Coupling the probability of connectivity and RUSLE reveals pathways of sediment transport and soil loss rates for forest and reclaimed mine landscapes

2021 ◽  
Vol 594 ◽  
pp. 125963
Author(s):  
D. Mahoney ◽  
B. Blandford ◽  
J. Fox
Keyword(s):  
Sediment Transport ◽  
Soil Loss ◽  
Probability Of Connectivity ◽  
Loss Rates

scholarly journals Water Erosion Risk Assessment in the Kenya Great Rift Valley Region

2021 ◽  
Vol 13 (2) ◽  
pp. 844
Author(s):  
George Watene ◽  
Lijun Yu ◽  
Yueping Nie ◽  
Jianfeng Zhu ◽  
Thomas Ngigi ◽  
...  
Keyword(s):  
Rift Valley ◽  
Soil Loss ◽  
Large Change ◽  
Risk Category ◽  
Lake Nakuru ◽  
Erosion Risk ◽  
Erosion Rates ◽  
3.0 T ◽  
Great Rift Valley ◽  
Loss Rates

The Kenya Great Rift Valley (KGRV) region unique landscape comprises of mountainous terrain, large valley-floor lakes, and agricultural lands bordered by extensive Arid and Semi-Arid Lands (ASALs). The East Africa (EA) region has received high amounts of rainfall in the recent past as evidenced by the rising lake levels in the GRV lakes. In Kenya, few studies have quantified soil loss at national scales and erosion rates information on these GRV lakes’ regional basins within the ASALs is lacking. This study used the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates between 1990 and 2015 in the Great Rift Valley region of Kenya which is approximately 84.5% ASAL. The mean erosion rates for both periods was estimated to be tolerable (6.26 t ha−1 yr−1 and 7.14 t ha−1 yr−1 in 1990 and 2015 respectively) resulting in total soil loss of 116 Mt yr−1 and 132 Mt yr−1 in 1990 and 2015 respectively. Approximately 83% and 81% of the erosive lands in KGRV fell under the low risk category (<10 t ha−1 yr−1) in 1990 and 2015 respectively while about 10% were classified under the top three conservation priority levels in 2015. Lake Nakuru basin had the highest erosion rate net change (4.19 t ha−1 yr−1) among the GRV lake basins with Lake Bogoria-Baringo recording annual soil loss rates >10 t ha−1 yr−1 in both years. The mountainous central parts of the KGRV with Andosol/Nitisols soils and high rainfall experienced a large change of land uses to croplands thus had highest soil loss net change (4.34 t ha−1 yr−1). In both years, forests recorded the lowest annual soil loss rates (<3.0 t ha−1 yr−1) while most of the ASAL districts presented erosion rates (<8 t ha−1 yr−1). Only 34% of all the protected areas were found to have erosion rates <10 t ha−1 yr−1 highlighting the need for effective anti-erosive measures.

scholarly journals Soil and Water Loss Rates in Oxisols Under No-tillage System in Western Paraná, Brazil

2018 ◽  
Vol 10 (10) ◽  
pp. 132
Author(s):  
Luana Salete Celante ◽  
Deonir Secco ◽  
Aracéli Ciotti de Marins ◽  
Daniela Trentin Nava ◽  
Flávio Gurgacz ◽  
...  
Keyword(s):  
Water Loss ◽  
Soil Loss ◽  
No Tillage ◽  
Linear Regression Models ◽  
Water Losses ◽  
Soil And Water Loss ◽  
Tillage System ◽  
Soil Losses ◽  
Soil And Water ◽  
Loss Rates

The objective of work was to quantify soil and water loss rates as a function of slope variation, correlating these rates with soybean yield. In addition to developing multiple linear regression models that associate water and soil loss rates in function of their physical attributes. The experiment was conducted in an Oxisols under a no-tillage system. The experiment was carried out in Cascavel, PR, Brazil. Four slopes (3.5%; 8.2%; 11.4% and 13.5%) were considered as treatments. The water and soil loss rates were monitored in the rainfall occurring during the crop development cycle. The water drained in each plot was collected in gutters made of polyvinyl chloride and stored in containers for the quantification of soil and water losses. The stepwise backward method was used to identify the variables that had a significant influence on water and soil losses. The unevenness of the terrain did not influence the soil and water loss rates. The maximum soil and water losses during the soybean cycle were, respectively, 0.01962 Mg ha-1 and 4.07 m3 ha-1. The maximum soil and water losses occurred when the precipitation volume was up to 82 mm. Soil and water losses showed a higher correlation with macroporosity and bulk density. Soybean grain yield showed a higher linear correlation with water, and soil loss and was higher at the slopes of 8.2% and 13.4%. The low water and soil losses demonstrate the soil capacity, managed under a no-tillage system, to minimize environmental impacts.

scholarly journals The effect of natural infrastructure on water erosion mitigation in the Andes

2021 ◽  
Author(s):  
Veerle Vanacker ◽  
Armando Molina ◽  
Miluska Rosas-Barturen ◽  
Vivien Bonnesoeur ◽  
Francisco Román-Dañobeytia ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Water Conservation ◽  
Soil Loss ◽  
Southern Oscillation ◽  
Soil And Water Conservation ◽  
The Andes ◽  
Conservation Measures ◽  
Erosion Mitigation ◽  
Soil And Water ◽  
Loss Rates

