FLO-2D Simulation of Mudflow Caused by Large Landslide Due to Extremely Heavy Rainfall in Southeastern Taiwan during Typhoon Morakot

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1 FLO-2D Simulation of Mudflow Caused by Large Landslide Due to Extremely Heavy Rainfall in Southeastern Taiwan during Typhoon Morakot PENG Szu-Hsien 1*, LU Shih-Chung 1 Department of Spatial Design, Chienkuo Technology University, Changhua City 500, Taiwan, Chinese Taipei * Corresponding author, shpeng@cc.ctu.edu.tw; Tel: ext. 2256; Fax: Abstract: Daniau Village in Daniau Creek Watershed, Taitung County, Taiwan, sustained damages from landslides and mudflows during Typhoon Morakot in The purpose of this study is to adopt the FLO-2D numerical model recognized by Federal Emergency Management Agency (FEMA) to simulate the mudflow, and the case of Daniau Village was used as a case study, along with rainfall and digital terrain data for this simulation. On the basis of sediment yields, the residual sediment volume in the landslide area was determined to be 33,276 m 3 by comparison of digital elevation models (DEMs) and by using the universal soil loss equation (USLE). In addition, this study performed a hydrological frequency analysis of rainfall to estimate the flow discharge as conditions of the simulation. Results of disaster surveys were collected to compare with outputs of the numerical model. Results of the simulation conducted with FLO-2D indicated that if the countermeasure was not destroyed, the drainage work would function without overflow. This study aimed to review the effectiveness of countermeasure on the basis of simulation results obtained by using the model to provide references for future disaster prevention and resident evacuation plans. Keywords: Typhoon Morakot; Sediment disaster; FLO-2D. Introduction In August 2009, Typhoon Morakot struck Taiwan and caused heavy rainfall, the highest of which was 2,884 mm in Alishan, Chiayi County. A comparison of rainfall during this typhoon and the extreme global records (WRA 2009) is shown in Table 1. Extreme rainfall has become more common in recent years and usually causes disasters such as floods, debris flows, or other sediment-related hazards. Mudflows or debris flows in highly concentrated sediment-related flows are generally considered as pseudo-homogenous fluids. Mudflows are usually expressed as Bingham fluids, which feature specific yield stresses such that a fluid begins to flow only when the shear stress is greater than the yield stress. Classic hydrodynamic studies focus only on the motion laws of pure gas (or mixed gas) and pure liquid (or 1

2 solution); sediment disasters are rarely discussed. Several previous studies have proposed effective models and numerical analysis methods (O Brien et al. 1993; Laigle and Coussot 1997; Jin and Fread 1999; Imran et al. 2001; Pastor et al. 2004; Jang and Shimizu 2007) for single fluids such as clear water flows, mudflows, and debris flows. After the occurrence of a mudflow or debris flow, investigations to determine the range of influences of sediment accumulation can be performed only through on-site investigation and aerial photography (Sosio et al. 2007; Toyos et al. 2007; Armento et al. 2008; Stolz and Huggel 2008). However, these methods require a significant amount of manpower and materials. Thus, simulations with numerical models were applied with limited field investigations to predict the possible ranges of influences for preventing disasters. FLO-2D, a software package developed by O Brien et al. (1993) that applies to floods and debris flows, uses the non-newton fluid model and the central finite difference scheme to solve motion-governing equations. This model can be applied to flood disaster management, construction design, urban floods, mudflows, and debris flows. O Brien et al. (1993) used FLO-2D to simulate the mudflow of Rudd Creek in The adopted method assumed that downstream debris was delivered by the flood from upstream. After comparing simulation results with the actual values, the greatest depth at the apex of the alluvial fan, depth of the front end of the flood range, and flow velocity were consistent with actual values. An additional example of FLO-2D application was documented by Hubl and Steinwendtner (2001), who used the software to simulate debris flows in Wartschenbach and Moschergraben in the Alps mountain area with a 5 m 5 m digital terrain, 55%-60% volume concentration, and diffusive wave equation. The rheological parameters were obtained through experiments, and sediment volume was measured on-site. Their conclusion suggested that digital terrain and rheological parameters were the most important factors of FLO-2D-simulated debris flows. In addition, Aleotti and Polloni (2003) used FLO-2D to simulate two debris flows in Sarno Town, South Italy. On the basis of the local terrain and rainfall records in May 1998, the debris flows were simulated by using the volume conservation law. They suggested that the important factors related to the range of influences of the deposited debris were rheological parameters and volume concentration. Small changes in these two factors may lead to significant changes in the range. Garcia et al. (2003) used a case in Vargas State, Venezuela, as an example to simulate the ranges of influences of deposited debris with various annual rainfall amounts. On the basis of debris flows occurrence probability, a hazard map with dangerous degrees of sediment disasters was created. The colors red, blue, and yellow were used to represent the various degrees of hazard; corresponding response measures were proposed on the basis of this analysis. Moreover, Chen et al. (2010) applied FLO-2D to simulate a debris flow that occurred in the Songhe Community in Taiwan for risk assessment purposes. These previous studies indicate that FLO-2D is reasonably well developed and effective for actual case applications. 2

