modeling the sediment yield from landslides in the shihmen...

EARTH SURFACE PROCESSES AND LANDFORMSEarth Surf. Process. Landforms (2012)Copyright © 2012 John Wiley & Sons, Ltd.Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/esp.3309

Modeling the sediment yield from landslides in theShihmen Reservoir watershed, TaiwanZong-Xian Tsai,1 Gene J.-Y. You,1* Hong-Yuan Lee1 and Yu-Jia Chiu21 Dept. of Civil Engineering, National Taiwan University, Taipei, Taiwan2 Hydrotech Research Institute, National Taiwan University, Taipei, Taiwan

Received 11 February 2010; Revised 17 July 2012; Accepted 19 July 2012

*Correspondence to: Gene J.-Y. You, Dept. of Civil Engineering, National Taiwan University, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, Taiwan. E-mail: [email protected]

ABSTRACT: Landslides generate enormous volumes of sediment in mountainous watersheds; however, quantifying the downstreamtransport of landslide-derived sediment remains a challenge. Landslide erosion and sediment delivery to the Shihmen Reservoirwatershed in Taiwanwas estimated using empirical landslide frequency–area and volume–area relationships, empirical landslide runoutmodels, and the Hydrological Simulation Program- FORTRAN (HSPF). Landslide erosion rates ranged from 0.4mmyr-1 to 2.2mmyr-1

during the period 1986–2003, but increased to 7.9mmyr-1 following Typhoon Aere in 2004. The percentage of landslide sedimentdelivered to streams decreased from 78% during the period 1986–1997 to 55% in 2004. Although the delivery ratio was lower, thevolume of landslide sediment delivered to streams was 2.81�106Mgyr-1 in 1986–1997 and 8.60� 106Mgyr-1 in 2004. Modelsimulations indicate that only a small proportion of the landslidematerial was delivered downstream. An average of 13% of the landslidematerial delivered to rivers was moved downstream during the period 1986–1997. In 2004, the period including Typhoon Aere, theannual fluvial sediment yield accounted for approximately 23% of the landslide material delivered to streams. In general, the transferof sediment in the fluvial system in the Shihmen Reservoir watershed is dominantly transport limited. The imbalance between sedimentsupply and transport capacity has resulted in a considerable quantity of landslide material remaining in the upper-stream regions of thewatershed. Copyright © 2012 John Wiley & Sons, Ltd.

KEYWORDS: watershed sedimentation; landslide; frequency–size distribution; landslide volume; sediment delivery ratio; HSPF

Introduction

Landslides cause considerable loss of human life and damage toproperty (Sidle and Ochiai, 2006): and sedimentation from land-slides impacts watersheds for years. Sediment from landslides isfrequently deposited in channels (Gomi et al., 2002; Sidle,2005; Chen, 2006). In watersheds with reservoirs the down-stream transport of landslide sediment reduces reservoir storagecapacity and hence diminishes reservoir function and lifespan(Tianchi, 1989). Landslide sediment can also degrade waterquality, aquatic habitat, and municipal water supply systems(Sidle and Ochiai, 2006). However, comprehensive understand-ing of the effects of landslide sediment requires quantification oflandslide sediment yields and sediment routing.Engineers and geologists have studied landslides for more than

a century, focusing primarily on detailed physical descriptions oflandslides. Few studies have endeavored to provide a compre-hensive investigation of landsliding and landslide sedimentrouting at the watershed scale, which requires knowledge oflandslide volumes, sediment delivery to streams, and fluvialsediment transport through the channel network.A landslide inventory is a record that documents the number,

area, and location of landslides. Landslide area and spatialfrequency data from landslide inventories can be used todetermine the frequency–area scaling of landslides, whichcan then be used to determine landslide erosion rates. The

frequency of landslides with medium-large areas can bedescribed as a negative power-law function of area (Fuyii,1969; Whitehouse and Griffiths, 1983; Ohmori and Hirano,1988; Sasaki et al., 1991; Sugi et al., 1994; Hovius et al.,1997; Dai and Lee, 2001). Empirical studies have generallyshown that the frequency of landslides with small areasdeparts from the power-law distribution, a phenomenontermed the rollover effect, for which there are multiple possi-ble explanations (Stark and Hovius, 2001; Brardinoni andChurch, 2004; Malamud et al., 2004a). Both double Pareto(Stark and Hovius, 2001; Guzzetti et al., 2002) and inverse-gamma (Malamud et al., 2004a) landslide area–frequencydistributions have been proposed, because, unlike negativepower-law scaling that applies to medium-large landslides,these two distributions fit the entire range of landslideareas.

Landslide inventories rarely contain information on thevolumes of individual landslides, although volume data isnecessary for calculating landslide erosion rates and sedimentdelivery. Landslide volume–area scaling relationships can beused to estimate the volume of a landslide with a given area(Hovius et al., 1997; Guzzetti et al., 2009; Larsen et al.,2010). Landslide volume–area scaling relationships can becombined with landslide frequency–area distributions toestimate the total volume of landslide material for a landslideinventory (Hovius et al., 1997; Malamud et al., 2004a).

m

m

igure 1. Sub-watersheds of the study area, Shihmen reservoiratershed, Northern Taiwan. This figure is available in colour onlinet wileyonlinelibrary.com/journal/espl

Z.-X. TSAI ET AL.

After a landslide occurs, the debris moves downslope butonly a fraction of the sediment is delivered to the streamchannel networks. According to field studies, the delivery oflandslide sediment to channels ranges from 20% to 70% anddepends primarily on the volume of the landslide, the mobilityof the material on the hillslope, the type of mass wastingprocess, the behavior of sediment at hillslope–channel junc-tions, and the characteristics of the terrain (Ziemer et al.,1991a, 1991b; Trustrum et al., 1999; Dadson et al., 2004; Sidleand Ochiai, 2006; Lin et al., 2008). Landslide sediment deliv-ery ratios vary considerably and determining the delivery ratiois a site-specific task that depends on the site characteristicsof each landslide (Ward, 1994). However, simple, empiricalrelationships can be used to estimate sediment delivery. Forexample, landslide volume, distance from landslide to stream,hillslope gradient, and tributary junction angles have beenidentified as important controls on landslide sediment delivery(James, 1985; Ward, 1994). Although more field measurementsand studies on the process of landslide sediment delivery arerequired (Burton and Bathurst, 1998; Claessens et al., 2007):the delivery of sediment to streams can be approximatedaccording to dominant geographical parameters.Upon reaching the channel, landslide sediment transported

downstream through the fluvial system can be modeled, butthe estimation of sediment yield in channels is primarilydetermined using sediment discharge rating curves. A sedimentrating curve is the relationship between streamflow dischargeand suspended sediment load. Hence sediment yield can beestimated according to observed flow using a given ratingcurve at each gauging station. However, the reliability ofsediment rating curves is often questionable due to, amongother factors, incomplete data and difficulties associated withsediment sampling during typhoon/flood events. Sedimenttransport modeling is common alternative for estimating fluvialsediment yield. The HSPF (Hydrological Simulation Program -FORTRAN) is a state-of-the-art modeling tool used for water-shed analysis (Donigian and Imhoff, 2006). The developmentof the HSPF began in the 1950s and 1960s. With advances inuser-interaction, geographic information systems (GIS): anddatabase technology, the current version of the HSPF 12 isintegrated as a submodel in Better Assessment Science Integrat-ing Point and Nonpoint Sources (BASINS) developed by theUS Environmental Protection Agency (EPA). The HSPF modelhas been widely and successfully applied to the prediction offluvial sediment transport (Moore et al., 1988; Chew et al.,1991; Laroche et al., 1996; Lee et al., 2006).The objective of this study is to explore each process

associated with landslide sedimentation, including estimationof landslide volume, the delivery of landslide sediment, andsediment routing, to gain a better understanding of the spatio-temporal distribution of sediment in watersheds. We providea discussion on these processes from a watershed scaleperspective. The framework proposed for assessing landslidesedimentation, as well as our results could be helpful in asses-sing the problem of landslide sediment in watersheds.

