Summary of Emerging Evidence from the Demonstration Test Catchments (DTC) Platform

Summary of emerging evidence from the Demonstration Test Catchments (DTC) Platform

Contents 1 Introduction ...... 7 1.1 The Context ...... 7 1.2 The Challenge ...... 7 1.3 Demonstration Test Catchments ...... 9 1.4 Beneficiaries and Impacts ...... 11 1.4.1 Informing and Supporting Policy ...... 11 1.4.2 Supporting Policy Delivery ...... 11 1.4.3 Demonstrating to Industry and Stakeholders ...... 11 1.4.4 Researchers and Research Funders ...... 12 2 Understanding the Issues ...... 12 2.1 Introduction ...... 12 2.2 Catchment Characteristics ...... 12 2.2.1 Hampshire Avon ...... 14 2.2.2 Tamar ...... 15 2.2.3 Eden ...... 16 2.2.4 Wensum ...... 17 2.3 Catchment Monitoring ...... 19 2.3.1 Sub-Catchment Approach...... 19 2.3.2 Hydrological and Water Quality Monitoring ...... 26 2.3.3 Ecological Monitoring ...... 28 2.3.4 Other Monitoring and Assessment Approaches ...... 29 2.4 Results and Interpretation...... 35 2.4.1 Hydrology and Water Quality across the DTC Platform ...... 36 2.4.1.1 Differences in Hydrological and Hydrochemical Behaviours and Chemistries in the DTC Sub-Catchments ...... 46 2.4.1.2 Nutrient and Sediment Delivery to Streams in the DTC Sub-Catchments: the Relative Importance of Flow Conditions under Different Geoclimatic Conditions ...... 52 2.4.1.3 Seasonal Pollutant Transfers ...... 55 2.4.1.4 Hydrology and Water Quality Conclusions ...... 60 2.4.2 Ecology ...... 61 2.4.3 Specific Groundwater Investigations ...... 72 3 Measures ...... 84 3.1 Background ...... 84 3.2 Selection of Measures ...... 85 3.3 Mitigation Measures Development Plans ...... 89 3.3.1 Avon ...... 89 3.3.2 Tamar Catchment, Caudworthy Water ...... 92 3.3.3 Development of a Mitigation Measures Plan in the Eden ...... 94 3.3.4 Development of Mitigation Measures Plan in the Wensum ...... 96 3.4 Implementation of Measures across the Catchments ...... 98 3.4.1 Avon ...... 98 3.4.2 Tamar ...... 106 3.4.3 Eden ...... 111 3.4.4 Wensum ...... 113 3.5 Rationale of Monitoring Approach ...... 118

3.5.1 Sub-Catchment Monitoring ...... 118 3.5.2 Avon Measures Monitoring Plan ...... 119 3.5.3 Eden Measures Monitoring Plan ...... 121 3.5.4 Wensum Measures Monitoring Plan ...... 124 4 Working with Stakeholders: Knowledge Exchange and Understanding Farmer Behaviour ...... 126 4.1 Introduction ...... 126 4.2 An Overview of Local Knowledge Transfer and Knowledge Exchange Activities .. 126 4.3 Media Activity and Dissemination ...... 128 4.4 Stakeholder Events ...... 129 4.4.1 EdenDTC Catchment Bus Tour ...... 130 4.4.2 Avon DTC Farm Advisor Workshop, May 2012 ...... 131 4.4.3 Wensum DTC Farmer Presentations and Field Visits, 2014 ...... 133 4.4.4 Farmer-Led Monitoring in the Wensum DTC ...... 135 4.5 Supporting the Catchment-Based Approach ...... 137 4.6 The Baseline Farm Survey...... 137 4.6.1 Understanding Farmer Behaviour ...... 137 4.6.2 Implementing the Survey ...... 138 4.6.3 Current Adoption of Mitigation Measures ...... 138 4.6.4 Attitudes to Future Adoption of Measures ...... 139 4.6.5 Farmer Priority Measures ...... 143 4.7 Farm Diaries ...... 143 4.8 Assessment of Progress and Future Priorities ...... 144 4.8.1 Future Priorities ...... 144 5 Monitoring and Evaluation of On-Farm Interventions for Informing Water Quality Policy ...... 145 5.1 Introduction ...... 145 5.2 The General Challenge Driving DTC ...... 145 5.2.1 The Specific Challenge Driving DTC ...... 146 5.2.2 A Twin-Track Approach to Iterative Science ...... 146 5.2.3 Developing a ‘Catchment Science Toolkit’ ...... 147 5.2.4 Lumped Descriptive Metrics for Characterising and Comparing Hydrochemical Response ...... 149 5.2.5 Detecting Change with Conventional Hydrochemical Time Series Data . 150 5.2.6 Detecting Change with Nutrient Speciation and Fractionation Data ...... 153 5.2.7 Detecting Change using Hysteretic Loops ...... 154 5.2.8 Detecting Change with Structural and Functional Aquatic Ecology Data 156 5.2.9 In Situ Monitoring of Benthic Communities...... 158 5.3 Potential Confounding Factors for Detecting Change with Conventional Water Quality and Aquatic Ecology Data ...... 161 5.4 Synthesis ...... 174 6 Key Messages from Phase 1 and Moving Forward to Phase 2 ...... 174 6.1 Introduction ...... 174 6.2 Key Messages from Phase 1 ...... 175 6.3 Moving Forward to DTC Phase 2 ...... 181 7 References ...... 182

8 Appendices ...... 194 Appendix 1.1 The Added Value of DTC ...... 194 Appendix 2.1 Problems Encountered with Sensor Performance and Bankside Analyser Data on the DTC Platform: Examples from the Hampshire Avon DTC ...... 201 Appendix 2.2 DTC Sample Collection, Analysis and Quality Assurance Protocols ...... 207 A2.2.1 Handling and Processing of Laboratory-Analysed Samples ...... 207 A2.2.2 Quality Assurance and Quality Control Protocols for Sensor Data from Bankside Monitoring ...... 209 A2.2.3 Quality Control Procedures ...... 210 Appendix 2.3 Biological Monitoring Approach and Methods; Diatoms, Macrophytes, Macroinvertebrates and Fish ...... 211 Appendix 2.4 Tabularised Summary Statistics for Water Quality Monitoring Data from all Four DTCs and their Sub-Catchments...... 214 Appendix 2.5 Analysis of Event Based Pollutant Transport in the Demonstration Test Catchments ...... 228 A2.5.1 Storm Events in the Eden ...... 230 A2.5.2 Storm Events in the Wensum ...... 233 A2.5.3 Soil Sampling and Soil Texture Mapping in the Wensum ...... 236 A2.5.4 Soil Moisture Monitoring in the Wensum ...... 240 A2.5.5 High-Temporal Resolution Fluvial Sediment Source Fingerprinting with Bayesian Uncertainty Analysis in the Wensum ...... 241 Appendix 2.6 Wensum Ecological Data ...... 244 Appendix 3.1 Faecal Indicator Organisms ...... 246 Appendix 3.2: Avon DTC Engagement with Farmers and Advisors ...... 251 Appendix 4.1 Pesticide Analysis in the Wensum Catchment ...... 253 9 Demonstration Test Catchment Programme Staff ...... 256

Abbreviations

A.S.L Above Sea Level AES Agri-environmental Schemes AMP Asset Management Plan AONB Area of outstanding Natural Beauty ASP Asset Management Plan AWS Anglian Water Services BACI Before-After Control-Impact (design for mitigation monitoring) BFI Base Flow Index BMP Best Management Plan BQE Biological Quality Element CaBA Catchment-Based Approach CHASM Catchment Hydrology and Sustainable Management CSF Catchment Sensitive farming (under England Catchment Sensitive Farming Delivery Initiative) Defra UK Department for Environment, Food and Rural Affairs DTC Demonstration Test Catchments DWPA diffuse water pollution EA Environment Agency ELS Entry Level Stewardship EM Electromagnetic induction EQR Ecological Quality Ratio ERT Electrical resistivity tomography FIO Faecal Indicator Organism FW Flow Weighted FYM Farm yard management GC-MS Gas Chromatography-Mass Spectrometry GPR Ground Penetrating Radar HLS Higher Level Stewardship IP Induced polarisation LIFE Lotic-invertebrate Index for Flow Evaluation N Nitrogen NE Natural England

NO3-N Nitrate as N NTU Nephelometric Turbidity Units (roughly equivalent to mg SS l-1) NVZ Nitrate Vulnerable Zone (define under EU Nitrates Directive) NVZ Nitrate Vulnerable Zone PARIS Phosphorus from Agriculture: Riverine Impacts Study PES Payments for Ecosystem Services PP Particulate Phosphorus (TP-TDP=PP) Q Discharge/Flow Q10 Flow equalled or exceeded 10% time (highflow threshold) Q90 Flow equalled or exceeded 90% time (lowflow threshold) RBMP River Basin Management Plans RC-UK Research Councils UK SAC Special Area of Conservation (area protected under Habitats Directive) SCIMAP Diffuse Pollution Risk Mapping Framework SEPA Scottish Environmental Protection Agency SPA Special Protection Area (area protected under Wild Birds Directive) SR seismic refraction

SRP Soluble Reactive Phosphorus SS Suspended Sediment/Solids SSSI Sites of Special Scientific Interest (Nationally protected areas, includes all SPAs and SACs) SUP Soluble Unreactive Phosphorus (TDP-SRP=SUP) SWW South West Water TDI Trophic Diatom Index TDP Total Dissolved Phosphorus TN Total Nitrogen TP Total Phosphorus TRP Total Reactive Phosphorus UV Ultraviolet WFD EU Water Framework Directive (2000/60/EC) WRT Westcountry Rivers Trust

1 Introduction

1.1 The Context Food production, and the intensification of farming practices to meet rising demand from a growing population, has led to the export of a range of pollutants to both the atmosphere and adjacent freshwater in farming landscapes. These include nutrients (nitrogen and phosphorus), sediment, microbes, and both pesticides and herbicides. These have contributed to a decline in water quality in the majority of water bodies across England and Wales and the loss of biodiversity and key services that these aquatic ecosystems provide to society.

In order to meet the WFD water body thresholds for ‘good ecological status’ in the UK, it will be necessary to target a reduction in the delivery of nitrogen, phosphorus and sediment to water bodies in places where agricultural land management is a significant contributory source and where the magnitude of such losses from farming poses challenges to meeting compliance targets. Achieving reductions in agricultural pollutant emissions will require a combination of changes to the way that land is managed and the implementation of pollution mitigation measures to tackle the principal reasons for failure. But, a major constraint on the design of effective pollution mitigation strategies at the landscape scale, is the lack of robust empirical evidence on the efficacy of combinations of on-farm interventions and the specifics in terms of density of measures, spatial extent and the requirement for ‘treatment-trains’ or otherwise. The mix of on-farm interventions must be flexible enough to accommodate the contrasting physiographic settings, farm types and practices across the country, yet still deliver effective and sustained mitigation.

Plot-scale experimental studies have helped us to understand the mobilisation of pollutants, and to test mitigation measures at this reductionist scale. The science emerging from these studies has been used in developing statistical and process-based models for policy support at the national scale and predicting the potential, or technically feasible, effectiveness of on-farm mitigation measures for improving water quality and aquatic ecology. However, these modelling approaches are frequently highly generalised and uncertain, particularly when applied at the scale of small water bodies (<20-25 km2) or indeed at the farm scale where daily decisions are made. As we become better able to conceptualise this complexity, we should manage and balance ecosystem services by effective interventions that are more integrated. It becomes increasingly important that we understand how the river, the land and the dependent ecosystems function together, within their socio-economic setting, at least sufficiently well to recognise the dependencies. Hence, we can ensure that management solutions are cost-effective and beneficial to both society and the environment.

The Demonstration Test Catchments (DTC) programme is part of the jigsaw of science-led policy initiatives to help address the challenges for steering rural economic and environmental policy. It is specifically focused on improving water quality and aquatic ecology impacted by diffuse agricultural pollution in contrasting sentinel catchments across England.

1.2 The Challenge The ways in which pollutants are mobilised from sources within the landscape and delivered to freshwater are complex, and current understanding of these processes is uncertain. The gaps in our knowledge and understanding, particularly at the larger scales at which we assess and manage the environment (water bodies, river catchments and drainage basins) need an improved evidence base. The Demonstration Test Catchments programme was commissioned by Defra in December 2009 to fill these gaps. It brought 7 together multi-disciplinary teams of researchers, practitioners and policymakers, and integrated their skill sets to determine how catchments responded to on-farm mitigation measures. Techniques employed included the use of novel and state of the art monitoring of water chemistry and aquatic ecology in manipulated and control sub-catchments, combined with local knowledge and expertise and socio- economic research on farming practices. The process is iterative, and allows hypotheses, assumptions and expert judgements to be tested against, and refined using, more detailed observations.

The major gap in our evidence base is robust empirical evidence regarding the effectiveness of on-farm mitigation measures for improving water quality and aquatic ecology at the catchment or landscape scale. The highly episodic nature of agricultural diffuse pollution means that regular, but infrequent, conventional monitoring carried out by national agencies is not sufficient on its own to understand fully the processes involved, nor detect improvements in a statistically robust manner, particularly those that result from the implementation of targeted on-farm mitigation measures. Research that focuses on the smaller scale of experimental test sub-catchments (typically up to ~10 km2) is needed to:

 test the impacts of combinations of on-farm mitigation measures on water quality and aquatic ecology at landscape scale;  understand the spatial coverage, targeting and formulation of on-farm mitigation measures required to achieve WFD goals;  predict the length of time that water quality recovery will take following the implementation of on- farm mitigation measures. This varies significantly between catchments on the basis of key controls including groundwater inputs, base flow indices and pollutant source proportions;  develop approaches to work with farmers and other stakeholders to target mitigation measures (on-farm and elsewhere) within a catchment (taking account of knock-on effects on agricultural production and wider environmental factors).

Addressing these challenges urgently has required:

 joining up the research community to deliver multi- and interdisciplinary evidence at the appropriate scales;  building closer working relationships between researchers, policy makers and practitioners so that research is more focused on the policy and operational questions and facilitating wider access to the evolving evidence base (including relevant knowledge gleaned from across the national/international research community);  setting in place studies to understand and detect long-term environmental changes that may take years or decades to become evident.

Defra research on diffuse water pollution from agriculture has historically consisted, largely, of individual research projects commissioned using a variety of separate contractors and undertaken in diverse and separate locations. The establishment of the DTC programme entailed a reallocation of resources away from the multiple plot- or field-scale, single-issue research projects, to a strategic initiative that aims to put water quality, aquatic ecology and catchment science into a real-world context. A major objective of DTCs was to establish a mechanism to bring researchers and Defra, policy delivery bodies and wider stakeholders into closer dialogue.

1.3 Demonstration Test Catchments By setting up a platform with a community of researchers working closely with local stakeholders (practitioners and policy-delivery agents) and policy-makers, DTCs are addressing the challenges described above. DTC has three main roles:

1. As a programme of linked and co-ordinated research projects: to provide underpinning research, from farm to catchment scale, that informs policy and practical approaches for the reduction of agricultural diffuse pollution and the improvement of ecological status in freshwaters, whilst maintaining economically viable food production. 2. As a research platform: to host longer-term collaborative research on diffuse pollution from agriculture, funded by multiple organisations. It has also developed a community of researchers and stakeholders enabling short and longer-term policy-relevant research questions to be answered, steering research and translating science into practice. 3. As a demonstration activity: To demonstrate scientifically robust approaches to diffuse pollution mitigation and explore ways to bring science into stakeholder-led catchment management.

Initially, the project comprised five component parts with interfaces as shown in Figure 1.1 below:

1. Design and implementation of the monitoring approach for each catchment including development of the catchment conceptual model 2. Implementation of measures (let as a separate project WQ0225 which runs to May 2017) 3. Knowledge exchange and knowledge transfer 4. Development of infrastructure for data management and dissemination 5. Predictive modelling and Decision Support Systems

Figure 1.1: Demonstration Test Catchment (DTC) project component parts and interfaces

Components 1, 2, 3 and 4 were let as separate commissioned projects, while Component 5 was subsumed into a joint initiative with the NERC Macronutrients Programme, UKWIR and the Scottish Government. Components 1 and 2 are closely linked in their technical content. Component 3 ran for 2 years, reporting separately in 2012, with the knowledge exchange aspects of DTC now being incorporated into the 2nd phase of Component 1. Component 4 involves the construction of a data archive in co-ordination with the Defra national GHG Inventory platform.

DTC has established a nationally coordinated programme of research focused on four sentinel study catchments, which are representative of >80% of UK soil/ rainfall combinations and the major farm types across England and Wales. These are the Eden (Cumbria), Wensum (Norfolk), Avon (Hampshire) and Tamar (Devon/Cornwall). Monitoring programmes, communities of researchers and suites of mitigation measures, applied experimentally, have been established in each catchment to provide evidence on the trajectory for water quality and aquatic ecology improvements towards WFD targets.

The research communities, monitoring infrastructure and data generated by the core DTC projects support a number of satellite projects, funded by Defra and other organisations, to test mitigation measures and further understand the physical, ecological and social functioning of river catchments (Appendix 1.1). By adopting the platform/community of practice approach the research undertaken by the academics can be more rapidly applied in practice, whilst the practitioners in the community can test the more practical questions.

The first five years of the DTC programme have been spent establishing the platform (building stakeholder relationships, designing experiments, finding sites and installing equipment) and undertaking baseline monitoring. The scientific findings that are beginning to emerge from DTC are detailed in the pages that follow. The mitigation measures that are being tested in the core target sub-catchments are being implemented through Component 2 (WQ0225).

Integrated catchment scale research requires longer-term investment than the more traditional approach of individual research projects. The first funding phase of Component 1 (characterisation and monitoring) ended on 31st March 2014 with a second phase to 2017 being procured to monitor the effectiveness of the experimental on-farm measures and achieve its original aims (Figure 1.2). Beyond 2017, Defra funding will need to be reviewed, but it is envisaged that on-going research activities funded by others should maintain the research activity within the DTC catchments.

2009 2010 2011 2012 2013 2014 2015 2016 2017 Phase 1 funding Phase 2 funding Scoping Monitor ing Baseline monitoring platfor Research to understand sub-catchment function

m Socio-economic research establis Knowledge Exchange, demonstration and sounding-board function

hed. Building stakehol der relation ships. Installation & testing of measures (through

WQ0225) Interpretation & up-scaling to other catchments

Figure 1.2: Timelines for the Demonstration Test Catchments

1.4 Beneficiaries and Impacts DTC provides benefits to a range of parties at different stages of the policy cycle including: (1) those developing policy; (2) those delivering policy (government agencies and NGOs); (3) stakeholders on the receiving end of policy; and (4) those undertaking and funding research to inform policy.

1.4.1 Informing and Supporting Policy DTC provides independent, scientifically robust, peer reviewed and internationally recognised research to improve the credibility of our evidence base on integrated catchment management for agricultural diffuse pollution mitigation to stakeholder and policy audiences, including the European Commission.

1.4.2 Supporting Policy Delivery DTC provides a strategic resource for a rapidly growing community of practitioners and stakeholders from local to catchment scales and above. These groups require evidence and guidance on the effectiveness of on-farm management options to combat pollution and the processes to target them most effectively. Stakeholder groups have provided DTC researchers with opportunities for new research to test mitigation options.

1.4.3 Demonstrating to Industry and Stakeholders DTC provides four regional demonstration hubs to show how farming can be carried out in ways that help reduce diffuse pollution. Each is well networked with local stakeholder groups (agricultural colleges, rivers trusts, local farming groups, NGOs, etc.) who contribute to DTC and draw on its emerging findings. Strong stakeholder relations have been established. The direct involvement of farmers in the research adds a sense of reality and credibility to the findings for both local and national level stakeholders.

1.4.4 Researchers and Research Funders DTC is helping improve the national capacity for translational (policy-relevant) research on land/catchment management and also helping to consolidate a previously fragmented UK catchment science community. By bringing them together with policy makers, delivery bodies, and local and national stakeholders, it allows researchers a more direct route to influence policy and practice (thereby increasing their impact). Currently hosting c. £7.5M of additional research funded by the Research Councils UK (RC-UK), the British Geological Survey, UK Water Industry Research (UKWIR) and the Environment Agency, DTC has helped strengthen linkages between Defra and other funders of research, especially providing the research councils with a vehicle of translation to improve the impact of the work they fund. The DTC data archive will make the data generated by DTC freely available to any researchers requiring it and the high-resolution data will support research on catchment processes and modelling into the future.

2 Understanding the Issues

2.1 Introduction This section of the DTC phase1 report focuses on ‘Understanding the Issues’. It introduces the four DTC focus catchments, discusses their characteristics, briefly describes the approach taken to monitoring in each of the catchments, presents the main results, and interprets and synthesises these in order to highlight the nature of the diffuse pollution problem and provide the context for effective mitigation.

2.2 Catchment Characteristics This section describes the background to the selection of the DTC focus catchments and justifies their inclusion in the project. It then describes the characteristics of the DTC focus and study sub-catchments.

In 2009, three initial DTCs were selected from a shortlist of nine catchments undergoing enhanced monitoring under the England Catchment Sensitive Farming Delivery Initiative (CSF; Defra, 2012), where intensive efforts are being made to engage farmers in priority areas through targeted one-to-one advice, clinics and capital grant funding for on-farm remedial measures. The three initial DTC focus catchments were chosen to maximise national coverage and representation of different physical and socio-economic factors influencing diffuse pollution. The level of stakeholder engagement through CSF and other initiatives and level of previous research investment in terms of infrastructure, existing knowledge and existing datasets was also considered (Table 2.1).

In autumn 2011, the Tamar catchment was adopted as a DTC focus catchment, providing an opportunity to assess the water quality and freshwater responses to mitigation strategies funded by South West Water (SWW) via the Payment for Ecosystem Services schemes being implemented by the Westcountry Rivers Trust (WRT).

The location of the four DTC study catchments is shown in Figure 2.1. Detailed catchment summary characteristics are shown in Table 2.2.

Table 2.1: Factors considered for selection of three initial DTC focus catchments. BFI = Base Flow Index

Factors Considered for Selection Hampshire Avon Eden Wensum Sediment Nitrate, Sediment, Pressures Phosphorus, Pesticides Phosphorus Nitrate Region South North West East Rainfall Moderate High Low Calcareous limestone, New Red Sandstone Chalk, Clay, Geology Chalk, Clay (Major Aquifer), Quaternary sediments Igneous Elevation Lowland Lowland to Upland Lowland Flow characteristics (BFI) High Low Moderate Dairy, Sheep, Arable, Poultry, Land-use and farm types Arable Beef, Arable Pigs Engagement and investment High High High

Figure 2.1: Location of the four DTC focus catchments

Table 2.2: Summary characteristics of the DTC study catchments – Land use from LCM2007 *Mayes et al. 2006 13

Characteristic Hampshire Avon Tamar Eden Wensum Area (km2) 1750 1800 2,288 677 River Length (km) 96 80 145 71 Rainfall Range (mm) 714-937 1000-2000 637-3359 624-675 Altitude Range (m A.S.L) 2-294 3-579 1-947 0-103 <3 (57%) <3 (14%) <3(39%) <3(91%) 3-7 (32%) 3-7 (69%) 3-7 (34%) 3-7 (7%) Slopes (°) 7-11 (8%) 7-11 (16%) 7-11 (13%) 7-11 (1%) 11-15 (2%) 11-15 (1%) 11-15 (6%) 11-15 (0.4%) >15 (1%) >15 (0%) >15(9%) >15(0.4%) Sandstone, Chalk, Granite, Limestone Chalk, Greensand, Geology Sandstones Mudstone with some Gravels, Clay, Mudstones igneous. Overlain with till Gravels Overlain with till. Heavy, Heavy, Heavy, Medium, Medium, Medium, Soil Types Medium, Sandy and light silty, Sandy and light silty, Sandy and light silty Peaty Chalk and limestone, Chalk and limestone Peaty Land Use (%) Arable 37a 9a 10b 62b Improved Pasture 29 a 65a 37b 19b Rough Grazing 11 a 7a 29b 4b Woodland 12 a 14a 10b 9b Urban 11 a 5a 2b 5b Average Farm Size (ha)c 94 62 96 117b a based on the ADAS land use database for reference year 2010 (cf. Comber et al., 2008). b Based on CEH Land Cover data 2007, for Eden includes 12% of other land covers including heather, bogs and montane habitats. c From 2012 June Census data for relevant administrative areas.

2.2.1 Hampshire Avon The Hampshire Avon rises in Wiltshire as two separate rivers: the West Avon and the East Avon rising just east of Pewsey, both of which drain the Vale of Pewsey (Figure 2.2). The two tributaries converge at Upavon, then flow south across Salisbury Plain and into the English Channel at Mudeford, Christchurch, in Dorset. The Hampshire Avon is a groundwater-dominated river catchment, with around 85% of main river flow supplied by the Cretaceous chalk and Upper greensand aquifers. The upper reaches of parts of the River Avon flow through chalk, where headwaters are drained by winterbournes – streams reliant on winter rain to flow which may dry up completely in summer. The western headwaters flow across clays, while Tertiary sands and gravels dominate the lower catchment. Base flow indices (BFIs) are typically >0.7 and as high as >0.95 in some parts of the catchment (Marsh and Hanaford, 2008). Topographical features such as open chalk downlands with steep scarp slopes, sheltered valleys, chalk hills, ridges and limestone plateaux are typical of the catchment. Land use mainly comprises arable land, improved pasture and woodland (Table 2.2), with the exception of the River Bourne tributary which is dominated by urban areas, fisheries management, historic milling and water meadow agricultural systems (EA, 2009a, 2010). Approximately 85% of the Hampshire Avon DTC is designated as a Nitrate Vulnerable Zone (NVZ; (81/676/EEC). Enhanced phosphorus, nitrate and sediment pressures from agricultural land are believed to have contributed to nutrient enrichment (Jarvie et al., 2005), siltation issues (Walling et al., 2008) and the occurrence of so-called ‘chalk stream malaise’ (UK Biodiversity Action Plan Steering Group for Chalk Rivers, 2004). Only 24% of river length and 37% of local freshwater bodies achieve good ecological status under the WFD.

Figure 2.2: The location of the Hampshire Avon DTC focus catchment and study sub-catchments, showing the location of the DTC Monitoring sites in relation to available EA and CSF monitoring.

2.2.2 Tamar The River Tamar is located in the South West of England and flows through the counties of Cornwall and Devon (Figure 2.3). It originates near Bude on the north Cornwall coast, runs south into the estuarine stretch of the tidal River Tamar at Plymouth (known as the Hamoaze), and enters the sea at Plymouth Sound in south-west Devon (Figure 2.3). The upper catchment is predominantly granite with the lower areas comprising of sandstones and mudstones overlain with alluvial silts and clays. Its catchment includes the upland areas of west Dartmoor and east Bodmin Moor, and is characterised by rolling farmland, valleys and heaths. Tributaries of the river include the rivers Inny, Ottery, Kensey and Lynher on the Cornish side, and the Deer and Tavy on the Devon side. Together, the Tamar, Tavy and Lynher are a designated Area of Outstanding Natural Beauty (AONB). The Tamar-Tavy estuary is a Site of Special Scientific Interest (SSSI) because of the habitat and wildlife found there. The River Tamar was adopted as a DTC focus catchment in autumn 2011, providing an opportunity to assess the water quality and freshwater responses to mitigation strategies funded by South West Water (SWW) via the Payment for Ecosystem Services schemes being implemented by the Westcountry Rivers Trust (WRT).

Figure 2.3: The location of the Tamar DTC focus catchment and study sub-catchments, showing the location of the DTC Monitoring sites in relation to available EA and CSF monitoring.

2.2.3 Eden The Eden in Cumbria, in the Solway Tweed River Basin District, rises in Mallerstang and flows north to the Solway Firth and into the Irish Sea (Figure 2.4). The catchment has a considerable elevation range, as it drains part of the edge of the Lake District to the east, and the North Pennines to the west. The range of slope within the catchment is from 0-30°, with the steepest slopes associated with the surrounding fells (highest elevation is at Cross Fell at 882 m), whilst the valley floor is characterised by gentle undulating slopes. Common grazing land in the uplands plays an important role within the catchment, but lowland agriculture is also important, with areas of intensive farming in the River Eden valley. Soil texture is mainly clay loam with large areas of sandy loam soils adjacent to the River Eden. On higher ground, the soil texture is a mixture of peat and peaty loams. The geology in the Eden varies greatly, underlain by sandstone, siltstone and mudstone in the northern part of the catchment, hard-rock dominated by Borrowdale Volcanics to the west (Allen et al., 2010), and Carboniferous limestone, shales and sandstones to the south. The major aquifers (groundwater stores) in the Eden catchment are the Permo-Triassic sandstones which underlie the valley floor in the Vale of Eden basin. The Carboniferous represents a smaller aquifer which can support minor abstractions or may discharge water to surface waters. The River Eden gains along most of its length within the Vale of Eden due to discharge from the underlying sandstone aquifers (Allen et al. 2012).

Substantial abstraction from Eden sources supports public water supply and industrial and small farm uses. Groundwater resources in the Eden are at risk from increasing nitrate concentration in recharge water due to previous agricultural intensification in the catchment (Butcher et al., 2008; 2003). Around 11% of the 16 catchment is located within an NVZ, and a very small portion of land is designated a groundwater safeguard zone for further protection of drinking water supplies. About 20 million litres per day is abstracted from the River Eden at Cumwhinton and the River Gelt (via the Castle Carrock reservoir) to service the Carlisle area. The Eden upstream of the abstraction point is now designated a surface water safeguard zone, with action targeted in these zones to address pollution so that extra treatment of raw water for pesticides and colour can be avoided. Haweswater, located in the west of the catchment, supports the water needs of the wider region, supplying 400 million litres a day to areas including the greater Manchester conurbation. The Eden is a largely rural catchment, dominated by farming – there are over 2000 farm holdings in the catchment representing 30% of Eden Valley businesses – and as a result pressures from phosphates and sediment are widespread. The River Eden is designated a Special Area of Conservation (SAC) under the EU Habitats Directive, and of the 39 units in the River Eden SAC only 23% are in favourable condition. Overall, only 41% of the 98 water bodies in the Eden achieve good status under the WFD. For more detail on the Eden catchment and understanding catchment scale issues see the Saving Eden Manifesto (ERT, 2014).

Figure 2.4: The location of the Eden DTC focus catchment and study sub-catchments, showing the location of the DTC Monitoring sites in relation to available EA and CSF monitoring.

2.2.4 Wensum The River Wensum in East Anglia flows from its source between the villages of Colkirk and Whissonsett to Norwich via Taverham, and on to its confluence with the River Yare at Whitlingham, before joining the sea at Great Yarmouth (Figure 2.5). The Cretaceous White Chalk bedrock underlying the catchment is exposed along the river valley close to Norwich and also in the upper catchment where the overlying deposits are thin or absent. To the east of the catchment the chalk is overlain by sands and gravels, while chalky, flint- rich boulder clays are interspersed with sands and gravels over much of the rest of the catchment, overlain with silty loess deposits and alluvium and river terrace deposits. The juxtaposition of glacial deposits is a significant control on hydrological processes in East Anglia and is typified by conditions found in the 17

Wensum catchment. The base-flow index of 0.74 for the downstream gauging station at Costessey Mill highlights the influence of the underlying Chalk aquifer in supporting river flow in the Wensum. Discharge of groundwater occurs in the valley bottoms where the river has cut through the overlying Quaternary deposits leading to a greater hydraulic connection between the Chalk and surface runoff. Extensive study of soil types has been undertaken (Hiscock et al., 1993, 1996; Lewis, 2011; Rawlins, 2011). Soils vary across the catchment, reflecting the complex geological history of the area, and are characterised by rich loams, silts and sandy peats. River valley soils are generally low permeability loams overlying clay, while soils on the valley slopes tend to be highly permeable and highly fertile sandy loams. The clay loam and sandy loam soils have a high potential for arable agriculture, with soils further improved by field drainage and through widening, straightening and deepening of tributaries and parts of main river channels. The main arable crops are barley, sugar beet, beans, potatoes, oil seed rape and maize.

Figure 2.5: The location of the Wensum DTC focus catchment and study sub-catchments, showing the location of the DTC Monitoring sites in relation to available EA and CSF monitoring

The River Wensum is an important chalk river habitat with over 100 plant species and a rich invertebrate fauna, and is designated a SSSI and SAC. Of the 91 hectares of ‘River and Stream’ habitat included in the SSSI, 99% is considered to be in an ‘unfavourable and declining’ state under the WFD, primarily due to excessive sediment and nutrient loadings (Sear et al., 2006). The main river channel currently has ‘poor’ ecological status and 40% of water bodies in the catchment are at risk of failing drinking water quality standards for nitrate. The Wensum is a CSF priority catchment.

2.3 Catchment Monitoring This section describes the sub-catchment approach taken within each focus catchment and presents the design implemented in order to gather baseline monitoring data and determine the effects of subsequent mitigation of diffuse pollution. It then briefly describes the hydrological and water quality monitoring and the ecological monitoring undertaken across all the DTC focus catchments. Finally, it introduces a number of other monitoring approaches which have been used within single or multiple DTC catchments in addition to the standard monitoring in order to ‘Understand the Issues’.

2.3.1 Sub-Catchment Approach Existing data on chemical quality elements for determining WFD status and water quality pressures within catchments rely on EA monitoring at monthly or bi-weekly intervals, supplemented by more detailed CSF monitoring data (generally weekly and event samples, up to twice-weekly for pesticides in the Wensum) in target catchments across a network of sites (Figures 2.2-5). Although these data provide information on the spatial differences between locations, the monitoring network is coarse by necessity, and the coverage is generally too patchy to be able to pick up the effects of recent water status improvements in response to mitigation of diffuse water pollution from agriculture. Within each DTC catchment therefore, a number of smaller study sub-catchments were selected as focus sites for high resolution monitoring at which the effects of improvements in water quality and ecology would be able to be assessed in relation to mitigation.

Each of the three DTC consortia adopted a broadly similar approach for the selection and design of DTC study sub-catchments, although the number of sites and specific placement and specification of monitoring stations was determined independently by each consortium in response to local needs and pressures. The focus was on small streams of approximately 10 km2, an area over which mitigation measures could be trialled and assessed intensively, with monitoring equipment installed immediately downstream of manipulated areas to capture the effects. The experimental approach used for assessing mitigation was the ‘Before-After Control-Impact’ (BACI) approach (Figure 2.6), which uses pre-mitigation instream data to provide a baseline against which to compare post-mitigation instream conditions, and also compares a ‘manipulated’ (mitigated) stream with a ‘non-manipulated’ (control) stream. Two BACI designs were used: BACI1, where a separate control stream provides additional spatial reference to account for the confounding effects of factors such as changes in land use, rainfall and flow; and BACI2 where a monitoring point upstream of the mitigation area is used to isolate a control area.

Figure 2.6: Conceptual diagrams of designs used in DTC for establishing control and mitigation sub- catchments; a) BACI1, and b) BACI2

Prerequisites for selecting DTC sub-catchments included accessibility, perennial (year round) flows, representative farm type and land use, bedrock and soils of the wider catchment, and access either to mains power for larger monitoring kiosks or compatibility with solar panels for smaller monitoring kiosks at the catchment monitoring point. Most important of all was engagement with land owners who were happy to host equipment and trial measures, as it was essential that these were carried out on working farms so that the implications on the farm business could be assessed simultaneously. Farm liaison officers with a thorough understanding of local issues were employed in each catchment, and these individuals were instrumental in building successful relationships between researchers and land owners. A risk matrix developed by the EA was used as the basis for sub catchment prioritisation within each DTC catchment, together with local knowledge and research data availability. Details of the study sub-catchment selection procedures and the designs used for each are provided here.

2.3.1.1 Hampshire Avon A total of 18 candidate sub-catchments were identified by the EA on the basis of a risk matrix and a further two catchments were proposed by the Hampshire Avon consortium based on previous research from the ‘Phosphorus from Agriculture: Riverine Impacts Study’ (PARIS) project. The list was narrowed down to five sub-catchments at the first local stakeholder group meeting. The consortium then aligned with local and regional EA officers and the local CSF team for an intensive process of field reconnaissance and farmer liaison to select the final Hampshire Avon DTC sub-catchments (Table 2.3).

Table 2.3: Summary characteristics of the Hampshire Avon DTC study sub-catchments. Characteristic Sem Ebble Wylye Cool’s Priors Ebbesbourne Kingston Brixton Monitored location Cottage Farm Wake Deverill Deverill ST901 297 ST891 284 ST 990 243 ST 841 371 ST 868 401 Area (km2) 2.6 4.6 16.7 25.2 50.2 Average rainfall (mm) 897 863 912 980 886-909 Base Flow Index (BFI) 0.5 0.2 1.0 0.9 0.93 Monitored elevation 163 126 165 190 189 (m A.S.L) Average slope (°) 7 2 7 5 5 Cretaceous Clays, Clays, Chalk, Geology Chalk Chalk, Upper Greensand Limestone Greensand Greensand Heavy, Soil types Heavy Heavy, medium, chalk and limestone medium Dominant land use Livestock Livestock Mixed Mixed Mixed Arable (%)a 14 0 20 40 49 Improved pasture (%)a 37 77 52 36 30 Rough grazing (%)a 9 14 11 12 11 Woodland (%)a 38 6 5 4 3 Urban (%)a 2 3 12 8 7 Constructed Notable features Wetland Overall WFD Classification Moderate Moderate Poor Poor Moderate (2012) a based on the ADAS land use database and for reference year 2010 (cf. Comber et al., 2008).

Three sub-catchments were chosen for the Hampshire Avon experimental design (Figure 2.2). These cover all the principle geological outcrops of the catchment, and all are located within CSF priority areas:

 The Sem is representative of a typical clay sub-catchment with dairy and lowland grazing livestock. The sub-catchment suffers from typical problems associated with livestock enterprises, including manure and slurry management, poaching of river margins, poor track management and efficient delivery of organic and artificial fertiliser applications to the watercourse via the land drainage network, which combines both agricultural and former military installations. Cools’s Cottage and Priors Farm are the sub-catchment monitoring points for the BACI1 design.  The Ebble is a predominantly lowland grazing livestock and mixed farm catchment, with steep-sided chalk valley slopes, purely underlain by chalk. The principal issues in this sub-catchment include elevated nutrient and sediment inputs to the watercourse associated with arable and livestock farming. Two monitoring points at Ebblesbourne Wake are used for the BACI 2 design.  The Wylye flows through areas of both greensand and chalk, all of which are underlain by a clay layer with steep sided chalk valley slopes. Farming systems in this sub-catchment tend to be mixed, suffering

from nutrient and sediment pressures associated with both livestock and arable enterprises, with tramlines running up and down the slope. Monitoring points at Kingston Deverill and Brixton Deverill are used for the BACI2 design.

The Hampshire Avon study areas involve 98 landowners across the three sub-catchments.

2.3.1.2 Tamar The Westcountry Rivers Trust advised the Hampshire Avon consortium on potential target sub-catchments in the Tamar catchment and a series of intensive field walks were undertaken to assess appropriate monitoring sites. The Caudworthy Ford sub-catchment of the Caudworthy Water (Figure 2.3) was selected as being representative of the River Tamar, with good seasonal flows, and is located in the priority area for the implementation of mitigation measures funded by the SWW PES scheme under the ‘Upstream Thinking’ programme. Within this catchment, monitoring at Winnacott Bridge provided the BACI2 design, while a control catchment outside the Tamar in the Neet catchment monitored at Burracott Bridge provided a control catchment for the BACI1 design. (It was not possible to find a suitable control catchment in the Tamar because of the implementation of measures under SWW funding). The Caudworthy Water and Neet sub-catchments have a combination of slowly permeable clay soils and non-alluvial loamy soils, are drained by flashy streams, and are dominated by lowland intensive mixed livestock farming with problems arising from manure/slurry management and high sediment loadings, and pressures associated with the production of crops such as cereals and maize (Table 2.4).

Table 2.4: Summary characteristics of the Tamar DTC study sub-catchments

Characteristic Neet Caudworthy Water Burracot Winnacott Caudworthy Ford Monitored location SS228 005 SX247 926 SX267 888 Monitored elevation 108 139 131 (m A.S.L) Area (km2) 10.9 18.0 26.0 Average Rainfall (mm) 1067 1137 1146 Base Flow Index (BFI) 0.4 0.4 0.4 Average Slope (°) 6 3 4 Geology Mudstones Soil Types Heavy, Medium Dominant Land Use Mixed Arable (%)a 11a 13a Improved Pasture (%)a 77a 73a Rough Grazing (%)a 0a 0a Woodland (%)a 10a 10a Urban (%)a 2a 4a Notable features Overall WFD Classification Moderate Moderate Moderate (2012) a based on the ADAS land use database and for reference year 2010 (cf. Comber et al., 2008).

2.3.1.3 Eden Priority areas identified through CSF in the Eden were added to the same risk matrix developed by the EA, which was used to select three sub-catchments. The involvement of the Eden Rivers Trust, which has long- standing relations with many stakeholders in the area in terms of negotiating access with land owners and having invaluable local in-depth knowledge of issues, was instrumental in the final site selection of the Eden 22 sub-catchments. Data from the Catchment Hydrology and Sustainable Management (CHASM) project in the Eden were also used in the site selection process.

Three sub-catchments were selected which are representative of the varied underlying geology and the different land use types in the Eden catchment, as well as areas where specific water quality issues had been identified (Figure 2.4).

 Morland is a sub-catchment of the Lyvennet, containing the upper reaches of Newby Beck, which flows into Morland Beck near Morland village. It is predominantly improved grassland over Calcareous limestone bedrock, encompassing a mixture of dairy and beef production with associated livestock grazing pressures. There are 44 separate land holdings in the catchment, with 14 participating farms. The dominant pollution pressures are from sediment and phosphorus. The Lyvennet is a CSF priority area;  Pow is drained by the upper reach of Pow Beck, a tributary of the Caldew, which flows into the Eden at Carlisle, and is located in the north of the Eden catchment south of Carlisle. The area is mainly grassland, over Calcareous limestone bedrock. It is the most intensively farmed of the three sub-catchments with a patchwork landscape of intensive dairy, beef, sheep, pig and poultry farming, and also contains a waste recycling facility and landfill site. There are 32 separate landholdings within this catchment, with 15 participating in DTC. It is currently failing WFD standards due to phosphorus, suffering from soil erosion associated with cultivation;  Dacre is drained by Thackthwaite Beck which lies within the Lake District National Park, and is a tributary of Dacre Beck which flows into the Eamont at Dalemain near Stainton in the west of the Eden north of Ullswater. The Dacre catchment drains to an outlet at Nabend, between Great Mell Fell (537 m) and Little Mell Fell (505 m), and is the highest altitude of the DTC sub-catchments. The area is dominated by improved grassland, over Siliceous (sandstone) bedrock. It has flashy flows responsive to rainfall, and is subject to soil erosion and associated phosphorus losses, and runoff from fertiliser and manure. This catchment contains 41 separate land holdings, with large areas managed by farmers resident outside the catchment, six of whom are participating in DTC.

The BACI 1 design has been implemented across all three sub-catchments in the Eden, with two further sub-catchments (Mitigation – Sub_M, and Control – Sub_C) identified within each DTC sub-catchment. The characteristics of these sub-catchments are very similar to those of the wider sub-catchments (Table 2.5), so no further detail about these is provided here.

Table 2.5: Summary characteristics of the Eden DTC study sub-catchments Morland Pow Beck Dacre Characteristic at Newby Beck at Nabend at Thackthwaite Beck Monitored location NY 600 213 NY 386 501 NY 412 259 Monitored elevation 151 60 256 (m A.S.L) Area (km2) 12.5 10.5 10.2 Average Rainfall (mm)a 1147 811 1570 Base Flow Index (BFI) 0.39 0.38 0.40 Average Slope (°) 4 2 9 Glacial till over Sandstone, Siltstone, Borrowdale Geology Carboniferous limestone Mudstone Volcanics Clay loam, Loam, Loam, Soil Types Sandy clay loam Clay Clay Dominant Land Use Lowland livestock Dairy Upland livestock Arable (%)b 6 37 6 Improved Pasture (%)b 76 46 35 Rough Grazing (%)b 14 12 34 Woodland (%)b 2 6 16 Urban (%)b 0.7 0.4 0.1 Waste recycling site ALFA site Nested sub-catchments: Landfill site Common grazing land Notable features Sub_M = 1.6 km2 Nested sub-catchments: Nested sub-catchments: Sub_C = 3.6 km2 Sub_M = 1.9 km2 Sub_M = 1.7 km2 Sub_C = 2.0 km2 Sub_C =1.3 km2 Overall WFD Classification Moderate Bad Good (2012) aBased on Met Office long term average data for 5 km grid (Perry and Hollis, 2005). bBased on CEH Land Cover Map 2007. Other land covers include heathland and bog (14% land area for Dacre).

2.3.1.4 Wensum The EA risk matrix was used to identify sub-catchments in the Wensum at risk from diffuse pollution and located within CSF priority areas. The web-tool ‘Nature on the Map’ (NE, 2012), which allows maps of agri- environment scheme uptake across England to be viewed, was then used to identify suitable farms. The Wensum farm liaison officer worked with the National Institute of Agricultural Botany and The Arable Group (NIAB-TAG, 2013) to engage the farm managers of two large estates in the Blackwater Drain sub- catchment near the village of Reepham. This resulted in farmer participation across four first order streams, which enabled a nested monitoring approach focused on one headwater sub-catchment of the Wensum. A combination of BACI1 and BACI2 designs is used in this catchment (Figure 2.7). This approach is different to that used in the other DTCs but is well suited to the Wensum due to the consistency in bedrock and topography throughout the catchment, and the fact that large estates are common in Norfolk, often farming areas larger than 1000 hectares.

In the Blackwater Drain tributary, the thickness of glacial till, sands and gravels can exceed 20 m and act to limit vertical recharge to the Chalk aquifer, so protecting the deeper groundwater from surface-derived contaminants. Variations in the composition of the glacial deposits means that vertical drainage is limited in headwater mini-catchments A and B where clay loam soils are developed on glacial tills, in contrast to the greater infiltration experienced in mini-catchments C and D which contain a greater extent of sandy loam soils developed on glacial sands and gravels. Hence, surface runoff in mini-catchments A and B tends to be flashier in response to rainfall events compared to the role of groundwater storage in reducing flood peaks 24

Figure 2.7: Response of nitrate, turbidity total phosphorus (TP) and total reactive phosphorus (TRP) to rainfall for the Wensum-Blackwater Drain sub-catchment in a) March 2013, showing a typical post- fertilisation nitrate peak, and b) August 2013, showing typical summer dilution of nitrate followed by a delayed peak in mini-catchments C and D. Although the Chalk does not outcrop in the Blackwater Drain sub-catchment, the combination of sand-rich deposits of less than 10 m thickness and the presence of an upward groundwater hydraulic gradient leads to saturated ground conditions at kiosk F at the outlet of the experimental study area. Chalk borehole records in the vicinity of kiosk F show overflowing artesian groundwater conditions in this vicinity. Hence, the role of groundwater in the Blackwater depends on the 25 nature and thickness of glacial deposits, with the existence of a deeper, slower Chalk groundwater flowpath and a shallower more rapid flowpath through the weathered glacial till, sands and gravels in the top 5-6 m of the sequence. In the west of the Blackwater are glacial tills with clay-rich, seasonally wet soils on chalky boulder clay, whereas in the east the deposits comprise glacial sands and gravels with well drained sandy loams and soils, both of which are typical of farms in Norfolk. Within the Blackwater catchment land use is predominantly arable, although there are some significant areas of woodland and some low density cattle grazing in the eastern part of the catchment (characteristics summarised in Table 2.6). The Blackwater catchment is at risk of failing WFD standards for nitrate and sediment and is probably at risk of failing standards for agricultural phosphate.

Table 2.6: Summary characteristics of Wensum DTC study sub-catchments in the Blackwater Drain catchment. Merrisons Swanhills Swanhills Stinton Brakehills Black Park M A B Hall (C) Bridge Farm Characteristic (Control (Mitigation) (Control Farm (D) (F) BACI2) BACI1) (E) TG 101 TG 110 256 TG 110 TG 116 TG 122 TG 127 TG 125 Monitored location 252 256 256 263 253 246 Monitored elevation 40 36 36 34 39 31 29 (m A.S.L) Area (km2) 3.7 5.3 1.5 7.1 3.5 6.6 19.7 Average rainfall (mm) 655 Base Flow Index (BFI) 0.80 Average Slope (°) 1 Cretaceous Cretaceous Chalk, Cretaceous Chalk, Chalk, All of these Geology Pleistocene Crag, Pleistocene Crag, Quaternary types Quaternary glacial till sands and gravels glacial till Clay loam Clay loam, Soil Types Sandy loam on chalky boulder clay sandy loam Dominant Land Use Arable Arable (%) 93 92 90 89 61 74 75 Improved Grassland (%) 3 3 1 5 19 8 12 Rough Grassland (%) 2 2 5 3 0 3 2 Woodland (%) 2 2 3 2 20 15 11 Urban (%) 1 1 1 1 0.3 1 1 Notable features Down- Down- Up-stream 1st Order 1st Order stream 1st Order 1st Order stream of of A of A & B all Overall WFD Moderate Classification (2012)

2.3.2 Hydrological and Water Quality Monitoring Each study sub-catchment was continuously monitored on a sub-hourly time step for flow and water quality using high-resolution instream sensors. A hierarchical monitoring approach was established in each catchment, comprising:

1. ‘Basic-specification’ stations, equipped with:  A pressure transducer to monitor stage height (calibrated to discharge using instantaneous gauging);  Continuous water quality monitoring of a limited range of parameters (turbidity (as a proxy for suspended sediment) and/or dissolved oxygen);

 An ISCO 3700 sampler for automated water sample collection at daily and sub-daily timescales in the Avon and Tamar. Samples collected by auto-samplers were analysed in the laboratory to determine suspended sediment (SS), nitrogen (N) species (Total N (TN), Total Dissolved N (TDN), Nitrate, Ammonium), and phosphorus (P) fractions (Total P (TP), Total Dissolved P (TDP), and Soluble Reactive P (SRP). Other particulate, dissolved organic and molybdate-unreactive fractions can be calculated from these as follows:

(i) TN-TDN= Particulate Organic N (PON) (ii) TDN-Nitrate-Ammonium=Dissolved Organic N (DON) (iii) TP-TDP=Particulate Phosphorus (iv) TDP-SRP=Soluble Unreactive P (SUP)

 The analysis of samples for full N speciation and P fractionation allowed the consortia to identify the most likely contributing source areas in each catchment, and in particular to discriminate between fresh nutrient fluxes to each stream from livestock manures (organic N and P fractions), sediment erosion (particulate N and P fractions) and from inorganic fertiliser applications to crops and grass;  Full N speciation and P fractionation was determined on samples from the Hampshire Avon and Tamar DTCs, while a limited set of species and fractions (excluding TDN, PON, DON) were determined on samples from the Wensum and Eden DTCs;  Suspended sediment data generated from these stations were used with turbidity data to develop a rating curve to allow conversion of continuous turbidity to suspended sediment concentrations.

2. ‘Low-specification’ stations, had the same equipment as Basic-specification stations, but were also equipped with a YSI 6600 V2 sonde or similar for measurement of a wider range of water chemistry parameters which could be used to indicate dynamic hydrochemical behaviours in each catchment, and to calculate a range of additional water quality indices such as the un-ionised ammonia concentration (using ammonium, dissolved oxygen and temperature data) which has a specific toxicity to fish and macroinvertebrate species, and to determine the stresses on stream ecology associated with extreme temperature fluctuations or low dissolved oxygen concentrations. The stream biota will respond to the full range of stresses in the stream environment and not only to the nutrient chemistry or sediment flux. The parameters included in these sondes are:  Electrical conductivity  pH  Dissolved oxygen  Temperature  Turbidity  Ammonium  Chlorophyll-a

3. ‘High-specification’ stations had the same equipment as the Low-specification stations but were also equipped with Hach Lange nutrient analysers for on-site analysis of a selection of nutrient fractions including TP, total reactive phosphorus (TRP = total P in an unfiltered sample which is molybdate reactive), and nitrate. The selection of parameters monitored in this manner was a function of those that were technologically available, and do not comprise the full range of nutrient species needed to provide a complete analysis of nutrient sources, delivery and impacts in stream systems. Nonetheless, the data provide an opportunity to explore: 27

 The extent to which daily sampling might miss key transport events in which nutrient and sediment loads are delivered from diffuse sources to stream ecosystems;  Whether there is significant diurnal fluctuation in stream chemistry that might have an impact on stream ecology, for example where stream production leads to a drop in oxygen status in the early hours of the morning, leading to impacts on stream ecosystem health;  The uncertainties associated with bank-side analysis versus quality controlled laboratory analysis of daily auto-sampler samples, and with different sampling frequencies and the data streams generated, in terms of our ability to detect change in hydrochemical time series (Lloyd et al., 2014; Lloyd et al., in review).