3 This study used the case of Daniau Village in the Daniau Creek Watershed, Taitung County, as a case study and adopted FLO-2D for the scenario simulation. On the basis of rainfall data, digital terrain information, and related parameters, the conditions before the disaster and after the stream regulation work were simulated, and the resulting deposition depth, flow velocity, and affected range of disaster were discussed. This countermeasure plan has been evaluated as a reference of disaster prevention for residential evacuation practices. 1 Material and Methods 1.1 Study area The study area, Daniau Creek Watershed, located in southeast Taiwan, has a fragile geology and is struck by frequent earthquakes; Taitung County has been affected by severe sediment disasters caused by typhoons and heavy rainfalls for approximately the past 30 years. The total area of the watershed is 1, ha, and the elevation from sea level altitude to 738 m. The geology and soil conditions mainly include the Miocene Lushan Formation, which is composed of argillite, slate, and phyllite mixed with sandstone. The soil is primarily yellow soil and has an area of 1, hectares, comprising 60.83% of the total area; colluvial soil occupies hectares. Daniau Creek is the main stream in this catchment and flows eastward; the length of the main stream is 7,150 m. On August 8-9, 2009, Typhoon Morakot struck Taiwan, and a large landslide of approximately 6.81 hectares occurred at the upstream catchment behind Daniau Village, Dawu Township, Taitung County (Figure 1). More than 300,000 cubic meters of sediment moved to the village along the branch of the Daniau Creek causing severe damage to industrial roads. Approximately 298 households were affected, and some residents were buried alive. In comparison when Typhoon Haitang affected the same area in July 2005, a significant amount of debris fell into streams, resulting in silt-clogged riverbeds and river water flooding residences along the streams up to 0.3 m (SWCB 2011). However, disaster areas appeared at the sub-catchment of Daniau Creek near Daniau Village after Typhoon Morakot in 2009 because the landslide had caused severe mudflow disasters (Figure 1). Since there are settlements, public facilities, and agricultural areas, the protected objects are over 15 households. The affected area may be expanded to overall Daniau Community. When the rainfall reaches high potential for flooding, the entire village, currently about 298 households should be evacuated. Therefore, our research will focus on this sub-catchment area. 1.2 Calculation of catchment sediment yield The catchment sediment yield includes the hillslope erosion, or slope soil loss; collapses including areas close to the river bank and those away from the bank collapse; and river sediment transport, including debris flows to yield the output of sediment (SWCB 2008) shown in Figure 2. The sediment budget analysis included sediment yields before and after the landslide caused by Typhoon Morakot in 2009 and before and after the earlier stage of dredging conducted by the local government for urgent treatment after 3

