A Hyper-concentrated Sediment Yield Prediction Model Using Sediment Delivery Ratio for Large Watersheds

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1 KSCE Journal of Civil Engineering (0000) 00(0): DOI /s Water Engineering A Hyper-concentrated Yield Prediction Model Using Delivery Ratio for Large Watersheds Jang Hyuk Pak and Joo Heon Lee Received May 23, 2011/Accepted September 23, 2011 Abstract This paper presents a sediment prediction model using sediment delivery ratio approach for prediction of sediment yields from large watersheds (larger than 800 ha). The Delivery Ratio (SDR) approach is effective for predicting the sediment yield as it moves through the stream system to a concentration point (debris basin) in the watershed. A statistical model, the Multi-Sequence Debris Prediction Model (MSDPM), was developed for use in relatively small watersheds ( ha) in the Los Angeles area. In this study, the MSDPM was extended to include a sediment delivery ratio for modeling of sediment transport through the stream network in the large watershed. The sediment delivery ratio approach was implemented to express the percent of sediment yield that is delivered through a stream system from the sub-watersheds to the debris basin. After adding the sediment delivery ratio to estimate the sediment yields from large watersheds, the revised MSDPM (MSDPM-R) was calibrated and validated based on precipitation, sediment yield and fire data collected from the William Fire (September 2002) and Grand Prix Fire (October and November 2003) events in southern California. Results from MSDPM-R were compared with the available field data obtained from several debris basins within Los Angeles and San Bernardino Counties. The MSDPM-R yields remarkably consistent results when compared with the measured field data. Keywords: sediment, fire, sediment delivery ratio, debris, watersheds 1. Introduction Alluvial fans are rapidly being urbanized in southern California because of their relatively mild terrain and aesthetic views. The mountain areas upslope from the alluvial fans are susceptible to fires which can significantly increase the amount of sediment material transported downstream during subsequent major storms. In this situation, the sediment material collected in debris basins is generated through a spectrum of processes, including surface runoff, flooding and debris flow. Development of these fan areas must consider the possibility of increasing sediment yield from mountain watersheds due to the frequent occurrence of fire events (Pak et al., 2008). Westerling et al. found that wildfire frequency is strongly associated with regional spring and summer temperatures and earlier spring snowmelts in forests based on 34 years of western U.S. wildfire history together with hydroclimatic data, (Westerling et al., 2006). Fires produce 2.1±0.8 petagrams of carbon emissions, or 66±24% of the CO 2 growth rate anomaly during the 1997 to 1998 El Niño (van der Werf et al., 2004). The main contributors were Southeast Asia (60%), Central and South America (30%), and boreal regions of Eurasia and North America (10%) (van der Werf et al., 2004). If global warming and early spring is increasing large wildfires, carbon emissions from wildfire will increase greenhouse gas emissions and this effect will further accelerate global warming (Westerling et al., 2006). Fires generally cause water repellency in soil to be temporarily hydrophobic, which effect infiltration, runoff and erosion in burned watersheds (DeBano, 2000). Several previous studies have shown that wildfire has a significant influence on the erosion of mountain watersheds in southern California (Cannon et al., 2003; Middleton et al., 2004). Rowe et al. (1954) estimated that a 100% burned watershed produces 35 times more sediment yield than in the unburned state. As mentioned above, there are strong relationships among the global warming, early snow melting, wildfire, flood, sediment yield, and green house gas emissions. An understanding of key surface erosion and watershed geomorphic processes is essential to the application of sediment yield prediction techniques. In particular, the variability of these processes in space and time is important in establishing limitation on the accuracy of estimates derived from sediment discharge data and/or predictive models (USACE, 1995). Although typical sediment yield processes are generally familiar, the interplay of factors that influence sediment yield from a watershed is less *Research Hydraulic Engineer, U.S. Army Corps of Engineers, Institute For Water Resources, Hydrologic Engineering Center, Davis, CA ( jay.h.pak@usace.army.mil) **Member, Professor, Joongbu University, Department of Civil Engineering, Kumsan , Korea (Corresponding Author, leejh@joongbu.ac.kr) 1

