Digital mapping of RUSLE slope length and steepness factor across New South Wales, Australia

Soil Research ◽  
2015 ◽  
Vol 53 (2) ◽  
pp. 216 ◽  
Author(s):  
Xihua Yang
Keyword(s):  
Land Use Planning ◽  
Soil Loss ◽  
Overland Flow ◽  
New South ◽  
New South Wales ◽  
Slope Angle ◽  
Universal Soil Loss Equation ◽  
Slope Length ◽  
South Wales ◽  
Soil Loss Equation

The Universal Soil Loss Equation (USLE) and its main derivate, the Revised Universal Soil Loss Equation (RUSLE), are widely used in estimating hillslope erosion. The effects of topography on hillslope erosion are estimated through the product of slope length (L) and slope steepness (S) subfactors, or LS factor, which often contains the highest detail and plays the most influential role in RUSLE. However, current LS maps in New South Wales (NSW) are either incomplete (e.g. point-based) or too coarse (e.g. 250 m), limiting RUSLE-based applications. The aim of this study was to develop automated procedures in a geographic information system (GIS) to estimate and map the LS factor across NSW. The method was based on RUSLE specifications and it incorporated a variable cutoff slope angle, which improves the detection of the beginning and end of each slope length. An overland-flow length algorithm for L subfactor calculation was applied through iterative slope-length cumulation and maximum downhill slope angle. Automated GIS scripts have been developed for LS factor calculation so that the only required input data are digital elevation models (DEMs). Hydrologically corrected DEMs were used for LS factor calculation on a catchment basis, then merged to form a seamless LS-factor digital map for NSW with a spatial resolution ~30 m (or 1 s). The modelled LS values were compared with the reference LS values, and the coefficient of efficiency reached 0.97. The high-resolution digital LS map produced is now being used along with other RUSLE factors in hillslope erosion modelling and land-use planning at local and regional scales across NSW.

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

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.

Reducing denitrification loss with nitrification inhibitors following presowing applications of urea to a cottonfield

1994 ◽  
Vol 34 (1) ◽  
pp. 75 ◽  
Author(s):  
DL Chen ◽  
JR Freney ◽  
AR Mosier ◽  
PM Chalk
Keyword(s):  
Soil Loss ◽  
New South ◽  
New South Wales ◽  
Calcium Carbide ◽  
Nitrification Inhibitors ◽  
N Loss ◽  
Mineral N ◽  
N Transformations ◽  
South Wales ◽  
Denitrification Loss

The effects of the nitrification inhibitors nitrapyrin, acetylene (provided by wax-coated calcium carbide), and phenylacetylene on nitrogen (N) transformations and denitrification losses following presowing applications of urea were determined in a cottonfield in the Namoi Valley of New South Wales. The study used 0.05-m-diameter microplots to follow the changes in mineral N, and 0.15-m-diameter microplots fertilised with 15N-labelled urea (6 g N/ m2; 5 atom % 15N) to assess losses of applied N. When urea was applied in February (34 weeks before sowing), 84% of applied N was lost from the soil. Loss of applied N was reduced by addition of nitrapyrin and phenylacetylene, to 53 and 57%, respectively. In the absence of nitrification inhibitors, less N was lost (72% of that applied) from an application in May than from the February application. Addition of acetylene, phenylacetylene, and nitrapyrin reduced losses over the 24 weeks to sowing to 57, 52, and 48%, respectively. These experiments show that N loss from presowing applications of urea can be significantly reduced by the use of nitrification inhibitors, but that the losses of N are still substantial.

scholarly journals An improved method for calculating slope length (λ) and the LS parameters of the Revised Universal Soil Loss Equation for large watersheds

