Study on the Soil Erosion Dynamic Changes in Songhuajiang River Watershed Based on RS and GIS

2011 ◽  
Vol 347-353 ◽  
pp. 1268-1271
Author(s):  
Feng Wen Gong ◽  
Li Yuan
Keyword(s):  
Soil Erosion ◽  
Optimal Allocation ◽  
Local Economy ◽  
Land Use Pattern ◽  
Dynamic Changes ◽  
Data Resource ◽  
Landsat Tm Image ◽  
Rs And Gis ◽  
Study Results ◽  
Micro Level

Based on the RS and GIS, the main data resource of Landsat TM image (1995 and 2005a) was used to study the soil erosion’s spatial-temporal dynamic changes. The results shown that: the reduced area of micro-level and slight soil erosion was 190.8 and 640 km2; the increased area of moderate and intensity soil erosion was 168 and 663 km2, the preserving ratio of micro-level erosion was greatest, intensity soil erosion’s persevering ratio was smallest, the transfer-out probability from intensity to micro-level soil erosion was greatest, the study results could give us some advice on making rational use of land and improving land use pattern the optimal allocation during developing the local economy.

Cross-provincial Cross-region Power Trading Optimization Modeling

2020 ◽  
Vol 13 (8) ◽  
pp. 1183-1189
Author(s):  
Jing-wen Chen ◽  
Yan Xiao ◽  
Hong-she Dang ◽  
Rong Zhang
Keyword(s):  
Network Flow ◽  
Optimal Allocation ◽  
Route Optimization ◽  
Regional Power ◽  
Power Resources ◽  
Gray Theory ◽  
Power Trading ◽  
Study Results ◽  
Multi Agent ◽  
Regional Economic Disparities

Background: China's power resources are unevenly distributed in geography, and the supply-demand imbalance becomes worse due to regional economic disparities. It is essential to optimize the allocation of power resources through cross-provincial and cross-regional power trading. Methods: This paper uses load forecasting, transaction subject data declaration, and route optimization models to achieve optimal allocation of electricity and power resources cross-provincial and cross-regional and maximize social benefits. Gray theory is used to predict the medium and longterm loads, while multi-agent technology is used to report the power trading price. Results: Cross-provincial and cross-regional power trading become a network flow problem, through which we can find the optimized complete trading paths. Conclusion: Numerical case study results has verified the efficiency of the proposed method in optimizing power allocation across provinces and regions.

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 Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia

2021 ◽  
Vol 11 (15) ◽  
pp. 6763
Author(s):  
Mongi Ben Zaied ◽  
Seifeddine Jomaa ◽  
Mohamed Ouessar
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scientific Evidence ◽  
Field Measurements ◽  
Spatiotemporal Variability ◽  
Soil Conditions ◽  
Storm Events ◽  
Erosion Processes ◽  
Study Results ◽  
Soil Losses

Soil erosion remains one of the principal environmental problems in arid regions. This study aims to assess and quantify the variability of soil erosion in the Koutine catchment using the RUSLE (Revised Universal Soil Loss Equation) model. The Koutine catchment is located in an arid area in southeastern Tunisia and is characterized by an annual mean precipitation of less than 200 mm. The model was used to examine the influence of topography, extreme rainstorm intensity and soil texture on soil loss. The data used for model validation were obtained from field measurements by monitoring deposited sediment in settlement basins of 25 cisterns (a traditional water harvesting and storage technique) over 4 years, from 2015 to 2018. Results showed that slope is the most controlling factor of soil loss. The average annual soil loss in monitoring sites varies between 0.01 and 12.5 t/ha/y. The storm events inducing the largest soil losses occurred in the upstream part of the Koutine catchment with a maximum value of 7.3 t/ha per event. Soil erosion is highly affected by initial and preceding soil conditions. The RUSLE model reasonably reproduced (R2 = 0.81) the spatiotemporal variability of measured soil losses in the study catchment during the observation period. This study revealed the importance of using the cisterns in the data-scarce dry areas as a substitute for the classic soil erosion monitoring fields. Besides, combining modeling of outputs and field measurements could improve our physical understanding of soil erosion processes and their controlling factors in an arid catchment. The study results are beneficial for decision-makers to evaluate the existing soil conservation and water management plans, which can be further adjusted using appropriate soil erosion mitigation options based on scientific evidence.

