Estimation of soil loss by the USLE model in a mountain basin in the south of Santa Catarina state, Brazil

Water erosion is a factor of soil degradation that is triggered by the impact of raindrops originated by intense rainfall disaggregating the soil, followed by the carrying of particles by surface runoff. In the erosion process, in addition to soil loss, nutrients, fertilizers, and pesticides are carried resulting in water courses and water pollution. Erosion can have a major impact on agricultural production, when soil use and management techniques are not used. Therefore, this study aimed to evaluate the soil loss in the Malacara river basin, which is a sub-basin of the Mampituba river basin characterized by a contrasting relief, with high altitudes in the escarps of Serra Geral and floodplain. The method used for the development of this research was the application of the Universal Soil Loss Equation (USLE). USLE soil loss estimation requires the following factors: rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), soil use and management (C), and erosion control practice factor (P). The estimated rainfall erosivity was 5,754.2 MJ mm ha-1 h-1 year-1. Erodibility was determined for the soils present in the basin, highlighting a high value for gleysoil. The topographic factor (LS) showed values from 0 to greater than 20, which corresponds to the low to very high runoff potential. The floodplain showed lower runoff rates, while for the locations close to the enclosed valleys in the Malacara canyon, the runoff potential varied from high to very high. The soil use and management factors and conservation practices (CP) obtained a maximum value of 0.404, corresponding to the exposed soil; the second most representative class was agricultural areas, with a value of 0.145. The soil loss in the Malacara river basin varied from 0 to more than 200 t ha-1 year-1. In fact, 87.38% of the area presents a degree of sheet erosion normal to slight and, only 2.94% of the area has a high or very high degree of erosion. Moreover, due to the relief characteristics with shallow soils and intense rainfall in mountainous basins, knowing and understanding soil losses due to erosion is crucial for the adequate management of water resources in river basins.

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Soil erosion assessment of a Himalayan river basin using TRMM data

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-366-200-2015 ◽

2015 ◽

Vol 366 ◽

pp. 200-200 ◽

Cited By ~ 2

Author(s):

A. Pandey ◽

S. K. Mishra ◽

A. K. Gautam ◽

D. Kumar

Keyword(s):

Soil Erosion ◽

River Basin ◽

Soil Loss ◽

Daily Rainfall ◽

Point Of View ◽

Rainfall Erosivity ◽

Severe Erosion ◽

Basin Area ◽

Very High ◽

Himalayan River

Abstract. In this study, an attempt has been made to assess the soil erosion of a Himalayan river basin, the Karnali basin, Nepal, using rainfall erosivity (R-factor) derived from satellite-based rainfall estimates (TRMM-3B42 V7). Average annual sediment yield was estimated using the well-known Universal Soil Loss Equation (USLE). The eight-year annual average rainfall erosivity factor (R) for the Karnali River basin was found to be 2620.84 MJ mm ha−1 h−1 year−1. Using intensity–erosivity relationships and eight years of the TRMM daily rainfall dataset (1998–2005), average annual soil erosion was also estimated for Karnali River basin. The minimum and maximum values of the rainfall erosivity factor were 1108.7 and 4868.49 MJ mm ha−1 h−1 year−1, respectively, during the assessment period. The average annual soil loss of the Karnali River basin was found to be 38.17 t ha−1 year−1. Finally, the basin area was categorized according to the following scale of erosion severity classes: Slight (0 to 5 t ha−1 year−1), Moderate (5 to 10 t ha−1 year−1), High (10 to 20 t ha−1 year−1), Very High (20 to 40 t ha−1 year−1), Severe (40 to 80 t ha−1 year−1) and Very Severe (>80 t ha−1 year−1). About 30.86% of the river basin area was found to be in the slight erosion class. The areas covered by the moderate, high, very high, severe and very severe erosion potential zones were 13.09%, 6.36%, 11.09%, 22.02% and 16.64% respectively. The study revealed that approximately 69% of the Karnali River basin needs immediate attention from a soil conservation point of view.