Study Area

The Shihmen Reservoir watershed is located in the Tahan Riverwatershed in northern Taiwan (Figure 1). The area of thewatershed is 760.2 km2, and the elevation ranges from 158m(Shihmen reservoir dam site) to 3524m. The Shihmen Reservoirwatershed comprises seven sub-watersheds: Shihmen, Hsiayun,Kaoyi, Lengchiad, Shankuang, Yunfeng, and Hsiuluan (Figure 1).The areas of these sub-watersheds are 144.3, 79.5, 39.1, 104.0,54.0, 220.0, 119.4 km2, respectively. Except for a small number

Copyright © 2012 John Wiley & Sons, Ltd.

Fwa

of igneous rock facies, the majority of the rocks in this area arelow grade metamorphic and sedimentary. Most of the soil isstony, with riverbanks and valleys composed of alluvium. Thereaders are referred to Lin et al. (2011): Keijsers et al. (2011):and Tsai et al. (2012) for more detailed lithological informationof the study area.

According to meteorological statistics (WRA, 1964–2004):the annual average temperature in this area is 19 �C and theannual average humidity is approximately 82%. The averageannual rainfall is approximately 2580mm, and is highly con-centrated between May and September due to heavy rainfallfrom typhoons. Rainfall also occurs during thunderstormstriggered by southwest air currents and intermittent tropicaldepressions. See Chang and Chiang (2009): Chiang and Chang(2009): Lin et al. (2011) and Tsai et al. (2012) for morebackground information on the Shihmen Reservoir watershed.

Methodology

Aerial photograph surveys of landslides

Landslide data for the Shihmen watershed were based on theinterpretation of aerial photographs from 1968, 1972, 1976,1986, 1998, 2003, and 2004. The aerial survey was conductedby the aerial survey office of the Taiwan Forestry Bureau,except for 1998. Industrial Technology Research Institute(ITRI) carried out the 1998 aerial survey. The Forestry Bureauconducted landslide identification, delineation and interpretationfor the 1968, 1972, 1976, 1986, and 2003 photos, while ITRIderived the sample data for the 1998 and 2004 photos. The scaleof the aerial photographs ranged between 1:10,000 and1:17,000. Aerial photograph based landslide mapping is not

Earth Surf. Process. Landforms, (2012)

SEDIMENT YIELD FROM LANDSLIDES IN THE SHIHMEN RESERVOIR WATERSHED

always able to differentiate between erosion and deposition;therefore, the possibility remains that inventory data may includean overestimation of landslide area. Measured landslide volumedata were only available for 1968 and 1972 by estimatingdifferences in elevation before and after the landslide event, usingstereoscopic parallax. The volume of each landslide was deter-mined by multiplying the estimated landslide depth by its area.The inventory included both fresh and old landslides. The

inclusion of old landslides occurring prior to the current periodwould lead to an overestimation of landslide volume. Thus, wedivided the landslide inventories into six time periods: 1968–1971,1972–1975, 1976–1985, 1986–1997, 1998–2003, and2004 and compared the landslide inventories between sequen-tial surveys to identify new landslides. Table I lists the basicinformation of the fresh landslide inventories including the totaland average number and area of landslides over the six periods.

Precipitation data

The average daily precipitation in the Shihmen Reservoirwatershed for the entire period 1986–2004 is shown in Figure 2.To investigate the influence of large storms, we have included

Table I. Summary of typhoon events and precipitation characteristics

Period 1968-19

Landslide Number Total (no.) 497Ave. (no. yr-1) 124

Total area Total (km2) 130Ave. (km2 yr-1) 33

Typhoon Which struck Taiwan Total ( no.) 12Ave. (no.yr-1) 3

Daily rainfall exceeds 250mm Total (no.) 4Ave. (no.yr-1) 1

Precipitation Annual Ave.(mm) 1899Max. (mm) 2201

Monthly maximum Ave.(mm) 592Max. (mm) 856

Daily maximum Ave.(mm) 246Max. (mm) 306

Figure 2. Records of daily precipitation for historical typhoon events in the Sin colour online at wileyonlinelibrary.com/journal/espl


the total and average number of typhoon events affectingTaiwan for each period, in which typhoon events with dailyrainfall exceeding 250mm are specifically identified (as notedin Figure 2). Table I also provides a summary of other hydrolog-ical characteristics, including the number of typhoons, maxi-mum and average daily, monthly, and annual precipitationaccording to the daily time series.

The year 2004 deserves more attention due to the occur-rence of Typhoon Aere, which played a significant role inthe landslide sediment production of the study area. TyphoonAere crossed specifically over northern Taiwan, bringingextreme heavy rainfall. Precipitation data indicate that thetotal rainfall during the typhoon was 1607mm, with 1262mmoccurring in 24h at the Baishi rainfall station in the Hsiuluansub-watershed (Central Weather Bureau and Water ResourcesAgency). The typhoon runoff to the reservoir carried extremelyhigh concentrations of sediment, considerably increasing theturbidity of the Shihmen Reservoir (WRA, 2005; Chen et al.,2010; Lin et al., 2011). Because the 1986–1997 period exhibitedthe largest increase in landslide area, we focus on landslidesediment issues from this time period, as well as the 1998–2003and 2004 periods, and model sediment routing from thelandslide initiation sites to the reservoir.

71 1972-1975 1976-1985 1986-1997 1998-2003 2004

415 752 288 626 1842104 75 24 104107 667 1427 163 54527 67 119 279 37 39 25 42.25 3.7 3.25 4.171 4 7 2 10.25 0.4 0.58 0.33

1895 1954 2453 2499 35972224 2648 3119 3384457 472 615 756 1394725 789 1048 1535144 200 222 223 556251 271 536 366

hihmen Reservoir watershed during 1968–2004. This figure is available


Z.-X. TSAI ET AL.