4 the disaster event in addition to the annual erosion amount calculated by the soil loss equation. The important events concerning Daniau Community are summarized in Table 2. In addition, the remaining sediment yields were estimated. The analysis process is shown in Figure 3. The calculation methods for annual erosion amounts and catchment sediment yields are described below Calculating soil erosion amounts by through USLE This study adopted the Universal Soil Loss Equation (USLE) to estimate erosions. Developed after many years of research (Wischmeier and Smith 1978), the USLE is an empirical formula with a standard unit plot that includes a slope length of m, a gradient of 9% to maintain more than two years of uncovered fallow land. USLE is now commonly used to estimate erosion and includes the following parameters: A = R K L S C P, (1) u m m where A u is annual average soil loss amount per ha (ton/(ha*year)), R m is the rainfall runoff factor (10 6 J*mm/(ha*hr*year)), K m is soil erodibility (ton*ha*hr*year/(10 6 J*mm*ha*year)), L is slope length, S is the slope factor, C is the crop management factor, and P is the soil and water conservation factor. The distribution of erosion of the study area was obtained by using the Equation (1), as shown in Figure 4. In this area, R m and K m were interpolated within the geostatistical analysis by using the empirical data provided by the Soil and Water Conservation Bureau, Taiwan. R m was between 21,000 and 32,661, and K m was between and These values indicate that the study area in Taiwan has a high rainfall index and low soil erosion resistance. According to the results of the calculation, the annual erosion was 3,600 m 3 /yr, and the annual average scour depth was 53 mm/yr in the area of the landslide Calculating landslide volume by comparing DEMs before and after landslide This study used a digital elevation model (DEM) to calculate landslide volume (Stolz and Huggel 2008; Vemu and Pinnamaneni 2011). The elevation data were obtained during three stages, in 2002 and before and after dredging in Due to a lack of DEM data before the landslide event in 2009, the terrain information was obtained in this study by digitizing the 1/5000 scale contours on the aerial photos of Taiwan in With the aerophotogrammetry and on-site measurements of the terrain in April 2010 after the landslide, the terrain information at an accuracy 1/1000 was obtained by the Soil and Water Consevation Bureau in Taiwan. The landslide volume was obtained by subtracting the DEM information after the landslide from that before the landslide, as shown in Figure 5. The negative values represent the depletion while positive values indicate deposition. Table 3 shows that the area of the deposition is 26,096 m 2, with volume of 93,574 m 3, and the area of the depletion is 51,873 m 2, with a volume of 319,875 m 3. After deducting the 200,000 m 3 of emergency dredging described by SWCB (2011), the difference was 26,301 m 3. This result could have been caused by sediment flushing to the sea through the Daniau Creek during Typhoon Morakot. Table 2 shows that the local government redredged approximately 60,000 m 3 of sediment in the 4

