GIS‐based rapid assessment of erosion risk in a small catchment in the wet/dry tropics of Australia

Boggs, Guy S.; Devonport, Christopher; Evans, K. G.; Puig, Patricia

doi:10.1002/ldr.457

Cited by 71 publications

(42 citation statements)

References 9 publications

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“…The SIBERIA erosion model has recently been tested and evaluated for erosion assessment of post-mining landforms (Boggs et al, 2000(Boggs et al, , 2001Evans et al, 1999Hancock et al, 2000Willgoose and Riley, 1998). A more detailed description of SIBERIA can be found in Willgoose et al (1991a,b,c,d).…”

Section: The Siberia Landscape Evolution Modelmentioning

confidence: 98%

The impact of different gridding methods on catchment geomorphology and soil erosion over long timescales using a landscape evolution model

Hancock

2006

Earth Surf Processes Landf

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Many digital elevation models are gridded from irregularly spaced data. This study examines differences in catchment geomorphology and hydrology as a result of kriging and Delaunay triangulation gridding methods. Both methods have been widely used as tools for placing irregularly spaced data onto a regular grid. In the past, numerical modelling has been performed with little assessment of model input variability and the variability produced by different gridding methods has not been fully assessed. Given the importance of the impact of digital elevation model error and potential impact on model output, little work has been done to understand the impact of this type of potential error. Examination of the different catchment realizations demonstrates that when using kriging (point or block) or Delaunay triangulation there are subtle differences in catchment area and elevation as well as networking properties. Subtle differences also exist in hillslope profile. Nevertheless, when comparing catchment descriptors such as the hypsometric curve, area-slope relationship and cumulative area distribution there is little hydrological and geomorphological difference between these catchments. Further, when these digital elevation models are used as landscape input in a long-term landscape evolution model (i.e. SIBERIA) there is little geomorphological difference or hydrological difference between the two digital elevation models after a 50 000 year modelled period. Consequently either method is appropriate for the study catchment. These findings provide confidence in the conversion of irregularly spaced data onto a regular grid using kriging or Delaunay triangulation and that either method can be used.

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Section: The Siberia Landscape Evolution Modelmentioning

confidence: 98%

The impact of different gridding methods on catchment geomorphology and soil erosion over long timescales using a landscape evolution model

Hancock

2006

Earth Surf Processes Landf

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“…The SIBERIA erosion model has recently been tested and evaluated for erosion assessment of proposed post-mining landforms (Willgoose and Riley 1998;Evans et al, 1999Boggs et al, 2000Boggs et al, , 2001Hancock et al, 2000Hancock et al, , 2002Lowry et al 2006). A more detailed description of SIBERIA can be found in Willgoose et al (1991a-d).…”

Section: The Siberia Landscape Evolution Modelmentioning

confidence: 99%

A catchment scale evaluation of the SIBERIA and CAESAR landscape evolution models

Hancock

Lowry²,

Coulthard

et al. 2010

Earth Surf Processes Landf

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Landscape evolution models provide a way to determine erosion rates and landscape stability over times scales from tens to thousands of years. The SIBERIA and CAESAR landscape evolution models both have the capability to simulate catchment-wide erosion and deposition over these time scales. They are both cellular, operate over a digital elevation model of the landscape, and represent fl uvial and slope processes. However, they were initially developed to solve research questions at different time and space scales and subsequently the perspective, detail and process representation vary considerably between the models. Notably, CAESAR simulates individual events with a greater emphasis on fl uvial processes whereas SIBERIA averages erosion rates across annual time scales. This paper describes how both models are applied to Tin Camp Creek, Northern Territory, Australia, where soil erosion rates have been closely monitored over the last 10 years. Results simulating 10 000 years of erosion are similar, yet also pick up subtle differences that indicate the relative strengths and weaknesses of the two models. The results from both the SIBERIA and CAESAR models compare well with independent fi eld data determined for the site over different time scales. Representative hillslope cross-sections are very similar between the models. Geomorphologically there was little difference between the modelled catchments after 1000 years but signifi cant differences were revealed at longer simulation times. Importantly, both models show that they are sensitive to input parameters and that hydrology and erosion parameter derivation has long-term implications for sediment transport prediction. Therefore selection of input parameters is critical. This study also provides a good example of how different models may be better suited to different applications or research questions.

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“…For example, a combination of remote sensing, GIS, and RUSLE provides the potential to estimate soil erosion loss on a cell-by-cell basis (Millward and Mersey, 1999). Boggs et al (2001) assessed soil erosion risk based on a simplified version of RUSLE using digital elevation model (DEM) data and land-units maps. Bartsch et al (2002) used GIS techniques to interpolate RUSLE parameters for sample plots to determine the soil erosion risk at Camp Wiliams, Utah.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS

et al. 2004

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This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall-runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0Á2, LS values of less than 2Á5, and C values of less than 0Á25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia.

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GIS‐based rapid assessment of erosion risk in a small catchment in the wet/dry tropics of Australia

Cited by 71 publications

References 9 publications

The impact of different gridding methods on catchment geomorphology and soil erosion over long timescales using a landscape evolution model

The impact of different gridding methods on catchment geomorphology and soil erosion over long timescales using a landscape evolution model

A catchment scale evaluation of the SIBERIA and CAESAR landscape evolution models

Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS

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