Abstract
Agriculture is a major contributor to widespread poor water quality through high losses of sediment and nutrients associated with activities such as tillage, fertiliser applications and slurry spreading, and due to poor livestock and grassland management. Often small-scale farm or field-based losses of nutrients and sediment to waterways can be significant in contributing to poor water quality, but currently, these losses are poorly quantified and understood. To effectively develop targeted catchment management techniques to improve water quality, greater identification, quantification and understanding of the contributing sources and transfer mechanisms of nutrient and sediment losses at the correct spatial scale is required. This research aimed to develop a multi-method approach at the sub-field scale to identify, quantify and manage nutrient and sediment losses for improved water quality. Using a novel combination of field data collection and laboratory analyses, with high spatial resolution GIS analyses of LiDAR-based imagery, this work was able to characterise and quantify the spatial variability in the sources and transfer pathways of nutrient and sediment losses from fields and riverbanks to streams. It focused on eight agricultural sites in Northern Ireland, all located in the cross-border Blackwater catchment.At the sub-field scale, it was found that soil nutrient sources of diffuse and point phosphorus (P) hotspots at-risk of contributing to eutrophication are best identified using high resolution 35 metre gridded soil sampling and GIS-based interpolation techniques. Comparisons were made to the soil nutrient content determined by soil sampling at a 70 metre interval and traditional bulked W sampling techniques. Subsequently, runoff-based nutrient and sediment losses were quantified from areas with varying soil P concentrations. Runoff-based P losses were found to be unrelated to soil P concentrations, with hydrological connectivity being the greatest determinant for increased risks of eutrophication. The controls influencing runoff-based nutrient and sediment losses were found to be spatially variable at the sub-field scale.
A new method was developed for quantifying riverbank erosion rates and associated nutrient and sediment losses to surrounding waterways. This was based on LiDAR image differencing in combination with field samples analysed for bulk density and total P content. Using this method, sites contributing high losses were identified and targeted for riverbank stabilisation techniques, with LiDAR-based image differencing used to determine the success of the stabilisation techniques in reducing erosion, sediment and nutrient loading rates to waterways. Soil erosion rates were also calculated through LiDAR applications and the agricultural activity of inversion tillage and grassland reseeding was found to generate high sediment and nutrient losses to waterways. However, this agricultural activity reduced soil surface nutrient hotspots, showing potential as a management tool for reducing the risk of nutrient losses from agricultural land.
The highest risk nutrient and sediment delivery zones to waterways were visualised by combining risk-assessed sub-field scale datasets on the sources and transfer mechanisms of sediment and nutrient losses within a GIS-based risk model using a weighted overlay approach. This visualisation allows the targeting of intervention strategies at specific in-field locations, aiding in the cost-effective introduction of water quality improvement schemes and increasing attractiveness to farmers by avoiding the removal of large areas of land from agricultural productivity. Validation with long-term water sampling data indicated that sites which had poorer water quality conditions correlated to higher risk weighting values, indicating that the risk modelling assessments have merit for determining agricultural areas which are contributing to nutrient and sediment losses.
| Date of Award | Dec 2023 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Sponsors | NERC QUADRAT |
| Supervisor | Donal Mullan (Supervisor) |
Keywords
- Phosphorus
- sediment
- GIS
- risk modelling
- water quality
- agriculture
- riverbank erosion
- surface runoff
- soil erosion