Modeling Sediment Yields and Stream Stability Due to Sediment-Related Disaster in Shihmen Reservoir Watershed in Taiwan

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Article Modeling Sediment Yields and Stream Stability Due to Sediment-Related Disaster in Shihmen Reservoir Watershed in Taiwan

Yu-Jia Chiu 1, Hong-Yuan Lee 1,2, Tse-Lin Wang 2, Junyang Yu 3, Ying-Tien Lin 3,* and Yeping Yuan 3 1 Hydrotech Research Institute, National Taiwan University, Taipei 106, Taiwan; [email protected] (Y.-J.C.); [email protected] (H.-Y.L.) 2 Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan; [email protected] 3 Ocean College, Zhejiang University, Hangzhou 310058, China; [email protected] (J.Y.); [email protected] (Y.Y.) * Correspondence: [email protected]; Tel.: +86-157-0015-1516

Received: 15 January 2019; Accepted: 11 February 2019; Published: 15 February 2019

Abstract: Accurate and reliable estimates of sediment yields from a watershed and identification of unstable stream reaches due to sediment-related disaster are crucial for watershed management, disaster prevention, and hazard mitigation purposes. In this study, we added hydrodynamic and sediment transport modules in a recently developed model to estimate sediment yields and identify the unstable stream reaches in a large-scale watershed (> 100km2). The calibrated and verified models can well reproduce the flow discharge and sediment discharge at the study site, the Shihmen Reservoir Watershed in Taiwan, during several typhoon events. For the scenario applications, the results revealed that the contribution (> 96%) of landslides on sediment supply is much more significant than compared to soil erosion (< 4%). The sediment contribution from the upstream of the hydrological station-Yufeng is approximately 36–55% of the total sediment supply for the rainfall events of 25, 50, 100, and 200 years return period. It also indicates that 22–52% of sediment still remain at foot of the slope and the streams, which become a potential source for sediment hazards in the future. Combining with the bed erosion and deposition depths, flow-induced shear stress from the SRH-2D model, and probability of slope failure within 250 m of stream reaches, the relatively stability of stream reaches can be identified. The results could provide the water resource authorities for reference to take precautionary measures in advance on the stream reaches with high-degree instability.

Keywords: sediment yield; landslide; stream stability; Shihmen Reservoir watershed

1. Introduction In tropical regions such as Taiwan, high intensity rainfall events from typhoons or plum rains often generate large amounts of sediments eroded from the upstream watershed [1]. These sediments are then brought to the downstream reservoir areas, and the decrease of ﬂow velocity causes sediment deposit, which would reduce the reservoir capacity and shorten the lifespan of reservoirs [2,3]. Therefore, it is important to accurately estimate the sediment yields suffering intense rainfall events for the purposes of disaster prevention, hazard mitigation, and watershed management. Since sediment yield from a watershed is involved with numerous parameters such as local topography, soil, lithology, meteorological conditions, vegetation cover, land use, watershed morphology, and ﬂow hydraulics [4,5], it is very complicated and highly variable in both time and hydromorphological location of watersheds.

Water 2019, 11, 332; doi:10.3390/w11020332 www.mdpi.com/journal/water Water 2019, 11, 332 2 of 22

Nowadays, three approaches are commonly used to estimate the sediment yield, which are field measurements, empirical-statistical models, and physically-based soil erosion-deposition models [6]. For field measurements, sediment yield is estimated based upon the relationship between the suspended sediment concentration and flow discharge (sediment rating curve) established at hydrological stations. However, the sediment rating curve during the flood period do not normally collapse into a single-value relationship, rather, they often exhibit a hysteretic loop [7]. Thus, the simplified relations between discharge and sediment yield cannot reflect temporal variations of sediment yield and generate significant estimate errors during the rising and falling stages of high flow events [8]. Empirical-statistical models ignore the physical processes of sediment supply, transport, and delivery and use the data from laboratory or field measurements to obtain the regression equations to firstly estimate the soil erosion [9]. Then, the soil erosion multiplies by the Sediment Delivery Ratio (SDR) to acquire the sediment yield. However, the SDR value is with high level of uncertainty and could become the major error source. The commonly-used empirical models are Universal Soil Loss Equation (USLE) [10], Modified Universal Soil Loss Equation (MUSLE) [11], and Revised Universal Soil Loss Equation (RUSLE) [12]. Physically-based soil erosion-deposition models adopt physical equations such as EUROSEM [13], HSPF [14], KINEROS [15], LASCAM [16], SHESED [17], and SWAT [18]. Nevertheless, the physically-based model needs to quantify numerous geographic and hydrological factors in a watershed. Though these models have provided acceptable estimates on sediment yield due to soil erosion, relatively few models include the landslide effects on total sediment yield. Landslides play an important role in determining sediment yield from a watershed [19,20]. Several studies attempted to establish the landslide component on total sediment yield [21–26]. For example, Burton and Bathurst [21] developed a physically-based model-SHETRAN to estimate the landslide-related sediment yield. Schuerch et al. [22] monitored the sediment yield in a small catchment and compared the different sediment source supplied to the channels. Hsu et al. [23] integrated the HSPF, TRIGRS [24], and FLO-2D [25] models to estimate the sediment yield from soil erosion, shallow landslide, and debris flow. Chen et al. [26] proposed an integrated model of Grid-based Sediment Production and Transport Model (GSPTM) at a watershed scale to evaluate the dynamic of sediment production and transport in the Shihmen Reservoir watershed in Taiwan. In their model, rainfall-runoff processes, landslide potential, sediment production from landslide and soil erosion, debris flow and mass movement, and sediment transport were included. This study aimed to estimate sediment yields and assess stream stability due to sediment- related disaster in a large-scale watershed (>100 km2). An integrated model originally proposed by Chen et al. [26] was adopted, but two hydrodynamic and sediment transport models were added in the model to better quantify the flow field and calculate suspended sediment concentration, sediment yields and sediment erosion and deposition patterns in streambeds. To identify the unstable reaches in streams, an evaluation system based on the integrated model that can provide the bed erosion and deposition patterns, flow-induced shear stress on the bed and bank of streams, and possibility of slope failure near the streams was developed. The integrated model was then applied to the Shihmen Reservoir watershed to evaluate its performance through calibration and validation and demonstrate its feasibility and potential to estimate the sediment yields. Finally, to identify the response of sediment yields and stream stability with different rainfall events, scenarios with four rainfall return periods ranging from 25 years to 200 years were used in the developed model.

2. Materials and Methods

2.1. Study Area Figure1 shows the map and sub-watersheds of the study area—the Shihmen reservoir watershed. The Shihmen Reservoir watershed is located on the middle reaches of the Dahan River, one of the largest and most important reservoirs in Taiwan. The reservoir was built in 1964 for multiple purposes such as industrial and domestic water supply, irrigation, hydro-power generation, and ﬂood prevention Water 2019, 11, 332 3 of 22

Water 2018, 10, x FOR PEER REVIEW 3 of 22 and recreation and had an original total capacity of 3.09 × 108 m3. The watershed area of the reservoir watershed area of the reservoir watershed is 760.2 km2 with elevation ranging from 158 m (Shihmen watershed is 760.2 km2 with elevation ranging from 158 m (Shihmen Reservoir dam site) to 3524 m Reservoir dam site) to 3,524 m and average slope angles of about 30 degrees. The upper reach of the and average slope angles of about 30 degrees. The upper reach of the Shihmen Reservoir watershed Shihmen Reservoir watershed has deep valleys and vulnerable geology characteristics, and, after the has deep valleys and vulnerable geology characteristics, and, after the Chi-Chi earthquake occurred in Chi-Chi earthquake occurred in 1999, several landslide areas appeared [27]. In 2004, Typhoon Aere 1999, several landslide areas appeared [27]. In 2004, Typhoon Aere hit Taiwan with a total precipitation hit Taiwan with a total precipitation of 1,600 mm in the Shihmen Reservoir watershed, which caused of 1600 mm in the Shihmen Reservoir watershed, which caused turbidity currents in the reservoir areas, turbidity currents in the reservoir areas, brought approximately 2.8 × 10 𝑚 sediments into the brought approximately 2.8 × 107 m3 sediments into the reservoir, and interrupted the water supply for reservoir, and interrupted the water supply for 17 days [28,29]. This typhoon event caused about 10% 17 days [28,29]. This typhoon event caused about 10% loss of the original reservoir storage capacity. loss of the original reservoir storage capacity. According to bathymetry survey conducted in 2016, According to bathymetry survey conducted in 2016, the deposited sediments occupied nearly 34% of the deposited sediments occupied nearly 34% of the total storage capacity of the Shihmen Reservoir the total storage capacity of the Shihmen Reservoir since 1964. To sustain the reservoir’s life and ensure since 1964. To sustain the reservoir’s life and ensure water supply, the mid- and long-term sediment water supply, the mid- and long-term sediment desilting projects were proposed and implemented. The desilting projects were proposed and implemented. The mid-term sediment management plan was mid-term sediment management plan was to modify the existing sluicing outlets [30]. The modification to modify the existing sluicing outlets [30]. The modification project was finished in 2013 and had project was finished in 2013 and had increased the sediment desilting rate from about 30% to 45% increased the sediment desilting rate from about 30% to 45% under the typhoon Aere condition [30]. under the typhoon Aere condition [30]. For the long-term sediment management plan, sediment For the long-term sediment management plan, sediment bypass tunnels—proposed as an important bypass tunnels—proposed as an important sediment remove measure,—will be built [31]. In addition, sediment remove measure,—will be built [31]. In addition, Ronghua Dam (grey box in Figure 1), built Ronghua Dam (grey box in Figure1), built in 1984, was within the Shihmen Reservoir Watershed and in 1984, was within the Shihmen Reservoir Watershed and 27 kilometers upstream of the Shihmen 27 kilometers upstream of the Shihmen Dam in the Dahan River gorge. Its main function is to prevent Dam in the Dahan River gorge. Its main function is to prevent sediments from moving and silting in sediments from moving and silting in the Shimen Reservoir. The original storage capacity of the dam the Shimen Reservoir. The original storage capacity of the dam was 1.67 × 10 𝑚 but has been filled was 1.67 × 107 m3 but has been filled by sediments at present [32]. by sediments at present [32].

