Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Liu, Yi Hsin; Li, Dong Huang; Chen, Walter; Lin, Bor-Shiun; Seeboonruang, Uma; Tsai, Fuan

doi:10.3390/w10101387

Cited by 22 publications

(27 citation statements)

References 29 publications

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“…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 K m factor adopted ranged from 0.004 to 0.042 t-hr MJ −1 mm −1 [44,45]. The L × S values are given by:…”

Section: Hillside Sediment Production Modelmentioning

confidence: 99%

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

Chiu

Lee

Wang

et al. 2019

Water

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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.

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Section: Hillside Sediment Production Modelmentioning

confidence: 99%

“…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].…”

Section: Hillside Sediment Production Modelmentioning

confidence: 99%

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

Chiu

Lee

Wang

et al. 2019

Water

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“…However, for other watersheds, all factors can be easily obtained in a similar way. One can also refer to other ways for soil erosion estimation (e.g., [50]). Wischmeier and Smith [51] found that soil erosion is highly correlated with the multiplication of the cumulative rainfall energy and maximum 30-min rainfall intensity.…”

Section: Soil Erosion Predictionmentioning

confidence: 99%

Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

Chen

Shen³

et al. 2018

Water

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Qualifying sediment dynamic in a reservoir watershed is essential for water resource management. This study proposed an integrated model of Grid-based Sediment Production and Transport Model (GSPTM) at watershed scale to evaluate the dynamic of sediment production and transport in the Shihmen Reservoir watershed in Taiwan. The GSPTM integrates several models, revealing landslide susceptibility and processes of rainfall-runoff, sediment production from landslide and soil erosion, debris flow and mass movement, and sediment transport. For modeling rainfall-runoff process, the tanks model gives surface runoff volume and soil water index as a hydrological parameter for a logistic regression-based landslide susceptibility model. Then, applying landslide model with a scaling relation of volume and area predicts landslide occurrence. The Universal Soil Loss Equation is then used for calculating soil erosion volume. Finally, incorporating runoff-routing algorithm and the Hunt's model achieves the dynamical modeling of sediment transport. The landslide module was calibrated using a well-documented inventory during 10 heavy rainfall or typhoon events since 2004. A simulation of Typhoon Morakot event was performed to evaluate model's performance. The results show the simulation agrees with the tendency of runoff and sediment discharge evolution with an acceptable overestimation of peak runoff, and predicts more precise sediment discharge than rating methods do. In addition, with clear distribution of sediment mass trapped in the mountainous area, the GSPTM also showed a sediment delivery ratio of 30% to quantify how much mass produced by landslide and soil erosion is still trapped in mountainous area. The GSPTM is verified to be useful and capable of modeling the dynamic of sediment production and transport at watershed level, and can provide useful information for sustainable development of Shihmen Reservoir watershed.

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“…Chiu et al [8] used 137Cs radionuclide collected from 60 hillslope sampling sites of the basin of the Shihmen reservoir to determine the soil erosion rate and found it to be one or two orders of magnitude lower than predicted by USLE. Recently, Liu et al [9] improved the analysis of the Shihmen reservoir watershed using the slope unit method in addition to the common grid cell approach. The results were verified with erosion pin measurements.…”

Section: Background and Introductionmentioning

confidence: 99%

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

Nguyen

Chen

Lin³

et al. 2020

Sustainability

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This study continues a previous study with further analysis of watershed-scale erosion pin measurements. Three machine learning (ML) algorithms—Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Artificial Neural Network (ANN)—were used to analyze depth of erosion of a watershed (Shihmen reservoir) in northern Taiwan. In addition to three previously used statistical indexes (Mean Absolute Error, Root Mean Square of Error, and R-squared), Nash–Sutcliffe Efficiency (NSE) was calculated to compare the predictive performances of the three models. To see if there was a statistical difference between the three models, the Wilcoxon signed-rank test was used. The research utilized 14 environmental attributes as the input predictors of the ML algorithms. They are distance to river, distance to road, type of slope, sub-watershed, slope direction, elevation, slope class, rainfall, epoch, lithology, and the amount of organic content, clay, sand, and silt in the soil. Additionally, measurements of a total of 550 erosion pins installed on 55 slopes were used as the target variable of the model prediction. The dataset was divided into a training set (70%) and a testing set (30%) using the stratified random sampling with sub-watershed as the stratification variable. The results showed that the ANFIS model outperforms the other two algorithms in predicting the erosion rates of the study area. The average RMSE of the test data is 2.05 mm/yr for ANFIS, compared to 2.36 mm/yr and 2.61 mm/yr for ANN and SVM, respectively. Finally, the results of this study (ANN, ANFIS, and SVM) were compared with the previous study (Random Forest, Decision Tree, and multiple regression). It was found that Random Forest remains the best predictive model, and ANFIS is the second-best among the six ML algorithms.

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Soil Erosion Modeling and Comparison Using Slope Units and Grid Cells in Shihmen Reservoir Watershed in Northern Taiwan

Cited by 22 publications

References 29 publications

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

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

Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan

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