infiltration, exhibited a distinctly different pattern compared to other bed types.The deposit expanded laterally from the original deposit during the deposition process, resulting in a branched deposition shape.In the scour flume experiments, a saturated erodible sand bed was laid in a low-slope flume.Through steady water supply and a recirculating system, the scouring behavior of the flow entering the erodible section was observed.When the scouring reached the horizontal equilibrium length (l_s),a camera was used to track the liquid surface line (LL) and sand surface line (SL),and Fourier analysis was performed to examine the periodic fluctuations of the sand surface caused by local scouring and backfilling.In addition, CMOS high-speed camera images combined with PIV techniques were used to track and analyze the spiral-shaped velocity field of sand particles in the water.The results demonstrate that the sand surface exhibits clear periodic fluctuations due to local scouring and backfilling, confirming that the experimental approach is suitable for investigating water–sediment interaction mechanisms.

Adrian, R. J. (1984). Scattering particle characteristics and their effect on pulsed laser measurements of fluid flow: speckle velocimetry vs particle image velocimetry. Applied Optics, 23(11), 1690–1691.
Ben Meftah, M., De Serio, F., De Padova, D., & Mossa, M. (2020). Hydrodynamic Structure with Scour Hole Downstream of Bed Sills. Water, 12(1), 186.
Berti, M., & Simoni, A. (2007). Prediction of debris flow inundation areas using empirical mobility relationships. Geomorphology, 90(1–2), 144–161.
D’Agostino, V., Cesca, M., & Marchi, L. (2010). Field and laboratory investigations of runout distances of debris flows in the Dolomites (Eastern Italian Alps). Geomorphology, 115(3–4), 294–304.
De Haas, T., Braat, L., Leuven, J. R., Lokhorst, I. R., & Kleinhans, M. G. (2015). Effects of debris flow composition on runout, depositional mechanisms, and deposit morphology in laboratory experiments. Journal of Geophysical Research: Earth Surface, 120(9), 1949–1972.
De Haas, T., Densmore, A., Stoffel, M., Suwa, H., Imaizumi, F., Ballesteros-Cánovas, J., & Wasklewicz, T. (2018). Avulsions and the spatio-temporal evolution of debris-flow fans. Earth-Science Reviews, 177, 53–75.
De Haas, T., van den Berg, W., Braat, L., & Kleinhans, M. G. (2016). Autogenic avulsion, channelization and backfilling dynamics of debris-flow fans. Sedimentology, 63(6), 1596–1619.
Griswold, J. P., & Iverson, R. M. (2008). Mobility statistics and automated hazard mapping for debris flows and rock avalanches (Tech. Rep.). US Geological Survey.
Haas, T. d., & Woerkom, T. v. (2016). Bed scour by debris flows: Experimental investigation of effects of debris-flow composition. Earth Surface Processes and Landforms, 41(13), 1951–1966.
Harris, F. J. (1978). On the use of windows for harmonic analysis with the discrete Fourier transform. Proceedings of the IEEE, 66(1), 51.
Huang, H. T., Fiedler, H. E., & Wang, J. J. (1993). Limitation and improvement of PIV: Part II: Particle Image Distortion, a Novel Technique. Experiments in Fluids, 15(4), 263–273.
Hürlimann, M., McArdell, B. W., & Rickli, C. (2015). Field and laboratory analysis of the runout characteristics of hillslope debris flows in Switzerland. Geomorphology, 232, 20–32.
Iverson, R. M., Logan, M., LaHusen, R. G., & Berti, M. (2010). The perfect debris flow? Aggregated results from 28 large-scale experiments. Journal of Geophysical Research: Earth Surface, 115(F3).
Johnson, C., Kokelaar, B., Iverson, R. M., Logan, M., LaHusen, R., & Gray, J. (2012). Grain-size segregation and levee formation in geophysical mass flows. Journal of Geophysical Research: Earth Surface, 117(F1).
Keane, R. D., & Adrian, R. J. (1990). Optimization of particle image velocimeters. I. Double pulsed systems. Measurement Science and Technology, 1(11), 1202.
Keane, R. D., & Adrian, R. J. (1992). Theory of cross-correlation analysis of PIV images. Applied Scientific Research, 49, 191–215.
Raffel, M., Willert, C., Wereley, S., & Kompenhans, J. (2007). Particle Image Velocimetry: A Practical Guide. Springer.
Scarano, F., & Riethmuller, M. L. (1999). Iterative multigrid approach in PIV image processing with discrete window offset. Experiments in Fluids, 26, 513–523.
Shavit, U., Lowe, R. J., & Steinbuck, J. V. (2007). Intensity capping: A simple method to improve cross-correlation PIV results. Experiments in Fluids, 42, 225–240.
Spinewine, B., Capart, H., Larcher, M., & Zech, Y. (2003). Three-dimensional Voronoï imaging methods for the measurement of near-wall particulate flows. Experiments in Fluids, 34, 227–241.
Stamhuis, E., & Thielicke, W. (2014). PIVlab – Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB. Journal of Open Research Software, 2(1), 30.
Stamhuis, E. J. (2006a). Basics and principles of particle image velocimetry (PIV) for mapping biogenic and biologically relevant flows. Aquatic Ecology, 40(4), 463–479.
Stamhuis, E. J. (2006b). Basics and principles of particle image velocimetry (PIV) for mapping biogenic and biologically relevant flows. Aquatic Ecology, 40(4), 463–479.
Westerweel, J. (1997). The effect of a discrete window offset on the accuracy of cross-correlation analysis of digital PIV recordings. Experiments in Fluids, 23(1), 20–28.
Wu, Y., Ji, J., Qi, S., Wang, X., Li, D., Li, H., … Yao, Q. (2023). Experimental study of erodible bed scoured by the debris flow in the narrow-steep gully. Scientific Reports, 13(1), 14894.