Figure 1. Sub-watersheds of the study area—Shihmen reservoir watershed, Taiwan. X(m and Y(m) are basedFigure upon 1. Sub-watersheds the TWD97/TM2 of the coordinate study area—Shi system. Thehmen inset reservoir ﬁgure showswatershed, the longitude Taiwan. X(m) and latitude and Y(m) of theare studybased areas.upon the TWD97/TM2 coordinate system. The inset figure shows the longitude and latitude of the study areas.

2.2. Proposed Model

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2.2. Proposed Model InWater this 2018 study,, 10, x FOR three PEER REVIEW individual models were integrated to estimate sediment yield4 fromof 22 the Shihmen Reservoir watershed, which include: (i) Hydrological model, i.e., the rainfall-runoff models, In this study, three individual models were integrated to estimate sediment yield from the to provideShihmen the Reservoir upstream watershed, inflow discharge;which include: (ii) (i) hillside Hydrological sediment model, production i.e., the rainfall-runoff model to estimatemodels, the amountto provide of sediments the upstream brought inflow from discharge; the hillslope (ii) hi tollside the rivers;sediment (iii) production hydraulic model and sedimentto estimate transport the modelsamount to obtain of sediments the suspended brought from sediment the hillslope concentration, to the rivers; flow-induced (iii) hydraulic shear and stresssediment and transport bed erosion and depositionmodels to obtain in the the downstream suspended sediment rivers; concentr and (iv)ation, an evaluation flow-induced system shear stress to assess and bed the erosion stability of streamand reaches. deposition Figure in 2the illustrates downstream the flowchartrivers; and for(iv) the an integratedevaluation modelsystem toto estimateassess the sediment stability of yields and assessstream thereaches. stability Figure of 2 stream illustrates reaches the flowchart in the studyfor the areas.integrated The model four individualto estimate sediment models yields are briefly describedand assess below. the stability of stream reaches in the study areas. The four individual models are briefly described below.

FigureFigure 2. 2.Framework Framework forfor thethe proposed integrated integrated model. model.

2.2.1.2.2.1. Hydrological Hydrological Model Model The conceptualThe conceptual Tank Tank model model was adoptedwas adopted to simulate to simulate the rainfall-runoff the rainfall-runoff processes, processes, thus providingthus the behaviorproviding of the rainwater behavior of in rainwater surface runoff,in surface soil runoff, infiltration, soil infiltration, and seepage and seepage [33– 35[33–35].]. The The Tank Tank model describesmodel the describes amount the of amount water storageof water instorage a watershed in a watershed with awith series a series of tanks of tanks with with the the outlets outlets on the side andon the bottom side and of thebottom tanks of [36the]. tanks In the [36]. study, In the we st referredudy, we toreferred the three-layer to the three-layer Tank model Tank model developed developed by [37], where the total water depth can be divided into three layers, i.e., the surface (𝐻 ), by [37], where the total water depth can be divided into three layers, i.e., the surface (H1), intermediate intermediate (𝐻), and base layers (𝐻). The sum of the water depths in the three tanks is called the (H2), and base layers (H3). The sum of the water depths in the three tanks is called the Soil Water Soil Water Index (SWI), i.e., 𝐻 +𝐻 +𝐻 . The Tank model is suitable to simulate surface runoff, Index (SWI), i.e., H + H + H . The Tank model is suitable to simulate surface runoff, reservoir flood reservoir flood 1discharging2 3and flood diversion [26]. discharging and flood diversion [26].

Water 2019, 11, 332 5 of 22

2.2.2. Hillside Sediment Production Model The hillside sediment production model includes the sediment from landslide areas and soil erosion. The models based upon two different mechanisms for sediment production are described as follows. (i) Landslide-induced sediment production: In this study, logistic regression (or binary regression analysis), a widely used statistical approach, was adopted to determine the landslide potential [38]. The advantage of logistic regression is that the dependent variable y is binary, e.g., landslide occurrence (y = 1) or nonoccurrence (y = 0) in this study, and the independent (or called explanatory) variables are x, the environmental factors possibly to affect the landslide occurrence [36,39]. The logistic regression model has been successfully applied to estimate the probability of landslide occurrence [38,40,41]. Some detailed veriﬁcation of applying the logistic regression model to landslide prediction can refer to literature [38,40,41]. The logit model from a logistic regression has the following form:

logit(y) = a + b1x1 + b2x2+, b3x3 + ··· + e, (1) where y is the dependent variable, xi is the i-th explanatory variable, a is a constant, bi is the i-th regression coefﬁcient, and e is the error term. The logit of y is the natural logarithm of the odds, which is: logit(y) = ln[p/(1 − p)], (2) where p is the probability of the occurrence of y, and p/(1 − p) is the odds. Combining Equations (1) and (2), the probability p can be rewritten as:

exp(logit(y)) exp(a + b x + b x +, b x + ···) p = = 1 1 2 2 3 3 . (3) 1 + exp(logit(y)) 1 + exp(a + b1x1 + b2x2+, b3x3 + ···)

Herein, we adopted the logit model developed by [29] to identify the potential areas for landslide. Twelve related environmental factors (explanatory variables)—eleven were numerical and the other one was categorical—were adopted. The eleven numerical variables are elevation, slope, sin of aspect, cos of aspect, proﬁle curvature, plan curvature, topographic wetness index (TWI), soil water index (SWI), distance to river, distance to ridge, and distance to road, respectively. The categorical variable is geological conditions. The geological data with a scale of 1:50000 published in 2000 was provided by the Central Geological Survey Bureau, Taiwan. There are twelve geological conditions in the Shihmen Reservoir watershed [26]. The coefﬁcients for the variables are determined from the landslide inventory during heavy rainfall events. For Shihmen Reservoir watershed, the logistic regression function is expressed as [26]

logit(y) = −8 × 10−4[elevation in meter] + 0.058[slope in degree] + 0.076[sin of aspect] −0.45[cos of aspect] − 1.84[proﬁle curvature in 1/meter] −1.67[plan curvature in 1/meter] + 0.25[TWI in meter] −9 × 10−4[distance to river in kilometer] (4) +5 × 10−3[distance to ridge inkilo meter] +4 × 10−5[distance to road in meter] + [geological conditions] +0.00657[SWI in meter] − 7.534.