scholarly journals Impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand

2012 ◽  
Vol 7 (No. 1) ◽  
pp. 10-17 ◽  
Author(s):  
S. Wijitkosum
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Land Cover ◽  
National Park ◽  
Land Use Changes ◽  
Influential Factors ◽  
Geographical Information ◽  
Adjacent Area ◽  
Erosion Risk ◽  
Study Results

Soil erosion has been considered as the primary cause of soil degradation since soil erosion leads to the loss of topsoil and soil organic matters which are essential for the growing of plants. Land use, which relates to land cover, is one of the influential factors that affect soil erosion. In this study, impacts of land use changes on soil erosion in Pa Deng sub-district, adjacent area of Kaeng Krachan National Park, Thailand, were investigated by applying remote sensing technique, geographical information system (GIS) and the Universal Soil Loss Equation (USLE). The study results revealed that land use changes in terms of area size and pattern influenced the soil erosion risk in Pa Deng in the 1990–2010 period. The area with smaller land cover obviously showed the high risk of soil erosion than the larger land cover did.

Study on Dynamic Changes of Soil Erosion in the North and South Mountains of Lanzhou

2021 ◽  
Author(s):  
Hua Zhang ◽  
Jinping Lei ◽  
Cungang Xu ◽  
Yuxin Yin
Keyword(s):  
Soil Erosion ◽  
Environmental Factors ◽  
Water Area ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
The North ◽  
Joint Influence ◽  
Bare Land ◽  
Temporal And Spatial ◽  
North And South

Abstract This study takes the north and south mountains of Lanzhou as the study area, calculates the soil erosion modulus of the north and south mountains of Lanzhou based on the five major soil erosion factors in the RUSLE model and analyzes the temporal and spatial dynamic changes of soil erosion and the characteristics of soil erosion under different environmental factors. The results show that the soil erosion intensity of the north and south mountains of Lanzhou is mainly micro erosion in 1995, 2000, 2005, 2010, 2015 and 2018. They are distributed in the northwest and southeast of the north and south mountains. Under different environmental factors, the soil erosion modulus first increased and then decreased with the increase of altitude; the soil erosion modulus increased with the increase of slope; the average soil erosion modulus of grassland and woodland was larger, and the average soil erosion modulus of water area was the smallest; except for bare land, the average soil erosion modulus decreased with the increase of vegetation coverage. The soil erosion modulus in the greening range is lower than that outside the greening scope, mainly the result of the joint influence of precipitation, soil and vegetation.

Experimental study on dynamic mechanism of sheet erosion processes on steep grassland in the loess region of China

2021 ◽  
Author(s):  
Qi Guo ◽  
Zhanli Wang
Keyword(s):  
Shear Stress ◽  
Soil Erosion ◽  
Power Function ◽  
Erosion Rate ◽  
Dynamic Mechanism ◽  
Erosion Process ◽  
Stream Power ◽  
Study Results ◽  
Loess Region ◽  
Sheet Erosion