The latter is explored in more detail in Part 4 and its associated appendices, and will provide a critical and robust platform from which further work to detect and report on the impact of on-farm mitigation measures on diffuse pollution of water from agriculture can be based.

The data from the all of the sondes and bankside analysers deployed in both the Low and High-specification stations were accessed remotely by each team using telemetry. This allowed the sensor data to be viewed remotely and in real time and for the teams to undertake prompt resolution of sensor drop outs, mis- calibration or other forms of malfunction common in field sensor networks, though some gaps inevitably occurred in the observational time series where solutions to sensor problems were not easy to derive. There are data gaps in the records for most of the stations across the platform because of problems such as sensor malfunction and power supply failure. Analysis of paired samples in the Hampshire Avon DTC, identified further problems resulting from reagent instability and decay between field visits in the bankside analysers, leading to periods where the Phosphax analyser in particular generated lower TP concentrations than were measured on the comparable samples taken through a quality controlled laboratory analysis step. Similar findings have been reported elsewhere in the literature for bankside analysers. Examples of the problems encountered with the sensor network and bankside analysers are illustrated in Appendix 2.1, based on example data streams from a selection of sites in the Hampshire Avon DTC. These can be common problems with sensor networks and bankside analysers and not easily resolved without significant additional time input by field staff and additional costs. Quality Assurance and Quality Control (QA/QC) procedures to minimise discrepancies between bankside and laboratory data and to remove any spurious data due to equipment failure etc. have been followed. The data presented here have been robustly checked and are of good quality. Further details on the approach and the methodology, including sample handling protocols and analytical and QA/QC procedures are provided in Appendix 2.2. Details on the uncertainties and relative precision and accuracy of load estimates derived from each of the observational approaches, based on data from the Hampshire Avon DTC are explored in Part 4.

2.3.3 Ecological Monitoring In conjunction with the hydrological and water quality monitoring, a monitoring regime was designed to detect how freshwater ecology responds to changes in agricultural diffuse pollutants. The data gathered have been used to confirm the WFD ecological status of the study sub-catchments, to explore spatial and temporal variability in biological quality elements, and to identify those pollutants that are most likely to be causing failure to achieve Good ecological status. This background data provides an evidence base to both inform, and assess the impact of, mitigation measures. DTC phase 1 will focus on tracking the extent and timing of any changes in the ecology in response to these targeted on-farm mitigation measures.

Invertebrates, fish, diatoms and macrophytes were monitored in each sub-catchment and WFD tools have been used to establish the status of these Biological Quality Elements (BQE). The biological monitoring sites

28 are closely matched to the hydrological and water quality monitoring stations, which will allow observed change in the BQEs to be linked to reductions in diffuse pollution and on-farm diffuse pollution mitigation measures. Further details on the approach and the methodology are provided in the Appendix 2.3.

2.3.4 Other Monitoring and Assessment Approaches In addition to the hydrological, water quality and ecological monitoring, a wide variety of additional monitoring and assessment approaches have been undertaken in order to understand the diffuse pollution problem. The approaches varied by catchment depending on partnerships and the needs of stakeholders, and the resources (e.g. other projects, MSc/PhD studentships) available to each consortium, but include:

 Pesticide monitoring (Wensum)  FIO monitoring (Eden, Wensum and Tamar)  Assessment of ecosystem functional responses to mitigation measures (Hampshire Avon)  Catchment hydrogeological function (Hampshire Avon and Eden)  Temperature sensing to investigate surface water-groundwater interaction (Wensum)  Sediment fingerprinting (Hampshire Avon and Wensum)  Dissolved organic matter fingerprinting (Hampshire Avon)  SCIMAP for assessing sediment risk (Eden, Wensum)  Uncertainty approaches to quantifying catchment responses (Hampshire Avon)

The results of the water quality monitoring and the ecological monitoring are discussed in detail here. In addition, the hydrogeological conceptual models for the Hampshire Avon, Tamar and Eden DTCs developed by the British Geological Survey in phase 1 and local investigations of groundwater in the Wensum catchment by the Wensum DTC, are also presented to assist in interpreting the role of groundwater as a receptor of and pathway for diffuse pollution in surface waters and wetlands in each DTC catchment. Further information on all approaches used, including the methodologies, and details on the outcomes from the other monitoring and assessment approaches undertaken is provided in appendices 2.4-5 to this report.

2.3.4.1 Real Time Camera Work Web Cameras A set of custom-made real time web cameras were installed at each of the EdenDTC catchment outlets as part of the first phase of the DTC project. These cameras captured Video Graphics Array (VGA) low resolution images (640 x 680 pixels) at 10 minute intervals, which were transmitted to the web-server via the mobile phone data network. The images were displayed on the EdenDTC website and archived for future analysis. The objectives for this installation were:  To allow the field team to see the current conditions at the sites to assist with maintenance; and  To allow stakeholders to see the sites and compare the monitoring results to the out visual record.

Results The live images from the web-cams were popular on the website. For example, the camera at the Morland outlet was viewed 245 times in 2013, while the water chemistry data was viewed 320 times, indicating that the users of the website were finding value in these images.

The cameras also captured some exemplar storm events and stream responses (Figure 2.8).

Figure 2.8: Storm flow response captured at Thackthwaite Beck with the real time web cameras

Problems There were problems with the reliability of the cameras, requiring frequent attention to keep them running. There were also issues with a lack of 3G mobile data signal at the Thackthwaite Beck site. Ultimately, the performance of the cameras was not sufficient to justify the continued time cost of maintaining the system. The camera supplier has since been bought out by Meteor Communications, who have updated and refined the cameras into a new product.

Concluding Remarks  The real-time cameras provided useful contextual information to both the field team and to website users;  The events captured by the cameras have supported presentations on the project;  The quality and level of maintenance required for the cameras meant that they were trialled for part of the project;  Future projects should consider installing new versions of this technology if the quality can be improved and maintenance required can be reduced.

2.3.4.2 Cattle Monitoring with Cameras Temporal dynamics between cattle in-stream presence and suspended solids in a headwater catchment In order to address the uncertainty about the extent to which cattle impact on sediment transfer processes within the fluvial channel, a comparison of high-resolution monitoring data of cattle activity within the streambed against high-resolution water quality data collected over a period of several months was conducted. Bushnell Trophy XLT 119455 motion sensor cameras, with infrared night-vision LEDs, were used to capture in-stream activity of cattle, with a time-delay of 10 seconds between each shutter response triggered by the motion sensors. The work highlights that cattle have an observable impact on water pollution, although a temporal lag between cattle in-stream presence and a critical amount of their contribution to sediment load was demonstrated. This study helps demonstrate the loss of environmental information that can result from low resolution monitoring programmes, and is available in full at http://pubs.rsc.org/en/content/articlepdf/2014/em/c3em00686g?page=search.

Abstract: Cattle in-stream activity is potentially an important contributor to water pollution from agriculture. This paper presents research on the physical movements of cattle within a stream on suspended solid concentrations (SSC). The study used camera surveillance to monitor in-stream activity of dairy cattle in an unfenced reach over a four-month period. Results were compared against high-resolution SSC data. Over the days that cattle grazed the field, 57.9% of the instances when SSC crossed the 25 mg l_1 Freshwater Fish Directive guideline threshold can be attributed to cattle presence in the stream. Flow was the main driver of total sediments transported over the study period, and no relationship was found between SSC and the absolute number of cattle feet in the water. Hysteresis analysis indicated a ‘first-flush’ of local sediments rapidly mobilised during the non-cattle related SSC events, a result of cattle proximity to channel margins. Results demonstrate a temporal lag between cattle in-stream presence and a critical amount of their contribution to sediment load, and that monitoring only instantaneously with cattle activity may lead to underestimation of their pollution impact.

2.3.4.3 Monitoring changes in diffuse pollution source risk with time-lapse photography There are many factors within the landscape that can affect diffuse pollution source risk, which vary over the year. Examples include snow cover dynamics driving overland flow not directly connected to precipitation on that day, and changes in solar receipt resulting from cloud cover and land cover changes ensuing from agricultural practices. An approach that was trialled within the first phase of DTC was the use of a time-lapse camera to create a visual record on the landscape, which could then be processed to give a continuous record of such factors. It is hoped that this extra information may provide useful context and background information for the interpretation of the in-stream monitoring data.

Location and Equipment A single camera was installed in the Dacre sub-catchment. The location was selected due to the suitability of the topography for the approach, and local issues with snow melt that are most relevant at this site. The camera location and a typical image are shown in Figure 2.9. The camera installed was a Canon 5D mk I DSLR with a 24mm f2.8 lens set to f5.0, with a fixed ISO of 200 in aperture priority mode. This setup gave a horizontal angle of view of 73.7°. The camera was triggered to take an image every 10 minutes and has been installed since Jan 2014.

Figure 2.9: The location of the time-lapse camera and a typical image acquired from the site (24/06/14). The red triangle in the map shows the camera direction and angle of view of the images

Converting Images to Information Images were processed with the Python Image Library and SciPy to extract information on the changing characteristics of the landscape. The approach taken for cloud cover and snow cover followed the same steps: 1. Define the area of interest within the image for analysis. For the clear skies, this is an area extracted from the sky, while for the snow cover this area was defined as the local foreground field. 2. A Gaussian blur filter was applied to the image with a high radius (200) to give a consistent colour value within the extracted area of interest image. 3. The red, green and blue components of the colour were extracted for analysis. a. For ‘clear skies’, the index was defined as blue / ((green + red) / 2) and hence the lower values represent cloudy days and higher values represent blue sky days b. For the ‘snowiness’, the index was defined as blue / green with values towards one representing snow cover

The changes in the overall scene were assessed by comparing the statistical differences between pairs of images. Two approaches were tested, the first compared the current day to the previous day and the second approach compared each day to the averaged scene from the whole year. The differences between the images were defined as the ‘Manhattan’ difference, whereby the mean movement in brightness levels per pixel is calculated after the images have been converted to greyscale and normalised.

Initial Results Figure 2.10a shows the initial results for the 2014 calendar year for the clear skies and snowiness indices, with example images (Figure 2.10b). These initial results are based on the midday images from each day. From the results, it can be seen that the snowiness index correctly identify days with snow cover when the index value exceeds 0.87. There is a strong seasonal trend in the snowiness index values with the lowest values in the summer. The ‘blue skies’ index shows the high level of cloud cover through the year, with relatively few days being cloud free (8.5% of days have an index value greater than 0.75). The example image in Figure 2.10b from 12/05/14 shows issues with condensation within the waterproof housing of the camera install.

Figure 2.10a: Changes in the clear skies and snowiness index over the 2014 calendar year

12/02/14 12/05/14 26/08/14 12/12/14 Clear Skies: 0.67 Clear Skies: 0.82 Clear Skies: 0.82 Clear Skies: 0.79 Snowiness: 0.96 Snowiness: 0.69 Snowiness: 0.53 Snowiness: 0.96

Figure 2.10b: Changes in the clear skies and snowiness index over the 2014 calendar year

This condensation reduces the image quality for fine detail, but it is still possible to extract information on the sky status. Figure 2.11 shows the changes over time for the daily differences when compared to the previous day and to the mean image. The mean image is shown in Figure 2.12.

11/02/14 14/04/14 09/08/14 11/12/14

12/02/14 15/04/14 10/08/14 12/12/14 From Previous: 35.3 From Previous: 44.6 From Previous: 32.7 From Previous: 52.4 From Average: 59.3 From Average: 46.2 From Average: 30.8 From Average: 14.8 Figure 2.11: Changes over time for daily differences when compared to the previous day and to the mean image

Figure 2.12: The average image from all midday images in 2014

The results in Figure 2.11 show that the difference based indices are capable of identifying the days of the year were the landscape or climate differ from the ‘normal’ conditions. The approach successfully identified the increased snow cover in February 2014, the occurrence of a change in the weather conditions from cloud to clear skies in April 2014 and the reverse in August, and finally the occurrence of snowfall in December 2014.

Concluding Remarks  Time-lapse photography of the catchment and landscape has the capability to add significant contextual information to complement and support the in-stream water chemistry datasets;  The approach generates significant amounts of data, and hence tools are needed to identify the important times and associated images;  The presented indices are capable of identifying properties of the environment such as cloud cover or snow accumulation/melt;  It is possible to identify the timing of large changes in the landscape, either compared to the previous day or to the annual average conditions.

2.3.4.4 Use of Unmanned Aerial Vehicles (UAVs) for Diffuse Pollution Understanding Original aim and need Part of the original conceptual model of the generation of diffuse pollution within the landscape was that a key part of the process was driven by interaction of the rainfall intensity and the status of the land surface. Land surface characteristics change significantly on intra- and inter-annual timescales and hence, to understand the catchment response to a storm event, knowledge of the current status of the landscape is needed. To monitor these changes, various remote sensing approaches were assessed. Both satellite based remote sensing and aerial photography were discounted due to prohibitive costs and potential issues with cloud cover. Therefore, a spatially targeted plan using a UAV was developed as a way to get some information on the landscape state.

Equipment The equipment sourced was a Microdrones MD4-200 quadcopter UAV1, which offered programmable flight routes using GPS and a ground station showing telemetry from the craft, including battery voltage, position and a video feed. This is the same UAV that was used by the Environment Agency and other UK public

1 http://www.microdrones.com/en/products/md4-200/at-a-glance/ 34 sector organisation at the time. The MD4-200 is capable of carrying 200g payload of a small RGB digital camera.

Proof of concept results The first image in Figure 2.13 is a true colour image of a spring sown barley field at Sewborwens Farm near Penrith; the second is a classified image which shows vegetation versus soil cover; and the final image show the change in vegetation cover over time for this site (more details in Dixon, 2012).

Example true colour image Land cover classified image. Changes in the ground vegetation Red are bare areas and blue cover are vegetated areas. Image shows 6.4% bare, erodible soil. Figure2.13: Example image processing for a spring sown barley field within the EdenDTC monitored area

Lessons learnt  Within phase 1 of EdenDTC, there was insufficient person time to implement the planned monitoring scheme without impacting on other measurements;  Dedicated time and resource is required to acquire high quality images and to process them into useful information;  With the MD4-200, there is a narrow wind speed window for effective and safe operation. The craft is sensitive to wind speed gusts, which are not always recorded in weather data;  Repair times can be long and can create issues with application;  People (stakeholders and public) are interested in the technology and it provides an effective engagement tool.

Next Steps Within DTC phase 1 we were unable to operationalise the spatial monitoring with the UAV due to lack of sufficient person time and technological limitations. Within DTC phase 2 we are planning to use UAV equipment from Durham University to monitor a set of key fields with high erosion risk and effective pathways (strong connectivity) to the river channels. The new equipment (DJI S1000 with Zenmuse gimbal, carrying a 24mp APS-C RGB camera) is capable of flying in far higher wind speeds and can capture higher resolution and higher precision images. These improved images will enable detailed terrain models and cover maps for the hotspot fields to be created. Examples will be shown in future project updates.

2.4 Results and Interpretation This section summarises the results of the monitoring data for the nine sub-catchments across the four DTC focus catchments. Although the DTC project began in 2010, much of 2010 and 2011 were used for initial site selection, land-access permissions, design, ordering and set-up of equipment, and in-field quality 35 control, meaning that the first robust hydrology and water quality data were not collected until well into 2011. The monitoring in the Hampshire Avon and Tamar DTCs also ended half way through a water year, in March 2014, whereas in the Eden and Wensum monitoring has been continued beyond the project end dates. Monitoring data for hydrology and water quality are therefore presented for the DTC focus catchments for two water years: Water year 2012 (1st October 2011 – 30th September 2012) and water year 2013 (1st October 2012 – 30th September 2013), and for three years of ecological data 2011-2013. Although there are differences in the monitoring approaches taken between catchments, common sets of observations are reported below across all catchments to allow comparisons to be made across the DTC platform, between differing sub-catchments, and enable differences in annual and seasonal transfers and patterns both within and across catchments to be highlighted. The more detailed N speciation and P fractionation results from the Hampshire Avon and Tamar which allowed finer discrimination of likely contributing source areas in those catchments are also reported. The results are presented by DTC and by sub-catchment, to highlight the contrasts between the differing sub-catchments in those DTCs, and the ways in which this was then used to direct mitigation measures in each catchment. Results of further targeted monitoring detailed are reported separately in appendices 2.6.

2.4.1 Hydrology and Water Quality across the DTC Platform Water years Water year 2012 The 2012 water year was unusual, described as a year of ‘dramatic contrast’ by the Met Office. A wet autumn led to widespread flooding in November and December across most of the UK affecting all the DTC catchments but particularly the Eden. The Environment Agency issued a record 1,000 flood warnings during these months. A very dry winter then followed, with an exceptionally warm and dry early spring. The lowest March rainfall total was recorded since 1953, and severe drought in the south of England (including in the Wensum catchment) resulted in a hosepipe ban from the first week of April, when soils reached their driest levels on record for the time of year. The weather then shifted abruptly, with the month of April actually recorded as the coldest since 1989 and the second wettest since records began in 1766, with much of the existing drought region receiving more than twice its average rainfall. This extreme weather switched the focus from drought stress to flood risk in many parts of the country. April to June was the wettest on record, with June being the wettest month in 2012. Flooding was again an issue in July after a month of high rainfall led to saturated soils.

Water year 2013 In contrast to 2012, the 2013 water year was drier, although there were some significant weather events throughout the year (Table 2.7). Autumn was wet, particularly the month of December when there was extensive disruption from flooding. The late winter and spring were exceptionally cold; the spring was the coldest recorded since 1962, and there were unseasonably late snowfalls. Summer was warm and sunny, and a heatwave in July – the sunniest July since 2006 – was a marked contrast to the cool and wet summers experienced from 2007-2012.

Table 2.7: Rainfall totals in mm for the four DTC catchments and sub-catchments for the 2012 and 2013 water years

DTC Hampshire Avon Tamar Eden Wensum catchment Sub- Sem a Ebbleb Wylye a Caudworthy Neetc Morlandd Dacred Powd Blackwater catchment Waterc Draine 2012 870 1129 1152 1270 1270 1207 1963 849 683 2013 824 914 1053 910 910 1184 1528 762 633 aFrom one tipping bucket rain gauge. bFrom Met Office rain gauge: Hurn, 4117E 978N. cFrom Met Office rain gauge: Chivenor, 2496E 1344 N. dAreal rainfall total calculated using Thiessen Polygon method from three gauges in each sub- catchment. eFrom 6 tipping bucket raingauges across the catchment (standard deviation = 6mm).

Temporal Variations in Nutrient and Sediment Transport in the DTC Sub-Catchments Phase 1 of the DTC programme spanned two markedly different water years, in terms of hydrological function, in each of the sub-catchments. As a key driver of pollutant delivery from diffuse sources to adjacent waters, where hydrology varies markedly so too will the connectivity of source areas to streams, the efficiency of nutrient and sediment delivery from land to water, and the relative proportion of the different nutrient fractions (organic, particulate, inorganic) mobilised and transported in any catchment. This contrast has provided an opportunity to investigate inter-annual variability in the baseline water quality data for each sub-catchment, and to evaluate the impact of this variability on the uncertainties associated with ability to detect shifts in hydrochemical time series (Lloyd et al., 2014). Further details of these analyses, and the implications for detecting hydrochemical and ecological responses to on-farm mitigation measures, are presented in Section 5. Figures 2.14-21 show the hydrochemical and hydrological time series data collected for a selection of the DTC sub-catchments for the 2012 and 2013 water years. The sites illustrated in these figures include:

 River Sem at Priors Farm, a target catchment for mitigation in the Hampshire Avon;  River Ebble, Ebbesbourne Wake downstream from the wetland mitigation feature, Hampshire Avon;  River Wylye at Brixton Deverill, downstream from the proposed mitigation reach, Hampshire Avon;  Caudworthy Water at Caudworthy Ford, downstream from proposed on-farm mitigation,Tamar;  River Neet at Burracott, a control catchment with intensive livestock production in the Tamar;  Morland Beck in the Eden;  River Pow in the Eden;  Blackwater in the Wensum.

Uncertainty analysis undertaken on the High Specification Hampshire Avon-Ebble sub-catchment data, discussed in Part 4, led to the selection of daily, quality controlled data from the auto-sampler unit rather than the half-hourly data from on-site analysers for report for the Ebble monitoring station. The Tamar sites at Caudworthy Ford and Burracott are Low-Specification stations and do not have on-site analysers. Data for the High-Specification Hampshire Avon-Wylye site at Brixton Deverill, and for the Eden and Wensum DTC sites, are from on-site analysers. To allow comparison of trends across all sites, the TP, TDP and nitrate- N concentrations are presented in these figures for the Hampshire Avon-Wylye, Eden and Wensum sites. TP, SRP and Nitrate-N concentrations are reported for the Hampshire Avon-Sem and Ebble and both Tamar DTC sites. The remainder of the N speciation and P fractionation data for these sites are discussed in Section 2.4.1.1 below.

The differences between the 2012 and 2013 water years and the atypical hydrological behaviour in 2012 is clear in all of the time series data presented in Figures 2.14-21. For many of the DTC focus catchments and sub-catchments, the difference between the dry winter of 2012 and the dry summer of 2013 is striking, and this is reflected in both groundwater-dominated catchments in the Hampshire Avon and Wensum catchments, and the less permeable catchments of the Eden, Tamar and the clay catchments of the Hampshire Avon (Sem) DTC.

Hampshire Avon, Sem at Priors Farm This catchment is characterised by quick-flow responses to rainfall events. Flow, total P and turbidity in the Sem sub-catchment demonstrate these quick flow responses throughout 2012 and 2013, with multiple peaks recorded throughout 2012 and the first half of 2013 (Figure 2.14). The total P response is dominated by particulate P mobilisation and delivery within the catchment (see Appendix 2.4, Tables A2.4 and A2.6).

Figure 2.14: Flow and water quality data for water years 2012 and 2013 for the Hampshire Avon-Sem sub- catchment at Priors Farm. Data plotted for TP, SRP and Nitrate are daily, quality controlled nutrient concentration data derived from lab analysis of samples collected by auto-sampler

Nitrate shows a classic dilution response to rainfall, with concentrations decreasing as nitrate-poor surface generated quick-flow arrives instream, recovering to pre-event concentrations as nitrate-rich through-flow arrives instream, lagged behind peak flow in the channel. However, the Sem also showed distinctive event- based nitrate peaks, with a clear shift in nitrate response from event dilution to peaks in late March 2013. 38

The shift suggests activation of near-stream nitrate sources in this period, possibly associated with surface dressing of soils with fertilisers in close proximity to the sampling station. The nutrient speciation data for this site are presented in Tables A2.4a and A2.4b in Appendix 2.4, and are discussed later in this section of the report. They demonstrate the significance of both SRP and SUP (primarily in the form of dissolved organic P as the major components of TP flux in this catchment).

Hampshire Avon, Ebble at Ebbesbourne Wake (downstream site) Flow in the Ebble is strongly controlled by the hydrogeology of this chalk catchment. The stream is ephemeral at this site and therefore dries up for part of the year when the water table falls in the mid to late summer period. The 2012 water year was particularly wet in the autumn and winter.

Figure 2.15: Flow and water quality data for water years 2012 and 2013 for the Hampshire Avon-Ebble sub- catchment at the monitoring station downstream from the wetland mitigation feature. Data plotted for TP, SRP and Nitrate are daily, quality controlled nutrient concentration data derived from lab analysis of samples collected by auto-sampler

Water started to flow in the channel in May 2012 and continued through to July 2013. The data collected show a damped flow regime, with peak turbidity and TP concentrations lagged behind peak flow, and lagged nitrate dilution in response to peak flow events. However, there are also a number of peaks in TP, SRP and nitrate in the stream which are not co-incident with flow variations. These are likely to reflect the decision of the land-owner at the site to introduce cattle grazing on the wetland. The cattle access the site over a plank bridge adjacent to the High Specification sampling station, and there is further evidence in the other nutrient fractions for this site (see Appendix 2.4, Tables A2.4a-b, and later in this section) that this introduced significant peaks in organic and particulate N and P to the stream at the downstream station.

Figure 2.16: Flow and water quality data for water years 2012 and 2013 for the Hampshire Avon-Wylye sub-catchment at Brixton Deverill. Data for TP, TRP and Nitrate are high resolution half hourly data from on-site analysis

Figure 2.17: Flow and water quality data for water years 2012 and 2013 for Caudworthy Water at Caudworthy Ford in the Tamar catchment. Data plotted for TP, SRP and Nitrate are daily, quality controlled nutrient concentration data derived from lab analysis of samples collected by auto-sampler

Hampshire Avon, Wylye at Brixton Deverill The ephemeral head for the River Wylye is upstream from Brixton Deverill at Kingston Deverill. Flow was therefore continuous in the Wylye at Brixton Deverill for the whole of the monitoring period. Step changes evident in the Wylye discharge data were caused by the groundwater pumping and stream augmentation regime operated by Wessex Water at the ephemeral head, as a method of stream support during dry periods. Total phosphorus peaks were much greater in the Sem and Ebble than in the Wylye, but clear peaks coinciding with nitrate dilution occurred in each of the main storm peaks captured in the monitoring programme. This is in agreement with the nutrient flux behaviours in the Wylye observed at daily frequency by Yates and Johnes (2013) for the preceding 3 year period, with TP concentrations peaking at reaching 1 mg l-1 in the Wylye, in contrast to 5 mg l-1 in the Sem at Priors farm. The TP signal in the Wylye at Brixton Deverill is dominated by soluble rather than particulate P transport, suggesting that mitigation measures would need to target sources mobilised along through-flow and groundwater flow pathways rather than sources mobilised along overland flow pathways.

Caudworthy Water at Caudworthy Ford, Tamar DTC The data collected are from a Basic specification station and are at daily sampling resolution. They show a rapid response to rainfall events in both the flow and the sediment transferred to and within the channel (inferred from the turbidity data). Total P flux shows similar coincident peaks in both water years, with peak concentrations reaching 1.8 mg l-1 P in the largest events. SRP is a minor component of this TP flux, which is dominated by the mobilisation and transport of P-rich sediment and manures via overland pathways or from near-channel sources. Nitrate shows a short-lived dilution in response to peak flow events, with concentrations substantially lower than those observed in the chalk catchments. Total N concentrations in this catchment are dominated by organic and particulate N fractions (Appendix 2.4), suggesting that mitigation measures need to be focused on reducing sediment transport efficiency via overland or near- surface quickflow pathways, reducing sources in the near-channel zone, and reducing the amount and/or vulnerability to mobilisation of animal manures in this catchment.

Figure 2.18: Flow and water quality data for water years 2012 and 2013 for the Neet at Burracott in the Tamar catchment. Data plotted for TP, SRP and Nitrate are daily, quality controlled nutrient concentration data derived from lab analysis of samples collected by auto-sampler

River Neet at Burracott, Tamar DTC The Neet data demonstrate very similar behaviours to those observed for the Caudworthy, confirming the suitability of this site for a BACI1 experimental design. The Neet is the control catchment, against which the response of Caudworthy Water to on-farm mitigation measures can be compared. As with the Caudworthy, peaks in TP, turbidity and flow are coincident, SRP is a minor component of the TP concentrations observed, and nitrate shows modest dilution responses to peak flow events in this catchment. The N speciation data for this catchment (Appendix 2.4) point to intensive livestock production as a key source of nutrient enrichment in this catchment.

Morland and Pow, Eden DTC Flow in the Eden catchments was characterised by frequent discharge events occurring in response to regular rainfall inputs (Figures 2.19 - Figure 2.20). Hydrographs were flashy, with steep rising and only slightly attenuated falling limbs during individual storm events compared to the Wensum and Avon sub- catchments, and with flow peaks of similar magnitude occurring throughout the year. The early part of 2012 was unusually dry for this part of the country, with only one discharge event recorded in February and

Figure 2.19: Rainfall, flow and water quality data for water years 2012 and 2013 for the Eden-Morland sub- catchment. Data for TP, TRP and Nitrate are high resolution half hourly data from on-site analysis

March, and 2013 being more typical. Nitrate concentrations were lower in the Eden sub-catchments than in the other DTC catchments, but there was a change in the pattern of transfer throughout the year, with a shift from dilution in response to rainfall and increasing discharge followed by delayed peaks, to sharply defined rapidly responding peaks of much greater magnitude. In 2012, this shift occurred between April and July, while in 2013 it extended throughout the summer period. Phosphorus peaks occurred very frequently and rapidly in response to rainfall with concentrations frequently exceeding 0.6 mg P l-1 and peaking at 1.0 mg P l-1. TRP peaks are co-incident with TP peaks and contribute a large component of the TP flux in these catchments. Turbidity peaks are also coincident with peak flows, suggesting that there are well-connected surface sources of particulate P, dissolved organic P and sediment which are rapidly mobilised during rainfall events. The data suggest that mitigation measures should be developed to control P and sediment mobilisation at source, and reducing the efficiency of delivery pathways linking source to receptor (stream).

Figure 2.20: Rainfall, flow and water quality data for water years 2012 and 2013 for the Eden-Pow sub- catchment. Data for TP, TRP and Nitrate are high resolution half hourly data from on-site analysis

Blackwater, Wensum DTC The flow hydrographs show long recession limbs during individual events, but to a lesser extent than in the Hampshire Avon sub-catchments. The dry winter in 2012 was less pronounced in the Wensum (Figure 2.21) than in the Hampshire Avon, with significant rainfall events in January and March, and a large discharge event in the flow record in March. Nitrate concentrations showed dilution patterns from October to December in response to rainfall and increasing flow, but from January 2012 onwards, a similar shift in the nitrate pattern to the Eden occurred, as rapidly responding peaks in nitrate concentration were recorded which became less pronounced by August 2012.

Figure 2.21: Rainfall, flow and water quality data for water years 2012 and 2013 for the Wensum- Blackwater sub-catchment. Data for TP, TRP and Nitrate are high resolution half hourly data from on-site analysis

In 2013, these nitrate peaks were also observed throughout the autumn and winter period and into spring, but the dry summer conditions then reduced opportunities for nitrate transfers. Total phosphorus concentrations responded rapidly to rainfall and increased discharge throughout the year, and even to small events, but peak TP concentrations were much lower than in the other DTC catchments. Turbidity peaks are also much lower than in the other DTC catchments, and the TRP data suggest that particulate P

45 transfer is not the major source of nutrient delivery in the Wensum catchment. Mitigation measures will be most effectively targeted at reduction of nitrate leaching from crops and grass, and reduction of P mobilisation in peak flow events.

The data presented in Section 2.4.1.1 indicate that for one or more sub-catchments within all DTC focus catchments, a large proportion of sediment, phosphorus and nitrate transfers occur during discrete events when runoff and pollutant transfers increase due to rainfall input, source mobilisation and the connection of source to receptor. A more detailed analysis of these trends, based on the Outram et al. (2014) paper on high-frequency monitoring of nitrogen and phosphorus response in the Eden, Hampshire Avon and Wensum DTC sub-catchments to the end of the 2011–2012 drought is presented in Appendix 2.5 to illustrate the type of analysis that is currently underway, based on the high resolution observational data streams being generated by the platform. Also in Appendix 2.5 is information on geochemical sediment fingerprinting work undertaken in the Wensum DTC, based on two papers (Cooper et al., 2014a; Cooper et al., 2014b), which has been used to apportion sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources during numerous precipitation events, alongside a realistic assessment of uncertainty

2.4.1.1 Differences in Hydrological and Hydrochemical Behaviours and Chemistries in the DTC Sub- Catchments The analysis presented in 2.4.1.1 focuses on a subset of variables which were monitoring across all of the DTC sites. However, the sampling programme was designed to capture a wider range of hydrochemical fractions and behaviours in order to identify the likely contributing source areas, and the variables most likely to reflect change in response to on-farm mitigation measures across the diverse range of landscape, land use and agricultural conditions captured in the DTC sub-catchment selection. Tables summarising the complete set of hydrochemical and flow data for each catchment are provided for reference in Appendix 2.4. In Tables A2.4a-b and A2.5 in this Appendix, summaries of mean annual concentrations for the nutrient fractions and turbidity in each of the four DTC focus catchments are presented, for the 2012 and 2013 water years. Bar charts highlighting the key trends in flow weighted concentrations for the initial subset of variables common to all catchments are presented below in Figure 2.22, for turbidity, TP, TRP and Nitrate-N concentrations.

Turbidity, measured in nephelometric turbidity units (NTU), is highest in the Tamar sub-catchments, with the Hampshire Avon-Sem, a clay catchment, also having high annual mean values. Turbidity in the Eden and Wensum sub-catchments was similar, but with much greater variability in the Eden, and when these values were converted to flow weighted suspended sediment concentrations it was clear that high flow events were much more important in driving sediment delivery to streams in the Eden than in the Wensum. This pattern was repeated in the data sets collected for both 2012 and 2013. The pattern reflects the relative dominance of surface flow versus throughflow and groundwater flow in each of the catchments, with the greatest turbidity observed in those catchments with bare soils, steep slopes and a high proportion of surface or near-surface throughflow from land to water. In these catchments (Eden, Tamar, Sem in the Hampshire Avon) there is an intrinsically higher risk of sediment erosion than in groundwater-dominated catchments (Ebble and Wylye in the Hampshire Avon, for example) where the dominant flow pathways are sub-surface. Local variations in actual sediment mobilisation and delivery result from local on-farm practices. Geochemical sediment fingerprinting in the Wensum revealed several critical source areas of sediment activated during high rainfall events (see Appendix 2.5; Cooper et al., 2014a; Cooper et al., 2014b). Reduction in sediment delivery will require targeted on –farm mitigation measures in these catchments (see Part 2).

12.0

9.0 N (mg/l) N - 6.0

3.0 2012

FW FW Nitrate 2013 0.0

Nitrate-N

2.0

1.5

1.0

0.5 2012 2013

0.0 FW FW Concentration TP (mg/l)

Total phosphorus

40.0

30.0

20.0

Turbidity(ntu) 10.0 2012 2013 0.0

Turbidity

Figure 2.22: Flow weighted mean annual Nitrate-N and TP concentrations (mg l-1) and Turbidity (NTU) in the four DTC catchments, 2012 and 2013. There were no turbidity sensors deployed in the Ebble upstream site and the Eden-Pow catchment

Total phosphorus data (Figure 2.22) were compared across all DTC sub-catchments through the use of flow weighted (FW) concentrations (see Appendix 2.4, Tables A2.4 and A2.5). FW TP concentrations were highest in the Priors Farm (Sem) sub-catchment, with FW concentrations rising 4-fold between 2012 and 2013 owing to changes in site management in the Ebble sub-catchment (see below). Across both years, FW concentrations were high in the Neet (Tamar DTC) and in the Wylye (Hampshire Avon). The Eden-Morland had lower FW TP concentrations, with higher concentrations recorded in the Eden-Pow in 2013. Mean TP concentrations for Eden-Morland were similar to the Wensum-Blackwater, but the high variability indicates the importance of storm transfers, and the linkage of TP to sediment transfers in this catchment. Although sediment concentrations were similar, TP concentrations were higher in the Eden-Pow compared to Eden- Morland, suggesting that TRP is more important than particulate P in this sub-catchment. TRP concentrations represent approximately half of the TP concentrations for Eden-Morland in both 2012 and 2013, and around two thirds for the Wensum-Blackwater in 2012, rising to three quarters in 2013, and 80% for the Eden-Pow in 2013. These data are presented in Appendix 2.5 for reference for the Eden and Wensum sites.

Nitrate concentrations (Figure 2.22) are particularly high in the Hampshire Avon-Ebble sub-catchment, with mean FW values well above the 11.3 mg l-1 drinking water limit in 2012, although in 2013 concentrations were much lower and more similar to those in the Hampshire Avon-Wylye and Wensum. Daily mean values were lower for the Ebble in both 2012 and 2013, but higher for the other Avon and Tamar sub-catchments. Mean annual nitrate concentrations were greater in 2012 for all sub-catchments except the Wylye and the Wensum. For the Wylye, daily nitrate concentrations were similar to those measured in the nearest borehole, which had an average nitrate concentration of 6.9 mg l-1, suggesting that the dominant flow pathway for nitrogen transport to the upper Wylye is via groundwater flow, pumping from the borehole during flow augmentation and upwelling through the river gravels into water column. Mitigation measures in chalk catchments like the Wylye and Ebble therefore need to focus on reducing source mobilisation and downward leaching of nitrate to the groundwater aquifer, if nitrogen flux to waters and its impact on stream ecosystem function is to be reduced from diffuse on-farm sources. This also applies to the Wensum, where the downward movement of nitrate from fertilisers to deeper groundwater can be intercepted by field drains, which provide a fast pathway to the stream.

This analysis of the subset of variables provides a useful means of comparing likely contributing sources across all of the DTC catchments. In the Wensum, weekly grab samples at all sites analysed for a greater number of determinands than the bankside analysers permit have revealed that nitrate is by far the most dominant form of nitrogen due to the high fertiliser application rates and the low presence of livestock farming in the catchment. Therefore, in the Wensum DTC the high resolution bankside analysis is an appropriate form of monitoring to capture the behaviour of nitrogen in the catchment. However, the complete nutrient speciation data sets for the Hampshire Avon and Tamar DTC sub-catchments provide an opportunity to explore the other nutrient fractions contributing to the total nutrient loading at a daily and sub-daily (storm sampling) temporal resolution delivered to the aquatic ecosystem from on-farm sources in each catchment, and to evaluate whether the focus on nitrate alone is providing robust evidence to underpin the selection of mitigation measures for each catchment. These are presented as mean annual concentration, mean annual FW concentration, and as annual load estimates for each of these 8 catchments in Tables A2.4a-b and A2.6 in Appendix 2.4. Summary bar charts illustrating the key trends are presented here in Figures 2.23 (phosphorus) and 2.24 (nitrogen). Comparable TP and nitrate-N load estimates for the Eden and Wensum DTC sub-catchments are presented in Appendix 2.4. TRP determined on the Eden and Wensum sites includes colloidal P fractions, as the samples were not filtered prior to analysis using the bankside analysers, so the data are not directly comparable with the SRP fraction

48 determined in the Hampshire Avon and Tamar sites. The data for the Eden and Wensum are not, therefore, included in this figure, but the loads are reported in Appendix 2.4, Table A2.6.

1.5

1.2

0.9

0.6 PP

0.3 SUP Annualload (kg/ha) SRP 0.0

Phosphorus fractions (2012)

3.5

3.0 2.5 2.0 1.5 PP 1.0

SUP Annualload (kg/ha) 0.5 SRP 0.0

Phosphorus fractions (2013)

2.0

1.5

1.0

0.5 2012

Annualload (kg/ha) 2013 0.0

Total phosphorus

Figure 2.23: P load fractionation in the Hampshire Avon and Tamar DTC sub-catchments, 2012 and 2013

25.0

20.0

15.0 PON 10.0 DON

5.0 Ammonium Annualload (kg/ha) Nitrate 0.0

Nitrogen fractions (2012)

25.0

20.0

15.0 PON 10.0 DON

5.0 Ammonium Annualload (kg/ha) Nitrate 0.0

Nitrogen fractions (2013)

20 2012 10 Annualload (kg/ha) 2013 0

Total nitrogen

Figure 2.24: N load speciation in the Hampshire Avon and Tamar DTC sub-catchments, 2012 and 2013

Total phosphorus load fractionation data (Figure 2.24) for the annual load concentrations observed in the Hampshire Avon and Tamar DTC sub-catchments (see also Appendix 2.4, Table A2.6) confirm the indications of dominant sources from the analysis of the turbidity data. P delivery to the livestock farming catchments in the Sem and Tamar sub-catchments was dominated by particulate-P (PP) flux during high flow events, with SUP as an important secondary component in the P load transported by the Sem at Priors Farm and in the Neet, where livestock farming is intensive. SRP was a minor fraction of total P at all sites, but proportionately more significant in the Wylye and Ebble where septic tanks in streamside properties upstream from the sampling infrastructure will be contributing soluble N and P to the water column. SUP was a larger fraction of TP delivery in the Neet than at Caudworthy Ford, reflecting the intensity of livestock farming in that catchment. High particulate and organic P loading will stimulate benthic community production in the stream ecosystem, leading to adverse and undesirable impacts on stream ecosystem function and health. In the Sem catchments at Priors Farm and Cools’ Cottage a similar pattern emerges, while in the chalk catchments at Kingston Deverill, Brixton Deverill and at the upstream Ebble site, TP loading is dominated by the soluble P fractions, including SRP from septic tank systems and dissolved organic P fractions originating from livestock wastes. The findings can be used to infer the likely balance of contributing source areas in each catchment, with sub-catchments with a higher proportion of PP likely to be dominated by livestock wastes and sediment erosion pathways, while the higher proportion of SRP in the Hampshire Avon is likely to reflect septic tank effluent discharges from villages along the course of the Upper Wylye.

Total nitrogen load speciation: nitrogen loading fractionation determined in the Hampshire Avon and Tamar DTCs, together with dissolved organic N (DON) and particulate N show marked variations between catchments. Those with clay soils (Sem, Hampshire Avon) or relatively impermeable bedrock (Caudworthy, Tamar and Neet) are dominated by livestock farming and demonstrate a quickflow response to peak rainfall events. In contrast, there is mixed arable farming on calcareous soils in the chalk catchments (Ebble and Wylye, both in the Hampshire Avon) which have permeable bedrock and are dominated by groundwater flow with time lags between peak rainfall and a runoff response. Mean annual total nitrogen loading reflects these differences in source character, vulnerability to mobilisation and transport, and the different flow pathways linking source to receptor in these catchments.

The TN loading varies markedly between catchments with the highest loadings in 2012 observed in the Ebble and the Neet. However, the fractionation of the TN load between the two sites differs markedly. Over 80% of the TN load in the Ebble is in the form of nitrate-N at both the upstream and downstream sites. This pattern mirrors that for the Wylye at both Brixton Deverill and Kingston Deverill, suggesting that in general terms, nitrogen flux to waters in chalk catchments is likely to be dominated by nitrate leaching to groundwater, and that on-farm mitigation in chalk landscapes should focus on reducing the mobilisation of nitrate within the soil system, and the efficiency of nitrate transport to groundwater. By contrast, in the intensive livestock farming catchments of the Neet and Sem, nitrate-N contributes about or less than half of the TN load. Mitigation measures in these catchments would need to target the mobilisation and transport of DON and particulate organic N (PON) in these landscape and farming types if they are to deliver effective reduction of nitrogen flux to, and impact on ecosystem function and health. These patterns are repeated in 2013, with 63% of the TN load in the Sem at Priors Farm and the Neet at Burracott in the form of DON and PON, and only 35% in the form of nitrate at each site. Clearly, if mitigation is to be effectively informed, and responses to measures are to be effectively detected, particularly given the uncertainties associated with the sensor network and on-site analyser data, and the lack of robust in situ technological capability for routine sensing of organic and particulate nutrient fractions, monitoring to determine the TN load exported

51 to waters is essential in any catchment dominated by livestock farming. Sensing of nitrate-N alone will be insufficient for the challenge in these systems.

Another clear feature of the data is the sharp increase in TN load at the High Specification sampling station downstream from the wetland mitigation feature at Ebbesbourne Wake. When compared with the data for the auto-sampler basic station upstream from the wetland, the increase is particularly marked. This reflects local decision making regarding the management of this artificial wetland system. The land owner decided to introduce cattle grazing as a maintenance regime on the wetland. Cattle manure voided on and near the plank bridge next to the sampling equipment may well have contributed to this peak. The cattle were not on site in 2012. The peak in TN mirrors that shown for TP in Figure 2.23, and may be accounted for by the flow-independent peaks in TP and turbidity at this site between January and May 2013 shown in Figure 2.15. Where mitigation features are proposed, the ongoing management of the site needs to be factored in if the benefits of the investment are to be realised in the longer term.

The annual pollutant loads analysis demonstrates both the marked contrast in the likely contributing source areas and flow pathways linking source to stream between different sub-catchment and provides a robust evidence base with which to inform mitigation planning. However, it has also drawn out the similarities in hydrochemical function and nutrient delivery pathways between catchments with similar geoclimatic and farming character, providing a valuable insight into the potential for upscaling the evidence from the DTC infrastructure and investment to develop generic guidance on the likely contributing source areas, and pathways linking source to receptor by catchment type, and the types of evidence agencies need to collect in order to provide a robust platform of evidence to inform the selection of targeted mitigation for each catchment. The question remains, however, of whether mitigation measures implemented in catchments should focus on mitigating nutrient and sediment transfers during peak flow conditions, or whether mitigation needs to be effective across a wider range of flow conditions in catchments. This is examined in the next section.

2.4.1.2 Nutrient and Sediment Delivery to Streams in the DTC Sub-Catchments: the Relative Importance of Flow Conditions under Different Geoclimatic Conditions Annual nutrient and sediment load contributions were examined in terms of flow conditions for 2013, with high flows categorized as the top 10% of the flow duration record and low flows categorized as the bottom 10%. Mid-flows represent the 10th-90th percent of flows. The data are presented for flow, turbidity and P fractions in Figure 2.25, while data for the N species including nitrate-N are presented in Figure 2.26.

For all DTC sites in 2013, the highest 10th percentile of flows accounted for around 50% of annual flow, the exceptions being the Wylye at Brixton Deverill and Kingston Deverill, and the upstream site on the Ebble, as well as the Sem at Cool’s Cottage and the Pow. The groundwater response of the Wylye and Ebble resulted in dampened storm responses. In the Morland sub-catchment, high flows were responsible for 94% of the annual sediment load, meaning that over 90% of the load mobilisation occurred in 10% of the time. This was an outlier, when comparing data across all sites, and while high flows were important in mobilising sediment, the majority of this transport occurred under mid-flow conditions in all of the Hampshire Avon and Tamar sub-catchments. In the Pow and Wensum, high flows accounted for over half of the sediment load mobilised. Low flows accounted for less than 10% of annual load at all sites.

Similar patterns were observed for TP with high flows being responsible for mobilisation, most markedly in the Ebble downstream from the wetland where cattle manure voided next to the sampling station was readily mobilised (80%) and Morland (79%). However, in the upstream site at the Ebble more than 80% of

Figure 2.25: Proportion of flow, turbidity and total P load transported in low, mid and high flow periods, 2003

the TP load was mobilised at mid flows, and this was a pattern repeated at each of the other chalk catchments (Wylye, Ebble, headwaters of Cool’s Cottage). The Wylye and Caudworthy Water stand out as having the majority of TP transported during mid flows (80% and 83%, respectively). Low flows accounted for less than 2% of TP loads across all sites. TRP transport was dominated by high flows at Morland (73%) but mid-flows at Pow and the Blackwater (57% and 50%, respectively; see Appendix 2.4, Table A2.7 for details). Field drains in the Blackwater are a source of dissolved P under mid flows, whereas the proportion of PP increases during storms due to topsoil and road verge particle mobilisation. SUP transport at all sites was dominated by mobilisation under mid-flow conditions, though high flows accounted for nearly half of the load mobilisation at Priors Farm, downstream in the Ebble and in the Neet. Each of these is intensive livestock farming systems or, in the case of the Ebble, had proximal sources of manures at the sampling station. This pattern is likely to reflect mobilisation of manure-based sources of dissolved organic P under mid-flow conditions.

Figure 2.26: Proportion of total annual N load transported in low, mid and high flow periods, 2013

Nitrate transport was dominated by high flows in the Ebble downstream from the wetland, Neet and Blackwater (58%, 50% and 54%, respectively; Figure 2.26). In the Blackwater, high flows represent the activation of field drains in the catchment, providing a quick pathway for soluble nitrogen to the stream, particularly during the fertiliser application window in March-May when groundwater levels are high at the end of the recharge period. For all other sites mid flows were most important for nitrate mobilisation and transport, a pattern repeated for dissolved organic N, suggesting that both nitrate and DON may be mobilised in all catchments from flushing of soil porewaters under mid flow conditions, with both lateral and vertical transport of nitrate-N and DON from soil porewater pools occurring as soils wet-up. A prolonged period of damp weather resulting in mid-flow discharge instream can lead to a significant increase in N loading delivered to streams and consequent impacts on the stream biota.

Hampshire Avon and Tamar sub-catchments) is likely to be mobilised from soil porewater pools under the same conditions as DON and nitrate-N. Ammonium is commonly considered to be mobilised under high flow conditions, but in the year analysed here this was not the case at any of the sites other than at the site downstream from the Ebble wetland. 60-95% of ammonium was mobilised under mid-flow conditions in the other three chalk catchments in the Wylye and Ebble, both of the clay catchments in the Sem, and the 54 impermeable catchments in the Tamar DTC. A complexity of sources, including instream sources, ammonium contributed to the chalk streams from septic tank systems in the river corridor, and the mobilisation of ammonium from animal manure stores may account for this behaviour. By contrast, the majority of PON is mobilised under high flow conditions in both the Ebble downstream from the wetland, and the Neet, suggesting that entrainment of organic N rich particulate matter such as animal manures under high flow conditions may be an important pathway for the delivery of N to stream systems in intensive livestock farming catchments.

The analysis of nutrient and sediment delivery under differing flow conditions provides a valuable insights into the extent to which mitigation measures should focus on controlling the impact of extreme flow events as a mechanism for the delivery of contaminants to stream systems from catchment sources. The analysis indicates that nutrient and sediment mobilisation occurs over a wide range of flow conditions, and that mitigation needs to consider measures for nutrient fractions and sediment mobilised under mid flow conditions as well.

In terms of ecosystem impacts of nutrient and sediment loads delivered to waters from diffuse agricultural sources, there are both the immediate impacts as the load arrives in the stream, particularly if that load contains nutrient fractions or sediment-associated pollutants which have a toxic impact on the stream biota (un-ionised ammonia, for example), and the longer term impact of that load over subsequent weeks, seasons and years, as it is cycled instream, spiralling through multiple biotic and physicochemical pools as it moves downstream. Both will have a significant impact on stream ecosystem function and health. The data collected from the DTC phase 1 programme have therefore been further analysed to determine the seasonal patterns in the delivery of each nutrient fraction from source to receptor in each of the sub- catchments. These data are presented in Appendix 2.5, Tables A2.8 and A2.9, and are interpreted in Section 2.4.1.3 below.

2.4.1.3 Seasonal Pollutant Transfers Seasonal variations in flow, suspended sediment, nitrate, and total P loads delivered to each catchment as a percentage of total loads are presented in Figure 2.27 for water year 2013. The data are presented in Appendix 2.4, Table A2.9, with the data for 2012.

