scholarly journals Adapting the WEPP hillslope model to predict unpaved road soil erosion in southern China

2021 ◽  
Author(s):  
Longxi Cao ◽  
Ting Zhang ◽  
Yi Wang
Keyword(s):  
South China ◽  
Soil Loss ◽  
Laser Scanning ◽  
Southern China ◽  
Critical Shear Stress ◽  
Red Soil ◽  
Unpaved Roads ◽  
Erosion Risk ◽  
Low Efficiency ◽  
Road Segments

Process-based erosion models are efficient tools that can be used to predict where and when erosion occurs. On unpaved roads that have been recognized as important sediment sources, soil loss along road segments should be precisely predicted. This study was performed using the hillslope version of the Water Erosion Prediction Project (WEPP) to estimate soil loss from 20 typical road segments in the red soil region of South China. Terrestrial laser scanning (TLS)-measured soil losses were used to validate the model simulations. The results showed that the WEPP model could reasonably predict the total soil loss in relatively short (less than 100 m) and gentle (slope gradient lower than 10%) road segments. In contrast, the WEPP-simulated soil loss was underestimated for long or steep road segments. Detailed outputs along roads revealed that most of the peak soil loss rates could not be adequately calculated. The linear critical shear stress and the sediment equilibrium theory in the WEPP model for soil detachment simulation might be responsible for the underestimation. Additionally, the lack of upslope flow and the curved road tortuosity were found to be connected to the relatively low efficiency of the model outputs. Nevertheless, the WEPP simulation could accurately fit the trend of soil loss variation along road segments despite underestimation. Furthermore, the simulated results could provide a reliable prediction of the maximum soil loss positions. Therefore, the WEPP model could be adopted to evaluate the erosion risk of unpaved roads in the red soil region of South China.

Modelling soil erosion on unpaved road surface with process-based model and terrestrial laser scanning

2021 ◽  
Author(s):  
Longxi Cao ◽  
Xin Li ◽  
Mingming Shi
Keyword(s):  
Soil Loss ◽  
Laser Scanning ◽  
Critical Shear Stress ◽  
Terrestrial Laser Scanning ◽  
Red Soil ◽  
Runoff Generation ◽  
Unpaved Roads ◽  
Low Efficiency ◽  
Soil Losses ◽  
Road Segments

<p>Unpaved roads are common man-made features that distributed in agricultural or forest watersheds. Road construction will change the underlying topography and alter the surface hydrology, in turn would enhance runoff generation and result in high soil loss risk. Therefore road erosion should be considered as one of the main sediment sources and should be properly evaluated. Process-based erosion models provide efficient tools to precisely evaluate soil loss along unpaved roads. This study was performed using the hillslope version of the Water Erosion Prediction Project (WEPP) to estimate soil loss from 20 typical road segments in the red soil region of South China. The terrestrial laser scanning (TLS)-measured soil losses were used to validate the model simulations. The results showed that the WEPP model could reasonably predict the total soil loss in relatively short (less than 100m) and gentle (slope gradient lower than 10%) road segments. On the contrary, the WEPP simulated soil loss would result in underestimation for long or steep road segments. Detailed WEPP plot outputs along roads revealed that most of the peak soil loss rates cannot be adequately calculated as comparing with the TLS-measured values. The linear critical shear stress theory in WEPP model for soil detachment simulation might responsible for the underestimation of non-linear peaked soil losses in long or steep road segments. Meanwhile, the lack of upslope flow and the curved road tortuosity were also found to be connected to the relatively low efficiency of the model outputs. Nevertheless, the WEPP simulation could accurately fit the detailed trend of soil loss variation along road segment despite the underestimation. Furthermore, the simulated results could provide a reliable prediction of the maximum soil loss positions. Therefore, the WEPP model could be adopted to evaluate erosion risk of unpaved roads in red soil region of China.</p>

scholarly journals Evaluating of the spatial heterogeneity of soil loss tolerance and its effects on erosion risk in the carbonate areas of southern China

Solid Earth ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 661-669 ◽  
Author(s):  
Yue Li ◽  
Xiao Yong Bai ◽  
Shi Jie Wang ◽  
Luo Yi Qin ◽  
Yi Chao Tian ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Spatial Heterogeneity ◽  
Soil Loss ◽  
Carbonate Rock ◽  
Southern China ◽  
Clastic Rock ◽  
Erosion Risk ◽  
Soil Loss Tolerance ◽  
Loss Tolerance ◽  
The Relationship

