Register      Login
Soil Research Soil Research Society
Soil, land care and environmental research
RESEARCH ARTICLE (Open Access)

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

Xihua Yang A C , Jonathan Gray A , Greg Chapman A , Qinggaozi Zhu B , Mitch Tulau A and Sally McInnes-Clarke A
+ Author Affiliations
- Author Affiliations

A New South Wales Office of Environment and Heritage, PO Box A290, Sydney South, NSW 1232, Australia.

B School of Life Sciences, University of Technology, Sydney, Australia.

C Corresponding author. Email: xihua.yang@environment.nsw.gov.au

Soil Research 56(2) 158-170 https://doi.org/10.1071/SR17058
Submitted: 14 February 2017  Accepted: 31 July 2017   Published: 13 October 2017

Journal compilation © CSIRO 2018 Open Access CC BY-NC-ND

Abstract

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.

Additional keywords: digital soil maps, geographic information system (GIS), hillslope erosion, Revised Universal Soil Loss Equation (RUSLE).


References

Belasri A, Lakhouili A (2016) Estimation of soil erosion risk using the Universal Soil Loss Equation (USLE) and geo-information technology in Oued El Makhazine watershed, Morocco. Journal of Geographic Information System 8, 98–107.
Estimation of soil erosion risk using the Universal Soil Loss Equation (USLE) and geo-information technology in Oued El Makhazine watershed, Morocco.Crossref | GoogleScholarGoogle Scholar |

Bonilla CA, Johnson OI (2012) Soil erodibility mapping and its correlation with soil properties in Central Chile. Geoderma 189–190, 116–123.
Soil erodibility mapping and its correlation with soil properties in Central Chile.Crossref | GoogleScholarGoogle Scholar |

Borselli L, Torri D, Poesen J, Iaquinta P (2012) A robust algorithm for estimating soil erodibility in different climates. Catena 97, 85–94.
A robust algorithm for estimating soil erodibility in different climates.Crossref | GoogleScholarGoogle Scholar |

Bronick CJ, Lal R (2005) Soil structure and management: a review. Geoderma 124, 3–22.
Soil structure and management: a review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2cXhtVOru7jP&md5=d1d0d5563f7af42ef6d8508a96fc8a36CAS |

Edwards K (1987) ‘Runoff and soil loss studies in New South Wales.’ Technical Handbook No. 10. (Soil Conservation Service of NSW: Sydney, NSW)

Gray JM, Thomas FA, Bishop TFA, Wilford JR (2016) Lithology and soil relationships for soil modelling and mapping. Catena 147, 429–440.
Lithology and soil relationships for soil modelling and mapping.Crossref | GoogleScholarGoogle Scholar |

Grundy MJ, Viscarra Rossel RA, Searle RD, Wilson PL, Chen C, Gregory LJ (2015) Soil and landscape grid of Australia. Soil Research 53, 835–844.
Soil and landscape grid of Australia.Crossref | GoogleScholarGoogle Scholar |

Hussein MH, Kariem TH, Othman AK (2007) Predicting soil erodibility in northern Iraq using natural runoff plot data. Soil and Tillage Research 94, 220–228.
Predicting soil erodibility in northern Iraq using natural runoff plot data.Crossref | GoogleScholarGoogle Scholar |

Isbell RF (2002) ‘The Australian soil classification (revised edition).’ (CSIRO Publishing: Melbourne, Vic.)

IUSS Working Group WRB (2015) World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. (FAO: Rome)

Knapen A, Poesen J, De Baets S (2007) Seasonal variations in soil erosion resistance during concentrated flow for a loess-derived soil under two contrasting tillage practices. Soil & Tillage Research 94, 425–440.
Seasonal variations in soil erosion resistance during concentrated flow for a loess-derived soil under two contrasting tillage practices.Crossref | GoogleScholarGoogle Scholar |

Lal R, Pimental D (2008) Soil erosion: a carbon sink or source? Science 319, 1040–1042.
Soil erosion: a carbon sink or source?Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXis1Kksrc%3D&md5=898ed8a318d85783dd00b3cf5a56e433CAS |

Loch RJ, Rosewell CJ (1992) Laboratory methods for measurement of soil erodibilities (K-factor) for the Universal Soil Loss Equation. Australian Journal of Soil Research 30, 233–248.
Laboratory methods for measurement of soil erodibilities (K-factor) for the Universal Soil Loss Equation.Crossref | GoogleScholarGoogle Scholar |

