scholarly journals Effects of Content of Soil Rock Fragments on Soil Erodibility in China

2022 ◽  
Vol 19 (2) ◽  
pp. 648
Author(s):  
Miaomiao Yang ◽  
Keli Zhang ◽  
Chenlu Huang ◽  
Qinke Yang
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Soil Type ◽  
Soil Profile ◽  
Redundancy Analysis ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Soil Erodibility ◽  
Rock Fragments ◽  
Normalised Difference Vegetation Index

Soil erosion is serious in China—the soil in plateau and mountain areas contain a large of rock fragments, and their content and distribution have an important influence on soil erosion. However, there are still no complete results for calculating soil erodibility factor (K) that have corrected rock fragments in China. In this paper, the data available on rock fragments in the soil profile (RFP); rock fragments on the surface of the soil (RFS); and environmental factors such as elevation, terrain relief, slope, vegetation coverage (characterised by normalised difference vegetation index, NDVI), land use, precipitation, temperature, and soil type were used to explore the effects of content of soil rock fragments on calculating of K in China. The correlation analysis, typical sampling area analysis, and redundancy analysis were applied to analyse the effects of content of soil rock fragments on calculating of K and its relationship with environment factors. The results showed that (1) The rock fragments in the soil profile (RFP) increased K. The rock fragments on the surface (RFS) of the soil reduced K. The effect of both RFP and RFS reduced K. (2) The effect of rock fragments on K was most affected by elevation, followed by terrain relief, NDVI, slope, soil type, temperature, and precipitation, but had little correlation with land use. (3) The result of redundancy analysis showed elevation to be the main predominant factor of the effect of rock fragments on K. This study fully considered the effect of rock fragments on calculating of K and carried out a quantitative analysis of the factors affecting the effect of rock fragments on K, so as to provide necessary scientific basis for estimating K and evaluating soil erosion status in China more accurately.

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

Effects of Content of Soil Rock Fragments on Calculating of Soil Erodibility

2021 ◽  
Author(s):  
Miaomiao Yang ◽  
Qinke Yang ◽  
Keli Zhang ◽  
Yuru Li ◽  
Chunmei Wang ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Soil Erosion ◽  
Soil Profile ◽  
Joint Effect ◽  
Saturated Hydraulic Conductivity ◽  
Soil Erodibility ◽  
Soil Permeability ◽  
Land Area ◽  
Rock Fragments ◽  
Global Land

<p>【Objective】Rock fragments (>2mm diameter) are an important component of soil, and its presence has a significant impact on soil erosion and sediment yield. So it is essential to take into full account content of the rock fragments for accurate calculation of soil erodibility factor (K). 【Method】In this paper, based on the data available of the content of rock fragments and classes of soil texture with a resolution of 30 arc-second, influence of the content of rock fragments, including rock fragments in the soil profile (RFP) and gravels on the surface of the soil (SC), on K was assessed at a global scale, using the equation (Brakensiek, 1986) of the relationship between saturated hydraulic conductivity and grade of soil permeability, and the equation (Poesen) of soil erodibility attenuation under a rock fragment cover. 【Result】Results show: (1) The existence of rock fragments in the soil increased K by 4.43% and soil permeability by 5.68% on average in grade and lowering soil saturated hydraulic conductivity by 11.57% by reducing water infiltration rate of the soil and increasing surface runoff. The gravels on the surface of the mountain land and desert/gobi reduced K by 18.7% by protecting the soil from splashing of rain drops and scrubbing of runoff; so once the content of rock fragments in the soil profile and gravels on the surface of the land are taken into account in calculation, soil K may be 5.52% lower; (2)In the areas dominated with the effect of rock fragments, about 62.7% of the global land area, soil K decreased by 0.0091( t•hm<sup>2</sup>•h)•( hm<sup>-2</sup>•MJ<sup>-1</sup>•mm<sup>-1</sup>) on average, while in the area affected mainly by rock fragments in profile, about 31.1% of the global land area, soil K increased by 0.0019( t•hm<sup>2</sup>•h)•( hm<sup>-2</sup>•MJ<sup>-1</sup>•mm<sup>-1</sup>); and (3)The joint effect of rock fragments in profile and gravels on the surface reduced the soil erosion rate by 11.8% in the 6 sample areas. 【Conclusion】 The presence of RFP increases soil K while the presence of SC does reversely. The joint effect of the two leads to decrease in soil erosion. In plotting regional soil erosion maps, it is essential to take both of the two into account so as to improve accuracy of the mapping.</p>

scholarly journals A Quantitative Analysis of Factors Influencing Organic Matter Concentration in the Topsoil of Black Soil in Northeast China Based on Spatial Heterogeneous Patterns

2021 ◽  
Vol 10 (5) ◽  
pp. 348
Author(s):  
Zhenbo Du ◽  
Bingbo Gao ◽  
Cong Ou ◽  
Zhenrong Du ◽  
Jianyu Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Soil Erosion ◽  
Soil Type ◽  
Northeast China ◽  
Black Soil ◽  
Factors Affecting ◽  
Soil Zone ◽  
Rainfed Farming ◽  
Study Results

