ASSESSMENT OF SOIL EROSION USING FOURNIER INDEXES TO ESTIMATE RAINFALL EROSIVITY

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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Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

10.5194/hess-2020-187 ◽

2020 ◽

Author(s):

Qiang Dai ◽

Jingxuan Zhu ◽

Shuliang Zhang ◽

Shaonan Zhu ◽

Dawei Han ◽

...

Keyword(s):

Soil Erosion ◽

Water Conservation ◽

Large Scale ◽

Weather Prediction ◽

Wrf Model ◽

Analysis Data ◽

Potential Effect ◽

Rainfall Erosivity ◽

Size Distributions ◽

Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driving force for soil erosion and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy (KE). As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops KE. With the development of global atmospheric re-analysis data, numerical weather prediction (NWP) techniques become a promising way to estimate rainfall KE directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware [TAA]) to obtain raindrop size distributions, rainfall KE and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, TAA had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

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Estimation of rainfall erosivity based on WRF-derived raindrop size distributions

Hydrology and Earth System Sciences ◽

10.5194/hess-24-5407-2020 ◽

2020 ◽

Vol 24 (11) ◽

pp. 5407-5422

Author(s):

Qiang Dai ◽

Jingxuan Zhu ◽

Shuliang Zhang ◽

Shaonan Zhu ◽

Dawei Han ◽

...

Keyword(s):

Soil Erosion ◽

Kinetic Energy ◽

Water Conservation ◽

Large Scale ◽

Weather Prediction ◽

Wrf Model ◽

Analysis Data ◽

Rainfall Erosivity ◽

Size Distributions ◽

Raindrop Size

Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.

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Mapping rainfall erosivity at a regional scale: a comparison of interpolation methods in the Ebro Basin (NE Spain)

Hydrology and Earth System Sciences ◽

10.5194/hess-13-1907-2009 ◽

2009 ◽

Vol 13 (10) ◽

pp. 1907-1920 ◽

Cited By ~ 71

Author(s):

M. Angulo-Martínez ◽

M. López-Vicente ◽

S. M. Vicente-Serrano ◽

S. Beguería

Keyword(s):

Soil Erosion ◽

Prediction Models ◽

Regional Scale ◽

Geostatistical Simulation ◽

High Temporal Resolution ◽

Rainfall Erosivity ◽

Ebro Basin ◽

Spatial Coverage ◽

Interpolation Techniques ◽

Ne Spain

Abstract. Rainfall erosivity is a major causal factor of soil erosion, and it is included in many prediction models. Maps of rainfall erosivity indices are required for assessing soil erosion at the regional scale. In this study a comparison is made between several techniques for mapping the rainfall erosivity indices: i) the RUSLE R factor and ii) the average EI30 index of the erosive events over the Ebro basin (NE Spain). A spatially dense precipitation data base with a high temporal resolution (15 min) was used. Global, local and geostatistical interpolation techniques were employed to produce maps of the rainfall erosivity indices, as well as mixed methods. To determine the reliability of the maps several goodness-of-fit and error statistics were computed, using a cross-validation scheme, as well as the uncertainty of the predictions, modeled by Gaussian geostatistical simulation. All methods were able to capture the general spatial pattern of both erosivity indices. The semivariogram analysis revealed that spatial autocorrelation only affected at distances of ~15 km around the observatories. Therefore, local interpolation techniques tended to be better overall considering the validation statistics. All models showed high uncertainty, caused by the high variability of rainfall erosivity indices both in time and space, what stresses the importance of having long data series with a dense spatial coverage.

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SVM-Based Geospatial Prediction of Soil Erosion Under Static and Dynamic Conditioning Factors

MATEC Web of Conferences ◽

10.1051/matecconf/201820304004 ◽

2018 ◽

Vol 203 ◽

pp. 04004

Author(s):

Muhammad Raza Ul Mustafa ◽

Abdulkadir Taofeeq Sholagberu ◽

Khamaruzaman Wan Yusof ◽

Ahmad Mustafa Hashim ◽

Muhammad Waris Ali Khan ◽

...