Loch RJ, Slater BK, Devoil C (1998) Soil erodibility (Km) values for some Australian soils. Australian Journal of Soil Research 36, 1045–1055.
Soil erodibility (Km) values for some Australian soils.Crossref | GoogleScholarGoogle Scholar |

Lu H, Prosser IP, Moran CJ, Gallant JC, Priestley G, Stevenson JG (2003) Predicting sheetwash and rill erosion over the Australian continent. Australian Journal of Soil Research 41, 1037–1062.
Predicting sheetwash and rill erosion over the Australian continent.Crossref | GoogleScholarGoogle Scholar |

Murphy CL, Fogarty PJ, Ryan PJ 1998. Soil regolith stability classification for state forests of eastern New South Wales. Technical Report No. 41, NSW Department of Land and Water Conservation, Sydney, NSW.

Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models, Part 1: a discussion of principles. Journal of Hydrology 10, 282–290.
River flow forecasting through conceptual models, Part 1: a discussion of principles.Crossref | GoogleScholarGoogle Scholar |

Office of Environment & Heritage (OEH) (2017a) The Soil and Land Information System (SALIS). Available at http://www.environment.nsw.gov.au/topics/land-and-soil/soil-data/salis [verified 5 February 2017].

Office of Environment & Heritage (OEH) (2017b) Digital soil mapping of key soil properties over NSW. Technical Report, NSW OEH, Sydney, NSW. Available at http://www.environment.nsw.gov.au/Research-and-publications/Publications-search/Digital-soil-mapping-of-key-soil-properties-over-NSW [verified 14 June 2017].

Office of Environment & Heritage (OEH) (2017c) Great Soil Group (GSG) Soil Type map of NSW. Available at http://data.environment.nsw.gov.au/dataset/great-soil-group-gsg-soil-type-map-of-nsw1cf19 [verified 11 September 2017].

Olson KR, Al-Kaisi M, Lal R, Cihacek L (2016) Impact of soil erosion on soil organic carbon stocks. Journal of Soil and Water Conservation 71, 61A–67A.
Impact of soil erosion on soil organic carbon stocks.Crossref | GoogleScholarGoogle Scholar |

Panagos P, Meusburger K, Ballabio C, Borrelli P, Alewell C (2014) Soil erodibility in Europe: a high-resolution dataset based on LUCAS. The Science of the Total Environment 479–480, 189–200.
Soil erodibility in Europe: a high-resolution dataset based on LUCAS.Crossref | GoogleScholarGoogle Scholar |

Paton TR, Humphreys GS, Mitchell PB (1995) ‘Soils: a new global view.’ (UCL Press: London, UK)

Poesen JW, Torri D, Bunte K (1994) Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141–166.
Effects of rock fragments on soil erosion by water at different spatial scales: a review.Crossref | GoogleScholarGoogle Scholar |

Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) ‘Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE).’ Agricultural Handbook, Vol. 703. (USDA: Washington, DC)

Riley SJ, Crozier P, Blong RJ (1981) An inexpensive and easily installed runoff plot. Journal of the Soil Conservation Service NSW 37, 144–147.

Römkens MJM, Poesen JWA, Wang JY (1988). Relationship between the USLE soil erodibility factor and soil properties. In ‘Conservation for future generations’. (Ed. S. Rimwanichland) pp. 371–38. (Department of Land Development: Bangkok, Thailand)

Rosewell CJ (1993) SOILOSS – a program to assist in the selection of management practices to reduce erosion. Technical Handbook No. 11, Soil Conservation Services, Sydney, NSW.

Rosewell CJ, Loch RJ (2002). Estimation of the RUSLE soil erodibility factor. In ‘Soil physical measurement and interpretation for land evaluation’. (Eds N. McKenzie, K. Coughlan, H. Cresswell) pp. 361–369. (CSIRO Publishing: Melbourne, Vic.)