2 Jang Hyuk Pak and Joo Heon Lee obvious and much more difficult to estimate quantitatively. Vegetation plays an essential role in the sediment yield, but knowledge of biological functions is poorly integrated into procedures for prediction of sediment yields especially under burn conditions. On the geological time scale, the surface of the earth is transformed by sediment production in the upper part of a watershed, transportation of sediments in a fluvial system, and deposition in low-lying lakes, alluvial fans, deltas, and in the oceans (USACE, 1995). The sediment production and transport system is extremely complex, involving the interaction of many hydrologic, geomorphic, and geological processes. Having a better understanding of their influence on sediment yield should make a more credible study by overcoming the corresponding limitations on sediment yield prediction models. Debris basins have been constructed in many areas to capture debris flows. The amount of solid materials (including boulders, gravel, sand, silt, clay, trees, etc.) accumulated in debris basins is called sediment, hyper-concentrated sediment, or debris yields. The yield often excludes fine sand, silts, and clays which pass through the debris basin in suspension during the storm event. yield prediction is necessary for debris basin design and can also help determine maintenance needs for debris basin management. During the 2003 debris disaster, a proper debris yield method was not in existence to estimate debris yields from large watersheds (USACE, 2005). The objective of the present study is to develop an accurate model to predict the sequential sediment yields for large watersheds caused by wildfire and subsequent storm events. The MSDPM is a statistical model, named the MultiSequence Debris Prediction Model (MSDPM). The MSDPM is based on a multiple regression analysis of measured sediment yield data collected from small watersheds between 1938 and This equation included variables of precipitation, drainage area, relief ratio, and a non-dimensional fire factor as well as threshold precipitation factors for rainfall-intensity and total rainfall. The MSDPM was calibrated and validated only for the small watersheds (smaller than 800 ha) (Pak, 2005; Pak et al., 2008, 2009). For this study, the MSDPM was modified to develop a sediment prediction model for large watersheds (larger than 800 ha) by adding the sediment delivery ratio. The most of current other methods including MSDPM were originally developed for use in relatively small watersheds ( ha), and, therefore there is no an adequate method for large watersheds. In many cases, a large number of non-point source sediment and water quality models, like the universal soil loss equation (USLE) (Wischmeier and Smith, 1978) or the revised version of USLE (RUSLE) (Renard et al., 1997), use the sediment delivery ratio to model erosion on hillslopes. The sediment delivery ratio is an approach used to predict the spatial variations of a sediment yield as it moves through a stream network from the sub-watersheds to the outlet of a watershed. The MSDPM is now referred to as the MSDPM-R after adding the sediment delivery ratio option. After including the sediment delivery ratio, the MSDPM-R was cali- brated and validated using measured sediment yields, wildfire data, and rainfall data collected from the 2002 and 2003 fire events in southern California. 2. Model Development The watersheds used in the analysis are located in the San Gabriel Mountains and San Bernardino Mountains within Los Angeles and San Bernardino Counties, as shown in Fig. 1. Debris cleanout data from 2002 to 2003 were obtained for debris basins owned by the Los Angeles County and San Bernardino County. Debris cleanout data were obtained based on the truck count or survey after excavating all material (clay, silt, sand, gravel, boulders, and organic materials) deposited in the debris basin. 