Abstract. Soil erosion by water is affecting natural and anthropogenic environments through its impacts on water quality and availability, loss of soil nutrients, flood risk, sedimentation in rivers and streams, and damage to civil infrastructure. Sustainable management aims to avoid, reduce and reverse soil erosion and can provide multiple benefits for the environment, population, and livelihoods. We conducted a systematic review of 121 case studies from the Andes to answer the following questions: (1) Which erosion indicators allow us to assess the effectiveness of natural infrastructure? (2) What is the overall impact of working with natural infrastructure on on-site and off-site erosion mitigation? and (3) Which locations and types of studies are needed to fill critical gaps in knowledge and research? Three major categories of natural infrastructure were considered: protective vegetation, soil and water conservation measures, and adaptation measures that regulate the flow and transport of water. From the suite of physical, chemical and biological indicators commonly used in soil erosion research, two indicators were particularly relevant: soil organic carbon (SOC) of topsoil, and soil loss rates at the plot scale. In areas with protective vegetation and/or soil and water conservation measures, the SOC of topsoil is –on average– 1.3 to 2.8 times higher than in areas under traditional agriculture. Soil loss rates in areas with natural infrastructure were reported to be 38 % to 54 % lower than rates measured in untreated croplands. Further research is needed to evaluate whether the reported effectiveness holds during extreme events related to, for example, El Niño–Southern Oscillation.

scholarly journals Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

2021 ◽  
Vol 13 (21) ◽  
pp. 4360
Author(s):  
Andrew K. Marondedze ◽  
Brigitta Schütt
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Climate Scenarios ◽  
Residential Areas ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Loss Rates

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

scholarly journals Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1124
Author(s):  
Salman A. H. Selmy ◽  
Salah H. Abd Al-Aziz ◽  
Raimundo Jiménez-Ballesta ◽  
Francisco Jesús García-Navarro ◽  
Mohamed E. Fadl
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Wind Erosion ◽  
Water Erosion ◽  
Soil Erodibility ◽  
Gis Techniques ◽  
Five Factors ◽  
Dakhla Oasis ◽  
Erosion Hazards ◽  
Loss Rates

Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor (R-Factor), soil erodibility (K-Factor), topography (LS-Factor), crop types (C-Factor), and conservation practice (P-Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity (CE-Factor), soil erodibility (E-Factor), soil crust (SC-Factor), vegetation cover (VC-Factor), and surface roughness (SR-Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha−1 yr−1 with an average of 1.30 t ha−1 yr−1 and from 0.26 to 3.09 t ha−1 yr−1 with a mean of 1.33 t ha−1 yr−1, respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (<6.7 t ha−1 yr−1), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation (r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression (R2 = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions.

scholarly journals Effects of Landscape Changes on Soil Erosion in the Built Environment: Application of Geospatial-Based RUSLE Technique

2020 ◽  
Vol 12 (15) ◽  
pp. 5898 ◽  
Author(s):  
Bilal Aslam ◽  
Ahsen Maqsoom ◽  
Shahzaib ◽  
Zaheer Abbas Kazmi ◽  
Mahmoud Sodangi ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
Built Environment ◽  
Land Cover ◽  
Soil Loss ◽  
Agricultural Land ◽  
Process Maps ◽  
Erosion Risk ◽  
Regulatory Decision ◽  
Efficient Planning

The world’s ecosystem is severely affected by the increase in the rate of soil erosion and sediment transport in the built environment and agricultural lands. Land use land cover changes (LULCC) are considered as the most significant cause of sediment transport. This study aims to estimate the effect of LULCC on soil erosion potential in the past 20 years (2000–2020) by using Revised Universal Soil Loss Equation (RUSLE) model based on Geographic Information System (GIS). Different factors were analyzed to study the effect of each factor including R factor, K factor, LS factor, and land cover factor on the erosion process. Maps generated in the study show the changes in the severity of soil loss in the Chitral district of Pakistan. It was found out that 4% of the area was under very high erosion risk in the year 2000 which increased to 8% in the year 2020. An increase in agricultural land (4%) was observed in the last 20 years which shows that human activities largely affected the study area. The outcomes of this study will help the stakeholders and regulatory decision makers to control deforestation and take other necessary actions to minimize the rate of soil erosion. Such an efficient planning will also be helpful to reduce the sedimentation in the reservoir of hydraulic dam(s) constructed on Chitral river, which drains through this watershed.

scholarly journals Assessment of Sediment Transport Functions with the Modified SWAT-Twn Model for a Taiwanese Small Mountainous Watershed

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1749 ◽  
Author(s):  
Lu ◽  
Chiang
Keyword(s):  
Sediment Transport ◽  
Soil Loss ◽  
Assessment Tool ◽  
Swat Model ◽  
Extreme Rainfall ◽  
Universal Soil Loss Equation ◽  
Calibration And Validation ◽  
Mountainous Catchment ◽  
Sediment Transportation ◽  
Soil Loss Equation