Statistical estimation of landslide volume usingfrequency–area distributions

Landslide frequency–area data extract from each landslideinventory were fit with an inverse-gamma distribution:

p AL;r; a; sð Þ ¼ 1aΓ rð Þ

aAL � s

� �rþ1

exp � aAL � s

� �(1)

where AL is the landslide area; p(AL;r, a, s) is the probabilitydensity function with respect to AL; r, a, and s are empiricalparameters; and Γ(r) is the gamma function of r (Malamudet al., 2004a). The parameter r controls the power-law decayfor the larger landslide areas, a controls the location of themaximum probability distribution, and s primarily controlsthe exponential decay for small landslide areas (Malamudet al., 2004a).To determine landslide volumes, we employed the general

form of the relationship between the landslide volume, VL(m3):

and its area, AL (m2): as proposed by Larsen et al. (2010):

VL ¼ aALg (2)

where g is the scaling exponent anda is the intercept. Thisequation then can be used to express the average volumefollowing the derivation of Malamud et al. (2004a):

VL� ¼ VLT

NLT¼

Z ALmax

0VLp ALð ÞdAL

¼Z ALmax

0aAL

gp ALð ÞdAL

(3)

where �VL is the average landslide volume in a watershed, VLT isthe total volume of landslides in the inventory, NLT is the totalnumber of landslides in the inventory, and ALmax is an upperbound of the landslide area for integration.Substituting p(AL) in Equation (3) and assuming that s can be

neglected compared with AL (Malamud et al., 2004a): we canrewrite Equation (3) as

VL� ¼ aar

Γ rð ÞZ ALmax

0

1

ALr�gþ1ð Þ exp � a

AL

� �dAL (4)

When r< g, divergence occurs when integrating Equation(4) and large landslides dominate the total landslide volume.The landslide volume determined by this equation wouldbe sensitive to the upper bound, ALmax. Being conservativeestimates of volumes, this study took maximum landslide area

Table II. Empirical models used to calculate run-out distance

Author Formula

Ikeya (1981) D= 8.6(V tanθ)0.42 D: Run-out distance, froLorente (2003) D=7.13(VH)0.271

Rickenmann(1994)*

D=25V0.3 V: The volume of landsl

Rickenmann(1999)

D=30(VH)0.25 H: Elevation differencebegins (meter)

Scheidegger(1973)

log(H/D) =�0.15666 log(V) + 0.62419

θ: Mean gradient of the

Takahashi (1994)* H/D= tana=0.2 a: An empirically deriveVandre (1985) D=aH a :set at 0.4 in the first d

*:Personal communication, Bathurst et al. (1997)Not in ref list


in an inventory as ALmax. In Equation (4): the variables thatrequire empirical measurements for parameterization are r, a,a, and g. The three parameters of the inverse gamma function,r, a, and s, were determined for each of the six time periodsusing the maximum likelihood method (s is not used for thecalculation of volume). The volume–area scaling parameters(a and g) were determined using regression with field datafrom 1968 and 1972, the only years with data. These fourparameters were used to estimate numerically the mean land-slide volume using Equation (4). Lastly, total landslide volumewas calculated by multiplying the mean volume by thetotal number of landslides. The volumes were then convertedto mass using a specific weight of 2.65Mgm-3, under theassumption that most of the material was sand. Thus, the massmay have been slightly overestimated if the actual materialscomprised mostly clay and silt.

To determine the yield of landslide sediment from each sub-watershed, we divided the total volume of landslides in theentire study area among the sub-watersheds. In accordancewith inventory datasets, we first converted landslide area tovolume for individual landslides using Equation (2). Thevolumes of individual landslides were then summed to obtainthe volumes for the sub-watersheds. Finally, we calculated thevolume ratios for the sub-watersheds over various periods andassessed the yield of landslide sediment from each region bymultiplying the total volume of landslides with the ratio.

Sediment delivery ratio to the stream

Sediment delivery to streams was modeled by empiricallyestimating landslide runout distance and the proportion oflandslide sediment delivered to streams. Bathurst et al. (1997)proposed a general form of the ratio of sediment delivered tothe stream as:

DEL ¼D �Δzð Þ

D� 100% D > Δz

0 D≤Δz

((5)

where DEL is the delivery ratio of a landslide. Here, D is therunout distance, and Δz is the distance between the centroidof the landslide area and the nearest downslope streamchannel. Landslide runout distances were calculated for threelandslide inventory periods, 1986–1997, 1998–2003, and2004. Runout distance was estimated using the mean predictedvalue from the seven models listed in Table II.

Most of the topographical parameters required for thesemodels were available from landslide inventory datasets andthe corresponding reports. The area of individual landslides as

Description

m landslide source to the point where deposition begins (meter)

ide (cubic meters)

between the head of the landslide and the point at which deposition

landslide (degrees)

d fractionistance



well as differences in elevation, mean slope, and GPS datawere obtained directly from these reports. As mentionedearlier, landslide volumes were unavailable; therefore, we usedthe relationship between volume and area (Equation (2)) toestimate the volume of each landslide. To determineDEL,stream channels were defined as areas with perennial flowbetween defined banks. GPS data related to stream channelswere obtained from the North Water Resources Bureau(Figure 1). According to the GPS data of stream channels andlandslide locations, Δz for each landslide can be determinedusing the spatial analysis function in commercial GIS in thecoordinate system of the Taiwan Datum 97 (TWD 97).This study first estimated runout distances using these

topographical parameters and different models. The mean ofthe seven runout distances was used to calculate the delivery ra-tio for each landslide using Equation (5). Finally, the estimatedvolume of landslide material delivered to streams was calculatedby multiplying the total landslide volume by the delivery ratio.

Simulation of fluvial sediment transport using theHSPF model

An HSPF model was developed for each sub-watershed in theShihmen Reservoir watershed to predict the proportion ofsediment transported to the reservoir after being delivered tochannels. The model development procedure is shown inFigure 3. The HSPF model is a comprehensive, lumped-parameter, and continuous watershed model that simulateswater quantity, quality, and sedimentation processes. The HSPFmodel incorporates spatial variability by dividing basins intohydrologically homogeneous land segments and simulatingeach segment independently using a lumped approach withvarious meteorological input data and model parameters. Bothphysically-based and empirical approaches are used in theHSPF model.The HSPF model simulates hydrologic processes by defining

pervious and impervious land areas, free flow reaches andreservoirs. More than 97% of the area of the Shihmen Reservoirwatershed is forest, hence the entire watershed was modeledwith pervious land use. Reservoir hydraulics was not simulatedbecause it was beyond the scope of this study. Six sub-watersheds(not including the Shihmen sub-watershed, which is dominatedby reservoir hydraulics): were included in the model. TheHSPF model comprises two application modules: PERLND andRCHRES. The PERLND module simulates the quality andquantity of water-related processes occurring in a pervious landsegment. The RCHRESmodule simulates hydraulic and sedimenttransport processes occurring within an open channel reach.The PERLND module models rainfall–runoff processes by

simulating interception, evapotranspiration, and infiltrationusing a spatially varied distribution relationship. Overland flowis simulated using an empirical outflow depth detention storagerelationship and the Chezy–Manning equation. Subsurfaceflow is simulated using empirical relationships to representthe interflow, outflow, percolation, and groundwater outflow.Channel routing using RCHRES is computed using kinematicwave routing and outflow from each stream reach, and theadvection of suspended/dissolved constituents is predicted.Measured channel cross–sections at gauge stations located atthe outlets of the Hsiuluan, Yunfeng, Lengchiad, Kaoyi, andHsiayun catchments (Figure 1) were used to establish channelhydraulic functions used in flow routing. HSPF models trans-port of sand, silt, and clay by simulating transport, erosion,and deposition. Erosion and deposition rates of silt and clayare calculated using bed shear stress. Sand transport capacityis estimated empirically.


The HSPF model requires a large number of parametersto adequately simulate hydrology, hydraulics, and sedimenttransport (Table III). The input parameters were initially estimatedusing the watershed characteristics, theoretical considerations,and parameters suggested in a previous study (Lee et al., 2006).

The hydrologic parameters were calibrated by attempting tominimize the error in simulated runoff volumes. Simulateddaily stream flow values were compared against 4383, stream-flow measurements from 1986–1997. Input parameters wereadjusted so that Nash–Sutcliffe model efficiency values (NSE,Nash and Sutcliffe, 1970) were ≥0.50 which indicate thatmodel performance is satisfactory.