Keyword(s):

Soil Erosion ◽

Watershed Management ◽

Land Use Planning ◽

Land Surface ◽

Vegetation Index ◽

Support Vector ◽

Susceptibility Map ◽

Rainfall Erosivity ◽

Kappa Index ◽

Conditioning Factors

Land degradation caused by soil erosion remains an important global issue due to its adverse consequences on food security and environment. Geospatial prediction of erosion through susceptibility analysis is very crucial to sustainable watershed management. Previous susceptibility studies devoid of some crucial conditioning factors (CFs) termed dynamic CFs whose impacts on the accuracy have not been investigated. Thus, this study evaluates erosion susceptibility under the influence of both non-redundant static and dynamic CFs using support vector machine (SVM), remote sensing and GIS. The CFs considered include drainage density, lineament density, length-slope and soil erodibility as non-redundant static factors, and land surface temperature, soil moisture index, vegetation index and rainfall erosivity as the dynamic factors. The study implements four kernel tricks of SVM with sequential minimal optimization algorithm as a classifier for soil erosion susceptibility modeling. Using area under the curve (AUC) and Cohen’s kappa index (k) as the validation criteria, the results showed that polynomial function had the highest performance followed by linear and radial basis function. However, sigmoid SVM underperformed having the lowest AUC and k values coupled with higher classification errors. The CFs’ weights were implemented for the development of soil erosion susceptibility map. The map would assist planners and decision makers in optimal land-use planning, prevention of soil erosion and its related hazards leading to sustainable watershed management.

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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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Effects of Climate and Land-Cover Changes on Soil Erosion in Brazilian Pantanal

Sustainability ◽

10.3390/su11247053 ◽

2019 ◽

Vol 11 (24) ◽

pp. 7053 ◽

Cited By ~ 4

Author(s):

Carina Colman ◽

Paulo Oliveira ◽

André Almagro ◽

Britaldo Soares-Filho ◽

Dulce Rodrigues

Keyword(s):

Climate Change ◽

Land Use ◽

Soil Erosion ◽

Land Cover ◽

Ecosystem Service ◽

Soil Loss ◽

Land Cover Changes ◽

Rainfall Erosivity ◽

Comprehensive Planning ◽

Brazilian Pantanal

The Pantanal biome integrates the lowlands of the Upper Paraguay Basin (UPB), which is hydrologically connected to the biomes of the Cerrado and Amazon (the highlands of the UPB). The effects of recent land-cover and land-use (LCLU) changes in the highlands, combined with climate change, are still poorly understood in this region. Here, we investigate the effects of soil erosion in the Brazilian Pantanal under climate and LCLU changes by combining different scenarios of projected rainfall erosivity and land-cover management. We compute the average annual soil erosion for the baseline (2012) and projected scenarios for 2020, 2035, and 2050. For the worst scenario, we noted an increase in soil loss of up to 100% from 2012 to 2050, associated with cropland expansion in some parts of the highlands. Furthermore, for the same period, our results indicated an increase of 20 to 40% in soil loss in parts of the Pantanal biome, which was associated with farmland increase (mainly for livestock) in the lowlands. Therefore, to ensure water, food, energy, and ecosystem service security over the next decades in the whole UPB, robust and comprehensive planning measures need to be developed, especially for the most impacted areas found in our study.

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Assessment of Soil and Water Conservation Practices in the Loess Hilly Region Using a Coupled Rainfall-Runoff-Erosion Model

Sustainability ◽

10.3390/su12030934 ◽

2020 ◽

Vol 12 (3) ◽

pp. 934 ◽

Cited By ~ 2

Author(s):

Mengfan Cai ◽

Chunjiang An ◽

Christophe Guy ◽

Chen Lu

Keyword(s):

Climate Change ◽

Soil Erosion ◽

Water Conservation ◽

Surface Runoff ◽

Rainfall Erosivity ◽

Conservation Practices ◽

Emission Scenarios ◽

Soil And Water Loss ◽

Hilly Region ◽

Soil And Water

Soil and water conservation practices (SWCPs) are widely used to control soil and water loss. Quantifying the effect of SWCPs and climate change on soil and water erosion is important for regional environmental management. In this study, the Soil Conservation Service Curve Number (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) were employed to investigate the patterns of surface runoff and soil erosion with different SWCPs in the hilly region on the Loess Plateau of China. The impact of climate change under RCP4.5 and RCP8.5 emission scenarios was considered from 2020 to 2050. Surface runoff grew with the increased rainfall and rainfall erosivity, while soil erosion presented large variations between years due to uneven distribution of rainfall and rainfall erosivity under two scenarios. Different SWCPs significantly reduced surface soil and water loss. Compared with bare slopes, the reduction rates were 15–40% for surface runoff and 35–67% for soil erosion under RCP4.5 and RCP8.5 emission scenarios, respectively. The combination of shrub and horizontal terracing was recommended due to its low water cost for sediment control among seven SWCPs.

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