Abstract. Soil loss tolerance (T value) is one of the criteria in determining the necessity of erosion control measures and ecological restoration strategy. However, the validity of this criterion in subtropical karst regions is strongly disputed. In this study, T value is calculated based on soil formation rate by using a digital distribution map of carbonate rock assemblage types. Results indicated a spatial heterogeneity and diversity in soil loss tolerance. Instead of only one criterion, a minimum of three criteria should be considered when investigating the carbonate areas of southern China because the one region, one T value concept may not be applicable to this region. T value is proportionate to the amount of argillaceous material, which determines the surface soil thickness of the formations in homogenous carbonate rock areas. Homogenous carbonate rock, carbonate rock intercalated with clastic rock areas and carbonate/clastic rock alternation areas have T values of 20, 50 and 100 t/(km2 a), and they are extremely, severely and moderately sensitive to soil erosion. Karst rocky desertification (KRD) is defined as extreme soil erosion and reflects the risks of erosion. Thus, the relationship between T value and erosion risk is determined using KRD as a parameter. The existence of KRD land is unrelated to the T value, although this parameter indicates erosion sensitivity. Erosion risk is strongly dependent on the relationship between real soil loss (RL) and T value rather than on either erosion intensity or the T value itself. If RL > > T, then the erosion risk is high despite of a low RL. Conversely, if T > > RL, then the soil is safe although RL is high. Overall, these findings may clarify the heterogeneity of T value and its effect on erosion risk in a karst environment.

scholarly journals A Remote Sensing Based Method to Detect Soil Erosion in Forests

2019 ◽  
Vol 11 (5) ◽  
pp. 513 ◽  
Author(s):  
Hanqiu Xu ◽  
Xiujuan Hu ◽  
Huade Guan ◽  
Bobo Zhang ◽  
Meiya Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Southern China ◽  
Red Soil ◽  
Forest Area ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Five Factors

Rainwater-induced soil erosion occurring in the forest is a special phenomenon of soil erosion in many red soil areas. Detection of such soil erosion is essential for developing land management to reduce soil loss in areas including southern China and other red soil regions of the world. Remotely sensed canopy cover is often used to determine the potential of soil erosion over a large spatial scale, which, however, becomes less useful in forest areas. This study proposes a new remote sensing method to detect soil erosion under forest canopy and presents a case study in a forest area in southern China. Five factors that are closely related to soil erosion in forest were used as discriminators to develop the model. These factors include fractional vegetation coverage, nitrogen reflectance index, yellow leaf index, bare soil index and slope. They quantitatively represent vegetation density, vegetation health status, soil exposure intensity and terrain steepness that are considered relevant to forest soil erosion. These five factors can all be derived from remote sensing imagery based on related thematic indices or algorithms. The five factors were integrated to create the soil erosion under forest model (SEUFM) through Principal Components Analysis (PCA) or a multiplication method. The case study in the forest area in Changting County of southern China with a Landsat 8 image shows that the first principal component-based SEUFM achieves an overall accuracy close to 90%, while the multiplication-based model reaches 81%. The detected locations of soil erosion in forest provide the target areas to be managed from further soil loss. The proposed method provides a tool to understand more about soil erosion in forested areas where soil erosion is usually not considered an issue. Therefore, the method is useful for soil conservation in forest.

scholarly journals Evaluating the spatial heterogeneity of soil loss tolerance and its effects on erosion risk in the carbonate areas of South China

2016 ◽  
Author(s):  
Yue Li ◽  
Xiaoyong Bai ◽  
Shijie Wang ◽  
Luoyi Qin ◽  
Yichao Tian ◽  
...  
Keyword(s):  
Spatial Heterogeneity ◽  
South China ◽  
Soil Loss ◽  
Carbonate Rock ◽  
Soil Thickness ◽  
Clastic Rock ◽  
Erosion Risk ◽  
Soil Loss Tolerance ◽  
Loss Tolerance ◽  
The Relationship