2.1 MSDPM Pak et al. briefly described the background of MSDPM as shown below (Pak et al., 2009) The MSDPM was developed for sediment prediction of relatively small watersheds ( ha). Development of a multiple regression equation was the first step to provide the fundamental statistical equation of MSDPM. The relief ratio (S), drainage area (A), maximum 1-hr rainfall intensity (Im) of each storm event, and fire factor (F) were finally selected as independent variables among other meteorologic and physiographic parameters through the stepwise multiple linear regression analysis. In the selected stepwise regression routine, independent variables are progressively added by the program in order of decreasing significance. Variables determined to be significant in earlier stages of the computations may be deleted upon introduction of more significant variables at a later stage. This process allows for determination of the effect of an independent variable on the dependent variable as well as the change in the relative value of this variable upon the inclusion of additional variables (Gatwood et al., 2000). The MSDPM allows the users to determine the sediment yield based on several parameters. These include rainfall amount, maximum 1-hr rainfall intensity, Threshold for Maximum 1-hr Rainfall 2 Fig. 1. Locations of Debris Basin Watersheds for Study KSCE Journal of Civil Engineering

3 A Hyper-concentrated Yield Prediction Model Using Delivery Ratio for Large Watersheds Intensity (TMRI), Total Minimum Rainfall Amount (TMRA), relief ratio (S), drainage area (A), antecedent precipitation events, and fire condition. The fire condition is defined on the percentage of the basin area burned, the time since the last fire and the number of antecedent effective precipitation events. These effective events include the number of previous events that generated sediment yield and have precipitation values exceeding the Threshold Maximum 1-hr Rainfall Intensity (TMRI) and Total Minimum Rainfall Amount (TMRA). The MSDPM does not consider the spatial variation of effective rainfall within the watershed. Thus, the MSDPM is applicable primarily for small watersheds and the accuracy will decrease as the watershed area increases. Regression analysis on the variables above resulted in the MSDPM equation, Eq. (1). N ( ) i I c ( ( P) 1 i P = c ( I m ) i I c ) + ( ( P) i P c ) N ( D ) y i i = 1 i = 1 I m ( I m ) i S A e 0.290F (1) where P P c and I m I c D y : Yield per Event, (m 3 ) I m : Maximum 1-hr Rainfall Intensity per Event, (mm/ hr) I c : Threshold Maximum 1-hr Rainfall Intensity (TMRI), (mm/hr) P: Total Rainfall Amount per Event, (mm) P c : Total Minimum Rainfall Amount (TMRA), (mm) : Absolute value S: Relief Ratio, (m/km) (h 2 h 1 )/L h 2 : Highest Elevation in the watershed, (m) h 1 : Lowest Elevation in the watershed, (m) L: Maximum stream length (km), measured through Geographic Information System (GIS) processing based on the digital elevation model (DEM) A: Size of Drainage Area, (ha) F: Fire Factor, 3.0 F 6.5 (dimensionless): 0.29 F 6.5 ( B p B y + ( 1 B p ) ( 20 B y ) 0.29 ) 2 e A p = ( ( 200) ) (2) where B p : % of Burn/100, (0 B p 1) B y : Number of Years since Burn, (1 B y 10 yr) A p : Number of Antecedent Effective Precipitation Events that have enough energy to generate sediment yield The rainfall events were screened to select the effective rainfall that can provide the required energy through Eq. (1). The threshold maximum 1-hr rainfall intensity for entrainment of sediment particles was determined as the TMRI (I c ) based on the relationship between the TMRI and relief ratio shown in Fig. 2(a). The threshold minimum rainfall amount for the transport capacity to move sediment to the concentration point was determined as TMRA (P c ) based on the relationship between the TMRA and TMRI shown in Fig 2(b) for each debris basin through calibration processes, which defined the critical conditions used in MSDPM (Detail discussion of I c and P c were given in Pak and Fig. 2. Regression Equations of TMRI and TMRA for Prediction Using MSDPM (a) Relationship between TMRI and Relief Ratio (b) Relationship between TMRA and TMRI Lee (2008)). The Fire Factor equation, Eq. (2), was developed based on the fire factor curve for watersheds in the range of 26 to 777 ha (0.1 to 3.0 mi 2 ) of Los Angeles District Debris Method (Gatwood et al., 2000) by adding effects of antecedent precipitation events. Tatum (1963) developed the fire factor curve of Los Angeles District Debris Method using a relationship established by Rowe et al. (1954), to correlate measured sediment yields and computed sediment yields by means of a single fire curve. The Fire Factor (F) was generated using the percentage of the watershed burned, the number of years since the fire, and the number of antecedent precipitation events above a certain threshold value that occurred after the fire (Pak and Lee, 2008). The impacts of fire are gradually reduced by re-vegetation, subsequent storms, and watershed management. Robichaud (2000) stated that hydrophobicity in soils is broken up or is washed away within one to two years after fire. The key to understanding soil recovery after fire is how quickly the bare soil can be covered again by vegetation or litter (Pierson et al., 2001). The final Fire Factor equation, Eq. (2), was calibrated in a manner that minimizes the differences between the measured sediment yields and estimated sediment yields within the main sediment yield equation, Eq. (1). 