The HSPF model was used to simulate hydrology andsediment transport only for the period 1986–2004 because ofthe significant increase in landslides during this time, asdiscussed earlier. An additional reason for restricting themodeling to recent time periods is that the current channelgeometry may differ considerably from past channel geometry.

The sedimentation model was calibrated following Donigianand Love (2003) and the US EPA (2006): which containdescriptions of the procedure we followed. The sedimentationmodel was calibrated using sediment yield data collected inthe Shihmen watershed from 1986–1995. Suspended sedimentwas measured at least twice monthly during the calibrationperiod. We calibrated the model by adjusting input parametersuntil the trend and slope of the modeled sediment rating curveswere consistent with rating curves based on measurements.Two logarithmic regression lines were fitted to the simulationand measurement data in each sub-watershed to evaluate theperformance of sediment simulation. It should be noted thatbedload transport was not calibrated due to a lack of measure-ment data; therefore, we employed standard bedload transportparameters. In the Shihmen Reservior watershed, 16% of totalsediment discharge is typically bedload (Lin et al., 2011;WRA, 1964–2004). Similar ratios of bedload versus suspendedsediment discharge were predicted by our model. Followingthe calibration and validation, sediment yields were simulatedfor the periods 1986–1997, 1998–2003, and 2004.

Results

Estimation of landslide volume

Historical landslide frequency–area distributions indicate thatinverse gamma frequency–area scaling parameters vary by timeperiod (Figure 4). The tail of the probability distribution forlarge landslide areas exhibits a power-law relationship withexponents ranging from �1.86 to �2.94 (as � r� 1). For mostdata sets, the rollover occurs for landslide areas as less than4.7�10� 4 to 1.04� 10� 2km2. Landslide volume–area scalingexhibits a power-law relationship (Figure 5) with a=0.23 andg=1.16 for the combined 1968–1971 and 1972–1975 data.Total landslide volume estimates of each sub-watershed for1968–2004 range from 1.07�106 to 2.02� 106 m3 and thecorresponding erosion rates range from 0.19 to 24mmyr-1

(Table IV).

Delivery ratio of landslide sediment

Estimates of the mean volume of landslide sediment deliveredto streams ranged from 3.62�104 to 8.09� 106 m3 for individ-ual sub-watersheds, which was 40% to 89% of the total land-slide volume (Table V). The proportion of sediment deliveredto channels from individual landslides varied over time, witha high proportion of landslides delivering >75% for the period1986–1997, relative to later periods (Figure 6).


Figure 3. Flow chart of the calibration, validation and application of the HSPF model. This figure is available in colour online at wileyonlineli-brary.com/journal/espl

Z.-X. TSAI ET AL.

Simulation of streamflow and sediment transport

The hydrologic calibration resulted in NSE values for therelationship between modeled and observed streamflow thatranged from 0.58 to 0.82 for the calibration period, and from0.46 to 0.81 for the validation period (Fig. 7). These resultsdemonstrate that this HSPF model provides a reasonablerepresentation of the hydrologic system in the watershed.There were power law relationships between sediment yield

and discharge for both the measured and simulated data. Thecalibrated sediment rating curve data were generally consis-tent, indicating the HSPF adequately simulates sedimenttransport within the Shihmen watershed. The simulatedmean annual sediment yield for each sub-watershed from1986–1997 was 0.06� 106, 0.18� 106, 0.25� 106, 0.03�106,0.30� 106, and 0.36� 106Mgyr-1 for the Hsiuluan, Yunfeng,Shankuang, Lengchiad, Kaoyi, and Hsiayun sub-watersheds,


respectively (Table VI). For the period from 1998–2003, thesediment discharge for each sub-watershed was 0.04�106,0.11�106, 0.16� 106, 0.05� 106, 0.26�106, and0.30� 106Mg yr-1, respectively. For 2004, the sedimentdischarge was 0.30� 106, 0.81�106, 1.10�106, 0.24� 106,1.47�106, and 2.02�106Mgyr-1, respectively. The sedimentyield from landslides and the delivery and discharge of channelsediment for each sub-watershed for the three different periodsis summarized in Table VII. The values for sediment yield anddischargewere converted to normalized erosion rates by dividingthem by the area of the sub-watersheds. In Table VII, sedimentdischarge refers to the accumulated sediment in all reaches andsub-watersheds upstream of the drainage outlet. To determinethe influence of individual sub-watersheds, we calculated theincrement of sediment discharge from each sub-watershed aswell the as proportion of sediment delivered downstream. Ourresults show that the proportion of landslide sediment in channels


Table III. Important parameters for (a) hydrology simulation and (b) sediment transport simulation of HSPF

(a)Category Item Name Definition Units

Pervious Land Hydrology(PWATER) Parameters PARM2 FOREST Fration forest cover noneLZSN Lower Zone Nominal soil Moisture Storage inchesINFILT Index to Infiltration Capacity in/hrLSUR Length of overland flow feetSLSUR Slope of overland flow plane ft/ftKVARY Variable groundwater recession 1/inchesAGWRC Base groundwater recession none

PARM3 INFEXP Exponent in infiltration equation noneINFILD Ratio of max/mean infiltration capacities noneDEEPFR Fraction of GW inflow to deep recharge noneBASETP Fraction of remaining ET from baseflow noneAGWETP Fraction of remaining ET from active GW none

PARM4 CEPSC Interception storage capacity inchesUZSN Upper zone nominal soil moisture storage inchesNSUR Manning’s n (roughness) for overland flow noneINTFW Interflow inflow parameter none IRC Interflow

recession parameternone

LZETP Lower zone ET parameter none(b)Category Item Name Definition UnitsPervious Land Accumulation and Removal ofSediment (SEDMNT) Parameters

SED-PARM2 SMPF Manegement Practice (P) factor from USLE noneKRER Coefficient in the soil detachment equation complexJRER Exponent in the soil detachment equation noneAFFIX Daily reduction in detached sediment per dayCOVER Fraction land surface protected from rainfall noneNVSI Atmospheric additions to sediment storage lb/ac-dy

SED-PARM3 KSER Coefficient in the sediment washoff equation complexJSER Exponent in the sediment washoff equation noneKGER Coefficient in soil matrix scour equation complexJGER Exponent in soil matrix scour equation none

Instream Sediment Transport (SEDTRN) Parameters SAND-PM KSAND Coefficient in sandload power function formula ComplexEXPSND Exponent in sandload power function formula Complex

SILT-CLAY-PM TAUCD Critical bed shear stress for deposition lb/ft2

TAUCS Critical bed shear stress for scour lb/ft2

M Erodibility coefficient lb/ft2.d

Figure 4. Probability density–area distribution for landslide data of each survey period in the Shihmen reservoir watershed. Data are plotted ondouble logarithmic axes. The gray line is the inverse gamma function fitted using the maximum likelihood method. The best fitting parametersfor three-parameter inverse-gamma function is obtained as r = 0.86, 1.32, 1.94, 0.89, 1.88, 1.30; a=4.7 � 10–4 , 1.29 �10–3, 1.04 �10–2,7.55 �10–3, 2.55 �10–3, 1.14 �10–3 km2; s=�5 � 10–5

~, –1.47 �10–4, –1.18�10–3, –9 �10–4

~, –2.4 �10–4, – 7.9�10–5 km2, R2 =0.75, 0.89,

0.79, 0.78, 0.87, and 0.94, for inventory 1972, 1976, 1986, 1998, 2003, and 2004, respectively.


