Combining plot measurements and a calibrated RUSLE model to investigate recent changes in soil erosion in upland areas in Southern Italy

2022 ◽  
Author(s):  
P. Porto ◽  
M. Bacchi ◽  
G. Preiti ◽  
M. Romeo ◽  
M. Monti
Keyword(s):  
Soil Erosion ◽  
Southern Italy ◽  
Rusle Model

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.

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 Environmental effectiveness of GAEC cross-compliance Standard 1.1a (temporary ditches) and 1.2g (permanent grass cover of set-aside) in reducing soil erosion and economic evaluation of the competitiveness gap for farmers

2016 ◽  
Vol 10 (1s) ◽  
Author(s):  
Paolo Bazzoffi ◽  
Rosa Francaviglia ◽  
Ulderico Neri ◽  
Rosario Napoli ◽  
Alessandro Marchetti ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Vegetation Cover ◽  
Bare Soil ◽  
Runoff Water ◽  
Rusle Model ◽  
Environmental Effectiveness ◽  
Additional Costs ◽  
Soil Erosion By Water ◽  
Cross Compliance ◽  
Made In

<p>This paper shows the results of the monitoring carried out in three hilly farms of the MONACO project in order to verify the effectiveness of the Standard 1.1 <sub>(commitment a)</sub> (temporary ditches) and Standard 1.2 <sub>(commitment g)</sub> (Vegetation cover throughout the year in set-aside land) in the reduction in soil erosion, contained in Rule 1: ‘minimum land management that meets specific conditions’ of the decree Mipaaf 2009 and following modifications, until the recent decree No. 180 of January 23, 2015. In addition, the assessment of the competitiveness gap was done. That is the evaluation of the additional costs borne by the beneficiary of the single payment determined from agronomic commitments. Monitoring has also compared the erosion actually observed in the field with that predicted by RUSLE model (Revised Universal Soil Loss Equation) (Renard et al., 1997) in the two situations: with and without the presence of temporary ditches, i.e. assuming Factual (compliance rules) and in that Counterfactual (infringement). This comparison was made in view of the fact that the RUSLE model was chosen by the 'European Evaluation Network for Rural Development (EEN, 2013) as a forecasting tool for the quantification of' Common Indicator ‘soil erosion by water’. The results of soil erosion survey carried out by using a new  UAV-GIS methodology  on two monitoring farms in two years of observations have shown that temporary ditches were effective in decreasing erosion, on average, by 42.5%, from 36. 59 t ha<sup>-1</sup> to 21.05 t ha<sup>-1</sup> during the monitoring period. It was also evaluated the effectiveness of grass strips (at variance with the commitment of temporary ditches). The results showed a strong, highly significant, reduction in erosion by about 35% times respect soil erosion observed in bare soil and also a significant reduction in the volume of runoff water.  With regard to Standard 1.2 <sub>(commitment g)</sub> the statistical analysis shows a strong and highly significant decrease in the erosion due to the vegetation cover of the soil compared to bare soil. The economic competitiveness gap of  Standard 1.1<sub>(commitment a)</sub> stood at € 4.07±1.42 € ha<sup>-1</sup> year<sup>-1</sup>, while CO<sub>2</sub> emissions due to execution of temporary ditches was 2.58 kg ha<sup>-1</sup>year<sup>-1</sup>. As for the Standard 1.2 <sub>(commitment g) </sub>the average differential competitiveness gap amounted to  50.22±13.7 € ha<sup>-1</sup> year<sup>-1</sup> and an output of CO<sub>2</sub> equal to 31.52  kg ha<sup>-1</sup> year.</p>

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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