scholarly journals Influence of Vegetation Coverage on Hydraulic Characteristics of Overland Flow

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1055
Author(s):  
Zekang Cai ◽  
Jian Wang ◽  
Yushuo Yang ◽  
Ran Zhang
Keyword(s):  
Reynolds Number ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Overland Flow ◽  
Resistance Coefficient ◽  
Vegetation Coverage ◽  
Hydraulic Characteristics ◽  
The Loess Plateau ◽  
Artificial Grass ◽  
The Relationship

Soil erosion is a major problem in the Loess Plateau (China); however, it can be alleviated through vegetation restoration. In this study, the overland flow on a slope during soil erosion was experimentally simulated using artificial grass as vegetation cover. Nine degrees of vegetation coverage and seven flow rates were tested in combinations along a 12° slope gradient. As the coverage degree increased, the water depth of the overland flow increased, but the flow velocity decreased. The resistance coefficient increased with increasing degree of coverage, especially after a certain point. The resistance coefficient and the Reynolds number had an inverse relationship. When the Reynolds number was relatively small, the resistance coefficient decreased faster; however, when it exceeded 600, the resistance coefficient decreased at a slower rate. A critical degree of vegetation cover was observed in the relationship between the resistance coefficient and submergence degree. When the degree of coverage was greater than 66.42%, the resistance coefficient first decreased and then increased with a higher submergence degree. Finally, the formula for the resistance coefficient under vegetation coverage was derived. This formula has a relatively high accuracy and can serve as a reference for predicting soil erosion.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

scholarly journals Overland Flow Resistance Law under Sparse Stem Vegetation Coverage

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1657
Author(s):  
Jingzhou Zhang ◽  
Shengtang Zhang ◽  
Si Chen ◽  
Ming Liu ◽  
Xuefeng Xu ◽  
...  
Keyword(s):  
Flow Resistance ◽  
Overland Flow ◽  
Vegetation Coverage ◽  
Dominant Factor ◽  
Roughness Coefficient ◽  
Hydraulic Parameters ◽  
Slope Condition ◽  
Manning Roughness ◽  
The Relationship ◽  
Manning Roughness Coefficient

To explore the characteristics of overland flow resistance under the condition of sparse vegetative stem coverage and improve the basic theoretical research of overland flow, the resistance characteristics of overland flow were systematically investigated under four slope gradients (S), seven flow discharges (Q), and six degrees of vegetation coverage (Cr). The results show that the Manning roughness coefficient (n) changes with the ratio of water depth to vegetation height (h/hv) while the Reynolds number (Re), Froude number (Fr), and slope (S) are closely related to vegetation coverage. Meanwhile, h/hv, Re, and Cr have strong positive correlations with n, while Fr and S have strong negative correlations with n. Through data regression analysis, a power function relationship between n and hydraulic parameters was observed and sensitivity analysis was performed. It was concluded that the relationship between n and h/hv, Re, Cr, Q, and S shows the same law; in particular, for sparse stem vegetation coverage, Cr is the dominant factor affecting overland flow resistance under zero slope condition, while Cr is no longer the first dominant factor affecting overland flow resistance under non-zero slope condition. In the relationship between n and Fr, Cr has the least effect on overland flow resistance. This indicates that when Manning roughness coefficient is correlated with different hydraulic parameters, the same vegetation coverage has different effects on overland flow resistance. Therefore, it is necessary to study overland flow resistance under the condition of sparse stalk vegetation coverage.

The Relationship Between Frictional Resistance and Roughness for Surfaces Smoothed by Sanding

2002 ◽  
Vol 124 (2) ◽  
pp. 492-499 ◽  
Author(s):  
Michael P. Schultz
Keyword(s):  
Reynolds Number ◽  
Frictional Resistance ◽  
Resistance Coefficient ◽  
Polished Surface ◽  
Average Height ◽  
Freestream Velocity ◽  
Number Range ◽  
Test Fixture ◽  
Plate Length ◽  
The Relationship

An experimental investigation has been carried out to document and relate the frictional resistance and roughness texture of painted surfaces smoothed by sanding. Hydrodynamic tests were carried out in a towing tank using a flat plate test fixture towed at a Reynolds number ReL range of 2.8×106−5.5×106 based on the plate length and freestream velocity. Results indicate an increase in frictional resistance coefficient CF of up to 7.3% for an unsanded, as-sprayed paint surface compared to a sanded, polished surface. Significant increases in CF were also noted on surfaces sanded with sandpaper as fine as 600-grit as compared to the polished surface. The results show that, for the present surfaces, the centerline average height Ra is sufficient to explain a large majority of the variance in the roughness function ΔU+ in this Reynolds number range.