There were marked differences between seasonal flows and pollutant transfers between years 2012 and 2013 for all DTC sites. Autumn in 2012 accounted for 17-39% of flow in 2012 compared to 37 - 58% in 2013 for all sites across the DTC platform. In the Sem, Wylye and the Blackwater, spring accounted for 39%, 30% and 34%, respectively, of annual flow in 2012, contrasted with 4%, 10% and 13%, respectively, in 2013. The timing of rainfall events throughout the year also had a profound effect on nutrient and sediment delivery from diffuse catchment sources. Although in both years in Morland autumn accounted for the largest seasonal sediment transfers, the percentage contribution in 2012 was 39% compared with 60% in 2013. The combined spring-summer delivery of SS to the Morland Beck accounted for 48% of annual sediment transfer in Morland in 2012, contrasting with only 26% in 2013. In the Blackwater, autumn accounted for 9% of annual sediment transfer, compared to 49% the following year. In the autumn, bare, ploughed fields and poor cover from the establishment of winter-sown cereals are common in the Wensum, and also more widely across the UK on arable land, particularly in permeable lowland landscapes, so any heavy rainfall occurring in autumn coincides with this period of low topsoil stability and high intrinsic erosion risk. In Morland, the harsh climate and steep slopes make this catchment vulnerable to soil erosion at this time of year also, and the steeper slopes in the chalk catchments of the Hampshire Avon (Ebble and Wylye) increases their vulnerability to sediment erosion and transport to adjacent waters in autumn and winter. 55

Figure 2.27: Seasonal patterns in proportion of flow and the total annual nutrient and sediment load delivered to selected streams on the DTC platform (data are presented as % of total load)

The seasonal patterns in TP and TRP mimicked those of sediment in Morland and the Blackwater in both years, emphasising the relationship between sediment and phosphorus transfers in these sub-catchments. In the Avon and Tamar sub-catchments, there was more variation between the TP and SRP fractions, and the SRP data show a marked difference in terms of seasonal trends than the TRP fraction. In the Wylye the majority of SRP was mobilised and transported to the streams in the Autumn period, with over 75% transported to the Sem in the Winter period, 65% in the Ebble, and 55% in the Wylye, reflecting the cultivation of spring cereals and the access of livestock to stream banks throughout the year (prior to mitigation). In the Tamar catchments delivery of SRP is roughly equally split between winter and spring, and there is evidence of livestock-mobilised SRP transfers to the Neet in the summer period.

For nitrate, the majority of the annual load was delivered to the chalk streams in the winter period, while in the impermeable catchments of the Sem and Caudworthy the dominant season was the spring. In the Neet, Morland, Pow and Wensum, Autumn was the key period for nitrate flux to waters. Seasonal nitrate transfers followed the same pattern as seasonal flow contribution. The starkest contrast between the two years was found in the Wylye, the Neet and the Blackwater. In 2012 in the Wylye, summer accounted for 47% of nitrate transport, whereas in 2013 winter was responsible for 53% of annual nitrate transport. In 56 the Neet, autumn accounted for only 15% of annual nitrate transport in 2012 but 71% for the same period in 2013. The marked contrasts in the hydrometeorological conditions in these catchments between 2012 and 2013 and the peak rainfall in the autumn 2013 water year which ended the 2012 drought are likely to have driven this trend

In the Blackwater, spring 2012 accounted for the largest proportion of annual nitrate transport, 36%, due to the wet summer and build-up of N in the soils following on from the drought which ended in early 2012. The following year, the autumn-winter period had more typical rainfall, resulting in seasonal contributions in autumn and winter of 43% and 41%, respectively. Nitrate transport in the Blackwater was proportional to flow volume; the higher runoff coefficient in 2013 resulted in larger N losses per hectare, which largely occurred in the winter period. Therefore, the timing of rainfall in the Blackwater is crucially linked to nitrate losses, where rainfall in the winter is more likely to result in runoff when shallow groundwater flow paths and field drains are active, as opposed to the summer when rainfall is likely to go into storage.

There are also marked seasonal trends in the fractionation of the nutrient load delivered to the DTC sub- catchments from catchment sources. These are outlined for the Hampshire Avon and Tamar DTC sites in Figures 2.22 (N species) and 2.23 (P fractions) for 2012 and 2013 water years. The graphs should be read with care as vertical axes are different for each species and year.

The N speciation data show some interesting trends which can help to diagnose the likely combination of contributing source areas and dominant transport pathways in each catchment. In the Sem at Priors Farm, N flux was dominated by organic nutrient fractions in 2012, primarily delivered during the winter-spring period. B contrast, in the drier 2013 water year N flux decreased 3-fold, though it was still dominated by organic N fractions rather than by nitrate. A similar pattern was evident in the data for Neet, in terms of load fractionation, but delivery prinarily occurred in the summer and winter. In 2013 the rate of delivery of N species in the Neet was higher in all seasons, with the dominant period of delvery being the autumn. The trends likely reflect farm licestock management practices and the differing hydrological behaviours in each catchment between the two water years.

In the chalk catchments, nitrogen loading is dominated by nitrate-N flux,in both years, with a peak in particulate organic N at the site downstream in the Ebble during the auturm and winter, reflecting cattle grazing on the wetland and defecation close to the sampling station. DON is a significant component of TN flux at all of the livestock farming catchments in both water years, and a significant minority component of flux in the permeable catchments where delivery is dominated by nitrate leaching from fertiliser grass and arable soils in the autumn-winter period.

The P fractionation data presented in Figure 2.29 support these findings, with marked differences in the rates and timing of P dlivery to waters from catchment sources between the two water years. Particulate P transfers are the dominant component of TP delivery to waters at most sites in the 2013 water year, particularly at the downstream Ebble site. SUP, including DOP, is also a key component of P flux to waters in all seasonsin 2013, but particularly in spring and summer. SRP is a minor component of TP flux even in the most contaminated waters. In the chalk catchments of the Wylye at Kingston and Brixton Deverill and the Ebble sites at Ebbesbourne Wake soluble P fractions (SRP and SUP) are the dominant form of P flux to waters. The data suggest that in a dry year mitigation measures would need to target the soluble P sources and transfer pathways in permeable catchments, but that particulate P would also need to be targeted through mitigation, as it is rapidly mobilised from catchment stores and delivered to the streams during periods of high flow.

Figure 2.28: Seasonal trends in nitrogen species delivery to sub-catchments in the Hampshire Avon and Tamar DTCs in 2012 and 2013

Figure 2.29: Seasonal trends in phosphorus fraction delivery to sub-catchments in the Hampshire Avon and Tamar DTCs in 2012 and 2013

Key conclusions which may be drawn from this analysis are that although it is possible to detect common hydrochemical function in catchments with similar land use, management and geoclimatic conditions, there are marked difference in these behaviours between contrasting water years, between catchments with superficially similar farming systems and landscapes but with local management activities which generate

59 deleterious effects in terms of their impact on nutrient and sediment flux in these catchments. It may not, therefore, be possible to draw analogies on the likely hydrochemical response to land management within any catchment, simply by drawing an interference from another study on a similar lithology, unless the management of nutrients, soils and farming activities are very similar. In equal measure, it is axiomatic that assumptions about the fractionation of the nutrient load, the seasons in which delivery primarily takes place, cannot be made based even upon the water quality observations in the same basin if the hydrometeorological conditions are very different. This poses a challenge for environmental managers charged with deriving measures to mitigate against pollutant transport from diffuse sources in agricultural catchments and points to the need to collect robust evidence of the range of behaviours and chemistries a catchment might exhibit under a range of environmental conditions in order to derive a robust understanding of the sources within the landscape and the pathways that allow delivery of nutrient and sediments from source to receptor.

2.4.1.4 Hydrology and Water Quality Conclusions The DTC hydrological and hydrochemical monitoring programme has delivered a strong evidence base from which to plan for mitigation of diffuse nutrient and sediment pollution of water bodies across a range of landscape types common to the UK. A number of conclusions have been reached concerning the dominant sources in each of the DTC catchments, the timing of nutrient export from land to water, and the range of nutrient chemistries mobilised and transported to streams in each of the catchments. Universal truths have not emerged from this analysis, and it is clear that care must be taken to ensure that the findings from one programme are not assumed to provide a perfect solution in another circumstance without prior testing of those solutions across multiple land management and geoclimatic conditions.

The DTC monitoring has, for example, shown that storm events are highly important in driving the flux of diffuse pollution in catchments where there is a high proportion of rapid runoff in preferential flow pathways (surface runoff, near-surface quick-flow and drain flow in drained landscapes like the Wensum catchment). However, it is the mid flow events which dominate nutrient and sediment deliver in catchments driven by subsurface hydrological function. Similarly, nitrate is the dominant form of nitrogen delivered to waterbodies in permeable catchments, based on the evidence collected here, but it is a minority component of the TN loading in livestock farming systems where the high stocking densities and abundance of manure production leads to enrichment of waters with both particulate and dissolved organic nutrient fractions which will stimulate both algal productivity and microbial metabolism instream. A monitoring programme that focused solely in nitrate in a livestock farming catchment would be unlikely to be sensitive to current management or to targeted on-farm mitigation efforts in the catchment.

Another point which has emerged is that there are significant uncertainties associated with any monitoring strategy to detect nutrient and sediment flux behaviours in catchments. Sensors provide on-site high frequency observations, but there can be technological problems to overcome with their use, if robust and reliable findings are to be generated. Simply relying on the sensors without testing the accuracy and precision of the observations within an uncertainty framework will constrain the user to imprecise and uncertain observations, data streams with significant gaps and a limited range of determinands which might not be those best suited to answering the question posed. Laboratory based analyses have their place, as quality control can ensure higher quality data, for a wider range of determinands, albeit at a lower temporal resolution. A combination of both approaches is likely to be needed to generate robust evidence streams for catchment mitigation efforts at any site.

In synthesising these nutrient and sediment flux behaviours for different landscape typologies across the DTC platform, careful consideration also needs to be given to the appropriate statistical techniques that are 60 applied (see Lloyd et al. 2014 and Section 5 of this report). This ensures core findings regarding the efficacy of mitigation strategies allow for the quality and uncertainty of different measurement strategies (such as identified above); the natural variability in time and space of catchment systems and related climatic factors; the duration of the evidence base pre and post mitigation in light of inherent natural variabilities; and the different QA procedures and monitoring strategies between DTC components. The DTC platform has developed number of toolkit approaches (see case studies in Section 5) to quantifying these uncertainties that should be embedded in any weight of evidence approach to providing decision support for diffuse pollution mitigation strategies and the detection of change.

Finally, the contrast between the responses of different catchments with varying soils and geology to differing degrees of rainfall has been shown to be critical in determining pollutant loads, and properly evidencing the selection of mitigation measures in any catchment, as is the proportion of groundwater that contributes to river flow. Consequently the importance of identifying and managing run-off pathways during storm events and subsurface pathways year round is a key finding of DTC. The manner in which these behaviours impact on the ecosystem function and health of the receiving water bodies are discussed below in section 2.4.2.

2.4.2 Ecology 2.4.2.1 WFD Status All experimental sub-catchments chosen for study across the three DTCs are below the required WFD Good-Moderate boundary based on their Biological Quality Elements (BQE; with the exception of Dacre, Table 2.8). It is expected that these sites will improve in response to mitigation of agricultural diffuse pollution with the aim of achieving the Good ecological status required by the WFD. More specific details on the status of each BQE at sites within the Avon, Tamar, Eden and Wensum are presented in Tables 2.9- 12. Water bodies that are classified as Moderate status or lower require the legal implementation of Programme of Measures (POM) under the WFD in order to restore these systems to at least Good ecological status. This emphasises the importance of research conducted through the DTC research platform on ecological structural diversity and the response of biological communities to key physicochemical drivers which provides a more holistic understanding of how stream systems function at the catchment scale. This will serve to inform mitigation efforts and wider catchment monitoring. The primary aim of the biological component of phase 1 of the DTC was to provide a solid baseline against which the impact of mitigation measures can be assessed: this has been achieved. Despite the variation in weather, the data describing the condition at the sites appears to be consistent both within (where duplicate samples have been taken) and between occasions. However, it is important that we establish the background variation in the metrics used to classify sites so that we can assess the significance of any change in response to changes in key diffuse pollutants from agriculture.

2.4.2.2 Variability in Data Figure 2.30 demonstrates how information captured in metrics based on single season sampling (it is usual to collect one sample in spring and one in autumn) may conceal important temporal dynamics. Monthly EQR values for diatoms demonstrated the greatest cyclic seasonal pattern within the Eden-Morland catchment with highest trophic status classification in the spring and lowest in winter. Across the Eden DTC, trends at the sub-catchment outlet (10 km2 scale) were partly tracked by those at smaller scales (2 km2) within the sub-catchments, which revealed high annual repeatability, sensitivity and robustness of EQR scores. This is important in terms of mitigation, demonstrating that the impacts on EQR values of intervention at farm scale and field scale could be reflected in EQR values at sub-catchment outlets. 61

Table 2.8: Overall assessment of WFD Class for DTC focus catchments and study sub-catchments. Ecological status of Biological Quality Elements: H = High, G = Good, M = Moderate, P = Poor, B = Bad. Water Framework Directive objective is Good or above

WFD Class Catchment Sub-catchment Site Issues 2010 2011 2012 2013 d/s Cool's Farm Control A M P M P Sediment, N, P, Organic Sem Priors Farm Manipulated M M M M Sediment, N, P, Organic Donhead Hall Control B P P P P Sediment, N, P Hampshire u/s wetland Control P P P N, P Ebble Avon d/s wetland Manipulated P B P N, P Kingston Deverill Control M P M N, P, Flow Wylye Brixton Deverill Manipulated A M M M P N, P, Flow Hill Deverill Manipulated B M M M M N, P, Flow Caudworthy Bridge Manipulated A M M M Sediment, Organic Caudworthy Water Tamar Caudworthy Ford Manipulated B M M M Sediment, Organic Neet Burracott Bridge Control P M M Sediment, Organic Eco-control Control B B B Physical habitat, Flow A P P B B Physical habitat, Flow, Sediment, N,P B M P B B Physical habitat, Flow, Sediment, N,P Wensum Blackwater C P P B P Sediment, N, P Drain D M P P P N,P E P P B P Physical habitat, Flow, Sediment, N,P F B B B B Sediment, N, P Newby Beck M M M Sediment, P Morland Dedra Banks Beck M P M Sediment, P Sleagill Beck M M M Sediment, P Pow Outlet M B M Sediment, N, P Eden Pow Tributary A M M M Sediment, N, P Tributary B M M M Sediment, N, P Thackthwaite Beck G G G Sediment, P Dacre Mellfell Beck G G Sediment, P Lowthwaite Beck M G G Sediment, P

Table 2.9: Ecological Status of the Hampshire Avon DTC study sub-catchments. Biological Quality Elements: H = High, G = Good, M = Moderate, P = Poor, B = Bad

Sub- INVERTEBRATES MACROPHYTES DIATOMS FISH Site Replicate catchment 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 a/c Control A H H H G P G G M P G P Cool's Farm A B H H H P M M

A G M M M M M M G M G M Sem Priors Farm Manipulated B M M P M M G

Control A H H H G M G H P P P P Donhead Hall B B H H H G G G

A M M P P P M M M H u/s wetland Control B G P P M M Ebble A P B P P M M P H H d/s wetland Manipulated B P P P M M

A M P M G G G G H H Kingston Deverill Control B M P G H G

Manipulated A M M M M M M P H G M P Brixton Deverill Wylye A B M M M M G P

A G G G M G M M M M G G Manipulated Hill Deverill B G G M G G M B B H H H H G

Table 2.10: Ecological Status of the Tamar DTC study sub-catchments. Biological Quality Elements: H = High, G = Good, M = Moderate, P = Poor, B = Bad

Sub- INVERTEBRATES MACROPHYTES DIATOMS FISH Site Replicate catchment 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 a/c Manipulated A H G H H H G M M M Caudworthy Bridge Caudworthy A B H H H G G

Water Manipulated A H G H H H H M M M Caudworthy Ford B B G G G H H

A H H H G G G P M M Neet Burracott Bridge Control B H H H H G

Table 2.11: Ecological Status of the Eden DTC study sub-catchments. Biological Quality Elements: H = High, G = Good, M = Moderate, P = Poor, B = Bad

Sub- INVERTEBRATES MACROPHYTES DIATOMS FISH Site catchment 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 Newby Beck H H H G H G M M M M Morland Dedra Banks Beck M P M Sleagill Beck M M M Pow Outlet G G H G H H M M M B Pow Unnamed trib A M M M Unnamed trib B M M M Thackthwaite Beck H H H H H H G G G H Dacre Mellfell Beck G G Lowthwaite Beck M G G

Table 2.12: Ecological Status of the Wensum DTC study sub-catchments. Biological Quality Elements: H = High, G = Good, M = Moderate, P = Poor, B = Bad

Sub- INVERTEBRATES MACROPHYTES DIATOMS FISH Site catchment 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 2010 2011 2012 2013 Eco-control M M G M B B B M M A P P P B P B P P P P B M M P M P B B M M M Blackwater C P M M M P B P P B M Drain D M P P M P M P P P P E P P P P P M M M B M F P M B B M M M M P P B B M

Moreover, within all three sub-catchments, considerable resilience of diatom communities was highlighted through the seasonal pattern in inter-monthly EQR scores with month-on-month changes in EQR values evident against the highly variable hydrological regime (see Section 5). Therefore, for flashy upland catchments such as the Eden, time and frequency of sampling is critical. As both spatial and temporal factors may impact classification status, results need to be considered in relation to discharge-nutrient dynamics. Further work will be undertaken in DTC phase 2 to determine how discharge-nutrient dynamics influence confidence of classification.

(a)

(b)

(c)

Figure 2.30: Monthly EQR metric scores for diatoms within (a) Morland; (b) Pow; and (c) Dacre catchments from March 2011 to March 2013. EQR WFD UK TAG classification boundaries: Bad (< 0.25), Poor/Bad (0.25), Poor/Moderate (0.50), Moderate/Good (0.75) and Good/High (1.00)

Temporal variation in biological communities is to be expected. The challenge is to extract the information from these biological data that reveals any change in the ecological quality of the site, rather than the natural background variation in response to seasonality, weather, etc. Whilst the WFD tools have been designed to best reflect the response to stressors, it is not possible to completely remove the influence of background variation. In this first phase we have quantified the variation in key biological metrics before mitigation measures were put in place, so that we can determine the significance of any biological response 65 to subsequent changes in key diffuse pollutants from agriculture. As an example, variations in the macroinvertebrate based WFD classification metrics across the four DTCs are shown (Figure 2.31). For the two WFD metrics there is a high degree of stability both within sites (where multiple samples have been collected) and within sites over time. As it is based on an average score, ASPT is a more stable metric than NTAXA.

Figure 2.31: Variation in Ecological Quality Index (EQI) for the two WFD classification indices based on invertebrates over time in A) the Hampshire Avon/Tamar, B) the Eden and C) the Wensum DTCs. The WFD classification boundaries (horizontal lines) are also shown.

2.4.2.3 Ecological Pressures The secondary aim of DTC phase 1 was to identify the issues acting in each sub-catchment that were causing the sites to fail to achieve WFD Good Ecological Status. For each catchment, stressor-specific diagnostic indices have been derived from the biological quality elements and the biological response related to the hydrochemical monitoring data. This has enabled the identification of stressors acting on the biota at individual sites (Table 2.8). The most prevalent issues appear to be nutrients and fine sediment. One such diagnostic index, the LIFE index (Lotic-invertebrate Index for Flow Evaluation), relates invertebrate community to flow (Extence et al., 1999): higher flows should result in higher LIFE scores. The scores, based on macroinvertebrate sensitivity to prevailing flow regimes, are calculated at either family or species level. The LIFE index suggests that there was a response of the invertebrate community to the increased flow in the groundwater-fed Hampshire Avon-Wylye during the 2012 summer wet period (Figure 2.32). Using such indices it is possible to separate the biological response to natural change (here this is change in discharge in response to weather) from changes in diffuse pollution, even though delivery pathways of diffuse pollutants are likely to be driven by rainfall.

Figure 2.32: Variation in the invertebrate based LIFE score (Lotic-invertebrate Index for Flow Evaluation) over time in the Wylye sub-catchment (Hampshire Avon) compared with mean daily discharge from Brixton Deverill. NB The increase in LIFE score in all sites in summer 2012, corresponding with the high flows during this unusually wet period

Despite nutrients being a key pressure on ecological communities, studies within the Wensum DTC demonstrate that EQRs based on diatoms (Figure 2.33a), benthic invertebrates (Figure 2.33b) and macrophytes (Figure 2.33c) showed limited variation in response to observed variation in N and P, suggesting that a substantial reduction in nutrient concentrations would be required to induce the required biological response or, perhaps, that other factors are constraining ecological quality. A major conclusion from ecological work in the Wensum is that, for most sites, physical habitat (artificially simplified channel morphology with even distribution of fine sediment), may be limiting the ecological quality of the aquatic communities. Invertebrate taxa that persist only when water is well oxygenated (invertebrates with high BMWP scores) were scarce, but were present in the agricultural drainage ditches of the Blackwater (Wensum) sub-catchment. Patches of habitat in the Blackwater are evidently suitable for pollution-sensitive

67 organisms but high quality patches are too few at present to raise the overall ecological status of the sub- catchment beyond poor to medium. Engineering of channel morphology to create meander sequences with consequent variations in flow velocity and sediment particle size distribution appears to offer good prospects for improving the ecological status of the waterways in the upper, intensively cultivated catchment of the Wensum (Appendix 2.6).

(a)

(b)

(c)

Figure 2.33: Ecological Quality Ratios (EQRs) for (a) diatoms (b) benthic invertebrates and (c) macrophytes and major nutrients averaged over the 21 days before sampling (note the log scale) in six waterways in the Blackwater sub-catchment of the River Wensum sampled in Spring, Summer and Autumn for diatoms and invertebrates and in Summer for macrophytes between November 2011 and September 2013

Diatoms in the Eden DTC appeared to respond to increased delivery of phosphorus during wetter, winter periods (Figure 2.34). High Trophic Diatom Index (TDI), indicating a higher percentage of nutrient-tolerant taxa in the community and thus indicative of more nutrient-enriched conditions, and low biomass periods are generally associated with high discharge events and corresponding peaks in TP concentration.

In addition to using the BQEs to diagnose the diffuse pollution issues at play in each sub-catchment, and hence target mitigation activities more effectively, by linking ecology and hydrochemistry data we have been exploring which aspects of variation in hydrochemistry are best reflected in the ecological data.

Figure 2.34: Monitoring data from River Eden DTC outflow stations for (a) Morland (Newby Beck) and (b) Pow (Pow Beck) for the period 2011–2013. Precipitation, discharge and TP values presented as daily averages. Monthly ecological sampling has been used to calculate the trophic diatom index (TDI) and in situ Fluorometric (ISF) chlorophyll-a (and fitted with spline curve)

Understanding the responses of BQEs to antecedent conditions is of critical importance to determining key physical and chemical drivers of biological communities, which could then be used to inform potential response times of the BQEs to mitigation. The continuous water chemistry, rainfall, and discharge data collected by the DTC project has been explored to establish the critical antecedent period determining diatom community structure (using TDI as a surrogate). The diatom community observed at any point in time will reflect the discharge-nutrient conditions over some preceding time period. TDI was positively correlated with mean discharge and the strength of the correlation varied according to the time period used to calculate mean discharge (Figure 2.35). For the Eden-Morland catchment, an initial correlation was found between TDI and mean discharge on the day of diatom sampling, and the correlation strengthened to a maximum after 19-20 days. Significant correlations were also observed between TDI and preceding TP concentrations, with the strongest correlation after 21 days (p < 0.05, r = 0.66). A similar pattern with discharge was observed in Pow Beck (maximum correlation after 21 days; p < 0.05; r = 0.63), although with lower coefficients. For Pow Beck, significant correlations were observed between TDI and TP between 7-12 days (p < 0.05, r = -0.6). Overall, this suggests that diatom community composition is responding to nutrient-discharge conditions over the preceding 15 - 21 days. Similarly, TDI analyses from the Hampshire Avon DTC suggest that diatom community composition responds to TP conditions over the preceding 20 days.

Data from the DTC focus catchments have shown that the link between measured pollutants/stressors and the indices supposed to be sensitive to these pressures is not always clear. Of particular note is the very strong correlation between the LIFE index (Lotic-invertebrate Index for Flow Evaluation), for assessing the impact of low flows, and the PSI index (Proportion of Sediment- sensitive Invertebrates), for assessing sediment stress. These two indices are 90% correlated for Hampshire Avon data both within and between sites (Figure 2.36). As flow and sediment load are intrinsically linked, the correlation between these indices makes it difficult to determine which of these two potential stressors is acting on the invertebrates. It is therefore difficult to determine the key community drivers in these multi-pressure systems.

Figure 2.35: Antecedent forcing periods of the trophic diatom index (TDI). Pearson’s r was calculated between TDI and mean discharge and TP for Eden-Pow (Pow Beck) and Eden-Morland (Newby Beck). The continuously sampled environmental data was averaged over periods from zero to 21 days. Curves are 3rd order polynomial regressions. The TDI were collected monthly over 25 months for Newby Beck (n=25) and 18 months for Pow Beck (n=18)

Scatterplot of PSIsp vs LIFEfam_index Scatterplot of PSIsp vs LIFEfam_index

90 90 SITE_ID 101 80 80 102 104 70 70 105 106 60 60 107 108 109

p 50

p 50 s

s 201

I S

S 202 P P 40 40 203 30 30

20 20

10 10

0 0 6.0 6.5 7.0 7.5 8.0 8.5 6.0 6.5 7.0 7.5 8.0 8.5 LIFEfam_index LIFEfam_index a

Scatterplot of PSIsp vs LIFEfam_index Scatterplot of PSIsp vs LIFEfam_index

90 90 SITE_ID 101 80 80 102 104 70 70 105 106 60 60 107 108 109

p 50

p 50 s

s 201

I S

S 202 P P 40 40 203 30 30

20 20

10 10

0 0 6.0 6.5 7.0 7.5 8.0 8.5 6.0 6.5 7.0 7.5 8.0 8.5 LIFEfam_index b LIFEfam_index Figure 2.36: Correlation between the invertebrate indices proposed to assess pressure from fine sediment, PSI, and from low flows, LIFE; a) across all Avon DTC sites and occasions, b) within Avon DTC sites. The interaction between sites and LIFE is not significant, i.e. the relationship between LIFE 2 and PSI is the same both within and between sites R adj = 90.34%, p = <0.0001

2.4.2.4 Ecology Conclusions and Recommendations The continuous water chemistry, rainfall, and discharge data collected by the DTC project alongside data on BQEs have demonstrated inter-annual variability in both general chemical (water quality) and biological elements used to determine WFD ecological status. In particular, the data have been used to establish the critical antecedent period determining diatom community structure (using TDI as a surrogate) and have enabled the identification of stressors acting on the biota at individual sites (Table 2.8, page 62). Focus now needs to be placed on the specific questions identified below to improve our mechanistic understanding of biological structure of these streams. This would provide a holistic understanding of how stream systems are functioning at the reach scale, providing greater accuracy when extrapolating the results from the reach to the catchment scale. Consequently, this would allow us to better inform and critique current WFD metric interpretation and classification of ecological status. Specific research focus should include:

1. Quantifying the concordance (agreement among groups) in response among WFD BQEs to key agricultural pollution sources and factors affecting the impact of agriculture on water quality in water bodies in the four different catchments. For example to better understand the role of

nitrogen this would require mapping of stable isotope ratios (δ15N) through the catchment from fertiliser inputs, through soils and into the aquatic system 2. Examining how resilient WFD BQEs are to the effects of dynamic (varying in frequency, intensity, etc.) perturbations (from storm events to seasonal trends). This will take into account interactions with other factors, such as the morphology of the river channel 3. Upscaling and extrapolating our understanding of BQE responses to key environment drivers from reach to catchment scale. This would involve extending ecological sampling beyond the sub-catchments and incorporating data from Environment Agency sampling localities. A wide spatial survey downstream of the sub-catchments would enable understanding of the interaction of the headwater communities with downstream locations through nutrient spiralling and propagation of pollution 4. Critique of ecological WFD assessment methods. This would draw on the findings of bullet points 1-3 and be supported by monitoring of BQEs together with functional measures (ecosystem process rates) to inform current monitoring strategies 5. Consideration of ecological response and lag time to mitigation measures. The ecological response to reductions in diffuse pollution from agriculture as a consequence of mitigation measures should be measured by quantifying change in pollutant sensitive indicators. Where the response is not immediate, modelling work should explore the extent and lag time of expected ecological response to mitigation.

2.4.3 Specific Groundwater Investigations Groundwater is both a receptor of diffuse pollution and a pathway to surface waters and wetlands. The role of groundwater in hydrological systems is often less well understood than the surface flow regime. Where groundwater inputs to base-flow occur it is important to understand their character and significance. Transport through groundwater may delay the impact of land use activity resulting in a wide range of travel times for pollutants from source to receptor. Unless flow routes are better characterised, it will be difficult to gauge the success of control measures in the short term.

2.4.3.1 Developing a hydrogeological conceptual model for the Avon/Tamar DTC As part of the DTC phase 1 project, Allen et al. (2014) undertook a groundwater study using 3D modelling and supplementary hydrochemical information to develop groundwater conceptual models of sub-catchments in the Hampshire Avon DTC focus catchment (Figure 2.37-3).

Figure 2.37: Schematic diagram showing the principal hydrogeological functioning of the Sem Cools Cottage sub-catchment. Dashed lines show possible potentiometric surfaces 72

Figure 2.38: Schematic diagram showing the principal hydrogeological features of the Ebble sub- catchment

Figure 2.39: Schematic section showing the Chalk aquifer below the Upper Wylye sub-catchment with age information added. The Central BH is screened in both the Upper Greensand (UGS) and Chalk, with its mean residence time (MRT) of 25 years implying that the age of water in the confined Upper Greensand is older. However, the artesian Eastern BH, screened only in the Upper Greensand, yields a similar MRT and therefore may be affected by lateral inflow of younger water

Data suggest that groundwater ages >25 years exist in parts of the catchments; clearly, observations like these must be used to judge the likely effectiveness of targeted control measures. The revealed hydrogeological complexity of the Avon catchment is unlikely to be unique, so the techniques described here should be applicable to other lowland river systems with moderate to high base-flow indices (>0.5). To support the WFD, groundwater conceptual models should inform the design of effective measures for diffuse pollution mitigation. The groundwater conceptual models of the Avon DTC sub-catchments also have a number of implications for the interpretation of monitoring data collected during the DTC study. Groundwater is significant in all but one of one of the target sub- catchments, and is dominant in most of them. Therefore, understanding the potential effects of mitigation measures on river chemistry must take groundwater flow routes into account. Three aspects of the groundwater contribution in particular are likely to be important, as follows:

(1) Flow systems are spatially complex: rivers in the Hampshire Avon DTC sub-catchments often do not gain flow from groundwater in a simple manner; instead, discrete inputs of water from springs are common (e.g. in the Rivers Wylye and Sem (Cools Cottage) sub-catchments). To understand the links between pollution mitigation measures and river chemistry it is therefore important to identify the recharge areas of significant springs. (2) The time scales of the various flow routes from the land surface to rivers may vary enormously. In particular, the variation in flow velocity between surface and groundwater systems may vary over orders of magnitude. Flow is likely to occur over a spectrum of time scales from rapid surface wash through slower near-surface interflow to a range of slow groundwater flow times up to decades long, depending approximately on the depth and length of the subsurface groundwater flow path. When nutrient transit through the unsaturated zone is coupled to these relatively slow flow paths, it is clear that measures put in place today are unlikely to reduce nutrient concentrations in groundwaters discharging to streams by 2027 (see Wang et al. 2012). The potential for delivery of elevated nutrient concentrations over a ≥25 year time frame puts the over-ambitious aspirations of the WFD in context (Hering et al. 2010). (3) In the sub-catchments where groundwater contributes significantly to river discharge, the nature of baseline groundwater chemistry needs to be taken into account when considering pollutant loading; for example, the potential addition of phosphate to the Upper River Wylye from natural sources in the Upper Greensand.

To evaluate the effects of targeted diffuse pollution mitigation measures on rivers draining catchments it is important to understand the nature of the flow routes between the areas where the measures are applied and the point at which the effect is monitored in-river. This is particularly significant where the time scale of flow may be variable or where the flow route is uncertain, as the effectiveness of mitigation measures may be characterized incorrectly if the receiving river is monitored in the wrong place or over an inappropriate duration.

Taking the Hampshire Avon DTC as an example, groundwater is seen to be an important component of the hydrology in all but one of the DTC target sub-catchments. The groundwater conceptual models of the sub-catchments developed in this study suggest that flow routes are both spatially and temporally complex with, for example, discrete discharge areas such as springs and a variety of flow time scales. The conceptual groundwater models are seen to be essential for interpreting river monitoring data correctly. The hydraulic characteristics of the Hampshire Avon DTC target sub- catchments are not likely to be unusual, either in the UK or elsewhere. Groundwater commonly forms a significant proportion of river flows, subsurface flow routes are often complex, and rivers often receive water with a range of ages, ranging from young rapid surface runoff to very old deep groundwater input. It is therefore concluded that it is important, indeed vital, that hydrological conceptual models are employed in the evaluation and projection of the effectiveness of targeted diffuse pollution mitigation measures.

2.4.3.2 Developing a hydrogeological conceptual model for the Eden DTC Bedrock geology of the Eden catchment The Eden valley lies between the uplands of the North Pennines to the east and the Lake District to the west. The locations of the three sub-catchments of the EdenDTC project in relation to the bedrock geology of the Eden catchment are reported in Figure 2.40.

Figure 2.40: Bedrock geology of the Eden catchment and location of the Pow Beck, Lyvennet Morland and Dacre sub-catchments (black catchment outlines) of the EdenDTC project

The major aquifers in the Eden catchment are the Permo-Triassic sandstones that underlie the valley floor in the Vale of Eden basin. Substantial abstraction from these aquifers supports public water supply, industrial and small farm uses, with recent work focussed on understanding the risks to these resources associated with agricultural intensification in the catchment and increasing nitrate concentration in recharge water (Butcher et al., 2008; 2003). The River Eden also gains along most of its length within the Vale of Eden, due to discharge from the underlying sandstone aquifers. The Carboniferous, comprising layered limestones, sandstones, mudstone and coals, represents a complex minor aquifer in which productive horizons can support minor abstractions or may discharge water to overlying Permo-Triassic units or to surface waters.

A preliminary geological and hydrological overview of the Eden DTC focus catchment was undertaken as part of a NERC-funded BGS study, involving a preliminary assessment of the likely nature and timing of groundwater flow-path, and is presented in Allen et al. (2010). The study concludes that the hydrogeology of all three of the Eden sub-catchments (Pow, Morland and Dacre) is likely to be dominated by superficial deposits rather than the deep aquifer. Major aquifers are only present in the Pow catchment, where they do not appear to support the river, the bedrock aquifers in Morland are likely to be localised, and much of Dacre is underlain by poorly-permeable material. Superficial deposits are likely to vary in composition, and perched near-surface local aquifers may be present. It is possible that the base-flow components of the streams in all the Eden sub-catchments may originate from groundwater flow over a range of timescales. Groundwater recharge may result in relatively rapid flow of shallow groundwater to the streams which means these pathways may operate within the tim4escale of the DTC project. Although the proportion of groundwater flow in the sub-catchments is unknown, it is likely to dominate summer base-flows as it does elsewhere in many headwater streams in the Eden.

Quaternary superficial deposits Approximately 75% of the bedrock geology in the Eden catchment is covered by Quaternary superficial deposits (Allen et al., 2010), varying in thickness across the catchment but reaching 20- 30m in places. Hydrogeological work under the EdenDTC project has focussed on these Quaternary deposits, based on the hypothesis that the superficial materials are likely to control the hydrogeology of the sub-catchments. The lithostratigraphy of the superficials is complex, involving interdigitations of sand, gravel, silt and clay, resulting in complex piezometric conditions including perched water tables. Surface waters within the sub-catchments may gain water from sources that span a range of residence times, from rapid surface runoff at one extreme to delayed shallow or deeper groundwater pathways at the other extreme. Because shallow groundwater flow-paths may have relatively short residence times, the impact of changes in agricultural practices at the land surface may be transferred through the sub-surface system to streams within the timescale of the DTC programme. Activity has therefore focussed on understanding the lithostratigraphy of superficial deposits in the DTC sub-catchments, as a basis for subsequently understanding the potential importance of groundwater in these deposits for the hydrological and hydrochemical functioning of the stream network.

Due to limited resources, and in an attempt to coordinate activity, work has predominantly focussed on the Pow sub-catchment during phase 1 of DTC Component 1. The work has sought to combine resources from the DTC programme, from BGS National Capability funding and from externally- funded PhD research to advance understanding of the hydrogeology of the superficial deposits in this catchment. This work has involved:

A. Drilling and core collection programme A total of 30 boreholes have been drilled into the superficials of the Pow sub-catchment to investigate the lithostratigraphy of these deposits, to collect core material for subsequent analysis, and to install piezometers where water-bearing strata were observed for subsequent sampling and testing. The drilling and core logs confirmed a complex lithostratigraphy of the superficial deposits within the Pow sub-catchment, with sand- and gravel-rich strata (up to 1-2 m thickness in places) interdigitated with clay-rich material (Figure 2.41). Core and bagged samples of the superficial deposits were recovered and subjected to hydraulic and electrical testing (see B below). In the majority of cases, sand/gravel-rich strata showed evidence of water saturation, suggesting perched water tables, and a network of 11 piezometers with intakes within the saturated strata were installed and completed at the ground surface. These piezometers have been monitored for hydrochemical parameters and been used for slug testing to help understand the potential importance of the superficial deposits for both the hydrological and hydrochemical functioning of the stream network within the Pow catchment (see C below).

Figure 2.41: Shell-and-auger drilling programme within the Pow sub-catchment (left). Window sampler cores recovered from the superficial deposits (right) demonstrating lithological contrasts between sand/gravel-rich material (A) and clay-rich material (B). Marker lines are at 5 cm intervals.

A further borehole within the Pow sub-catchment was drilled to 64 m below ground surface into the Permo-Triassic sandstone to establish the water table depth within the sandstone aquifer in relation to the bed elevation of the Pow stream. Rest water level within the borehole was substantially (>10 m) below the elevation of the nearby Pow stream and a tributary to this stream, suggesting that groundwater in the Permo-Triassic sandstone is unlikely to discharge to the Pow and that stream water may be lost through recharge to groundwater within the sandstone, depending on the permeability of materials overlying the head of the sandstone. These conclusions are further supported by an analysis of rest water levels in boreholes across the Pow sub-catchment in relation to stream bed elevation that also concluded that groundwater discharge to the Pow stream was unlikely, with the Pow catchment seemingly perched above groundwater in the sandstone bedrock (Allen et al., 2010). These observations again emphasise the potential importance of superficial deposits for controlling the hydrogeology of the Pow sub-catchment.

B. Geophysical surveys of the superficial deposits Multiple geophysical techniques have been used to characterise the superficial deposits in the Pow sub-catchment as part of a PhD project funded by the Malaysian government (Mejus, unpublished data). Electrical resistivity tomography (ERT), electromagnetic induction (EM) and induced polarisation (IP) methods were used to map variation in electrical conductivity (or resistivity) of the subsurface vertically and horizontally, while ground penetrating radar (GPR) and shallow seismic refraction (SR) methods were used to identify the stratigraphic structures. The ultimate objective of this work is to develop geophysical-lithological and geophysical-hydraulic relationships, enabling spatially-extensive geophysical data collection followed by lithological or hydraulic interpretation of the geophysical data. Such approaches could enable lithological and hydraulic characterisation of heterogeneous strata in a way that is currently prohibitively expensive across anything other than small spatial areas.

Figure 2.42 summarises a sub-set of the geophysical data collected as part of this project. Electromagnetic induction (terrain conductivity) surveys of the upper 6 m of deposits confirm

Figure 2.42: Geophysical data from the Pow sub-catchment (Mejus, unpublished data). Focus area for the work given by the black rectangular outline in the 10 km2 Pow catchment (red boundary) in the top left of the figure. Data from ERT (shown as resistivity) and IP (shown as imaginary conductivity, i.e. a measure of polarisation) lines are reported in individual figures that surround a plan view of the electrical conductivity survey. Black hashed ovals in the plan view of electrical conductivity data mark the location of linear resistive features heterogeneous electrical properties of the superficial materials, with electrical conductivity ranging from <8 mS m-1 to >13 mS m-1. On-going work within this PhD project is evaluating whether spatial variation in electrical conductivity of the superficial deposits is related to spatial variation in lithological properties, and specifically whether electrical conductivity is inversely correlated with particle size of the superficial deposits. Data from ERT and IP surveys indicate stratigraphic variation in the electrical properties of the superficial deposits (e.g. ERT04; ERT06 and IP06; and ERT and IP07 in Figure 2.42). Initial interpretation of these data leads to a hypothesised three-layer stratigraphic model incorporating a shallow (<5 m) electrically resistive layer overlying a deeper, electrically conductive layer of variable thickness, which in turn overlies a deeper layer of electrically resistive material. Based on interpretation of drilling and core logs, alongside recovered core material, this three-layer electrical model has been interpreted to reflect lithological variations between a shallow layer of mixed materials comprising variable quantities of clay, gravel and sand, overlying a clay-rich layer, which in turn overlies the head of the sandstone aquifer.

Geophysical surveys have revealed further variations in electrical properties of the superficials that are of potential hydrogeological importance. Firstly, the electrically-conductive, possibly clay-rich, layer at mid-depth in the stratigraphy of the superficial deposits is consistently thick along some ERT 78 survey lines (e.g. ERT02 in Figure 2.42), suggesting protection of the underlying aquifer from recharge that may be contaminated by intensive agricultural activity at the land surface. However, other ERT survey lines reveal a thinning of the electrically-conductive layer (e.g. ERT06) or potential windows through this layer (e.g. ERT07), indicative of increased opportunity for recharge of the underlying aquifers. Secondly, electrically-resistive, linear features have been observed in some ERT survey areas (dashed ovals in Figure 2.42, running east-west or north-south), possibly indicating the presence of paleo-channels or glacio-fluvial features. Given the proximity of these resistive features to the main Pow stream and a tributary to the north of the Pow, if this unit is confirmed to be permeable then it may offer a distinct pathway from the subsurface to the stream, or from the subsurface/stream to the underlying sandstone aquifer.

Bagged and core samples of the superficial deposits and the Permo-Triassic sandstone have also been analysed for saturated hydraulic conductivity, K, particle size, cation exchange capacity and complex electrical conductivity (in term of the conductive and capacitive properties of the material). These analyses will be used to determine the extent to which geophysical-lithological and geophysical-hydraulic relationships can be established for the Pow sub-catchment. This work is currently on-going, but initial results suggest contrasts in K between the three electrically-defined strata from ERT surveys, for the electrically-resistive upper stratum = 3.6 × 10-4; 2.8 × 10-4; 12, for electrically-conductive middle stratum = 1.8 × 10-6; 3.2 × 10-6; 13, for electrically-resistive bottom stratum = 4.1 × 10-7; 1.0 × 10-6; 7 (푥̅; 휎; 푛 (with K in m s-1); Mejus, unpublished data). These K data begin to support hydrogeological interpretation of the field geophysical survey data, but further work is required to establish geophysical proxies for lithological or hydraulic properties of the superficial deposits.

C. Field testing and sampling of the superficial deposits The piezometers installed within water-bearing strata have been subjected to a range of sampling and testing. This activity includes: i) assessment of groundwater age using a combination of CFC-12 and SF6 dating techniques, revealing ages of 28-34 years (n=4) for groundwater between 2 and 19 m below ground surface in the superficial deposits, based on CFC-12 and the piston flow model; ii) completion of a programme of slug tests on nine of the piezometers to determine saturated hydraulic conductivity (maximum – minimum range = 1.0 × 10-2 – 6.9 × 10-5 m s-1, n = 32; Sipthorpe, unpublished data); iii) monthly determination of field hydrochemical parameters and collection of samples for nutrient analyses revealing, for example, the presence of elevated and seasonally- variable nitrate-nitrogen concentrations in some piezometers (Figure 2.43). Taken together, these data suggest the presence of reasonably permeable strata within the superficial deposits, containing groundwater of relatively modern age that has been recharged with water enriched in compounds likely to be derived from intensification of agricultural production at the land surface.

Figure 2.43: Monthly nitrate-nitrogen concentrations in groundwater sampled from two piezometers in superficial deposits within the Pow sub-catchment. Intake of piezometer 1B is 3.0 – 4.0 m below ground level, intake of piezometer 2 is 2.4 – 3.0 m below ground level

2.4.3.3 Hydrogeological setting of the Wensum DTC The whole of the Wensum catchment is underlain by the Chalk aquifer. In the eastern part of the catchment, the Chalk is overlain by the Wroxham Crag Formation. These bedrock strata are overlain by a complex sequence of Pleistocene (Anglian tills and glaciofluvial sands and gravels) and Holocene (blown sand, alluvium, peat and river terrace deposits) deposits, with no bedrock occurring at the ground surface in the Blackwater sub-catchment (Figure 2.44). In the Wensum DTC area (Blackwater sub-catchment), the aquifers therefore comprise Holocene alluvium and river terrace deposits, Pleistocene glaciofluvial and glaciolacustrine sands and gravels (of the Briton’s Lane, Sheringham Cliffs, Lowestoft and Happisburgh Formations), the Wroxham Crag Formation and the underlying Chalk. Interdigitated with these, are five different tills (Weybourne Town Till, Bacton Green Till, Walcott Till, Lowestoft Till and Happisburgh Till Members) resulting in a complex, multi-layered aquifer system (Figures 2.45 and 2.46). The aquifer properties are further complicated by the sands and gravel formations above the Chalk having intergranular permeability whilst the Chalk is a microporous limestone in which fracture flow predominates.

Water in the alluvium and river terrace deposits is generally in hydraulic continuity with the associated river. Recharge to the underlying deposits is inhibited by the presence of the low permeability tills. Any glacial sand and gravel horizons within them can be saturated and if basal glaciofluvial sands and gravels (below the lowest till) are present, they are generally in hydraulic continuity with the underlying Wroxham Crag and Chalk. However, where overlain by other deposits (both superficial deposits and Crag) an upper layer of soft, ‘putty’ Chalk is often present, and this may affect hydraulic connection.

Figure 2.44: Map of superficial deposits map produced using the British Geological Survey GSI3D model (Lewis, 2014)

Figure 2.45: Expanded view of the strata in the Blackwater sub-catchment viewed from the southwest (Lewis, 2014)

Figure 2.46: Schematic cross-section across the Blackwater sub-catchment (Lewis, 2014).

The Chalk aquifer is confined (with water levels rising above where first struck) by the thick sequence of Quaternary deposits with water levels likely to lie against these superficial deposits over the whole of the Wensum DTC area. In the National Well Record Archive there is only a single borehole, at Docking Farm (TG 1538 2619), where this was not the case and here the rest water level was the same level as the boundary between the superficial deposits and the Crag. The water levels fluctuate seasonally by 1-2 metres (see Figures 2.40-41) with the highest levels generally occurring in April and the lowest in autumn. Artesian overflow, often seasonal, has been recorded in the lower lying parts of the Blackwater to the east of Salle (TG 12 25).

Chalk yields and transmissivities are both highest in the valleys. The Chalk in the Wensum catchment area has been attributed with a mean transmissivity value of 685 m2 d-1 and a mean storage coefficient of 0.064 for the whole catchment (Toynton, 1983). This storage coefficient was derived from base-flow and groundwater recessions and therefore represents the whole aquifer system (including the overlying deposits) rather than solely the Chalk aquifer itself. Allen et al. (1997) found no evidence of delayed yield affecting storage coefficients in this area.

The base-flow index of 0.74 for the downstream gauging station at Costessey Mill highlights the influence of the underlying Chalk aquifer in supporting river flow in the Wensum. Discharge of groundwater occurs in the valley bottoms where the river has cut through the overlying Quaternary deposits leading to a greater hydraulic connection between the Chalk and surface runoff. In the headwaters, as in the Blackwater sub-catchment, the thickness of glacial till, sands and gravels can exceed 20 m and act to limit vertical recharge to the Chalk aquifer, so protecting the deeper groundwater from surface-derived contaminants.

Variations in the composition of the glacial deposits means that vertical drainage is limited in headwater mini-catchments A and B where clay loam soils are developed on glacial tills, in contrast to the greater infiltration experienced in mini-catchments C and D which contain a greater extent of sandy loam soils developed on glacial sands and gravels. Hence, surface runoff in mini-catchments A and B tends to be flashier in response to rainfall events compared to the role of groundwater storage in reducing flood peaks in mini-catchments C and D. Although the Chalk does not outcrop in the Blackwater sub-catchment, the combination of sand-rich deposits of less than 10 m thickness and the presence of an upward groundwater hydraulic gradient leads to saturated ground conditions at kiosk F at the outlet of the experimental study area. This possibility of surface water-groundwater 82 interaction in the Blackwater was explored by the use of a fibre-optic distributed temperature sensor cable emplaced in the streambed upstream of Kiosk E, the results from which are presented in Case Study 4. Hence, the role of groundwater in the Blackwater depends on the nature and thickness of glacial deposits, with the existence of a deeper, slower Chalk groundwater flowpath and a shallower more rapid flow-path through the weathered glacial till, sands and gravels in the top 5-6 m of the sequence.

Pressure transducers (Divers) to monitor water levels were installed in two sets of four boreholes at Site A (Merrison’s Lane) and Site F (Park Farm) drilled as part of the Wensum DTC groundwater investigations (Lewis, 2014). Data for the first few months of water level monitoring are shown in Figures 2.47 and 2.48. The similarity in levels in boreholes A2 and A3 indicate that possibly these boreholes are monitoring the same aquifer unit, and the borehole completions may not have hydraulically isolated different units. Both Sites A and F show a small downward hydraulic gradient indicative of slow groundwater recharge to the underlying Chalk aquifer. Borehole 4 at Site A is particularly responsive to rainfall events as a result of the greater permeability of the top 5-6 metres of weathered glacial till at this location.

Figure 2.47: Water levels at Site A (Merrison’s Lane). Borehole 1 monitors the Chalk aquifer and boreholes 2-4 monitor deep, intermediate and shallow levels in the overlying Quaternary deposits, respectively. See Figure 1 for the location of Site A (Lewis, 2014)

Figure 2.48: Water levels at Site F (Park Farm). Borehole 1 monitors the Chalk aquifer and boreholes 2-4 monitor deep, intermediate and shallow levels in the overlying Quaternary deposits, respectively. See Figure 1 for the location of Site F (Lewis, 2014)

3 Measures

3.1 Background Efforts to demonstrate the effectiveness of mitigation measures for controlling diffuse pollution have a long history in the UK. Considerable work was carried out on nitrate leaching during the 1970s and 1980s, which is well summarised by Addiscott (1991). Substantial effort has also focussed on the control of soil erosion on agricultural land (Chambers et al., 2000; Quinton and Catt, 2004) and on the mobilisation and delivery of phosphorus from agricultural fields (Deasy et al., 2009; Quinton et al., 2001; Haygarth et al., 2009). Much of this work has been funded by Defra, who continue to fund work through WQ0127 and WQ201. In addition, User Guides have been prepared (e.g. Newell Price et al., 2011) under WQ0106 that summarise the current state of knowledge. However, most of the work to date focuses on demonstrating the effectiveness of individual mitigation measures with few addressing the cumulative effect of introducing combinations into defined catchments. Since such spatial units are the key framework for compliance reporting under the EU Water Framework Directive providing evidence on how sets of measures can be used to deliver diffuse pollution control in catchments seems a logical and urgently required next step. The proposals for planning and implementation of measures made here are fully integrated with the experimental design that underpins DTC Component 1 activities. Including locations within each existing DTC catchment enables the impact of mitigation measures to be assessed across the range 84 of dominant farming systems in the UK agricultural sector allowing effective scaling up and the development of guidance.

3.2 Selection of Measures A conceptual model was based on the sources, mobilisation, pathways and delivery of pollution in the individual catchments. This enabled the identification of key pollutant sources, mobilisation mechanisms and delivery routes. The biophysical component of the conceptual model was organised around a source-mobilisation-delivery-impact framework. Within the catchments likely sources of nutrients that could be mobilised were identified through walk over surveys and discussions with local farmers, agricultural advisers, Catchment Sensitive Farming Officers, Environment Agency staff and other consortium members. Sources of nutrients identified are predominantly due to the application of fertilisers, presence of livestock and the management of slurry and farmyard management (FYM). Farmyard management becomes increasingly important during winter when livestock are housed. Other sources are from bought in feed for animals, direct stock access to streams in some locations, and the use of inorganic fertilisers.