By using Equation (3), the logit(y) can be converted to the probability p value of landslide occurrence. According to the results from previous literature [e.g., 26,38], the threshold of the p value set to 0.5 can obtain the best area concordance ratio (>70%), which is the overlapped landslide areas between the prediction and the total landslide area on the digital map divided by total landslide area on the digital map. Therefore, when the p value in this study was larger than 0.5, the cell was classiﬁed Water 2019, 11, 332 6 of 22 as a landslide cell. On the other hand, when the probability p of landslide occurrence was less than 0.5, the cell was regarded as stable. Based upon the analysis of landslide potential, the location and area of landslide can be obtained. Secondly, the landslide area needs to be converted to the landslide volume. In this study, the landslide volume-area relationship was given by [42]:

r VL = a × AL, (5) where VL is the landslide volume, AL is the landslide area, and a and r are the coefﬁcients related to the geological properties for the selected watershed. In the Shihmen Reservoir Watershed, the landslide volume-area relationship was obtained as shown below [26]:

1.179 2 VL = 0.458 × AL , R = 0.94, (6) where R2 is the determination coefﬁcient. To estimate the sediment amount delivered to streams, the Sediment Delivery Ratio (SDR) between landslide runout distance and the landslide sediment brought to streams is required. In this study, the landslide runout distance L was given by Ikeya’s formula [43]:

0.42 L = 8.6·(VL tan θ) , (7)

SDR = (L − D)/L, (8) where θ is the landslide slope (degrees) and D (m) is the distance between the centroid of the landslide area and the nearest downslope stream channel. Landslide migration distance L (m) represents the maximum possible moving distance of the sediment produced in a newly added landslide area. Once the SDR value was obtained, we could estimate the sediment amount from landslide into streams. (ii) Sediment production from soil erosion: In addition to the landslide-derived sediment production, the soil loss from sheet and rill erosion also needs to be taken into account. In this study, MUSLE, an improved soil erosion over USLE and RUSLE, was adopted. In general, MUSLE can be expressed as follows [11]:

0.56 Vs = 11.8 Ve f f × Qp × Km × L × S × C × P, (9)

3 where Vs is sediment yield to the streams in metric tons, Ve f f is the effective runoff volume in m (the summation of the runoff obtained from the Tank model in each grid for a given rainfall event), Qp is the peak runoff ﬂowrate in m3 (the maximum value from the runoff hydrography obtained in the Tank model), Km is the soil erodibility factor, L and S are the slope length and gradient factor, C is the cover management, and P is the erosion control practice factor. In this study, the grid size derived from the digital elevation model (DEM) data provided by Center for Space and Remote Sensing Research, National Central University, Taiwan, was 40 m × 40 m, and the runoff and soil erosion were calculated in each grid and then summed up to the total runoff and soil erosion amount. The Km factor adopted ranged from 0.004 to 0.042 t-hr MJ−1 mm−1 [44,45]. The L × S values are given by:

X m L × S = × 65.41 sin2 θ × 4.56 sin θ + 0.065 , (10) 22.13 where X is the slope length in meters; θ is the angle of the slope in degrees; and m is 0.5 (the slope is 5% or more), 0.4 (on slopes of 3% to 5%), 0.3 (on slopes of 1% to 3%), and 0.2 (on slopes less than 1%) [36]. To determine the slope length X, the DEM data were used to delineate the watershed topography. In addition, a speciﬁed threshold needs to be assigned to distinguish between overland and channels. Water 2019, 11, 332 7 of 22

As suggested by Liu et al. [46], a threshold of 100 m can be used as a default value to evaluate the slope length in most areas. The C factor was converted from a land-use map in [45]. Assuming there is no protection measure, the P value is set to be 1 [44].

2.2.3. Flow and Sediment Transport Models in Streams In this study, two hydrodynamic and sediment transport models were used. The first one was quasi two-dimensional (2-D) hydraulic and sediment routing models called “Network of Stream Tube Model for Alluvial River Simulation (NETSTARS)”, which can provide the suspended sediment concentration and subsequently estimate sediment yields in the study areas. Though the NETSTARS model solves the 1-D flow and sediment governing equations, the NETSTARS applies the concept of stream tubes—imaginary tubes bounded by streamlines—to perform lateral variability in hydraulics and morphodynamics [47]. However, the NETSTARS cannot simulate second currents, recirculating flow, or lateral erosion of stream banks. The second one: Sedimentation and River Hydraulics-Two-Dimensional River Flow Modeling (SRH-2D) model developed by Bureau of Reclamation, Department of the Interior, U.S. was applied to provide the spatial-temporal flow field and bed erosion and deposition patterns in detail for the downstream reach of the Shihmen Reservoir Watershed. The model solves the depth-averaged St. Venant equations and uses a flexible mesh that may contain arbitrarily shaped cells [48]. In addition, the SRH-2D model adopts very robust and stable numerical schemes with a seamless wetting-drying algorithm. Therefore, the SRH-2D model can simultaneously model all flow regimes (sub-, super-, and trans-critical flows), both steady and unsteady flows, and handle flows over dry surfaces [49]. Furthermore, the mobile-bed sediment transport module in the SRH-2D model can predict vertical stream bed changes by tracking multi-size, non-equilibrium sediment transport for suspended, mixed, and bed loads, and for cohesive and non-cohesive sediments [50].

2.2.4. Stability of Stream Reaches In this study, three indicators associated with (i) erosion and deposition depth on channel beds, (ii) shear stress on channel wet boundary (channel bed and channel bank), and (iii) probability of slope failure were used to evaluate the stability in different stream reaches. The ﬁrst two indicators can be obtained from the SRH-2D model, and the third one was calculated using the landslide model. The evaluation system including the three indicators for stream stability are described below. (i) Erosion and deposition depth on channel beds: According to simulation results from the SRH-2D model, the erosion and deposition depths on channel beds can be obtained. When the erosion or deposition depths range between −1 m (erosion) and 1 m (deposition), the stream is in the safest conditions. As the erosion or deposition depths become larger, the threat to stability of stream and adjacent hydraulic structures such as embankment, dam, groin, etc. is also increasing. Therefore, the indicator Sd according to bed erosion or deposition depths that vary from 0 to 1 is given in Table1 to reﬂect the threat level on stream stability.

Table 1. Sd for erosion and deposition depth on streambeds.

Erosion (Positive Value) or Deposition Erosion or Deposition S Value Depth (Negative Value) (m) d Degree on Streambeds <−3 1 Intense −2–−3 0.7 Obvious −1–−2 0.5 Distinguishable −1–1 0 Insigniﬁcant 1–2 0.5 Distinguishable 2–3 0.7 Obvious > 3 1 Intense Water 2019, 11, 332 8 of 22

(ii) Flow-induced shear stress on channel wet boundary (channel bed and channel bank):

The ﬂow-induced shear stress τw on the channel boundary can be used to evaluate the erosion potential to the streambed and stream bank. Herein, the maximum shear stress τw,max during the intense rainfall events output from the SRH-2D model was divided into six classes, which were = ∼ N ∼ N ∼ N ∼ N ∼ N τw,max 0 250 m2 , 250 500 m2 , 500 750 m2 , 750 1000 m2 , 1000 2000 m2 , > N and 2000 m2 . The corresponding value Sτ for the ﬂow-induced shear stress τw,max to the six classes is listed in Table2.

Table 2. Sτ for ﬂow-induced shear stress on streambed boundary

N τw,max ( m2 ) Sτ Value Possibility to Damage the River Structures 0–250 0 Very unlikely 250–500 0.2 Unlikely 500–750 0.4 Low possibility 750–1000 0.6 Possibly 1000–2000 0.8 Likely >2000 1 Very likely

(iii) Probability of slope failure: When the slope near the riverbank fails, the streambed stability and adjacent hydraulic structures in streams may be affected or even damaged. Therefore, the probability of slope failure needs to be taken into account when we analyzed the stream stability. According to the landslide inventory collected in Typhoon Aere, 2004, the averaged landslide migration distance L in the Shihmen Reservoir watershed is approximately 250 m [51]. It can be inferred that within 250 m on both sides of the stream bank, the slope failure may affect stability of streambed and hydraulic structures in streams. Therefore, the probability of slope failure was estimated using the logistic regression method in the 250 m range around streams. Similar to Sd and Sτ, the factor Sp is dependent on the probability of slope failure in the 250 m range around the streams. Five classes of probability of slope failure p were proposed to determine the value of the factor Sp, as listed in Table3.

Table 3. Sp for probability of slope failure near the streambank.

Probability of Slope Failure Sp Value Possibility to Damage the River Structures 0–0.1 0 Very unlikely 0.1–0.2 0.25 Unlikely 0.2–0.3 0.50 Low possibility 0.3–0.4 0.75 Possible >0.4 1 Very likely

The stability factor Stab can be obtained by simply taking the average of the above-mentioned three indicators in streams, which is

Sd + Sτ + Sp Stab = . (11) 3 Since there was no sufﬁcient history data about the streambank failure or damage of river revetment, it is unlikely to give a speciﬁc threshold of the Stab value to determine if the stream would become unstable at the present stage. The magnitude of this value represents the relative stability of different regions of the stream course. The larger the Stab value, the more chances the stream reaches could become unstable during intense rainfall events. Therefore, the Stab value could help us identify the stream reaches with high chances to become unstable suffering heavy rain. Water 2019, 11, 332 9 of 22

2.2.5. Data Source In this study, the digital elevation model (DEM) data with the grid size of 40 m × 40 m was provided by Center for Space and Remote Sensing Research, National Central University, Taiwan. The boundary conditions such upstream ﬂow discharge, downstream water level, and sediment particle size of suspended and bed materials for numerical modelling were provided by the Water Resources Agency of Taiwan. The rainfall data were obtained from the Central Weather Bureau of Taiwan. The bathymetry was interpolated from a river cross-section survey (the interval between each cross-section is 500–600 m) conducted by the Northern Region Water Resource Ofﬁce, the Water Resources Agency of Taiwan. In 2009, two river cross-section surveys were conducted on January 12 and August 29, respectively. For the case of January 12, 2009, the survey was from St.3 to St.8, while for the case of August 29, 2009, the survey was from St.2 to St.8, i.e., extension from St.2 to St.3.