5 deposition zone above the Daniau Community in The dredging volume was obtained by subtracting the post-dredging DEM information from the post-landslide DEM information. Between the times of the two measurements, an amount of sediment was washed down and deposited. Therefore, the part from -0.5 m to 0.5 m was not considered as variation. The part above 0.5 m was the deposition area, and that below -0.5 m was the dredging area Sediment budget of the research area Sediment transport mechanisms include suspended loading and bed loading. The main source of suspended load can be assumed as soil erosions and can be calculated by using USLE. The annual erosion amount of the landslide in Daniau Village was 3,600 m 3 /yr, and the annual average erosion rate was 53 mm/yr. Therefore, the landslide can be considered as the main source of bed load. After deducting the sediment brought to the sea by the flood event of Daniau Creek and that manually dredged, the volume of landslide determined after calculation by using the DEMs before and after the landslide was 319,875 m 3. The official record (SWCB 2011) states that 260,000 m 3 of sediment was dredged and that 26,000 m 3 of sediment was moved to the sea. The volume of residual sediment was 33,000 m 3, and affected zone was shown in Figure 6. FLO-2D was used for simulating the method of mudflow movement and determining the influence of sediments induced by the landslide to evaluate the effectiveness of the countermeasure. 2 Hydrological Analyses 2.1 Frequency analysis Because no relevant stage-discharge station is present in the study area, the data recorded by the Shao-Jia rainfall station of the Water Resources Agency in Taiwan are considered. The frequency analysis of hydrology is based on Shao-Jia station data. Therefore, all such records were collected for a statistical hydrology frequency analysis of cumulative rainfall and rainfall intensity, which included return periods of 5 years, 10 years, 25 years, 50 years, 100 years, and 200 years. In addition, the five most serious typhoon or heavy rainfall events were also analyzed. The Thiessen polygon method was used to determine the significance of rainfall gauges located in close proximity to the research region (Bedient and Huber 2002). The results show that data from the nearest gauge, Shao-Jia Station, could represent the entire watershed; thus, the historic rainfall records ( ) from this station were used to analyze the maximum cumulative rainfall for 1 day, 2 days, and 3 days. Moreover, five probability distributions including normal, lognormal, Pearson type III, log Pearson type III, and extreme value type I were also employed to fit the rainfall data. We used the Hazen, Weibull, and California plotting methods to calculate the sum of squared deviations (SSE) and standard deviation (SE). With the exception of the Pearson type III distribution which exhibited smaller SSE and SE values for 1-day rainfall within the Hazen method, the other conditions for the log Pearson type III distribution obtained the minimum values of SSE and SE for 2- or 3-day rainfall analysis and with all plotting methods. As determined through Chi-square and Kolmogorov-Smirnov goodness of fit tests (Chow 1988), the best 5

6 goodness of fit was reached by using the log Pearson type III distribution. Thus, this study used the results of this type of distribution for storm frequency analysis. The rainfalls of 1 day, 2 days, and 3 days for the 50-year return period were mm, 1,170.5 mm, and 1,279.7 mm, respectively. 2.2 Rainfall pattern The five most important typhoons or heavy rainfall events chosen from the historic rainfall data to analyze the rainfall pattern include Typhoon Morakot (August 8, 2009), Typhoon Nat (September 22, 1991), Typhoon Haitang (July 19, 2005), Typhoon Sepat (August 13, 2007), and Typhoon Thelma (July 24, 1977). Figure 7 shows the rainfall histogram of Typhoon Morakot, which is the most severe event. Frequency analysis was used to determine that the rainfall return period of Typhoon Morakot was longer than 50 years but not more than 100 years (theoretical value: 1,530.1 mm for 3 days). In addition, we chose 24 continuous rainfall hours from the five representative major typhoon events recorded by the Shao-Jia Station to obtain the percentage of rainfalls for each event against the total torrential rain for every time period. By reconfiguring and calculating the results with the averaging method used at a similar location, the 24-hour rainfall pattern was determined (Figure 8). 2.3 Flow discharge estimation The Triangle Unit Hydrograph was employed to estimate the flow discharge hydrograph (Wanielista 1990; Coroza et al. 1997; SWCB 2008; Li et al. 2010; Croke et al. 2011) for modeling the mudflow movement described in the next section. The peak flow was estimated by using the unit hydrograph approach: 0.208ARe Qp =, (2) Tp D T p = T c, (3) where Q p is the peak discharge of unit hydrograph (m 3 /s), A is the catchment area (km 2 ), R e is excess rainfall of unit hydrograph (mm), T p is the time of the flow discharge from zero to peak (hr), D is rainfall duration (hr), and T c is the time of concentration (hr). The 24-hour rainfall for various return periods and rainfall pattern (Figure 8) were employed to calculate the flow discharge hydrograph with the unit hydrograph method. 3 Simulation of Mudflow Conditions 3.1 Model description FLO-2D software simulates two-dimensional mudflow or debris flow simulation to solve the average velocity u in the x-axis direction, the average velocity v in the y-axis direction, and the flow depth h. The governing equations are given as (O Brien 2006): Continuity equation 6