Mobilisation within the catchments is promoted by soil compaction. Soil surveys indicate that silage production fields are significantly affected by compaction, in addition to fields with grazing livestock. The dominant pathways as inferred from DTC monitoring data are rapidly responding, surface runoff and drain flow, once the catchment is at or near to field capacity. It is hypothesised that surface water–groundwater interaction is important in the catchments. Farm tracks have also been identified as features promoting high connectivity between sources and receiving waters. The main impacts on receptors are elevated sediment, nitrate and total phosphorus loads, which in turn are hypothesised to influence the ecological status of the rivers.

Evidence from research undertaken as part of previous studies in the catchments (e.g. PARIS, Avon, CSF Wensum and CHASM Eden) was also assessed for relevance to the approach taken above. Consultation with leading catchment scientists was carried out together with engagement with existing Defra projects (MOPS2, PEDAL2) to identify research gaps in the application of measures in catchments across England and Wales. A key point arising from this assessment was that while we are developing knowledge of how individual measures may work we have less evidence about how effective they may be in combination at the catchment scale.

An initial set of mitigation measures that could be used to tackle these issues was then selected from those in relevant reports (e.g. the Defra User Guides from projects ES0203 and WQ0106, Cuttle et al., 2007; Newell Price et al., 2011 and Haygarth et al., 2009). The process therefore identified a set of measures of potential relevance not only within the chosen sub–catchments discussed below, but also across the main catchments and nationally.

Overall, this resulted in a subset of measures which:

 Demonstrated a lack of evidence for diffuse pollution mitigation at the catchment scale.  Were applicable to the remainder of the catchment  Were applicable to many catchments across England and Wales.  Were known to have the capacity to be delivered through existing policy funding mechanisms.  Had the ability to be incorporated within new policy funding mechanisms.  Could be readily incorporated into guidance for improved delivery of diffuse pollution mitigation at the catchment scale.  Spanned the source-mobilisation-delivery continuum. 85

These measures were then reviewed in more detail with the farmers (and their advisers) who had expressed interest in participating in the research, particularly with respect to the practicalities of implementation given their current farming system (e.g. crop rotation) and equipment, as well as the cost implications. Additionally agronomic expertise both was sought. Results from the monitoring equipment installed under Component 1 were also used where activities have already demonstrated distinct peaks of sediment, phosphate and nitrate during and following rainfall events, aiding in the identification of sources. Additionally, insights from the conceptual modelling in Component 1 about how the sub-catchments function were incorporated. This included work undertaken by the British Geological Survey on sub surface flow paths identification and surface discharge points such as spring. This understanding was then employed in connectivity models such as Scimap and Topmodel to help identify the likely extent of the measures needed for demonstration of success at the sub-catchment scale. Subsequently, models such as Pond Flow and Farmscoper were employed to make preliminary assessments of the impacts that certain candidate measures could have within the target sub -catchments. Logistical problems (e.g. Environment Agency or drainage board consents), the potential for CSF capital grant aid and local engagement were also considered.

Measures identified for funding as part of this DTC component and possible additional measures are listed in Tables 3.1 and 3.2. Table 3.1 describes the measures in terms of the sub-catchments where they will be applied, the farm types involved and the elements of the source-mobilisation-delivery continuum they address. In Table 3.2 we summarise for each measure the evidence of success demonstrated to date, the evidence used to support location of the measure, the anticipated reduction in diffused pollutants and the likely policy delivery mechanisms or windows of opportunity.

Table 3.1: Target sub-catchments for work under Component 2, with details of the farm types present, proposed measures and points on the source-mobilisation-delivery continuum which they address

Catchment Sub-catchment Continuum and geology Farm Types Proposed Measures Point

Avon Grazing livestock (lowland), dairy, Yard infrastructure. Integrated manure and fertiliser Source Nadder(Sem) mixed. planning. Arable reversion. Clay/Greensand Farm track resurfacing. Mobilisation Wetlands. Extension of current buffer strip. Delivery Stream bank fencing. Avon Cereals, mixed, grazing livestock Wetland. Delivery Ebble (lowland). Chalk Avon Cereals, mixed, grazing livestock Measures will be implemented as/when additional funding Wylye (lowland), dairy, specialist pig. is agreed and a robust baseline has been collated. Chalk Eden Improved grazing (cows & sheep) - Farmyard infrastructure. Biobed. Integrated manure and Source & Morland 83%, rough grazing (sheep). fertiliser planning. Mobilisation Limestone/Sandstone Track resurfacing. Rural SuDS Delivery

Wensum Arable - 97%. Reduced cultivation. Biobed. Cover crops. Source Blackwater Extension of current buffer strips. Rural SuDS. Tree Mobilisation Quaternary deposits overlying planting along watercourses. Delivery Chalk

Table 3.2: Assessment of proposed measures and possible policy delivery mechanisms

Success Criteria: Likely Evidence Used to Anticipated % Policy Measure Evidence of Success Support Location Reduction in Delivery/ Catchment Identified Demonstrated so Far of Measure Diffuse Pollution Windows

Avon (CSF) Yard Very little documented Local knowledge, N 30 ELS Eden Infrastructure evidence. Conceptual model P 50 – 60 CAP reform S 50 – 60 Water Companies SSAFO regs Farmer/land owner Rivers trusts Avon Integrated Individual measures have Local Knowledge N, 20 Cross Eden (CSF) manure and been examined but little Conceptual model P, 20 Compliance Wensum fertiliser planning work on integration of S,10 CAP reform measures. Rivers trusts Avon (CSF) Farm track re Little direct evidence. GIS based analysis N, 0 ELS Eden surfacing Biological and P, 0-10 CAP reform hydrochemical S, 0-10 Rivers trusts monitoring Avon Rural SuDS Some evidence generated GIS based analysis N, 0 HLS Eden (CSF) but more needed. Biological and P, 6-80 Water Wensum Previous work site hydrochemical S, 50-80 companies specific. monitoring CAP reform WFD Floods Directive Local Authority Flood Risk Management /EA FCRM Avon Extension of Moderate evidence but GIS based analysis N, 0 HLS Wensum current buffer little known of the impact Local Knowledge P, 50-60 CAP reform strips at the catchment scale. S, 50-60 Rivers trusts Habitats Directive Floods Directive WFD Wensum Reduced Moderate evidence but GIS based analysis N, 10-80 Cross cultivation little known of the impact Local Knowledge P, 10-80 Compliance at the catchment scale. S, 10-80 Water companies CAP reform

Wensum Cover crops Moderate evidence but GIS based analysis N, 35-65 Cross little known of the impact Local Knowledge P, 25-80 Compliance at the catchment scale. S, 25-80 Water companies CAP reform

Avon Stream bank re Some evidence at the GIS based analysis P, 10 Voluntary fencing catchment scale provided Local Knowledge N, 10 HLS but only for SW England. S, 30 –50 CAP reform Rivers trusts Habitats Directive WFD Wensum Tree planting Little evidence on GIS based analysis P, 10 HLS (Stewardship) watercourses catchment scale Local Knowledge N, 10 CAP reform Eden S, 30 –50 WFD CSF Habitats Direcitve Floods Directive Forestry Commission Rivers trusts Wensum (CSF) Biobed Moderate evidence but Local Knowledge N, 0 HLS lacking at the catchment Conceptual model P, 6-80 CAP reform scale S, 50-80

Notes: Evidence demonstrated so far and anticipated reductions in diffuse pollutants are based on evaluations in Cuttle et al. (2007) and Newell-Price et al. (2011) but these are not based on studies at the sub-catchment scale. Where additional funding is being sought for measures the potential source is indicated in brackets.

In the course of selecting measures it became apparent that the totality of options was likely to be more expensive than could be directly supported with Defra funding so a multi-track approach has been developed with some key priorities and a number of complementary measures which could be added through funding from other sources. Where additional funding will be necessary potential sources are highlighted in Table 3.2, while the costs of the measures to be funded directly by Defra through the Component 2 budget are given in Table 3.3.

Table 3.3: Estimated costs of Defra funded measures in the three catchments

Area Applied Catchment & Measures (ha or metres) Unit Cost (£) Number Overall Cost (£)

Avon

Improved integration of manure and fertiliser management and na 5,317 1 5,317 improved timings of dirty water application

na 5 per m 1000 5,000 (CSF) Riparian Fencing

na 1000 1 1000 Separation of clean and dirty water

Covering of yard 262 m2 84 per m2 22000

428 m 48 20544 Farm track resurfacing na 2625 4 10500 V-notch weirs/settling ponds 400m 5 2000 Buffer strip extension (fencing) na 48 200 6240 Farm track resurfacing V-notch weirs/settling ponds na 2625 2 5250 Eden

Hardstanding runoff management (Towcett, Dedra Banks and Brown na 14000 3 42000 Howe farm) Mops/wetland features 10000 2 20000 Revised track management including interception features and na 1000 10 10000 sediment trap Farmer advice – farm nutrient na 0 3 0 plans, soil testing, etc Wensum

Sediment/nutrient traps 10,500 10,500 Cover crops,reduced tillage and 145.45 55 cultivation controls 2013 8,000 Cover crops, reduced tillage and 145.45 55 8,000 cultivation controls 2014 Cover crops, reducedtillage and 145.45 55 8,000 cultivation controls 2015 Cover crops, reduced tillage and 145.45 55 8,000 cultivation controls 2016

3.3 Mitigation Measures Development Plans 3.3.1 Avon The over-arching approach adopted in the Avon catchment has been to ensure that selected measures are complementary, forming a ‘treatment-train’ or sequence of improvements to water quality from farmyard to surface water, with inherent or built-in capacity to operate and function successfully across a range of weather conditions (e.g. Figure 3.1 below). Additionally, most features are multi-functional, tackling most of the major issues identified within the manipulated target sub- catchment. These approaches are closely linked to the source-mobilisation-transport-impact approach for the identification of problems in a pollution impaired catchment. Financial constraints have also been recognised, hence the protracted negotiations to ensure buy-in from the farmers as necessary (Table 3.4).

Table 3.4: Summary of experimental measures within the Avon DTC

Measure Coverage/ targeting Funding Cost mechanism 1 Yard roofing; separation of Colemans Farm, TN, TP SS & CSF/DTC TBC clean and dirty water FIOs 2 Track resurfacing and Hays Farm, TN, TP SS & FIOs DTC See below management 3 settling ponds, installation of Hays Farm, TN, TP SS & FIOs DTC £43K – total v-notch weirs/ in the ditch 2 &3 running at the bottom of the problem track 4 Fencing of watercourses Hays Farm, TN, TP SS & FIOs CSF TBC 5 Sub-catchment wide nutrient Sub-catchment, TN, TP, SS & DTC TBC management advice FIOs 6 Reversion of maize field to Priors Farm, SS DTC Via advice grassland 7 Extension of buffer strip Priors Farm, TN, TP SS & FIOs CSF TBC

Development of mitigation plans within the Avon DTC began in 2011 with initial infrastructure farm surveys completed in August 2011. Some of the recommendations within these surveys, such as yard coverage and gutter replacements at Colemans Farm, were undertaken through CSF funding. In August 2014, following a site visit in conjunction with the CSF officer, Jodene Williams, and the farmer, it was agreed that a new infrastructure survey at this farm would be carried out to reflect the changing priorities identified by the farmer and the research team. The infrastructure report was completed by Charles Bentley from ADAS and key priorities (Figures 3.1-3) and recommendations are summarised below.

Recent new guttering and downpipes installed has led to standing water of over 0.5m with water overflowing into the silage clamp (Figure 3.1, arrow 2) and across the yard to the slurry lagoon (Figure 3.1, arrow 3). To reduce the amount of standing water it is recommended to relocate the water trough to the corner of the yard, which will allow overflow through the sleeper wall to the corner of the straw barn. Additionally re-alignment of the kennels downpipe to the feed trough and installing a rainwater harvesting tank on the existing concrete base on the south west corner of the straw barn. Concreting is suggested over the new stoned surface in front of the two end bays of the straw barn, (approximately 6m x 12m) and sloping the concrete to the south west corner to connect

89 to a proposed chamber adjacent to the new drain should reduce the amount of water collecting. It is also anticipated that installing a pre-cast concrete chamber approximately 1m deep x 1m wide x 2.4m long (section of slurry channel) with slatted lid and T pipe outfall to a new drain and constructing rendered blockwork end walls to the channel will help control water flow across the area.

Priority 1: Failed drain/standing water.

Figure 3.1: Straw Barn standing water

• Straw barn has new gutters and downpipes (CSF funded) which feed a water trough. • Excess water should drain via a collection point (located at the corner of the barn (arrow 1). This should drain via a pipe away from the yard to a ditch which runs along the road, A350. • Standing water – in this picture the standing water is > 50 cm. • Water overflows into the silage clamp (arrow 2) and across the yard to the slurry lagoon (arrow 3) • Collection point is a stone filled hole connected to a field drain (i.e. a pipe with slits in the surface)

Priority 2: Loose housing guttering

Figure 3.2: Loose housing guttering

 Loose housing has no gutter and rainwater flows directly into the adjoining slurry lagoon

Outbuildings with no guttering were identified leading to rainwater flowing directly into the adjoining slurry lagoon. It was recommended that the installation of 4 x 5m (15’) sections of new steel box section guttering on fabricated brackets to the rear of the loose housing above the slurry lagoon would reduce water ingress with connection to a new downpipe on the north east corner of the building (Figure 3.2, arrow 4) leading to a 1.8m3 (400 gallon) water trough with overflow back into the slurry lagoon.

Priority 3: Ditch from water pools

Figure 3.3: Water pools and ditch

 The “water” pools are at the downslope end of the farmyard. They take yard runoff and over flow from the slurry store, which is essentially dirty water. There is a ditch that runs via an underground pipe at the rear of the stables (Figure 3.2, arrow 5). The pipe runs to the edge of the water pools and connects to an open ditch, which discharges to the stream at the boundary of the farm.

Recommendation relating to priority 3 (Figure 3.3) included clearing of vegetation and cleaning the existing ditch down to original invert level for approximately 100m from the inlet. Three timber sleeper weirs where installed across the channel at 30, 60 and 90 m from the head of the ditch. It was also recommended that each weir comprise an erosion resistant drop point (pitched stone, concrete slab or similar) below a sleeper wall consisting of two horizontal sections of sleeper stacked edge-on and set into the banks. A V notch was to be cut into the top sleeper to provide a defined spill point. Sleepers were to be retained within the banks by clay packing and a treated timber post installed on each quarter of the barrier. The cleared section of ditch required fencing to exclude stock. The headwall and inlet pipe to the ditch from the yard/roof drainage system was to be exposed and extended to a minimum distance of 10 m from the inside of the dirty water lagoon, and install new proprietary or concrete headwall. 3.3.2 Tamar Catchment, Caudworthy Water Faecal indicator organisms (FIOs) are the key faecal derived pollutant from livestock and a major cause of water quality failure in the UK under the EU WFD. The empirical evidence to show the 92 relative significance of FIOs derived from direct inputs resulting from livestock access to water courses versus diffuse sources is poor in the UK. Stream bank and drinking bays are measures to restrict both direct and indirect livestock access to water courses, thereby reducing the direct input of FIOs. Understanding the efficacy of such measures is important in order that policy can support the most appropriate measures for FIO reduction.

The focus of mitigation plan development in the Caudworthy Water sub-catchment is on key issues with regard to water quality which have been identified by the WRT as being related largely to livestock movements/access and management of animal wastes. Therefore, it was decided to monitor (FIO) concentrations within the catchment along with TN and TP concentrations in surface waters. FIOs were used to assess the microbiological water quality of recreational waters 9 sites within Caudworthy Water (Figure 3.4). See Appendix 3.1 for method details.

Figure 3.4: Sample sites on Caudworthy Water

In addition to FIOs investigation, Westcountry Rivers Trust (WRT), funded by South West Water (SWW) via their Payment for Ecosystem Services (PES) scheme, mitigation strategies and advice have been targeted in as many farms as possible in the Caudworthy Water sub-catchment in order to improve water quality. An indicative summary of investment activities is given in Table 3.5. Repeat source apportionment for organic wastes and minerogenic sediment is also being undertaken as a complement to the conventional sub-catchment outlet water quality monitoring funded by DTC component 1. Details of negotiations with individual farmers are included in Appendix 3.2.

Table 3.5: Measures selection rational in the Tamar

Farm Main issue Measures Investment 1 Poor slurry management/storage Silage Clamp/Slurry £100K 93

store/Roofing/tracks/fencing 2 Poor slurry management, uncovered slurry storage Cover yard underground slurry £90K store shed umbilical slurry pipe 3 Uncovered slurry store Cover slurry pit £25K 4 Uncovered yard Cover open yard/handling area £15K 5 Poorly maintained handling area, walls of sludge store Concrete and roof handling £75K in poor repair yard, improve sludge store 6 Unrestricted access to surface waters, poorly Concrete for improved access £30K maintained slurry store to slurry store, riparian fencing 7 Poor storage facilities resulting in waste, poor yard Chicken shed, repairs to yard £30K concrete impairing water management concrete. 8 Poor yard water management, unrestricted access to Riparian fencing, Alternative £54K surface waters water infrastructure 9 Unmanaged chicken manure chicken manure store £20K 10 Unrestricted access to surface waters, poor cattle Fencing/riparian and track £5K tracks 11 Poor slurry storage, unmanaged track, uncovered Extend shed roof 2 yards repair £75K handling yards. slurry store concrete track 12 Poor cattle housing facility New cattle shed £11K

3.3.3 Development of a Mitigation Measures Plan in the Eden Within the Eden catchment, focus was placed on mitigation within the 2km2 mitigation sub- catchment of Morland Beck (Figure 3.5), following the experimental design laid out by Defra for Component 1. There are three tenanted farms within the 2km2 sub-catchment: Brown Howe is owned by a different land owner to Towcett and Dedra Banks farms. The tenant at Brown Howe is an elderly man and it is expected that the farm will change hands in the short to medium term. With this in mind, mitigation has been concentrated at Towcett and Dedra Banks.

Figure 3.5: Towcett and Dedra Banks Farms in the Morland sub-catchment of the Eden DTC

The key focus of mitigation measures was reducing sediment and associated nutrient (in particular P) movement through the catchment by addressing flow pathway connectivity. Monitoring data from DTC Component 1 indicated that stream nutrient concentrations can increase rapidly following

94 a rainfall event within the catchment. Combined with evidence from catchment walks, farm surveys, dye-tracing studies and local knowledge from farmers a number of issues were identified within the Morland catchment (see Table 3.6). This suggested that rapidly-responding surface (overland flow) and sub-surface (drain flow) pathways contribute to diffuse pollution within the Morland catchment. A key objective of mitigation was therefore to retain/delay water and associated sediment-nutrient pollutant movement within the catchment. Source management was identified as a priority, particularly farmyard infrastructure and hard-standing management. Mobilisation and delivery of diffusion pollutants to in-stream ecological receptors in response rainfall was also recognised as a key priority for mitigation and was addressed through attenuation of fast surface and subsurface flow paths from tracks, farmyards and overland flow. Reduced mobilization through improvement of soil compaction and physical status of soils also formed a key part of the mitigation measure plan (consult Table 3.6).

Table 3.6: List of issues and actions

Issue Action 1 Volume of run-off on yard is high and silage clamp is pre-1991 Renew infrastructure and roofs. (NB Silage clamp was and we believe it is connected to the river, through fractured renewed with grant and farmers own funds. limestone or a very old drain that we cannot locate 2 High volume of clean water from dairy and roof, and dirty Resurfacing and level adjustments in yard to redirect run-off run-off from yard bypassing yard drain causing erosion on to in-ditch barrier to encourage sedimentation. track and field. 3 ‘Pinch point of the farm as connects one part of the farm to RAF using off line pond, a raised track & soil bund. the other. Area is highly compacted with high erosion in field and highly connected to river. (CSA) Area floods during bigger storms. 4 Eroding and poached banks by livestock accessing river. Fence remaining areas to keep out stock and allow re- Majority of sub-catchment that takes livestock has been vegetation and stabilisation of bank. fenced by ERT. These highlighted areas have livestock and no fence. 5 Field run-off and surface drain connected to river. Lowest Run-off captured by off-line pond (RAF). part of farm –outlet of farm. 6 High volume of run-off from yard and upslope field is eroding Run-off directed into RAF at earliest opportunity using a cross track. Also making field adjacent to track very wet, causing drain. Two further cross drains from track lower down direct compaction and encouraging chickweed vegetation that is RO into swallow scrapes. Vegetated scrape at lowest point of not good for grazing. track, collects run-off from upslope (arable) field and allows sediment to be captured. 7 Dirty run off from yard connected to land drain (green line) To reduce volume of yard run-off (Action 8) and to use on-line that discharges into this ditch. Upslope field drains also pond (barrier in ditch) to enhance sedimentation. discharge into ditch. 8 Clean and dirty water separation. Drains to capture clean water into drain towards ditch. Drains to capture pig washings into slurry store 9 Soil compaction occurs in a number of fields used for silage Use of ERT soil aerator. production and by grazing livestock.

The EdenDTC, through the Eden Rivers Trust, has strongly felt that the Defra Funding for mitigation should be used as seed corn to lever further funding for mitigation from other sources including the tenants and landowners. This was achieved by the following process for selection of measures on Towcett and Dedra Banks below:

1) Stakeholder mapping – carried out with the two tenant farmers in order to gather information on issues in the catchment that may have been missed. 2) Stakeholder mapping – carried out with members of the Saving Eden (CaBA) coalition including EA, NE and CSF in order to gain their input into issues. 3) 1st drafts of individual measures discussed with tenants and landowner 4) Fine tuning of designs 5) Implementation of clean and dirty water separation 6) Implementation of RAF’s 95

7) Snagging of RAF’s 8) Soil aeration and sub soiling carried out during the project

This process has allowed both farmers and landowners to buy into the process, empowering them to provide ‘both ends’ solutions which we believe are key to sustainable catchment management. By using this concept £18,000 of investment was received from the Farming and Forestry Improvement Scheme, approximately £30,000 from the Catchment Sensitive Farming Capital Grant Scheme, £20,000 of contribution from farmers and £15,000 from the Catchment Restoration Fund. A summary of measures chosen to mitigate issues highlighted in Figure 3.5 including cost are presented in Table 3.7.

Table 3.7: Summary of experimental measures

Measure Coverage/ targeting Funding mechanism* Cost (£)

Clean and dirty water separation See Action 1 & 5, Figure 3.5 DTC, CRF, CSF c.79,300 Farmer, FFIS On-line pond (within ditch barrier) See Action 2 & 6, DTC c.2,000 Figure 3.5 Runoff attenuation into offline ponds See Action 3, 4 & 7, DTC, CSF c.12,500 & scrapes Figure 3.5 Riparian fencing at Towcet See Action 8, Catchment Restoration Fund £3000 Figure 3.5 via ERT Riparian fencing at Dedra See Action 8, Catchment Restoration Fund £6000 Figure 3.5 via ERT Use of soil aerator See Action 9, ERT (in kind) c.600 Figure 3.5 Farmer Integrated manure & fertilizer At farm scale ERT/DTC planning Rural sud feature at Dedra Issue 3, Figure 3.6 DTC £2500 Settlement pond at Towcet Issue 6 and 7, DTC £6500 Figure 3.5 Leaky Dam at Towcet Issue 6 &7, DTC £500 Figure 3.5 Total c.112,900 DTC = Defra funding under DTC Component 2; CRF = Catchment restoration funding; CSF = Catchment sensitive farming capital grant; FFIS = Farming and forestry improvement scheme funding; Farmer = funding from tenant/landowner

Based on the collection of evidence gathered through Component 1 of the DTC a systemic approach to developing and implementing a mitigation plan has been proposed for headwater catchments, typical of NE England:

1. Build a shared conceptual model, combining biophysical with social and economic data and understanding (builds on Component 1) 2. Develop a mitigation plan for the sub-catchment, prioritising risks and mitigation actions (Component 2) 3. Combine sources of funding to deliver the mitigation plan: DTC, CSF, CRF, FFIS, private funds (Component 2 contributes ) 4. Monitor the effectiveness of individual measures and of the mitigation plan (Requires Component 1 alongside Component 2)

3.3.4 Development of Mitigation Measures Plan in the Wensum Through discussions with local farmers and agronomists four main measures were identified as feasible and agreed with the main landowner (Salle Farms; Figure 3.6). These included: 96

1. The use of reduced cultivation methods; 2. Cover crops; 3. Woodland planting and associated channel profile changes to act as a sediment/nutrient trap; 4. A Biobed for attenuation of sprayer washings.

The measures were also highlighted in the baseline farmer survey of the wider Wensum catchment as measures which at least 40% of respondents anticipated adopting in the future. Since there is still debate about how best to implement several of these measures or aspects of their cost- effectiveness it was concluded that providing further evidence through DTC research would attract interest from local farmers and support wider adoption. A summary of measure chosen to mitigate issues highlighted in Figure 3.6 including cost is present in Table 3.8.

Figure 3.6: Focus area for mitigation in the Wensum catchment (see Table 3.8 for explanation)

Table 3.8: Summary of experimental measures

Measure Coverage/ targeting Funding mechanism Cost Reduced Cultivation Systems (Discordan 7 fields (Blocks P and L on map) DTC Measures Funding, Salle Farms, ~ £75k or similar cultivator and direct drill in totalling 102 ha, Block J fields (41 Vaderstad (agricultural machinery 2013/14 farming year) ha) as ploughed control manufacturer) Cover Crops (oilseed radish) 7 fields (Blocks P and L on map) DTC Measures Funding, Salle Farms ~ £10k totalling 102 ha in 2013/14 farming year Woodland planting and channel profile 1 ha site above Kiosk E (See Fig DTC Measures Funding, Forestry ~ £25k changes to act as sediment/nutrient trap 3) Commission, Salle Farms Biobed Installation at Manor Farm to CSF Capital Grant, Salle Farms, ~ £70k serve Salle Farms local Environment Agency, DTC Project operations on 2050 ha WQ0212

3.4 Implementation of Measures across the Catchments Results from the monitoring kiosks have demonstrated that there are pronounced peaks of sediment, phosphate and nitrate during and following rainfall events (Component 1). Other sediment fingerprinting research undertaken across the catchments has highlighted the role of different sources such as surface run-off, field drains, road verges and channel bank remobilisation. In addition, there is important groundwater connectivity with seepage from the underlying aquifers into the surface water system. This all means that a combination of measures that reduces sources, limits mobilisation and prevents delivery in an integrated manner is necessary. It is also important to monitor multiple pathways in order to assess the impact of different mitigation measures.

Following assessment of key issues and negotiations with farmers, priority measures that were practicable and acceptable to implement were identified as those that target:

1. Farmyard management, steading covering and the separation of clean and dirty water. 2. The improvement of tracks for reduction of sediment and animal derived pollutants. 3. The attenuation of runoff along key pathways prior to discharge into main water courses. 4. Better management of nutrient sources (both organic and inorganic) for the protection of surface and ground waters. 5. The use of reduced cultivation methods; 6. Cover crops; 7. Woodland planting and associated channel profile changes to act as a sediment/nutrient trap; 8. Biobed for attenuation of sprayer washings.

3.4.1 Avon 3.4.1.1 Source Soil and nutrient management Soil nutrient management is to be addressed over the next year of the DTC Measures Component 2 project. Independent advisors will be brought in to give advice across the whole sub-catchment in an attempt to improve excessive spreading of dirty water and slurry and improve the timings of organic and inorganic fertiliser applications. The scope for converting a splasher plate tanker in to a trailing shoe will also be investigated. An ADAS advisor known well to the local farmers is likely to deliver this work for Component 2 of DTC.

3.4.1.2 Mobilisation/Pathway Track Improvement Works The track running from the farmyard, around the fields and across the River Sem, was producing high rates of runoff, both from the farmyard and surrounding fields as well as the track itself acting as an erosive feature (Figure 3.7). Large volumes of slurry and farm waste were observed flowing down this degraded farm track, particularly in wet weather. A ditch alongside the lower part of the track was also connected to the Sem, providing another entry point for farm track runoff.

Figure 3.7: Farm track at Hays Farm before remediation works

Works at Hays Farm, Hampshire Avon DTC, commenced in the summer of 2013; mid-June to end of July (Figure 3.8). Weather during construction was dry and hot; this made it necessary to damp down the concrete track to avoid it drying out too quickly. This was done during the evenings by the farmer, an extra cost (not funded) in terms of resource, water and person time. Figure 3.9 shows the original farm track, Figure 3.10 a-c show the track through construction.

Figure 3.8: Hays Farm: orange lines = CSF funded fencing; red dashes = farmer self-funded fencing; blue shaded areas = location of concrete track, settling pond and v-notch weirs (DTC funded)

Figure 3.9: The original track

(a )

(b )

(c)

Figure 3.10: (a) Track construction July 2013; (b) Collection ditch and camber of the track July 2013; and (c) The track in use – ‘self-cleaning’ October 2013

100

Riparian Vegetation (targeting edge-of-field connectivity) Priors farm sits directly downstream of Hays, within the Priors sub-catchment. It is an un-intensive hobby farm with a small herd of beef cattle. Along this reach of the Sem is a well-established woodland riparian buffer strip (Figure 3.11). A tributary feeding into the Sem is surrounded either side by a grass riparian buffer strip (Figure 3.12). These two buffer zones are being monitored upstream and downstream to analyse their efficacy.

Figure 3.11: Experimental area of Priors Farm

Figure 3.12: Left: Grass riparian buffer strip on Priors Farm. Right: unfenced area upstream of the woodland riparian buffer strip, often poached by cattle

101

3.4.1.3 Landscape and attenuation features As part of the negotiations concerning the tracks at Hays Farm, it was considered that although the concrete track would no longer pose a threat as a source of sediment, there was a risk of improved connectivity between the farmyard and the surface water courses to which it discharged. Consequently, the track was designed to slope gently towards a grass swale constructed parallel to the concrete track, which at relatively low flows, would attenuate water velocity and this the transport of sediment and nutrients. In order to address issues associated with high flows, a pond was constructed into which the track and swale discharge and runoff from upslope fields could be attenuated. Figure 3.13a-b shows the pond’s construction. Figure 3.13 c shows the water level rising in the pond and the establishment of bank vegetation. The pond discharged into a ditch into which two attenuation weirs were constructed, resulting in ponding of water and hence slowing of flows, and removal of pollutants via deposition and biological processing. These features additionally enable the monitoring of performance via discharge measurements and locations for the collection of water samples for subsequent analysis.

HOBBO® U20 water level loggers were installed at the three V notch weirs (Figure 3.14a-b). A fourth logger was installed centrally to the site (close to the end of the pond) to record atmospheric pressure and ambient temperature. The weir loggers are used to measure stage height from which discharge can be calculated, recording absolute pressure and temperature at 15 minute intervals. They are highly accurate (± 0.5 – 1.0 cm @ 9000 cm depth) and maintenance free with the potential to record 21,000 combined pressure and temperature measurements. A number of options were explored for measuring discharge from the pond, but because of the sporadic nature of discharge from this feature, a purpose built thin plate V-notch (28°) weir fitted with a HOBBO® U20 water level logger to measure stage height was installed in September 2014 (see Figure 3.14c).

Extensive fencing has been erected on Hays Farm, negotiated and funded primarily by CSF, using the capital grant scheme, but with a major contribution from the farmer. Two tables are included in Appendix 3.2 highlighting the ongoing engagement with farmers and advisors. The high quality fencing complements the track, ditch and pond works, but also now provides livestock exclusion across the whole of Hays Farm in the Priors target sub-catchment. The new fencing protects the new mitigation features (e.g. ponds and weirs) and stream banks from damage and erosion via livestock poaching and additionally prevents livestock entering the streams where they can be a direct source of pollution via defecation or re-mobilisation of deposited bed sediment.

Preliminary stage height data with temperature are shown in Figure 3.15, for November 2013 to November 2014. Figure 3.15a refers to weir 3, the temperature is water temperature, and clearly there is less recorded temperature fluctuation than weir 1 (Figure 3.15b). This is because the temperature recorded at weir 1 is predominantly ambient air temperature due to the intermittent flow at this location. Interestingly, there are only seven recorded discharge events from March to November 2014 at weir 1, i.e. only seven rain events that generated sufficient flow from the concrete track to result in discharge over the weir.

102

(a)

(b)

(c)

Figure 3.13: (a) Pond construction, July 2013; (b) Pond inlet, July 2013; and (c) Pond level rising and vegetation establishing, November 2013

103

(a)

(b)

(c)

Figure 3.14: (a) Water level sensor in place at Weir 1 – end of the ditch that runs alongside the concrete track. Note the stage height board – one is fitted at each weir; (b) Water level sensor in place at Weir 3. Note the stage height board; and (c) A weir is incorporated to measure the discharge from the pond, September 2014

104

(a)

(b)

Figure 3.15: (a) Stage heights with water temperature at weir 3, November 2013 to November 2014; (b) Stage height with ambient temperature at weir 1 November 2013 to November 2014. Note that relative few Discharge events (seven in total) were recorded after March 2014

Pond and Wetland (targeting in-stream connectivity) The River Ebble is an ephemeral chalk stream that flows through Ebbesbourne Wake, where a well- established wetland (over a decade) is situated directly upstream of a historical pond (Figure 3.16). The aim here is to analyse the efficacy of these two mitigation options combined, in reducing conveyance of sediment and pollutants downstream. This provides an opportunity to test the potentially beneficial impact of a constructed wetland and pond in improving water quality; the presence of both features in a small reach of river is quite unique, and allows simultaneous evaluation of both mitigation options. 105

Figure 3.16: The River Ebble in Ebbesbourne Wake. Left Photograph looks upstream at the wetland and the right photograph looks downstream at the historical pond

3.4.2 Tamar FIOs were used to assess the microbiological water quality of with the Tamar, with results presented in Figures 3.17-18 from two water quality (FIOs) monitoring studies carried out a) over summer 2013 and b) an intensive, hourly survey over a single day.

Mean FIO concentrations for samples taken during a high flow event was a factor of 2 Log10 units higher (110.6 and 121.1) than the mean E.coli and I.E. concentrations (respectively) for samples taken when the river was in low flow. A one way ANOVA test shows flow to have a significant effect on E.coli and I.E. concentrations. Similarly, site is shown to have a significant effect on E.coli and I.E. concentrations when the river is in the low flow and high flow state (Table 3.9).

106

E.coli Enterococci

Figure 3.17: FIO concentrations at the 8 sites sampled over the summer of 2013 and 2014. Data collected during low flow is marked by triangles and data points from high flow events are represented by diamonds

107

E.coli

Enterococc i

Figure 3.18: Data collected in the summers of 2013 and 2014 have been plotted separately for when the river was in high flow and low flow. Low flow data has been plotted as boxplots, the lack of high flow events necessitates that individual high flow data points have been plotted

Table 3.9: A one way ANOVA of flow and site with regard to the effect on FIO concentration. The level of significance was set at 0.05, significant results (P<0.05) are presented in bold font

DF Sum Sq Mean Sq F value Pr(>F) Effect of flow on E.coli Flow 1 102.1 102.06 172.5 <2e-16 concentration Residuals 282 166.8 0.59 Effect of flow on I.E. Flow 1 106.02 106.02 3.14 <2e-16 concentration 282 94.95 0.34 Effect of site on E.coli Site 1 41.63 41.63 88.79 2e-16 concentration, low flow Residuals 255 119.55 0.47 Effect of site on I.E. Site 1 7.98 7.981 24.4 1.42e-6 concentration, low flow Residuals 255 83.42 0.327 Effect of site on E.coli Site 1 1.510 1.5095 9.157 0.00567 concentration, high flow Residuals 25 4.121 0.1648 Effect of site on Site 1 0.5954 0.5954 5.035 0.0339 enterococci Residuals 25 2.9563 0.1183 concentration, high flow

108

Kay et al (2008) report the elevation of FIO concentrations by a factor of 10 during high flow events. The results reported here broadly support this finding, showing E.coli and I.E. concentrations to be elevated by a factor of 110 and 121, respectively, during high flow events. This pattern is likely to be a function of multiple processes including a reduced transport time under high flow resulting in reduced die-off of FIOs in the water column, reduced deposition of FIOs under high flow conditions (Crowther et al., 2011) and increased entry of FIOs into the water column during high flow events, via both overland runoff (Crowther et al., 2011) and the re-suspension of FIOs from river sediment (Smith et al., 2008). For data collected during both high and low flow the FIO concentrations do not appear to increase as a function of distance downstream (see Figure 3.18) suggesting that either the number of FIOs entering the river decreases and/or FIO die-off and/or they settle out. The implications of the increased FIO concentration during high flow events and apparent lack of increasing FIO concentrations further downstream for the recommendation of best management practices is heavily dependent on the relative importance of the processes producing these patterns. If FIO die-off is responsible for preventing FIO concentrations increasing as a function of distance from the river source (and is primarily a function of time), then any intervention which slows the flow of the river will be beneficial with respect to microbial pollution. Similarly if particulate settling drives the patterns observed under low flow and re-suspension is not an important process under high flow then interventions which increase particulate settling under low flow should be prioritised and farms located further downstream should be prioritised for interventions. If the particulate settling is the key process driving patterns during low flow and the re-suspension of settled FIOs drives the increase in FIO concentrations in the water during high flow events then reducing FIO concentrations during low flow could come at a cost during high flow events.

The effect of excluding cattle from the river (2013) Water samples were collected from 9 locations on Caudworthy Water (Figure 3.4). Samples were collected at regular intervals throughout the bathing season (April – September) during 2013. By sampling at relatively high frequency the aim was to capture the spatial and temporal relationship of microbiologically derived pollution from livestock. And determine the effect of excluding livestock by fencing of water courses, drinking bays and direct access in terms of FIO concentrations.

The dendrogram in Figure 3.19 shows the percentage similarity as clusters for each sample site where the canonical variance is scaled to equal 1. Group numbers are expressed as sample site number (Ntotal=162) and cattle score (e.g. site number 3 cattle score 1, as 3 1). Cattle scores were given as (1=cattle absent; 2=cattle present; 3=cattle in stream) and all were scored immediately after sample collection. The group number is calculated by combining the canonical variance of sample sites and cattle scores between all pollution parameters (Log10 transformation of E. coli, intestinal enterococci, TN and TP). Some group combinations were not observed (e.g. no group 5 2). Clusters of similar groups are labelled for example as cluster 1 (C1), cluster 2 (C2)...... , cluster 5 (C5).

109

Figure 3.19: Percentage similarities as clusters for each sample site

• Fenced fields significantly decrease FIO concentrations (CFU/100ml) (p<0.05) • An improvement was observed with all pollution parameters in drinking bays compared to unfenced sites

The effect of excluding cattle from the river (2014) Data was pooled by the type of access livestock had to the river to test if this had a significant effect on mean FIO count. Data from sites 2 and 5 were pooled to form the group “Drinking Bay”, and data from sites 1 and 7 were pooled to form “No Access”, data from sites 3 formed the group “Unrestricted Access” and data from the groups 4, 6 and 8 were pooled to form the group “No Livestock” (during the summer of 2014 five cattle were observed at site 6 on one occasion, on all other occasions no livestock were present). Figure 3.20 shows a plot of these data.

Figure 3.20: Boxplots for the log10 transformed concentrations of FIOs (low flow data for 2014 data only) with data separated by the type of access that livestock had to the river at the site. Data for E.coli and enterococci are plotted separately.

110

It was not possible to analyse the data for both 2013 and 2013 together, because of the observed differences in stocking rates of the fields at the sample sites. However, an ANOVA (Table 3.10), using the data for 2014 (for which data regarding the access cattle had to the river were available) showed the level of access livestock had to the river (no livestock observed at site, livestock present but with no access to river, livestock with access to river via drinking bay and livestock with unrestricted access to river) to have a significant effect on the FIO concentration in water samples at these sites during the low flow state and high flow events, broadly in keeping with the results from 2013.

Table 3.10: An ANOVA showing the type of access to the river to have a significant effect on FIO concentrations

E.coli DF Sum Sq Mean Sq F value Pr(>F) Access 3 19.02 6.342 17.72 1.51e-9 Residuals 117 41.88 0.358 I.E. DF Sum Sq Mean Sq F value Pr(>F) Access 3 5.303 1.768 6.596 0.0004 Residuals 117 31.357 0.268

Studies have focussed around the efficacy of unrestricted access vs drinking bays vs fenced streams with drinking troughs in relation to water quality. The studies are ongoing, but in summary, to date the main findings have been:

 FIO concentrations are regularly exceeded during the bathing water season.  High FIO values occurred despite dry weather reducing activation of pathways for transfer of FIOs to water course.  High FIO values are associated with grazing livestock having direct entry to the stream.  Despite high percentage of fenced waterways in the catchment, the presence of only a few unfenced areas can result in rBWD guideline failure.  Drinking bays may therefore be ineffective – on-going research is establishing whether drinking bays will improve water quality compared to installation of drinking troughs.

3.4.3 Eden 3.4.3.1 Sources Soil and nutrient management Information on existing soil and nutrient data on farms has been collated and used to create a baseline of nutrient management activity and awareness on the farms within the Morland DTC catchment. From this we identified the gaps in farmer knowledge, which were a) soil compaction and remediation thereof b) trace element management, and c) animal nutrition. Taking this as guidance we then organised a series of meetings and workshops both for groups of farmers and one-to-one advice in conjunction with CSF, DairyCo (AHDB) and Cumbria Farm Environment Partnership (formally Cumbria FWAG). We brought in different independent specialist advisors who were known to the agricultural industry to ensure that the advice was realistic and relevant to the farming business, maintaining the ethos of the DTC hypothesis. As a follow on from these meetings we have made available a ‘free to hire’ soil aerator for the farmers in the Morland mitigation catchment and fields that have been aerated have been recorded.

111

3.4.3.2 Mobilisation Hard standings and yards Infrastructure surveys of the farm yards in the Morland Mitigation catchment were carried out and appraised alongside existing plans carried out by CSF, Promar and others. Measures that could be funded through existing grant funding mechanisms (CSF and FFIS) were then negotiated with farmers these included clean and dirty water separation and yard drainage (Figure 3.21); roofing silage clamps (Figure 3.22); and installing cow tracks. These were generally more time consuming to agree because they needed match funding from the farmer and negotiation with both the EA and CSF and were therefore addressed earlier in the project, the main objective for these options is to reduce pressure on the slurry systems by reducing a) the amount of rain water going into the system and b) increasing the length of the grazing season through track management. DTC mitigation funding was then used on options that fell outside of existing grant funding to provide a more ‘bespoke’ solution to yard issues, packaging up the measures in this way help to provide a plan that the farmers could work to therefore making agreement easier whilst adding significant value to the overall work package in terms of impact on the ground.

Figure 3.21: New drains installed in the Morland sub-catchment to reduce surface runoff

Figure 3.22: Covering of buildings in the Morland catchments to reduce mobilisation of contaminants

112

3.4.3.3 Landscape features and attenuation features These features theoretically are more difficult to agree with farmers as they are ‘location sensitive’ in terms of their effectiveness in intercepting flow pathways. However in the case of the Eden DTC the features we proposed worked well within the farmed landscape as they were best suited to areas that were either already fenced off from agricultural production or were generally unproductive areas (Figure 3.23). We had to alter one feature significantly as it became clear that the farmer did not fully understand the implications of the work and the longer term management, this highlighted the difficulty of working both spatially in the wider catchment and temporally within the agricultural rotation.

Figure 3.23: Installation of a wetland to capture surface runoff from a farm in the Morland catchment

3.4.4 Wensum 3.4.4.1 Source/Mobilisation Cover Crops and Reduced Cultivation The experimental work with cover crops and reduced cultivation techniques is taking place in a group of nine fields (shaded in Figure 3.6, page 97) that total ~143 ha of land around the key surface drainage in one mini-catchment. These fields are all scheduled to receive the same cropping in the Salle estate rotation and for project purposes have been divided into three blocks. Two fields (Block J) will be managed in accordance with normal estate practice and the other seven (Blocks L and P) will have differing variants of reduced cultivation practices applied. Block P will be direct drilled (i.e. no inversion) and Block J cultivated prior to drilling (an intermediate solution). A tributary of the Blackwater passes through the fields and there is a monitoring kiosk (M) above the reduced tillage blocks and two further ones below it. All of the fields are under-drained and a set of field drains have been highlighted for weekly monitoring (see red triangles in Figure 3.6) so that any differences in drainage water can be identified. The cropping measures on the nine fields will run through to summer 2017 so that it will be possible to assess the implications for soil characteristics and yields as the rotation changes. All of the fields were in cereals crops in 2012-13 and this 113 will be followed by spring beans, winter wheat, winter barley and winter oilseed rape in subsequent years. Normally the fields would be ploughed and left over winter without any ground cover before planting spring beans but as part of the experimental design the seven reduced cultivation fields were planted with an oilseed radish cover crop immediately after harvest and the impact of this on soil and field drain water quality was monitored over the 2013-14 winter.

The oilseed radish cover crop was established during 16-30th August 2013 (Figure 3.24). The radish was selected as a cover crop on agronomist advice due to the long tap root (a benefit for soil improvement), the nutrient retention capabilities and the suitability for BCN nematode control (important given the presence of sugar beet in the farm rotation). Since there was some debate about the need for a starter N fertiliser a rate of 30 kg/ha was applied to five of the cover crop fields and two (one in each of Blocks P and L) received no application. In the mild autumn conditions the cover crop grew rapidly and Figure 4 shows a photo looking across one field on 2 February 2014. To control the crop beans it was sprayed with Glyphosate prior to the drilling of the spring beans in mid-March. Figure 3.25 shows photos taken of several fields on 21 March 2014 after the drilling had been completed. Figure 3.25a and 3.25b are of a field where the beans were directly drilled into the remains of the cover crop (Block L), while 3.25c had cultivator passes prior to drilling (Block P) and 3.25d shows a control field (no cover crop, Block J). In the directly drilled fields the remains of the cover crop were clearly visible, but this was not the case in Block P.

Figure 3.24: View of oilseed radish cover crops on 2 February 2014

Figure 3.25: Views of mitigation measures fields on 21 March 2014 following completion of drilling for spring bean crops. 5a and 5b show a directly drilled field (Block J), 5c is of Block P (intermediate cultivation and drill) and 5d is of a control field (spring cultivation and drill after autumn ploughing) 114

The impacts of the cover crops and differences in cultivation methods were monitored in terms of soil properties (physical, chemical and biological) and nutrient levels in soil water and field drains. The radish itself was sampled in late January and the results summarised in Table 3.11 revealed no statistically significant differences according to whether a starter fertiliser was applied. However, it should be noted that the cover crop was established early in good conditions and if it had been later or the autumn weather poorer then the effects of fertiliser might have been more apparent.

Table 3.11 Oilseed radish leaf and root matter analysis (samples taken 22 January 2014)

Mean N Mean dry Mean N Mean dry Mean N Mean dry content matter yield content matter yield content matter yield TOTAL TOTAL LEAF LEAF ROOT ROOT (root & leaf) (root & leaf) (kg N/ha) (t/ha) (kg N/ha) (t/ha) (kg N/ha) (t/ha) Without 57.31 1.91 13.15 0.64 70.46 2.55 starter N With 63.57 2.17 11.97 0.61 75.54 2.78 starter N

Porous pot sampling of nitrate in soil water at 90 cm depth was undertaken in February and April 2014 (i.e. before and after the establishment of the spring beans). The results summarised in Table 3.12 indicate a substantial contrast between the fields with and without cover crops, many of the concentrations in the former being an order of magnitude lower than the latter. The contrast remained considerable following the establishment of the spring beans, though there was a consistent increase across the cover crop fields by late April.

Table 3.12 Porous pot sampling of nitrate concentrations in soil water (90 cm depth)

5-6 February 2014 28-29 April 2014 Mean NO -N (mg l-1) Mean NO -N (mg l-1) Field 3 3 Middle Hempsky 0.50 (n = 10) 3.01 (n = 4) First Hempsky 0.42 (n = 8) 2.75 (n = 7) Sheds Field 0.45 (n = 8) 2.76 (n = 9) Moor Hall Field 0.25 (n = 19) 2.60 (n =18) Gatehouse Hyrne 1.37 (n = 10) 2.39 (n =4) Far Hempsky 17.52 (n = 20) 15.52 (n = 8) (No cover crop) Potash 10.95 (n = 10) 9.81 (n = 8) (No cover crop) n = number of samples

The impact of the cover crops was also very evident in the results of the field drains. Figure 3.26 shows the recorded concentrations from 11 drains between late October 2013 and the end of March 2014 (the period of time when most drains were running). In the legend to Figure 3.6 (page 97) the drains are ordered according to their sequence downstream, with the lines shaded blue and purple representing drains from control fields or where other crops (i.e. winter wheat and oilseed rape) were growing and those of red, orange, yellow or green signifying those draining cover crop fields. The consistently lower values in the latter group are very apparent, as is the tendency for there to be a slight increase in nitrate levels once the beans were drilled in March. The only blue line amongst those for the cover crop drains is from a field of 115 oilseed rape, suggesting that the early autumn establishment of this crop had taken up nitrogen in the soil. The same explanation may also account for the two other blue lines which show steady declines in nitrate concentrations between December/January and early March. Both of these were drains from fields of winter wheat. A final relevant point is that the corresponding data for TRP showed a much more spiked pattern over time with no clear separation of concentrations from cover crop drains compared to those from other fields.

Figure 3.26: Results from monitoring of nitrates in field drains, October 2013 – March 2014

Other monitoring of soil properties is ongoing, but several sets of data will be needed to assess characteristics such as infiltration rates and the impact of the cover crops and cultivation methods can only be fully evaluated over several year of crop rotations. Financial returns over the 2013-14 farming year were evaluated in September 2014 and are summarised in Table 3.13. The most striking feature of these results is that even though the variable and application costs were higher in Blocks L and P (the cover crop fields) so were the bean yields so that ultimately there was very little difference in the gross margins, all three blocks providing a good return. Valuable practical experience was also gained with management of the cover crop and direct drilling systems in 2013-14, which should benefit more efficient use of these mitigation methods in subsequent years.

Table 3.13 Comparative outputs, costs and financial returns across the three cultivation blocks

Block J Block L Block P Gross output: Yield (t/ha) 5.80 6.24 6.55 Output at £230/t (£/ha) 1334 1435 1506 Costs: Establishment (£/ha) 96 67 128 Applications (£/ha) 90 120 120 Variable costs (£/ha) 318 432 415 Harvesting (£/ha) 85 85 85 Total costs (£/ha) 589 704 748 Gross Margin (£/ha) 745 731 758 Data supplied by Salle Farms Co.

116

Woodland Planting and Sediment/Nutrient Trap Agreement was reached to support woodland planting through a Forestry Commission grant scheme, but due to the deep nature of the channel banks the cost of the earthworks required to create a feature where high-flow water would be dispersed through an area of wet woodland have proved beyond the available budget. As these complications became apparent it was also felt that if such a feature was created it would not be something that could be readily replicated elsewhere and consequently subsequent monitoring and evaluation would have limited merit in terms of wider applicability. At the present time this plan is being reviewed, with the aim of possibly establishing a simpler planting scheme and using some of the available DTC budget to support creation of a sediment trap for road runoff near the Swanhills road bridge since ongoing sediment fingerprinting research has identified this as an important pathway in rainfall events and the such a mitigation measure would have considerable applicability elsewhere in the catchment and more widely.

Biobed A substantial biobed facility with a capacity to handle liquid pesticide waste to a maximum of 15,000 litres per year was constructed at Manor Farm, Salle in 2012-13. Financial support came from a CSF Capital Grant and the DTC measures budget, but the great majority of the cost was paid by the estate. Figure 3.27 shows the sprayer washdown area under construction and at completion with the adjoining infiltration area. A schematic diagram of the biobed layout is given in Figure 3.28.

(a) (b)

Figure 3.27: (a) Sprayer washdown area under construction in July 2012; and (b) at completion

Figure 3.28: Schematic diagram of the biobed layout

117

Since the biobed became operation in late 2013 samples have been collected from different parts of the facility and analysed for a range of pesticides through the support of the Catchment Sensitive Farming initiative and the Environment Agency. Initial results for Mecoprop (commonly used to control broadleaf weeds) are given in Table 3.14, with the contrast in concentrations between the input sump, output sump and porous pots in the drain field suggesting that substantial attenuation is occurring and that the biobed is operating effectively. Further sampling and analysis is ongoing.