3. Results and Discussion In this section, the hydrodynamic and sediment transport models were firstly calibrated and verified to ensure their capability on providing accurate and reliable flow and sediment transport information. Typhoon Sinlaku and Morakot events were chosen for model calibration and verification. Typhoon Sinkalu stroke Taiwan in September 2008 with heavy rain and strong winds and produced 887 mm rainfall in total, the maximum cumulative rainfall for the past 15 years in the study areas. Typhoon Morakot swept Taiwan from August 7–9, 2009, carried record-breaking rainfall that the maximum five days cumulative rainfall reached 3,004.5 mm in Southern Taiwan, and triggered massive landslides in mountainous area. Though Typhoon Morakot only brought 510 mm rainfall in the study area, it is still valuable for understanding the spatiotemporal variations of flow discharge and sediment movement. The developed model was then applied for different rainfall scenarios to obtain flow and sediment discharge, and sediment erosion and deposition on streambeds. Finally, the stability of stream reaches were evaluated.

3.1. Sediment Yielding Prediction Using NETSTARS Model This study firstly simulated the river discharge and sediment discharge during Typhoon Sinlaku in the Lofu Bridge (St.8 on Figure1), which is regarded as the reservoir entrance, to calibrate the NETSTARS model. Then, the Typhoon Morakot event was used to verify the calibrated model. The following simulation conditions were used in the calibration and verification processes: (1) Simulation reach was between the Yufeng Station (St.2 on Figure1) to the Lofu Bridge (St.8 on Figure1) of the Dahan river. The river cross-section data input to the NETSTARS model was from the survey (from St.2 to St.8) conducted on 29 August 2009; (2) Upstream boundary conditions were inflow river discharge and sediment discharge, which were obtained by the tank model and the sediment production model, respectively; (3) Downstream boundary conditions were water levels measured in the Hsiayun station (St.7 on Figure1). The Manning’s n values were initially based on the mean sediment diameter d50 and then adjusted to calibrate the developed model until an acceptable agreement between the measured and modelled flow and sediment discharges was reached. The calibrated Manning’s n values range from 0.027–0.039. Figure3 provides the calibration and verification results of sediment discharge in the Lofu Bridge during Typhoons Sinlaku and Morakot. The simulation and measurement data showed a similar trend, and the difference of the peak sediment discharge between the simulation and measurement data was approximately 7–15%. Considering the difficulties in the measurement and prediction of sediment transport, the model performance was reasonable and acceptable for engineering applications [52,53]. Water 2019, 11, 332 10 of 22 Water 2018, 10, x FOR PEER REVIEW 10 of 22

450,000 120,000

) Measurement ) Measurement -1 400,000 Simulation -1 hr Simulation · 100,000 350,000

300,000 80,000

250,000 60,000 200,000

150,000 40,000

100,000 Sediment Discharge (ton DischargeSediment Sediment Discharge (ton·hr DischargeSediment 20,000 50,000

0 0 1 5 9 1317212529333741454953576165697377818589 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 Time (hr) Time (hr)

(a) Calibrated sediment discharge in Typhoon (b) Verified sediment discharge in Typhoon Morakot Sinlaku

Figure 3. Calibration and verification results for the sediment discharge. Figure 3. Calibration and verification results for the sediment discharge. 3.2. Streambed Eroison and Depostion Patterns Using the SRH-2D Model 3.2. StreambedIn addition Eroison to the and NETSTARDepostion Patterns model, Using the SRH-2D the SRH-2D model Model was used to simulate the erosion andIn deposition addition patternsto the NETSTAR in the downstream model, the SRH-2D reaches ofmodel the Shihmenwas usedReservoir to simulate Watershed, the erosion where and depositionthe bathymetry patterns data in (from the downstream St.3 to St.8 on reaches Figure of1) werethe Shihmen interpolated Reservoir from Watershed, the river cross-section where the bathymetrysurvey. The data total (from length St.3 of to simulatedSt.8 on Figure reach 1) we wasre approximatelyinterpolated from 28 the km river with cross-section an average slopesurvey. of The0.013. total The length bathymetry of simulated for the simulatedreach was reachapproximately and the seven 28 km cross-sections with an average used toslope compare of 0.013. with The the bathymetrymeasurements for arethe shown simulated in Figure reach4. Thereand werethe seven totally cross-sections 14074 unstructured used to triangular compare grids with in the measurementscomputational domain,are shown which in Figure is better 4. There fit to the were geometry totally of14074 the studyunstructured reach. The triangular boundary grids conditions in the computationalabout upstream domain, inflow, lateralwhich inflowis better from fit Shankuangto the geometry Creek (St.4of the on Figurestudy 1reach.), downstream The boundary water conditionslevel, bed changesabout duringupstream the studyinflow, period, lateral and infl sedimentow from particle Shankuang size of suspendedCreek (St.4 and on bed Figure materials 1), downstreamwere provided water by the level, Water bed Resources changes Agency during ofthe Taiwan. study Theperiod, Manning’s and sedimentn values calibratedparticle size in theof suspendedNETSTARS and model bed was materials used in were the SRH-2D provided model. by the The Water simulation Resources time wasAgency from of January Taiwan. 12, 2009The Manning’sto August 29,n values 2009, coveringcalibrated the in date the ofNETSTARS Typhoon Morakot model was passage used and in correspondingthe SRH-2D model. to the dateThe simulationof the two time cross-section was from survey January conducted. 12, 2009 to InAugust the SRH-2D 29, 2009, model, covering four the sediment date of Typhoon transport Morakot capacity passageequations and could corresponding be selected. to Based the date upon of suggestionthe two cross-section from Lai et survey al. [54 ],conducted. the equation In proposedthe SRH-2D by model,Parker four [55] wassediment adopted transport in this capacity study. In equation the equations could (see be Equation selected. 10 Based in Lai upon et al. suggestion [54]), the hiding from Laifactor et al. and [54], reference the eq non-dimensionaluation proposed shearby Parker stress [55] (reference was adopted Shield’s in parameter) this study. need In the to beequation determined (see Equationin advance. 10 in The Lai hiding et al. [54]), factor the accounts hiding forfactor the and processes reference of non-uniform non-dimensional sediment shear movement, stress (reference where Shield’sthe larger parameter) particles haveneed higherto be determined chance exposed in advance. to the flow,The hiding and the factor required accounts critical for shear the processes stress for ofsediment non-uniform incipient sediment motions movement, is reduced where [56]. the On thelarger contrary, particles the have smaller higher particles chance more exposed likely to to the be flow,covered and by the larger required particles critical would shear be stress initiated for bysediment larger critical incipient shear motions stress is from reduced the sediment [56]. On beds. the contrary,The reference the smaller non-dimensional particles more shear likely stress wasto be used covered to calculate by larger the particles initiation would of motion be initiated of sediment by largerin a fluid critical flow shear [50]. Accordingstress from to the Lai sediment et al. [54 ],beds. the hidingThe reference factor and non-dimensional reference non-dimensional shear stress shearwas usedstress to were calculate set to the 0.905 initiation and 0.0385 of motion for streams of sediment in Taiwan. in a fluid flow [50]. According to Lai et al. [54], the hiding factor and reference non-dimensional shear stress were set to 0.905 and 0.0385 for streams in Taiwan.

Water 2019, 11, 332 11 of 22 Water 2018, 10, x FOR PEER REVIEW 11 of 22

Figure 4. The bathymetry and seven cross-sections used to compare with the measurements for the Sedimentation and River Hydraulics-Two-DimensionalHydraulics-Two-Dimensional River Flow ModelingModeling (SRH-2D)(SRH-2D) model.model. The coordinate is basedbased uponupon thethe TWD97/TM2TWD97/TM2 system. system.

