Understanding and improving mitigation strategies for reducing catchment scale nutrient loads using high resolution observations and uncertainty analysis approaches

One of the biggest challenges in catchment water quality management is tackling the problem of reducing water pollution from agriculture whilst ensuring food security nationally. Improvements to catchment management plans are needed if we are to enhance biodiversity and maintain good ecological status in freshwater ecosystems, while producing enough food to support a growing global population. In order to plan for a more sustainable and secure future, research needs to quantify the uncertainties and understand the complexities in the source-mobilisation-delivery-impact continuum of pollution and nutrients at all scales. In the UK the Demonstration Test Catchment (DTC) project has been set up to improve water quality specifically from diffuse pollution from agriculture by enhanced high resolution monitoring and targeted mitigation experiments. The DTC project aims to detect shifts in the baseline trend of the most ecologically-significant pollutants resulting from targeted on-farm measures at field to farm scales and assessing their effects on ecosystem function. The DTC programme involves three catchments across the UK that are indicative of three different typologies and land uses. This paper will focus on the Hampshire Avon DTC, where a total of 12 parameters are monitored by bank-side stations at two sampling sites, including flow, turbidity, phosphate and nitrate concentrations at 30 min resolution. This monitoring is supported by daily resolution sampling at 5 other sites and storm sampling at all locations. Part of the DTC project aims to understand how observations of water quality within river systems at different temporal resolutions and types of monitoring strategies enable us to understand and detect changes over and above the natural variability. Baseline monitoring is currently underway and early results show that high-resolution data is essential at this sub-catchment scale to understand important process dynamics. This is critical if we are to design cost efficient and effective management strategies. The high-resolution dataset means that there are new opportunities to explore the associated uncertainties in monitoring water quality and assessing ecological status and how that relates to current monitoring networks. For example, concurrent grab samples at the high-resolution sampling stations allow the assessment of the uncertainties which would be generated through coarser sampling strategies. This is just the beginning of the project, however, as the project progresses, the high resolution dataset will provide higher statistical power compared with previous data collection schemes and allow the employment of more complex methods such as signal decomposition e.g. wavelet analysis, which can allow us to start to decipher the complex interactions occurring at sub-catchment scale which may not be immediately detectable in bulk signals. In this paper we outline our methodological approach, present some of the initial findings of this research and how we can quantify changes to nutrient loads whilst taking account the main uncertainties and the inherent natural variability.