Table 3.14: Mecoprop concentrations in Manor Farm biobed samples, November 2013 to March 2014

Date Input sump (μg/L) Output sump (μg/L) Porous Pots (μg/L) 14-Nov-13 18,300 235 44.2 (n=10) 18-Dec-13 6,310 221 1.5 (n=10) 10-Jan-14 644 432 26.0 (n=10) 24-Jan-14 704 348 5.5 (n=10) 18-Feb-14 209 93 6.1 (n=10) 04-Mar-14 127 107 5.4 (n=10) 19-Mar-14 354 216 3.1 (n=10)

3.5 Rationale of Monitoring Approach A three-stage monitoring strategy is proposed to be implemented consistently across the DTCs, although with sufficient flexibility to reflect the specific characteristics of each catchment and the measures proposed.

3.5.1 Sub-Catchment Monitoring Each catchment is using water quality and biological monitoring data from the outlets of mitigated and control sub-catchments to assess the effectiveness of combinations of measures at this scale. Analysis of monitoring data from this Before-After, Control-Impact (BACI) sampling design will follow the protocols established under Component 1. Monitoring data from larger-scale catchment outlets (combining mitigated and control sub-catchments), also established under Component 1, will be analysed for evidence that the effects of measures are transferred to these larger scales. This assessment will use time series analyses based on the same BACI design, data management and quality assurance protocols already established under Component 1 and so these details are not repeated here. In addition, visual assessments on farm walks and farm surveys focussed on changes in behaviour and practice will be used to assess the effectiveness of measures.

3.5.1.1 Source Apportionment Techniques Each catchment has used source-apportionment techniques based on a before-after sampling design to assess the sub–catchment scale impact of measures on sediment and organic waste delivery to water courses.

In order to help disentangle the impacts of measures implemented across the DTC target sub-catchments, a multi-pollutant (sediment, slurries/manures, nutrients) source tracking procedure will be applied as part of the national DTC project (Defra project WQ0208). This work will provide a basis for detecting change in response to measures targeting arable or grass land sub-sectors as well as their specific components including steadings or yards and farm tracks. The approach permits the detection of change at sub- catchment scale over the short-term as recently demonstrated by Defra project WQ0208 in the SW of England (channel bank fencing) and CSF work under phase 2 (a suite of measures). Synthesis of the source tracing data with conventional pollution metrics, such as water quality or channel retention, permits the 118 conversion of the source apportionment into magnitude, thereby allowing an assessment of the true impact of the measures. The pollutant tracking framework links sources directly to point of biological impact and thereby short cuts the conventional source-mobilisation-delivery continuum and the inherent delays in detecting change in response to measures using the components of that conceptual framework. The tracking framework also provides a means of undertaking ‘edge of field’ or ‘edge of measure’ work, again, to help detect change in the short-term. An additional benefit of the source tracking framework is that it will support the refinement of the DWPA modelling under DTC by providing a basis for refining the efficacy values for measures. This latter point is founded on the cross sector information provided by the source tracking approach and will build upon the recent work under Defra project WQ0128. A range of additional metrics (e.g. engagement, attitudes, implementation and behaviour data; pollutant mobilisation; soil surveys) combined in an integrated toolkit for catchment appraisals will also be used as a means of maximising the opportunity for reporting positive outcomes to the user community within the lifespan of the current DTC project. The same integrated toolkit is beginning to be used as part of wider CSF work under phase 3 of this specific delivery programme.

3.5.1.2 Site Scale Monitoring Thirdly, site-scale monitoring approaches are planned to assess the effectiveness of individual measures, disaggregating these effects from those observed in sub-catchment-scale water quality and biological monitoring data. These specific monitoring activities will use a combination of before-after and upstream- downstream sampling designs based on both automatic and manual sample collection (e.g. to assess outputs from field drains).

3.5.2 Avon Measures Monitoring Plan Disturbance Experiments Disturbance experiments have been performed following the method developed by Lambert and Walling (1988). A cylinder 50 cm in width and 70 cm in depth is placed on the riverbed to create a seal. The riverbed within this area is then manually disturbed using a pole to suspend the sediment stored within it (Figure 3.29). A total of 2.5 litres of water is then collected from this cylinder and used to determine mass and characteristics of the sediment stored per unit area of the bed, when calculated with the depth of the water within the cylinder. This experiment is repeated three times at each location, to gain an instantaneous representation of the sediment mass stored on the riverbed. Recent work has demonstrated that this method provides a high level of confidence in terms of capturing the variability in riverbed sediment storage (Duerdoth et al., 2015).

Figure 3.29: Riverbed Disturbance experiments at Ebbesbourne Wake on the left and Hays Farm on the right 119

Time-integrated Sediment Traps Sediment traps similar to those developed by Phillips et al. (2000) have been deployed upstream and downstream of each mitigation option (Figure 3.30). These are emptied monthly, simultaneously with the disturbance experiments. The mass and characteristics of the sediment within it can be used to characterise the sediment in suspension. These traps consist of 10 litre tubes, 1 m in length and 11 cm in diameter. At each end there is a 4 mm hole, through which channel water flows in, loses velocity by a factor of 600 and deposits sediment, before flowing out again. A funnel is fixed at the upstream end, to reduce ambient flow disruption. These traps are submerged and attached with cable ties to upright dexion that has been sunk into the bed.

Use of a trap is beneficial as a widespread method for testing efficacy, because it can be left in situ, without need for power or supervision, for any chosen length of time. This removes problems associated with the practicality of high volumes of samples, and safety associated with collection during high flows. The resulting samples are a composite of all suspended sediment carried through the stream in that time period.

Figure 3.30: Time-integrated sediment traps

Dustpan Sampling This piece of equipment has been deployed at one particular site, in order to analyse the sediment running off a farm track and therefore its contribution to sedimentation and pollution in the stream. It consists of a dustpan with a hole at the base; a tube runs from this into a large 25 litre jerry can. As runoff leaves the track it collects in this, and the sediment can then be used for laboratory analysis (Figure 3.31).

Figure 3.31: Dustpan Sampling.

120

Riparian Buffer Sampling Astroturf matting has been installed using pegs pushed into the soil, so that sediment in higher flows is trapped within it (e.g. Lambert & Walling, 1988; Gruszowski, 2003; Figure 3.32). These capture sediment during periods of enhanced connectivity within the catchment, and therefore potentially different sources. This sediment can then be used for sediment fingerprinting. This method is capable of withstanding high, fast flows, as well as being relatively representative of vegetation.

Figure 3.32: Riparian buffer sampling with AstroTurf matting.

Water Quality A Multi-parameter probe and colorimeter are being used to test water quality upstream and downstream of each mitigation option. A portable colorimeter is being used to test levels of nitrate, nitrite, phosphate and ammonia. A portable multi-parameter probe (AP-800 Aquaprobe) is used to measure pH, temperature, dissolved oxygen, redox potential, total dissolved solids and electrical conductivity of the water. Nitrate concentrations in groundwater are being monitored in the Cholderton Catchment.

Sediment Source Fingerprinting All sediment collected is dried at 40°C, weighed, then disaggregated using a pestle and mortar; samples are then sieved to 63 μm. These samples are analysed for geochemistry, mineral magnetism, gamma emitting radionuclides, particle size and organic content. The data can then be used for sediment fingerprinting. This will give information on the major sources of sediment being supplied to the river, as well as showing the efficacy of the mitigation option in potentially reducing the source supply (cf. Collins et al., 2010).

3.5.3 Eden Measures Monitoring Plan Determining accumulation of sediment, nitrogen and phosphorus in runoff detention features Marker pads have been installed in each runoff detention feature that has been completed within the mitigation sub-area of the Morland catchment (Figure 3.33). These marker pads identify a baseline from which sediment and nutrient accumulation will be determined on an annual basis throughout the duration of the mitigation project. Cores of sediment will be collected from each marker pad by driving sediment sampling tubes through the accumulated sediment and into the marker horizon (see Noe and Hupp, 2005). Sediment that is retrieved will be analysed for dry mass; total phosphorus (P) and total nitrogen (N) contents. Installing multiple marker pads within each runoff detention feature will enable spatial variation in sediment, N and P accumulation to be evaluated, providing a more accurate measure of annual accumulation within each feature. Annual accumulation within the runoff detention features installed in

121 the Morland sub-catchment will be compared to annual sediment and total P export from the Morland sub- catchment.

Figure 3.33: Sediment marker pads (white circles) installed at the base of a runoff detention feature in the Morland sub-catchment

Upstream-downstream monitoring of pollutant concentration change through runoff detention features A network of level-triggered ISCO automatic water samplers has been installed within the Morland sub- catchment to capture changes in pollutant concentration during the passage of water through runoff detention features. Sampling will target both storm events (a total of 10 events over the duration of the mitigation project, with up to eight samples collected per event) and base-flow conditions. Samples will be analysed for concentrations of total P, SRP, total N, ammonium-N, nitrate-N and suspended solids. Initial results from these samplers indicate that extremely high pollutant concentrations can be delivered to the stream network within the sub catchment during rainfall-runoff events (e.g. Figure 3.34), and that significant reductions in total pollutant loads within runoff detention features should be possible based on the high suspended sediment concentration within storm water runoff (e.g. Figure 3.35). Where possible, we will consider flow gauging at upstream-downstream ISCO sites to enable reductions in pollutant load to be calculated. Where this is not possible, the assumption that concentration change provides a reasonable surrogate for load change through the runoff detention features will be used

122

) 1 - 60

30 Total phosphorus (mg P L P (mg phosphorus Total 20

1900 1300 1430 1600 1730 2030 2200 2330 0100 0230 0400 0530 0700 0830 1000 1130 1300 1430 1600 1730 1900 2030 2200 2330

Time (GMT)

Figure 3.34 Total phosphorus concentration entering a runoff detention feature during one rainfall-runoff event within the Morland sub-catchment.

1800

1600

1400

)

1 - 1200 1000

800

600 L (mg solids Suspended 400 200

1900 1300 1430 1600 1730 2030 2200 2330 0100 0230 0400 0530 0700 0830 1000 1130 1300 1430 1600 1730 1900 2030 2200 2330

Time (GMT)

Figure 3.35 Suspended solid concentration (left) entering the same runoff detention feature during the same rainfall-runoff event depicted in Figure 3.34, and visual representation of the change in suspended solid concentration over the course of the same runoff event (right).

Calculation of theoretical reductions in mass of N, P and sediment exported from farmyards For mitigation measure that have focussed on farmyard infrastructure (e.g. roofing of silage clamps, re- configuration of farmyard drainage), theoretical calculations of the reduction in the load of P and N delivered to receiving waters will be made. These calculations will be based on the area of roofing/hardstanding captured by the revised drainage system, the depth of rainfall and estimates of the typical concentration of P and N in runoff from uncovered areas of silage stores/farmyards. These latter estimates will be based on empirical work within the Morland sub-catchment, or on published data.

123

Additional monitoring activity still under development (as of January 2015) In addition to the points above, a number of additional pieces of monitoring work related to the mitigation measures in the Morland sub-catchment may be implemented over the duration of the mitigation project:

a. Targeted monitoring of changes in soil compaction/soil bulk density/infiltration capacity in response to soil aeration/sub-soiling; b. Biomonitoring, focussing particularly on the diatom community, to assess biological impacts associated with the mitigation features; c. Sampling focussed on degradation of veterinary antibiotics within runoff detention features; d. Use of remotely operated cameras and terrestrial Lidar scanning to characterise behaviour/effectiveness of runoff detention features.

3.5.4 Wensum Measures Monitoring Plan Monitoring specific to mitigation measures is taking two main forms. The first relates to the cultivation measures on 143 ha of arable farmland and the second concerns the biobed installed at Manor Far, Salle. Further details are given below.

Cultivation Measures Sampling of environmental parameters has taken place to assess variations both within and between the nine fields in the three cultivation blocks. Soil sampling campaigns were conducted in 2013 and 2014 to assess characteristics such as soil texture, infiltration rate, bulk density, % dry matter, soil mineral N, pH, and P, K and MG indices. These surveys will be repeated in an annual basis. Figure 3.36 shows the distribution of the soil sampling sites used for baseline data collection in 2013 where the percentages of clay, sand and silt have been interpolated to generate a composite colour scheme illustrating variations in soil texture. The areas of more purplish colour are heavier land, while the green shades indicate lighter (sandier) conditions. Given the variations across certain fields, this information was then used to select sites to locate sets of porous pots to measure variations in soil water. The locations selected are labelled in Figure 3.36 and these sites will be sampled on at least three occasions each farming year to provide evidence on soil water characteristics.

Figure 3.36: Sampling site locations and variations in soil texture across the cultivation trial fields 124

During the cover crop trial in 2013-14 further sampling (Figure 3.37) took place to assess variations in the dry matter yield and N content of the radish plants across the different fields. This was supplemented by an assessment of soil invertebrate diversity. Similar assessments will take place in subsequent years to build up an evidence base on change over time.

Figure 3.37: Sampling the oilseed radish cover crop in January 2014

The arable fields in the cultivation area are extensively under-drained so the field drains are an important pathway for soluble diffuse pollutants to reach watercourses. Field drain maps maintained by Salle Farms were digitised so that they could be overlaid with other map data in a GIS and in winter 2012 a pilot survey of field drains was undertaken to identify those which flowed most regularly. From this information a set of 12 drains were identified for regular monitoring (see Figure 3.26, page 116). Grab samples are collected from these drains (when they are flowing) as part of a weekly sample collection routine and subsequently analysed for a range of chemical parameters by the analytical laboratories at UEA. In 2013-14 the majority of the drains flowed from late October to the end of March and this provided an invaluable dataset to compare discharges from the different cultivation blocks and adjoining fields. This sampling will be continued in future years as part of the regular monitoring operations.

Since the cultivation trial fields lie between three min-kiosks and above a main monitoring kiosk (see Figure 2.5, page 18) information from routine monitoring at these sites can be used to help assess the impact of the mitigation measures. The same is true of the routine monitoring of aquatic ecology in the Blackwater which takes place on an annual basis at sites above and below the cultivation blocks.

Economic assessment of the cultivation measures is taking place through access to the Gatekeeper records maintained by Salle Farms. These provide full details of field operations, costs and yields, allowing summary measures (such as gross margins) to be calculated on a field-by-field basis.

Biobed Monitoring Since the biobed became operation in late 2013 samples have been collected on a monthly or bi-monthly basis to assess the effectiveness of attenuation through the facility. Samples are collected by Wensum DTC staff from the input sump, output sump and from porous pots in the infiltration area (see Figure 3.28, page 117, for a schematic of the layout). Figure 3.38 shows the infiltration area and one of the porous pots used 125 for sampling purposes. The samples are sent to the Environment Agency national facility for analysis of a range of pesticides. Sample collection is continuing at present, but future results will be dependent on the availability of the analytical services provided by the Environment Agency.

Figure 3.38: Biobed infiltration area and example of the porous pots used for sampling

4 Working with Stakeholders: Knowledge Exchange and Understanding Farmer Behaviour

4.1 Introduction Two distinctive features of the DTC programme are the emphasis on reducing diffuse agricultural pollution whilst maintaining economically viable food production potential and the desire to develop new approaches to catchment management involving partnerships between local stakeholders and scientists/practitioners (McGonigle et al., 2014). In phase 1 of the DTCs it has consequently been essential to complement the catchment monitoring and implementation of mitigation measures with stakeholder engagement activities and socio-economic research. These initiatives have focused primarily on the farming communities in each catchment, but have also involved interactions with a wide variety of other organisations and sectors with relevant interests. This section of the phase 1 report presents a synthesis of the activities undertaken by the three DTC consortia from early 2010 through to spring 2014. It begins by considering the common themes in the local knowledge transfer and knowledge exchange initiatives in each catchment. This is followed by a summary of key findings from a baseline farm survey, particularly those relating to current uptake and potential future adoption of different diffuse pollution mitigation measures. The final section evaluates progress to date, discussing how the local role of the DTC consortia has evolved during phase 1, identifying several key lessons learnt and highlighting some future priorities for socio-economic research and enhanced knowledge exchange at local and national scales.

4.2 An Overview of Local Knowledge Transfer and Knowledge Exchange Activities It is widely recognised that the complexities and trade-offs associated with catchment management require an adaptive management cycle, collaboration between agencies and a ‘twin-track’ of stakeholder engagement alongside scientific research. Such a cycle typically begins with a phase of building partnerships, followed by catchment characterisation and prioritisation, plan implementation and subsequent monitoring and adjustment (e.g. US Environmental Protection Agency, 2008; Smith et al., 126

2010). This sequence is at the heart of the Catchment-Based Approach (CaBA) as promoted in the UK and is illustrated in Figure 4.1.

Figure 4.1: The main stages in the Catchment-Based Approach (Source: Catchment-Based Approach, 2014)

Following their establishment in late 2009 all of the DTCs needed to begin with a similar process of engagement and partnership building. While the DTCs have not been engaged in directly creating catchment plans, there was a requirement to develop an underpinning conceptual model of each catchment which could then inform monitoring strategies and help identify priorities regarding the implementation and evaluation of mitigation measures. For these steps to be effective it was essential to utilise existing knowledge and to recognise that this was held by a variety of local agencies and stakeholders.

A broadly-based strategy of local engagement was therefore a feature of the activities undertaken by each DTC during the first few years of phase 1. These initiatives sought to raise awareness of the DTC programme, identify key stakeholders (at the level of both individuals and organisations) and invite collaboration where there were particular opportunities for exchange of information and/or joint working. Specific details are given in three accompanying reports (Winter et al., 2014; Cleasby et al., 2014; Dockerty et al., 2014), each of which illustrates how the DTCs have become successfully embedded in a network of local partnerships. Reviewing these documents highlights three factors that have been particularly important for these achievements:

i) Recognising the local setting – each of the DTCs operates in a very different environment in terms of physical characteristics, the nature of the farm businesses (in terms of type of farming operations, size and profitability) and the presence of other organisations (river trusts, water companies etc.). The planning and implementation of engagement strategies has needed to be sensitive to these contrasting realities. For instance, there has been a need to recognise the sensitivities of tenant-landlord or existing farmer-advisor relationships, the pressures on farmer

127

time during particular months of the year, and the established roles and reputations of other parties with interests in the catchments; ii) Utilising knowledge brokers – reflecting the differences in setting each DTC has needed to have individuals who can act as intermediaries between the research team, the farming community and other stakeholders. In some cases these people have been directly employed by the DTCs (e.g. as a farm liaison officer), while in others they are staff members of a partner organisation (e.g. river trusts) or a relevant public agency (e.g. a Catchment Sensitive Farming officer). There has also been a need to have several individuals undertake such roles in each DTC, since the background and expertise that may be highly relevant in one context (e.g. farming) may be less so in another (e.g. water companies). The importance of such individuals for knowledge exchange has been recognised in other contexts (e.g. Proctor et al., 2011), but the DTC experience certainly highlights their relevance for catchment management; iii) Having the time to build relationships and trust – each of the DTCs had a background of previous research in their catchment, but in all three cases it took at least two years to get locally known and begin to receive requests to participate in other events and activities. It is a distinct advantage of the research platform concept (McGonigle et al., 2014) that it provides the necessary timescale for such relationships to evolve, contrasting with the shorter duration of most conventional research projects and, for example, the difficulties that the Catchment Sensitive Farming initiative has encountered with fixed-term funding leading to a substantial turnover of personnel in some catchments.

The following sub-sections provide some examples of knowledge transfer and knowledge exchange activities, drawing on the detail in the individual DTC reports and identifying some common themes.

4.3 Media Activity and Dissemination Each DTC has created and maintained a website as means of providing background information, news about activities and events, research summaries or briefing notes and data feeds. Table 4.1 summarises information on the use of the websites from 2010-14. Provision of monitoring data and downloadable summaries of research findings have been features that have been particularly appreciated by users.

Table 4.1: DTC website activity 2010-14

Page Loads Unique Visits Avon 41,104 11,768 Eden 60,800 9,738 Wensum 30,000 11,000

Total 131,904 32,506 Note: the websites are at http://www.avondtc.org.uk/, http://www.edendtc.org.uk/ and http://www.wensumalliance.org.uk/.

Alongside the websites a range of media dissemination activities have been carried out. These include a Twitter feed for the Eden DTC (over 200 followers as of April 2014; see Table 4.2 for summary), press articles (e.g. in the Farmers Weekly 5/3/2010 and Farmers Guardian 18/11/2014) and radio and TV interviews (Radio 4 Farming Today 4/11/11 and BBC Countryfile 3/2/12). Presentations of findings have also been given at a range of local, regional and national meetings, including those organised by Catchment Sensitive Farming, rivers trusts, farm advisers (e.g. ADAS and Frontier Agriculture), water companies and public sector agencies.

128

Table 4.2: Summary of stakeholder engagement activity and events in the Eden

Mechanism for KE Description Evaluation Live data feeds

Online presence on the EdenDTC updating on news, events and Twitter feeds/updates EdenDTC website activities in the EdenDTC platform Staff introductions Enquiry form

Public engagement Public events where the EdenDTC had a presence to engage with the 1255 people spoken to events wider catchment community on diffuse pollution issues

35 attendees Engagement events aimed at increasing awareness of diffuse Farmer events pollution and the EdenDTC project amongst the wider farming community 233 farmers spoken to

Site visits by Events and visits hosted by EdenDTC for industry stakeholders, other stakeholders and 7 events academics and interested sectors other academics

Presentations and 7 public presentations aim at those on the periphery of the 7 events talks agricultural industry and in the wider catchment community Cumberland and Westmorland Herald

Westmorland Gazette Press articles Cumberland News 17 press articles Stackyard.com Anglersnet.co.uk Radio 4 Farming Today TV and Radio Radio Cumbria 7 interviews interviews Countryfile

4.4 Stakeholder Events Media initiatives have been invaluable for the dissemination of information, but for the two-way exchange of knowledge and opinion it has been essential to organise and participate in a range of face-to-face meetings or events. These have taken a variety of forms, ranging from open day displays for the general public, annual consortium meetings to discuss progress and findings with representatives of relevant stakeholder organisations and smaller workshops focused on specific themes (such as aspects of farming practice). Table 4.3 summarises details of these events. Over the period 2010-14 attendees at events organised in the DTC catchments have included over 500 farmers, 400 representatives from other organisations and over 1,500 members of the general public. Examples of such events are discussed below. These also illustrate how the knowledge exchange activities have evolved during phase 1 from the initial focus on engagement and building partnerships to more emphasis on improving on-the-ground practice and providing scientific support for catchment-scale management.

129

Table 4.3: Summary of stakeholder engagement activity and events

Type of Event Description Numbers of Events or Participants Stakeholder conferences, Annual conferences or  Annual stakeholder conferences or local workshops or meetings and meetings providing the workshops in all three catchments (over 600 specialist group, academic and opportunity for local participants) stakeholder site visits or stakeholders to interact with  National KE event, Oct 2012 (Avon) presentations the research programme.  34 site visits or presentations with over 850 participants Public engagement events and Outreach events with members  27 events (over 1,500 participants), many activities of the public (adults and organised by other farming, wildlife or other children) to inform them about civic organisations. the project, educate on water quality issues, and facilitate engagement. Farmer events and site visits Outreach events to engage  34 events (over 600 participants), many with members of the farming conducted jointly with river trusts, community to inform them Catchment Sensitive Farming or other about the project, educate on providers of advice such as agronomists or water quality issues, and where agricultural colleges applicable facilitate involvement in DTC activities. Research collaboration / Fostering the development of  12 collaborative projects arising Facilitating the development of the DTC as a broader research  6 development initiatives the catchment-based approach platform, and the development of further collaborative research e.g. with Catchment Sensitive Farming, local river trusts, agricultural research organisations (British Beet Research Organisation).

4.4.1 EdenDTC Catchment Bus Tour The catchment bus tour was held in August 2012 and was a follow-on from an earlier Farming Think Tank discussion meeting held in May. It visited three dairy farms that were carrying out ‘water friendly’ mitigation works. Members of the farming community were invited along with representatives of the Defra family, United Utilities (the local water company), academics and other third sector organisations. Each participant was ‘paired’ with another from a different sector, the objective being to give all attendees a better understanding of each perspectives and challenges in order to identify common ground and a joined up approach to tackling diffuse pollution. Figure 4.2 shows some photographs taken during the day.

This type of event does not appear to have been tried before and it received a great deal of positive feedback from all parties. It was particularly successful in terms of breaking down perceived barriers to delivery and good practice and developing a more positive relationship between regulator and industry. These contacts have since been further developed through the Saving Eden coalition hosted by the Eden Rivers Trust.

130

First stop Newton Rigg College. Newton Rigg is a key partner in the EdenDTC, supporting engagement with farmers and the farming workforce of the future in Eden. The DTC through the Eden Rivers Trust has played a key role in advising on the development a new purpose built water friendly commercial dairy unit on the college campus

Prof. Phil Haygarth of Lancaster University discussing evidence and the role of science in tackling diffuse agricultural pollution

Trevor Marsh, Senior Environment Officer with the Environment Agency, discussing the role of regulation and inspection in agriculture

Figure 4.2: Scenes from the Eden catchment bus tour, 28 August 2012

4.4.2 Avon DTC Farm Advisor Workshop, May 2012 FARMSCOPER is a decision support software programme developed by ADAS which is designed to assist in the assessment and mitigation of risks of diffusion pollution from agriculture (Zhang et al., 2012). During an earlier Farmer-Scientist workshop held in December 2011 the suggestion was made that it could be useful as part of the professional advice provided to farmers so this follow-up event was arranged with the participation of 15 farm advisors. Figure 4.3 shows some photographs taken during these workshops.

131

Figure 4.3: Scenes from the FARMSCOPER workshops

Like the earlier discussions with farmers, the workshop generated many reactions to FARMSCOPER and provided opportunities for advisors and scientists to share insights in ways that inform future model development. The facilitating team captured views primarily in a qualitative format, but some of the reactions are also expressed quantitatively (Table 4.4).

Table 4.4: Advisor assessments of FARMSCOPER

Evaluation Criteria Mean Response General applicability of Farmscoper to the advice giving process 3.9 Potential usefulness of Farmscoper in your own advisory work 3.1 Capacity of Farmscoper to identify potential problems 4.4 Capacity of Farmscoper to provide information that will help support farm profitability 6.4

Criteria ranked by participants on scale of 1 to 8 where 8 = maximum positive score.

In general terms, participants felt that the tool was best placed to provide information on risks and opportunities at the strategic catchment/sub-catchment level and highlight what key measures in an area might be employed given its general landscape attributes (such as soil types) and types of farming taking place there. Some also suggested that the tool might helpfully be deployed at open meetings and technical events as a device for awareness raising among farmers and demonstrating the cost/benefit of undertaking particular practices.

If FARMSCOPER was to be used at the farm level, it was widely argued that the link to local circumstances would need to be developed further. Advisors persistently highlighted that information produced by the tool tended to work from generic assumptions about farm circumstances and missed salient local information, and this issue therefore currently restricted its wider practical utility:

“As it takes averages and was designed at the national scale, I don’t think it can really effectively identify potential problems/situations reliably but I can see it could identify them at the larger scale. Advisers need specifics, not generalities”

“There is no such thing as an average farm - if it’s to be used at farm scale needs to include greater complexity and real farm data”

As a result, capacity to manipulate data ‘inputs’ to reflect individual circumstances was highlighted as an area of future tool development such as including information on topographical risk factors, and risk

132 associated with soil and crops types as well as housing and grazing patterns. The ability to carry out assessments of risks and options at the field level was also emphasised.

Keeping these points in mind, it was nonetheless felt that when used by “farm advisors with a good knowledge of farm circumstances” the tool could help provide a generic ‘base line’ indication/direction of risk that may be helpful as the basis for targeted farm level guidance. There was recognition that FARMSCOPER could never be a substitute for the “detailed local knowledge stored by the farm adviser” but had potential utility as a “background guide” to be used at the formative stage of the advice giving process:

“Having run through the programme, I can see that whilst giving generic answers, this information could be used by an advisor to develop a more targeted plan”

“It’s applicable to me on a sub-catchment scale to give me an indicator of issues and mitigation methods prior to going to a farm and then adjusting my advice depending on the individual farm”

“[I would use FARMSCOPER] before going to the farm to give 1:1 advice or getting to know a patch....providing general advice on mitigation methods”

Advisors further felt that the range of alternative measures presented as options to help mitigate risks was useful especially since these could be grouped according to types of risk and then linked to costs. However, there was some concern that the list of measures was was perhaps too long to be meaningfully used. As one advisor suggested it was “difficult to see how [FARMSCOPER] can be used effectively due to the number of measures considered”. One suggested that the tool should direct users to the “top three” measures. Nonetheless, it was also argued that such information could be a useful ‘prompt’ for advisors and could encourage them to think about measures that they might otherwise not consider. Again, the focus of advisor comments was that information on mitigation measures was a generic and high level first step in advice giving and targeting; the basis for generally highlighting “potential impact reduction/cost saving mitigation measures for further investigation”. Advisors were keen that the tool told them more about how particular mitigation measures impacted on farm productivity and desired that the tool provide a final “cost-benefit/effectiveness as a result of measures used. Putting this in to a graph format would also be useful. In terms of translating such data into information of interest to farmers, the link to nutrient management and system profitability was cited as important. The general point made was that any results had to make economic sense as well as environmental sense. Finally, some advisors also suggested the tool should also be clearer regarding which mitigation methods were simplest to implement.

Overall, the workshop suggested that FARMSCOPER is a useful tool for provide generic information on risks and approaches to mitigation at the catchment/sub-catchment level. Advisors were also keen to see the tool evolve in such a way as to allow salient local information to be further incorporated. The workshop discussion contributed to a published paper (Gooday et al., 2014) and further workshops with advisors are planned because their role means they are well-placed to present research findings in ways that makes them most relevant to local farming communities.

4.4.3 Wensum DTC Farmer Presentations and Field Visits, 2014 The mitigation measures being evaluated as part of the Wensum DTC programme include the use of cover crops and reduced tillage cultivation methods. Seven fields of oilseed radish cover crop were established in August 2013 at Salle and the impact on nutrient levels in soil water and field drain discharges was monitored over the winter. In March 2014 spring beans were established in these fields using either a cultivator pass followed by drilling or direct drilling. Two other fields were managed as controls (autumn

133 ploughing followed by spring cultivation and drilling) and monitoring continued through to harvest of all three variants so that their environmental and economic performance could be compared.

Experiments on this scale (140 ha of farmland) are novel and attracted considerable interest from local farmers and agricultural organisations. The research was also particularly timely given the inclusion of cover crops as an Ecological Focus Area option in the greening requirements of the revised CAP (Defra, 2014). As a consequence, the Wensum DTC team were asked to help host a number of visits to Salle by farmer groups and other organisations to see the field sites and hear about the emerging findings. These included a large meeting organised by British Sugar in February 2014, Defra staff in March and June 2014 and a CSF event for local farmers in early November 2014. Other local presentations were given at a CSF field meeting at Metton in February, a farmer discussion group in March 2014 and Frontier Agriculture event at Gressenhall in October 2014. Figure 4.4 shows some photographs taken during the field meetings at Salle.

Figure 4.4: Field visits to examine the cover crop and reduced cultivation method trials at Salle in 2014. Figure 4a) shows sampling of the radish cover crop in January, 4b and 4c were taken in March shortly after the beans had been drilled and 4d in May when the crop was becoming established

An important feature of these events is that they have been characterised by a much more in-depth and multi-directional discussion with members of the farming community than typically occurred at previous meetings. This reflects a change from presenting the aims of the project and initial monitoring results to having findings that were perceived as of more immediate relevance and use by farmers. In essence, by being able to present ongoing practical experience (including equipment performance) and both economic and environmental aspects of mitigation measures (e.g. the reduction in nitrogen losses under cover crops and the financial implications) has helped frame issues of diffuse pollution mitigation in a much more accessible manner and provided a setting in which farmers have been very willing to offer opinions and

134 comments based on their own experience. This, in turn, has helped the Wensum DTC team to become part of a local Community of Practice (where this is defined as a group of people who share a passion for something they know how to do and who interact regularly in order to learn how to do it better, Watson at al., 2013) which will be of great benefit for future knowledge exchange activities. Such developments should also start to become more common across the whole DTC programme as more findings emerge from the implementation and evaluation of mitigation measures.

4.4.4 Farmer-Led Monitoring in the Wensum DTC As part of the knowledge exchange activities of the Wensum DTC, several small trials with hand-held monitoring equipment were conducted. Several Hanna HI 9829 probes were initially purchased, which can measure nitrate, turbidity, dissolved oxygen and water temperature, but these were not sufficiently reliable in validation tests using samples that were also processed by the analytical laboratory at UEA.

Several farmers attending the Wensum DTC/NFU meeting in November 2011 offered to participate in undertaking monitoring of field drains on their own farms. Since the Hanna probes did not prove effective in spring 2012 we supplied three farmers with nitrate test strips so that they could assess variations in water quality from field drains under different crops. These strips typically cost about £10 for a tube of 50 strips, but have limited sensitivity with colour changes that can be matched to indicator levels of (for -1 example) 0, 5, 10, 25, 50, 100, 250 and 500 mg l NO3. Three farmers used the strips to take measurements on three or four fields every fortnight or so for three months (March to May 2012). Between them, they collected 65 data points on 14 dates from 11 locations. Table 4.5 shows the range of readings recorded from drains on fields with a variety of different crops.

Table 4.5: Nitrate test strip measurements from three farms in the Wensum (Apr – May 2012)

Minimum Maximum -1 -1 (mg l NO3) (mg l NO3)

Oilseed rape 0 5

Winter wheat 2 20

Sugar beet 20 80

Field beans 20 50

Linseed 5 20

Herbs 10 20

Pasture 5 5

The results were sufficiently reliable to highlight differences between crops (e.g. concentrations <10 mg l-1

NO3 from fields of oilseed rape where the crop was actively taking up nitrogen fertiliser, and values in the -1 range 20-80 mg l NO3 where sugar beet had been planted). However, it was not possible to identify any consistent changes following rainfall events and all three farmers noted that there was a degree of subjectivity in matching the colour changes to the shade scale on the side of the tubes.

135

Based on this experience it was decided that we needed equipment with greater sensitivity and that could provide a readable concentration (rather than requiring comparison with colour shades). It took some time to identify a possible solution, but in late 2012 the DTC purchased five Lovibond MD 600 Photometers (costing £815 each, including VAT) from Tintometer Ltd (http://www.lovibond.com/) and have been pleased with their subsequent performance.

The photometers can be set up to analyse quite a wide range of water quality parameters, although so far -1 only phosphate (PO4) and nitrate (mg l N) concentrations have been analysed. The phosphate test involves crushing two tablets in a water sample and leaving them to dissolve for 10 minutes before taking a reading. A set of 250 phosphate tablets costs about £50. The nitrate test is more expensive (£150 for 25 tests) and involves tubes of reagents with a high concentration of sulphuric acid. Completed risk assessment forms are required for both analyses and it has been useful to purchase additional plastics boxes, trays, test tube stands etc. to store and manage the equipment and reagents (see Figure 4.5). To date there have not been any problems with safety issues.

Figure 4.5: Lovibond MD 600 Photometer

The nitrate test has an advertised range of 1-30 mg l-1 N. Repeated analyses of the same water sample from a field drain have typically generated variations of 1-2 mg l-1 N around the mean value. For phosphate -1 there is an advertised range of 0.05-4 mg l PO4 and experience shows that it is quite possible to get results varying by 0.10 or 0.15 mg l-1 from repeat analyses on the same water sample. The issue with the phosphate tests is to ensure that the tablets are completely crushed and dissolved before putting the sample in the photometer. Beyond these caveats regarding precision, the equipment proved to be reliable and straightforward to explain to potential users.

Over winter 2013-14 several of the photometers were loaned to the Catchment Sensitive Farming team in the Wissey catchment who are collaborating with farmers recruited through Frontier Agriculture to test their use. Another set of equipment was used by a project officer from the Norfolk Rivers Trust. Experiences reported back to date are that the equipment is certainly capable of identifying differences in field drain nutrient levels between crops (e.g. oilseed rape and wheat) and reductions in concentrations between late November and February. On a practical level there have been comments that the photometers are certainly an improvement on test strips or equipment from other suppliers, but issues have been encountered in getting all the phosphate tablets to dissolve and the view has been expressed that many farmers would require a training session and on-farm support to get consistently reliable results.

136

Working with local Catchment Sensitive Farming officers, the Wensum DTC hopes to develop such training and gather further insights on experience during the next two years.

4.5 Supporting the Catchment-Based Approach Since the DTCs were established in 2009 there has been substantial promotion of the catchment-based approach (CaBA) and a growing prominence of river trusts. The DTC study catchments vary between those where river trusts are well-established (the Eden and Tamar) and those where such organisations are a much more recent development (the Avon and Wensum). Nevertheless, the growth in activity across all four catchments has been substantial and it has become apparent that there is an important role for the DTCs to support such organisations with scientific and technical expertise. Examples of such collaboration have occurred through the involvement of the DTCs in the Evidence and Impacts forum of the Saving Eden coalition and the Broadland Rivers Catchment Plan. This trend is expected to continue and it is likely that more future DTC knowledge exchange activities will be embedded amongst those of other organisations (e.g. participating in meetings or initiatives which they arrange) rather than stand-alone events. These partners will include river trusts, Catchment Sensitive Farming and other organisations involved in on-farm advice (e.g. agronomists, environmental NGOs and water companies).

4.6 The Baseline Farm Survey 4.6.1 Understanding Farmer Behaviour Extensive research has been carried out to determine the best agricultural practices for water pollution control (e.g. Deasy et al., 2010). However it is recognised that the implementation of such measures will only be effective with the co-operation of stakeholders. Whilst many agricultural management options remain voluntary, farmer participation is increasingly seen as a necessary ingredient for catchment management. As a consequence, there is a need for more information on the realistic farmer uptake and acceptability of different measures to enhance the potential for pollution mitigation.

As part of the DTC programme a survey was conducted to create a baseline regarding current agricultural practices and provide insights regarding farmer attitudes to the future adoption of other mitigation measures. The main objectives of the survey were to:

• Determine the nature of the farm businesses in the Avon, Eden and Wensum catchments. • Ascertain the current uptake of mitigation measures by farmers. • Investigate the attitudes of farmers towards future adoption of measures. • Discover which measures farmers prioritise for implementation. Opinions were obtained on 86 diffuse water pollution (DWPA) mitigation measures taken from the Defra User Guide (Newell-Price et al., 2011). This allowed the survey to provide a wider perspective on current farm practices and attitudes to mitigation measures than much previous research. It was appropriate to focus on the list of measures within the User Guide as this was the most relevant and comprehensive review for the UK. Full details of the survey and findings are given in the accompanying report by Vrain et al. (2014) with a summary provided below.

137

4.6.2 Implementing the Survey Eighty eight farmers in total were surveyed between February 2012 and February 2013 in the contrasting DTC catchments: the grassland dominated Eden catchment; the arable dominated Wensum catchment and the mixed farming of the Hampshire Avon catchment. There was a great variation in size amongst the surveyed farms, varying from relatively small livestock farms in the Eden to large arable farms in the Wensum. Overall 87% of farmers surveyed currently participate in Entry Level Stewardship (ELS) and 40% in Higher Level Stewardship (HLS).

4.6.3 Current Adoption of Mitigation Measures As might be anticipated, there was a wide variation in the extent to which the 86 measures were currently adopted. Particular contrasts were apparent between farm types and catchments. Since some measures were only relevant to a few of the surveyed farms (e.g. those keeping pigs or poultry), Figure 4.6 illustrates the current uptake only for those options which were applicable to at least 75% of participants.

Figure 4.6: Current uptake of DWPA mitigation measures applicable to ≥ 75% of survey participants

A number of comments can be made about the findings summarised in Figure 4.6:

138

• Measures with the highest uptake were concerned with fertiliser or manure management and most were part of cross compliance requirements for receipt of the CAP Single Farm Payment. • Measures compatible with current farm practice were more likely to have been adopted than those which required radical management or land use change. Mitigation measures involving land use change are some of the most effective at reducing pollutants and providing other ecosystem services but tend to be more expensive for farmers. • There was no obvious difference in uptake of measures according to whether they related to source minimisation, pathway reduction or receptor protection. This suggests that farmers had no particular preference regarding adoption of any of these categories of measure. • Some of the measures in Figure 4.6 are supported by agri-environmental schemes (AES) or other incentives, but a number of those towards the top of the list are not, therefore providing an insight into what is considered as general good farming practice. Examples include cultivating compacted tillage soils and maintaining field drainage systems. It is also important to recognise that what is regarded as the ‘norm’ is likely to vary between catchments. For instance, reduced tillage methods were relatively common amongst Wensum arable farmers, but not in the other two catchments. • No particularly strong trends were apparent between uptake of measures and whether they were currently supported within AES. Certain AES measures such as establishing riparian buffer strips were found to be quite widely adopted, whilst others (e.g. cover crops or in-field buffer strips) were not. • Several measures with known benefits (e.g. cover crops) were less widely used than might have been anticipated. There was a distinct reluctance towards covering manure stores with sheeting or growing biomass crops.

4.6.4 Attitudes to Future Adoption of Measures Knowledge of whether attitudes are more positively or negatively inclined towards future adoption can help inform the identification of appropriate policy mechanisms and the effort that may be required to encourage uptake. Survey participants currently not practicing a particular mitigation measure were asked how ‘likely’ they would be to adopt the measure in the future. Tables 4.6 and 4.7 summarise the attitudes of participants from the four main farming systems (arable, lowland livestock, dairy and mixed) towards future adoption of measures. The main trends are as follows:

• Measures particularly likely to be adopted in the future were those which decreased the use of fertiliser and fuel, therefore reducing costs. • Farmers in the survey were more negative towards future adoption of livestock and manure management measures than those concerning soil and fertiliser management. • Measures involving land use change were less likely to be adopted than those improve farm infrastructure. Farm infrastructure options may involve large commitments on behalf of the farmer. Despite this, several measures such as farm track management and re-siting gateways generally gained positive responses from farmers who had not already adopted them. When considering attitudes to land use change, the most likely to be adopted in the future was the establishment of woodland. • Many in-field measures which would require a change in the current timing of cultivation, crop rotation or farm management (e.g. establishment of cover crops) received generally negative responses for future adoption. However, one in-field measure - managing overwinter tramlines – was

139

rated more positively, thus indicating the potential for more farmers to be encouraged to adopt this measure. • Although uptake of riparian buffer strips was high amongst the arable farmers surveyed, those who currently did not have them were unlikely to introduce them in the future. This suggests a great deal of effort would be required to encourage adoption by the few remaining farmers. • Results indicated that the dairy farmers surveyed were keen improve their current manure management as many of the measures in this category had positive attitudes for future uptake.

140

Table 4.6: Summary of surveyed arable and lowland livestock farmers current uptake and attitudes to future adoption of DWPA mitigation measures

Medium to low uptake with Medium to low uptake with Medium to low uptake with High current uptake ( 75%) ≥ positive future attitudes mixed future attitudes negative future attitudes

 Cultivate and drill cross slope  Use fertiliser placement technologies  Establish permanent woodlands  Establish cover crops in Autumn  Establish riparian buffer strips  Re-site gateways  Use plants with improved nitrogen use  Loosen compacted soil layers in grassland fields  Early harvesting/establishment in Autumn  Manage over-winter tramlines efficiency  Grow biomass crops  Cultivate compacted tillage soils  Store solid manure heaps on concrete and collect  Reduce fertiliser applications rates effluent  Fertiliser spreader calibration  Cultivate land for crops in Spring rather than

 Adopt field heap storage of solid manure Autumn  Incorporate manure into the soil  Use clover in place of grass  Adopt reduced cultivation systems  Irrigate crops to achieve maximum yield Arable  Maintain field drainage systems  Replace urea fertiliser with another nitrogen form  Farm track management (e.g. ammonium)  Establish new hedges  Convert arable land to unfertilised grass  Leave Autumn seedbed rough  Cover solid manure stores with sheeting  Arable reversion to low fertiliser input extensive grazing  Establish and maintain artificial wetlands

 Reduce field stocking rates if soils are wet  Re-site gateways  Establish new hedges  Manure spreader calibration

 Adopt field heap storage of solid manure  Move feeders at regular intervals  Establish permanent woodlands  Cover solid manure stores with sheeting  Farm track management  Construct troughs with a firm but  Establish and maintain artificial wetlands permeable base  Grow biomass crops  Fence off rivers and streams  Reduce overall stocking rates  Compost solid manure  Store solid manure heaps on concrete and collect effluent  Construct bridges for livestock

 Establish tree shelter belts around livestock housing Lowland Livestock Lowland and slurry storage

141

Table 4.7: Summary of surveyed dairy and mixed farmers current uptake and attitudes to future adoption of DWPA mitigation measures

Medium to low uptake with Medium to low uptake with Medium to low uptake with

High current uptake (≥ 75%)

positive future attitudes mixed future attitudes negative future attitudes

 Reduce field stocking rates if soils are wet  Use anaerobic digestion for farm manures  Cover solid manure stores with sheeting  Allow field drainage systems to  Maintain field drainage systems  Reduce fertiliser applications rates  Establish tree shelter belts around livestock deteriorate  Fertiliser spreader calibration  Minimise volume of dirty water and slurry produced housing and slurry storage  Grow biomass crops  Construct bridges for livestock  Transport manure to neighbouring farms  Establish permanent woodlands  Use fertiliser placement technologies  Establish & maintain artificial wetlands  Out-wintering of cattle on woodchip  Install covers on slurry stores  Manure spreader calibration stand-off pads  Use slurry injection application techniques  Establish riparian buffer strips  Reduce length of grazing day/grazing  Additional targeted straw-bedding for cattle housing  Compost solid manure season  Fence off rivers and streams  Reduce overall stocking rates  Adopt reduced cultivation systems  Construct troughs with a firm but permeable base

 Store solid manure heaps on concrete & collect effluent  Re-site gateways

 Use clover in place of grass Dairy  Increase the capacity of slurry stores  Use nitrification inhibitors  Reduce dietary N and P intakes  Establish new hedges  Farm track management  Loosen compacted soil layers in grassland fields  Cultivate compacted tillage soils  Make use of improved genetic resources  Use plants with improved nitrogen use efficiency  Ditch management  Incorporate manure into the soil

 Cultivate land for crops in Spring rather  Adopt reduced cultivation systems  Move feeders at regular intervals  Grow biomass crops than Autumn  Use plants with improved nitrogen use efficiency  Manage over-winter tramlines  Arable reversion to low fertiliser input  Cultivate and drill across slope  Make use of improved genetic resources  Reduce fertiliser applications rates extensive grazing

 Incorporate manure into the soil  Establish new hedges  Establish tree shelter belts around livestock  Establish and maintain artificial wetlands  Farm track management  Maintain field drainage systems housing and slurry storage  Reduce length of grazing day/grazing  Fertiliser spreader calibration  Establish cover crops in Autumn  Establish permanent woodlands season  Reduce field stocking rates if soils are wet  Use fertiliser placement technologies  Fence off rivers and streams  Convert arable land to unfertilised grass  Cultivate compacted tillage soils  Manure spreader calibration  Store solid manure heaps on concrete &  Adopt field heap storage of solid manure  Establish riparian buffer strips collect effluent

Mixed Farming Mixed  Loosen compacted soil layers in grassland fields  Use clover in place of grass  Re-site gateways  Cover solid manure stores with sheeting  Compost solid manure  Reduce overall stocking rates  Early harvesting/establishment in Autumn

142

4.6.5 Farmer Priority Measures To gain further insight into farmer attitudes towards DWPA mitigation, participants were asked to prioritise three measures they would like to implement within their farm business. Sixty-five farmers responded, each listing between 0 and 3 measures (22% of farmers identified no priorities). These answers have been categorised in Figure 4.7 by a) management type and b) location of measures. Nearly two-thirds of the priorities involve changing farm infrastructure, particularly additional concrete areas. A variety of uses were identified, including concrete for manure heaps, diverting dirty water and track repair. The manure and fertiliser management priorities included better timing and application efficiency, as well as storage cover. It is clear from Figure 4.7b that the location of priorities had a significant bias towards in farmyards, with in- field and field boundary measures being less common.

Figure 4.7: Numbers of priority measures categorised by a) management type and b) location

4.7 Farm Diaries Some of the farmers we work with in the Eden were asked to keep a diary of activities to improve our spatial understanding of the catchments. There has been varying success with the farm diary concept, as some farmers have kept useful details of activities while others have not had the time to fill them in. The most helpful information they have provided is the location and dates of activities such as slurry and fertiliser applications.

With the more widespread use of smart phones we are now starting to get images sent to us of mitigation features and storm events from farmers within the EdenDTC. It may be helpful to formalise this in phase 2 to help record flow pathways and other landscape functions/reactions.

There have been five farms in the Eden (two in Morland, two in Pow and one in Dacre) involved with the farm diary process. Combined with the work performed on nutrient losses with the CSF program there is scope to use the farm diary system as a means to target specific ‘events’ within the data.

143

4.8 Assessment of Progress and Future Priorities The activities summarised in this section of the phase 1 report indicate that the DTC consortia have been successful in establishing networks of local contacts, disseminating information and, as results from the implementation of mitigation measures emerge, moving towards deeper forms of knowledge exchange and participation in local communities of practice. There have also been changes in the organisational settings in which the DTCs operate, particularly in terms of the growing profile of river trusts and catchment partnerships. This has been reflected in some realignment of DTC activities to profile scientific support to local implementation of the catchment-based approach and an increased emphasis on working through other organisations than arranging stand-alone events.

Undertaking the baseline survey of current uptake and attitudes towards future adoption of mitigation measures provided a wider perspective on current practices and opinions than previously existed for England. Insights from the survey have already been discussed with staff involved in the Catchment Sensitive Farming initiative and Defra policy teams to help support their work.

4.8.1 Future Priorities The nature of the baseline survey meant that there was limited opportunity to investigate why certain measures had been adopted or particular attitudes existed. Studying the role of different mechanisms, such as various sources of advice delivery, would also help inform decisions as to where policy initiatives should be focused. These issues are a focus of ongoing DTC research (Vrain, 2014a; 2014b) and will be reported in greater detail in due course. Other local knowledge exchange worked planned as part of phase 2 of the DTCs (2015-17) will investigate:

• Definitions of ‘good agricultural practice’ in terms of the types of diffuse pollution mitigation measures adopted by the main farming systems in each catchment and an assessment of the degree to which they are currently being achieved. • The extent to which farmer collaboration with the DTC consortia has influenced their practices and attitudes compared to other members of the local farming communities. • Farmer attitudes, motivations and constraints regarding diffuse pollution mitigation measures and the implications for policy makers and advisors in the design and implementation of regulatory and incentive schemes. • The governance arrangements needed for successful implementation of the catchment-based approach. More generally, there will be a need to enhance the nature of the knowledge exchange achieved. This will focus on further developing the Community of Practice concept in each DTC. To be successful, such a network requires mutual understanding, recognition, and common purpose across a range of stakeholders and individuals as a prerequisite for change. Crucially, it requires translation of science outputs into tools (techniques, management systems, farm business models etc.) that have direct applicability in the farmed landscape. As more findings emerge from the implementation of mitigation measures, the DTCs will be in a stronger position to stimulate such discussion of experiences and to take advantage of the opportunities created. These aspirations will also benefit from collaboration with the new Defra Sustainable Intensification Platform, since the landscape-scale element of this programme aligns with the DTC catchments and discussions are now underway to plan a number of joint activities. A second broad priority will be to support the ongoing development of the catchment-based approach in England. The individual DTCs are already well-placed to provide scientific input on a local and regional level, 144 but it is also apparent that many river trusts and catchment partnerships will need substantially more in the way of financial resources to fully implement their plans. There may be a role for the DTCs in helping to build broader coalitions with other funders (e.g. water companies), as well as contributing to the wider dissemination of insights and experience from the DTC catchments to other parts of the country.

