scholarly journals Dynamic Changes of Soil Erosion in the Taohe River Basin Using the RUSLE Model and Google Earth Engine

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1293 ◽  
Author(s):  
Hao Wang ◽  
Hu Zhao
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Water Conservation ◽  
Yellow River ◽  
Google Earth ◽  
Large Area ◽  
Conservation Area ◽  
Conservation Practice ◽  
Rusle Model ◽  
Google Earth Engine

The Taohe River Basin is the largest tributary and an important water conservation area in the upper reaches of the Yellow River. In order to investigate the status of soil erosion in this region, we conducted a research of soil erosion. In our study, several parameters of the revised universal soil loss equation (RUSLE) model are extracted by using Google Earth Engine. The soil erosion modulus of the Taohe River Basin was calculated based on multi-source data, and the spatio-temporal variation characteristics of the soil erosion intensity were analyzed. The results showed the following: (1) the average soil erosion modulus of the Taohe River Basin in 2000, 2005, 2010, 2015 and 2018 were 1424, 1195, 1129, 1099 and 1124 t·ha−1·year−1, respectively, and the overall downward trend was obvious. (2) The ranges of soil erosion in the Taohe River Basin in 2000, 2005, 2010, 2015 and 2018 are basically the same—mainly with slight erosion—and the soil erosion in the middle and lower reaches was more serious. (3) When dealing with the vegetation cover factor and conservation practice factor in the RUSLE model, Google Earth Engine provided a new approach for soil erosion investigation and monitoring over a large area.

RUSLE Model based Assessment of Soil Erosion in Parbati River Basin, Central India using Google Earth Engine and GIS

2021 ◽  
Author(s):  
Rohit Kumar ◽  
Benidhar Deshmukh ◽  
Kiran Sathunuri
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Pattern ◽  
River Basin ◽  
Soil Loss ◽  
Google Earth ◽  
Rusle Model ◽  
High Soil ◽  
Basin Scale ◽  
Google Earth Engine

<p>Land degradation is a global concern posing significant threat to sustainable development. One of its major aspects is soil erosion, which is recognised as one of the critical geomorphic processes controlling sediment budget and landscape evolution. Natural rate of soil erosion is exacerbated due to anthropogenic activities that may lead to soil infertility. Therefore, assessment of soil erosion at basin scale is needed to understand its spatial pattern so as to effectively plan for soil conservation. This study focuses on Parbati river basin, a major north flowing cratonic river and a tributary of river Chambal to identify erosion prone areas using RUSLE model. Soil erodibility (K), Rainfall erosivity (R), and Topographic (LS) factors were derived from National Bureau of Soil Survey and Land Use Planning, Nagpur (NBSS-LUP) soil maps, India Meteorological Department (IMD) datasets, and SRTM30m DEM, respectively in GIS environment. The crop management (C) and support practice (P) factors were calculated by assigning appropriate values to Land use /land cover (LULC) classes derived by random forest based supervised classification of Sentinel-2 level-1C satellite remote sensing data in Google Earth Engine platform. High and very high soil erosion were observed in NE and NW parts of the basin, respectively, which may be attributed to the presence of barren land, fallow areas and rugged topography. The result reveals that annual rate of soil loss for the Parbati river basin is ~319 tons/ha/yr (with the mean of 1.2 tons/ha/yr). Lowest rate of soil loss (i.e. ~36 tons/ha/yr with mean of 0.22 tons/ha/yr) has been observed in the open forest class whereas highest rate of soil loss (i.e. ~316 tons/ha/yr with mean of 32.08 tons/ha/yr) have been observed in gullied area class. The study indicates that gullied areas are contributing most to the high soil erosion rate in the basin. Further, the rate of soil loss in the gullied areas is much higher than the permissible value of 4.5–11 tons/ha/yr recognized for India. The study helps in understanding spatial pattern of soil loss in the study area and is therefore useful in identifying and prioritising erosion prone areas so as to plan for their conservation.</p>

scholarly journals RUSLE Model Based Annual Soil Loss Quantification for Soil Erosion Protection in Fincha Catchment, Abay River Basin, Ethiopia.