Black soil is fertile, abundant with organic matter (OM) and is exceptional for farming. The black soil zone in northeast China is the third-largest black soil zone globally and produces a quarter of China’s commodity grain. However, the soil organic matter (SOM) in this zone is declining, and the quality of cultivated land is falling off rapidly due to overexploitation and unsustainable management practices. To help develop an integrated protection strategy for black soil, this study aimed to identify the primary factors contributing to SOM degradation. The geographic detector, which can detect both linear and nonlinear relationships and the interactions based on spatial heterogeneous patterns, was used to quantitatively analyze the natural and anthropogenic factors affecting SOM concentration in northeast China. In descending order, the nine factors affecting SOM are temperature, gross domestic product (GDP), elevation, population, soil type, precipitation, soil erosion, land use, and geomorphology. The influence of all factors is significant, and the interaction of any two factors enhances their impact. The SOM concentration decreases with increased temperature, population, soil erosion, elevation and terrain undulation. SOM rises with increased precipitation, initially decreases with increasing GDP but then increases, and varies by soil type and land use. Conclusions about detailed impacts are presented in this paper. For example, wind erosion has a more significant effect than water erosion, and irrigated land has a lower SOM content than dry land. Based on the study results, protection measures, including conservation tillage, farmland shelterbelts, cross-slope ridges, terraces, and rainfed farming are recommended. The conversion of high-quality farmland to non-farm uses should be prohibited.

scholarly journals Hydrological Response Unit Analysis Using AVSWAT 2000 for Keuliling Reservoir Watershed, Aceh Province, Indonesia

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
. Azmeri ◽  
Alfian Yulianur ◽  
Maimun Rizalihadi ◽  
Shafur Bachtiar
Keyword(s):  
Land Use ◽  
Soil Type ◽  
Soil Erodibility ◽  
Hydrologic Response ◽  
Response Unit ◽  
Area 5 ◽  
Soil Erodibility Factor ◽  
Hydrologic Response Unit ◽  
Factor C ◽  
Basin Area

<p>Sediments deposition derived from the erosion in upstream areas can lead to river siltation or canals downstream irrigation. According to the complexity of erosion problem at Keuliling reservoir, it is essential that topography, hydrology, soil type and land use to be analyzed comprehensively. Software used to analyze is AVSWAT 2000 (Arc View Soil and Water Assessment Tools-2000), one of the additional tool of ArcView program. The results obtained are the watershed delineation map, soil type map to produce soil erodibility factor (K) which indicates the resistance of soil particles toward exfoliation, land use map to produce crop management factor (C) and soil conservation and its management factors (P). Hydrology analysis includes soil type, land use and utility for the erosion rate analysis through Hydrologic Response Unit (HRU). The biggest HRU value of sub-basin is on area 5 and the lowest one is on area 10. All four HRU in sub-basin area 5 are potentially donating high value for HRU. In short, this area has the longest slope length so that it has a large LS factor. About 50% of the land was covered by bushes which gain higher C factor rather than forest. Moreover, it has contour crop conservation technique with 9-20 % declivity resulting in having dominant factor of P. Soil type is dominated by Meucampli Formation which has soil erodibility factor with high level of vulnerable toward the rainfall kinetic energy. All in all, the vast majority of HRU parameters in this sub-basin area obtain the highest HRU value. Hydrology analysis, soil type, and use-land are useful for land area analysis that is susceptible to erosion which was identified through Hydrologic Response Unit (HRU) using GIS. As the matter of fact, spatially studies constructed with GIS can facilitate the agency to determine critical areas which are needed to be aware or fully rehabilitated.</p>

scholarly journals Using local climate zones to compare remotely sensed surface temperatures in temperate cities and hot desert cities

2020 ◽  
Vol 28 (1) ◽  
pp. 48-60
Author(s):  
Cathy Fricke ◽  
Rita Pongrácz ◽  
Tamás Gál ◽  
Stevan Savić ◽  
János Unger
Keyword(s):  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Coverage ◽  
Temperate Climate ◽  
Local Climate ◽  
Local Climate Zones ◽  
Normalised Difference Vegetation Index ◽  
Urban Rural ◽  
Novi Sad

AbstractUrban and rural thermal properties mainly depend on surface cover features as well as vegetation cover. Surface classification using the local climate zone (LCZ) system provides an appropriate approach for distinguishing urban and rural areas, as well as comparing the surface urban heat island (SUHI) of climatically different regions. Our goal is to compare the SUHI effects of two Central European cities (Szeged, Hungary and Novi Sad, Serbia) with a temperate climate (Köppen-Geiger’s Cfa), and a city (Beer Sheva, Israel) with a hot desert (BWh) climate. LCZ classification is completed using WUDAPT (World Urban Database and Access Portal Tools) methodology and the thermal differences are analysed on the basis of the land surface temperature data of the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor, derived on clear days over a four-year period. This intra-climate region comparison shows the difference between the SUHI effects of Szeged and Novi Sad in spring and autumn. As the pattern of NDVI (Normalised Difference Vegetation Index) indicates, the vegetation coverage of the surrounding rural areas is an important modifying factor of the diurnal SUHI effect, and can change the sign of the urban-rural thermal difference. According to the inter-climate comparison, the urban-rural thermal contrast is the strongest during daytime in summer with an opposite sign in each season.