5 Monitoring and Evaluation of On-Farm Interventions for Informing Water Quality Policy

5.1 Introduction This part of the phase 1 report reminds us of both general and more specific challenges driving the DTC programme. It then discusses our general approach to iterative catchment scale science, before presenting a ‘weight-of-evidence’ science toolkit for detecting change in biophysical and socio-economic end points and specifically for decomposing the impacts of on-farm interventions on water quality and aquatic ecology, in the context of natural variability and observational uncertainties. A range of methods for assessing change in hydrochemical time series and aquatic ecology is briefly discussed, before readers are reminded of potential confounding factors using the DTC experimental design for such analyses at landscape scale. Finally, some case studies are presented to illustrate how DTC has started to address the need for an explicit framework for uncertainty analyses and some of the additional confounding factors in phase 1.

5.2 The General Challenge Driving DTC Whilst a large body of plot and field scale research exists on the key mobilisation processes, behaviour, and mitigation of diffuse water pollution from agriculture, significant outstanding evidence gaps remain in placing this evidence into a catchment context to inform evolving policy. Interdisciplinary, catchment-scale research to investigate the behaviour and impacts of major agricultural pollutants (nutrients, sediment, microbes and pesticides) and the effectiveness of targeted mitigation strategies over long timeframes requires new integrated research approaches. Robust evidence of the cost-effectiveness of measures included in regulatory, voluntary or incentive schemes is needed both to ensure that policies are proportionate and effective, and to engender support and uptake from the farmers and other stakeholders who will ultimately need to adopt and sustain them. Against this context, the DTC programme was established to bring together researchers from different disciplines and institutions to address the gap in empirical evidence on the cost-effectiveness of combinations of agricultural diffuse pollution mitigation measures at sub-catchment scale.

To close the evidence gaps for water quality policy, research is needed to synthesize existing strands of knowledge, integrating the physical, ecological and human aspects of catchment systems. This requires integration of targeted reductionist plot scale experimentation and more holistic, multi-disciplinary sub- catchment to catchment-scale investigations. Integrated studies need to take account of multiple, cross- sector pollutant sources, pathways and impacts (cf. D’Arcy and Frost, 2001; Collins and Walling, 2004; Haygarth et al., 2005, Hewett et al., 2009; Winter et al., 2011; Collins et al., 2014). This integrative research also needs to combine data on biophysical processes with the socio-economic and ethical dimensions of catchment management (cf. Lankester et al., 2009; Spash et al., 2009; Friberg, 2010b; Palmer et al., 2010). Long-term datasets are required to provide robust appraisals of environmental state and responses to interventions (Harremoes et al., 2002; Parr et al., 2003).

145

The lack of robust consideration of spatial and temporal scale complexities, together with study design (e.g. lack of robust pre- and post- data), are key problems for most diffuse pollution management and river restoration projects (e.g. Bernhardt et al., 2005; Clews and Ormerod, 2010). As a result, the detection of positive outcomes post intervention appears to be equivocal (Palmer et al., 2010) and this raises challenges for keeping stakeholders, including farmers, engaged with implementing best practice.

5.2.1 The Specific Challenge Driving DTC The overarching objective of the DTC programme is to test the hypothesis that it is possible to reduce cost- effectively the impact of agriculture on water quality and aquatic ecology whilst sustaining a competitive farming industry. This means that developing methods for assessing the effectiveness of on-farm interventions for diffuse pollution control, taking explicit account of uncertainties, are core to the DTC programme of work. Developing a general framework for uncertainty evaluation is the only way to benchmark effectively the quality of data from different sub-catchments and with differing sampling resolutions. Without this, robust procedures cannot be implemented that can separately identify the relative efficacy of different on-farm mitigation measures implemented in each DTC from changes which are attributable to observational and laboratory uncertainties and natural variability (e.g. hydrological) within each target sub-catchment (Beck, 1987; Alexander, 1993; Beven and Alcock, 2012; Page et al., 2012; McMillan et al., 2012). Although modelling can be used as an approach to predict the technically benefits of on-farm interventions, uncertainty analysis is again important given issues including uncertain input data and outputs potentially confounded by equifinality (Arhonditsis et al., 2008; Dean et al., 2009).

5.2.2 A Twin-Track Approach to Iterative Science A twin-track approach (Figure 5.1) is being used in the DTC programme to improve our understanding of diffuse agricultural pollution and the effectiveness of on-farm interventions. The first strand of this approach consists of targeted experimentation and observation for monitoring changes in water quality and aquatic ecology against a baseline used to inform the establishment of on-farm mitigation measures. Water quality data collected at nested spatial scales and farm practice data will be analysed using statistical approaches to assess the effectiveness of pollution mitigation measures at field to farm to sub- catchment/catchment scales. The second strand is an iterative conceptual/process-based modelling approach to interpret and extrapolate emerging experimental and observational data for policy support. The conceptual/process-based models are, in turn, tested against observational data at a variety of scales and iteratively adapted.

146

Figure 5.1: A twin-track approach to iterative catchment science designed to target and assess the environmental outcomes of on-farm interventions for diffuse pollution control

5.2.3 Developing a ‘Catchment Science Toolkit’ An iterative ‘weight of evidence’ approach is being developed by DTC to assist the decomposition of effects of on-farm interventions. This involves gathering layers of evidence, gradually improving conceptual models of how the monitored systems function and where remedial actions need to be targeted to optimise environmental benefits. Multiple approaches and techniques are used in combination as a ‘catchment science toolkit’ to build up layers of evidence over time to help detect change (Figure 5.2 and Table 5.1).

147

• Farmer attitudes to best management >1 year (short- • Uptake of on-farm measures term)

• Reductions at source (recording practices and identifying risks) • Reduced mobilisation (soil surface monitoring) 5 years • Reductions in pathways (monitoring at edge-of- (medium term) field, farm scale) • Economic and agronomic performance

• Hydrochemical monitoring at sub-catchment outlets 10+ years • Ecological monitoring at sub-catchment outlets (long term)

Figure 5.2: Timelines of potential outcomes or responses for detecting change following the targeted implementation of on-farm interventions for the benefit of water quality and aquatic ecology

It is envisaged that the ‘catchment science toolkit’ will be used to answer both key scientific as well as operational questions, with some examples including: Key questions driving the development of the ‘catchment science toolkit’ • How do we best monitor and characterise degraded sub-catchment systems to inform the targeted implementation of on-farm pollution control interventions? • How do we best design combinations of the on-farm measures to be targeted? • Are on-farm mitigation options being targeted at sufficient scale and intensity across landscapes? • Are the most useful on-farm mitigation options being selected for inclusion in policy instruments for protecting the aquatic environment? • How do we best influence land managers to take up the most effective on-farm measures? • What is the timeframe within which, or even can we achieve environmental targets for water quality and ecological status using on-farm interventions? • How do we best sustain any environmental benefits that manifest from targeted on-farm intervention strategies? • How do we best detect changes through time in response to targeted on-farm interventions to report outcomes for keeping stakeholders engaged with the need for better practice?

148

Table 5.1: Examples of approaches that can be used in combination to form a ‘catchment science toolkit’ to monitor changes in water quality and aquatic ecology following targeted on-farm pollution mitigation interventions (Revised from McGonigle et al., 2014)

Pollutant Approaches: Timescale to cascade measure changes component Source - Repeat visual assessment and mapping Short - Repeat farm practice surveys " - Repeat farm-gate nutrient balances " Mobilisation - Repeat visual assessment and mapping Short - Soil risk mapping approaches " - Repeat soil dispersion tests " - Monitoring soil pore water (e.g. using porous pots) or Medium soil mineral nitrogen (SMN) - Repeat soil shear strength tests " - Hill slope run-off studies " - Tracer studies (e.g. magnetic/ fluorescent/ rare earth) "

Delivery - Repeat visual assessment and mapping Short - Repeat field / measure scale water quality sampling Medium - Monitoring field drains " Impact - Repeat visual assessment and habitat assessment Short - Tracer studies (e.g. magnetic/ fluorescent/ rare earth) Medium - Repeat source apportionment (sediment/ nutrient / DOM fingerprinting) " - Microbial source tracking " - Sub-catchment scale water quality monitoring Long - Ecological monitoring (community and functional " metrics) - Groundwater monitoring and modelling "

The final question above represents a key driver for the development of the ‘catchment science toolkit’ as understanding and communicating the causes and likely duration of time lags in water quality and ecological response to targeted on-farm interventions is an important stakeholder relations challenge (Reckhow et al., 2011). Recognition, involvement and engagement of stakeholders are critical in developing iterative catchment management and can be used as a metric of success per se (Bergfur et al., 2012). The visual establishment of interventions, especially where supported by robust objective data on performance (biophysical and socio-economic outcomes), can lend enormous support to the voluntary expansion of interventions across impaired catchments.

5.2.4 Lumped Descriptive Metrics for Characterising and Comparing Hydrochemical Response A range of lumped descriptive metrics (e.g. ASCE Task Committee, 1970; Jordan et al., 2005, 2012; Meybeck and Moatar, 2012; Moatar et al., 2013; Shore et al., 2014; Meybeck and Moatar, 2014) can be used to characterise and compare hydrochemical response simply and to monitor potential change through time

149

(Table 5.2). Some of these are dimensionless and provide a basis for comparison of multiple sites (paired or grouped).

Table 5.2: Descriptive metrics for characterising and comparing hydrochemical response

Metric type Metric Comment Flow and pollutant export Annual export (% annual loss) versus Used as a pollutant transfer risk metric Q5:Q95 embodying the propensity for landscapes to convert rainfall to runoff and to deliver pollution to the stream system Upper quartile as a % of the total Quick flow in response to rain Upper 5th percentile as a % of the total Peak flows Time window export (% annual loss; closed A pollutant transfer risk metric embodying periods, ecological windows including fish the propensity for landscapes to convert spawning or macrophyte growth seasons) rainfall to runoff and to deliver pollution to versus Q5:Q95 the stream system during legislative or ecologically relevant windows

Flow and pollutant transport regime (flux M2% Mass of pollutant exported in 2% time variability indicators)

W2% Cumulative flow volume exported during the upper 2% daily flow (hydrological reactivity)

B50SUP Truncated exponent for the concentration- discharge relationship where + indicates concentrating behaviour and – indicates diluting behaviour

Dimensionless inter-station general Qmean / Q50 For discharge temporal variability indicators

Cmean / C50 For pollutant concentrations

Yieldmean / yield50 For pollutant yields

Dimensionless inter-station extreme Q99 / Q50 For discharge variability indicators

C99 / C50 For pollutant concentrations

Yield99 / yield50 For pollutant yields

5.2.5 Detecting Change with Conventional Hydrochemical Time Series Data The core of the DTC experimental design is a Before-After, Control-Intervention (BACI) approach (cf. Stewart-Oaten et al., 1986; Smith, 1993, 2002) that monitors changes in sub-catchment response following the implementation of on-farm pollution mitigation measures over time and against “business as usual” control areas. BACI designs are commonly used to monitor potential environmental impacts arising from management interventions. BACI experiments are good for detecting:

a) Significant potential changes post the implementation of interventions b) Whether those changes are sustained BACI experiments are less robust for detecting: a) Small potential changes post the implementation of interventions b) Gradual changes c) The potential for changes other than at the experimental sites and years monitored.

Scientific studies based on a BACI design have been used to quantify changes in water quality at scales ranging from sub-field (Clausen et al., 1996) to multiple land use large catchments (Meals, 2001). Where BACI has been used to assess the environmental impact of on-farm mitigation measures, the norm has been to undertake between-stream comparisons (i.e. to compare areas with and without increased uptake

150 of interventions) rather than assessing changes at specific sites pre- and post- measure implementation (Quinn et al., 1997; Rhodes et al., 2007).

The DTC BACI experiments are underpinned by semi-automated, web-enabled/manual monitoring networks to collect water quality at high temporal resolution and different scales. Critically, these data are being interpreted alongside aquatic ecological surveys, farm practice and socio-economic information to detect changes in pollutant sources/mobilisation, delivery and impact at field to farm to sub-catchment scales.

Ultimately, BACI experiments aim to assess the effect size which is a measure of the magnitude of the impact of the on-farm interventions. Effect sizes can be assessed using annual or seasonal models (Michener, 1997; Bishop et al., 2005; Schilling and Spooner, 2006; Lemke et al., 2011; Bergfur et al., 2012) and a variety of metrics including: a) The mean difference (treatment – control) b) The standardized mean difference (treatment – control) c) Log ratio (treatment to control)

The latter two options are dimensionless, but the standardized mean difference is sensitive to low sample numbers as these impact on the accuracy of the STD estimate of variance. Covariance of data also needs to be taken into consideration e.g. using ANCOVA (USEPA, 1993, 1997a, b; Bishop et al., 2005). Effect sizes need to be evaluated in the context of statistical power and the so-called minimum detectable treatment effect (MDTE; Galeone, 1999; Bishop et al., 2005; Schilling et al., 2014). The work of Schilling et al. (2014) reported a MDTE range of 7-13% for nitrate concentrations, with an average of 8%.

Establishing a correlation between treatment and control sub-catchments is a fundamental requirement of that specific version of BACI. A minimum correlation of 0.6 is advisable for most response variables (Loftis et al., 2001; King et al., 2008; Spooner, 2011; Schilling et al., 2014). A correlation of >0.6 permits an evaluation of the statistically significant change necessary to determine an effect in the context of background variability.

Change in monitored water quality data might be abrupt or more gradual and different methods need to be employed to isolate the types of response which might occur within complex hydrochemical datasets (Lloyd et al., 2014). These methods include parametric and nonparametric tests (Table 5.3).

The most important thing when detecting change in response to a specific stimulus (e.g. on-farm interventions for pollution control) is to have a robust estimation of the natural variability in the system, without which it is impossible to truly determine whether state change has taken place. The sampling resolution should be fine enough to capture the dominant processes the programme aims to encompass, without being finer than needed as this can lead to wasted resource and will increase problems associated with autocorrelation. Against this background, Lloyd et al. (2014) proposed a generic framework for change detection analysis using hydrochemical datasets (Figure 5.3).

151

Table 5.3: Examples of parametric and nonparametric approaches which can be used for analysing hydrochemical time series (adapted from Lloyd et al., 2014)

Method Description Advantages Assumptions Parametric t-test Abrupt changes in Simple to implement Normal data, no sample means autocorrelation, no underlying trend ANOVA Abrupt changes in the Simple to implement; Data should be normally means of three or more allows the comparison distributed, no independent sample of multiple groups of autocorrelation, no groups data underlying trend ANCOVA Blend between ANOVA Allows control over Normality of residuals, and regression other continuous homogeneity of variance variables and regression slopes, variables should be linearly related and the errors should be independent Nonparametric Pettitt method Detects change in Change point can be Data should not have an median when exact unknown underlying trend; deals change point is unknown with only one change point Lepage test Combination of Mann- Works even if the Assumes observations Whitney and Ansari- distribution of a sample are independent Bradley statistics is unknown; shown to be statistically more powerful than other similar non-parametric tests Kruskal-Wallis H-test Compares the median of Extension of Mann- Assumes data are two or more samples Whitney for 2 or more independent; samples (non-parametric AVOVA) samples; less sensitive to should have >5 data outliers; extensions points; subsamples available to determine should have identical differences between data scales and individual sample pairs distribution shapes

152

Figure 5.3: A framework for change detection analysis using hydrochemical data. The reference to methods table refers to Table 2 (Lloyd et al., 2014)

5.2.6 Detecting Change with Nutrient Speciation and Fractionation Data The analysis of samples for full N speciation and P fractionation provides a basis for identifying the most likely contributing sources in each DTC sub-catchment, and in particular to discriminate between fresh nutrient fluxes to each stream from livestock manures (organic N and P fractions), soil erosion and sediment delivery (particulate N and P fractions) and from inorganic fertiliser applications to improved grass and crops. Repeat analyses of such data for the pre- and post-treatment time periods permits an assessment of changes in key nutrient sources. In turn, the latter can be evaluated in the context of the key sources targeted by specific interventions in a given sub-catchment. This approach underscores the utility of performing full speciation and fractionation for nutrients and avoids the risk of missing detectable change following on-farm interventions by focussing on specific sub-fractions (Figure 5.4).

153

Artificial fertilisers

Farm manures /slurries

Erosion and sediment delivery

Artificial fertilisers

Farm manures/slurries, septic tanks

Erosion and sediment delivery

Figure 5.4: Example pie charts showing fractionation of N and P and corresponding key nutrient sources over a period of monitoring during phase 1 on the Priors Farm sub-catchment in the Avon DTC

5.2.7 Detecting Change using Hysteretic Loops Hysteresis loops summarise the relationships between hydrographs and chemographs evident in sub-daily resolution records for individual storm events. If the hysteresis loop is clockwise through time, this is likely to represent a rapid flushing of nutrients or sediment, as the concentration responds very rapidly after flow increases in the channel. Such loops are interpreted as reflecting mobilisation and delivery from near or in- channel sources. On the other hand, if there is a lag between increasing flow and an increase in concentration, represented by an anticlockwise loop, this typically suggests mobilisation and delivery from more distal sources. The differing patterns of behaviour can therefore be used to infer the location and connectivity of pollutant source areas and on this basis, changes in the direction of the loops can be assessed through time as an elementary method of detecting response to targeted mitigation. Examples of hysteretic loops from phase 1 are illustrated in Figure 5.5.

In addition to assessing hysteresis loops in a qualitative way, metrics can be calculated to quantify their primary characteristics. For example, the H-index (modified from Lawler et al. 2006) is a measure based on the ratio between paired values on the rising and falling limb at various points in the hydrograph. A positive H-value represents clockwise hysteresis and a negative value anticlockwise behaviour. Additionally, the wider the hysteresis loop the larger the value of the H-index. As an example from phase 1 (Figure 5.6), data for storms sampled in both a chalk (Brixton Deverill) and clay (Priors Farm) sub-catchment of the Hampshire Avon shows how the H-index varied through time and between the sub-catchments.

154

Figure 5.5: Concentration-discharge plots showing TP hysteresis during storm events in (a-b) the Hampshire Avon Wylye, (c-d) Wensum Blackwater and (e) Eden Newby Beck sub-catchments

155

Figure 5.6: H-index values for turbidity hysteresis loops through time in phase 1 for storms at Brixton Deverill (River Wylye sub-catchment; chalk) and Priors Farm (River Sem; clay) in the Avon DTC

5.2.8 Detecting Change with Structural and Functional Aquatic Ecology Data To assess the ecological condition of a site, structural measures of aquatic ecology (i.e. based on community composition) are compared to a reference condition (i.e. what would be expected if the site were not subjected to any stress) to produce an Environmental Quality Ratio (EQR). This EQR is derived from observed/expected scores, ranges from 0 to 1.

Although the reference condition approach allows comparison across different ecological community types, any assessment is obtained by sampling/surveying one or more biological quality elements at one or more spatial locations within the water body at one or more points in time during the period for which the assessment is intended to apply. The results of all bioassessment techniques are influenced by variation in the observed fauna, which affects the certainty of the estimate of quality. By dividing assessment quality into classes (as stipulated by the Water Framework Directive), there is a possibility that sites will be misclassified, and the probability of misclassification occurring is influenced by how much variation there is in the observed biota relative to the width of the classes (Figure 5.7). This variation is attributable to:

1. Sampling variation due to random or systematic differences between samples collected 2. Sample processing errors caused by sub-sampling, sorting and identification 3. ‘Natural’ temporal variation 4. The effects of remediation.

The probability of misclassification is particularly important at the good/moderate status boundary: the aim of remediation is to achieve the WFD target of good ecological status where sites are currently failing.

156

Probability of Probability Misclassification

Figure 5.7: Effect of uncertainty (standard deviation of potential returned values as a percentage of quality class width) in EQR on the probability of misclassification of ecological quality class for any returned EQR value. Note that greater uncertainty leads to a higher probability of misclassification, and that the probability of misclassification increases as values approach a status class boundary

To determine the influence of variation attributable to sampling, processing and natural temporal differences (1, 2, and 3 above) relative to the response to remediation (4 above) multiple samples have been collected from each location in the DTC target sub-catchments. The relationships between factors influencing observed temporal variation (e.g. variation in discharge) and EQR have been established and the remaining unattributed variance quantified.

Detecting change with functional measures is more difficult as “reference condition” values have not been established for these novel techniques. We are relying on a BACI design with increased replication of measures of ecological process rates (corrected for variation in temperature using degree days) within each site and occasion to provide robust pre- and post-implementation data. The BACI approach is being used to study the effect of land management on water quality and the consequences for in-stream ecosystem function in the two headwater clay sub-catchments at Priors Farm and Cools Cottage, tributaries of the River Sem, in the Avon DTC. Using a baseline questionnaire, weekly field observations and discussions with landowners and farmers, the two sub-catchments were identified as having distinct management practices. The Priors Farm sub-catchment is characterised as predominantly extensive dairy farming on improved pasture fertilised by inorganic N and P, and with manure management dominated by slurry storage and spreading. The Cools Cottage sub-catchment is characterised as a mixture of intensive dairy farming using the ‘strip grazing’ model of New Zealand, limited arable cultivation and organic beef farming where manure is managed as solid waste.

157

The ecosystem processes we have measured are detritivory (invertebrate and microbial), through breakdown of leaf litter, and herbivory, through grazing on algae. We have also measured primary production and respiration, using changes in dissolved oxygen concentration in light and dark chambers over 24 hours.

Invertebrate detritivory appears suppressed in the Priors Farm sub-catchment compared with Cools Cottage (Figure 5.8). There was a parallel but smaller difference in microbial detritivory. Differences in the invertebrate community structure as a consequence of the pulses of high ammonia and low dissolved oxygen are likely to have contributed to the lower leaf litter processing rates in the Priors Farm sub- catchment.

In situ incubations to assess community primary production and respiration, using changes in dissolved oxygen concentration in light and dark chambers over 24 hours, show consistently higher respiration at Priors Farm, at both the upstream and downstream experimental sites used for this work, when compared with Cools Cottage. By contrast, only the downstream experimental site at Priors Farm shows significant primary production using this method.

Figure 5.8: Seasonal changes in invertebrate and microbial detritivory in the River Sem target sub- catchments of the Avon DTC

5.2.9 In Situ Monitoring of Benthic Communities. DTC phase 1 required the development of methods that can be used to detect changes in ecological communities following mitigation. Here we report the ability to rapidly measure the biomass and composition of biofilms. Benthic biofilms within freshwaters are central to energy, matter and nutrient cycling and are shaped by the chemical, ecological and physical conditions within stream ecosystems. Through the EdenDTC platform, the potential contribution of an in situ fluorometry (ISF) technique, the BenthoTorch© spectrofluorometer, to understanding the spatial and temporal heterogeneity in benthic biofilm communities was investigated. This handheld fluorometer allows for an immediate and cost- effective in-field assessment of benthic chlorophyll-a concentrations within what are hypothesised to be

158 diatom, cyanobacterial and green algal groups, based on their distinct spectral responses. Here we present two exemplar applications which show that ISF generates estimates of total benthic chlorophyll-a that differ significantly compared to traditional approaches, and can resolve differences in benthic chlorophyll-a between physical biotopes in streams. Seasonal dynamics in benthic composition and biomass revealed through ISF are presented in Snell et al., 2014. Understanding of algal community development through ISF in theory provides a new dimension to ecological assessment, accompanying traditional diatom counts and compositional surveys of wider algae assemblage. However, while ISF offers new opportunities to analyse benthic biofilm communities and provide significant advances in benthic research and assessment, further research is required to fully evaluate ISF, in particular to understand the accuracy of the fluorescence signal according to biofilm characteristics and the need for site-dependant calibration.

Case study 1: Catchment-scale differences in annual average chlorophyll-a concentration Annual average chlorophyll-a varied across the individual Eden sub-catchments, from 5.4-2.3 µg/cm2 based on ISF, and from 3.5-1.6 µg/cm2 using laboratory-based determinations of chlorophyll-a over the hydrological year 2011-12 (Figure 5.9). Laboratory-determined mean annual chlorophyll-a across the three sub-catchments ranged from 20-29% of that determined by ISF. This likely reflects incomplete removal of benthic chlorophyll-a by the cobble-scraping technique used for traditional ex situ biomass assessments. For example, Carpentier et al. (2013) recently demonstrated that an average of only 54% of ISF-determined benthic chlorophyll-a was removed from cobbles using a similar scraping technique to that applied in this research (range = 6-88%, n = 8). It is also important to note that the relative differences in median chlorophyll-a concentration between the three sub-catchments of the Eden (Morland>Dacre>Pow) were consistent for both ISF- and laboratory-based techniques, with no significant difference in chlorophyll-a concentration observed between the Dacre and Pow catchments. This suggests that the most productive catchments may not always support the highest biomass concentrations. Other factors, such as high bed sedimentation, can prove more important than nutrients in determining biomass concentrations (Snell et al., in prep). It is, therefore, important that chlorophyll-a concentrations are interpreted in the context of multiple site specific physical and chemical drivers at appropriate temporal resolution.

4 ISF Chla Lab Chla

3 Chlorophyll(µg/cm²) a

0 Morland Outlet Dacre Outlet Pow Outlet

Figure 5.9: Comparison of ISF-determined chlorophyll-a (‘ISF Chla’; n = 36 for each catchment) and laboratory-determined chlorophyll-a corrected for phaeophytin (‘Lab Chla’; n = 12 for each catchment) across three ~10 km2 sub-catchments of the River Eden for the hydrological year 2011-12. Mean values from all samples given by columns, error bars show ± one standard deviation 159

Case study 2: Reach-scale variation in chlorophyll-a concentration associated with physical biotopes Significant spatial patchiness in chlorophyll-a was revealed both within and among hydraulically-defined biotopes (riffles and pools) using ISF (Figure 5.10), a finding that was not observed in chlorophyll-a data derived from traditional laboratory techniques. A mean increase in water depth of 19.2 cm was observed when moving from riffle to pool units, suggesting that significantly higher biomass concentrations were observed in riffles compared to deeper pool area. This highlights the sensitivity of ISF determined chlorophyll-a concentrations to differences in microhabitat characteristics, such as water depth, and is consistent with previous research focused on reach-scale controls on benthic chlorophyll-a concentration (e.g. Cardinale et al., 2002). While the reach-scale currently remains the predominant scale of assessment in river ecology, the evaluation of ISF reported here suggests that system assessments based on reach-scale sampling networks and using infrequent sample collection can lead to bias. However, the ability of the ISF technique, using the BenthoTorch© spectrofluorometer, to enhance the spatial and temporal intensity of monitoring and capturing natural and anthropogenic driven changes in benthic algal communities, while at the same time facilitating more rapid field-based evaluations of community structure and distribution, should better inform instream monitoring and assessment strategies required under current legislation. 35 7 A

30 6

25 5

)

20 4 Water depth

µg/cm²

ISF chla a ( a -

15 3 Water depth(cm) Water Chlorophyll 10 2

5 1

0 0 Riffle Pool 35 Riffle Pool 7

B 30 6

25 5 ) 20 4 Water depth

µg/cm² Lab chla

a ( a -

15 3

depth(cm) Water Chlorophyll

10 2

5 1

0 0 Riffle Pool Figure 5.10: Water depthRiffle and benthic chlorophyllPool -a concentration from two riffle (n = 18) and two pool (n = 18) units at the outlet of the Morland sub-catchment. A: based on chlorophyll-a concentrations determined using the ISF technique, and B: based on chlorophyll-a concentrations determined using laboratory techniques

160

5.3 Potential Confounding Factors for Detecting Change with Conventional Water Quality and Aquatic Ecology Data Detection of positive environmental outcomes for water quality and aquatic ecology following the implementation of on-farm interventions for pollution control can be hampered or confounded by a range of factors (Meals, 2010; Jarvie et al., 2013; Table 5.4), including:

a) Sampling methodologies for water quality and aquatic ecology (Meals, 2010) b) Inter-annual variability in weather and runoff (Longabucco and Rafferty, 1998) c) The spatial heterogeneity of landscapes and combinations of pollutant mobilisation, transport and delivery processes therein (Sharpley et al., 2009) d) Unexpected changes in cropping or on-farm activities (Boesch et al., 2001) e) Inadequate targeting and intensity of measures (Meals, 1990; Sharpley et al., 2009) f) Inadequate pre- and post-intervention monitoring (Kyllmar et al., 2006; Palmer et al., 2007) g) Lagged and complex ecological responses (e.g. Table 3) due to physical, chemical and biological multiple stressors and interactions/feedbacks (Cardinale et al., 2012) h) Inputs from multiple (e.g. non-agricultural) pollutant sources (Jordan et al., 2005, 2007, 2012; Sharpley et al., 2009; Collins et al., 2014) i) Biogeochemical buffering, hydrological damping and internal catchment residence and remobilisation dynamics generating legacy effects including those associated with terrestrial P, river P and standing water P (Kirchner et al., 2000; Kleinman et al., 2011; Haygarth et al., 2012; Hamilton, 2012; Spears et al., 2012; Sharpley et al., 2013) j) Connectivity pathway dynamics (Scanlon et al., 2005; Jordan et al., 2012) k) Site–specificity of mitigation option reduction efficiencies (Sharpley et al., 2009).

Table 5.4: Examples of lag times reported in response to environmental impact or treatment (from Meals et al., 2010)

Pollutant Scale Impact/treatment Response lag Reference (years) Sediment River basin Land >50 Clark and clearing/agriculture Wilcock (2000) Sediment Large Cropland erosion 19 Newson watershed control (2007)

NO3-N Large Nutrient management ≥5 STAC (2005) watersheds

NO3-N Small Nutrient management 5-39 Galeone watershed (2005)

NO3-N Small Grassland restoration 10 Shilling and watershed Spooner (2006)

NO3-N Small Riparian forest buffer 10 Newbold et al. watershed (2008) Macroinvertebrates Small Livestock exclusion 3 Meals (201) watersheds Fish Large Conservation reserve 25 Marshall et al. watershed programme (2008)

161

As illustrative case studies from phase 1 of DTC, the following examples demonstrate how data streams can be analysed and integrated to help address some of the key challenges and confounding factors for testing suites of on-farm interventions at landscape scale. In phase 2, more detailed before and after data analyses will be undertaken following the completion of the on-farm measure installations in the manipulated target sub-catchments.

Case Study 1: characterising the uncertainties associated with conventional hydrochemical data and implications for sampling design Analysing the DTC monitoring data within an uncertainty framework is critical to understand inherent observational uncertainties, the influence of natural variability and thus enable the effective attribution of change to on-farm mitigation measures implemented in any target sub-catchment. As part of this work on the Hampshire Avon and Tamar DTCs, uncertainties have been calculated in the observed parameters for each of the sampling stations in each target sub-catchment, based on the errors associated with sensor measurements, sample collection, storage and laboratory analyses, and/or QA procedures applied to the observational data streams. Three examples from these analyses are illustrated below.

Discharge uncertainty was calculated using a non-parametric Loess regression technique applied to paired stage height and stream discharge data. Figure 5.11 provides an example of 95% confidence intervals for the stage-discharge relationship at Brixton Deverill (River Wylye sub-catchment; Hampshire Avon DTC) during winter and spring, where the relationship is relatively stable meaning that error estimates are representing measurement errors.

Figure 5.11: Uncertainty analysis of the stage- discharge relationship at Brixton Deverill. Red lines represent 95% confidence limits

Detailed analysis showed that the small headwater streams monitored as part of the DTC programme display quite different stage-discharge relationships throughout the year. Figure 5.12 shows paired stage and discharge values for the Prior’s Farm sub-catchment (Hampshire Avon DTC), coloured by season. This shows that the stage-discharge relationship is different during the summer and autumn months compared with winter and spring. This is most probably caused by the presence of more stream vegetation during the summer and autumn months which slows water velocity in the channel and therefore changes the shape of the relationship.

162

Figure 5.12: Plot showing paired stage-discharge measurements for the Prior’s Farm sub-catchment, split by season

This work highlights two important points: firstly, the importance of calculating discharge using a velocity- area method rather than relying on a stage-discharge curve, and; secondly, the importance of dividing the data into stable chunks before characterising measurement uncertainty, as otherwise, the uncertainty will be over-estimated as natural variability around the stage-discharge curve will be misclassified as measurement error.

Nutrient chemistry uncertainty: in this example, uncertainty in the data streams generated from the nutrient chemistry sensors was estimated by comparing the sensor data to paired daily lab samples. The lab sample observations had been subject to a rigorous QA and calibration procedure using spiked standards with a known concentration across a representative concentration range for each determinand.

Figure 5.13 provides an example of a scatterplot of the analysis for Nitrate-N at Brixton Deverill (River Wylye sub-catchment; Hampshire Avon DTC). From these plots residuals can be calculated and integrated to look for any bias which may be present in the sensor data. The standard deviation of these residuals can then be used to construct error bars.

Figure 5.14 shows example time series of sensor data with error estimates for discharge, Nitrate-N and TP. These uncertainty estimates can then be cascaded through future analyses to provide more robust assessment of the extent to which natural variability, versus changed in changes in diffuse pollution loads resulting from mitigation measures applied in a target sub-catchment might be driving a trend in observational data from the DTC platform.

163

Figure 5.13: Plot showing paired sensor and lab data for Nitrate-N at Brixton Deverill

Figure 5.14: Example discharge, nitrate-N and total phosphorus data for the Brixton Deverill (River Wylye; Hampshire Avon) field site. Dashed lines show 95% confidence intervals for the estimates

164

3. Temporal resolution uncertainty: analysis was also carried out to quantify the uncertainty associated with the temporal resolution at which sampling occurred.

In step 1, the impact of sampling resolution on load estimation was assessed. This was performed by calculating the total nutrient load using the sub-hourly resolution sensor data, then degrading the dataset to produce hourly, daily, weekly and monthly datasets. A bootstrapping methodology was then applied to produce at least 200 replicate datasets at each temporal resolution. Figure 5.15 shows the load estimates produced for nitrate-N at Brixton Deverill (River Wylye sub-catchment; Hampshire Avon DTC) at each sample resolution.

A relatively simple approach was adopted here in order to examine the sole effect of sample resolution on the load estimate uncertainties. This relies on maintaining a ‘structure’ to the decimation of the records: thus there will be a range of possible ‘weekly’ sampling programmes tested, selecting data for 3 time slots on a Monday (3 replicates), 3 on a Tuesday and so on, representing a likely set of sampling programmes that might be conducted by a monitoring agency. The analysis also assumes that the sub-hourly Figure 5.15: Boxplots showing the range of nitrate-N dataset and the use of load estimation load estimates calculated from replicate datasets at methodology 5 (Johnes, 2007) represents a different sampling resolutions ‘true load’ against which loads calculated using the same method, but at a lower sampling frequency can be compared. In reality, there will be other uncertainties associated with the sub-hourly dataset and Method 5 which are not considered in this analysis.

The analysis is nevertheless useful, allowing isolation of the effect of sampling resolution alone on the uncertainties associated with load estimation. The outputs from this analysis, as presented in Figure 13, show that as sampling resolution is coarsened towards monthly sampling, the level of uncertainty increases and therefore our ability to quantify the nutrient load accurately diminishes. This is because nutrient flux associated with extreme catchment events tends to be very dynamic and, as a result, it is very easy to miss important periods of flux with infrequent sampling methodologies. The analysis presented here suggests that hourly sampling will produce load estimates within -2% and +1.6% of the sub-hourly load estimate, while daily sampling will produce estimates within -11.5% and +10.6%, weekly sampling within -30.9% and +36.4% and monthly sampling within -63.3% and +110.4% of the sub-hourly estimate.

In step 2 of this analysis of the impact of temporal sampling resolution on uncertainties in data products generated from monitoring programmes, exceedance curves were constructed for TP observed at Brixton Deverill (River Wylye sub-catchment; Hampshire Avon DTC) using the replicate datasets generated in step 1. These are presented in Figure 5.14. As with the load estimation, uncertainty increases as sample resolution coarsens, and exceedance is progressively underestimated as sampling frequency decreases. This uncertainty can have direct implications for the reliability of observational data products collected at

165 low temporal resolution to inform management decisions: for example, in the classification of the status of water bodies under the EU Water Framework Directive.

Figure 5.16 also includes a boxplot of mean TP concentrations calculated at each sampling resolution. The data illustrate that as the temporal resolution of the sampling coarsens, the uncertainty in the mean concentration increases, with a proportion of the monthly sample resolution datasets classifying the site as being in ‘good ecological status’, despite the higher resolution datasets indicating that the site should be classified as having only ‘moderate ecological status’. The conclusion that can be drawn from this analysis is that using infrequent sampling regimes can increase the chance of misclassifying a catchment under the WFD criteria.

Figure 5.16: Concentration exceedance plots and boxplots of WFD guideline statistics for high-resolution data and degraded versions (area = 5th-95th percentiles)

The uncertainty analysis undertaken on the high resolution data streams generated from the Hampshire Avon and Tamar DTCs illustrates the importance of understanding errors within observational data, both in terms of measurement technology errors in sensor networks, errors associated with the transport, storage and analysis of water samples to and in laboratories, and errors associated with the frequency of monitoring for each determinand.

A composite analysis of these errors provides clear guidance on the relative merits of sensor networks versus high frequency laboratory analysis, and the extent to which realistic and robust advice on appropriate targets for mitigation can be provided, based on these different measurement approaches.

Case Study 2: characterising the uncertainties associated with conventional biological monitoring data Understanding the role of uncertainty in the biological data is critical for us to be able to attribute effectively any observed change to the on-farm mitigation measures implemented. Considerable effort has been put into characterising the influence of potential sources of variation (spatial, temporal and sampling variation, together with processing error) on EQR assessments for some of the tools used to produce WFD classifications (e.g. Davy-Bowker et al. 2008).

166

Nevertheless, it is necessary to characterise the uncertainty in biological response to hydrochemical drivers within the DTCs, so that any change in ecological quality can be confidently ascribed to the effect of targeted mitigation. In essence, we will need to demonstrate the causal pathway linking both biological and hydrochemical responses to the on-farm interventions. Whilst biotic indices have been developed to reflect pressure gradients, the high resolution hydrochemical data collected with matched biological samples in the DTC programme has enabled us uniquely to explore what aspect of the hydrochemical gradient the biota are responding to.

To investigate these linkages, high resolution hydrochemical data from the Avon and Tamar DTCs were compiled summarising the potential drivers of biological change in a number of different ways, including measures of the average, the range and the variability, with each measure calculated over a number of different time periods or windows (1 day – 90 days) prior to the collection of the biological data. Biological response variables were summarised as biotic indices. General linear models were then used with a stepwise forward selection procedure to identify the hydrochemical summary statistic(s) that best described the variation in the biotic indices. Both continuous (e.g. hydrochemistry) and categorical (site) variables were used. Once the best hydrochemical summary statistic (or combination of statistics) had been identified, a further analysis was undertaken to partition the variation to the potential sources.

An example of the results of these analyses is presented here with the invertebrate based ASPT (Average BMWP Score per Taxon present) index which is used to assess organic pollution. Our analysis indicated that the Q5 of % oxygen saturation calculated using data from the 10 days prior to sampling was the best summary statistic for predicting ASPT, suggesting that the invertebrates were reflecting oxygen sags during the short time period before sample collection (Figure 5.17).

6 70

60 5 50

4 40 ASPT

3 30 %Variance 20 2 10

1 0 Site 0 Year

0 20 40 60 80 100 120 Season Q5 % Q5 Sat Q % Dissolved Oxygen (10 days) 5 Unexplained Figure 5.17: Relationship between Q5 of % Dissolved Oxygen over the 10 days preceding the invertebrate sample and ASPT (different sub-catchments indicated by different symbols). Proportion of variance in ASPT explained by Q5 of % DO, spatial, temporal and unexplained factors

167

Other summary measures of dissolved oxygen concentration were not as effective, and neither was shorter or longer time periods over which hydrochemical summaries were made. Q5 of % oxygen saturation calculated using data from the 10 days prior to sampling explained 63% of the variation in ASPT, with spatial differences explaining a further 28%, and temporal differences <1%. Only 9% of the variation was unexplained. These results will help us understand how much of any observed difference in this biotic index could be attributed to a directional response to on-farm mitigation, and how much to additional unexplained factors.

Case Study 3: combining cost-effectiveness modelling and source tracing to better represent the technically feasible impact of on-farm interventions for sediment control The transfer of estimates of the efficacy of on-farm interventions to farmers needs to be timely and to take account of the influence of those pollutant sources not targeted by the mitigation measures in question. The former ensures that farmers remain engaged with the need for improved management in impaired areas, while the second converts efficacy estimates to better reflect impact in-river (Collins et al., 2014). Without the latter correction, edge-of-measure or farm scale estimates of efficacy will over-estimate impact and fail to take account of the confounding influence of multiple sources. Timeliness is commonly delivered by the use of computer simulation of the technically feasible impact of on-farm pollution control measures. Pollutant source apportionment techniques provide a basis for helping estimate the efficacy of on-farm measures for point of impact in-river. By way of illustrating this type of data integration, the Hampshire Avon team used the returns from the DTC baseline farm practice survey in phase 1 to identify those on-farm sediment control options currently implemented in the Priors Farm sub-catchment (Table 5.5) and their corresponding level (prior implementation) of uptake. This information was used in combination with farm business data to generate inputs for the FARMSCOPER (FARM SCale Optimisation of Pollutant Emission Reductions) DST (Zhang et al., 2012; Gooday et al., 2014). Compared to a baseline with no prior implementation of on-farm interventions for sediment control, FARMSCOPER predicted that the current uptake in the Priors Farm sub-catchment had technically reduced annual agricultural sediment loss by 7.5%. Additional model iterations suggested that uptake of all FARMSCOPER on-farm interventions for sediment management could technically reduce annual agricultural sediment loss by 8.4%, relative to a baseline without any interventions.

Table 5.5: On-farm sediment mitigation measures currently implemented in the Priors Farm sub-catchment of the Avon DTC

Establish riparian buffer strips Intensive ditch management on arable land Intensive ditch management on grassland Reduce field stocking rates when soils are wet Move feeders at regular intervals Construct bridges for livestock crossing rivers/streams RE-site gateways away from high risk areas

A sediment source tracing investigation was undertaken to help correct the simulated efficacy of current or projected implementation of on-farm sediment control measures, to better reflect the confounding effect of additional sediment sources in the Priors Farm sub-catchment. The source tracing study included the major potential sediment sources identified by field walking and field observations; agricultural topsoils 168

(grass and maize at the time of reporting), agricultural field drains, damaged road verges and channel banks/subsurface sources. Some preliminary source tracing over a year in phase 1 suggested that the typical median contributions from these key sources, taking into account the outputs of Monte Carlo simulations for uncertainty analysis using both local and genetic algorithm global optimisation were: grass surface soils (35%), maize surface soils (16%); agricultural field drains (17%); damaged road verges (15%), and; channel banks/subsurface sources (17%). On this basis, agriculture was estimated to be responsible for about 68% of the fine-grained sediment delivered to the channel system. Integrating these preliminary sediment source estimates with the FARMSCOPER predictions discussed above suggested that the current uptake of on-farm sediment control options has reduced baseline annual sediment loss to the river system by 5.1% (compared with 7.5% considering agriculture in isolation). Similarly, the projected efficacy of the uptake of all FARMSCOPER on-farm interventions for sediment management was corrected from 8.4% to 5.7%. This case study illustrates how the simple integration of data streams can help better inform stakeholders of the potential benefits of on-farm interventions for controlling in-river sediment problems (Figure5.18).

Figure 5.18: Evidence of sediment mobilisation by rilling from a maize stubble field in the Priors Farm sub- catchment; Hampshire Avon DTC

Case Study 4: Sediment-associated organic matter source tracing in the Hampshire Avon using bulk isotopes and near infra-red reflectance spectroscopy (NIRS) Context The target sub-catchments in the Hampshire Avon DTC have been assessed as being under stress from excessive fine sediment loadings. Fine sediment stress in the EU WFD is defined as both the inorganic (minerogenic) and organic fractions. Consequently, fine sediment source tracing work as part of the DTC programme has been focussing on both fractions in order to be comprehensive. The focus on fine sediment-associated organic matter is important since this fraction, through oxygen consumption in conjunction with its decomposition, can have a deleterious impact on the supply of dissolved oxygen available to aquatic ecology in watercourses (Collins et al., 2011; Kemp et al., 2011; Jones et al., 2012a, 2012b and 2014).

169

Method Work during phase 1 of the DTC programme applied a source tracing procedure for fine sediment- associated organic matter (Collins et al., 2013 and 2014). This procedure combined bulk stable isotopes of C and N and spectra generated using NIRS to distinguish four primary sources of sediment-associated organic matter in the target sub-catchments: farm yard manures/slurries, damaged road verges, decaying instream vegetation and human septic waste (septic tanks). The composite fingerprints identified using a combination of statistical procedures including two versions of the Kruskal-Wallis H-test (KW1 and KW2), principal component analysis (PCA) and genetic-algorithm driven discriminant function analysis (GA-DFA) all correctly distinguished 100% of the samples collected from the Cools Cottage or Priors Farm target sub- catchments. In turn, the composite fingerprints were used to apportion the relative contributions of these sources to the fine sediment-associated organic matter sampled from the river substrate in each sub- catchment over the period December 2012 - November 2013. Representative samples of fine sediment- associated organic matter were collected using retrievable basket traps installed in artificial salmonid redds constructed in the river substrate.

Results As an example, Table 5.6 presents the results of the tracing exercise in the Priors Farm target sub- catchment for the phase 1 sampling period. The estimates generated using the individual composite signatures were used to generate final source proportions for each basket extraction period (Figure 5.19) and for the entire duration of the in-river sampling (Figure 5.20).

Table 5.6: The relative frequency-weighted median source contributions to the fine sediment-associated organic matter sampled during phase 1 in the Priors Farm target sub-catchment, generated using the individual composite signatures Basket Signature Farm yard Damaged Decaying Human extraction manures/slurries road instream septic month verges vegetation waste February 13 KW1 0.04 0.25 0.58 0.13 February 13 KW2 0.04 0.30 0.47 0.20 February 13 PCA 0.03 0.28 0.53 0.15 February 13 GA1 0.10 0.59 0.21 0.09 February 13 GA2 0.06 0.52 0.26 0.16 February 13 GA3 0.08 0.57 0.31 0.04 May 13 KW1 0.02 0.03 0.74 0.21 May 13 KW2 0.02 0.05 0.63 0.30 May 13 PCA 0.02 0.04 0.69 0.25 May 13 GA1 0.07 0.62 0.26 0.05 May 13 GA2 0.11 0.47 0.22 0.20 May 13 GA3 0.06 0.68 0.23 0.04 August 13 KW1 0.03 0.24 0.62 0.12 August 13 KW2 0.03 0.22 0.64 0.11 August 13 PCA 0.03 0.24 0.59 0.14 August 13 GA1 0.16 0.35 0.45 0.04 August 13 GA2 0.13 0.24 0.40 0.22 August 13 GA3 0.15 0.45 0.27 0.12 November 13 KW1 0.04 0.13 0.70 0.13 November 13 KW2 0.03 0.18 0.59 0.20 November 13 PCA 0.03 0.17 0.62 0.18 November 13 GA1 0.10 0.64 0.22 0.04 November 13 GA2 0.12 0.43 0.25 0.20 November 13 GA3 0.08 0.55 0.33 0.04 170

Figures 5.19 and 5.20 confirm that both agricultural (farm manures/slurries) and anthropogenic sources (human septic waste) do contribute to the sediment-associated organic matter sampled in the river substrate. Decaying in-stream vegetation and damaged road verge material consistently dominate the cumulative (i.e. time-integrated) relative contributions from the individual sources. Interpretation of these estimates must bear in mind that the biological oxygen demand of the farm manures/slurries will be higher than that of the organic matter originating from the other sources, e.g. human septic waste. Accordingly, the impact of the sources on oxygen depletion in the river substrate will not be directly proportional to the source tracing estimates. a) December 2012 - February 2013 b) December 2012-May 2013

Farm yard Farm yard manures/slurries manures/slurries

Damaged road Damaged road 13 6 verges 17 5 verges 31 42 39 Decaying 46 Decaying instream instream vegetation vegetation Human septic Human septic waste waste

c) December 2012 – August 2013 d) December 2012 – November 2013

Farm yard Farm yard manures/slurries manures/slurries

Damaged road Damaged road 13 9 verges 13 7 29 35 verges 50 45 Decaying Decaying instream instream vegetation vegetation Human septic Human septic waste waste

Figure 5.19: Final estimates of the relative frequency-weighted median source contributions to the fine sediment-associated organic matter sampled during the individual basket extraction periods of phase 1 in the Priors Farm target sub-catchment

171

Farm yard manures/slurries

Damaged road 11 5 verges 27 36 Decaying instream vegetation Human septic waste

Figure 5.20: Final estimates of the relative frequency-weighted median source contributions to the fine sediment-associated organic matter sampled during the entire sampling period (December 2012-November 2013) of phase 1 in the Priors Farm target sub-catchment

Case Study 5: Application of fibre-optic distributed temperature sensing to investigate surface water- groundwater interaction in the Wensum DTC Whilst the overarching interest of the DTC project is in the surface water hydrochemistry, hydrochemical monitoring programmes have limitations in terms of the frequency and spatial coverage of sampling. In streams, temperature can act as a natural tracer that can be used to locate inflows and quantify them through simple energy balances (Selker et al., 2006). This is useful since temperature is readily measureable over time with thermistors and, with fibre-optic distributed temperature sensing (DTS), can be readily monitored over both space and time (Tyler et al., 2009). Therefore, the use of fibre-optic sensing to monitor stream temperatures can be used to determine the locations and quantity of flow to a stream, providing important information if trying to identify contributions to surface water nutrient and sediment loads. The rationale for deploying DTS in the Wensum DTC is that flow from field drains or groundwater inflow to the streams potentially has a different temperature from the resident stream water. Thus, inflows, if significant, should be observable in temperature profiles along the stream. For this purpose, a steel armoured fibre-optic cable was installed in stream A of the Blackwater sub-catchment in March 2012 (Figure 5.21). With the technology of DTS, this fibre-optic cable acts as a long (1 km) thermometer in the stream with a spatial sampling interval of 1 m, as set by the Sensornet Oryx base unit housed in Kiosk E. Different temporal averaging lengths were trialled, although typically, during the monitoring period, the temperature distribution along the stream was averaged over 1-minute intervals.

Figure 5.21: Fibre-optic cable deployment in the Blackwater sub-catchment upstream of Kiosk E (Stinton Hall Farm) in the Wensum DTC

172

DTS temperature data collected on 20th February 2013 are shown in Figure 5.22, as an example. February is considered an ideal month for this approach given that surface water temperatures should be significantly lower than groundwater temperature, and groundwater levels should be in recovery. Furthermore, stream gauging at sites A, B and E (Figure 5.21) suggests that the stream gains along this reach. In DTS data, discrete flow sources may appear as spikes (e.g. Matheswaran et al., 2013), either cold or warm, depending on the contrast between stream and source temperature, which may or may not cause a downstream temperature change following complete mixing with the resident surface water. The cold anomalies at 16:00 located at around 480, 570 and 920 m are likely due to part of the cable being exposed to the air, since these locations also have relatively high temporal standard deviations in temperature. Of the remaining warm temperature anomaly locations at around 360 and 420 m, the former is almost 1oC colder at 16:00 relative to 06:00. This is in contrast to the rest of the profile which is 1oC warmer. The anomaly at 420 m has characteristics expected of an inflow but does not correspond to any of the known field drains. If it is a subsurface inflow, then the flow contribution is likely to be small given that there is no significant downstream temperature change. The only clear inflow that results in a step-like temperature change is at 660 m and is at the confluence of streams A and B (Figure 5.21).

Figure 5.22: DTS temperature data from stream A, upstream of Kiosk E on 20 February 2013 showing: (top) temperature profile at 16:00 and 24 hour standard deviation of temperature; (middle) 24 hour mean temperature; and (bottom) temperature difference between 16:00 and 06:00

In the case of the field drains, the flow contribution to the stream was too small to result in a DTS resolvable temperature signal. Groundwater upwelling to the reach was also likely to be either too weak or too diffuse to be detected. The latter seems possible given that the top of the Chalk aquifer is approximately 15 m below the stream. Even if discrete groundwater seeps are present, it should be noted

173 that the DTS approach is not always a failsafe method for locating subsurface inflows when there is surface water flow, as has been recently shown in a flume set-up (Selker and Selker, in press).