7 h ( uh) ( vh + + ) = i t x y where h is mudflow or debris flow depth (m), u is average flow velocity in the x-axis direction (m/s), v is average flow velocity in the y-axis direction, and i is rainfall intensity (mm/hr). Momentum equation S S fx fy h u u u = Sbx u v (5) x g t g x g y h v v v = Sby u v y g t g x g y where S fx, S fy are the friction slopes of the x- and y-axis directions; S bx, S by are the bed slopes of the x- and y-axis directions; and g is acceleration of gravity (m/s 2 ). Equations (5) and (6) indicate the representative forces balanced in the momentum equations of the x- and y-directions, respectively, including, from left to right, contact forces in the material strength of the friction slopes, bed slopes due to the gravity term, pressure gradients, local acceleration term of inertia forces, and convective acceleration term. FLO-2D provides three modes for simulating various physical problems based on the governing equations that represent physical meaning. The dynamic wave model includes the overall momentum equations shown as equations (5) and (6); the diffusion wave model ignores local acceleration term of inertia forces, and convective acceleration term of equations (5) and (6); and the kinematic wave model ignores the same parameters of the diffusion wave model in addition to pressure gradients. Although ignore more items will save computation time, more errors can be introduced. For example, the kinematic wave model does not apply to flat slopes. Considering the computing capacity of the present computer, the dynamic wave model is generally used to obtain a more accurate analysis. 3.2 Model calibration To simulate the terrain, a topographic map digitized from the aerial photos of 2002 was transformed into a 5 m 5 m DEM. On the basis of the terrain before and after-disaster countermeasure works, the model parameters including yield stress and dynamic viscosity of mudflow were calibrated. The 24-hour maximum rainfall (781 mm) during Typhoon Morakot was used to simulate the inflow condition. With the previously-mentioned rainfall type, the runoff hydrograph was estimated by the triangular unit hydrograph, as shown in Figure 9. The parameters were calibrated by a trial and error method by comparing the mudflow influence with aerial photos obtained after the disaster. The degree of similarity was determined by the similarity ratio of the simulated and the actual ranges. The similarity ratio (omission accuracy) was defined as (Congalton and Story 1986): A α = 2 100%, (7) A 1 where α is the similarity ratio of the simulated and actual ranges, A 1 is the area of actual range of sedimentation, and A 2 is the overlapped area of simulated and actual ranges. (4) (6) 7

8 The mudflow volume concentration of this simulation was calculated on the basis of the landslide sediment yields and runoff volume of Typhoon Morakot determined through hydrological analysis, while other mudflow parameters were initially calculated by using the empirical formulas proposed by FLO-2D user s manual. After several sets of parameters were tested, the set with the following parameters was deemed acceptable for follow-up simulations because the corresponding similarity ratio calculated by Eq. (7) had better value (Figure 10): volume concentration, 35%; yield stress, 204 Pa; and dynamic viscosity, 0.51 pa s. The area of sedimentation range was 57,840 m 2 ; the area of the simulated range was 73,208 m 2 ; and the overlapping area was 39,205 m 2. The similarity ratio was 67.8%, which is close to the acceptable value of 70%. 3.3 Simulation results For simulation of the conditions, a 5 m 5 m grid was obtained on the basis of the DEM created in 2010 and the interpolation method used after the countermeasure and during the construction, respectively. Because some features of the terrain were smoothed due to the interpolation method, the plan of the constructions overlapped with the grid for partial elevation modifications to reflect the terrain effects of the constructions. To re-create the inflow conditions for simulations, runoff hydrographs were calculated with 24-hour rainfalls of 200 mm, 400 mm, 600 mm, 800 mm, and 1000 mm, and the simulation results are shown in Figure 11. The FLO-2D simulation results indicate that when the 24-hour accumulated rainfall was below 400 mm and volume concentrations mudflows were approximately 35%, mudflows can be controlled by the countermeasure works; that is, mudflows were almost completely sent downstream without spilling over the submerged dike. The outlet of the drainage channel must be without obstruction. However, when the 24-hour accumulated rainfall exceeded 600 mm, mudflows spilled over the downstream submerged dike and moved downstream along the slope, spilling over the 3-m-high retaining wall. Thus, to reduce the loading on the retaining wall, a 3-m-high training dike was assumed to be built between the downstream region of the sedimentation area and the retaining wall. The 24-hour maximum rainfall of 781 mm that occurred during Typhoon Morakot was used as the inflow condition for simulation; the results are shown in Figure 12. According to the simulation results, such a dike could effectively prevent spillage from 781 mm of accumulated rainfall in one day. However, errors, difference between the simulated results and actual conditions, were present in simulated values because of the assumptions; thus the construction includes risks (Shiau et al. 2010). Therefore, besides planning for disaster prevention constructions, it is suggested that evacuation exercises shall still be frequently performed with village residents (Chen and Chen 2002; Chen and Huang 2010). 4 Conclusions Landslides can be caused by earthquakes or heavy rainfalls. In this paper, we performed a hydrological frequency analysis of rainfalls of almost 100 years and obtained a return period of 24 8