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Estimating storm erosion with a rainfall simulator

Canadian Journal of Soil Science ◽

10.4141/s96-079 ◽

1997 ◽

Vol 77 (4) ◽

pp. 669-676 ◽

Cited By ~ 18

Author(s):

S. C. Nolan ◽

L. J. P. van Vliet ◽

T. W. Goddard ◽

T. K. Flesch

Keyword(s):

Soil Loss ◽

Conventional Tillage ◽

Reduced Tillage ◽

Natural Soil ◽

Rainfall Erosivity ◽

Slope Length ◽

Rainfall Simulator ◽

Rainfall Events ◽

Plot Size ◽

Natural Rainfall

Interpreting soil loss from rainfall simulators is complicated by the uncertain relationship between simulated and natural rainstorms. Our objective was to develop and test a method for estimating soil loss from natural rainfall using a portable rainfall simulator (1 m2 plot size). Soil loss from 12 rainstorms was measured on 144-m2 plots with barley residue in conventional tillage (CT), reduced tillage (RT) and zero tillage (ZT) conditions. A corresponding "simulated" soil loss was calculated by matching the simulator erosivity to each storm's erosivity. High (140 mm h−1) and low (60 mm h−1) simulation intensities were examined. The best agreement between simulated and natural soil loss occurred using the low intensity, after making three adjustments. The first was to compensate for the 38% lower kinetic energy of the simulator compared with natural rain. The second was for the smaller slope length of the simulator plot. The third was to begin calculating simulator erosivity only after runoff began. After these adjustments, the simulated soil loss over all storms was 99% of the natural soil loss for CT, 112% for RT and 95% for ZT. Our results show that rainfall simulators can successfully estimate soil loss from natural rainfall events. Key words: Natural rainfall events, simulated rainfall, erosivity, tillage

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Estimation of annual soil erosion dynamics (2005 - 2015) in Pakistan using Revised Universal Soil Loss Equation (RUSLE)

10.22541/au.160946369.92099648/v1 ◽

2021 ◽

Author(s):

Hammad Gilani ◽

Adeel Ahmad ◽

Isma Younes ◽

Sawaid Abbas

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Management Practices ◽

Climatic Factors ◽

Universal Soil Loss Equation ◽

Rainfall Erosivity ◽

Slope Length ◽

Conservation Practice ◽

Administrative Units ◽

Soil Loss Equation

Abrupt changes in climatic factors, exploitation of natural resources, and land degradation contribute to soil erosion. This study provides the first comprehensive analysis of annual soil erosion dynamics in Pakistan for 2005 and 2015 using publically available climatic, topographic, soil type, and land cover geospatial datasets at 1 km spatial resolution. A well-accepted and widely applied Revised Universal Soil Loss Equation (RUSLE) was implemented for the annual soil erosion estimations and mapping by incorporating six factors; rainfall erosivity (R), soil erodibility (K), slope-length (L), slope-steepness (S), cover management (C) and conservation practice (P). We used a cross tabular or change matrix method to assess the annual soil erosion (ton/ha/year) changes (2005-2015) in terms of areas and spatial distriburtions in four soil erosion classes; i.e. Low (<1), Medium (1–5], High (5-20], and Very high (>20). Major findings of this paper indicated that, at the national scale, an estimated annual soil erosion of 1.79 ± 11.52 ton/ha/year (mean ± standard deviation) was observed in 2005, which increased to 2.47 ±18.14 ton/ha/year in 2015. Among seven administrative units of Pakistan, in Azad Jammu & Kashmir, the average soil erosion doubled from 14.44 ± 35.70 ton/ha/year in 2005 to 28.03 ± 68.24 ton/ha/year in 2015. Spatially explicit and temporal annual analysis of soil erosion provided in this study is essential for various purposes, including the soil conservation and management practices, environmental impact assessment studies, among others.

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

10.21203/rs.3.rs-250848/v1 ◽

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.

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ASSESSMENT OF VULNERABILITY IN AGRICULTURAL LAND IN FLOOD PRONE AREAS AND APPLICATION OF MOBILE SMART PHONE IN PROVIDING FLOOD HAZARD INFORMATION IN LAM RIVER BASIN (VIETNAM)

Sustainable Development of Mountain Territories ◽

10.21177/1998-4502-2021-13-2-254-264 ◽

2021 ◽

Vol 13 (2) ◽

pp. 254-264

Author(s):

Nguyen DUNG ◽

◽

Dang MINH ◽

Bui AN ◽

Nguyen NGA ◽

...