One of the findings of the deployment of the DTS in this study is that temperature anomalies that seemed to have traits symptomatic of an inflow of relatively constant temperature were found to almost always be explained by other processes. Temperature anomalies similar to that at 420 m were observed at many more locations at other times of the year and could be associated with partial cable burial in the streambed sediments, vegetation mass, and in the spring months with decomposing plant material. When the cable was removed and immediately replaced back on top of the streambed, such anomalies in the DTS data disappeared. The prevalence and ambiguity of the temperature anomalies made analysis of the data for water inflows challenging, especially since the channel is inaccessible due to macrophyte growth for much of the year preventing detailed in-field validation. It is concluded that these issues are also likely to be encountered when DTS is deployed long term in similar streams with a high volume of mobile sediment and vigorous plant growth.

5.4 Synthesis During phase 1, the DTC programme has initiated the development of a ‘catchment science toolkit’ for undertaking a ‘weight-of-evidence’ assessment of the multiple impacts of on-farm interventions at landscape scale. Core to this approach is the development of an uncertainty analysis framework for using hydrochemical and aquatic biology datasets. Additional data streams are being collected and analysed in an attempt to provide positive information on outcomes to keep stakeholders engaged in the short and medium terms.

6 Key Messages from Phase 1 and Moving Forward to Phase 2

6.1 Introduction The Demonstration Test Catchments (DTC) programme was established in December 2009 to gather empirical evidence on the cost-effectiveness of combinations of diffuse pollution mitigation measures at sub-catchment scales. Such evidence is needed to meet the objectives of the EU WFD, as well as broader challenges associated with the sustainable intensification of food production.To date, the DTC programme has involved over 40 organisations, as well as acting as a research platform to support additional research investments (e.g. via the NERC Macronutrients, Changing Water Cycle and BESS initiatives and the DOMAINE large grant), and capturing interactions and outcomes of various policy-driven initiatives such as Catchment Sensitive Farming (CSF) and the Catchment Restoration Fund (CRF).

The previous sections have outlined work during phase 1, presenting the background characteristics of the core study areas, hydrochemical and aquatic ecology data, ongoing work on the targeted implementation of on-farm interventions driven and informed by the baseline characterisation, knowledge exchange activites and the development of a ‘catchment science toolkit’ for dealing with the confounding issues for testing the underpinning research hypothesis at landscape scale.

174

6.2 Key Messages from Phase 1 Some key messages from phase 1 include:

Understanding the issues  In order to ‘Understand the Issues’, a sub-catchment approach was adopted within across the DTC focus catchments in order to gather baseline-monitoring data and determine the effects of subsequent mitigation of diffuse pollution. The approach used hydrological, water quality, and ecological monitoring undertaken across all the DTC focus catchments. Each of the three DTC consortia adopted a broadly similar approach for the selection and design of DTC study sub-catchments, although the number of sites and specific placement and specification of monitoring stations was determined independently by each consortium in response to local needs and pressures. The focus was on small headwater streams of approximately 10 km2, an area over which mitigation measures could be trialled and assessed intensively, with monitoring equipment installed immediately downstream of manipulated areas to capture the effects. The experimental approach used for assessing mitigation was the ‘Before-After Control-Impact’ (BACI) approach, which uses pre-mitigation instream data to provide a baseline against which to compare post-mitigation instream conditions, and also compares a ‘manipulated’ (mitigated) stream with a ‘non-manipulated’ (control) stream.  Phase 1 of the DTC programme, spanned two markedly different water years, in terms of hydrological function in each of the sub-catchments. The hydrology varied considerably between hydrological years due to different rainfalls. In 2012 a wet autumn led to widespread flooding in November and December across most of the UK affecting all the DTC catchments but particularly the Eden. A very dry winter then followed, with an exceptionally warm and dry early spring. In contrast to 2012, the 2013 hydrological year was drier, although there were some significant weather events throughout the year. Autumn was wet, particularly the month of December when there was extensive disruption from flooding. The late winter and spring were exceptionally cold; the spring was the coldest recorded since 1962, and there were unseasonably late snowfalls. Summer was warm and sunny, and a heatwave in July – the sunniest July since 2006 – was a marked contrast to the cool and wet summers experienced from 2007-2012. As a key driver of pollutant delivery from diffuse sources to adjacent waters, where hydrology varies markedly so too will the connectivity of source areas to streams, the efficiency of nutrient and sediment delivery from land to water, and the relative proportion of the different nutrient fractions (organic, particulate, inorganic) mobilised and transported in any catchment.  This contrast provided an opportunity to investigate inter-annual variability in the baseline water quality data for each sub-catchment. For many of the DTC focus catchments and sub-catchments, the difference between the dry winter of 2012 and the dry summer of 2013 is striking, and this is reflected in both groundwater-dominated catchments in the Hampshire Avon and Wensum catchments, and the less permeable catchments of the Eden, Tamar and the clay catchments of the Hampshire Avon (Sem) DTC.  The DTC hydrological and hydrochemical monitoring programme has delivered a strong evidence base from which to plan for mitigation of diffuse nutrient and sediment pollution of water bodies across a range of landscape types common to the UK. A number of conclusions have been reached concerning the dominant sources in each of the DTC catchments, the timing of nutrient export from land to water, and the range of nutrient chemistries mobilised and transported to streams in each of the catchments. Universal truths have not emerged from this analysis, and it is clear that care must be taken to ensure that the findings from one programme are not assumed to provide a perfect solution in another circumstance without prior testing of those solutions across multiple land management and geoclimatic conditions.

175

 The DTC monitoring has, for example, shown that storm events are highly important in driving the flux of diffuse pollution in catchments where there is a high proportion of rapid runoff in preferential flow pathways (surface runoff, near-surface quickflow and drain flow in drained landscapes like the Wensum catchment). However, it is the mid flow events which dominate nutrient and sediment deliver in catchments driven by subsurface hydrological function. Similarly, nitrate is the dominant form of nitrogen delivered to waterbodies in permeable catchments, based on the evidence collected here, but it is a minority component of the TN loading in livestock farming systems where the high stocking densities and abundance of manure production leads to enrichment of waters with both particulate and dissolved organic nutrient fractions which will stimulate both algal productivity and microbial metabolism instream. A monitoring programme that focused solely in nitrate in a livestock farming catchment would be unlikely to be sensitive to current management or to targeted on-farm mitigation efforts in the catchment.  There are significant uncertainties associated with any monitoring strategy to detect nutrient and sediment flux behaviours in catchments. Sensors provide on-site high frequency observations, but there can be significant technological problems to overcome with their use, if robust and reliable findings are to be generated. Simply relying on the sensors without testing the accuracy and precision of the observations within an uncertainty framework will constrain the user to imprecise and uncertain observations, data streams with significant gaps and a limited range of determinands which might not be those best suited to answering the question posed. Laboratory based analyses have their place, as quality control can ensure higher quality data, for a wider range of determinands, albeit at a lower temporal resolution. A combination of both approaches is likely to be needed to generate robust evidence streams for catchment mitigation efforts at any site.  In synthesising these nutrient and sediment flux behaviours for different landscape typologies across the DTC platform, careful consideration also needs to be given to the appropriate statistical techniques that are applied. This ensures core findings regarding the efficacy of mitigation strategies allow for the quality and uncertainty of different measurement strategies (such as identified above); the natural variability in time and space of catchment systems and related climatic factors; the duration of the evidence base pre and post mitigation in light of inherent natural variabilities; and the different QA procedures and monitoring strategies between DTC components. The DTC platform has developed a toolkit of options for quantifying these uncertainties that should be embedded in any weight of evidence approach to providing decision support for diffuse pollution mitigation strategies and the detection of change.  The contrast between the hydrochemical responses of different catchments with varying soils and geology to differing degrees of rainfall has been shown to be critical in determining pollutant loads, and properly evidencing the selection of mitigation measures in any catchment, as is the proportion of groundwater that contributes to river flow. Consequently the importance of identifying and managing run-off pathways during storm events and subsurface pathways year round is a key finding of DTC.  Biota are likely to respond to multiple pressures in any environment, dependent upon individual species susceptibilities and stage of life cycle. The ecological monitoring programme has therefore been undertaken in the context of the multi-stressor focus of the hydrochemical and hydrological monitoring programmes, to provide a solid baseline against which the impact of mitigation measures can be assessed. Differences in the emphases given to different indicators varied between catchments reflecting the diversity of ecohydrological settings although all catchments were assessed using standard WFD tools.  A key focus of the ecological monitoring programme was to establish the background variation in the metrics used to classify the ecological status of the water bodies across the DTC platform so that the

176

significance of any change in response to reductions in diffuse pollutants from agriculture resulting from on-farm mitigation measures could be assessed. This also extended to addressing the utility of measures in detecting mitigation changes in headwater catchments.  A key finding from the phase 1 ecological monitoring and analysis of the trends in the data is that metrics based on single season sampling (it is usual to collect one sample in spring and one in autumn) may conceal important temporal dynamics, particularly over seasonal cycles in flashy upland headwaters of the Eden, but also between contrasting years where hydrology and hydrochemistry vary markedly.  Testing of EQR scores revealed high annual repeatability despite seasonal variation, sensitivity to water quality parameters and robustness as a means of assessing ecosystem quality.  The secondary aim of DTC phase I was to identify the issues acting in each sub-catchment that were causing the sites to fail to achieve WFD Good Ecological Status. One sub-catchment, the Dacre in the Eden, offers a useful counterpoint as a site that has achieved good status but is under pressure. For each catchment, stressor-specific diagnostic indices have been derived from the biological quality elements and related to the hydrochemical monitoring data. This has enabled the identification of stressors acting on the biota at individual sites from the biological response. The most prevalent issues for the biota at all of the DTC sites appeared to be nutrients and fine sediment.  Despite nutrients being a key pressure on ecological communities, studies within the Wensum DTC demonstrate that EQRs based on diatoms, benthic invertebrates and macrophytes showed limited variation in response to observed variation in N and P, suggesting that a substantial reduction in nutrient concentrations would be required to induce the required biological response or, perhaps, that other factors are also constraining ecological quality at these DTC sites. An effective mitigation, in order to deliver a significant improvement in ecological quality, would therefore need to address multiple stressors acting upon the ecosystem, rather than focusing on one particular stressor deemed to be ‘controlling’ the ecological health of any water body.  Data from the DTC focus catchments has found that the link between measured pollutants/stressors and the indices supposed to be sensitive to these pressures is not always clear. Of particular note is the very strong correlation between the LIFE index (Lotic-invertebrate Index for Flow Evaluation), for assessing the impact of low flows, and the PSI index (Proportion of Sediment-sensitive Invertebrates), for assessing sediment stress. These two indices are 90% correlated with each other in the data sets for the Hampshire Avon sites, both within and between sites. As flow and sediment load are intrinsically linked, the correlation between these indices makes it difficult to determine which of these two potential stressors is acting on the invertebrates. As it is difficult to determine the key community drivers in these multi-pressure systems, a multi-stressor approach is therefore advocated.  Finally, a focus on evaluating the role of groundwater in the hydrochemical and ecosystem function of each site was also undertaken. Groundwater is both a receptor of diffuse pollution and a pathway to surface waters and wetlands, particularly in the chalk catchments where more than 90% of streamflow is routed through the groundwater aquifer. The role of groundwater in hydrological systems is often less well understood than the surface flow regime. Where groundwater inputs to baseflow occur it is important to understand their chemical character and their physical, chemical and ecological significance.  Groundwater was found to be a significant component of the flow regime and its chemistry in all of the sub-catchments on the platform and to be a dominant component of flow in all of the permeable sub- catchments. Even in catchments where surface flow pathways are dominant as sources of streams, the hydrogeological characterisation work has indicated the presence of reasonably permeable strata within the superficial deposits, containing groundwater of relatively modern age that has been

177

recharged with water enriched in compounds likely to be derived from intensification of agricultural production at the land surface. Understanding the potential effects of mitigation measures on stream hydrology hydrochemistry and the stream ecology will therefore need to take groundwater flow routes and chemistries into account, particularly in permeable catchments where time lags in flow routing and the flushing of accumulated pollutant stores may delay the instream hydrochemical and ecological response to on-farm mitigation measures.

Measures  To provide evidence of the effectiveness of mitigation measures for diffuse pollution control requires the development of a conceptual model. This model must be based on the sources, mobilisation, pathways and delivery of pollution in individual catchments. This enables the identification and assessment of their influence of key pollutant sources, mobilisation mechanisms and delivery routes. The biophysical component of the conceptual models developed across the catchments were organised around a source-mobilisation-delivery-impact framework.  Results from the monitoring kiosks commissioned under Component 1 have demonstrated that there are pronounced peaks of sediment, phosphate and nitrate during and following rainfall events which have been delivered through differing pathways within a range of timescales.  Other sediment fingerprinting research undertaken across the catchments has highlighted the role of different sources such as surface run-off, field drains, road verges and channel bank remobilisation which again impacts on different timescales. In addition, there is important groundwater connectivity with seepage from the underlying aquifers into the surface water system.  This means that a combination of measures that reduces sources, limits mobilisation and prevents delivery in an integrated manner is necessary. It is also important to both understand and monitor multiple pathways in order to assess the impact of different mitigation measures over different time scales.  Additionally successfully understanding how the catchments function and which measures are best suited and where located has benefited immensely, through involving farmers from the start of the process. This was achieved by allowing the farmers and landowners to buy into the process, empowering them to provide ‘both ends’ solutions, which we believe are key to sustainable catchment management. This involved explaining the problem, sharing the data, and building trust and a long standing partnership to fully realise multiple benefits on farms and have a real opportunity to address Diffuse Water Pollution from Agriculture. The most effective way to do this is through a process of enlightened self-interest, this being the only way to ensure that benefits will be realised for a considerable time.  Therefore the over-arching approach adopted across the catchments has been to ensure that selected measures are complementary, forming a ‘treatment-train’ or sequence of improvements to water quality from farmyard to surface water, with inherent or built-in capacity to operate and function successfully across a range of weather conditions. Additionally, most features are multi- functional, tackling most of the major issues identified within the manipulated target sub- catchment. These approaches are closely linked to the source-mobilisation-transport-impact approach for the identification of problems in a pollution impaired catchment. Financial constraints have also been recognised, hence the protracted negotiations to ensure buy-in from the farmers as necessary.

178

 Unravelling the effectiveness of measures will require a greater understanding of how the catchments function in the next phase. Pollutant sources, mobilisation, pathways, and timescales to impact and the level of impacts itself will need greater understanding.  The role of landowners and farmers in land management and diffuse pollution will also require increased understanding of the social and economic barriers to mitigating diffuse pollution.

Working with stakeholders: knowledge exchange and understanding farmer behaviour  A broadly-based strategy of local engagement was a key feature of the activities undertaken by each DTC during the first few years of phase 1. These initiatives sought to raise awareness of the DTC programme, identify key stakeholders (at the level of both individuals and organisations) and invite collaboration where there were particular opportunities for exchange of information and/or joint working. Over the duration of phase 1 each DTCs has become successfully embedded in a network of local partnerships. Three factors have been particularly important contributors to this achievement and are relevant for other initiatives involved in developing catchment partnerships.  Recognising the local setting – each of the DTCs operates in a very different environment in terms of physical characteristics, the nature of the farm businesses (in terms of type of farming operations, size and profitability) and the presence of other organisations (river trusts, water companies etc.). The planning and implementation of engagement strategies has needed to be sensitive to these contrasting realities.  Utilising knowledge brokers – reflecting the differences in setting each DTC has needed to have individuals who can act as intermediaries between the research team, the farming community and other stakeholders. In some cases these people have been directly employed by the DTCs (e.g. as a farm liaison officer), while in others they are staff members of a partner organisation (e.g. river trusts) or a relevant public agency (e.g. a Catchment Sensitive Farming officer). There has also been a need to have several individuals undertake such roles in each DTC, since the background and expertise that may be highly relevant in one context (e.g. farming) may be less so in another (e.g. water companies).  Having the time to build relationships and trust – each of the DTCs had a background of previous research in their catchment, but in all three cases it took at least two years to get locally known and begin to receive requests to participate in other events and activities. It is a distinct advantage of the research platform concept that it provides the necessary timescale for such relationships to evolve, contrasting with the shorter duration of most conventional research projects.  Since the DTCs were established in 2009 there has been substantial promotion of the catchment- based approach (CaBA) and a growing prominence of river trusts. The DTC study catchments vary between those where river trusts are well-established (the Eden and Tamar) and those where such organisations are a much more recent development (the Avon and Wensum). Nevertheless, the growth in activity across all four catchments has been substantial and it has become apparent that there is an important role for the DTCs to support such organisations with scientific and technical expertise. Examples of such collaboration have occurred in all the DTC consortia. This trend is expected to continue and it is likely that more future DTC knowledge exchange activities will be embedded amongst those of other organisations (e.g. participating in meetings or initiatives which they arrange) rather than stand-alone events.  Alongside the broader knowledge exchange activities a survey was conducted to create a baseline regarding current agricultural practices relevant to diffuse pollution and provide insights regarding farmer attitudes to the future adoption of other mitigation measures. Eighty eight farmers were

179

surveyed between February 2012 and February 2013 with the results indicating wide variation in the current adoption of 86 mitigation measures reviewed in the Defra WQ0106 inventory.  Measures with the highest current uptake were concerned with fertiliser or manure management and most were part of cross compliance requirements for receipt of the CAP Single Farm Payment. In addition, those compatible with current farm practice were more likely to have been adopted than those which required radical management or land use change. There was no obvious difference in uptake of measures according to whether they related to source minimisation, pathway reduction or receptor protection.  When asked about future adoption those measures which decreased the use of fertiliser and fuel (therefore reducing costs) were commonly mentioned. In general, measures associated with improvements in farm infrastructure were rated positively, while those involving in-field changes tended to receive more negative reactions.  Undertaking the baseline survey of current uptake and attitudes towards future adoption of mitigation measures provided a wider perspective on current practices and opinions than previously existed for England. Insights from the survey have already been discussed with staff involved in the Catchment Sensitive Farming initiative and Defra policy teams to help support their work.  In future activities there will be a need to enhance the nature of the knowledge exchange achieved. This will focus on further developing the Community of Practice concept in each DTC. To be successful, such a network requires mutual understanding, recognition, and common purpose across a range of stakeholders and individuals as a prerequisite for change. Crucially, it requires translation of science outputs into tools (techniques, management systems, farm business models etc.) that have direct applicability in the farmed landscape. As more findings emerge from the implementation of mitigation measures, the DTCs will be in a stronger position to stimulate such discussion of experiences and to take advantage of the opportunities created. These aspirations will also benefit from collaboration with the new Defra Sustainable Intensification Platform, since the landscape-scale element of this programme aligns with the DTC catchments and discussions are now underway to plan a number of joint activities.  A second broad priority will be to support the ongoing development of the catchment-based approach in England. The individual DTCs are already well-placed to provide scientific input on a local and regional level, but it is also apparent that many river trusts and catchment partnerships will need substantially more in the way of financial resources to fully implement their plans. There may be a role for the DTCs in helping to build broader coalitions with other funders (e.g. water companies), as well as contributing to the wider dissemination of insights and experience from the DTC catchments to other parts of the country.

Monitoring and evaluation of on-farm interventions for informing water quality policy has been core to the DTC phase 1 programme.  This has enabled and resulted in developing methods for assessing the effectiveness of on-farm interventions for diffuse pollution control, taking explicit account of uncertainties, is core to the DTC programme of work.  Establishing a general framework for uncertainty evaluation is the only way to benchmark effectively the quality of data from different sub-catchments and with differing sampling resolutions.  Without this framework, robust data analysis procedures cannot be implemented that can separately identify the relative efficacy of different on-farm mitigation measures implemented in

180

each DTC from changes which are attributable to observational and laboratory uncertainties and natural variability.  A twin-track approach is being used in the DTC programme to improve our understanding of diffuse agricultural pollution and the effectiveness of on-farm interventions, combining: targeted experimentation and observation for monitoring changes in water quality and aquatic ecology against a baseline, and; iterative conceptual/process-based modelling to interpret and extrapolate emerging experimental and observational data for policy support.  An iterative ‘weight of evidence’ approach is being developed by DTC to assist the decomposition of effects of on-farm interventions.  Multiple approaches and techniques are used in combination as a ‘catchment science toolkit’ to build up layers of evidence over time to help detect change and keep stakeholders engaged with the need for best practice on farms.  Detection of positive environmental outcomes for water quality and aquatic ecology following the implementation of on-farm interventions for pollution control at landscape scale can be hampered or confounded by a range of factors and the DTC ‘catchment science toolkit’ is being used as a means on integrating data streams to address these issues explicitly.

6.3 Moving Forward to DTC Phase 2 DTC is currently working in an interim period of funding up to the end of March 2015. It is planned that phase 2 of DTC will run from April 2015 to September 2017, inclusive. During this extension, the national DTC consortia will maintain the unique research platforms established in the experimental target sub- catchments and evaluate the extent to which these are indicative of the wider catchments. Socio-economic aspects of catchment management and research will be more prominent than in phase 1 and there will be particular priorities to:  address a series of policy-relevant questions;  support the on-farm measures work particularly through the provision of economics data, to allow the impact of interventions on farm businesses to be assessed;  collect information and develop analytical approaches that will allow upscaling and extrapolation of the local findings to regional and national scales;  provide a sounding board on topics relevant to policy development on water quality and aquatic ecology.

Phase 2 of DTC will focus on four main themes:  ongoing characterisation of the target sub-catchments and iterative conceptual modelling;  ongoing implementation and testing of on-farm interventions;  the socio-economics of on-farm mitigation for diffuse pollution control;  monitoring and evaluation techniques for assessing the efficacy of on-farm interventions using biophysical and socio-economic data streams in an iterative ‘weight-of-evidence approach’.

181

7 References

ADAS (2011) An Inventory of Mitigation Methods and Guide to their Effects on Diffuse Water Pollution, Greenhouse Gas Emissions and Ammonia Emissions from Agriculture. User Guide. Prepared as part of Defra Project WQ0106. 158p. http://www.avondtc.org.uk/Portals/0/Farmscoper/DEFRA%20user%20guide.PDF

Alexander, R.B., Smith, R.A. and Schwarz, G.E. (1993). Correction of stream quality trends for the effects of laboratory measurement bias." Water Resources Research 29, 3821-3833.

Allen DJ, Darling WG, Davies J, Newell AJ, Gooddy DC & Collins AL (2014) Groundwater conceptual models: implications for evaluating diffuse pollution mitigation measures. Quarterly Journal of Engineering Geology and Hydrogeology 47: 65-80

Allen, D.J., Newell, A.J., and Butcher, A.S. (2010) Preliminary review of the geology and hydrogeology of the EdenDTC sub-catchments. British Geological Survey Open Report, OR/10/063.

Allen, DJ, Brewerton, LJ, Coleby, LM, Gibbs, BR, Lewis, MA, Macdonald, AM, Wagstaff, SJ & Williams, AT (1997) The physical properties of major aquifers in England and Wales. British Geological Survey Technical report WD/97/34, 312 pp and Environment Agency R&D publication 8.

Anon (2000) Council of the European Communities. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Official Journal of the European Union L327, 1-72.

Anon (2006) Directive 2006/7/EC of The European Parliament and of The Council of 15th February 2006 concerning the management of bathing water quality and repealing Directive 76/160/EEC. Official Journal of the European Union L64 (4.3.2006), 37-51.

Beck, M. B. (1987). Water-quality modelling - a review of the analysis of uncertainty. Water Resources Research 23, 1393-1442.

Bergfur, J., Demars, O.L., Stutter, M.I., Langan, S.J. and Friberg, N. (2012). The Tarland catchment initiative and its effect on water quality and macroinvertebrate indices. Journal of Environmental Quality 41, 314- 321.

Bernhardt, E.S., and Palmer, M.A.. 2011. River restoration: the fuzzy logic of repairing reaches to reverse catchment scale degradation. Ecological Applications 21, 1926–1931.

Bernhardt, E.S., Palmer, M.A., Allan, J.D., Alexander, G., Barnas, K., Brooks, S., Carr, J., Clayton, S., Dahm, C., Follstad-Shah, J., Galat, D., Gloss, S., Goodwin, P., Hart, D., Hassett, B., Jenkinson, R., Katz, S., Kondolf, G.M., Lake, P.S., Lave, R., Meyer, J.L., O’Donnell, T.K., Pagano, L., Powell, B. and Sudduth, E. (2005). Ecology: Synthesizing US river restoration efforts. Science 308, 636–637.

Beven, K.J. and Alcock, R.E. (2012). Modelling everything everywhere: a new approach to decision-making for water management under uncertainty. Freshwater Biology 57, 124-132.

Bishop, P.L., Hively, W.D., Stedinger, J.R., Rafferty, M.R., Lojpersberger, J.L. and Bloomfield, J.A. (2005). Multivariate analysis of paired watershed data to evaluate agricultural best management practice effects on stream water phosphorus. Journal of Environmental Quality 34, 1087-1101.

182

Boesch, D.F., Brinsfield, R.B. and Magnien, R.E. (2001) Chesapeake Bay, eutrophication: scientific understanding, ecosystem restoration and challenges for agriculture. Journal of Environmental Quality 30, 303-320.

Butcher A.S., Lawrence A.R., Jackson C., Cunningham J., Cullis E L, Hasan K. and Ingram J. (2003) Investigation of rising nitrate concentrations in groundwater in the Eden Valley, Cumbria: Scoping Study. Environment Agency Technical Report NC/00/24/14.

Butcher, A., Lawrence, A., Mansour, M., Burke, S., Ingram, J., and Merrin, P. (2008) Investigation of Rising Nitrate Concentration in Groundwater in the Eden Valley, Cumbria: Catchment Water Quality Survey. British Geological Survey Commissioned Report No. OR/08/023N.

Cardinale, B.J., Palmer, M., Swan, C., Brooks, S., Poff, N. LeRoy. (2002) The influence of substrate heterogeneity on biofilm metabolism in a stream ecosystem. Ecology, 83 (2), 412-422

Cardinale, B.J., Duffy, J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, G.B., Larigauderie, A., Srivastava, D.S. and Naeem, S. (2012). Biodiversity loss and its impact on humanity. Nature 486, 59–67.

Carpentier, C., Dahlhaus, A., Van De Geisen, N. & Marsalek, B. 2013. The influence of hard substratum reflection and calibration profiles on in situ fluorescence measurements of benthic microalgal biomass. Environmental Science: Processes and Impacts 15: 783-793

Carty, A., Scholz, M., Heal, K., Keohane, J., Dunne, E., Fabrice, G., and Mustafa, A. (2008) Constructed Farm Wetlands (CFW) Design Manual for Scotland and Northern Ireland 61p. http://www.bing.com/search?q=SEPA%20Constructed%20Farm%20wetlands&pc=cosp&ptag=A84014190B 7D34506AEF&form=CONBDF&conlogo=CT3210127

Catchment-Based Approach, (2014) Best practice showcase. Available at: [Accessed 23 December 2014]. Chalk. Quarterly Journal of Engineering Geology, 16, 169-185.

Clark, J.J., and Wilcock, P.R. (2000) Effects of Land-use change on channel morphology in northeastern Puerto Rico. Geological Society of America Bulletin 112, 1763–1777.

Clausen, J.C., Jokela, W.E., Potter, F.I. and Williams, J.W. (1996) Paired watershed comparison of tillage effects on runoff, sediment and pesticide losses. Journal of Environmental Quality 25, 1000-1006.

Cleasby, W. and the Eden DTC Team, (2014) Extending knowledge exchange in the Eden DTC: Report on activities 2010-2014. Report to Defra, Eden Rivers Trust, Penrith.

Clews, E., and Ormerod, S.J. (2010) Appraising riparian management effects on benthic macroinvertebrates in the Wye River system. Aquatic Conservation 20, S73–S81.

Collins, A.L. and Walling, D.E. (2004). Documenting catchment suspended sediment sources: problems, approaches and prospects. Progress in Physical Geography 28, 159-196.

Collins, A.L. Naden, P.S., Sear, D.A., Jones, J.I., Foster, I.D.L. and Morrow, K. (2011). Sediment targets for informing river catchment management: international experience and prospects. Hydrological Processes 25, 2112-2129.

183

Collins, A.L. Williams, L.J., Zhang, Y.S., Marius, M., Dungait, J.A.J. Smallman, D.J., Dixon, E.R., Stringfellow, A., Sear, D.A., Jones, J.I. and Naden, P.S. (2013). Catchment source contributions to the sediment-bound organic matter degrading salmonid spawning gravels in a lowland river, southern England. Science of the Total Environment 456-457, 181-195.

Collins, A.L., Williams, L.J., Zhang, Y.S., Marius, M., Dungait, J.A.J., Smallman, D.J., Dixon, E.R., Stringfellow, A., Sear, D.A., Jones, J.I. and Naden, P.S. (2014). Sources of sediment-bound organic matter infiltrating spawning gravels during the incubation and emergence life stages of salmonids. Agriculture, Ecosystems and Environment 196, 76-93.

Collins, A.L., Stutter, M. and Kronvang, B. (2014). Mitigating diffuse pollution from agriculture: international approaches and experience. Science of the Total Environment 468-469,1173-1177.

Comber, A., Anthony, A., Proctor, C., 2008. The creation of a national agricultural land use dataset: combining pycnophylactic interpolation with dasymetric mapping techniques. Transactions in GIS 12, 775- 791.

Cooper RJ, Rawlins BG, Léze B, Krueger T & Hiscock KM (2014a) Combining two filter paper-based analytical methods to monitor temporal variations in the geochemical properties of fluvial suspended particulate matter. Hydrological Processes, DOI: 10.1002/hyp.9945.

Hiscock KM, Rawlins BG (2014b) High-temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach. Earth Surface Processes and Landforms, DOI: 10.1002/esp.3621.

CREH (2005) Monitoring the effectiveness of field and steading measures to reduce the diffuse pollution from agriculture to bathing waters in the Ettrick, Cessnock, Nairn and Sandyhills catchments. Reference ENV/7/4/04. Scottish Executive Water Division. 75p. CREH Aberystwyth.

CREH (2010a) Source Strengths and Attenuation of Faecal indicators Derived from Livestock Farming Activities: Literature Review Interim Report DEFRA Project WQ0203 Demonstration Test Catchments Initiative. 32p. CREH Aberystwyth.

CREH (2010b) The use of farm ponds to reduce pollution from farmyard dirty water: performance and effectiveness. Final report for project FF/05/23 33p plus figures and tables. CREH Aberystwyth.

D’Arcy, B. and Frost, A. (2001). The role of best management practices in alleviating water quality problems associated with diffuse pollution. Journal of Environmental Quality 265, 359-367.

Davy-Bowker, J., Clarke, R., Corbin, T., Vincent, H., Pretty, J., Hawczak, A., Blackburn, J., Murphy, J. and Jones, J.I. (2008). River Invertebrate Classification Tool. Final report. SNIFFER Project WFD72C.

Deasy, C., Quinton, J.N., Silgram, M., Bailey, A.P., Jackson, B. & Stevens, C.J., 2010. Contributing understanding of mitigation options for phosphorus and sediment to a review of the efficacy of contemporary agricultural stewardship measures. Agricultural Systems, 103, 105–109.

Defra, 2014. The new Common Agricultural Policy schemes in England: August 2014 update. Available at: [Accessed 19 November 2014].

184

Defra. Catchment Sensitive Farming @ http://www.defra.gov.uk/food-farm/land-manage/nitrates- watercourses/csf/ accessed June 2012

Dils R, 2009. Hampshire Avon catchment description & background research document for DEFRA-EA Demonstration Test Catchments project, October 2009. Avon DTC support document.

Dixon, S. 2012. The Effects of Land Management and Predicted Climate Change on Hydrological Connectivity and Diffuse Fine Sediment Pollution Risk Within The River Eden Catchment; Durham University Master by Research thesis

Dockerty, T., Lovett, A.A., Hiscock, K.M. & Noble, L., 2014. Extending knowledge exchange in the Wensum DTC: Report on activities 2010-2014. Report to Defra, University of East Anglia, Norwich. Environmental Quality 38, 1981–1988.

Friberg, N., Skriver, J., Larsen, S.E., Pedersen, M.L. and Buffagni, A. (2010). Stream macroinvertebrate occurrence along gradients in organic pollution and eutrophication. Freshwater Biology 55, 1405–1419.

Funtowicz, S.O., and Ravetz, J.R. (1993). Science for the post normal age. Futures 25, 739-755.

Galeone, D.G. (1999). Calibration of paired basins prior to streambank fencing of pasture land. Journal of Environmental Quality 28, 1853-1863.

Galeone, D.G. (2005). Pequea and Mill Creek Watersheds Section 319 NMP Project: Eff ects of Streambank Fencing on Surface-water Quality. NWQEP Notes 118: August, 2005. North Carolina State Univ., Raleigh.

Gooday, R.D., Anthony, S.G., Chadwick, D.R., Newell-Price, P., Harris, D., Deuthmann, D., Fish, R., Collins, A.L. and Winter, M. (2014). Modelling the cost-effectiveness of mitigation methods for multiple pollutants at farm scale. Science of the Total Environment 468-469, 1198-1209.

Grabow, G.L., Spooner, J., Lombardo, L. and Line, D.E. (1999). Detecting water quality changes before and after BMP implementation: use of SAS for statistical analysis. NWQEP Notes, Number 93, NCSU Water Quality Group, Raleigh, NC.

Hamilton, S.K. (2012). Biogeochemical time lags may delay responses of streams to ecological restoration. Freshwater Biology 57, 43–57.

Harremoes, P., Gee, D., MacGarvin, M., Stirling, A., Keys, J., Wynne, B., Guedes Vaz, S. (2002). Late lessons from early warnings: the precautionary principle 1896–2000.Envir onmental issue report, 22.OPOCE.

Harrington, R., Dunne, E.J., Carroll, P., Keohane, J. and Ryder, C. (2005). The concept, design and performance of integrated constructed wetlands for the treatment of farmyard dirty water’. Pp. 179-188 in Dunne, E.J., Reddy, K.R. and Carton, O.T. (Eds.): Nutrient Management in Agricultural Watersheds: A Wetlands Solution, Wageningen Academic Publishers, The Netherlands.

Harris, G.P. and Heathwaite, A.L. (2012). Why is achieving good ecological outcomes in rivers so difficult? Freshwater Biology 57, 91–107.

Haygarth, P. M., Condron, L. M., Heathwaite, A. L., Turner, B. L., Harris, G. P., (2005). The phosphorus transfer continuum: Linking source to impact with an interdisciplinary and multi-scaled approach. Science of the Total Environment 344, 5-14.

185

Haygarth, P.M., Page, T.J.C., Beven, K.J., Freer, J., Joynes, A., Butler, P., Wood, G.A. and Owens, P.N. (2012). Scaling up the phosphorus signal from soil hillslopes to headwater catchments. Freshwater Biology 57, 7– 25.

Hering, D., Borja, A., et al. 2010. The European Water Framework Directive at the age of 10: A critical review of the achievements with recommendations for the future. Science of the Total Environment, 408, 4007–4019.

Hewett, C.J.M., Quinn, P.F., Heathwaite, A.L., Whitehead, P.G., Lerner, D.N. and Doyle, A. (2009). A multi- scale framework for strategic management of diffuse pollution. Environmental Modelling and Software 24, 74-85.

Hiscock KM (1993) The influence of pre-Devensian glacial deposits on the hydrogeochemistry of the chalk aquifer system of north Norfolk, UK. Journal of Hydrology 144: 335-369

Hiscock KM, Dennis PF, Saynor PR & Thomas MO (1996) Hydrochemical and stable isotope evidence for the extent and nature of the effective Chalk aquifer of north Norfolk, UK. Journal of Hydrology 180: 79-107.

Hodgson, C. J., J. Perkins and J. C. Labadz (2003). Evaluation of biotracers to monitor effluent retention time in constructed wetlands. Letters in Applied Microbiology 36(6), 362-371.

Hodgson, C. J., J. Perkins and J. C. Labadz (2004). The use of microbial tracers to monitor seasonal variations in effluent retention in a constructed wetland. Water Research 38(18), 3833-3844.

Holmes NTH & Whitton BA (1977) The macrophytic vegetation of the River Tees in 1975: observed and predicted changes. Freshwater Biology 7: 43-60

Holmes NTH, Newman JR, Chadd S, Rouen KJ, Saint L and Dawson FH (1999) Mean Trophic Rank: A User's Manual. Environment Agency. R&D Technical Report E38

Jarvie HP, Neal C, Withers PJA, Wescott C, Acornley RM, 2005. Nutrient hydrochemistry for a groundwater- dominated catchment: The Hampshire Avon, UK. Science of the Total Environment 344 (1-3 SPEC. ISS.): 143-158.

Johnes, P. J. (2007). Uncertainties in annual riverine phosphorus load estimation: Impact of load estimation methodology, sampling frequency, baseflow index and catchment population density. Journal of Hydrology 332, 241-258

Johnes, P. J. and Heathwaite, A. L. 1992: A procedure for the simultaneous determination of total nitrogen and total phosphorus in freshwater samples using persulphate microwave digestion. Water Research 26, 10, 1281-1287.

Jones, J.I., Murphy, J.F., Collins, A.L., Sear, D.A. and Naden, P.S. (2012a). The impact of fine sediment on macro-invertebrates. River Research and Applications 28, 1055-1071.

Jones, J.I., Collins, A.L., Naden, P.S, and Sear, D.A. (2012b). The relationship between fine sediment and macrophytes in rivers. River Research and Applications 28, 1006-1018.

Jones, J.I., Duerdoth, C.P., Collins, A.L., Naden, P.S. and Sear, D.A. (2014). Interactions between diatoms and fine sediment. Hydrological Processes 28, 1226-1237.

186

Jordan, P., Arnscheidt, J., McGrogan, H. and McCormick, S. (2007). Characterizing phosphorus transfers in rural catchments using a continuous bank-side analyzer. Hydrology and Earth System Sciences 11, 372–381.

Jordan, P., Melland, A.R., Mellander, P.-E., Shortle, G. and Wall, D. (2012). The seasonality of phosphorus transfers from land to water: implications for trophic impacts and policy evaluation. Science of the Total Environment 434, 101-109.

Jordan, P., Menary, W., Daly, K., Kiely, G., Morgan, G., Byrne, P., et al. (2005). Patterns and processes of phosphorus transfer from Irish grassland soils to rivers—integration of laboratory and catchment studies. Journal of Hydrology 304, 20–34.

Kay ,D., Francis, C., Edwards, T., Kay, ., McDonald, A., Lowe, N., Stapleton, C., Watkins, J., and Wyer, M. (2005) The efficacy of natural wastewater treatment systems in removing faecal indicator bacteria. UKWIR Report for project WW21. 51p.

Kay, D., Aitken, M., Crowther, J., Dickson, I., Edwards, A.C., Francis, C., Hopkins, M., Jeffrey, W., Kay, C., McDonald, A.T., McDonald, D., Stapleton, C.M., Watkins, J., Wilkinson, J. and Wyer, M. (2007) Reducing fluxes of faecal indicator compliance parameters to bathing waters from diffuse agricultural sources, the Brighouse Bay study, Scotland. Environmental Pollution 147, 139-149. (doi:10.1016/j.envpol.2006.08.019). Kay, D., Bartram, J., Pruss, A., Ashbolt, N., Wyer, M. D., Fleisher, J. M., Fewtrell, L., Rogers, A., and Rees, G. 2004: Derivation of numerical values for the World Health Organization guidelines for recreational waters. Water Research 38, 1296-1304.

Kay, D., Crowther, J., Stapleton, C.M., Wyer, M.D., Anthony, S., Bradford, M., Edwards, A., Fewtrell, L., Francis, C., Hopkins, M., Kay, C., McDonald, A.T., Watkins, J. and Wilkinson, J. (2008a) Faecal indicator organism concentrations and catchment export coefficients in the UK. Water Research 42, 2649-2661. (doi:10.1016/j.watres.2008.01.017).

Kay, D., Fleisher, J. M., Salmon, R. L., Jones, F., Wyer, M. D., Godfree, A. F., Zelenauchjacquotte, Z., and Shore, R. 1994: Predicting the likelihood of gastroenteritis from sea bathing - Results from randomized exposure. Lancet 344, 905-909.

Kay, D., Stapleton, C.M., Crowther, J., Wyer, M.D., Fewtrell, L., Edwards, A., McDonald, A.T., Watkins, J., Francis, F. and Wilkinson, J. (2008b) Faecal indicator organism compliance parameter concentrations in sewage and treated effluents. Water Research 42, 442-454. (doi:10.1016/j.watres.2007.07.036).

Kemp, P., Sear, D., Collins, A., Naden, P. and Jones, I. (2011). The impacts of fine sediment on riverine fish. Hydrological Processes 25, 1800-1821.

King, K.W., Smiley, P.C., Baker, B.J. and Fausey, N.R. (2008). Validation of paired watersheds for assessing conservation practices in the Upper Big Walnut Creek watershed, Ohio. Journal of Soil and Water Conservation 63, 380–395.

Kirchner, J.W., Feng, X.H. and Neal, C. (2000). Fractal stream chemistry and its implications for contaminant transport in catchments. Nature 403, 524–527.

187

Kleinman, P.J.A., Sharpley, A.N., Buda, A.R., McDowell, R.W. and Allen, A.L.. (2011). Soil controls of phosphorus in runoff: Management barriers and opportunities. Canadian Journal of Soil Science 91, 329– 338.

Kyllmar, K., Carlsson, C., Gustafson, A., Ulén, B. and Johnsson, H. (2006). Nutrient discharge from small agricultural catchments in Sweden. Characterisation and trends. Agriculture, Ecosystems and Environment 115, 15–26.

Lankester, A., Valentine, R. and Cottrell, A. (2009). ‘The sweeter country’: Social dimensions to riparian management in the Burdekin rangelands, Queensland. Australas. J. Environ. Manage. 16, 94–102.

Larsen, R.E., Krueger, W.C., George, M.R., Barrington, M.R., Buckhouse, J.C. and Johnson, D.E. (1998) Livestock influences on riparian zones and fish habitats: literature classification. Journal of Range Management 51, 661–664.

Lemke, A.M., Kirkham, K.G., Lindenbaum, T.T., Herbert, M.E., Tear, T.H., Perry, W.L., Herkert, J.R. (2011). Evaluating agricultural best management practices in tile-drained subwatersheds of the Mackinaw River, Illinois. Journal of Environmental Quality 40, 1215-1228.

Lewis MA (2011) Borehole drilling and sampling in the Wensum Demonstration Test Catchment. British Geological Survey Commissioned Report, CR/11/162, pp. 38

Lloyd, C. E. M., Freer, J. E., Johnes, P. J., Coxon, G. and Collins, A. L. (in review). Discharge uncertainty: implications for nutrient flux estimation in small streams. Submitted to Hydrological Processes, 15/09/2014. Awaiting reviewer comments.

Lloyd, C. E. M., Freer,J. E., Collins, A. L., Johnes, P. J. and Jones, J. I. (2014) Methods for detecting change in hydrochemical time series in response to targeted pollutant mitigation in river catchments. Journal of Hydrology, 514, 297-312. http://dx.doi.org/10.1016/j.jhydrol.2014.04.036

Loftis, J.C., McDonald, L.H., Street, S., Hariharan, K.I. and Bunte, K. (2001). Detecting the cumulative watershed effects: The statistical power of pairing. Journal of Hydrology 251, 49–64.

Longabucco, P. and Rafferty, M. (1998). Analysis of material loading to Cannonsville reservoir: advantages of event-based sampling. Lake and Reservoir Management 14, 197-212.

Marsh, T. J., and Hannaford, J.: Hydrometric Register. Hydrological Data UK Series., Centre for Ecology and Hydrology, Wallingford, 2008.

Marshall, D.W., Fayram, A.H., Panuska, J.C., Baumann, J. and Hennessey, J. (2008). Positive effects of agricultural land use changes on coldwater fish communities in Southwest Wisconsin streams. North American Journal of Fisheries Management 28, 944–953.

Matheswaran, K., Blemmer, M., Rosbjerg, D. and Boegh, E. (2014). Seasonal variations in groundwater upwelling zones in a Danish lowland stream analyzed using Distributed Temperature Sensing (DTS). Hydrological Processes 28, 1422–1435.

McGonigle DF, Burke SP, Collins AL, Gartner R, Haft MR, Harris RC, Haygarth PM, Hedges MC, Hiscock, KM & Lovett, AA (2014) Developing demonstration test catchments as a platform for transdisciplinary land management research in England and Wales. Environmental Science: Processes & Impacts DOI: 10.1039/c3em00658a

188

McGonigle, D.F., Burke, S.P., Collins, A.L., Gartner, R., Haft, M.R., Harris, R.C., Haygarth, P.M., Hedges, M.C., Hiscock, K.M. & Lovett, A.A., 2014. Developing demonstration test catchments as a platform for transdisciplinary land management research in England and Wales. Environmental Science: Processes & Impacts, DOI: 10.1039/c3em00658a.

McMillan, H., Krueger, T. and Freer, J. (2012). Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality. Hydrological Processes 26, 4078-4111.

Meals, D.W. (1990). LaPlatte River watershed water quality monitoring and analysis program: Comprehensive fi nal report. Program Rep. 12. Vermont Water Resources Research Centre, Univ. Vermont, Burlington.

Meals, D.W. (2001). Lake Champlain basin agricultural watersheds Section 319 National Monitoring Program, Final Project Report, May 1994 to November 2000. Vermont Dep. of Environ. Conserv.,Water Qual. Div., Waterbury.

Meals, D.W., Dressing, S.A. and Davenport, T.E. (2010). Lag time in water quality response to best management practices: a review. Journal of Environmental Quality 39, 85-96.

Melland, A.R., Mellander, P.-E., Murphy, P.N.C., Wall, D.P., Mechan, S., Shine, O., Shortle, G. and Jordan, P. (2012). Stream water quality in intensive cereal cropping catchments with regulated nutrient management. Environmental Science and Policy 24, 58-70.

Meteorological Office (2013) UK climate averages: Reepham 1981-2010. Meteorological Office, Exeter. Online: http://www.metoffice.gov.uk/public/weather/climate/#?tab=climateTables

Meybeck, M and Moatar, F. (2014). Daily variability of suspended particulate concentrations and yields and their effect on river particulates chemistry. In: Sediment dynamics from the summit to the sea (Ed. by Jun, X., Allison, M., Bentley, S.J., Collins, A.L., Erskine, W.D., Golosov, V., Horowitz, A., Stone, M.), International Association of Hydrological Sciences Publication No. 367, pp 12-27.

Meybeck, M. and Moatar, F. (2012). Daily variability of river concentrations and fluxes: indicators based on the segmentation of the rating curve. Hydrological Processes 26, 1188–1207.

Michener, W.K. (1997). Quantitatively evaluating restoration experiments: research design, statistical analysis and data management considerations. Restoration Ecology 5, 324-337.

Moatar, F., Meybeck, M., Raymond, S., Birgand, F. and Curie, F. (2013). River flux uncertainties predicted by hydrological variability and riverine material behaviour. Hydrological Processes 27, 3535-3546.

Murray-Bligh JAD, Furse MT, Jones FH, Gunn RJM, Dines RA and Wright JF (1997) Procedure for collecting and analysing macroinvertebrate samples for RIVPACS. Institute of Freshwater Ecology & Environment Agency, 155pp.

Newbold, J.D., Herbert, S., Sweeney, B.W. and Kiry, P. (2008). Water quality functions of a 15-year-old riparian forest buffer system. p. 1–7. In Riparian ecosystems and buff ers: Working at the water’s edge. AWRA Summer Specialty Conf. Am. Water Resources Assoc., Virginia Beach, VA.

Newell-Price, Harris, D., Taylor, M., Williams, J.R., Anthony, S.G., Duethmann, D., Gooday, R.D., Lord, E.I., Chambers, B.J., Chadwick, D.R. & Misselbrook, T.H., 2011. An inventory of mitigation methods and guide to

189 their effects on diffuse water pollution, greenhouse gas emissions and ammonia emissions from agriculture. Report prepared as part of Defra Project WQ0106.

Newson, J. (2007). Measurement and modeling of sediment transport in a northern Idaho stream. Master’s thesis. Biological and Agric. Eng., College of Graduate Studies, Univ. of Idaho, Moscow.

Outram, F. N., Lloyd, C., Jonczyk, J., Benskin, C. McW. H., Grant, F., Dorling, S. R., Steele, C. J., Collins, A. L., Freer, J., Haygarth, P. M., Hiscock, K. M., Johnes, P. J., and Lovett, A. L (2014) High-frequency monitoring of nitrogen and phosphorus response in three rural catchments to the end of the 2011–2012 drought in England. Hydrol. Earth Syst. Sci., 18, 3429–3448, doi:10.5194/hess-18-3429-2014.

Page, T., Heathwaite, A.L., Moss, B., Reynolds, C., Beven, K.J., Pope, L. and Willows, R. (2012). Managing the impacts of nutrient enrichment on river systems: dealing with complex uncertainties in risk analyses. Freshwater Biology 57, 108-123.

Palmer, M., Allan, J.D., Meyer, J. and Bernhardt, E.S. (2007). River restoration in the twenty-fi rst century: Data and experiential future eff orts. Restoration Ecology 15, 472–481.

Palmer, M.A., Menninger, H.L. and Bernhardt, E.. (2010). River restoration, habitat heterogeneity and biodiversity: A failure of theory or practice? Freshwater Biology 55, 205–222.

Parr, T.W., Sier, A.R.J., Battarbee, R.W., Mackay, A., Burgess, J. (2003). Detecting environmental change: science and society—perspectives on long-term research and monitoring in the 21st century. Science of the Total Environment 310, 1-8.

Perry M.C. and Hollis D.M. 2005. The generation of monthly gridded datasets for a range of climatic variables over the UK. International Journal of Climatology 25: 1041-1054

Proctor, A., Phillipson, J., Lowe, P. & Donaldson, A., 2011. Field advisors as agents of knowledge exchange. RELU Policy and Practice Note No. 30. Available at [Accessed 27 December 2014].

Quinn, J.M., Cooper, A.B., Davies-Colley, R.J., Rutherford, J.C. and Williamson, R.B. (1997). Land-use effects on habitat, water quality, periphyton and benthic invertebrates in Waikato, New Zealand, hill country streams. New Zealand Journal of Marine and Freshwater Research 31, 579–597.

Rawlins BG (2011) A pilot study to assess soil spectroscopic methods for mapping key topsoil properties in the Blackwater sub-catchments (Wensum DTC). British Geological Survey Internal Report, OR/11/053

Reckhow, K.H., Norris, P.E., Budell, R.J., Di Toro, D.M., Galloway, J.N., Greening, H. Sharpley, A.N. Shirmhhammadi, A. and Stacey, P.E.. (2011). Achieving nutrient and sediment reduction goals in the Chesapeake Bay: An evaluation of program strategies and implementation. Th e National Academies Press, Washington, DC.

Rhodes, H.M., Closs, G.P., Townsend, C.R. (2007). Stream ecosystem health outcomes of providing information to farmers and adoption of best management practices. Journal of Applied Ecology 44, 1106- 1115.

Robson, A. and Reed, D.: Flood estimation handbook – FEH CDROM 3, Institute of Hydrology, Wallingford, 1999.

190

Scanlon, T.M., Kiely, G. and Amboldi, R. (2005). Model determination of non-point source phosphorus transport pathways in a fertilized grassland catchment. Hydrological Processes 19, 2801–14.

Schilling, K.E., and Spooner, J. (2006). Effects of watershed-scale land use change on stream nitrate concentrations. Journal of Environmental Quality 35, 2132–2145.

Schilling, K.E., Jones, C.S. and Seeman, A. (2014). How paired is paired? Comparing nitrate concentrations in three Iowa drainage districts. Journal of Environmental Quality 42, 1412-1421.

Scientific and Technical Advisory Committee, Chesapeake Bay Program. (2005). Understanding ‘lag times’ affecting the improvement of water quality in the Chesapeake Bay. STAC Publ. 05–001. Chesapeake Bay Program, Edgewater, MD.