Copyright © 2012 John Wiley & Sons, Ltd. Earth Surf. Process. Landforms, (2012)

Lan

dsl

ide

volu

me(

m3 )

Landslide area(m2)

1968-1972

1972-1976

Depth=0.1m

Depth=0.2m

Depth=0.5m

Depth=1m

Depth=2m

Depth=3m

Figure 5. The relation between landslide volume and area for entireShihmen reservoir Watershed. This figure is available in colour onlineat wileyonlinelibrary.com/journal/espl

Z.-X. TSAI ET AL.

transported to the downstream varied from 7% to 260%, accord-ing to time and the specific sub-watersheds involved.

Discussion

The frequency–area distributions and volume–area scaling aretwo important factors to determine landslide sediment yield.In the Shihmen Reservoir watershed, landslide frequency–areadistributions can be modeled well using an inverse gamma

Table IV. Landslide volume estimation for six landslide inventories

Sub-Watershed Category

1968-1972 1972-1

Estimation Inventory Estimation

Hsl V 0.309 0.315 0.211�V 0.077 0.079 0.053R 0.650 0.660 0.440

Yuf V 0.391 0.398 0.266�V 0.098 0.100 0.067R 0.440 0.450 0.300

Shk V 0.307 0.246 0.100�V 0.077 0.062 0.025R 1.420 1.140 0.460

Lec V 0.225 0.233 0.159�V 0.056 0.058 0.040R 0.540 0.560 0.380

Kay V 0.050 0.054 0.085�V 0.013 0.014 0.021R 0.320 0.350 0.540

Hsy V 0.067 0.059 0.163�V 0.017 0.015 0.041R 0.210 0.190 0.510

Shm V 0.102 0.119 0.083�V 0.026 0.030 0.021R 0.180 0.210 0.140

Whole watershed V 1.450 1.420 1.070�V 0.363 0.356 0.267R 0.480 0.470 0.350

V: Total landslide volume (106m3); �V :Annual average of landslide volume (1Watershed symbols: Hsl, Hsiuluan; Yuf, Yunfeng; Shk, Shankuang; Lec, Leng


function. The volume–area relationship provides a convenientmeans to estimate volume coupling frequency–area distribu-tions. The parameters a and g used in Equation (2) determinethe volume of a landslide with respect to area. Hovius et al.(1997) and Malamud et al. (2004a) assumed that landslidesare generally geometrically similar; therefore, they usedg=1.5. However, Larsen et al. (2010) proposed that thisrelationship is determined by hillslope material and thatgeometric similarity is not always maintained. This studyadopted the volume–area equation used by Larsen et al.(2010). Our results show that the depth of 94% of the landslidesin the Shihmen Reservoir watershed ranged from 0.2 to 2mwith only a few in the range of 2 to 3m. The scaling exponentg is approximately 1.16 with little variation among the sub-watersheds. According to the categories suggested by Larsenet al. (2010): the value of g implies that landslides in theShihmen Reservoir watershed are primarily shallow soil land-slides. For this kind of landslide, the depth of scars is limitedby the soil thickness. This result is consistent with surveys con-ducted by several agencies (Forestry Bureau, 1976; CentralGeological Survey, 2008; Chung, 2008; Soil and WaterConservation Bureau, 2009, 2010; Chiang and Chang, 2011).These studies indicate that the soil thickness in the study areais usually less than 2m.

One crucial factor in the occurrence of shallow landslides isthe thickness of soil regolith, which is influenced by a numberof physical, hydrological, chemical, and biological processes(Okimura and Ichikawa, 1985; Dietrich et al., 1995; Iida,1999; Burbank, 2002; Kasai et al., 2005; Larsen et al., 2010).The thickness of soil regolith represents a balance betweenthe production and erosion of soil. A dynamic equilibrium inthe thickness of regolith involves short-term fluctuations withthe long-term mean remaining unchanged (Burbank, 2002).Our results indicate that shallow landslides are currently the

Year

976 1976-1986 1986-1998 1998-2003 2004(Aere)

Inventory Estimation Estimation Estimation Estimation

0.269 1.850 1.660 0.234 2.6400.067 0.185 0.138 0.047 2.6400.560 1.550 1.160 0.390 22.1200.329 2.430 10.300 0.428 1.0600.082 0.243 0.895 0.0857 1.0600.370 1.100 3.910 0.390 4.8300.072 0.663 0.375 0.058 1.0600.018 0.066 0.031 0.012 1.0600.330 1.230 0.580 0.210 19.6000.130 0.648 2.100 0.226 0.2650.032 0.065 0.175 0.045 0.2650.310 0.620 1.680 0.430 2.5500.0501 0.396 0.584 0.136 0.3380.013 0.040 0.049 0.027 0.3380.320 1.010 1.250 0.700 8.6500.267 1.060 1.500 0.290 0.5370.067 0. 0.125 0.058 0.5370.840 1.330 1.570 0.730 6.7600.128 0.702 3.640 0.166 0.0920.032 0.070 0.303 0.033 0.0920.220 0.490 2.100 0.230 0.6401.240 7.740 20.200 1.540 6.0000.311 0.774 1.680 0.308 6.0000.410 1.020 2.210 0.400 7.890

06m3 yr-1);R: Erosion Rate (mmyr-1).chiad; Kay, Kaoyi; Hsy, Hsiayun; Shm,Shihmen


Table V. Average landslide sediment delivery ratio and total landslide volume delivered to the stream

Sub-watershed

1986–1998 1998–2003 2004 (Aere)

AverageDEL (%)

Landslide volume delivered tostreams (m3)

AverageDEL (%)


AverageDEL (%)


Hsl 75 1.25�106 64 1.50�105 55 1.45�106

Yuf 78 8.09�106 60 2.55�105 55 5.82�105

Shk 66 2.48�105 63 3.62�104 56 5.91�105

Lec 69 1.45�106 56 1.25�105 48 1.26�105

Kay 84 4.89�105 52 7.05�104 58 1.95�105

Hsy 83 1.24�106 62 1.81�105 54 2.91�105

Shm 89 3.23�106 48 8.04�104 40 3.69�104

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0-12.5 12.5-25 25-37.5 37.5-50 50-62.5 62.5-75 75-87.5 87.5-100 100

Rel

ativ

e F

req

uen

cyR

elat

ive

Fre

qu

ency

Rel

ativ

e F

req

uen

cy

DEL(%)

0 0-12.5 12.5-25 25-37.5 37.5-50 50-62.5 62.5-75 75-87.5 87.5-100 100DEL(%)

0 0-12.5 12.5-25 25-37.5 37.5-50 50-62.5 62.5-75 75-87.5 87.5-100 100

DEL(%)

Shihmen

Hsiayun

Kaoyi

Lengchiad

Shankuang

Yunfeng

Hsiuluan

1986-1997

(a)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7Shihmen

Hsiayun

Kaoyi

Lengchiad

Shankuang

Yunfeng

Hsiuluan

1998-2003

(b)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7Shihmen

Hsiayun

Kaoyi

Lengchiad

Shankuang

Yunfeng

Hsiuluan

2004

(c)

Figure 6. Landslide sediment delivery ratio calculated for three differ-ent periods. Different hatch pattern are used for seven sub-watersheds.