2.2 Delivery Ratio for Large Watersheds The determination of the sediment delivery ratio is of primary importance to provide realistic estimates of total sediment yield at the concentration point based on estimated sub-watershed Vol. 00, No. 0 /

4 Jang Hyuk Pak and Joo Heon Lee sediment yields. The sediment delivery ratio is a simple process used to predict the spatial variations of a debris flow as it moves through the stream network. The short-term storage of sediment throughout a stream system plays an important role in the sediment transport. For rain occurring during the storm season, sediment yields from sub-watersheds can be estimated at the sub-watershed outlets by MSDPM. Then sediment yields are delivered through the stream system based on the delivery ratio to account for the storage effects on the stream system. The sediment delivery ratio is developed based on the simple linear reservoir routing model concept traditionally used in the lag-androute method. The sediment delivery ratio, Eq. (4), represents the effect of the short-term storage concept (Ponce, 1989). The sediment outflow from the stream is obtained by Eq. (3). Osed = SDR Ised (3) Fig. 3. Fire Maps of William Fire and Grand Prix Fire 3 where Osed: Outflow from the Stream (m ) Ised: Inflow to the Stream (m3) SDR: Delivery Ratio 1 K SDR = (1 K) (4) K: SDR Constant 3. Calibration and Validation In practice, historically sediment yield data from large watersheds are very limited and may not be sufficient for calibration and validation purposes. In this study, however, the SDR Constant (K) for the large watershed analysis was calibrated and validated based on currently available data generated from two fire events that occurred between 2002 and On September 22, 2002, the William Fire in the Azusa to Claremont area burned over 15,054 ha including the watershed of Little Dalton Wash. Due to high temperatures, Santa Ana winds, steep topography, and intense fire, control of the fire s perimeter was severely hampered. The fire destroyed over 60 residences burning at high to moderate intensity (LACDPW, 2003). The Big Dalton Dam precipitation gage (223C) was chosen for the data analysis because the location is closest in proximity to the Little Dalton Watershed and its data appeared to be the most consistent with measured sediment yield data (debris basin cleanout record) for the Little Dalton Debris Basin. The watershed of Little Dalton Debris Basin was burned 89% by the William Fire as shown on Fig. 3. On October and November, 2003, the Padua Fire, Grand Prix Fire, and Old Fire in the San Gabriel Mountains and San Bernardino Mountains burned nearly 36,826 ha including the watersheds of Cucamonga Creek Debris Basin, Deer Creek Debris Basin, and Day Creek Debris Basin. Precipitation data were collected from three precipitation gages [Demens Creek Debris Basin (DCDB), Mt. Baldy (MTBY), and San Antonia Dam (SNTO)], located in the vicinity of the Grand Prix Fire area. After analyzing data from three precipitation gages, the Mt. Baldy (MTBY) precipitation gage was selected for the data analysis because its data were the most reliable and its elevation is closer to the average elevation of watersheds. The watersheds of three debris basins (Cucamonga Creek Debris Basin, Deer Creek Debris Basin, and Day Creek Debris Basin) burned from 89% to 100% by the Grand Prix Fire as shown in Fig. 3. Three Debris Basins (Little Dalton Debris Basin, Cucamonga Creek Debris Basin, and Deer Creek Debris Basin) were selected to determine the SDR Constant (K) via the model calibration that minimized the residuals between the measured and estimated sediment yields. The characteristics of the three debris basins used for calibration are shown in Table 1. For using MSDPM with the sediment delivery ratio, the watershed of debris basin was divided into several sub-watersheds based on tributary junctions, slope, and drainage area (less than 800 ha). The MSDPM was applied to calculate the sediment yields generated by each effective storm event from all subwatersheds using watershed characteristics. The sediment delivery ratio was used to calculate the total sediment yield generated through the stream network from all sub-watersheds to the concentration point (debris basin) for each effective storm event. Through the calibration process, the SDR constant (K) was determined for each debris basin. The Threshold Maximum 1-hr Rainfall Intensity and the Total Minimum Rainfall Amount for all sub-watersheds were determined for MSDPM, Eq. (1), through the equation y=175.05x of Fig 2(a) and the equation y = X of Fig. 2(b), respectively. 