Figure5 5provides provides the the flow flow field field and and contours contours of flow of velocityflow velocity at 10 am.at 10 On am. August On August 8, 2009 (during8, 2009 the(during Typhoon the Typhoon Morakot event).Morakot The event). results The showed results that showed the maximum that the velocitymaximum reached velocity 20 msreached−1 when 20 the𝑚𝑠 overflow when the occurred overflow at theoccurred Ronghua at the Dam Ronghua (Figure Dam5a).Figure (Figure5b 5a). shows Figure that 5b the shows SRH-2D thatmodel the SRH- is also2D model able to is simulate also able and to identify simulate the and flow identify field in the the flow main field channel in the and main adjacent channel floodplains and adjacent (white color).floodplains Figure (white6 displays color). the Figure flow 6 field displays and thethe bedflow erosion field and and the deposition bed erosion patterns and deposition of the simulated patterns reachesof the simulated at 10 am reaches on August at 10 8, am 2009, on where August the 8, position 2009, where values the represent position bedvalues erosion represent and thebed negative erosion valuesand the denote negative bed values deposition. denote The bed maximum deposition bed. The erosion maximum and deposition bed erosion depths and candeposition reach 6 depths m and 2can m, reach respectively, 6 m and thus2 m, implyingrespectively, the thus significant implying redistribution the significant of bottomredistribution sediments of bottom during sediments typhoon events.during Furthermore,typhoon events. the areasFurthermore, with greater the flowareas velocity with greater usually flow suffer velocity to bed erosion.usually suffer to bed erosion.The comparisons between the measurement and simulation at seven cross sections are shown in Figure7. In Section 1 (Figure7a), the difference of the maximum erosion depth between the simulation and measurements is 1.5 m, and the simulations and measurements both show erosion on the right bank of the stream. For Sections 2 and 3 (Figure7b,c), although the maximum difference between the simulation and measurements in bed elevation reached 2 m, the trend of sediment erosion and deposition between the measurement and simulation is still similar. In Section 4 (Figure7d), the simulation and measurement both exhibit the obvious bed erosion at the right bank of the channel. The simulation and measurement are close with each other, indicating that the simulation can well reproduce the sediment transport processes. For Section 5 (Figure7e), on the left bank of the channel, the simulation and measurement have the same trend on bed changes. However, the simulation displays approximately 10 m erosion on the left bank of the channel. For Section 6 (Figure7f), the bed erosion and deposition between the simulations and measurements are similar. Due to the channel shrinkage at Section 7 (Figure7g), some discrepancy between the simulation and measurement was found. The discrepancy between the numerical results and measurements can be attributed to several possible aspects: (1) The bathymetry in the computational domain was interpolated from river cross-section survey; (2) the two-dimensional depth-averaged model could not completely reflect the

Water 2019, 11, 332 12 of 22 three-dimensional ﬂow patterns in the study site; and (3) the temporal and spatial variations of the Manning’sWater 2018, 10n, xvalues FOR PEER were REVIEW not taken into account. Overall, the differences between the measurements12 of 22 andWater numerical 2018, 10, x resultsFOR PEER are REVIEW still acceptable in terms of engineering applications. The SRH-2D model12 stillof 22 can reproduce the bed erosion and depositional patterns close to the measurement in a general sense.

Figure 5. Flow field and contours of flow velocity at 10 am on August 8, 2009 (during the Typhoon Figure 5. Flow ﬁeld and contours of ﬂow velocity at 10 am on August 8, 2009 (during the Typhoon Morakot event). MorakotFigure 5. event). Flow field and contours of flow velocity at 10 am on August 8, 2009 (during the Typhoon Morakot event).

FigureFigure 6.6. FlowFlow ﬁeldfield andand erosionerosion andand depositiondeposition patternspatterns atat 1010 amam onon AugustAugust 8,8, 20092009 (during(during thethe TyphoonTyphoonFigure 6. Morakot Morakot Flow field event). event). and erosion and deposition patterns at 10 am on August 8, 2009 (during the Typhoon Morakot event). The comparisons between the measurement and simulation at seven cross sections are shown in FigureThe 7. comparisonsIn Section 1 between (Figure the7a), measurement the difference and of simu thelation maximum at seven erosion cross sectionsdepth betweenare shown the in simulationFigure 7. Inand Section measurements 1 (Figure is 1.7a),5 m, the and differ the simulationsence of the and maximum measurements erosion both depth show between erosion theon thesimulation right bank and of measurements the stream. For is Sections 1.5 m, and 2 and the 3 simulations (Figure 7b and and c), meas althoughurements the bothmaximum show differenceerosion on betweenthe right thebank simulation of the stream. and For measurements Sections 2 and in 3 bed (Figure elevation 7b and reached c), although 2 m, the the maximum trend of differencesediment between the simulation and measurements in bed elevation reached 2 m, the trend of sediment

Water 2018, 10, x FOR PEER REVIEW 13 of 22 erosion and deposition between the measurement and simulation is still similar. In Section 4 (Figure 7d), the simulation and measurement both exhibit the obvious bed erosion at the right bank of the channel. The simulation and measurement are close with each other, indicating that the simulation can well reproduce the sediment transport processes. For Section 5 (Figure 7e), on the left bank of the channel, the simulation and measurement have the same trend on bed changes. However, the simulation displays approximately 10 m erosion on the left bank of the channel. For Section 6 (Figure 7f), the bed erosion and deposition between the simulations and measurements are similar. Due to the channel shrinkage at Section 7 (Figure 7g), some discrepancy between the simulation and measurement was found. The discrepancy between the numerical results and measurements can be attributed to several possible aspects: (1) The bathymetry in the computational domain was interpolated from river cross-section survey; (2) the two-dimensional depth-averaged model could not completely reflect the three-dimensional flow patterns in the study site; and (3) the temporal and spatial variations of the Manning’s n values were not taken into account. Overall, the differences between the measurements and numerical results are still acceptable in terms of engineering Waterapplications.2019, 11, 332 The SRH-2D model still can reproduce the bed erosion and depositional patterns 13close of 22 to the measurement in a general sense.

540 540

535 535

530 530

525 525

520 520 Bed elevation (m) Bed elevation Bed elevation (m) Bed elevation 515 515

510 510 0 40 80 120 160 0 50 100 150 200 250 300 350 400 Distance from the left bank (m) Distance from the left bank (m)

(a) Section 1 (b) Section 2

530 530

525 525

520 520

515 515

Bed elevation (m) elevation Bed 510 (m) Bed elevation 510

505 505 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 Distance from the left bank (m) Distance from the left bank (m)

515 510

510 505 500 505 495 500 490 Bed elevation (m) Bed elevation 495 (m) Bed elevation 485

490 480 0 40 80 120 160 0 50 100 150 200 250 300 Distance from the left bank (m) Distance from the left bank (m)

WaterWater 20182018,, 1010,, xx FORFOR PEERPEER REVIEWREVIEWWater 2018 , 10, x FOR PEER REVIEW 1414 ofof 2222 14 of 22 (e) Section 5 (f) Section 6

475475 475

470470 470

465465 465

460460 460 Bed elevation (m) Bed elevation

Bed elevation (m) Bed elevation 455 Bed elevation (m) Bed elevation Bed elevation (m) Bed elevation 455455

450450 450 040801201600408012016004080120160 DistanceDistance fromfrom thethe leftleft bankbank (m)(m)Distance from the left bank (m)

((gg)) SectionSection 77 (g) Section 7

FigureFigure 7.7. ComparisonsComparisons betweenbetweenFigure thethe 7. measurementComparisonsmeasurement betweenandand simulationsimusimu thelationlation measurement atat sevenseven crosscross and sections.sections.simusections.lation Note:Note: at seven thethe cross sections. Note: the blackblack lineline ‘‘ ’ ’ indicatesindicates thethe black initialinitial line bed bed ‘ location ’ indicates ( (12(1212 Januarythe January initial 2009); 2009); bed location thethe greygrey ( 12 linelineline January ‘‘ ’ ’ is 2009);the the measuredmeasured the grey line ‘ ’ is the measured bedbed locationlocation (29(29 AugustAugust 2009);2009);bed the thelocation dashdash line(29line August ‘‘ ’ ’ isis 2009); the the simulated simulated the dash bed bedline location location‘ ’ is the (29 (29 simulated August August 2009).2009). bed location (29 August 2009).