9 continuous hours of rainfall during Typhoon Morakot. Although geological conditions are additional factors causing such severe landslide disasters, the study still focuses on the effect of heavy rainfall. On the basis of the sediment yields and annual erosion levels before and after the Daniau landslide and early dredging, this study analyzed and calculated the residual sediment volume in the landslide area by comparison of DEMs and by using USLE. 260,000 m 3 of landslide sediment above Daniau Village was dredged by the local government after the typhoon with a residual deposition volume of 33,276 m 3. Through simulation performed with FLO-2D, it was determined that when the accumulated rainfall within 24 hours was below 400 mm, the volume concentration was expected to be 35%, which was within the controlling range of the conservation construction. These results show that the mudflow would not overflow the planned retaining basin downstream and would instead flow down along the channel only if the channel remains unblocked. However, when the accumulated rainfall within 24 hours exceeded 600 mm, the retaining basin would be clogged with silt, and mudflows would flow down along the slope and overflow the 3-m-high retaining wall constructed to protect the community. The results suggested that the present concrete wall should be extended upwards, and a 3-m-high training dike should be constructed on the present slope accumulation area. The simulation of rainfall accumulated during Typhoon Morakot before the dam body was destroyed, i.e. 781 mm in 24 hours, showed that drainage improvements will result in proper drainage function without flooding. When the reconstructed work is completed, it is expected to effectively reduce the sediment disaster area, and protect land and houses, maintain industrial activities, enhance social value, and conserve the ecological environment. Acknowledgement The paper was supported in part by the National Science Council (NSC B MY3) and Taitung Branch, Soil and Water Conservation Bureau, Council of Agriculture, Taiwan. References Aleotti P, Polloni G (2003) Two-dimensional model of the 1998 Sarno debris flows (Italy): preliminary results. In: Rickenmann, D. and Chen, C.L. (eds.), Third International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment. Davos, Switzerland. pp Armento MC, Genevois R, Tecca PR (2008) Comparison of numerical models of two debris flows in the Cortina d'ampezzo area, Dolomites, Italy. Landslides 5(1): Bedient PB, Huber WC (2002) Hydrology and Floodplain Analysis. Prentice Hall Publishing, New Jersey. Chen SC, Chen LK (2002) The Planning and Enforcement of Evacuation and Shelter System for Debris Flow Disaster. In: Chen, S.C. (eds.), Proceedings of the International Conference on Debris-Flow Disaster Mitigation Strategy. Chinese Taipei. pp Chen SC, Huang BT (2010) Non-structural mitigation programs for sediment-related disasters after the 9