3.1 Little Dalton Debris Basin The watershed of Little Dalton Debris Basin was divided based 4 Table 1. Characteristics of Debris Basins used for Calibration Debris Basin Little Dalton Cucamonga Creek Deer Creek Drainage Area (ha) 853 2, Burn (%) Relief Ratio (m/km) KSCE Journal of Civil Engineering

5 A Hyper-concentrated Yield Prediction Model Using Delivery Ratio for Large Watersheds on tributary junctions, slope, and drainage area as shown on Fig. 4. MSDPM was applied to calculate the sediment yields generated by each effective storm event from all sub-watersheds based on watershed characteristics of each sub-watershed indicated in Table 2. yields estimated for storm events which occurred between September 28, 2002 and December 29, 2002 from 9 sub-watersheds are summarized in Table 3. The sediment delivery ratio was used to calculate the total sediment yield generated at the concentration point through the stream network as depicted in Fig. 4. The measured sediment yield generated was 48,932 m 3 (LACDPW, 2003) while the estimated sediment yield calculated by the MSDPM-R with precipitation data obtained from the Big Dalton Dam precipitation gage was 48,984 m 3. The difference of the two values was 52 m 3, (0.1%). The final SDR Constant (K) was determined as for the Little Dalton Debris Basin via the calibration process. 3.2 Cucamonga Creek Debris Basin The watershed of Cucamonga Creek Debris Basin was divided Table 3. Yield Calculation for Little Dalton Debris Basin Date D y (m 3 ) Accumulated D y (m 3 ) I (mm/hr) P (mm) 9/28/ /29/ /8/2002 8,088 8, /9/2002 1,198 9, /29/ , /30/ ,009 21, /16/ ,690 48, /17/ , /20/ , /29/ , Total 48,984 m 3 based on tributary junctions, slope, and drainage area as shown on Fig. 5. MSDPM was applied to calculate the sediment yields generated by each effective storm event from sub-watersheds based on watershed characteristics as contained in Table 4. yields estimated for the storm events which occurred between November 1, 2003 and January 2, 2004 are summarized in Table 5. The sediment delivery ratio was used to calculate the total sediment yield generated at the concentration point through the stream network as depicted in Fig. 5. The measured sediment yield was 175,848 m 3 (USACE, 2005) while the estimated sediment yield by the MSDPM-R was 176,091 m 3 with the precipitation data obtained from the Mt. Baldy (MTBY) precipitation gage. The difference of the two values was 243 m 3, (0.1%). The final SDR Constant (K) was determined as for the Cucamonga Creek Debris Basin via the calibration process. Fig. 4. Sub-Watershed Delineation for Little Dalton Watershed Table 2. Sub-Watershed Characteristics of Little Dalton Debris Basin Sub-Watershed I c (mm/hr) P c (mm) S (m/km) A (ha) WS WS WS WS WS WS WS WS WS Fig. 5. Sub-Watershed Delineation for Cucamonga Creek Watershed Vol. 00, No. 0 /

6 Jang Hyuk Pak and Joo Heon Lee Table 4. Sub-Watershed Characteristics of Cucamonga Creek Debris Basin Sub-Watershed I c (mm/hr) P c (mm) S (m/km) A (ha) WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS WS Table 5. Yield Calculation for Cucamonga Creek Debris Basin Date D y (m 3 ) Accumulated D y (m 3 ) I (mm/hr) P (mm) 11/1/ /12/ ,344 40, /25/ , , /2/ , Total 176,091 m Deer Creek Debris Basin The watershed of Deer Creek Debris Basin was subdivided based on tributary junctions, slope, and drainage area as shown on Fig. 6. MSDPM was applied to estimate the sediment yields based on each effective storm event which occurred between November 1, 2003 and January 2, 2004 from sub-watersheds based on watershed characteristics as contained in Table 6. yields estimated for storm events summarized in Table 7. The sediment delivery ratio was used to calculate the total sediment yield generated at the concentration point through the stream network as depicted in Fig. 6. The measured sediment was 120,112 m 3 (USACE, 2005) while the estimated sediment yield by