dominant type of erosion in the Shihmen Reservoir watershed.Shallow landslides commonly involve only regolith, such thatthe rate of sediment delivery by such landslides is limited bythe rate at which rock is converted to regolith (Burbank,2002; Heimsath et al., 2012). As discussed by Larsen et al.(2010): in the short term, this process is transport-limited if therate of landslide erosion is less than the depth of the regolith;it is supply limited over the long term, as rock-to-regolithconversion rates dictate the availability of landslide material.In the study, the landslide erosion rate ranges from 0.19 to24mmyr-1. Because rock-to-regolith conversion rates aretypically less than 1mmyr-1 (Monaghan et al., 1992; Heimsathet al., 1997, 2000, 2012): we infer that the proportion ofbedrock landslides in this area will gradually increase, with asubsequent acceleration in the formation of soil because soilformation rates decline exponentially with an increase in soildepth. In addition, long-term hydrological conditions also playa role in the dynamic equilibrium by influencing the frequencywith which landslides are triggered. Episodic soil landslides(that reduce the depth of soil) increase the rate of bedrockweathering in proportion to their frequency. Considering thelong-term dynamic equilibrium in the rapidly rising mountainsof this study area, if the formation of soil did not proceed at arate equal to or greater than that of landslide erosion, onlybedrock landslides would be able to match river incision andexhumation rates (Burbank, 2002). However, it could takecenturies or millennia for this process to achieve equilibrium,and this change would not be directly reflected in our data,which only covers decades. Therefore, we were unable todetermine whether the current situation represents short-termfluctuations or long-term equilibrium.

Few studies have attempted to compare the volume oflandslides and erosion rates obtained from survey measurementsagainst values estimated using statistical methods based onlandslide frequency–area and volume–area scaling. Estimatinglandslide volumes using frequency–area and volume–areascaling is a convenient means of estimating total landslidevolume when a comprehensive survey has not been performed,as the total volume of landslides in a watershed can be calculatedwithout the need for any additional information. Table IV showsthat the difference between estimated values and those obtainedfrom surveys is within an acceptable range (+2% to �14%).Several factors contribute to close correspondence between theestimated and measured values. The first factor is the goodnessof fit in the curves related to frequency–area distribution andvolume–area scaling. The second factor is the selection of theupper limit of integration for the frequency–area equation, whichresulted in a considerable degree of uncertainty (Malamud et al.,2004b; Korup et al., 2012). Korup et al. (2012) evaluated theimpact of uncertainty in these factors on the calculation oflandslide volume. Their results showed small numerical errors


-

lu

mi

-

(a) (b)

1997)Daily discharge calibration (1986 Daily discharge validation (1998 2003)

)smc(

egra

hcsiD

detaSS

imu

late

d D

isch

arg

e (c

ms)

Observed Discharge (cms) Observed Discharge (cms)

Hsiuluan, NSE=0.77

Yunfeng, NSE=0.64

Lengchiad, NSE=0.46

Kaoyi, NSE=0.77

Hsiayun, NSE=0.81

1:1 line

Hsiuluan, NSE=0.58

Yunfeng, NSE=0.72

Lengchiad, NSE=0.63

Kaoyi, NSE=0.79

Hsiayun, NSE=0.82

1:1 line

Figure 7. HSPF simulation of daily discharge for (a) the calibration period 1986–1997 and (b) the validation period 1998–2003. The 1:1 line indi-cates perfect agreement between the observations and model simulations.

Table VI. Annual fluvial sediment transport predicted by HSPF

Unit: Mg

Year Hsl Yuf Shk Lec Kay Hsy

1986 23800 27000 68700 29100 148000 3370001987 23100 47100 97800 41400 200000 3580001988 4630 10900 23300 13200 26500 271001989 38400 262000 264000 38500 404000 3200001990 178000 586000 737000 34100 873000 10200001991 3640 11200 23800 9240 30200 274001992 30700 81000 116000 17600 188000 2280001993 8230 12700 15500 2390 19800 175001994 168000 214000 339000 18400 394000 3280001995 17800 20300 25400 8210 38900 323001996 142000 421000 476000 111000 638000 8880001997 132000 466000 581000 21800 667000 7580001998 98400 354000 457000 98100 637000 6430001999 3500 3960 11200 19800 24600 257002000 17700 26300 31100 34100 107000 3610002001 45800 135000 234000 154000 479000 4770002002 83300 163000 246000 9760 294000 2830002003 2830 3500 4890 2740 9140 70102004 295000 807000 1100000 242000 1470000 2020000

Z.-X. TSAI ET AL.

in statistical scaling parameters, in which an inaccurate estima-tion of the size of the largest landslide may lead to considerablebias in volumetric estimates. In accordance with Korup et al.(2012): we also recommend caution in the use of these statisticalestimation models for interpretation. Despite the fact that ourresults indicate a reasonable match between statistical estimationand the survey, errors in the estimation of volume (which cannotbe resolved with the available data): may still exist. Such errone-ous estimates would falsely suggest changes in erosion rates.In this study, the use of landslide inventories covering various

spans of time could influence completeness of the inventory.The consequences can be observed in Table I. The periodscovering 1976–1985 and 1986–1997 are relative long (10


and 12 years, respectively): compared with other periods. Inthese two periods, the number of landslides is lower but thetotal area is larger. One possible reason is that with the passageof time, many smaller landslides were obscured by subsequentlandslides, erosion, anthropic influences, and the growth ofvegetation (Malamud et al., 2004a; Van Den Eeckhaut et al.,2007). In another study on post-failure sediment, we confirmedthat landslide retrogression/enlargement is caused by rainfall,while the growth of vegetation in the landslide area increaseswith time (Tsai et al., 2012).

Landslide erosion rates were greater during the surveyperiods 1986–1997 and 2004, relative to other periods. The land-slide erosion rate from 1986–1997was approximately seven-foldgreater than in 1972–1975. In the period 1998–2003, thelandslide erosion rate decreased to the low level observed in1968–1976. In 2004, Typhoon Aere significantly increased thenumber of landslides. The number of landslides triggered by thissingle event and the total area covered were several times greaterthan the annual average of any preceding period. The landslideerosion rate in 2004 was estimated to be 7.89mmyr-1, whilethe annual erosion rates of previous periods ranged from only0.40 to 2.21mmyr-1.