Geoderma ◽  
2017 ◽  
Vol 308 ◽  
pp. 36-45 ◽  
Author(s):  
Hongming Zhang ◽  
Jicheng Wei ◽  
Qinke Yang ◽  
Jantiene E.M. Baartman ◽  
Lingtong Gai ◽  
...  
Keyword(s):  
Soil Loss ◽  
Universal Soil Loss Equation ◽  
Improved Method ◽  
Slope Length ◽  
Soil Loss Equation

scholarly journals Digital mapping of soil erodibility for water erosion in New South Wales, Australia

Soil Research ◽  
2018 ◽  
Vol 56 (2) ◽  
pp. 158 ◽  
Author(s):  
Xihua Yang ◽  
Jonathan Gray ◽  
Greg Chapman ◽  
Qinggaozi Zhu ◽  
Mitch Tulau ◽  
...  
Keyword(s):  
Soil Erosion ◽  
Soil Properties ◽  
Soil Loss ◽  
New South ◽  
New South Wales ◽  
Field Measurements ◽  
Soil Erodibility ◽  
South Wales ◽  
Australian Continent ◽  
K Factor

Soil erodibility represents the soil’s response to rainfall and run-off erosivity and is related to soil properties such as organic matter content, texture, structure, permeability and aggregate stability. Soil erodibility is an important factor in soil erosion modelling, such as the Revised Universal Soil Loss Equation (RUSLE), in which it is represented by the soil erodibility factor (K-factor). However, determination of soil erodibility at larger spatial scales is often problematic because of the lack of spatial data on soil properties and field measurements for model validation. Recently, a major national project has resulted in the release of digital soil maps (DSMs) for a wide range of key soil properties over the entire Australian continent at approximately 90-m spatial resolution. In the present study we used the DSMs and New South Wales (NSW) Soil and Land Information System to map and validate soil erodibility for soil depths up to 100 cm. We assessed eight empirical methods or existing maps on erodibility estimation and produced a harmonised high-resolution soil erodibility map for the entire state of NSW with improvements based on studies in NSW. The modelled erodibility values were compared with those from field measurements at soil plots for NSW soils and revealed good agreement. The erodibility map shows similar patterns as that of the parent material lithology classes, but no obvious trend with any single soil property. Most of the modelled erodibility values range from 0.02 to 0.07 t ha h ha–1 MJ–1 mm–1 with a mean (± s.d.) of 0.035 ± 0.007 t ha h ha–1 MJ–1 mm–1. The validated K-factor map was further used along with other RUSLE factors to assess soil loss across NSW for preventing and managing soil erosion.

scholarly journals The Australian Great Flood of 1954: Estimating the Cost of a Similar Event in 2011

2013 ◽  
Vol 5 (3) ◽  
pp. 199-209 ◽  
Author(s):  
Kevin M. Roche ◽  
K. John McAneney ◽  
Keping Chen ◽  
Ryan P. Crompton
Keyword(s):  
Land Use Planning ◽  
Residential Buildings ◽  
New South ◽  
New South Wales ◽  
Economic Loss ◽  
Flood Vulnerability ◽  
South Wales ◽  
Insured Loss ◽  
Insurance Penetration ◽  
The Cost

Abstract As in many other parts of the globe, migration to the coast and rapid regional development in Australia is resulting in large concentrations of population and insured assets. One of the most rapidly growing regions is southeastern Queensland and northern New South Wales, an area prone to flooding. This study reexamines the Great Flood of 1954 and develops a deterministic methodology to estimate the likely cost if a similar event had occurred in 2011. This cost is estimated using council flood maps, census information, historical observations, and Risk Frontiers' proprietary flood vulnerability functions. The 1954 flood arose from heavy rainfall caused by the passage of a tropical cyclone that made landfall on 20 February near the Queensland–New South Wales border, before heading south. Responsible for some of the largest floods on record for many northern New South Wales' river catchments, it occurred prior to the availability of reliable insurance statistics and the recent escalation in property values. The lower-bound estimate of the insurance loss using current exposure and assuming 100% insurance penetration for residential buildings and contents is AU$3.5 billion, a cost that would make it the third-highest ranked insured loss due to an extreme weather event since 1967. The corresponding normalized economic loss is AU$7.6 billion but the uncertainty about this figure is high. The magnitude of these losses reflects the accumulation of exposure on the floodplains. Risk-informed land-use planning practices and improved building regulations hold the key to reducing future losses.