the MSDPM-R was 120,319 m 3 with the precipitation data obtained from the Mt. Baldy (MTBY) precipitation gage. The difference of the two values was 207 m 3, (0.2%). The final SDR Constance (K) was determined as Fig. 6. Sub-Watershed Delineation for Deer Creek Watershed Table 6. Sub-Watershed Characteristics of Deer Creek Debris Basin Sub-Watershed I c (mm/hr) P c (mm) S (m/km) A (ha) WS WS WS WS WS WS WS WS WS WS WS Table 7. Yield Calculation for Deer Creek Debris Basin Date D y (m 3 ) Accumulated D y (m 3 ) I (mm/hr) P (mm) 11/1/ /12/ ,918 37, /25/ , , /2/ , Total 120,319 m 3 for the Deer Creek Debris Basin via the calibration process. The SDR Constant (K) for the watersheds of Little Dalton, Cucamonga Creek, and Deer Creek debris basins were plotted with the Relief Ratio (S) of each watershed in Fig. 7. The calibration process is described: (1) Assume an initial K value and then calculate sediment yield at the concentration point. (2) Compare the estimated total sediment yield from all subwatersheds to debris basin (concentration point) with measured 6 KSCE Journal of Civil Engineering

7 A Hyper-concentrated Yield Prediction Model Using Delivery Ratio for Large Watersheds Fig. 7. Relationship of SDR Constant (K) and Relief Ratio (S) sediment yield. (3) Make adjustments to K to obtain the best overall fit between estimated and measured sediment yields. The sediment delivery ratio is affected by physical characteristics of a watershed. It varies with the drainage area, relief ratio, land use, and soil properties. This study has found that the SDR Constant (K) has a stronger relationship with the Relief Ratio (S) than other physical characteristics of a watershed. The logarithmic regression equation, K = Ln(S) , was generated based on the relationship between the SDR Constant (K) and Relief Ratio (S) from the three watersheds with the high R-squared value (R 2 = ). In an ungaged situation, a value for SDR Constant (K) can be estimated based on this relationship because determination of the SDR Constant (K) value in ungaged areas can be very difficult. The MSDPM-R was developed based on the MSDPM because the SDR was incorporated into MSDPM with the logarithmic regression equation, K = Ln(S) , to predict sediment yields from large watersheds. The SDR Constant (K) was determined based on this regression equation for the Day Creek watershed. The MSDPM-R was directly applied to predict the sediment yields from the Day Creek Debris Basin watershed caused by subsequent storm events after Grand Prix fire and the results were then compared with the field data. The characteristics of watersheds used for validation are shown in Table Day Creek Debris Basin The watershed of Day Creek Debris Basin was divided based on tributary junctions, slope, and drainage area as shown on Fig 8. The SDR Constant value (K), 0.029, was determined based on the regression equation, K = Ln(S) , using a Relief Ratio of 211 m/km for the Day Creek watershed. MSDPM-R was applied to calculate the sediment yields generated by each effective storm event from all sub-watersheds using watershed characteristics as contained in Table 9. yields generated by storm events which occurred Table 8. Characteristics of Debris Basin used for Validation Drainage Area Relief Ratio Debris Basin Burn (%) (ha) (m/km) Day Creek Fig. 8. Sub-Watershed Delineation for Day Creek Watershed Table 9. Sub-Watershed Characteristics of Day Creek Debris Basin Sub-Watershed I c (mm/hr) P c (mm) S (m/km) A (ha) WS WS WS WS WS WS WS WS WS WS WS WS WS WS Table 10. Summary of Calculation for Day Creek Debris Basin Date D y (m 3 ) Accumulated D y (m 3 ) I (mm/hr) P (mm) 11/1/ /12/ ,623 45, /25/ ,037 94, /2/ Total 139,660 m 3 between November 1, 2003 and January 2, 2004 summarized in Table 10. The sediment delivery ratio was used to calculate the total routed sediment yield generated at the concentration point through the stream network as depicted in Fig. 8. Vol. 00, No. 0 /

