data is a fundamental prerequisite for prioritising rehabilitation and providing the baseline data for rehabilitation design .
• Capturing LiDAR over a significant portion of the remainder of the BBB should be a high priority ( except in steep terrain ).
• The largest source of error in determining sediment abatement associated with gully remediation is the determination of the baseline sediment ( and nutrient ) yield . The whole program will benefit from having greater investment in the baseline sediment and nutrient yield assessment .
• There is a need to accurately monitor sediment yield reductions from all treatment intensity and sediment yield gully classes , but particularly the low intensity gullies . These represent a significant portion of the gully population and collectively a significant proportion of the estimated sediment yield . Given that only moderate to high yielding gullies were examined in the field , current estimated yields from lower yielding gullies are likely to be an overestimate .
• A set of long-term monitoring sites should be established at sites of low and minimal intensity treatments . It is important to establish whether yield reductions associated with these measures are maintained through drought and flood and associated grazing cycles . Note that at these sites the investment in monitoring may need to be larger than the investment in actual intervention .
• Further soil material sampling undertaken as part of the design phase for the next priority sites should be fed back into the dataset underpinning this analysis to further improve the model .
7 . Acquisition of high-resolution multi-spectral remote sensing data ( including radiometrics ) and further field sampling of alluvial landscapes should be a priority for investment to improve alluvial soil landscape mapping .
8 . A standardised approach to collating gully rehabilitation cost data should be adopted across all projects . The hierarchical approach outlined in this study is a robust approach that can ensure comparability between all projects .
9 . The modelled gully-specific remediation cost estimates produced in this study should be tested against real cost data as they become available . Site and project cost data should be accumulated by a central agency . Particular attention should be focused on economies of scale data .
10 . The treatment effectiveness across all treatment intensity classes , but particularly the lower intensity classes , is the weakest analysis in the study . The establishment of a range of monitoring sites to assess the effectiveness of different treatments is a high priority , along the lines of those conducted under NESP projects 3.1.7 and 5.9 . Baseline monitoring at sites planned for future rehabilitation works should be established as soon as possible . Results from NESP research associated with the Strathalbyn gully treatments shows that the average Remediation Effectiveness Ratio at the site is 98 %. Hence the 80 % figure used to model the reductions from high intensity treatments in this study is extremely conservative .
The study highlighted several policy and management implications of the findings . The types of considerations discussed within the full report include :
• Predicted time constraints to meeting the Reef 2050 WQIP targets ;
• Considerations between short term reduction opportunities at sites with the greatest costeffectiveness benefits , compared to the trade-offs of longer-term clustered approaches to gully selection .
• Sensitivity of data management , sharing and communication around “ hot spots ”.
Readers should refer to the full report for further details .
91