In Taiwan, the steep landscape and highly vulnerable geology make it difficult to predict soil erosion and sediment transportation via variable transport conditions. In this study, we integrated the Taiwan universal soil loss equation (TUSLE) and slope stability conditions in the soil and water assessment tool (SWAT) as the SWAT-Twn model to improve sediment simulation and assess the sediment transport functions in the Chenyulan watershed, a small mountainous catchment. The results showed that the simulation of streamflow was satisfactory for calibration and validation. Before model calibration and validation for sediment, SWAT-Twn with default sediment transport method performed better in sediment simulation than the official SWAT model (version 664). The SWAT-Twn model coupled with the simplified Bagnold equation could estimate sediment export more accurately and significantly reduce the overestimated sediment yield by 65.7%, especially in highly steep areas. Furthermore, five different sediment transport methods (simplified Bagnold equation with/without routing by particle size, Kodoatie equation, Molinas and Wu equation, and Yang sand and gravel equation) were evaluated. It is suggested that modelers who conduct sediment studies in the mountainous watersheds with extreme rainfall conditions should adjust the modified universal soil loss equation (MUSLE) factors and carefully evaluate the sediment transportation equations in SWAT.

scholarly journals Estimation of soil loss rate using the USLE model for Agewmariayam Watershed, northern Ethiopia

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Gebrehana Girmay ◽  
Awdenegest Moges ◽  
Alemayehu Muluneh
Keyword(s):  
Water Conservation ◽  
Soil Loss ◽  
Loss Rate ◽  
Sub Saharan Africa ◽  
Annual Rainfall ◽  
Rainfall Erosivity ◽  
Northern Ethiopia ◽  
Spatial Risk ◽  
Soil Loss Rate ◽  
Loss Rates

Abstract Background Soil erosion and nutrient depletion threaten food security and the sustainability of agricultural production in sub-Saharan Africa. Estimating soil loss and identifying hotspot areas support combating soil degradation. The aim of this paper is to estimate the soil loss rate and identify hotspot areas using USLE model in the Agewmariam watershed, northern Ethiopia. Methods Rainfall erosivity factor was determined from annual rainfall, soil erodibility factor from soil data, slope length and gradient factor were generated from DEM, cover factor and conservation practice factor obtained from land use cover map. Finally, the parameters were integrated with ArcGIS tools to estimate soil loss rates of the study watershed. Results Mean annual soil loss rates were estimated to be between 0 and 897 t ha−1 year−1 on flatter and steeper slopes, respectively. The total annual soil loss was 51,403.13 tons from the watershed and the annual soil loss rate of the study area was 25 t ha−1 year−1. More than 33% of the study areas were above tolerable soil loss rate (11 t ha−1 year−1). The spatial risk categorization rate was 67.2% severe (> 51 t ha−1 year−1), 5.4% very high (31–50 t ha−1 year−1), 5.8% high (19–30 t ha−1 year−1), 3.2% moderate (12–18 t ha−1 year−1) and 18.3% slight (0–11 t ha−1 year−1). Conclusion The results showed that the severity of erosion occurred on the steep slope cultivation, absence of conservation measures, and sparse nature of the vegetation cover. This area required immediate action of soil and water conservation which accounts for about 33.5% of the total watershed.

Experimental assessment of soil protection by vegetation for current crops and for up-coming EU glyphosate ban

2021 ◽  
Author(s):  
Jakub Stašek ◽  
Josef Krása ◽  
Adela Roudnická ◽  
Tomáš Dostál ◽  
Martin Mistr ◽  
...  
Keyword(s):  
Sediment Transport ◽  
Soil Loss ◽  
Management Practices ◽  
Field Experiments ◽  
Cropping Systems ◽  
Bare Soil ◽  
Soil Protection ◽  
Local Conditions ◽  
Reference Plot ◽  
Factor C

&lt;p&gt;There is still uncertainty in determining vegetation cover and management factor (C factor) for Universal Soil Loss Equation (USLE). Data we use today are often outdated, not specific and not representing local conditions. Current technologies in agriculture and recent crop varieties substantially vary from processes known during USLE (RUSLE) development.&lt;/p&gt;&lt;p&gt;Use of a rainfall simulator on a defined field crop is one way to obtain data for vegetation protection effect. Simulated rainfall is applied on experimental field with crop and bare soil as a reference. Plot size is 8x2 m and runoff and sediment transport is measured. Soil loss ratios are measured for three crop-development stages. Pre-sowing and post-harvest phases are measured as well. All measured data give information about soil protection for the whole season. In the span of 5 years, we have conducted over 340 field experiments on 15 typical, but also newly used crops and various management practices. The results are used in soil erosion and sediment transport analyses or models&amp;#8217; calibration. Metadata of experiments and results are added into a complex and public available database.&lt;/p&gt;&lt;p&gt;The contribution was prepared in the frame of projects No. QK1920224 (Possibilities of anti-erosion protection on farms to avoid the use of glyphosate), and H2020 SHUi (Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping systems).&lt;/p&gt;

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