10 Chichi Earthquake in Taiwan. Journal of Mountain Science 7(3): Chen SC, Wu CY, Huang BT (2010) The efficiency of a risk reduction program for debris-flow disasters - a case study of the Songhe community in Taiwan. Natural Hazards and Earth System Sciences 10(7): Chow VT (1988) Applied Hydrology. McGraw Hill Publishing, New York. Congalton RG, Story M (1986) Accuracy assessment: a user s perspective. Photogrammetric Engineering and Remote Sensing 55(9): Coroza O, Evans D, Bishop I (1997) Enhancing runoff modeling with GIS. Landscape and Urban Planning 38: Croke BFW, Islam A, Ghosh J, Khan MA (2011) Evaluation of approaches for estimation of rainfall and the unit hydrograph. Hydrology Research 42(5): Garcia R, López JL, Noya M,et al. (2003) Hazard mapping for debris flow events in the alluvial fans of northern Venezuela. In: Rickenmann, D. and Chen, C.L. (eds.), Third International Conference on Debris-Flow Hazards Mitigation: Mechanics, Prediction and Assessment. Davos, Switzerland, pp Hubl J, Steinwendtner H (2001) Two-dimensional simulation of two viscous debris flows in Austria. Physics and Chemistry of the Earth Part C-Solar-Terrestial and Planetary Science 26(9): Imran J, Parker G, Locat J, Lee H (2001) 1D numerical model of muddy subaqueous and subaerial debris flows. Journal of Hydraulic Engineering-ASCE 127(11): Jang CL, Shimizu Y (2007) Numerical analysis of braided rivers and alluvial fan deltas. Engineering Applications of Computational Fluid Mechanics 1(1): Jin M, Fread DL (1999) 1D modeling of mud/debris unsteady flows. Journal of Hydraulic Engineering-ASCE 125(8): Laigle D, Coussot P (1997) Numerical modeling of mudflows. Journal of Hydraulic Engineering-ASCE 123(7): Li CC, Lin A, Tsai YJ (2010) A Study of the Sediment Problem Caused by Typhoon Morakot - Taking the Watershed of Tsengwen Reservoir as an Example. Journal of the Taiwan Disaster Prevention Society 2(1): (In Chinese) O'Brien JS (2006) FLO-2D user s manual. Version , FLO-2D Software, Inc., Nutrioso. O'Brien JS, Julien PY, Fullerton WT (1993) Two-dimensional water flood and mudflow simulation. Journal of Hydraulic Engineering-ASCE 119(2): Pastor M, Quecedo M, Gonzalez E, Herreros MI, Merodo JAF, Mira P (2004) Simple approximation to bottom friction for Bingham fluid depth integrated models. Journal of Hydraulic Engineering-ASCE 130(2): Shiau JT, Wang HY, Tsai CT (2010) Copula-based depth-duration-frequency analysis of typhoons in Taiwan. Hydrology Research 41(5):

11 Sosio R, Crosta GB, Frattini P (2007) Field observations, rheological testing and numerical modelling of a debris-flow event. Earth Surface Processes and Landforms 32(2): Stolz A, Huggel C (2008) Debris flows in the Swiss National Park: the influence of different flow models and varying DEM grid size on modeling results. Landslides 5(3): SWCB (2008) Integrated Watershed Investigation and Planning Manual. Report of Soil and Water Conservation Bureau, Council of Agriculture, Chinese Taipei. (In Chinese) SWCB (2011) Slopeland Conservation Investigation and Planning of Daniau Coastal Watershed. Report of Soil and Water Conservation Bureau, Council of Agriculture, Chinese Taipei. (In Chinese) Toyos G, Dorta DO, Oppenheimer C, Pareschi MT, Sulpizio R, Zanchetta G (2007) GIS-assisted modelling for debris flow hazard assessment based on the events of May 1998 in the area of Sarno, Southern Italy: Part I. Maximum run-out. Earth Surface Processes and Landforms 32(10): Vemu S, Pinnamaneni UB (2011) Estimation of spatial patterns of soil erosion using remote sensing and GIS: a case study of Indravati catchment. Natural Hazards 59(3): Wanielista MP (1990) Hydrology and Water Quantity Control. John Willey & Sons Publishing, New York. Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses. U.S. Department of Agriculture, Agricultural Research Service, Agriculture Handbook 537. WRA (2009) Hydrological Analysis Report of Typhoon Morakot. Presentation Document of Water Resources Agency, Ministry of Economic Affairs, Chinese Taipei. (In Chinese) Tables: Table 1 Comparison of rainfall levels during Typhoon Morakot and extreme rainfall. (Source: Water Resources Agency, Ministry of Economic Affairs, Taiwan) Duration (hr) Rainfall of Typhoon Morakot* (mm) Extreme records in the world** Rainfall (mm) Location of occurrence Data of occurrence Shangdi, Nei Monggol, China 1975/7/ Muduocaidang, China 1977/8/1 24 1, ,825 Foc Foc, La Réunion 1966/1/7-1966/1/8 48 2,361 2,467 Aurere, La Réunion 1958/4/7-1958/4/9 72 2,748 3,130 Aurere, La Réunion 1958/4/6-1958/4/9 *Maximum record of Typhoon Morakot in Alishan, Chiayi County, Taiwan. **Source: World record point precipitation measurements, Hydrometeorological Design Studies Center, NOAA s National Weather Service ( 11