Keyword(s):

River Basin ◽

Agricultural Land ◽

Flood Hazard ◽

Smart Phone ◽

Flood Management ◽

Drainage Density ◽

Slope Length ◽

Risk Levels ◽

High Hazard ◽

Very High

Floods are considered to be one of the most costly natural hazards in the Lam river basin causing infrastructure damages as well as devastating the affected area and relatively high death toll. So prevention is necessary for shielding lives and properties. The flood management on the Lam River basin has been considering for many years to minimize damages caused by flooding. The flood hazard zoning map is one of the indispensable tools to provide information about hazard and risk levels in a particular area and to perform the necessary preventive and preparedness procedures. The multicriteria decision analysis based on geographic information systems is used to build a flood hazard map of the study area. The analytic hierarchy process is applied to extract the weights of six criteria affecting the areas where are prone to flooding hazards, including rainfall, slope, relative slope length, soil, land cover, and drainage density. The results showed in 91.32 % (20103.83 km2) of the basin located in the moderate hazard zones to very high hazard zones. Accordingly, this study also determined 4 vulnerability levels to agricultural land including low, medium, high, and very high. About 94% of the total area of agricultural land in the basin are classified into moderate to the very high hazard of flood vulnerability. The paper presents a method that allows flood risk areas in the Lam River basin to receive information about flood risks on a smartphone, making them more aware.

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Effect of Land Use Land Cover and Climate Change on River Flow and Soil Loss in Didessa River Basin, South West Blue Nile, Ethiopia

Hydrology ◽

10.3390/hydrology6010002 ◽

2018 ◽

Vol 6 (1) ◽

pp. 2 ◽

Cited By ~ 4

Author(s):

Kinati Chimdessa ◽

Shoeb Quraishi ◽

Asfaw Kebede ◽

Tena Alamirew

Keyword(s):

Climate Change ◽

Land Use ◽

Land Cover ◽

River Basin ◽

Sediment Yield ◽

Soil Loss ◽

River Flow ◽

Land Use Land Cover ◽

The Impact ◽

Soil Losses

In the Didessa river basin, which is found in Ethiopia, the human population number is increasing at an alarming rate. The conversion of forests, shrub and grasslands into cropland has increased in parallel with the population increase. The land use/land cover change (LULCC) that has been undertaken in the river basin combined with climate change may have affected the Didessa river flow and soil loss. Therefore, this study was designed to assess the impact of LULCC on the Didessa river flow and soil loss under historical and future climates. Land use/land cover (LULC) of the years 1986, 2001 and 2015 were independently combined with the historical climate to assess their individual impacts on river flow and soil loss. Further, the impact of future climates under Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5) scenarios on river flow and soil loss was assessed by combining the pathways with the 2015 LULC. A physically based Soil and Water Assessment Tool (SWAT2012) model in the ArcGIS 10.4.1 interface was used to realize the purpose. Results of the study revealed that LULCC that occurred between 1986 and 2015 resulted in increased average sediment yield by 20.9 t ha−1 yr−1. Climate change under RCP2.6, RCP4.5 and RCP8.5 combined with 2015 LULC increased annual average soil losses by 31.3, 50.9 and 83.5 t ha−1 yr−1 compared with the 2015 LULC under historical climate data. It was also found that 13.4%, 47.1% and 87.0% of the total area may experience high soil loss under RCP2.6, RCP4.5 and RCP8.5, respectively. Annual soil losses of five top-priority sub catchments range from 62.8 to 57.7 per hectare. Nash Stuncliffe Simulation efficiency (NSE) and R2 values during model calibration and validation indicated good agreement between observed and simulated values both for flow and sediment yield.

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GIS estimation of annual average soil loss rate from Hangar River watershed using RUSLE

Journal of Water and Climate Change ◽

10.2166/wcc.2019.181 ◽

2019 ◽

Vol 11 (2) ◽

pp. 529-539 ◽

Cited By ~ 1

Author(s):

Mahmud Mustefa ◽

Fekadu Fufa ◽

Wakjira Takala

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Loss Rate ◽

Study Finding ◽

Mitigation Measures ◽

Rainfall Erosivity ◽

Vegetative Cover ◽

Annual Average ◽

Slope Length ◽

Soil Loss Rate

Abstract Currently, soil erosion is the major environmental problem in the Blue Nile, Hangar watershed in particular. This study aimed to estimate the spatially distributed mean annual soil erosion and map the most vulnerable areas in Hangar watershed using the revised universal soil loss equation. In this model, rainfall erosivity (R-factor), soil erodibility (K-factor), slope steepness and slope length (LS-factor), vegetative cover (C-factor), and conservation practice (P-factor) were considered as the influencing factors. Maps of these factors were generated and integrated in ArcGIS and then the annual average soil erosion rate was determined. The result of the analysis showed that the amount of soil loss from the study area ranges from 1 to 500 tha−1 yr−1 with an average annual soil loss rate of 32 tha−1 yr−1. Considering contour ploughing with terracing as a fully developed watershed management, the resulting soil loss rate was reduced from 32 to 19.2 tha−1 yr−1. Hence, applying contour ploughing with terracing effectively reduces the vulnerability of the watershed by 40%. Based on the spatial vulnerability of the watershed, most critical soil erosion areas were situated in the steepest part of the watershed. The result of the study finding is helpful for stakeholders to take appropriate mitigation measures.