<p>Sheet erosion has been the major erosion process on steep grassland since the Grain-for-Green project was implemented in 1999 in the Loess Plateau with serious soil erosion, in China. Quantifying sheet erosion rate on steep grassland could improve soil erosion estimation on loess hillslopes and provide scientific support for effectively controlling soil erosion and rationally managing grassland. Simulated rainfall experiments were conducted on grassland plot with vegetation coverage of 40% under complete combination of rainfall intensities of 0.7, 1.0, 1.5, 2.0 and 2.5 mm min<sup>-1</sup> and slope gradients of 7°, 10°, 15°, 20° and 25°. Results showed that sheet erosion rate (<em>SE</em>), varying from 0.0048 to 0.0578 kg m<sup>-2</sup> min<sup>-1</sup>, was well described by binary power function equation (<em>SE</em> = 0.0026 <em>I</em><sup>1.306</sup><em>S</em><sup>0.662</sup>) containing rainfall intensity and slope gradient with <em>R<sup>2</sup></em> = 0.940. The logarithmic equation of shear stress (<em>SE</em> = 0.084 + Ln (<em>τ</em>)) and the power function equation of stream power (<em>SE</em> = 1.141 <em>ɷ</em><sup>1.073</sup>) could be used to predict sheet erosion rate. Stream power (<em>R<sup>2</sup></em> = 0.903) was a better predictor of sheet erosion than shear stress (<em>R<sup>2</sup></em> = 0.882). However, predictions based on flow velocity, unit stream power, and unit energy were unsatisfactory. The stream power was an excellent hydrodynamic parameter for predicting sheet erosion rate. The sheet erosion process of grassland slope was also affected by the raindrop impact except the dynamic action of sheet flow. The combination of stream power and rainfall kinetic energy (<em>KE</em>) among different rainfall physical parameters had the most closely relationship with the sheet erosion rates, which is also better than the stream power only, and a binary power function equation (<em>SE</em> = 0.221 <em>ω</em><sup>0.831</sup><em>KE</em><sup>0.416</sup>) could be used to predict sheet erosion rate on grassland slope with <em>R<sup>2</sup></em> = 0.930. The study results revealed the dynamic mechanism of the sheet erosion process on steep grassland in the loess region of China.</p>

Fighting for Every Inch of Land: Greed and Grievance in Petition Mobilization in Zhejiang

Modern China ◽  
2019 ◽  
Vol 46 (4) ◽  
pp. 400-432 ◽  
Author(s):  
Christopher Heurlin
Keyword(s):  
Real Estate ◽  
Local Governments ◽  
Local Economy ◽  
County Level ◽  
Economic Approach ◽  
Resource Value ◽  
Real Estate Markets ◽  
Cognitive Mechanism ◽  
Original Dataset ◽  
Micro Level

Despite a proliferation of studies of the micro-level dynamics of protests and petitions against land takings in China, we know very little about how meso-level factors, such as the local economy, influence petitions to Beijing and provincial governments. Drawing upon the economic approach to civil war, this article examines the roles played by grievances and greed in determining the scale of mobilization at the county level in Zhejiang province. Through archival evidence and interviews in Ningbo and Lishui, as well as an original dataset of petitions, this article suggests that both grievances and greed influence petitioning. Mobilization is especially high in Ningbo, where valuable real estate markets have prompted landless farmers to compete with local governments over control of the rents from land. The article proposes the concept of resource value activation as a cognitive mechanism that has contributed to this process of mobilization.

scholarly journals A Stochastic Optimization Model for Agricultural Irrigation Water Allocation Based on the Field Water Cycle

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1031 ◽  
Author(s):  
Zehao Yan ◽  
Mo Li
Keyword(s):  
Irrigation Water ◽  
Water Allocation ◽  
Optimal Allocation ◽  
Water Cycle ◽  
Decision Makers ◽  
Irrigation District ◽  
Agricultural Irrigation ◽  
Dynamic Changes ◽  
Irrigation Water Allocation ◽  
Allocation Strategies

Agricultural water scarcity is a global problem and this reinforces the need for optimal allocation of irrigation water resources. However, decision makers are challenged by the complexity of fluctuating stream condition and irrigation quota as well as the dynamic changes of the field water cycle process, which make optimal allocation more complex. A two-stage chance-constrained programming model with random parameters in the left- and right-hand sides of constraints considering field water cycle process has been developed for agricultural irrigation water allocation. The model is capable of generating reasonable irrigation allocation strategies considering water transformation among crop evapotranspiration, precipitation, irrigation, soil water content, and deep percolation. Moreover, it can deal with randomness in both the right-hand side and the left-hand side of constraints to generate schemes under different flow levels and constraint-violation risk levels, which are informative for decision makers. The Yingke irrigation district in the middle reaches of the Heihe River basin, northwest China, was used to test the developed model. Tradeoffs among different crops in different time periods under different flow levels, and dynamic changes of soil moisture and deep percolation were analyzed. Scenarios with different violating probabilities were conducted to gain insight into the sensitivity of irrigation water allocation strategies on water supply and irrigation quota. The performed analysis indicated that the proposed model can efficiently optimize agricultural irrigation water for an irrigation district with water scarcity in a stochastic environment.

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