12 Table 2 Important events concerning Daniau Community. Year Event Description The maximum cumulative rainfall of Typhoon Haitang (07.18) in 48 hours was 1,159 mm, which caused a large amount of sediment in the stream channel. The river overflowed and flooded households on both sides. Maximum flood depth was m. Continuing heavy rainfall by Typhoon Morakot (08.08) resulted in a 24-hour accumulated rainfall of 781 mm in the study area. The torrential rainfall induced a landslide of 6.81 hectares in the upstream slope to form a downward mudflow of 300 thousand cubic meters of sediment, which buried 14 houses and road traffic. Local government dredged approximately 200 thousand cubic meters of sediment after disaster. Taitung Branch of Soil and Water Conservation Bureau performed aerophotogrammetry in April to obtain a topographic map at an accuracy of 1/1000. Local government redredged the sediment of the collapsed zone above Daniau Community in summer, amounting to approximately 60,000 cubic meters. Typhoon Fanapi on September 19 brought days of rain to the landslide region above Daniau Community to increase the gully depth from 2 m to 8 m, and portions of sediment were transported by water to the downstream channel to increase the riverbed elevation downstream. Table 3 Calculation of deposition region and depletion region. Period Region Area (m 2 ) Difference of maximum elevation (m) Volume of variation (m 3 ) Average depth (m) Before dredging Deposition region 26, , Depletion region 51, , Excavation region 9, , After dredging Unchanged region* 67, , Fill region 1, , *Unchanged region indicates that this area was not artificially dredged. 12

13 Figures: Figure 1 Aerial photograph of sediment-related hazard in Daniau Creek Watershed. Figure 2 Schematic diagram of sediment yield and outflow on hillslope. Solid lines represent statements that are generally acceptable; dashed lines represent undetermined statements. (Source: Soil and Water Conservation Bureau 2008) 13

14 Figure 3 Flowchart showing the process of estimating the sediment volume of the landslide in Daniau Village. 14

15 Figure 4 Erosion distribution within the study area. Figure 5 Results of deducting the digital elevation model (DEM) information before and after the landslide. 15

16 Figure 6 Zone of residual sediment of landslide area. Figure 7 Rainfall histogram based on Typhoon Morakot data recorded at Shao-Jia Meteorological Station. 16

17 Figure 8 Rainfall pattern determined through examination of representative major typhoon incidents. Figure 9 Hydrograph of estimated runoff from Typhoon Morakot. 17

18 Figure 10 Comparison of simulated and actual sediment distribution area. (a) 18

19 (b) (c) (d) 19

20 (e) Figure 11 Three-dimensional graph of the maximum flow depth simulated by FLO-2D: (a) 24-hour accumulated rainfall: 200 mm; (b) 24-hour accumulated rainfall: 400 mm; (c) 24-hour accumulated rainfall: 600 mm; (d) 24-hour accumulated rainfall: 800 mm; (e) 24-hour accumulated rainfall: 1000 mm. Figure 12 Three-dimensional graph simulated by FLO-2D with 24-hour accumulated rainfall of 781 mm, including a 3-m-high training dike. 20