Hydrological conditions are an important factor in thechange of erosion rates, both temporally and spatially. Acomparison of Tables I and III indicates the potential influenceof hydrological conditions. Among the various hydrologicalparameters included, the extreme values of daily maximumprecipitation and the average number of typhoons with heavyrainfall show a strongest correlation to erosion rates over theentire watershed. These results are in agreement with those ofprevious studies, indicating that daily precipitation is an impor-tant factor in the occurrence of landslides (Dai and Lee, 2001;Jakob et al., 2006; Chiang and Chang, 2011). One exceptionis the period 1968–1971, which had a higher frequency oftyphoons with heavy rainfall (1 times yr-1) and a high maximumdaily precipitation (306mm). The conditions in this periodwere more severe than those in the two subsequent periods;however, the erosion rate was close to or even less than whatit was in these periods. Hence, these indices do not necessarily


Table VII. Summary of sediment yields from landslides, delivery and channel sedimentation for each sub-watershed

(A) 1986–1997

Items Hsl Yuf Shk Lec Kay Hsy

Landslide sediment M 0.37 2.28 0.08 0.46 0.13 0.33R 1.16 3.91 0.58 1.68 1.25 1.57

Landslide sediment to channels M 0.27 1.78 0.05 0.32 0.11 0.27R 0.87 3.05 0.38 1.16 1.05 1.30

Sediment discharge (at sub-watershed outlet) M 0.06 0.18 0.25 0.03 0.31 0.37Increment of sediment discharge in sub-watershed M 0.06 0.12 0.07 0.03 0.03 0.06

R 0.19 0.21 0.49 0.11 0.29 0.28Sediment delivered to the downstream % 22 7 128 9 28 22(B) 1998–2003

Items Hsl Yuf Shk Lec Kay HsyLandslide sediment M 0.12 0.23 0.03 0.12 0.07 0.15

R 0.39 0.39 0.21 0.43 0.70 0.73Landslide sediment to channels M 0.08 0.14 0.02 0.07 0.04 0.09

R 0.25 0.23 0.13 0.24 0.36 0.45Sediment discharge(at sub-watershed outlet) M 0.04 0.11 0.16 0.05 0.25 0.29Increment of sediment discharge in sub-watershed M 0.04 0.07 0.05 0.05 0.04 0.04

R 0.13 0.12 0.35 0.18 0.39 0.19Sediment delivered to the downstream % 22 7 128 9 28 22(C) 2004 (Aere)

Items Hsl Yuf Shk Lec Kay HsyLandslide sediment M 7.00 2.82 2.81 0.70 0.90 1.42

R 22.12 4.83 19.60 2.55 8.65 6.76Landslide sediment to channels M 3.85 1.55 1.57 0.34 0.52 0.77

R 12.16 2.66 10.98 1.22 5.02 3.65Sediment discharge(at sub-watershed outlet) M 0.30 0.81 1.10 0.24 1.47 2.02Increment of sediment discharge in sub-watershed M 0.30 0.51 0.29 0.24 0.13 0.55

R 0.95 0.87 2.03 0.87 1.25 2.61Sediment delivered to the downstream % 8 33 18 71 25 72

M: average mass of sediment material per year (106Mg yr-1); R: Erosion ratio normalized with respect to sub-watershed area (mmyr-1).


provide a comprehensive indication of hydrological condi-tions. During the period 1976–1985, the maximum dailyprecipitation and the number of typhoons with heavy rainfallwere below those 1968–1971 (several storm events with dailyrainfall in the range of 200mm can be observed in Figure 2).During 1968–1971, the average number of landslides and thearea affected were double those of previous periods. Themaximum annual precipitation was 2648mm, which is obvi-ously larger than in the other periods. The higher erosion rate(1.20mmyr-1) might be explained by an increase in the numberof landslides, resulting from more frequent hydrological eventsof moderate intensity.The last three periods had relatively severe hydrological

conditions. As shown in Table I and Figure 2, seven typhoonswith heavy rainfall occurred in the first 12 years (0.58 timesyr-1) and only two in the following six years (0.33 times yr-1).The intensity of rainfall during 1986–1997 was also muchhigher, with maximum daily precipitation in the study areareaching 536mm during Typhoon Herb in 1996. During1998–2003, Typhoon Zeb and Nari produced maximumdaily rainfall amounts of 333mm and 366mm, respectively.In 2004, Typhoon Aere set new records for rainfall reaching556mm in 24 h. This temporal variation in precipitationrates, especially the extreme of maximum daily precipitation,may explain the trend in erosion rates over these threeperiods. However, the longer time span of datasets thatinclude extreme rainfall events may mask exceptionally highshort-term landslide erosion rates For example, the rainfallintensity of Typhoon Herb in 1996 is very close to that ofTyphoon Aere, similar magnitudes of landslides were sup-posed to be triggered. This possibility was confirmed by Linet al. (2011) but not observed in our result. They suggestedthe total landslide volumes during Typhoon Herb and


Typhoon Aere were 13.96� 106 and 6.71�106m3, respec-tively, a little larger than our estimates. Due to the long timeperiod, the influence of Typhoon Herb was averaged out sothe landslide erosion rate in 1986–1997 is lower than thatin 2004. In addition to precipitation, other studies have sug-gested that the 1999 Chi-chi earthquake (Mw 7.5) increasedthe fragility of geological formations, leading to an increasein the frequency of landslides (Lin et al., 2006; Liu et al.,2009; Chen and Chao, 2010). This phenomenon was moreevident in central Taiwan and our results do not support thisinference. Despite a rapid increase in landslide erosion to7.9mmyr-1 following Typhoon Aere in 2004, we did notobserve an increase in erosion rates during 1998–2003 (theperiod following the earthquake).

Another issue that must be addressed is the proportion oflandslide material moved downslope and transported bystreams. In general, two factors decide the proportion of DEL:(1) the distance to the stream; and (2) the runout distance ofthe landslide. The results of this study show that the value ofthe sediment delivery ratio does not vary much with respectto the sub-watersheds. However, the period of 1986–1997and the two later periods reveal different patterns in thedelivery ratio. During the period 1986–1997, most of thelandslides were located near streams. As a result, the deliveryratio in this period was relatively high, compared with laterperiods. A total of 65% of the landslides have a DEL in therange 87.5% to 100%, and 28% of the landslides have a DELas high as 100%, which indicates a landslide located immedi-ately next to a river. In later periods (1998–2003 and the periodduring which Typhoon Aere occurred) the distance betweenlandslides and streams was larger, with generally lower DELs.Most of the landslides were located at distances of approxi-mately 200–500m from streams; therefore, the average DEL


Z.-X. TSAI ET AL.

was approximately 50% as shown in Figure 6(b) and (c). Toexplain the change of distances and DEL, we hypothesize thatthe sites with the highest number of landslides, which arealways located beside a river, are those at which a landslidehas previously occurred. In locations where landslides havealready occurred, the conditions specific to that site may befavorable for a second failure and could possibly be triggeredagain by subsequent events. Since other potential landslidesites are already occupied by existing landslides, less area isavailable for new triggered landslides. With the passage oftime, the average distance of new triggered landslide will likelyincrease, due to an increase in the proportion of new distantlandslides. Thus, landslides that occurred later were shown tobe farther from rivers, with a subsequent reduction in deliveryratio. Comparing the periods of 1986–1997, 1998–2003 andthat in which Typhoon Aere occurred, demonstrate that thelatter two periods had a much lower delivery ratio. During theperiod 1998–2003, the landslide material that was deliveredaccounted for only 0.43� 106Mg, which is approximately 15%of the amount delivered in 1986–1997 (2.81�106Mg). Thedelivery ratio for the period in which Typhoon Aere occurred isalso low. However, due to the significant number of landslidesthat were triggered, the overall volume of landslide materialdelivered to streams was still 3 times greater than in the periodof 1986–1997. As shown in Table VII, the average total quantityof landslide material that was delivered to streams was2.81�106Mgyr-1 during 1986–1997, but this amount reached8.60� 106Mg following Typhoon Aere. It should be noted thatthe analysis of delivery ratio in this study is limited by the assump-tions of the empirical models employed. Due to the difficultyinvolved in quantifying the influence of rainfall characteristicson individual landslides, this was not considered in this study.Rainfall influences the viscosity of the landslide mass with asubsequent effect on runout distance. In some cases, a continueddecrease in viscosity results in the development of debris flowsand the erosion of unstable material in their paths. The debriscontinues moving downslope until the gradient falls belowthat required to maintain the flow (Bathurst et al., 1997). Tosimulate the behavior of debris-flow precisely would require acomplete dynamic simulation model with a large number ofparameters, which could be problematic for long-term studiesand those performed on the scale of watersheds. Empiricalstatistical methods are well suited to the purpose of such predic-tions, as general debris-flow characteristics can be reasonablywell simulated (Rickenmann, 2005).Lastly, we discuss the fluvial transport of landslide material.