Design and Implementation of Regional LS Factor Computing Tool Based on GIS and Array Operation

2013 ◽  
Vol 394 ◽  
pp. 509-514
Author(s):  
Hong Ming Zhang ◽  
Qin Ke Yang ◽  
Shu Qin Li ◽  
Mei Li Wang ◽  
Ming Ying ◽  
...  
Keyword(s):  
Soil Loss ◽  
Large Scale ◽  
Universal Soil Loss Equation ◽  
Graphic User Interface ◽  
Slope Length ◽  
Long Time ◽  
Area Unit ◽  
User Friendly ◽  
Gis Software ◽  
Soil Loss Equation

For over 40 years, the universal soil loss equation (USLE) and its revised version the revised universal soil loss equation (RUSLE) have been used all over the world for soil mean annual loss per area unit. Because of the watershed erosion models are under developing, many researchers applied the USLE and RUSLE to estimate soil loss in watershed estimations. However, a major limitation is the difficulty in extracting the LS factor. The geographic information system-based (GIS-based) methods which have been developed for estimating the slope length for USLE and RUSLE model also have limitations. A series of ARC/INFO AML program was created that can calculate LS factor for the USLE, however the program need a very long time to run in wide-ranging areas. The flowpath and cumulative cell length-based method (FCL) overcomes this disadvantage but does not consider the following questions: (1) Some original AML program functions are not achieved, so results are different. (2) Using USLE to calculate LS factor that do not adapt to the erosion environment of China. (3) There isnt a friendly graphic user interface. The purpose of this research was to overcome these limitations and extend the FCL method through Integrating CSLE equation. We developed a LS calculation tool (LS-TOOL) in Microsofts .NET environment using C# with a user-friendly interface. Comparing the LS factor calculated with the FCL method and AML method, LS factor values generated by using LS-TOOL method delivers improved results. The LS-TOOL algorithm can automatically calculate slope length, slope steepness, L factor, S factor, and LS factors, providing the results as ASCII files which can be easily used in some GIS software. This study is an important step forward in conducting fast large-scale erosion evaluation.

Assessment of Soil Loss using Revised Universal Soil Loss Equation (RUSLE): A Remote Sensing and GIS Approach

2018 ◽  
Vol 2 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ajaykumar Kadam ◽  
B. N. Umrikar ◽  
R. N. Sankhua
Keyword(s):  
Remote Sensing ◽  
Soil Loss ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Remote Sensing And Gis ◽  
Universal Soil Loss Equation ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Gis Environment ◽  
Soil Loss Equation

A comprehensive methodology that combines Revised Universal Soil Loss Equation (RUSLE), Remote Sensing data and Geographic Information System (GIS) techniques was used to determine the soil loss vulnerability of an agriculture mountainous watershed in Maharashtra, India. The spatial variation in rate of annual soil loss was obtained by integrating raster derived parameter in GIS environment. The thematic layers such as TRMM [Tropical Rainfall Measuring Mission] derived rainfall erosivity (R), soil erodibility (K), GDEM based slope length and steepness (LS), land cover management (C) and factors of conservation practices (P) were calculated to identify their effects on average annual soil loss. The highest potential of estimated soil loss was 688.397 t/ha/yr. The mean annual soil loss is 1.26 t/ha/yr and highest soil loss occurs on the main watercourse, since high slope length and steepness. The spatial soil loss maps prepared with RUSLE method using remote sensing and GIS can be helpful as a lead idea in arising plans for land use development and administration in the ecologically sensitive hilly areas.

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