3.3.3.3. ModelModel ScenarioScenario 3.3. Model Scenario

3.3.1. Sediment Yield Predictions Under Different Rainfall Return Periods 3.3.1.3.3.1. SedimentSediment YieldYield PredictionsPredictions UndeUnderr DifferentDifferent RainfallRainfall ReturnReturn PeriodsPeriods The developed model was used to estimate sediment yields of the Shihmen Reservoir Watershed TheThe developeddeveloped modelmodel waswas usedused toto estimateestimate sedimesedimentnt yieldsyields ofof thethe ShihmenShihmen ReservoirReservoir WatershedWatershed under different rainfall scenarios. Herein, the rainfall data were designed as a 24-hour hyetograph underunder differentdifferent rainfallrainfall scenarios.scenarios. Herein,Herein, thethe rarainfallinfall datadata werewere designeddesigned asas aa 24-hour24-hour hyetographhyetograph with the return periods of 25, 50, 100, and 200 years, respectively. By using the logistic model with withwith thethe returnreturn periodsperiods ofof 25,25, 50,50, 100,100, andand 200200 yearyears,s, respectively.respectively. ByBy usingusing thethe logisticlogistic modelmodel withwith the given hyetograph, the probability p distribution of landslide occurrence can be obtained. Figure thethe givengiven hyetograph,hyetograph, thethe probabilityprobability pp distributiondistribution ofof landslidelandslide occurrenceoccurrence cancan bebe obtained.obtained. FigureFigure 8a provides the landslide probability map with the rainfall return period of 200 years. The results 8a8a providesprovides thethe landslidelandslide probabilityprobability mapmap withwith thethe rainfallrainfall returnreturn periodperiod ofof 200200 years.years. TheThe resultsresults reveal that the landslide-prone areas are mostly located on concave slopes or near the stream courses. revealreveal thatthat thethe landslide-pronelandslide-prone areasareas areare mostlymostly localocatedted onon concaveconcave slopesslopes oror nearnear thethe streamstream courses.courses. As mentioned above, the p value of 0.5 (50%) was set as a threshold, and the p value larger than 0.5 AsAs mentionedmentioned above,above, thethe pp valuevalue ofof 0.50.5 (50%)(50%) waswas setset asas aa threshold,threshold, andand thethe pp valuevalue largerlarger thanthan 0.50.5 can be regarded as a landslide area. Therefore, the reactivated landslide areas can be estimated and cancan bebe regardedregarded asas aa landslidelandslide area.area. Therefore,Therefore, ththee reactivatedreactivated landslidelandslide areasareas cancan bebe estimatedestimated andand provided in Table 4. It is shown that the Shihmen, Shankuang, and Kaoyi watersheds are on much providedprovided inin TableTable 4.4. ItIt isis shownshown thatthat thethe ShihmeShihmen,n, Shankuang,Shankuang, andand KaoyiKaoyi watershedswatersheds areare onon muchmuch lower levels of reactivated landslide areas than the rest of the watersheds. The possible explanations lowerlower levelslevels ofof reactivatedreactivated landslidelandslide areasareas thanthan ththee restrest ofof thethe watersheds.watersheds. TheThe possiblepossible explanationsexplanations are as follows. For the Shihmen watershed, the slope—the most important factor for areare asas follows.follows. ForFor thethe ShihmenShihmen watershed,watershed, thethe slope—theslope—the mostmost importantimportant factorfactor forfor landslide—is gentlest among the seven sub-watersheds. Furthermore, the smaller elevation landslide—islandslide—is gentlestgentlest amongamong thethe sevenseven sub-watersheds.sub-watersheds. Furthermore,Furthermore, thethe smallersmaller elevationelevation in the Shihmen watershed can also lead to lighter rainfall and lessen weathering effects on inin thethe ShihmenShihmen watershedwatershed cancan alsoalso leadlead toto lighterlighter rainfallrainfall andand lessenlessen weatheringweathering effectseffects onon soil. For the Shankuang and Kaoyi watersheds, the slope and elevation are moderate among soil. For the Shankuang and Kaoyi watersheds, the slope and elevation are moderate among soil.soil. ForFor thethe ShankuangShankuangthese andand KaoyiKaoyiwatersheds. watersheds,watersheds, However, thethe slopeslope there andand are elevationelevation less developments areare moderatemoderate of among amongagriculture, business, and thesethese watersheds.watersheds. However,However,industry therethere in the areare Shankuang lessless dedevelopmentsvelopments and Kaoyi watersheds, ofof agriculture,agriculture, thus indicatingbusiness,business, betterandand conservation of soil industryindustry inin thethe ShankuangShankuangand andwaterand KaoyiKaoyi and watersheds,watersheds,less chances thusthusfor landslide indicatingindicating occu betterbetterrrence. conservationconservation On the other ofof soilsoil hand, for the Hsiayun andand waterwater andand lessless chanceschanceswatershed, forfor landslidelandslide although occuoccu the rrence.rrence.elevation OnOn and thethe slopeotherother arehand,hand, also forfor moderate, thethe HsiayunHsiayun the region has become a watershed,watershed, althoughalthough thethepopular elevationelevation site forandand tourism, slopeslope areare mountain alsoalso moderate,moderate, training, thethe and regionregion camp has hascenters. becomebecome The a anewly-built road and popularpopular sitesite forfor tourism,tourism,associated mountainmountain facility training,training, for tourists andand campcamp may centers. centers.have caused TheThe newly-builtnewly-builtthe undercuts roadroad of slope, andand thus increasing the associatedassociated facilityfacility forfor touriststouristsfrequency maymay of havelandslidehave causedcaused occurrence thethe undercutsundercuts [57]. ofof slope,slope, thusthus increasingincreasing thethe frequencyfrequency ofof landslidelandslide occurrenceoccurrence [57].[57].

Water 2019, 11, 332 14 of 22

3.3. Model Scenario

3.3.1. Sediment Yield Predictions Under Different Rainfall Return Periods The developed model was used to estimate sediment yields of the Shihmen Reservoir Watershed under different rainfall scenarios. Herein, the rainfall data were designed as a 24-hour hyetograph with the return periods of 25, 50, 100, and 200 years, respectively. By using the logistic model with the given hyetograph, the probability p distribution of landslide occurrence can be obtained. Figure8a provides the landslide probability map with the rainfall return period of 200 years. The results reveal that the landslide-prone areas are mostly located on concave slopes or near the stream courses. As mentioned above, the p value of 0.5 (50%) was set as a threshold, and the p value larger than 0.5 can be regarded as a landslide area. Therefore, the reactivated landslide areas can be estimated and provided in Table4. It is shown that the Shihmen, Shankuang, and Kaoyi watersheds are on much lower levels of reactivated landslide areas than the rest of the watersheds. The possible explanations are as follows. For the Shihmen watershed, the slope—the most important factor for landslide—is gentlest among the seven sub-watersheds. Furthermore, the smaller elevation in the Shihmen watershed can also lead to lighter rainfall and lessen weathering effects on soil. For the Shankuang and Kaoyi watersheds, the slope and elevation are moderate among these watersheds. However, there are less developments of agriculture, business, and industry in the Shankuang and Kaoyi watersheds, thus indicating better conservation of soil and water and less chances for landslide occurrence. On the other hand, for the Hsiayun watershed, although the elevation and slope are also moderate, the region has become a popular site for tourism, mountain training, and camp centers. The newly-built road and associated facility for tourists may have caused the undercuts of slope, thus increasing the frequency of landslide occurrence [57].

Table 4. Predicted reactivated landslide areas under different rainfall return periods.

Reactivated Landslide Area (ha) Sub-watershed 25-year 50-year 100-year 200-year Shihmen 2.56 9.12 21.28 55.20 Hsiayun 40.00 92.32 188.16 332.16 Kaoyi 4.48 14.24 34.08 87.04 Lengchiad 32.96 67.36 117.92 228.32 Shankuang 3.68 9.92 27.84 64.48 Yufeng 72.80 172.48 335.68 618.08 Hsiuluan 30.08 81.12 170.08 324.96 Total 186.56 446.56 895.04 1710.24

By applying Equation (6), the landslide areas can be converted to the landslide volume. The soil erosion from the hillslope was calculated using the MUSLE in Equation (9). Figure8b,c provide the distribution of landslide-driven erosion and soil erosion in the Shihmen Reservoir Watershed. Table5 lists the landslide-driven erosion and soil erosion in the Shihmen Reservoir Watershed. The erosion from landslide accounts for a large proportion (> 96%) of sediment production, while the soil erosion plays an insigniﬁcant role on sediment supply. According to the ﬁeld measurement from Tsai et al. [58] and Cs 137 measurement from Chiu [59], the ratio of soil loss from landslide and watershed soil erosion was approximately 10:1, close to the results in this study. As the rainfall amount increases, landslides will occur more frequently, thus leading to the decreasing contribution of soil erosion to total sediment supply. Water 2018, 10, x FOR PEER REVIEW 15 of 22

Table 4. Predicted reactivated landslide areas under different rainfall return periods.