How spatial vegetation distribution affects soil erosion and sediment transport

2021 ◽  
Author(s):  
Malte Kuegler ◽  
Thomas Hoffmann ◽  
Jana Eichel ◽  
Lothar Schrott ◽  
Juergen Schmidt
Keyword(s):  
Soil Erosion ◽  
Vegetation Cover ◽  
Vegetation Pattern ◽  
Vegetation Coverage ◽  
Future Research ◽  
Initial Increase ◽  
Vegetation Patterns ◽  
Erosion Rates ◽  
Catchment Areas ◽  
The Impact

&lt;p&gt;There are a multitude of factors that affect soil erosion and the process of sediment movement. One particular factor known to have a considerable impact is vegetation coverage within catchment areas.&amp;#160; Previous studies have examined the impact of vegetation cover on erosion.&amp;#160;However, there is a lack of research on how the spatial distribution of vegetation influences erosion rates.&lt;/p&gt;&lt;p&gt;A greater understanding of hillslope erosion is fundamental in modelling previous and future topographic changes under various climate conditions. Here, the physical based erosion model EROSION 3D &amp;#169; is used to evaluate the impact of a variety of vegetation patterns and degrees of vegetation cover on sediment erosion and transport. The model was applied on a natural catchment in La Campana (Central Chile). For this purpose, three different vegetation patterns were created: (i) random distribution, (ii) water-dependent distribution (TWIR) and (iii) banded vegetation pattern distribution. Additional to this, the areas covered by vegetation generated in the first step were expanded by steps of 10% [0...100%]. The Erosion3D &amp;#169; model then was applied on all vegetation patterns and degrees of cover.&lt;/p&gt;&lt;p&gt;Our results show an initial increase of soil erosion with increasing plant coverage within the catchment up to a certain cover threshold ranging between 10 and 40%. At larger vegetation cover soil erosion rates decline. The strength of increase and decline, as well as the cover-threshold is strongly conditioned by the spatial vegetation pattern. In the light of this, future research should pay particular attention to the properties of the plants and their distribution, not solely on the amount of biomass within catchment areas.&lt;/p&gt;

scholarly journals A Conversion Method to Determine Regional Vegetation Cover Factor from Standard Plots based on Large Sample Theory and TM Images: A Case Study in the Eastern Farming-pasture Ecotone of Northern China

2017 ◽  
Author(s):  
Degen Lin ◽  
Yuan Gao ◽  
Yaoyao Wu ◽  
Peijun Shi ◽  
Huiming Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Regional Scale ◽  
Strong Support ◽  
Vegetation Coverage ◽  
Large Sample ◽  
Coverage Ratio ◽  
Conversion Method ◽  
Large Sample Theory

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot directly use the C factor formula. That is because the C factor formula is obtained by experiment, and needs the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = &minus;0.03, df = 0.12, 2-tail significance p &lt; 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improves the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

scholarly journals A Conversion Method to Determine Regional Vegetation Cover Factor from Standard Plots based on Large Sample Theory and TM Images: A Case Study in the Eastern Farming-pasture Ecotone of Northern China

2017 ◽  
Author(s):  
Degen Lin ◽  
Yuan Gao ◽  
Yaoyao Wu ◽  
Peijun Shi ◽  
Huiming Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Vegetation Cover ◽  
Regional Scale ◽  
Strong Support ◽  
Vegetation Coverage ◽  
Large Sample ◽  
Coverage Ratio ◽  
Conversion Method ◽  
Large Sample Theory

