6 Phase II: GAP analysis - Method development
6.1 Overview of approach
The aim of Phase II of the project was to estimate the extent to which diffuse pollution will be managed by 2015 and beyond, and therefore required the adjustment of pollutant losses from a baseline (2004) to 2015 taking into account:
- Changed land-use and animal numbers.
- Uptake of measures.
- Spatial differences in uptake.
The WFD77 project generated loads (and concentrations) for 1 km 2 cells and WFD catchments. This is a single number e.g. kg N/ha lost, without the underpinning source apportionment e.g. the amount lost from grassland or arable land within that area, or the amount lost per unit of fertiliser or manure applied. This immediately makes validation of the WFD77 output (Phase I) and manipulation to take account of land-use change and uptake of measures (Phase II) complex. Therefore, the approach taken in the methodology used in Phase II had to be at a moderately coarse scale.
Previous work for Defra (DPI project) approached this by starting at individual farm levels and scaling up using complex spreadsheet models. As this project was based on the existing content of the WFD77 Screening Tool at a local scale, lessons learnt from developing the DPI methodology were used to ensure that this project's method:
- Focused on pollutant loads.
- Provided the most robust estimates for nitrogen (N), phosphorus (P), sediment and Faecal indicator Organisms ( FIOs).
- Gave an output scale of a 10 km 2 grid.
A simple export coefficient model from the BAUIII project ( Appendix V has a full description) was used to calculate an index of change in the pollution loss from 1997/2000 to 2015. The change index was responsive only to agricultural land-use inputs (fertiliser and manure loadings), and estimated uptake of mitigation measures. The WFD77 database and Screening Tool contents were derived using models that were sensitive to local environment conditions (slope, drainage status, soil type). These environment conditions remain constant even with the introduction of mitigation measures.
Therefore, it was appropriate to use the BAUIII export coefficient model to calculate a change coefficient that is multiplied against the WFD77 database contents to calculate pollutant loads for 2015. |
The simple BAUIII export coefficient model was only developed to estimate loads for N, P and sediment. It was therefore not possible to estimate how BOD loads would develop beyond 2004. Appendix I presents a review of the BOD loads estimated by the Screening Tool.
Due to the differences in the BAU model and the WFD77 database, a number of subsidiary steps also needed to be addressed. In summary the methodology was to:
1. Use ArcMap to spatially sum the 1 ha WFD77 pollutant values up to the 10-by-10 km grid square.
2. Update the BAUIII model to take into account any changes in export coefficient values arising from the model evaluation exercise.
3. Run the BAUIII model to produce 2004 pollutant loads (without measures).
4. Scale the WFD77 pollutant loads (no measures) to take into account the changes in agricultural census data between 1997 and 2004.
5. Use the ratio between the WFD77 pollutant loads (no measures) and the 2004 BAUIII pollutant loads (with measures) to scale the WFD77 pollutant loads to take into account baseline measure uptake for 2004.
6. Points 3 and 4 will give the project a set of baseline pollutant losses including current mitigation measure uptake.
7. List measures under the 2015 policy scenarios of revised NVZ, GBR and High Engagement GBR.
8. Estimate measure effectiveness, percent implementation and efficiency of implementation for revised NVZ, GBR and High Engagement GBR measures.
9. Update the BAUIII 'Methods Calculator' to include the new mitigation measures (for revised NVZ, GBR and High Engagement GBR), measure effectiveness, area implementation and efficiency of implementation values arising from this work.
10. Use the 'Methods Calculator' to calculate scenario multiplier coefficients describing pollutant losses relative to baseline.
11. Incorporate scenario multiplier coefficients into the 'Diffuse Calculator'.
12. Incorporate 2015 changes in livestock numbers from the BAUIII and FAPRI-adjusted BAUIII analysis by scaling the BAUIII agricultural census data.
13. Re-distribute BAUIII and FAPRI-adjusted BAUIII agricultural census data by farm-type and 10-by-10 km grid square.
14. Input this data in to the census files used in the 'Diffuse Calculator' and re-run the BAUIII export coefficient model (see Section 2). This will give new pollutant losses for 2015 for revised NVZ measures, GBR and High Engagement GBR for both BAU and FAPRI-adjusted BAUIII data.
15. Scale the WFD77 pollutant losses with the new 2015 BAUIII values for revised NVZ measures, GBR and High Engagement GBR
16. Compare 2015 results to baseline data to give a percentage change against the baseline for sub-basin ( AAG region) and farm-type.
6.2 Uncertainty and sensitivity
The modelling, as described above, produces a single number as an index of change in diffuse pollution loadings as affected by the policy scenarios. There are several assumptions underpinning the model calculations, each with a level of uncertainty surrounding it:
- Changes in land-use and livestock numbers to 2015 and beyond.
- Baseline levels of farm practices (including fertiliser inputs).
- Impacts of mitigation measures on diffuse pollution losses.
- Effects of policy mechanisms on uptake of mitigation measures.
- Robustness of the underpinning diffuse pollution models.
The nature of this work and the type of information generated relies on expert judgement and consensus, especially for changes in behaviour (cropping/livestock, changed practices). This uncertainty has to be recognised. However, placing confidence limits around each step and multiplying up would place a high level of uncertainty around each final number. Defining uncertainty around these numbers is uncertain itself.
The approach we have adopted is to provide an indication of the sensitivity of the final numbers to variations in the size of our assumptions, for:
- Changes in livestock numbers.
- Changes in effectiveness of measures.
- Changes in the upland P export coefficients.
6.2.1 Changes in Livestock and Cropping
As a test of the sensitivity of changes in diffuse pollution losses to changes in agricultural projections, it was agreed that we would investigate the effects of other policy changes on land-use and diffuse pollution losses. The Food and Agricultural Policy Research Institute ( FAPRI) project undertook an analysis of baseline policies for Scotland, as described in Section 5.1. Whilst the conclusions were no major changes in crop areas compared with BAU III, there were differences in livestock numbers. We have therefore run Phase II of this project with BAUIII projections and rerun with livestock numbers according to FAPRI-adjusted BAUIII estimates.
6.2.2 Effects of changes in measure effectiveness percentages
The review of the effectiveness of measures (Section 3.2) suggested that it was difficult to draw absolute values from literature alone and that much of this assessment had already previously been undertaken within the DPI work. Effectiveness values were therefore based on DPI values, reassessed for Scotland. To test the sensitivity of the outcomes to changes in effectiveness, the model was rerun after adjusting effectiveness values by +/- 20%.
6.2.3 Effects of P export coefficient value changes
After initial investigation into the use of the BAU methodology it was found necessary to increase the P and sediment export coefficients for rough grazing by 50%. This was to reflect the increased landscape connectivity of rough grazing area in Scotland that would not have been properly represented in the BAU export coefficient model. In order to investigate the sensitivity of total P loads to over estimations in P losses from upland areas, we also ran the model separately with the BAUIII P and sediment export coefficients unchanged (i.e. lower than predicted by WFD77). See section 2.5.1 for a more detailed explanation.