2021 ◽  
Author(s):  
Habtamu Tamiru ◽  
Meseret Wagari
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Landsat 8 ◽  
Land Resources ◽  
Rusle Model ◽  
Factor C ◽  
Significant Factors

Abstract Background: The quantity of soil loss as a result of soil erosion is dramatically increasing in catchment where land resources management is very weak. The annual dramatic increment of the depletion of very important soil nutrients exposes the residents of this catchment to high expenses of money to use artificial fertilizers to increase the yield. This paper was conducted in Fincha Catchment where the soil is highly vulnerable to erosion, however, where such studies are not undertaken. This study uses Fincha catchment in Abay river basin as the study area to quantify the annual soil loss, where such studies are not undertaken, by implementing Revised Universal Soil Loss Equation (RUSLE) model developed in ArcGIS version 10.4. Results: Digital Elevation Model (12.5 x 12.5), LANDSAT 8 of Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Annual Rainfall of 10 stations (2010-2019) and soil maps of the catchment were used as input parameters to generate the significant factors. Rainfall erosivity factor (R), soil erodibility factor (K), cover and management factor (C), slope length and steepness factor (LS) and support practice factor (P) were used as soil loss quantification significant factors. It was found that the quantified average annual soil loss ranges from 0.0 to 76.5 t ha-1 yr-1 was obtained in the catchment. The area coverage of soil erosion severity with 55%, 35% and 10% as low to moderate, high and very high respectively were identified. Conclusion: Finally, it was concluded that having information about the spatial variability of soil loss severity map generated in the RUSLE model has a paramount role to alert land resources managers and all stakeholders in controlling the effects via the implementation of both structural and non-structural mitigations. The results of the RUSLE model can also be further considered along with the catchment for practical soil loss quantification that can help for protection practices.

scholarly journals Forest ecological monitoring of the Shiyang River basin based on Google Earth Engine

2021 ◽  
Vol 932 (1) ◽  
pp. 012011
Author(s):  
Y Wang
Keyword(s):  
River Basin ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Ecological Environment ◽  
Ecological Quality ◽  
Natural Forests ◽  
Shiyang River Basin ◽  
Natural Factors ◽  
Google Earth Engine

Abstract The Shiyang River basin is a typical inland arid region and one of the most fragile and sensitive areas of terrestrial ecosystems in China, and it is important to understand its ecological changes in a timely and accurate manner. This article selects the Shiyang River basin forest as the research area and uses Google Earth Engine (GEE) to evaluate and monitor the ecological environment quality of the Shiyang River basin from 1990 to 2020. The geographical detector model (GDM) was also used to analyse the sensitivity of the forest ecological environment to three natural factors: elevation, temperature and altitude. The results showed that the ecological quality of the natural forest is significantly better than that of the man-made forest area, and the ecological quality grade is higher. The forest change area RSEI has a large annual variation in ecological quality and is vulnerable to external factors. Among the influencing natural factors, the sensitive factors of precipitation and altitude are both greater than 84%. The temperature sensitivity of natural forests is stronger than that of man-made forests, ranging from 66% to 92% overall.

scholarly journals Soil Erosion and Crop Productivity Loss in Palghar and Thane Districts of Maharashtra

Agropedology ◽  
2019 ◽  
Vol 28 (2) ◽  
Author(s):  
S. V. Shejale ◽  
◽  
S. B. Nandgude ◽  
S. S. Salunkhe ◽  
M. A. Phadtare ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Soil Loss ◽  
Productivity Loss ◽  
Research Work ◽  
Crop Productivity ◽  
Soil And Water Conservation ◽  
Conservation Practice ◽  
Conservation Measures ◽  
Soil And Water

Present research work was carried out on soil erosion and crop productivity loss in Palghar and Thane districts. The study also describes tolerable soil loss and relationship between top-soil loss and yield loss. The estimated average annual soil loss was 40.45 t ha-1yr-1 before adoption of the soil and water conservation measures (by USLE method) and estimated average tolerable soil loss was 9.36 t ha-1 yr-1, for Palghar district. Similarly, for Thane district the estimated average annual soil loss and tolerable soil loss were found to be 35.89 t ha-1 yr-1 and 9.61 t ha-1 yr-1, respectively for Thane district. The estimated average conservation practice factor (P) factors were obtained as 0.32 for Palghar district and 0.30 for Thane district to bring the soil loss below the tolerable limit. After adoption of soil and water conservation measures, the estimated soil loss were 9.02 t ha-1 yr-1 and 9.38 t ha-1 yr-1 for Palghar and Thane districts, respectively.