Abstract. Soil loss tolerance (T value) is the ultimate criterion to determine the necessity of erosion control measures and ecological restoration strategy. However, the validity of this criterion in subtropical karst regions is strongly disputed. In this study, T value is computed based on soil formation rate by using a digital distribution map of carbonate rock assemblage types. Results indicated spatial heterogeneity and diversity in such values; moreover, a minimum of three criteria should be considered instead of only one criterion when investigating the carbonate areas of South China given that the “one region, one T value” concept may not apply to this region. T value is proportionate to the amount of argillaceous material in formations that determine surface soil thickness in homogenous carbonate rock areas; such values are 20 and 50 t/(km2 · a) in carbonate rock intercalated with clastic rock areas and 100 t/(km2 · a) in carbonate/clastic rock alternation areas. These three areas are each extremely, severely, and moderately sensitive to soil erosion. This erosion is extreme in karst rocky desertification (KRD) land and reflects the degree of erosion risk. Thus, the relationship between T value and erosion risk is determined with KRD as a parameter. The existence of KRD land is unrelated to T value, although this parameter indicates erosion sensitivity. In fact, erosion risk is strongly dependent on the relationship between real soil loss (RL) and T value rather than on either erosion intensity or the T value itself. If RL >> T, then erosion risk is high despite a low RL. Conversely, if T >> RL, the soil is safe although RL is high. Overall, these findings may clarify T value heterogeneity and its effect on erosion risk in a karst eco-environment; hence, innovative technological assessment solutions need not be invented.

scholarly journals Response of runoff and soil loss to reforestation and rainfall type in red soil region of southern China

2010 ◽  
Vol 22 (11) ◽  
pp. 1765-1773 ◽  
Author(s):  
Zhigang Huang ◽  
Zhiyun Ouyang ◽  
Fengrui Li ◽  
Hua Zheng ◽  
Xiaoke Wang
Keyword(s):  
Soil Loss ◽  
Southern China ◽  
Red Soil ◽  
Rainfall Type ◽  
Runoff And Soil Loss

scholarly journals Analysis of Ownership Data from Consolidated Land Threatened by Water Erosion in the Vlára Basin, Slovakia

2020 ◽  
Vol 13 (1) ◽  
pp. 51
Author(s):  
Alexandra Pagáč Mokrá ◽  
Jakub Pagáč ◽  
Zlatica Muchová ◽  
František Petrovič
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Agricultural Land ◽  
Water Erosion ◽  
Land Consolidation ◽  
Land Fragmentation ◽  
Erosion Risk ◽  
Definition Of ◽  
The Relationship ◽  
Soil Loss Equation

Water erosion is a phenomenon that significantly damages agricultural land. The current land fragmentation in Slovakia and the complete ambiguity of who owns it leads to a lack of responsibility to care for the land in its current condition, which could affect its sustainability in the future. The reason so much soil has eroded is obvious when looking at current land management, with large fields, a lack of windbreaks between them, and no barriers to prevent soil runoff. Land consolidation might be the solution. This paper seeks to evaluate redistributed land and, based on modeling by the Universal Soil Loss Equation (USLE) method, to assess the degree of soil erosion risk. Ownership data provided information on how many owners and what amount of area to consider, while taking into account new conditions regarding water erosion. The results indicate that 2488 plots of 1607 owners which represent 12% of the model area are still endangered by water erosion, even after the completion of the land consolidation project. The results also presented a way of evaluating the territory and aims to trigger a discussion regarding an unambiguous definition of responsibility in the relationship between owner and user.

scholarly journals Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

2021 ◽  
Vol 13 (2) ◽  
pp. 844
Author(s):  
George Watene ◽  
Lijun Yu ◽  
Yueping Nie ◽  
Jianfeng Zhu ◽  
Thomas Ngigi ◽  
...  
Keyword(s):  
Rift Valley ◽  
Soil Loss ◽  
Large Change ◽  
Risk Category ◽  
Lake Nakuru ◽  
Erosion Risk ◽  
Erosion Rates ◽  
3.0 T ◽  
Great Rift Valley ◽  
Loss Rates

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (<10 t ha−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha−1 yr−1) while most of the ASAL districts presented erosion rates (<8 t ha−1 yr−1). Only 34% of all the protected areas were found to have erosion rates <10 t ha−1 yr−1 highlighting the need for effective anti-erosive measures.

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