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

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1387 ◽  
Author(s):  
Yi-Hsin Liu ◽  
Dong-Huang Li ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Grid Cell ◽  
Extreme Rainfall ◽  
Rainfall Erosivity ◽  
Grid Cells ◽  
Cell Method ◽  
Slope Unit ◽  
Shihmen Reservoir ◽  
Reservoir Watershed ◽  
Northern Taiwan

Soil erosion is a global problem that will become worse as a result of climate change. While many parts of the world are speculating about the effect of increased rainfall intensity and frequency on soil erosion, Taiwan’s mountainous areas are already facing the power of rainfall erosivity more than six times the global average. To improve the modeling ability of extreme rainfall conditions on highly rugged terrains, we use two analysis units to simulate soil erosion at the Shihmen reservoir watershed in northern Taiwan. The first one is the grid cell method, which divides the study area into 10 m by 10 m grid cells. The second one is the slope unit method, which divides the study area using natural breaks in landform. We compared the modeling results with field measurements of erosion pins. To our surprise, the grid cell method is much more accurate in predicting soil erosion than the slope unit method, although the slope unit method resembles the real terrains much better than the grid cell method. The average erosion pin measurement is 6.5 mm in the Shihmen reservoir watershed, which is equivalent to 90.6 t ha−1 yr−1 of soil erosion.

scholarly journals Estimating sediment delivery ratio by stream slope and relief ratio

2018 ◽  
Vol 192 ◽  
pp. 02040 ◽  
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen
Keyword(s):  
Soil Erosion ◽  
Storage Capacity ◽  
Length Ratio ◽  
Delivery Ratio ◽  
Sediment Delivery Ratio ◽  
Sediment Delivery ◽  
Transportation Process ◽  
Shihmen Reservoir ◽  
Reservoir Watershed ◽  
Steep Terrain

Nowadays, the storage capacity of a reservoir reduced by sediment deposition is a concern of many countries in the world. Therefore, understanding the soil erosion and transportation process is a significant matter, which helps to manage and prevent sediments entering the reservoir. The main objective of this study is to examine the sediments reaching the outlet of a basin by empirical sediment delivery ratio (SDR) equations and the gross soil erosion. The Shihmen reservoir watershed is used as the study area. Because steep terrain is a characteristic feature of the study area, two SDR models that depend on the slope of the mainstream channel and the relief-length ratio of the watershed are chosen. It is found that the Maner (1958) model, which uses the relief-length ratio, is the better model of the two. We believe that this empirical research improves our understanding of the sediment delivery process occurring in the study area.

scholarly journals Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1808 ◽  
Author(s):  
Yi-Chin Chen ◽  
Ying-Hsin Wu ◽  
Che-Wei Shen ◽  
Yu-Jia Chiu
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
Landslide Susceptibility ◽  
Delivery Ratio ◽  
Mountainous Area ◽  
Sediment Discharge ◽  
Rainfall Runoff ◽  
Sediment Production ◽  
Shihmen Reservoir ◽  
Reservoir Watershed

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.

scholarly journals Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed

2019 ◽  
Vol 11 (2) ◽  
pp. 355 ◽  
Author(s):  
Bor-Shiun Lin ◽  
Chun-Kai Chen ◽  
Kent Thomas ◽  
Chen-Kun Hsu ◽  
Hsing-Chuan Ho
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Large Scale ◽  
Chemical Properties ◽  
Past Research ◽  
Area Of Interest ◽  
K Factor ◽  
Shihmen Reservoir ◽  
The Difference ◽  
Reservoir Watershed

The estimation of soil erosion in Taiwan and many countries of the world is based on the widely used universal soil loss equation (USLE), which includes the factor of soil erodibility (K-factor). In Taiwan, K-factor values are referenced from past research compiled in the Taiwan Soil and Water Conservation Manual, but there is limited data for the downstream area of the Shihmen reservoir watershed. The designated K-factor from the manual cannot be directly applied to large-scale regional levels and also cannot distinguish and clarify the difference of soil erosion between small field plots or subdivisions. In view of the above, this study establishes additional values of K-factor by utilizing the double rings infiltration test and measures of soil physical–chemical properties and increases the spatial resolution of K-factor map for Shihmen reservoir watershed. Furthermore, the established values of K-factors were validated with the designated value set at Fuxing Sanmin from the manual for verifying the correctness of estimates. It is found that the comparative results agree well with established estimates within an allowable error range. Thus, the K-factors established by this study update the previous K-factor system and can be spatially estimated for any area of interest within the Shihmen reservoir watershed and improving upon past limitations.

scholarly journals Comparing watershed soil erosion of Taiwan and Thailand

2018 ◽  
Vol 192 ◽  
pp. 02041
Author(s):  
Yi-Hsin Liu ◽  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Jatuwat Wattanasetpong ◽  
Uma Seeboonruang
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Soil Erodibility ◽  
Erosion Model ◽  
Shihmen Reservoir ◽  
Soil Erodibility Factor ◽  
Tropical Watersheds ◽  
Reservoir Watershed ◽  
Soil Loss Equation ◽  
Steepness Factor