Saha RJ, Tomar JMS, Ghosh PK (2007) Evaluation and selection of multipurpose tree for improving soil hydro-physic behavior under hilly eco-system of north east India. Agroforestry Systems 69, 239–247.
Evaluation and selection of multipurpose tree for improving soil hydro-physic behavior under hilly eco-system of north east India.Crossref | GoogleScholarGoogle Scholar |

Sekhar KR, Rao BV (2002) Evaluation of sediment yield by using remote sensing and GIS: a case study from the Phulang Vagu watershed, Nizamabad District (AP), India. International Journal of Remote Sensing 23, 4499–4509.
Evaluation of sediment yield by using remote sensing and GIS: a case study from the Phulang Vagu watershed, Nizamabad District (AP), India.Crossref | GoogleScholarGoogle Scholar |

Sharply AN, Williams JR (1990) EPIC-Erosion/Productivity Impact Calculator I, model documentation. U.S. Department of Agriculture Technical Bulletin No. 1768, USDA Agricultural Research Service, Washington, DC.

Silburn DM (2011) Hillslope runoff and erosion on duplex soils in grazing lands in semi -arid central Queensland. III. USLE erodibility (K-factors) and cover–soil loss relationships. Soil Research 49, 127–134.
Hillslope runoff and erosion on duplex soils in grazing lands in semi -arid central Queensland. III. USLE erodibility (K-factors) and cover–soil loss relationships.Crossref | GoogleScholarGoogle Scholar |

Soil and Landscape Grid of Australia (2017) Soil and Landscape Grid of Australia. Available at http://www.clw.csiro.au/aclep/soilandlandscapegrid/index.html [verified 5 February 2017].

Stace HCT, Hubble GD, Brewer R, Northcote KH, Sleeman JR, Mulcahy MJ, Hallsworth EG (1968) ‘A handbook of Australian soils.’ (CSIRO and International Society of Soil Science, Rellim Technical Publications: Glenside, SA)

Teng HF, Viscarra Rossel RA, Shi Z, Behrens T, Chappell A, Bui E (2016) Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia. Environmental Modelling & Software 77, 156–167.
Assimilating satellite imagery and visible–near infrared spectroscopy to model and map soil loss by water erosion in Australia.Crossref | GoogleScholarGoogle Scholar |

Torri D, Poesen J, Borselli L (1997) Predictability and uncertainty of the soil erodibility factor using a global dataset. Catena 31, 1–22.
Predictability and uncertainty of the soil erodibility factor using a global dataset.Crossref | GoogleScholarGoogle Scholar |

Torri D, Poesen J, Borselli L (2002) Corrigendum to ‘Predictability and uncertainty of the soil erodibility factor using a global dataset’. Catena 46, 309–310.
Corrigendum to ‘Predictability and uncertainty of the soil erodibility factor using a global dataset’.Crossref | GoogleScholarGoogle Scholar |

USDA (2013) Revised Universal Soil Loss Equation, version 2 (RUSLE2). Available at http://fargo.nserl.purdue.edu/rusle2_dataweb/ [verified 5 February 2017].

Wang B, Zheng FL, Guan YH (2016) Improved USLE-K-factor prediction: a case study on water erosion areas in China. International Soil and Water Conservation Research 4, 168–176.
Improved USLE-K-factor prediction: a case study on water erosion areas in China.Crossref | GoogleScholarGoogle Scholar |

Wischmeier WH, Smith DD (1978) ‘Predicting Rainfall Erosion Losses: A Guide to Conservation Planning.’ Agricultural Handbook Vol. 537. (USDA: Washington, DC)

Wischmeier WH, Johnson CB, Cross BV (1971) A soil erodibility nomograph for farmland and construction sites. Journal of Soil and Water Conservation 26, 183–189.

Yang X (2014) Deriving RUSLE cover factor from time-series fractional vegetation cover for hillslope erosion risk monitoring in New South Wales. Soil Research 52, 253–261.
Deriving RUSLE cover factor from time-series fractional vegetation cover for hillslope erosion risk monitoring in New South Wales.Crossref | GoogleScholarGoogle Scholar |

Yang X (2015) Digital mapping of RUSLE slope length and steepness factor across New South Wales. Soil Research 53, 216–225.

Yang X, Yu BF (2015) Modelling and mapping rainfall erosivity in New South Wales, Australia. Soil Research 53, 178–189.

Zhang KL, Shu AP, Xu XL, Yang QK, Yu B (2008) Soil erodibility and its estimation for agricultural soils in China. Journal of Arid Environments 72, 1002–1011.
Soil erodibility and its estimation for agricultural soils in China.Crossref | GoogleScholarGoogle Scholar |