Reactivated landslide area (ha) Sub-watershed 25-year 50-year 100-year 200-year Shihmen 2.56 9.12 21.28 55.20 Hsiayun 40.00 92.32 188.16 332.16 Kaoyi 4.48 14.24 34.08 87.04 Lengchiad 32.96 67.36 117.92 228.32 Shankuang 3.68 9.92 27.84 64.48 Yufeng 72.80 172.48 335.68 618.08 Hsiuluan 30.08 81.12 170.08 324.96 Total 186.56 446.56 895.04 1710.24

By applying Equation (6), the landslide areas can be converted to the landslide volume. The soil erosion from the hillslope was calculated using the MUSLE in Equation (9). Figure 8b,c provide the distribution of landslide-driven erosion and soil erosion in the Shihmen Reservoir Watershed. Table 5 lists the landslide-driven erosion and soil erosion in the Shihmen Reservoir Watershed. The erosion from landslide accounts for a large proportion (> 96%) of sediment production, while the soil erosion plays an insignificant role on sediment supply. According to the field measurement from Tsai et al. [58] and Cs 137 measurement from Chiu [59], the ratio of soil loss from landslide and watershed soil erosion was approximately 10:1, close to the results in this study. As the rainfall amount increases, landslidesWater 2019, 11 will, 332 occur more frequently, thus leading to the decreasing contribution of soil erosion15 of to 22 total sediment supply.

Water 2018, 10, x FOR PEER REVIEW (a) Probability of landslide occurrence 16 of 22

(b) Landslide-driven erosion (c) Soil erosion

Figure 8. Results for the return periods of 200 years in the Shihmen Reservoir Watershed. Figure 8. Results for the return periods of 200 years in the Shihmen Reservoir Watershed.

Table 5. Predicted sediment production under different rainfall return periods.

Landslide-driven erosion Soil erosion Sub- (×𝟏𝟎𝟒 m3) (×𝟏𝟎𝟒 m3) watershed 25-year 50-year 100-year 200-year 25-year 50-year 100-year 200-year Shihmen 4.85 17.28 41.89 116.11 1.38 1.68 2.00 2.34 Hsiayun 94.99 218.80 485.00 985.40 1.91 2.30 2.71 3.13 Kaoyi 9.38 28.43 77.33 206.92 0.99 1.18 1.39 1.61 Lengchiad 70.17 141.20 258.63 539.92 1.98 2.35 2.74 3.15 Shankuang 6.96 19.35 56.40 137.42 1.71 2.05 2.38 2.71 Yufeng 170.06 412.46 852.37 1715.79 5.48 6.58 7.76 9.01 Hsiuluan 68.50 179.63 406.84 831.61 1.38 1.68 2.00 2.34 Total 424.90 1017.14 2178.46 4533.18 14.83 17.82 20.98 24.29

According to the landslide and soil erosion data and applying Equation (8) to estimate the sediment delivered to streams, the flow and sediment discharges in the Shihmen Reservoir Watershed could be estimated from the NETSTARS model. For the 25-year, 50-year, 100-year, and 200-year return period rainfall events, the sediment discharge from the upstream of the Yufeng station was the greatest. Figure 9 displays the 24-hour 200-year return period design hyetograph, simulated river discharge, and sediment discharge at the upstream inlet (Yufeng station, St.2 on Figure 1) and downstream outlet (Lofu Bridge, St.8 on Figure 1). The sediment contribution from the upstream watershed of the Yufeng station was approximately 55% of the total sediment yield for the 200-year return period rainfall. As the rainfall amount decreased to the 25-year return period level, the ratio of the sediment brought from the Yufeng station to the total sediment supply also declined to 36%. The second greatest sediment contribution was from the upstream of the Shankuang station (St.3 on Figure 1), where the sediment contribution was about 7~9% to the total sediment supply in the Shihmen Reservoir Watershed. In addition, the simulation results also show that 48% (25-year

Water 2019, 11, 332 16 of 22

Table 5. Predicted sediment production under different rainfall return periods.

Landslide-driven Erosion (×104 m3) Soil Erosion (×104 m3) Sub-watershed 25-year 50-year 100-year 200-year 25-year 50-year 100-year 200-year Shihmen 4.85 17.28 41.89 116.11 1.38 1.68 2.00 2.34 Hsiayun 94.99 218.80 485.00 985.40 1.91 2.30 2.71 3.13 Kaoyi 9.38 28.43 77.33 206.92 0.99 1.18 1.39 1.61 Lengchiad 70.17 141.20 258.63 539.92 1.98 2.35 2.74 3.15 Shankuang 6.96 19.35 56.40 137.42 1.71 2.05 2.38 2.71 Yufeng 170.06 412.46 852.37 1715.79 5.48 6.58 7.76 9.01 Hsiuluan 68.50 179.63 406.84 831.61 1.38 1.68 2.00 2.34 Total 424.90 1017.14 2178.46 4533.18 14.83 17.82 20.98 24.29

According to the landslide and soil erosion data and applying Equation (8) to estimate the sediment delivered to streams, the flow and sediment discharges in the Shihmen Reservoir Watershed could be estimated from the NETSTARS model. For the 25-year, 50-year, 100-year, and 200-year return period rainfall events, the sediment discharge from the upstream of the Yufeng station was the greatest. Figure9 displays the 24-hour 200-year return period design hyetograph, simulated river discharge, and sediment discharge at the upstream inlet (Yufeng station, St.2 on Figure1) and downstream outlet (Lofu Bridge, St.8 on Figure1). The sediment contribution from the upstream watershed of the Yufeng station was approximately 55% of the total sediment yield for the 200-year return period rainfall. As the rainfall amount decreased to the 25-year return period level, the ratio of the sediment brought from the Yufeng station to the total sediment supply also declined to 36%. The second greatest sediment contribution was from the upstream of the Shankuang station (St.3 on Figure1), where the sediment contribution was about 7~9% to the total sediment supply in the Shihmen Reservoir Watershed. In addition, the simulation results also show that 48% (25-year return period rainfall)~78% (200-year return rainfall) of the sediment produced by landslide and hillslope soil erosion were brought to the reservoir areas, while 22–52% of sediment still remained at foot of the slope and the streams. The amount of sediment delivered to the downstream increased with the increasing rainfall and resulting flow discharge. However, there are still certain amounts of sediments left in the upstream watershed. If the next intense rainfall event occurs, the induced flow discharge could reactivate these remaining sediments, which could possibly lead to secondary sediment disasters such as turbidity current in the reservoir areas. Water 2018, 10, x FOR PEER REVIEW 17 of 22 return period rainfall)~78% (200-year return rainfall) of the sediment produced by landslide and hillslope soil erosion were brought to the reservoir areas, while 22–52% of sediment still remained at foot of the slope and the streams. The amount of sediment delivered to the downstream increased with the increasing rainfall and resulting flow discharge. However, there are still certain amounts of sediments left in the upstream watershed. If the next intense rainfall event occurs, the induced flow

Waterdischarge2019, 11 could, 332 reactivate these remaining sediments, which could possibly lead to secondary17 of 22 sediment disasters such as turbidity current in the reservoir areas.

Figure 9. Result forfor thethe return return periods periods of of 200 200 years years in thein the Shihmen Shihmen Reservoir Reservoir Watershed. Watershed. The blueThe solidblue solidand dot and lines dot representlines represent the ﬂow the discharge flow discharge at the downstreamat the downstream (Lofu Bridge) (Lofu andBridge) upstream and upstream (Yufeng (YufengStation), Station), and the and brown thesolid brown and solid dot and lines dot denote lines denote the sediment the sediment discharge discharge at the downstreamat the downstream (Lofu (LofuBridge) Bridge) and upstream and upstream (Yufeng (Yufeng Station). Station).

3.3.2. Stream Stability under Different Rainfall Return Periods According toto the the ﬂow flow and and sediment sediment discharge discharge from thefrom three-layer the three-layer Tank and Tank sediment and production sediment productionmodels with models the combination with the combination of the SRH-2D of the model,SRH-2D the model, erosion the erosion and deposition and deposition patterns patterns in the indownstream the downstream of the Shihmenof the Shihmen Reservoir Reservoir Watershed Watersh withed different with different rainfall returnrainfall periods return can periods be obtained. can be obtained.Figure 10 Figureshows 10 the shows sediment the sediment erosion and erosion deposition and deposition amount of amount the study of the streams study forstreams the 200-year for the 200-yearreturn period return rainfall. period Therainfall. results The reveal results that reveal the amountthat the ofamount sediment of sediment deposited deposited in streams in isstreams much ismore much than more the than amount the ofamount sediment of sediment eroded from eroded streams, from indicatingstreams, indicating that the net that amount the net of amount sediment of sedimentis increased is increased in streams. in This streams. corresponds This correspon to the conclusionds to the conclusion drawn from drawn the NETSTARS from the NETSTARS model that modelcertain that amounts certain of amounts sediments of are sediments left in the are upstream left in the watershed. upstream In watershed. addition, FigureIn addition, 10 also Figure displays 10 alsothat thedisplays depth that of sedimentthe depth deposition of sediment in depositio most streamn in reachesmost stream is larger reaches than 3is m.larger than 3 m.