For the periods of 1986–1997 and 1998–2003, the amountof channel sediment did not change much, with only slightdifferences observed from one sub-watershed to another. Theproduction of sediment from landslides had no obviousinfluence on the sediment transport capacity during differentperiods. For Typhoon Aere in 2004, the volume of channelsediment was approximately six times greater than that inthe period 1986–1997, increasing from 0.37� 106Mg to2.02�106Mg for the entire six upper sub-watersheds, exceptfor the Shihmen reservoir sub-watershed. For each time period,the amount of sediment that was transported in the channelswas always less than the landslide material delivered to thestream. As shown in Table VII, an average of only 13% of thelandslide material in the river moved downstream in the period1986–1997. From 1998–2003, the proportion of landslidematerial delivered was relatively high due to the small numberof landslides generated. For the period in which TyphoonAere occurred, the channel sedimentation for the entire yearaccounted for approximately 23% of the landslide material thatmoved to the stream. The sediment transport data imply thata significant quantity of landslide material remained in the


riverbed. By dividing annual volume with sediment transportcapacity of reaches in 2004, the channel residence time ofthe landslide material were roughly estimated under thestationary assumption. The residence time could be longer ifthe annual flow is smaller. This landslide material was nottransported downstream immediately after being delivered tothe rivers, but remained there for 3–25 years or even longerbefore being completely removed. We interpret these resultsto mean that the channel sediment yield in the study area wascontrolled more by transport capacity than by the supply ofsediment. These findings differ from those of Hovius et al.(2000) in the Hualien catchment in eastern Taiwan. Usingdatasets on landslides and sediment transport, they concludedthat supply dominates sediment load in bedrock-flooredstreams in the Hualien catchment. The sediment transportcapacity of the Hualien Chi is very high due to steep slopesand rapid runoff (Kao and Milliman, 2008). According to thedata from the Water Resource Agency, the annual depths ofrunoff are 253mm and 191mm for the Hualien Chi catchmentand the Shihmen Reservoir watershed, respectively. The meangradient does not show a significant difference between thesetwo regions (4.0% vs. 2.8%; data from Water ResourcesAgency): but it should be noted that 123 check dams exist inthe Shihmen reservoir watershed (Chen et al., 2010). Checkdams reduce the effective slope of channels, thereby loweringthe velocity of flowing water, which decreases sedimenttransport capacity by allowing sediment to settle. Due to theconstruction of check dams, the sediment transport capacityof channels in the Shihmen Reservoir watershed is much lowerthan that in the Hualien Chi catchment. Hovius et al. (2000)stated that detailed knowledge of spatial and temporal patternsis required to understand the conditions of fluvial sedimenttransport. Spatial variations in transport conditions were alsoobserved in the study area. During 1986–2003, the quantityof transported sediment exceeded the volume of new triggeredlandslides in the Shankuang sub-watershed. Because transportcapacity has been constrained by check dams, this discrepancycould only be caused by spatial variations in precipitation. Thespatial distribution of precipitation associated with threetyphoons: Zeb (1998): Nari (2001): and Herb (1996) in thisperiod (Li et al., 2005): indicates that this sub-watershed hasrelatively low precipitation intensity and that the spatial patternof this precipitation is not always consistent. During TyphoonAere, rainfall was concentrated in the central and southern partsof this watershed. As a result, the sediment produced by landslideswas relative high compared with other sub-watersheds. As inthe study by Hovius et al. (2000): we observed that the factorscontrolling fluvial sediment loads may vary due to spatial andtemporal variations in sediment supply and transport mechanics.In general, the transfer of sediment in the fluvial system in theShihmen Reservoir watershed is dominantly transport limited,rather than supply limited, as it is in the Hualien Chi catchment.

The percentage of sediment that was transported down-stream as a result of Typhoon Aere was lower in the uppersub-watersheds, such as in Hsiuluan and Yunfeng. Accordingly,a significant quantity of landslide material that was triggered bythis typhoon may still remain in the upper-stream area. In thefuture, these areas will be susceptible to serious problemsassociated with channel sediment with consequences fordownstream regions and the Shihmen Reservoir.

Conclusion

The spatial and temporal distribution of landslide sedimenta-tion in the Shihmen Reservoir watershed in Taiwan wasquantified and modeled. Landslide volumes for different time



periods were estimated from mapped landslide areas andlandslide volume and area data. According to our estimation,landslide erosion rates were between 0.4 and 2.2mmyr-1 during1972–2003, reaching 7.9mm in 2004. Due to the occurrence oftwo severe typhoons (Herb and Aere): the amount of landslidematerial in the period of 1986–1997 and 2004 was several timesgreater than in other survey periods. The average annual land-slide sediment produced in the period 1986–1997 was approxi-mately 3.65� 106Mgyr-1, and Typhoon Aere, resulted in thegeneration of 15.65�106Mg of landslide sediment.From a long-term perspective, new triggered landslides in the

future may be located farther from streams, which could resultin a reduced delivery ratio. However, the ratio will not neces-sarily result in a decrease in the quantity of landslide materialtransported to streams. Our results show that approximately78% of the landslide material was delivered to streams in theperiod 1986–1997, 60% in the period 1998–2003, and 50%in 2004. However, the total mass of landslide delivered to thestreams in 2004 was approximately three times greater thanin the period 1986–1997 due to the significant number oflandslides in that period.Landslide material is the primary source of sediment trans-

ported to stream channels in the Shihmen Reservoir watershedand this study applied a calibrated HSPF model to determinethose volumes. Our analysis revealed that the channel sedi-ment yield in the study area was controlled by transportcapacity rather than the supply of sediment. One possiblereason is reduced transport capacity resulting from the con-struction of check dams, which helps to prevent sedimentationwithin the reservoir. However, this engineering measure mayonly postpone the ensuing threat. The imbalance betweensediment supply and transport capacity results in considerablequantities of landslide material accumulating in the upper-stream regions of the watershed. Even if the reservoir authorityapplies landslide prevention and soil conservation practices toreduce sediment discharge to the Shihmen Reservoir, thesepractices will probably require several years to take effect.Due to the discrepancy between the supply of sediment andtransport capacity over long periods of time in the past, theShihemn Reservoir will continue to have problems associatedwith high sediment inflow, despite only small increases in thenumber of landslides.

Acknowledgements—We would like to express our sincere gratitudeand appreciation to editor Professor Lane, associate editor and twoanonymous reviewers for their comments and suggestions, especiallythe first reviewer, who was so professional, patient and warmhearted.He/she made considerable effort in editing the manuscript and provid-ing a comprehensive and critical review which helped us to improvethis work. We also thank the North Water Resources Bureau andShihmen Reservoir Authority for providing data on the historical aerialsurvey, hydrological and sediment observation, and the financial supportfrom North Water Resources Bureau and National Science Council,Taiwan (NSC-99-2221-E-002-090, NSC-100-2221-E-002-193)

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