scholarly journals Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Veera Narayana Balabathina ◽  
R. P. Raju ◽  
Wuletaw Mulualem ◽  
Gedefaw Tadele
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Erosion Rate ◽  
Soil Erodibility ◽  
Lake Tana ◽  
Erosion Rates ◽  
Soil Erosion Rate ◽  
Soil Loss Equation

Abstract Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

Corrigendum to: Influence of climatic factors on variation in the Normalised Difference Vegetation Index in Mongolian Plateau grasslands

2018 ◽  
Vol 40 (2) ◽  
pp. 205
Author(s):  
Xu-Juan Cao ◽  
Qing-Zhu Gao ◽  
Ganjurjav Hasbagan ◽  
Yan Liang ◽  
Wen-Han Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Limiting Factor ◽  
Spatial And Temporal Variability ◽  
Climate Factors ◽  
Mongolian Plateau ◽  
Normalised Difference Vegetation Index

Climate change will affect how the Normalised Difference Vegetation Index (NDVI), which is correlated with climate factors, varies in space and over time. The Mongolian Plateau is an arid and semi-arid area, 64% covered by grassland, which is extremely sensitive to climate change. Its climate has shown a warming and drying trend at both annual and seasonal scales. We analysed NDVI and climate variation characteristics and the relationships between them for Mongolian Plateau grasslands from 1981 to 2013. The results showed spatial and temporal differences in the variation of NDVI. Precipitation showed the strongest correlation with NDVI (43% of plateau area correlated with total annual precipitation and 44% with total precipitation in the growing season, from May to September), followed by potential evapotranspiration (27% annual, and 30% growing season), temperature (7% annual, 16% growing season) and cloud cover (10% annual, 12% growing season). These findings confirm that moisture is the most important limiting factor for grassland vegetation growth on the Mongolian Plateau. Changes in land use help to explain variations in NDVI in 40% of the plateau, where no correlation with climate factors was found. Our results indicate that vegetation primary productivity will decrease if warming and drying trends continue but decreases will be less substantial if further warming, predicted as highly likely, is not accompanied by further drying, for which predictions are less certain. Continuing spatial and temporal variability can be expected, including as a result of land use changes.

scholarly journals Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Ecological Red Line Areas

2020 ◽  
Vol 12 (3) ◽  
pp. 399 ◽  
Author(s):  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Huan Wang ◽  
Liyuan Zuo
Keyword(s):  
Land Use ◽  
Spatial Distribution ◽  
Soil Erosion ◽  
Spatial Heterogeneity ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Water Yield ◽  
Land Use Type ◽  
Functional Areas ◽  
Red Line

Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing’s ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (>55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas.

scholarly journals Analysis of the Changes in the Water Yield Coefficient over the Past 50 Years in the Huang-Huai-Hai River Basin, China

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Xiaoqing Shi ◽  
Tianling Qin ◽  
Denghua Yan ◽  
Ruochen Sun ◽  
Shuang Cao ◽  
...  
Keyword(s):  
Land Use ◽  
River Basin ◽  
Vegetation Index ◽  
Yellow River ◽  
Rate Of Change ◽  
Vegetation Coverage ◽  
Water Yield ◽  
Yield Coefficient ◽  
Positive Trend ◽  
Land Use Types

This study analysed the temporal and spatial changes in the water yield coefficient (WYC), which represents the ratio of the gross amount of water resources to precipitation. Factors such as precipitation, rainstorm days, rainless days, vegetation cover change, and land use/cover change were considered to determine the causes of these changes. The results led to the following conclusions: (1) The average annual WYC of the Huang-Huai-Hai River Basin is between 0.03 and 0.58, with an average value of 0.17, which is smaller than the national average WYC of 0.4. (2) Temporally, the WYC varied slightly, with the western part showing a negative trend and the eastern part showing a positive trend. The WYC is positively correlated with precipitation, rainstorm days, and the normalized difference vegetation index (NDVI) and negatively correlated with rainless days. However, a slower change in NDVI produced a faster change in WYC. In areas with land use types exhibiting a large evapotranspiration decrease, the rate of change in the WYC increased. (3) Spatially, the distribution is fairly regular, exhibiting a gradual increase from the northern part of the Yellow River Basin (WYC < 0.1) to the surrounding areas. When the WYC is correlated with precipitation, rainstorm days, rainless days, and NDVI, the R2 values of the linear fitting results are 0.98, 0.91, 0.96, and 0.73, respectively. The WYC is positively correlated with precipitation, rainstorm days, and vegetation coverage and negatively correlated with rainless days, but the correlation coefficient is greatly influenced by the precipitation characteristics and land use types. In areas featuring high proportions of land use types associated with high evapotranspiration, the average WYC is low.

scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

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