The key to simulating soil erosion is to calculate the vegetation cover (C) factor. Methods that apply remote sensing to calculate C factor at regional scale cannot be directly using the C factor formula. That is because the C factor formula obtain by experiment, and need the coverage ratio data of croplands, woodlands and grasslands at standard plot scale. In this paper, we present a C factor conversion method from a standard plot to a km-sized grid based on large sample theory and multi-scale remote sensing. Results show that: 1) Compared with the existing C factor formula, our method is based on the coverage ratio of croplands, woodlands and grasslands on a km-sized grid, takes the C factor formula obtained from the standard plot experiment and applies it to regional scale. This method improves the applicability of the C factor formula, and can satisfy the need to simulate soil erosion in large areas. 2) The vegetation coverage obtained by remote sensing interpretation is significantly consistent (paired samples t-test, t = &minus;0.03, df = 0.12, 2-tail significance p &lt; 0.05) and significantly correlated with the measured vegetation coverage. 3) The C factor of the study area is smaller in the middle, southern and northern regions, and larger in the eastern and western regions. The main reason for that is the distribution of woodlands, the Hunshandake and Horqin sandy lands and the valleys affected by human activities. 4) The method presented in this paper is more meticulous than the C factor method based on the vegetation index, improved the applicability of the C factor formula, and can be used to simulate soil erosion on large scale and provide strong support for regional soil and water conservation planning.

scholarly journals Impact of Vegetation Cover Structure on Birds' Community at Tianfu National Wetland Park in Jiangsu Kunshan, China

2021 ◽  
Author(s):  
Fathielrahaman. H. Ajloon ◽  
Dong Xie ◽  
Shao Junxue ◽  
Zhang RuiTing ◽  
Aniefiok Ini Inayng
Keyword(s):  
Linear Relationship ◽  
Vegetation Cover ◽  
Diversity Index ◽  
Vegetation Coverage ◽  
Plant Abundance ◽  
Biological Communities ◽  
Avian Abundance ◽  
Avian Populations ◽  
The Relationship ◽  
Strong Linear Relationship

Abstract Vegetation cover has an essential role in wetland habitats in controlling avian populations throughout the world. The vegetation cover structure in grassland systems varies dramatically among seasons on the same sites. Variation in vegetation cover-abundance richness and diversity has been studied through one hundred forty-seven quadrate samples during summer and autumn, 2019, winter, and spring 2020. Avian spe cies richness and diversity were recorded during the same period. Meanwhile, correlation analysis results confirmed that: (1) there was no apparent seasonal difference in the abundance of vegetation cover while avian abundance was statistically different. (2) Plant abundance in summer was positively correlated with the number of avian, while in autumn it was negatively correlated. Plant and avian abundance at the genus level showed a positive correlation while maintaining a negative correlation at the speci es level (p < 0.05). However, during summer and autumn, a strong linear relationship exists between vegetation coverage and avian. The Shannon diversity index and Simpson diversity index have a positive linear relationship between vegetation coverage and a vian families and genera. Therefore, we conclude that vegetation coverage and richness significantly impact avian communities. We suggest further research into the relationship between other biological communities and farming practices in the wetlands.

Soil conservation on the Loess Plateau and the regional effect: impact of the ‘Grain for Green' Project

2018 ◽  
Vol 109 (3-4) ◽  
pp. 461-471
Author(s):  
Xiaofeng WANG ◽  
Feiyan XIAO ◽  
Xiaoming FENG ◽  
Bojie FU ◽  
Zixiang ZHOU ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Land Cover ◽  
Loess Plateau ◽  
Soil Conservation ◽  
Yellow River ◽  
Vegetation Coverage ◽  
The Yellow River ◽  
The Loess Plateau ◽  
Regional Effect ◽  
Grain For Green

ABSTRACTSoil conservation on the Loess Plateau is important not only for local residents but also for reducing sediment downstream in the Yellow River. In this paper, we report a decrease in soil erosion from 2000 to 2010 as a result of the ‘Grain for Green' (GFG) Project. By using the Revised Universal Soil Loss Equation and data on land cover, climate and sediment yield, we found that soil erosion decreased from 6579.55tkm–2yr–1 in 2000 to 1986.66tkm–2yr–1 in 2010. During this period, there was a major land cover change from farmland to grassland in response to the GFG. The area of low vegetation coverage with severe erosion decreased dramatically, whereas the area of high vegetation coverage with slight erosion increased. Our study demonstrates that the reduction in soil erosion on the Loess Plateau contributed to the decrease in the sediment concentration in the Yellow River.

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