Tropical watersheds in Taiwan and Thailand face the same severe soil erosion problem that is increasing at an alarming rate. In order to evaluate the severity of soil erosion, we quantitatively investigate the issue using a common soil erosion model (Universal Soil Loss Equation, USLE) on the Shihmen reservoir watershed of Taiwan and the Lam Phra Ploeng basin of Thailand, and compare their respective erosion factors. The results show an interesting contrast between the two watersheds. Some of the factors (rainfall factor, slope-steepness factor) are higher in the Shihmen reservoir watershed, while others (soil erodibility factor, cover and management factor) are higher in the Lam Phra Ploeng basin. The net result is that these factors cancel each other out, and the amount of soil erosion of the two watersheds are very similar at 68.03 t/ha/yr and 67.57 t/ha/yr, respectively.

scholarly journals Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

2019 ◽  
Vol 11 (13) ◽  
pp. 3615 ◽  
Author(s):  
Kieu Anh Nguyen ◽  
Walter Chen ◽  
Bor-Shiun Lin ◽  
Uma Seeboonruang ◽  
Kent Thomas
Keyword(s):  
Random Forest ◽  
Soil Erosion ◽  
Random Sampling ◽  
Training Data ◽  
Stratified Random Sampling ◽  
Rill Erosion ◽  
Random Forest Algorithm ◽  
Testing Data ◽  
Shihmen Reservoir ◽  
Reservoir Watershed

Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.

ASSESSMENT OF SOIL EROSION USING FOURNIER INDEXES TO ESTIMATE RAINFALL EROSIVITY

2019 ◽  
Vol 18 (8) ◽  
pp. 1739-1745 ◽  
Author(s):  
Gabriel Lazar ◽  
Alina Maria Coman ◽  
Georgiana Lacatusu ◽  
Ana Maria Macsim
Keyword(s):  
Soil Erosion ◽  
Rainfall Erosivity

scholarly journals Wavelet Support Vector Machines for Forecasting Precipitation in Tropical Cyclones: Comparisons with GSVM, Regression, and MM5

2012 ◽  
Vol 27 (2) ◽  
pp. 438-450 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Statistical Models ◽  
Mesoscale Model ◽  
Gaussian Kernel ◽  
Support Vector ◽  
State University ◽  
Rainfall Prediction ◽  
Fifth Generation ◽  
Shihmen Reservoir ◽  
Reservoir Watershed ◽  
Lag Times

Abstract This study presents two support vector machine (SVM) based models for forecasting hourly precipitation during tropical cyclone (typhoon) events. The two SVM-based models are the traditional Gaussian kernel SVMs (GSVMs) and the advanced wavelet kernel SVMs (WSVMs). A comparison between the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) and statistical models, including SVM-based models and linear regressions (regression), was made in terms of performance of rainfall prediction at the Shihmen Reservoir watershed in Taiwan. Data from 73 typhoons affecting the Shihmen Reservoir watershed were included in the analysis. This study designed six attribute combinations with different lag times for the forecast target. The modified RMSE, bias, and estimated threat score (ETS) results were employed to assess the predicted outcomes. Results show that better attribute combinations for typhoon climatologic characteristics and typhoon precipitation predictions occurred at 0-h lag time with modified RMSE values of 0.288, 0.257, and 0.296 in GSVM, WSVM, and the regression, respectively. Moreover, WSVM having average bias and ETS values close to 1.0 gave better predictions than did the GSVM and regression models. In addition, Typhoons Zeb (1998) and Nari (2001) were selected for comparison between the MM5 model output and the developed statistical models. Results showed that the MM5 tended to overestimate the peak and cumulative rainfall amounts while the statistical models were inclined to yield underestimations.

scholarly journals Description and Analysis of a 20-Year (1960-79) Digital Ice-Concentration Database for the Great Lakes of North America

1983 ◽  
Vol 4 ◽  
pp. 14-18 ◽  
Author(s):  
Raymond A. Assel
Keyword(s):  
North America ◽  
Great Lakes ◽  
Surface Area ◽  
Information Service ◽  
Grid Cell ◽  
Ice Cover ◽  
Unit Surface Area ◽  
Grid Cells ◽  
Data Set ◽  
Ice Concentration

A digital ice-concentration database spanning 20 years (1960 to 1979) was established for the Great Lakes of North America. Data on ice concentration, i.e. the percentage of a unit surface area of the lake that is ice-covered, were abstracted from over 2 800 historic ice charts produced by United States and Canadian government agencies. The database consists of ice concentrations ranging from zero to 100% in 10% increments for individual grid cells of size 5 × 5 km constituting the surface area of each Great Lake. The data set for each of the Great Lakes was divided into half-month periods for statistical analysis. Maxinium, minimum, median, mode, and average ice-concentrations statistics were calculated for each grid cell and half-month period. A lakewide average value was then calculated for each of the half-month ice-concentration statistics for all grid cells for a given lake. Ice-cover variability and the normal extent and progression of the ice cover is discussed within the context of the lakewide averaged value of the minimum and maximum ice concentrations and the lakewide averaged value of the median ice concentrations, respectively. Differences in ice-cover variability among the five Great Lakes are related to mean lake depth and accumulated freezing degree-days. A Great Lakes ice atlas presenting a series of ice charts which depict the maximum, minimum, and median icecover concentrations for each of the Great Lakes for nine half-monthly periods, starting the last half of December and continuing through the last half of April will be published in 1983 by the National Oceanic and Atmospheric Administration (NOAA). The database will be archived at the National Snow and Ice Data Center of the National Environmental Satellite Data and Information Service (NESDIS) in Boulder, Colorado, USA, also in 1983.

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