Water 2019, 11, 332 18 of 22

Water 2018, 10, x FOR PEER REVIEW 18 of 22

Figure 10.10. The sedimentsediment erosion (a)) andand depositiondeposition ((b)) amountamount ofof thethe studystudy streamstream forfor thethe 200-year200-year returnreturn periodperiod rainfall.rainfall.

Figure 1111 provides provides the the flow-induce flow-induce shear shear stress, stress, probability probability of slope of slope failure, failure, and the and stability the stability factor Stabfactorover 𝑆𝑡𝑎𝑏 the over stream the reaches. stream reaches. The results The show results that show the that maximum the maximum flow-induced flow-induced shear stresses shear stresses fall in a 2 2 widefall in range a wide from range less from than less 500 N/mthan 500to N/m larger2 to than larger 2000 than N/m 2000(see N/m Figure2 (see 11 Figurea). Larger 11a). shear Larger stresses shear usuallystresses occurusually at theoccur narrow at the streams narrow or streams concave or banks concave of streams. banks of The streams. probability The probability of slope failure of slope also showfailure great also spatialshow differencegreat spatial (Figure difference 11b). Comparing(Figure 12b). Figure Comparing 10, Figure Figure 11a,b, 10, the 11a distributions and 11b, the of thedistributions three indicators of the usedthree for indicators stream stability used for estimation stream stability are not estimation identical. Onlyare not the identical. maximum Only erosion the depthmaximum is relevant erosion to depth the maximum is relevant flow-induced to the maximum shear flow-induced stress. The threeshear indicatorsstress. The can three be indicators regarded almostcan be independentregarded almost with independent each other. Therefore, with each it isother. reasonable Therefore, to use it theis reasonable three indicators to use to the evaluate three theindicators stream to stability. evaluate According the stream to stability. the Stab Accordingvalues, the to relative the 𝑆𝑡𝑎𝑏 stream values, stability the relative is shown stream in Figure stability 11c, whereis shown the higherin Figure values 11c, imply where that the the higher stream values has greater imply probability that the stream to become has unstablegreater probability under intense to rainfallbecome events.unstable The under locations intense of rainfall buildings events. (purple The colorlocations in Figure of buildings 11c) and (purple stream color revetments in Figure (grey 11c) colorand stream in Figure revetments 11c), also (grey provided color in in Figure Figure 11 11c),c, reveal also provided that some in people Figure live 11c, near reveal the that study some stream people but therelive near are verythe study few revetments stream but constructed there are very along few the revetments stream. If the constructed stream reaches along near the thestream. buildings, If the theystream could reaches easily near become the unstable,buildings, and they stream could protection easily become engineering unstable, such and as revetmentsstream protection may be necessary.engineering During such as heavy revetments rainfall periods,may be necessary. the local residents During livingheavy nearrainfall the periods, unstable the reaches local mayresidents need toliving be evacuated. near the unstable Therefore, reaches Figure may11c canneed also to be be a evacuated. reference to Therefore, the water resourcesFigure 11c authorities can also asbe toa whichreference stream to the reaches water areresources priorities authorities for improvement as to which and stream management. reaches are priorities for improvement and management.

Water 2019, 11, 332 19 of 22 Water 2018, 10, x FOR PEER REVIEW 19 of 22

FigureFigure 11.11. Flow-inducedFlow-induced shearshear stressstress ((aa),), probabilityprobability ofof slopeslope failurefailure ((bb),), andand streamstream stabilitystability factorfactor ((cc)) ofof thethe studystudy streamstream forfor thethe 200-year200-yearreturn return period period rainfall. rainfall.

4.4. ConclusionsConclusions InIn thisthis study, study, hydrodynamic hydrodynamic and and sediment sediment transport transport modules modules were were added added in an integratedin an integrated model tomodel estimate to estimate sediment sediment yields and yields identify and theidentify possibly the unstablepossibly streamunstable reaches stream in reaches the Shihmen in the Reservoir Shihmen 2 Watershed,Reservoir Watershed, a large-scale a watershedlarge-scale (>100watershed km ) (>100 in Taiwan. km2) Throughin Taiwan. calibration Through and calibration verification, and theverification, hydrodynamic the hydrodynamic and sediment and transport sediment model transport successfully model successfully reproduced reproduced river flow river patterns, flow flowpatterns, discharge, flow discharge, sediment discharge,sediment discharge, and streambed and streambed changes during changes typhoon during events. typhoon Therefore, events. theTherefore, developed the developed model is feasible model is and feasible capable and toca providepable to provide the flow-induced the flow-induced boundary boundary shear stress shear andstress accurate and accurate estimates estimates of sediments of sediments yield causedyield caused by two by mechanisms two mechanisms such such as landslide as landslide and soiland 2 erosionsoil erosion and flowand flow patterns patterns in a large-scalein a large-scale watershed watershed (>100 (>100 km ). km Combining2). Combining with with the bed the erosionbed erosion and depositionand deposition depths, depths, flow-induced flow-induced shear shear stress, stress, and probability and probability of slope of failure slope failure within 250within mof 250 stream m of reaches,stream reaches, an evaluation an evaluation system wassystem proposed was proposed to assess to the assess relative the streamrelative stability. stream stability. TheThe modelmodel was then applied applied to to different different scenarios scenarios such such as as rainfall rainfall events events of of25, 25, 50, 50,100, 100, and and 200 200years years return return period period in inthe the study study areas. areas. The The resu resultslts show show that that the the sediment induced byby landslidelandslide accountsaccounts forfor moremore thanthan 96%96% ofof sedimentsediment supplysupply inin thethe ShihmenShihmen ReservoirReservoir Watershed.Watershed. TheThe sedimentsediment contributioncontribution fromfrom thethe upstreamupstream ofof thethe YufengYufeng stationstation isis approximatelyapproximately 36–55%36–55% ofof thethe totaltotal sedimentsediment yieldyield forfor differentdifferent scenarios.scenarios. TheyThey alsoalso indicateindicate thatthat 22–52%22–52% ofof sedimentsediment stillstill remainremain atat footfoot ofof thethe slopeslope andand thethe streams,streams, whichwhich willwill becomebecome aa potentialpotential sourcesource forfor hazardhazard inin thethefuture. future. UsingUsing thethe evaluationevaluation system,system, the the relatively relatively stability stability of of stream stream reaches reaches can can be identified,be identified, which which could could provide provide the waterthe water resource resource authorities authorities for reference for reference to take to precautionarytake precautionary measures measures on the on stream the stream reaches reaches with high-degreewith high-degree instability instability before before the hazards the hazards occur. occur.

AuthorAuthor Contributions:Contributions:Conceptualization, Conceptualization, Y.-J.C., Y.-J.C., H.-Y.L. H.-Y.L. and Y.-T.L.;and Y.-T Methodology,.L.; Methodology, Y.-J.C.and Y.-J.C. Y.-T.L.; and Software, Y.-T.L.; Y.-J.C.,Software, T.-L.W., Y.-J.C., J.Y. T.-L.W., and Y.-T.L.; J.Y. and Data Y.-T.L.; curation, Data Y.-J.C. curation, and T.-L.W.;Y.-J.C. and Writing-Original T.-L.W.; Writing- DraftOriginal Preparation, Draft Preparation, Y.-J.C. and H.-Y.L.;Y.-J. C. and Writing-Review H.-Y.L.; Writing-Review and Editing, Y.Y.and andEditing, Y.-T.L.; Y.Y. Funding and Y.-T.L.; Acquisition, Funding Y.-J.C. Acquisit andion, H.-Y.L. Y.-J. C. and H.-Y.L. Funding: This research was funded by National Key Research and Development Project of China [grant numberFunding: 2016YFC0402406, This research was 2016YFC1401404], funded by National and Key National Research Science and Development Council of Project Taiwan of [grantChina [grant number number NSC 105-2221-E-002-063-MY3].2016YFC0402406, 2016YFC1401404], and National Science Council of Taiwan [grant number NSC 105-2221-E- 002-063-MY3]. Conﬂicts of Interest: The authors declare no conﬂict of interest. Conflicts of Interest: The authors declare no conflict of interest.

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