Grazing (hillslope erosion) projects

Grazing (hillslope erosion) project computation

November 2023 release

The P2R Projector Grazing project type uses survey responses to assess the grazing management practices driving the distribution of ground cover over a project area of interest. The survey responses allow the ‘before’ (current) and ‘after’ (improved) practices to be aligned with the Reef Water Quality Risk Management Framework. This provides the opportunity to use the Revised Universal Soil Loss Equation (RUSLE) to estimate potential changes in hillslope erosion rate caused through (assumed) increases in ground cover. The assumed increases of ground cover in response to improved management practices are applied as adjusted C (cover) Factor values in the RUSLE and are taken from the matrix of C Factor Change Responses used in the Great Barrier Reef Catchment Modelling Program. This matrix varies the parameters a and b of the equation taking the form:

Adjusted CFactor = a * Cfactor^b

(Equation 1)

With the variation of parameters aligned to a spatial layer representing the Grazing Land Management Land Types that has been intersected with a representation of the degree to which grazing activities are considered ‘open’ or ‘closed/forested’ (GLM). The features of this intersected data set have been simplified to assist in storage and analysis.

To apply the RUSLE to each of the GLM polygons within a defined project area of interest, P2R Projector submits the footprint of each intersection (with relevant C Factor change parameters a and b) to a computation engine known as PEPER. PEPER is part of the VegMachine suite of analysis tools, however, is only accessible through P2R Projector. PEPER is called from P2R Projector via a specific API, which triggers an analysis of 11 Spring (late dry season, 2012-2022) representations of seasonal ground cover combined with Rainfall Erosivity (R Factor), Soil Erodibility (K Factor) and Slope Steepness (S Factor) from the Queensland Spatial Catalogue (RKS layer combined from individual factor layers).

Note: Prior to the 2023 version upgrade, PEPER-based P2R Projector estimates of erosion rates used a Statewide RKLS (see the inclusion of Slope Length (L) Factor) representation and 11 Spring ground cover layers spanning 2004-2014. The 2023 PEPER data upgrade will, in most cases, provide lower erosion estimates. However, changing the time span of consideration for PEPER calculations does mean that localised erosion estimate increases might be observed.

The PEPER computation returns an estimated average BASELINE total soil erosion rate (tons per hectare per year, BASERATE) for each feature submitted, and an estimated average ADJUSTED total soil erosion rate for a cover adjusted scenario (tons per hectare per year, ADJUSTRATE).

BASERATE is the average of all RKS * C Factor cell values within the feature’s footprint (that is, the average of 11 representations of RKS * C Factor for the feature). C Factor is converted from cover (%) using an efficient transformation that combines 2 conversions:

Convert satellite (objective) cover to visual cover according to Trevithick and Scarth (2013)
Convert visual cover to C Factor according to Rosewell (1997) (found conveniently in Searle and Ellis 2009)

Ln(C Factor) = -0.000545 * Cover^1.9 – 0.802962

(Equation 2)

With C Factor converted to groundcover (%) using

Cover = ((-1 * (Ln(C Factor) + 0.802962)) / 0.000545)^(1.0/1.9)

(Equation 3)

To provide ADJUSRATE, PEPER adjusts the cover cell values accordingly and performs the same average of 11 RKS * (adjusted) C Factor calculations. The cover adjustment performed by PEPER is calculated for each of the 11 layers of ground cover data independently. For each cover layer, an initial mean cover value is calculated for the feature. This mean cover value is converted to a mean C Factor value (Equation 2), with an adjusted C Factor (representing improved practices) calculated from Equation 1. The adjusted C Factor is converted back to a target improved cover value (%) (Equation 3), which is used to iteratively adjust observed cover values until the adjusted mean cover resembles the target improved cover value.

P2R Projector combines the PEPER-derived erosion rates (baseline and adjusted) proportionately according to the area of each GLM intersection within the project area of interest. Project area baseline and improved erosion rates are converted to a mass (tons per year) of total eroded soil (fine + coarse sediment). These site/local sediment masses are converted to an estimated end-of-river fine sediment export mass (tons per year) by applying assumed fine sediment proportion, fine sediment (to-stream) delivery ratio, and riverine system delivery ratio (RSDR, delivery to Great Barrier Reef Lagoon):

Fine Sediment to GBR = Total Eroded Sediment (site) * FSPROPDR * RSDR

(Equation 4)

Where:

RSDR is sourced from P2R catchment water quality models (Report Card 2019)
FSPROPDR is combined fine sediment proportion and to-stream delivery ratio, assumed 0.05

Fine sediment saved, report by P2R Projector, is the difference between baseline and adjusted fine sediment to GBR estimates.

April 2024 release

The K factor and S factor layers used in the Universal Soil Loss Equation (USLE) to predict fine sediment savings from grazing management projects have been replaced by new layers developed by the Qld Government. This effects all existing grazing projects and they will need to be recalculated before any further editing.

The new K factor (soil erodbility) utilises improvements in the Qld Government’s Soil and Landscape Information database, an expanded representation of the presence of significant rockiness, and greater use of Soil Profile Classes to characterise soil erodibility.

The new S factor (slope steepness) uses improved terrain analysis environments to better represent the expected range of slope steepness values derived from a digital elevation model.

The use of updated K and S factor layers brings P2R Projector calculations of hillslope erosion rates (and assumed erosion reductions) into closer alignment with the P2R Catchment Scale Water Quality Modelling approach.

References

Rosewell (1997). Potential sources of sediments and nutrients: sheet and rill erosion and phosphorus sources. Department of the Environment, Sport and Territories, Canberra, Australia State of the Environment Technical Paper Series (Inland Waters)

Searle, R.D. and Ellis, R.J. (2009). Incorporating variable cover in erosion algorithms for grazing lands within catchment scale water quality models. In Anderssen, R.S., R.D. Braddock and L.T.H. Newham (eds) 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation, July 2009, pp. 3542-3548. https://www.mssanz.org.au/modsim09/I8/searle.pdf

Trevithick, R. and Scarth, P. (2013). Estimating RUSLE C-Factor Values for Great Barrier Reef Catchments using Satellite Derived Ground Cover Estimates. In Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2013, pp 3246-3252. https://www.mssanz.org.au/modsim2013/L22/trevithick.pdf

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