8 Jang Hyuk Pak and Joo Heon Lee Debris Basin Watershed Area (ha) Table 11. Comparison of Yield with and without Delivery Ratio Burn Area (%) Relief Ratio (m/km) Measured Yield (m 3 ) MSDPM Yield 3 (m 3 ) MSDPM Difference (%) MSDPM-R Yield 4 (m 3 ) MSDPM-R Difference (%) Little Dalton , , Cucamonga Creek 1 2, , , , Deer Creek , , Day Creek 2 1, , , , Debris Basin for the calibration 2 Debris Basin for the validation 3 yield estimated based on the one watershed without the sediment delivery ratio 4 yield estimated based on multi sub-watersheds and stream network with the sediment delivery ratio The measured sediment yield was 146,871 m 3 (USACE, 2005) while the estimated sediment yield by the MSDPM-R was 139,660 m 3 with the precipitation data obtained from the Mt. Baldy (MTBY) precipitation gage. The difference of the two values was -7,211 m 3,(-4.9%). When the MSDPM-R was applied to predict the sediment yields for the Day Creek Debris Basin, the result (-4.9% difference) was in good agreement with the measured amount. It should be noted that the measured amount was computed using survey data from SBCDPW. Based on the predicted result compared with field data, one could reasonably conclude that the MSDPM-R can be used to predict the accumulated sediment yield using the sediment delivery ratio for the coastal southern California watersheds in the range of 800-3,000 ha. The difference between with and without the sediment delivery ratio approach is shown in Table 11. The MSDPM was used to estimate the sediment yield without the sediment delivery ratio based on one large watershed model while the MSDPM-R was used to estimate the sediment yield with the sediment delivery ratio based on multi sub-watersheds. The results from the two methods are very different because the MSDPM was developed based on the data generated from small watershed ( ha). To extend the use of MSDPM for large watershed, the sediment delivery ratio should be implemented using MSDPM-R. 4. Conclusions The MSDPM-R was developed by adding a sediment delivery ratio component to the MSDPM for the prediction of sediment yields for large watersheds in the San Gabriel Mountains and San Bernardino Mountains in southern California. The present research advances sediment yield prediction from a different perspective since most existing methods were developed for small watersheds. The MSDPM-R will provide a means to rapidly estimate debris yield with maximum 1-hr rainfall intensity and total rainfall amount of each storm event based on precipitation data for large watersheds. The modeling results suggest the MSDPM- R can be used to predict the accumulated sediment yield for coastal southern California watersheds with an area in the range of 800-3,000 ha. The MSDPM-R using sediment delivery ratio was developed based on limited data, therefore it should be modified when additional data available. The MSDPM-R was developed based on data generated from watersheds in the San Gabriel Mountains and San Bernardino Mountains within southern California. Expanded use of the MSDPM-R to wide areas should be confirmed with additional debris data in order to apply this model with confidence for a wider range of conditions in engineering applications. The MSDPM-R is a viable tool for the effective management of existing large debris basins, to determine whether they should be excavated immediately to regain storage capacity based on the remaining debris basin capacity before subsequent storms occur. This will enable operators to have more control in scheduling cleanout operation for large debris basins. The MSDPM-R can be used for responding to the emergency situations to protect human lives and to lessen the risks of economic damage by predicting more accurately the post-fire sediment yields based on the remaining debris basin capacity and National Weather Service forecast information. In this research, although the MSDPM-R was developed for rapid estimation of sediment yield based on limited field data, there is a huge potential for useful application once the MSDPM- R is calibrated based on future available field data. Expanded use of the model to other areas should be verified with additional sediment measurements from large watersheds in order that this model may be applied with confidence for a wider range of conditions. Acknowledgments The author is thankful to Matt Fleming and Dr. Michael Gee of the Hydrologic Engineering Center, Institute for Water Resources, Kerry Casey of the Los Angeles District, U.S. Army Corps of Engineers, and Dr. Jiin-Jen Lee of the University of Southern California for their helpful comments, insights, and suggestions. Dr. Iraj Nasseri, Loreto Soriano, and Ben Willardson of the Water Resources Division at the Department of Public Works of the Los Angeles County have provided useful data and helpful comments. References Cannon, S. H., Gartner, J. E., Rupert, M. G., and Michael J. A. (2003). Emergency assessment of debris-flow hazards from basins burned by the grand prix and old fires of 2003, Southern California. U.S. 8 KSCE Journal of Civil Engineering

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