scholarly journals Evaluating the integrity of forested riparian buffers over a large area using LiDAR data and Google Earth Engine

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamdi A. Zurqani ◽  
Christopher J. Post ◽  
Elena A. Mikhailova ◽  
Michael P. Cope ◽  
Jeffery S. Allen ◽  
...  
Keyword(s):  
Google Earth ◽  
Riparian Buffers ◽  
Lidar Data ◽  
Large Area ◽  
Google Earth Engine

scholarly journals Causes of Variations in Sediment Yield in the Jinghe River Basin, China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jinliang Zhang ◽  
Yizi Shang ◽  
Jinyong Liu ◽  
Jian Fu ◽  
Shitao Wei ◽  
...  
Keyword(s):  
River Basin ◽  
Sediment Yield ◽  
Water Conservation ◽  
Yellow River ◽  
Soil And Water Conservation ◽  
Water Diversion ◽  
Sediment Discharge ◽  
Jinghe River Basin ◽  
Jinghe River ◽  
Soil And Water

Abstract The Jinghe River remains the major sediment source of the Yellow River in China; however, sediment discharge in the Jinghe River has reduced significantly since the 1950s. The objective of this study is to identify the causes of sediment yield variations in the Jinghe River Basin based on soil and water conservation methods and rainfall analyses. The results revealed that soil and water conservation projects were responsible for half of the total sediment reduction; sediment retention due to reservoirs and water diversion projects was responsible for 1.3% of the total reduction. Moreover, the Jinghe River Basin has negligible opportunity to improve its vegetation cover (currently 55% of the basin is covered with lawns and trees), and silt-arrester dams play a smaller role in reducing sediment significantly before they are entirely full. Therefore, new large-scale sediment trapping projects must be implemented across the Jinghe River Basin, where heavy rainfall events are likely to substantially increase in the future, leading to higher sediment discharge.

scholarly journals Application of Revised Universal Soil Loss Equation (Rusle) Model to Assess Soil Erosion in “Kalu Ganga” River Basin in Sri Lanka

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
D. L. D. Panditharathne ◽  
N. S. Abeysingha ◽  
K. G. S. Nirmanee ◽  
Ananda Mallawatantri
Keyword(s):  
Land Use ◽  
Sri Lanka ◽  
Soil Erosion ◽  
River Basin ◽  
Soil Loss ◽  
Management Practices ◽  
Agricultural Land ◽  
Universal Soil Loss Equation ◽  
Rusle Model ◽  
Soil Loss Equation

Soil erosion is one of the main forms of land degradation. Erosion contributes to loss of agricultural land productivity and ecological and esthetic values of natural environment, and it impairs the production of safe drinking water and hydroenergy production. Thus, assessment of soil erosion and identifying the lands more prone to erosion are vital for erosion management process. Revised Universal Soil Loss Equation (Rusle) model supported by a GIS system was used to assess the spatial variability of erosion occurring at Kalu Ganga river basin in Sri Lanka. Digital Elevation Model (30 × 30 m), twenty years’ rainfall data measured at 11 rain gauge stations across the basin, land use and soil maps, and published literature were used as inputs to the model. The average annual soil loss in Kalu Ganga river basin varied from 0 to 134 t ha−1 year−1 and mean annual soil loss was estimated at 0.63 t ha−1 year−1. Based on erosion estimates, the basin landscape was divided into four different erosion severity classes: very low, low, moderate, and high. About 1.68% of the areas (4714 ha) in the river basin were identified with moderate to high erosion severity (>5 t ha−1 year−1) class which urgently need measures to control soil erosion. Lands with moderate to high soil erosion classes were mostly found in Bulathsinghala, Kuruwita, and Rathnapura divisional secretarial divisions. Use of the erosion severity information coupled with basin wide individual RUSLE parameters can help to design the appropriate land use management practices and improved management based on the observations to minimize soil erosion in the basin.

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