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Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany

Water ◽

10.3390/w11050904 ◽

2019 ◽

Vol 11 (5) ◽

pp. 904 ◽

Cited By ~ 7

Author(s):

Gericke ◽

Kiesel ◽

Deumlich ◽

Venohr

Keyword(s):

Soil Loss ◽

Heavy Rainfall ◽

Climate Models ◽

Climate Model ◽

Arable Land ◽

Rainfall Erosivity ◽

Erosion Risk ◽

Ne Germany ◽

The Impact ◽

R Factors

The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-minutes rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.

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Development Land Erosion Model Using Model Builder GIS (Case Study: Citepus Watershed)

MATEC Web of Conferences ◽

10.1051/matecconf/201814703003 ◽

2018 ◽

Vol 147 ◽

pp. 03003

Author(s):

Dina PA Hidayat ◽

Sih Andajani

Keyword(s):

Soil Loss ◽

Universal Soil Loss Equation ◽

Rainfall Erosivity ◽

Model Builder ◽

Watershed Analysis ◽

Runoff Discharge ◽

The Impact ◽

Impervious Areas ◽

Soil Loss Equation

Land erosion is the impact of increasing runoff discharge and land use conversion to impervious areas. Land erosion usually calculated by formula called USLE (Universal Soil Loss Equation) then modified as MUSLE (Modified Universal Soil Loss Equation). These formula calculate average annual soil loss in tons/areas depends on rainfall erosivity (R), soil erodibility factor (K), topographic factor (LS), cropping and conservation factor (CP). GIS (Geographic Information System) is a system designed to capture, manipulate, and analyze spatial/geographic data. There are some tools related water resources analysis in ArcGIS such as: watershed analysis and also have a tools for user to create their own model called model builder. This research was aim to create a model to calculate land erosion using MUSLE formula by model builder in ArcGIS. The output for this research is the model which can be used to calculate annual soil loss in watershed area based on GIS systems. For the model trial and case study, we use Citepus watershed located on Bandung West Java, that has 5 river branches: Cibogo, Cikakak, Cilimus, Cipedes and Ciroyom. As the result of the model, the value of average annual soil loss in Citepus watershed can be calculated automatically by developed model.

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Time Scale Effects and Interactions of Rainfall Erosivity and Cover Management Factors on Vineyard Soil Loss Erosion in the Semi-Arid Area of Southern Sicily

Water ◽

10.3390/w11050978 ◽

2019 ◽

Vol 11 (5) ◽

pp. 978 ◽

Cited By ~ 12

Author(s):

Giorgio Baiamonte ◽

Mario Minacapilli ◽

Agata Novara ◽

Luciano Gristina

Keyword(s):

Soil Erosion ◽

Soil Loss ◽

Scale Effects ◽

Management Practice ◽

Temporal Analysis ◽

Temporal Scale ◽

Rainfall Erosivity ◽

Erosion Risk ◽

Best Management ◽

The Impact

Several authors describe the effectiveness of cover crop management practice as an important tool to prevent soil erosion, but at the same time, they stress on the high soil loss variability due to the interaction of several factors characterized by large uncertainty. In this paper the Revised Universal Soil Loss Equation (RUSLE) model is applied to two Sicilian vineyards that are characterized by different topographic factors; one is subjected to Conventional Practice (CP) and the other to Best Management Practice (BMP). By using climatic input data at a high temporal scale resolution for the rainfall erosivity (R) factor, and remotely sensed imagery for the cover and management (C) factor, the importance of an appropriate R and C factor assessment and their inter and intra-annual interactions in determining soil erosion variability are showed. Different temporal analysis at ten-year, seasonal, monthly and event scales showed that results at events scales allow evidencing the interacting factors that determine erosion risk features which at other temporal scales of resolution can be hidden. The impact of BMP in preventing soil erosion is described in terms of average saved soil loss over the 10-year period of observation. The evaluation of soil erosion at a different temporal scale and its implications can help stakeholders and scientists formulate better soil conservation practices and agricultural management, and also consider that erosivity rates are expected to raise for the increase of rainfall intensity linked to climate change.

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