Sear DA, Newson M, Old JC & Hill C (2006) Geomorphological appraisal of the River Wensum Special Area of Conservation. English Nature Research Reports, No 685

Selker, F., and Selker, J.S. (in press). Flume testing of underwater seep detection using temperature sensing on or just below the surface of sand or gravel sediments. Water Resources Research, DOI: 10.1002/2014WR015257

Selker, J., N. van de Giesen, M. Westhoff, W. Luxemburg, and M. B. Parlange (2006). Fiber optics opens window on stream dynamics, Geophysical Research Letters, 33, L24401, DOI:10.1029/2006GL027979.

Sharpley, A.N., Jarvie, H.P., Buda, A., May, L., Spears, B. and Kleinman, P. (2013). Phosphorus legacy: overcoming the effects of past management practices to mitigate future water quality impairment. Journal of Environmental Quality 42, 1308-1326.

Sharpley, A.N., Kleinman, P.J.A., Jordan, P., Bergstrom, L. and Allen, A.L. (2009). Evaluating the success of phosphorus management from field to watershed. Journal Environmental Quality 38, 1981-1988.

Shore, M., Jordan, P., Mellander, P.-E., Kelly-Quinn, M., Wall, D.P., Murphy, P.N.C. and Melland, A.R. (2014). Evaluating the critical source area concept of phosphorus lossfrom soils to water-bodies in agricultural catchments. Science of the Total Environment 490, 405-415.

Smith, E. P. (2002). BACI design. Encyclopedia of Environmetrics 1, 141-148.

Smith, E.P. (1993). Impact assessment using the before-after-control-impact (BACI) model: concerns and comments. Canadian Journal of Fisheries and Aquatic Sciences 50, 627-637.

Smith, L., Hiscock, K. & Porter, K., 2010. Catchment management for the protection of water resources: A template. Available at < http://www.watergov.org/index.html> [Accessed 27 December 2014]

Snell, M.A., Barker, P.A., Surridge, B.W.J., Large, A.R. G., Jonczyk, J., Benskin, C. MCW., W., Reaney, S., Perks, M.T., Owen, G.J., Cleasby, W., Deasy, C., Burke, S. and Haygarth, P.M. 2014. High frequency variability of environmental drivers determining benthic community dynamics in headwater streams. Environmental Science: Processes and Impacts 16: 1629-36

191

Spash, C.L., Urama, K., Burton, R., Kenyon, W., Shannon, P. and Hill. G. (2009). Motives behind willingness to pay for improving biodiversity in a water ecosystem: Economics, ethics, and social psychology. Ecological Economics 68, 955–964.

Spears, B.M., Carvalho, L., Perkins, R., Kirika, A. and Paterson, D.M.. (2012). Long term variation and regulation of internal phosphorus loading in Loch Leven. Hydrobiologia 681, 23–33.

Spooner, J. (2011). Minimum detectable change (MDC). NWQEP Notes. NCSU Water Quality Group Newsletter 136. North Carolina State Univ., Raleigh.

Spooner, J., Jamieson, C.J., Maas, R.P. and Smolen, M.D. (1987). Determining statistically significant changes in water pollutant concentrations. Lake and Reservoir Management 3, 195–201.

Stapleton, C., Lowe, N., Kay, D. and Watkins, J. (2006) Reducing the amount of bacteria discharged from small sewage treatment works: Results of field studies at ten tertiary treatment plants. Report to Environment Agency (North West Region). ICREW Pilot Action 6: Sustainable Sewage Solutions.

Stewart-Oaten, A.,, Murdoch, W.W, and Parker, K.R. (1986). Environmental impact assessment: pseudoreplication in time? Ecology 67, 929-940.

Terry JA, Benskin CMcWH, Eastoe EF & Haygarth PM (2014) Temporal dynamics between cattle in-stream presence and suspended solids in a headwater catchment. Environmental Science: Processes & Impacts DOI: 10.1039/c3em00686g

Toynton, R (1983) The relation between fracture patterns and hydraulic anisotropy in the Norfolk twenty-fi rst century: Data and experiential future eff orts. Restoration Ecology 15, 472–481.

Tyler, S.W., J.S. Selker, M.B. Hausner, C.E. Hatch, T. Torgersen and S. Schladow (2009). Environmental temperature sensing using Raman spectra DTS fiber optic methods. Water Resources Research, DOI:10.1029/2008WR007052 4(187):673-679.

UK Biodiversity Action Plan Steering Group for Chalk Rivers, 2004. The State of England’s Chalk Rivers. Environment Agency, Bristol.

US Environmental Protection Agency, 2008. Handbook for developing watershed plans to restore and protect our waters, EPA 841-B-08-002. Available at [Accessed 19 November 2014].

USEPA. (1993). Paired watershed study design. EPA 841-F-93-009. USEPA Office of Water, Washington, DC

USEPA. (1997a). Techniques for tracking, evaluating, and reporting the implementation of nonpoint source control measures: agriculture. EPA 841-B-97-010. USEPA Office of Water, Washington, DC.

USEPA. (1997b). Linear regression for nonpoint source pollution analyses. EPA 841-B-97-007. USEPA Office of Water, Washington DC.

Vought, L.B.M., Dahl, J., Pedersen, C.L. and Lacoursiere, J.O. (1994). Nutrient retention and processing in riparian ecotones. Ambio 23, 342–348.

Vrain, E., 2014a. The role of farm advisors in improving the uptake of water quality and other environmental measures. Available at: [Accessed 12 December 2014] 192

Vrain, E., 2014b. The niche of Catchment Sensitive Farming in the provision of advice to farmers. Available at: < http://www.wensumalliance.org.uk/briefing_notes/CSF_farm_advisors_Emilie_ Vrain.pdf > [Accessed 12 December 2014].

Vrain, E., Lovett, A.A., Noble, L., Grant, F., Blundell, P. & Cleasby, W., 2014. Farmer attitudes towards diffuse pollution mitigation measures in England. Report to Defra, University of East Anglia, Norwich.

Walling DE, Collins AL, Stroud RW, 2008. Tracing suspended sediment and particulate phosphorus sources in catchments. Journal of Hydrology 350 (3-4): 274-289.

Wang, L., Lyons, J. and Kanehl, P. (2002). Effects of watershed best management practices on habitat and fish in Wisconsin streams. Journal of the American Water Resources Association 38, 663-680.

Wang, L., Stuart, M.E., et al 2012. Prediction of the arrival of peak nitrate concentrations at the water table at the regional scale in Great Britain. Hydrological Processes, 26, 226–239.

Watson, N., Oughton, L. & Collins, K., 2013. Developing knowledge exchange approaches to secure sustained behaviour changes to deliver multiple environmental benefits through sustainable land management. Final report on Project FFG 1007 to Defra, Lancaster Environment Centre, Lancaster.

WHO 2003: Guidelines for safe recreational water environments Volume 1: Coastal and freshwaters World Health Organisation, Geneva.

Winter, M., Collins, A.L., Fish, R., Lobley, M., Spencer, N. & Winter, H., 2014. Extending knowledge exchange in the Avon DTC: Report on activities 2010-2014. Report to Defra, University of Exeter, Exeter.

Winter, M., Oliver, D.M., Fish, R., Heathwaite, A.L., Chadwick, D. and Hodgson, C. (2011). Catchments, sub- catchments and private spaces: scale and process in managing microbial pollution from source to sea. Environmental Science and Policy 14, 315-326.

Yates, C. A. and Johnes, P. J. (2013) Nitrogen speciation and phosphorus fractionation dynamics in a lowland chalk catchment. Sci. Total Environ. 444, 466–479. http://dx.doi.org/10.1016/j.scitotenv.2012.12.002.

Zhang, Y., Collins, A.L. & Gooday, R.D., 2012. Application of the FARMSCOPER tool for assessing agricultural diffuse pollution mitigation methods across the Hampshire Avon Demonstration Test Catchment, UK. Environmental Science and Policy, DOI: 10.1016/ j.envsci.2012.08.003.

193

8 Appendices

Appendix 1.1 The Added Value of DTC The research communities, monitoring infrastructure and data generated by the core DTC projects support a number of satellite projects to test mitigation measures and further understand the physical, ecological and social functioning of river catchments. Activities hosted on the DTC research platform are funded by Defra and other organisations. Significant in-kind contributions are provided by the Rivers Trusts and farmers from various sources. This annex gives details of the work supported by Defra’s investment in the DTC platform. The on-farm measures that are being tested have been funded from several sources.

There are a growing number of research projects funded from outside of the core DTC budget using the facilities and the accumulated knowledge developed and assimilated by the DTC project. These are adding to the accumulating knowledge and understanding and helping to establish a linked inter- and multidisciplinary research community. Some examples include:

Research Councils and Universities:  The British Geological Survey (BGS) contributing funds to augment the installation of boreholes, geological core sampling and chemical analysis of pore waters and groundwaters and undertaking surveys of shallow groundwater sources and hydrochemistry (£320k)  Two NERC Macronutrients Cycle thematic programme research projects working on the Hampshire Avon, investigating the role of lateral exchange in modulating the seaward flux of C, N and P and examining macronutrient fluxes and impacts in Christchurch harbour (£4m)  The NERC DOMAINE Large Grant programme on Characterisation of the nature, origins and ecological significance of dissolved organic matter in freshwater ecosystems (£2.75m). The programme started 1st April 2014 and involves nine project partner organisations including Defra, Environment Agency, Natural England, Natural Resources Wales, Wessex Water, Welsh Water, Scottish Water, The Rivers Trust and the Institute for Catalysis, Madrid.  An ESRC project based in the Eden and Wensum on ‘Spatially targeted and coordinated regulation of agricultural externalities: An economic perspective’.  The NERC Environmental Virtual Observatory built on the Eden DTC to develop decision support and knowledge management tools, and used data from the Hampshire Avon DTC to calibrate the new National Biogeochemical Modelling Framework for geoclimatic regions occurring in the catchment (£1.6m)  A NERC “Changing Water Cycles” project (£2m)  Nearly 30 PhD projects and post-doctoral research fellows engaged across the DTC catchments that are funded through a variety of sources (including matched DTC and university funds). Their outputs are adding value to our accumulated understanding of the land/water interactions and systems and building partnerships with stakeholder groups such as the Rivers Trusts.

Defra:  Defra investment in a number of research projects including work on sediment source tracing and the Measures Component of DTC which is dependent on the continuation of monitoring (£1.5m)  Defra investment via project WQ0124 – Assessing the status of drainage in UK agriculture: a case study in the Demonstration Test Catchments (~£200k)  Defra investment as part of the Sustainable Intensification Platform (SIP) theme 2 – Opportunities and risks for farming and the environment at landscape scale

194

 Defra investment for a national soil erosion project (Scaling up the benefits of field scale soil protection measures to understand their impact at the landscape scale) involving Cranfield University, ADAS, Rothamsted Research-North Wyke, the University of Northampton and Anglia Ruskin University) is using the Avon DTC as the development landscape for this new national tool (~£200k)

Environment Agency/Catchment Sensitive Farming (CSF): There are a number of linked initiatives between the EA and/or CSF and the DTC consortia that have taken place or are in progress. Amongst them are:  CSF supporting a study of the effectiveness of a biobed for attenuating the runoff of pesticides at a wash-down facility in a DTC farmyard.  Helping knowledge exchange between DTC and CSF. In an attempt to find out what CSFOs needed by way of evidence, and where gaps could be plugged by generating, accessing or synthesising the evidence a team from University of Exeter and ADAS (part of the Avon DTC research consortia) ran 3 workshops with CSFOs using a facilitated discussion. The resultant report details 95 individual ‘asks’ gleaned from the sessions.  Targeting faecal indicator organism sources within a catchment in relation to their relative impact on bathing/shellfish waters is a project arising from these workshops.  Sediment fingerprinting guide - CSF and others have successfully used a variety of fingerprinting techniques to characterise sediment and phosphorus sources in priority catchments, which are developing into powerful tools for catchment scale diffuse pollution management. A synthesis of the different techniques in an accessible guide form will greatly benefit CSFOs undertaking these studies in their catchments.  An investigation into the influencing of farmers in relation to CSF is being undertaken by The Exeter University Social Science team, part of the DTC research consortia. The aim of this project is to suggest practical ways to predict the likely successful influencing of different farmer ‘types’ (sector/regions etc.) by the CSF (voluntary) approach.  Combining remote sensing with local knowledge to guide catchment work reducing diffuse pollution from farming. This project will produce a set of spatial data outputs (maps) developed collaboratively with farmers to; help increase involvement in diffuse pollution (DP) schemes and to help select and target mitigation options within the landscape.  Catchment Matcher is a spatial data tool being developed through the DTC project (led by UEA) that will allow river catchments to be compared to one another in a flexible fashion. The tool will help the ‘read across’/extrapolation of results from one catchment to others that are similar where data may not exist.  A PhD at UEA (Andrew Lovett/ Emilie Vrain of the Wensum DTC) on the ‘Role of Farm Advisors in Improving the Uptake of Measures’ is using CSF data and CSF is benefitting from the analysis of measures take-up through different agents and schemes.  DTC and CSF biologists are networking over analysis techniques for biological improvements in relation to diffuse pollution studies.  CSF and Regional EA survey work on pesticides is being synthesised with the Wensum DTC (Appendix 4).  CSFOs in the three DTC catchments are working closely with the research groups on tackling local issues in the study areas. CSF has provided some grants for measures being tested as part of the DTC work.

195

 The DTC baseline farm business survey data were used in a modelling exercise (Rothamsted Research-North Wyke and ADAS) commissioned by Defra, the Environment Agency and Natural England, for identifying revised ‘basic measures’ for farmers for delivering the WFD.  CSF and DTC phase 2 are co-funding a new project in the Pewsey Vale area of the Avon DTC investigating farmer receptiveness to phosphorus mitigation measures and modelling the potential benefits of those benefits (Rothamsted Research and the University of Exeter working with CSF).  The EA has supplied monitoring technology and in kind contributions in terms of staff time during the design and setup to the DTC research consortia. Feedback from the use of this equipment will be useful in further developments for diffuse pollution operational work.  The EA is augmenting ecological surveying and sample analysis on two of the DTC catchments.

Water Industry:  The UKWIR programme on Phosphorus Contributions from Wastewater Treatment Works and their impacts on river ecology relative to diffuse catchment P sources in the Hampshire Avon DTC catchment (£120k)  The Environment Agency and Anglian Water are supporting a survey of pesticides in the Wensum catchment (£30k)  Links with Southwest Water’s Upstream Thinking project on the Tamar is funding the mitigation measures that DTC is testing on the Tamar (£500k)  Farmers’ in-kind contributions across the DTC catchments have been significant in helping to characterise the catchment areas and helping to install mitigation measures. In some cases farmers are funding additional experimental measures themselves (c. £75-100k)  The UKWIR programme (Extending and updating UKWIR’s source apportionment tool) has used understanding from the Avon DTC to help revised agricultural pollutant loss predictions (~£80k – Rothamsted Research-North Wyke, ADAS, Atkins)

Farming Industry:  An agronomy consultancy is assisting with soil moisture monitoring in hosting and providing field support for its web-based sensor system.  The partnership between Eden DTC, Newton Rigg College and Eden Rivers Trust is bringing the following benefits: o Demonstrating the benefits of simple on farm measures for farm businesses and environment o Educating the next generation of land managers through the demonstration centre at Newton Rigg. o Trialling novel high-risk or uncertain measures on the college farm (e.g. experimenting with cattle dietary nutrients, precision farming etc).

Non-governmental Organisations/ charities:  The Morland Beck mitigation plan in the Eden has been jointly developed between the Eden Rivers Trust (ERT) and DTC researchers, pooling ideas and resources to provide a more coordinated, efficient solution – better faster outcomes. This has brought in additional resources to deliver mitigation measures in the Morland DTC sub-catchment (c. £55k)

196

 In the Eden (Pow experimental area) the DTC evidence and presence in the area has helped develop the Pow Beck Catchment Restoration Fund project, which is now addressing issues of P and sediment in the sub-catchment (£370k)  In the Eden (Dacre experimental area) we have been able to link the Eden DTC initiative with the ALFA (Adaptive Land use for Flood Alleviation) project to address surface runoff impacts on both water quality and flood risk (£200k)  The Eden DTC project has linked with ERT’s own biological monitoring (e.g. fisheries and crayfish surveys) to build a more complete picture and develop a fuller understanding of the impacts of agricultural diffuse pollution on the ecology of the river. The Eden DTC provides a conduit to link the wider catchment community with academics to answer key questions from the community and provide evidence to support management decisions on the ground.

197

PhD Students Working on the DTC Platform DTC Consortium Name of Student Affiliation (University) Title of Project Award from & Value (£)

Avon Victoria Janes Exeter Evaluation of climate and land use change Co-funded by ADAS and University of impacts on fine sediment transport and Exeter ~£66K storage within UK rivers

Avon Christopher Yates Reading Characterising dissolved organic matter University of Reading funded ~£65k transport in UK catchments

Avon Eleni Reading Climatic controls on the transformations and Co-funded by DTC and University of Geropanagioti aquatic impacts of Carbon exported from Reading ~£30K from Reading riparian wetlands

Avon Moragh Stirling Reading Stream ecosystem functional responses to DTC funded catchment mitigation measures in the Hampshire Avon DTC

Avon Mattida Biddulph Northampton Assessing the efficacy of mitigation options for Co-funded by DTC and Northampton diffuse pollution from agriculture ~£30K from Northampton

Avon Sarah Lewis Bristol (+Bangor) Tracing the cycling and leaching of fertiliser N NERC tied studentship from agricultural land to water courses using novel environmental metabolomic and 15N- (DOMAINE Large Grant programme) stable isotope approaches

Avon Jon Pemberton Bristol Comprehensive determination of dissolved NERC CASE studentship (+FERA as CASE organic matter at the molecular scale to partner) underpin enhanced river water quality assessments

198

Avon Matthew Copes Bangor (+Bristol) The role of dissolved organic matter character NERC tied studentship in carbonaceous and nitrogenous disinfection by-product formation in water supplies (DOMAINE Large Grant programme)

Eden Caroline Mills Newcastle Sediments

Eden Claire Walsh Newcastle Salmon habitats

Eden Federico Fragalla Newcastle Groundwater

Eden Chris Murphy Durham Hydrological pathways

Eden Sam Townsend Durham Run off generation

Eden Katie Smith Durham Land management

Eden Steph Dixon Durham Hydrological connectivity

Eden Gareth Owens Newcastle Modelling water quality

Eden Will Roberts Lancaster Buffer strips

Eden Megan Webb Lancaster Biomonitoring

Eden Xing Wang Lancaster Phosphorus transport *

Eden Maria Snell Lancaster Biomonitoring

Eden Rosie Law Lancaster Nutrients

Wensum Ali Albaggar UEA Microbiological community composition of Saudi Arabian Government (£100k) surface waters in the Wensum catchment

Wensum Sarah Taigel UEA Visioning catchment futures ESRC Quota Studentship (£60k)

199

Wensum Richard Cooper UEA The role of organo-mineral suspended UEA Studentship with British Geological sediment dynamics as a control on phosphorus Survey as CASE partner (£60k) export from an instrumented agricultural catchment

Wensum Tom Read UEA The use of fibre optic temperature sensing in NERC Quota Studentship (£60k) hydrology and hydrogeology

Wensum Emilie Vrain UEA Farmer attitudes to diffuse pollution mitigation Defra/UEA funding measures

Wensum Sam Taylor UEA Application of a climate-hydro-biogeochemical EPSRC studentship model cascade for the prediction of regional impacts of land use and management practices on water quality under climate change

Wensum Zanist Hama-Aziz UEA Assessment of the effects of reduced Iraq government studentship cultivation practices in an arable system on soil properties and nutrient losses

Wensum Richard Cooper UEA Investigating organo-mineral suspended NERC Case studentship with BGS sediment dynamics as controls on phosphorus export from instrumented, agricultural test catchments

*China Bridge Project

200

Appendix 2.1 Problems Encountered with Sensor Performance and Bankside Analyser Data on the DTC Platform: Examples from the Hampshire Avon DTC A number of the sensors deployed in phase 1 have delivered unreliable data streams with significant data gaps owing to power outages, sensor drift, sensor mis-calibration, and in the case of the Phosphax analyser, reagent instability and decay between field visits. Examples of the problems generated in phase 1 are illustrated in Figures A2.1, A2.2 and A2.3, based on data from phase 1 monitoring on the Wylye at Brixton Deverill, and the Sem at Priors Farm, both in the Hampshire Avon DTC catchment. Similar problems were encountered across the four DTCs.

1 2

Figure A2.1: Reliability of sensors in action 1: Ammonium (NH4-N) and flow (Q) data at Brixton Deverill, Hampshire Avon DTC, 01/02/2012 – 25/07/2012. Blue line = ammonium sensor data; red line = Q. Red circles highlight 1sensor mis-calibration with sensor concentrations at between 10- 100 times higher than laboratory concentrations; 2sensor drift over an order of magnitude; and 3sensor drop-out

201

1.5

) 1

- 1.0

mg l mg 1 TP( 0.5

01/02 21/02 12/03 01/04 21/04 11/05 31/05 20/06 10/07 20/07

1.5

)

1 -

1.0 mg l mg

TP(

0.5

0 01/02 21/02 12/03 01/04 21/04 11/05 31/05 20/06 10/07 20/07

1.5

)

1 1.0 -

mg l mg

2 TP( 0.5

01/02 21/02 12/03 01/04 21/04 11/05 31/05 20/06 10/07 20/07

Figure A2.2: Reliability of sensors in action 2: Total P data at Brixton Deverill, Hampshire Avon DTC, 01/02/2012 – 25/07/2012. Green dots = daily samples; blue line = Phosphax sensor data. Red circles indicate 1Phosphax drop-out; and 21 to 5-fold underestimation of peak concentrations compared to the QA lab data

202

1200

1000

800

600

(NTU) Turbidity Turbidity 400

200

0 19/09/11 07/04/11 24/10/11 11/05/12 27/11/12 15/06/13 01/01/14 Figure A2.3: Reliability of sensors in action 3: Turbidity sensor data at Priors Farm, Hampshire Avon DTC, 19/09/2011 – 01/01/14. Data indicate significant and unresolved problems with bubble- trapping

While there are undoubtedly examples from phase 1 where sensor data are reliable and have been used to shed new insights into process controls on nutrient and sediment flux behaviours over short time periods within the river system, the staff resource required to achieve and then maintain these standards is high. Even in phase 1 there was inadequate staff resource to allow prompt resolution of these issues at all sites, and the sensor and high resolution cabinet instrumentation data, if used in isolation, may offer a misleading picture of nutrient and sediment flux dynamics across the platform. The uncertainties that this generates are explored in the main report in Part 4, and are also reported in a recent publication by Lloyd et al. (currently in review; pdf available on request).

Nevertheless, the parallel programme of daily sampling with sensor deployment operated across the DTC platform has allowed, for the first time, a rigorous analysis of uncertainties in the data streams generated from high resolution sensor networks and allowed the team to identify the nature and scale of these problems and the limitations of sensor networks in environmental monitoring. The outcomes of this analysis have fed into the revised thinking on monitoring for phase 2, given the substantially reduced budgets available to support the continued monitoring of nutrient and sediment behaviours in the control and mitigated catchments. As an example, a summary of monitoring infrastructure and biological and morphological endpoints in phase 1 in the Hampshire Avon and Tamar DTCs is given in Table A2.1, while the proposed amendments to the monitoring in these catchments in phase 2 are summarised in Table A2.2.

203

Table A2.1: Detailed breakdown of the sub-catchment monitoring infrastructure and biological endpoints for phase 1 of component 1

Flow Sub-catchment Kiosk type and Determinands Biological and Determinands parameters (with Size Geology surface water monitored in morphological Catchment monitored in measured treatment (km2) /soil monitoring grab/ISCO monitoring kiosk (equipment type) equipment samples used)

Diatoms, Avon invertebrates, Sem at Cools macrophytes, fish, Cottage 1.7 RHS (control) bed sediment, ecosystem Sub-station Level, flow, function Clay Analite turbidity velocity (Mace Diatoms, probe, FloPro) invertebrates, Aanderaa DO Turbidity, Sem at Priors macrophytes, fish, probe, DO, temperature Farm 4.97 RHS ISCO 3700 (manipulated) bed sediment, autosampler ecosystem

function Ebble at Diatoms, Ebbesbourne invertebrates, Wake, macrophytes, RHS upstream site bed sediment (control) TP, SRP, SUP, TDP, Level, flow, Ebble at 8.32 High-spec kiosk PP, NH4-N, TN, velocity (Nivus Diatoms,

Ebbesbourne Hach Lange PON, TON, TDN, OCM F) invertebrates, TP, TRP, NO3, Wake, Sigmatax and DON, TSS, NPOC, macrophytes, RHS NH4, downstream Phosphax pH bed sediment

site Sigma, Nitratax

(manipulated) and NH4-D sc

probes, Diatoms, Turbidity, YSI 6600sonde, invertebrates, chlorophyll-a, DO, Level, flow, Wylye at Chalk Aanderaa DO macrophytes, fish, pH, temperature, velocity (using Brixton Deverill 49.97 probe, RHS conductivity, NH4- EA gauging (manipulated) bed sediment N, DO and data) ISCO 3700 temperature autosampler Diatoms, Sub-station invertebrates, Analite turbidity macrophytes, RHS Wylye at probe, Level, flow, bed sediment Kingston Turbidity, 24.26 Aandera DO velocity (Mace Deverill DO, temperature probe, FloPro) (control) ISCO 3700 autosampler

Diatoms, Tamar Neet at Turbidity, Level, flow, invertebrates, Burracott 11.23 Sub-station chlorophyll-a, DO, velocity (Nivus macrophytes, fish, TP, SRP, SUP, TDP, Bridge (control) YSI 6600 sonde, pH, temperature, OCM F) RHS PP, NH -N, TN, Mudstone/ Aanderaa DO conductivity, 4 bed sediment PON, TON, TDN, Caudworthy shale probe, Diatoms, DON, TSS, NPOC, Water at ISCO 3700 DO, temperature Level, flow, invertebrates, pH Winnacott 15 autosampler velocity (Mace macrophytes, fish, Bridge FloPro) RHS (manipulated) bed sediment

204

Caudworthy Diatoms, Water at Level, flow, Invertebrates, Caudworthy velocity (Mace macrophytes, 25.96 Ford FloPro and fish, RHS (manipulated) Time-of-Flight) bed sediment

Flow Kiosk type and Determinands Sub-catchment Determinands parameters Biological and Size Geology surface water monitored in Catchment (with treatment monitored in measured morphological (km2) /soil monitoring grab/ISCO type) kiosk (equipment monitoring equipment samples used)

Morland; Level and flow Newby Beck Hach Lange (Barometric (control and 11 Limestone Diatoms, Sigmatax and TP, SRP, N-NO3, diver and manipulated invertebrates, Phosphax, N-NH4, turbidity, argonaut) areas) macrophytes, fish, Nitrax and NH4- chlorophyll-a, DO, Pow; RHS D probes, YSI pH, temperature, Pow Beck sonde, ISCO conductivity TP, SRP, N-NO3, (control and 7 Sandstone groundwater autosampler N-NH4, SS, DOC, manipulated Ca, Alkalinity, areas) Eden turbidity, Dacre; turbidity, chlorophyll-a, DO, Thackthwaite Level and flow chlorophyll-a, DO, pH, Temperature, Beck (control (Barometric 10 Mixed pH, temperature, Conductivity Diatoms, and diver) conductivity, invertebrates, manipulated YSI sonde, ISCO ammonium macrophytes, fish, areas) autosampler RHS turbidity, Newton Rigg chlorophyll-a, DO, (Demonstration 2 Sandstone pH, temperature, site) conductivity Diatoms, invertebrates, Blackwater macrophytes, fish, Drain A 5.4 RHS (manipulated) Glacial Till/

clay loam turbidity, groundwater chlorophyll-a, DO, Level (Diver) Blackwater YSI sonde, ISCO 1.3 pH, temperature, and regular Drain B (control) autosampler conductivity, TP, TPP, TRP, SRP, flow gauging ammonium NO3, NO2, NH4, Blackwater TN, TPN, TDN, 3.5 Diatoms, Drain C (control) DON, TSS, TC, invertebrates, TOC, Ca, Total Wensum macrophytes, fish, Blackwater Alkalinity, pH, 6.6 RHS Drain D (control) conductivity, Mg,

Blackwater Glacial Na, K, SO4, Cl, Drain E (A+B) sands & HCO3, Si, B, Al, Fe, (control and 7.1 gravels Mn Hach Lange TP, TRP, NO3, manipulated over Till/ Level (Diver and Sigmatax and turbidity, areas) clay loam Sontek Phosphax, chlorophyll-a, DO, Argonaut) and Blackwater Nitrax probe, pH, temperature, Diatoms, regular flow Drain F YSI sonde, ISCO conductivity, invertebrates, (A+B+C+D) gauging macrophytes, fish, 19.7 autosampler ammonium (control and RHS manipulated areas) groundwater

205

Table A2.2: Revised breakdown of the proposed sub-catchment monitoring infrastructure and biological endpoints for phase 2 of component 1. Changes are shown in green boxes

Diatoms, Avon invertebrates, Sem at Cools macrophytes, fish, Cottage 1.7 RHS (control) bed sediment, ecosystem Level, flow, function Clay Sub-station velocity (Mace Diatoms, Aanderaa DO FloPro) invertebrates, probe, Sem at Priors DO, temperature macrophytes, fish,

Farm 4.97 RHS ISCO 3700 (manipulated) bed sediment, autosampler ecosystem function Ebble at Diatoms, Ebbesbourne invertebrates, Wake, macrophytes, RHS upstream site bed sediment (control) TP, SRP, SUP, TDP, Level, flow,

Ebble at 8.32 PP, NH4-N, TN, velocity (Nivus Diatoms, Ebbesbourne PON, TON, TDN, OCM F) invertebrates,mac Wake, DON, TSS, NPOC, rophytes, RHS downstream pH bed sediment site Sub-station

(manipulated) YSI 6600 sonde, DO,temperature Diatoms,

Aanderaa DO invertebrates, pH, conductivity Level, flow, Wylye at Chalk probe, macrophytes, fish, velocity (using Brixton Deverill 49.97 RHS EA gauging (manipulated) ISCO 3700 bed sediment data) autosampler

Diatoms, invertebrates, Sub-station macrophytes, RHS Aandera DO Level, flow, bed sediment Wylye at 24.26 probe, DO, temperature velocity (Mace Kingston FloPro) Deverill ISCO 3700 (control) autosampler

Diatoms, Tamar Neet at Sub-station Level, flow, invertebrates, Burracott 11.23 YSI 6600 sonde, velocity (Nivus macrophytes, fish, TP, SRP, SUP, TDP, Bridge (control) OCM F) RHS PP, NH -N, TN, Mudstone/ Aanderaa DO DO, temperature 4 bed sediment PON, TON, TDN, Caudworthy shale probe, Diatoms, DON, TSS, NPOC, Water at pH, conductivity Level, flow, invertebrates, pH Winnacott 15 ISCO 3700 velocity (Mace macrophytes, fish, Bridge autosampler FloPro) RHS (manipulated) bed sediment

206

Appendix 2.2 DTC Sample Collection, Analysis and Quality Assurance Protocols A2.2.1 Handling and Processing of Laboratory-Analysed Samples A2.2.1.1 Field collection and transportation to the laboratory a) Samples were labelled in a clear manner that was durable and given an identifier that records the site, date and time that the sample was collected. b) Samples were protected, sealed and kept at 4oC in the dark in cool boxes for transportation to the laboratory. c) A ‘chain of custody’ was established that records the sample handler, sample collection time, number of samples taken and time delivered to the lab. The count of sample containers received in the lab was verified against the chain of custody.

A2.2.1.2 Quality Assurance Procedures were in place to monitor the effectiveness of sampling methodology, demonstrate sampling errors have been controlled adequately and to give an indication of the error encountered as a result of the variability of the sampling. This was done by regular collection of replicate samples as a check on the precision of sampling, the use of field blank samples to monitor sources of sample contamination and the use of spiked samples as quality controls to assess sample stability during transport and storage.

A2.2.1.3 Sample handling, filtration and analysis procedures  Each sample was booked and given a lab identifier for use in all procedures.  Samples were tightly sealed and stored at 4oC in the dark before analysis.  The maximum storage time for a sample before analysis was stated in the Standard Operating Procedure for each analytical method. These were as follows: a. All samples were filtered on arrival at the laboratory according to the methods outlined in 3.5 below. b. All samples for inorganic nutrient analysis were first filtered, then analysed within 24

hours of collection. For soluble reactive phosphorus (PO4-P), analyses were only completed on samples which were less than 24 hours old on collection as this fraction was unstable. c. All samples for DOC (NPOC) analysis were filtered and acidified to pH 2, stored at 4oC in the dark until analysis, with all analyses completed within 7 days of sample collection d. All samples for particulate and organic nutrient analysis were digested and analysed within 7 days of collection. e. All repeat digests and analysis identified within the QA process were re-digested within 7 days of collection, with analysis repeated within a further 3 days.  All samples were brought to room temperature before analysis.  Samples were collected in a 500 ml bottle. On arrival in the laboratory:  400 ml of each sample was filtered through a pre-washed, dried (105oC, 24 hours) and oC for 24 hours before being re-weighed to determine suspended sediment concentration.  The filtrate on all samples was analysed within 24 hours of collection to determine all

inorganic N (TON, NH4-N) fractions.

207

 The filtrate on the 7th sample (collected fresh on the day of the weekly field visit) was

analysed within 24 hours of collection to determine the soluble reactive P (PO4-P) concentration.  In the Hampshire Avon and Tamar DTC sites only, the remainder of the filtrate was set aside for digestion using persulphate oxidation to determine the total dissolved N (TDN) and total dissolved P (TDP) concentration in each sample (after Johnes and Heathwaite, 1992). A duplicate digestion and analysis was conducted on every tenth sample.  50 ml of the original 500 ml sample was filtered within 24 hours of collection through a 0.7 quent determination of DOC concentration by the NPOC method. with a duplicate analysis on every tenth sample  In the Hampshire Avon and Tamar DTC sites only, the remaining 50 ml unfiltered subsample was set to one side for subsequent digestion using persulphate oxidation under high temperature and pressure to determine the Total N (TN) and Total P (TP) concentration in that sample. A duplicate digestion and analysis was conducted on every tenth sample.  For samples collected from the Hampshire Avon and Tamar DTC sites, the difference between the TN and TDN sample gave the particulate organic N (PON) concentration. The difference between TP and TDP gave the particulate P (PP) concentration. The difference

between TDN and the sum of the inorganic N species (TON, NH4-N) gave the dissolved organic N (DON) concentration, while the difference between the SRP and TDP concentration gave the soluble unreactive P (primarily dissolved organic P or DOP) concentration. Where the calculated fraction concentrations lay between zero and the limit of detection for the analytical instrument and procedure, the concentration was recorded as zero. Where the calculated fraction concentration was negative, the digestion and analysis steps were repeated for all fractions.  A flow chart showing the sampling filtration and analytical protocols for full N speciation and P fractionation analysis is shown in Figure A2.4.

A2.2.1.4 Reagents and standards  All glassware and plastic ware was soaked for 24 hours in 10% HCl then rinsed at least 3 times with Milli-Q water and air dried before use.  Analytical grade reagents were used to make up standards.  Fresh standards were made up according to the Standard Operating Procedure for each analytical method.  Where probes were used they were rinsed with Milli-Q in between measurements.  Field sensors were cleaned weekly with a toothbrush to remove biofilms

A2.2.1.5 Precision and Accuracy  Each method was tested with a series of blank measurements to determine the limit of detection due to noise on the signal etc. The standard deviation of the data was multiplied by 3 to give the 3 Sigma limit of detection.  Multiple analysis of a bulk sample of river water was used to ascertain the ratio of standard deviation (r.s.d.).Here r.s.d. should be < 2%. However for methods involving highly corrosive digests running through analytical instruments we accepted an r.s.d of < 10%. These values were quoted in the appropriate Standard Operating Procedure.

208

 Reference materials were used in every run of each instrument to check the calibration and to provide a record of analytical performance. For known values this would be expressed as:

(Measured value-Known value)/ Known value* 100

Within a run the tolerance allowed would be 5%.

 For agreement between different batches of analyte we would expect better than 10 %.  QA controls use the reference recoveries to pass the instrument ready for analysis. Only results from passed criteria are quoted. These values are quoted in the appropriate Precision and Accuracy section of the Standard Operating Procedure.

Figure A2.4: Sample preparation and analysis protocol for determination of N speciation, P fractionation and suspended sediment concentration

A2.2.2 Quality Assurance and Quality Control Protocols for Sensor Data from Bankside Monitoring Quality Assurance (QA) procedures are followed to make sure that the data collected by the bankside monitors are of good quality, such as regular calibration of sensors, cleaning of sampling flow cells etc. Quality control (QC) procedures are followed for all datasets produced by the sensors so that any data that are deemed to be untrustworthy are removed, such as during known equipment failures. Following both QA and QC procedures ensures that the final dataset is trustworthy. There are, inevitably, discrepancies between laboratory analysed samples and samples analysed in situ, but following these procedures ensures that the sensor data which is used for greater data analysis is within acceptable limits of laboratory analysed samples.

209

A2.2.2.1 Quality Assurance procedures

 The Nitratax Plus SC nitrate sensors are calibrated every three months using a standard solution. The Phosphax Sigma is automatically cleaned and calibrated daily using reagents that are replaced every three months. Additional cleaning of removable parts within the Sigmatax is carried out monthly, with pump tubing replaced every three months. This frequency of calibration and maintenance is sufficient to minimise drift in the in situ measurements.  Hampshire Avon DTC YSI multi-parameter sondes are calibrated monthly by the EA National Laboratory Service (NLS), while Analyte turbidity probes and Aanderaa DO probes are calibrated annually at ADAS labs. Wensum YSI sondes are calibrated every six weeks following standard operating procedures at the University of East Anglia, while YSI sondes in the Eden are calibrated every eight weeks at the EA laboratories in Penrith, by an Eden DTC team member.  Regular maintenance activities are carried out at different frequencies across the three sub- catchments but involve at least monthly cleaning of flow-through cells, weekly clearing of in- channel vegetation and debris where stage is monitored and cleaning of rain gauges.  All DTCs perform manual flow gauging during periods of extreme high and low flows, which show good agreement with discharge produced by in situ flow meters. All field work and maintenance activities are entered into maintenance logs for each site, which are used during data quality control (QC) procedures.  Telemetered data are checked on a daily basis in order to identify any equipment or maintenance issues that need to be addressed.  All field work and maintenance activities are entered into maintenance logs for each site, which are used during data quality control (QC) procedures.

A2.2.3 Quality Control Procedures  QC procedures include the validation of high-frequency nutrient data using routine daily spot samples in the Wylye, weekly spot samples in the Blackwater and monthly spot samples in Newby Beck. These samples are analysed in laboratories following standard methods. Inter-laboratory comparisons are used to check consistency in analytical procedures between sub-catchments. A Pearson correlation has been used to assess the strength of relationship between laboratory data and the in situ equipment during the hydrological year 2011-12 (Table A2.3) where a positive residual represents an over estimation of nutrient concentration by the in situ equipment. Corrections of high-frequency data against laboratory measurements were not necessary for the data included in this report.  QC procedures also include the identification of errors in all data sets. Errors flagged as critical include: periods of maintenance when the data may be unrepresentative; equipment and power failures; or data below limits of detection. Flagged data were not included in this analysis.

210

Table A2.3. Relationship between laboratory samples and samples collected using bankside analysers using a Pearson correlation for each of the DTC tributary sites

Blackwater Wylye Newby Beck

TP TRP NO3-N TP NO3-N TP TRP NO3-N

(mg P L-1) (mg P L-1) (mg N L-1) (mg P L-1) (mg N L-1) (mg P L-1) (mg P L-1) (mg N L-1)

Pearson 0.86 0.81 0.91 0.87 0.87 0.97 0.86 0.89 correlation coefficient p value <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01 <0.01

Mean of 0.01 <0.01 0.05 -0.04 -0.31 <0.01 <0.01 <0.01 residuals

Standard 0.01 0.01 0.42 0.07 0.38 0.03 0.03 0.13 deviation of residuals

Appendix 2.3 Biological Monitoring Approach and Methods; Diatoms, Macrophytes, Macroinvertebrates and Fish Biological samples have been collected from sites across the three demonstration catchments using EU Water Framework Directive-compatible methods, i.e. the DARES methodology for diatoms, LEAFPACS for macrophytes, and RIVPACS for invertebrates. Electric fishing was used to determine the fish present at the sites, with the results assessed using the Fish Classification Tool 2 (FCS2). The hydro-geomorphology of all sites has also been assessed by the Environment Agency using the River Habitat Survey (RHS) methodology. Sites were matched for all Biological Quality Elements (BQEs) and positioned at chemical monitoring stations. The frequency and spatial coverage of the biological sampling varied among the three consortia, according to the arrangement of the sub- catchments being investigated.

The biological sampling was conducted to satisfy two aims: i) To establish a base line of ecological status against which any change in response to mitigation can be compared. Specifically this would enable the extent and the rate of change of the ecological response to mitigation of agricultural pollution to be quantified.

211

ii) To identify the causes of failure to achieve Good ecological status in the individual sub-catchments being monitored and, hence, link biological response to ecologically-relevant pollutants.

Biological data were summarised as indices related to specific pressures or general degradation. Data were assessed using the appropriate WFD compliant tools to compare against reference condition (the predicted state of the site in the absence of degradation) and thus provide an Ecological Quality Ratio (EQR) for each site. This EQR, which ranges from 1 to 0, is divided into the WFD quality classes (High, Good, Moderate, Poor, Bad) used to classify the Ecological Status of the sites. Again it should be noted that difficulties gaining access to the FCS2 tool have resulted in poor representation of EQR based on fish. Further statistical approaches were used to link biological responses to pressures, with the aims of linking stressors to ecological response and of identifying temporal scales of ecological responses.

Macroinvertebrates To sample macroinvertebrates a semi-quantitative kick sample representative of the reach was collected, following standard RIVPACS methodology (3 minute kick sample covering all habitats in proportion to their occurrence; Murray-Bligh et al. 1997), and preserved. The relative abundance of taxa, resolved to species level, was determined upon return to the laboratory. The physical characteristics of the reach was assessed (width, depth, discharge, etc.) together with substrate composition as percentage cover. In terms of specific pollutants and targeted mitigation, the monitoring of macroinvertebrates will provide a biotic response to organic, flow and sediment pressures.

Macrophytes River macrophytes were assessed using the LEAFPACS methodology. The LEAFPACS system is a biological method to assess the trophic status of streams and rivers in the UK and was primarily developed to monitor the impacts of eutrophication. The LEAFPACS system is based on the presence and abundance of species of aquatic macrophyte. A macrophyte is defined as ‘any plant observable with the naked eye and nearly always identifiable when observed’ (Holmes and Whitton 1977). This definition includes all higher aquatic plants, vascular cryptograms and bryophytes, together with groups of algae which can be seen to be composed predominantly of a single species (Holmes et al. 1999). A 100 m reach of watercourse was carefully walked by the surveyor and the presence and cover (on a 9-point scale) of all macrophyte and bryophyte species recorded. Supplementary information was collected on the physical nature of the reach.

Benthic algae (diatoms) Representative samples of attached benthic algae were collected for the estimation of the diatom community following the DARES methodology. 5 replicate stones (or macrophytes where suitable stones were lacking) were randomly selected from the benthos and attached algae removed from the surface with a toothbrush and rinsed with algal free water into

212 clean nalgene bottles. Samples were preserved with Lugol’s iodine for identification. On return to the laboratory, samples were digested with hydrogen peroxide and mounted on microscope slides. The slides were examined under x 1000 microscopy, with 300 diatom valves from random fields of view in each sample being identified to species level. Data are summarised for each sample as a list of species present and their percentage abundance. Scores are allocated to species using the TDI system, where the total score for the sample is the average weighted by relative abundance.

213

Appendix 2.4 Tabularised Summary Statistics for Water Quality Monitoring Data from all Four DTCs and their Sub-Catchments Table A2.4a: Summary of mean annual concentrations (and standard deviations) and flow weighted (FW) concentrations for N species and P fractions, together with rainfall, flow and turbidity data for the Hampshire Avon and Tamar DTC sub-catchments in 2012

DTC catchment Hampshire Avon Tamar Caudworthy Sub-catchment Sem Ebble Wylye Neet Water Prior's Cools Kingston Brixton Caudworthy 2012 Upstream Downstream Burracott Farm Cottage Deverill Deverill Ford Rain (mm) 870 870 -- -- 1152 1152 -- -- Flow (mm) 378 160.00 89 89 53 144 321 622 Flow (m3 s-1) 0.05 0.02 0.11 0.11 0.10 0.23 0.43 0.25 Turbidity (NTU) 22.6 38.8 -- 0.9 14.8 5.1 23.1 35.1 Mean conc. Nitrate 2.36 1.93 6.80 6.27 6.79 6.77 2.55 1.54 (mg/L) Flow weighted mean 1.34 1.11 11.01 13.19 9.52 5.94 0.55 1.02 Nitrate (mg/L) Mean conc. Ammonium 0.22 0.06 0.10 0.09 0.07 0.08 0.08 0.07 (mg/L) Flow weighted mean 0.09 0.04 0.16 0.21 0.13 0.07 0.03 0.07 Ammonium (mg/L) Mean conc. DON (mg/L) 2.17 1.15 1.59 1.65 1.51 1.32 1.10 1.27 Flow weighted mean 1.17 0.81 1.88 2.85 1.97 0.75 0.21 0.81 DON (mg/L) Mean conc. PON (mg/L) 0.82 0.63 0.24 0.39 0.22 0.23 0.30 0.25 Flow weighted mean 0.09 0.48 0.40 0.89 0.45 0.15 0.07 0.22 PON (mg/L) Mean conc. TN (mg/L) 5.58 3.77 8.60 8.30 8.44 8.22 4.04 3.11 Flow weighted mean TN 3.14 2.44 13.79 17.63 12.19 6.86 0.86 2.13 (mg/L) Mean conc. SRP (mg/L) 0.18 0.03 0.08 0.08 0.13 0.10 0.01 0.02 Flow weighted mean 0.09 0.02 0.15 0.19 0.33 0.08 0.00 0.01 SRP (mg/L) Mean conc. SUP (mg/L) 0.10 0.07 0.05 0.05 0.05 0.05 0.07 0.07 Flow weighted mean 0.05 0.04 0.07 0.10 0.11 0.04 0.01 0.04 SUP (mg/L) Mean conc. PP (mg/L) 0.41 0.21 0.03 0.10 0.03 0.03 0.04 0.06 Flow weighted mean PP 0.26 0.14 0.05 0.19 0.09 0.03 0.01 0.06 (mg/L) Mean conc. TP (mg/L) 0.69 0.31 0.15 0.23 0.21 0.17 0.12 0.14 Flow weighted mean 0.40 0.20 0.27 0.52 0.54 0.15 0.03 0.12 Total P (mg/L)

214

Table A2.4b: Summary of mean annual concentrations (and standard deviations) and flow weighted (FW) concentrations for N species and P fractions, together with rainfall, flow and turbidity data for the Hampshire Avon and Tamar DTC sub-catchments in 2013

DTC catchment Hampshire Avon Tamar Caudworthy Sub-catchment Sem Ebble Wylye Neet Water Prior's Cools Kingston Brixton Caudworthy 2013 Upstream Downstream Burracott Farm Cottage Deverill Deverill Ford Rain (mm) 824 824 -- -- 1053 1053 -- -- Flow (mm) 458 275.00 278 278 199 271 841 621 Flow (m3 s-1) 0.07 0.02 0.23 0.23 0.21 0.44 0.72 0.24 Turbidity (NTU) 26.8 14.1 -- 5.4 40.7 6.9 24.7 29.1 Mean conc. Nitrate 1.52 2.83 7.29 6.38 6.86 7.30 1.73 1.06 (mg/L) Flow weighted mean 0.40 0.73 2.05 8.39 2.55 6.57 0.38 0.75 Nitrate (mg/L) Mean conc. Ammonium 0.19 0.06 0.06 0.10 0.08 0.07 0.06 0.06 (mg/L) Flow weighted mean 0.10 0.01 0.02 0.09 0.05 0.08 0.02 0.04 Ammonium (mg/L) Mean conc. DON (mg/L) 2.63 1.86 1.68 1.93 1.84 1.76 1.87 1.81 Flow weighted mean 0.77 0.41 0.47 2.58 0.83 1.73 0.27 1.10 DON (mg/L) Mean conc. PON (mg/L) 0.26 0.80 0.24 1.72 0.23 0.20 0.14 0.27 Flow weighted mean 0.08 0.14 0.10 3.70 0.19 0.14 0.05 0.22 PON (mg/L) Mean conc. TN (mg/L) 4.67 5.69 9.62 10.18 9.06 9.47 3.85 3.22 Flow weighted mean TN 1.36 1.34 2.68 14.85 3.64 8.59 0.78 2.13 (mg/L) Mean conc. SRP (mg/L) 0.15 0.03 0.06 0.09 0.10 0.09 0.02 0.02 Flow weighted mean 0.03 0.01 0.02 0.15 0.06 0.10 0.01 0.01 SRP (mg/L) Mean conc. SUP (mg/L) 0.08 0.07 0.06 0.06 0.06 0.05 0.08 0.08 Flow weighted mean 0.02 0.02 0.01 0.06 0.02 0.04 0.01 0.05 SUP (mg/L) Mean conc. PP (mg/L) 0.20 0.21 0.06 0.52 0.09 0.05 0.03 0.05 Flow weighted mean PP 0.06 0.04 0.03 1.12 0.09 0.05 0.01 0.06 (mg/L) Mean conc. TP (mg/L) 0.43 0.33 0.17 0.68 0.26 0.20 0.13 0.15 Flow weighted mean 0.11 0.07 0.07 1.38 0.17 0.19 0.03 0.12 Total P (mg/L)

215

Table A2.5: Summary of mean annual concentrations (and standard deviations) and flow weighted (FW) concentrations for TP, TRP and NO3-N, and turbidity in the Eden and Wensum DTC sub- catchments, 2012-2013

DTC catchment Eden Wensum

Sub-catchment Morland Dacre Pow Blackwater Drain

2012

Turbidity (NTU) 6.7 - - 6.6 Mean conc. Nitrate 2.0 - - (mg/L) 5.8 Flow weighted mean 2.1 - - Nitrate (mg/L) 6.5 Mean conc. TRP (mg/L) 0.0 - - 0.06 Flow weighted mean 0.1 - - TRP (mg/L) 0.06 Mean conc. TP (mg/L) 0.1 - - 0.09 Flow weighted mean 0.2 - - Total P (mg/L) 0.09 2013 Turbidity (NTU) 5.4 - 9.7 7.8 Mean conc. Nitrate 1.8 - 2.9 (mg/L) 6.2 Flow weighted mean 1.7 - 3.0 Nitrate (mg/L) 7.1 Mean conc. TRP (mg/L) 0.0 - 0.2 0.07 Flow weighted mean 0.1 - 0.3 TRP (mg/L) 0.06 Mean conc. TP (mg/L) 0.1 - 0.2 0.09 Flow weighted mean 0.2 - 0.4 Total P (mg/L) 0.09

N.B. For the Eden-Pow in 2012 turbidity/sediment calibrations are not robust enough for suspended sediment estimations. For the Eden-Pow in 2012 and the Eden-Dacre in 2012 and 2013, only flow data are currently of good enough quality for analysis

216

Table A2.6: Summary of annual pollutant loads across the four DTCs and their sub-catchments

DTC catchment Hampshire Avon Tamar Eden Wensum

Sem @ Sem @ Wylye Wylye Ebble Ebble Caudworthy Neet @ Blackwater Sub-catchment Prior's Cool's @Kingston @Brixton Morland Dacre Pow (Upstream) (Downstream) @ Ford Burracott Drain Farm Cottage Deverill Deverill 2012 Rain (mm) 870 870 1129 1129 1152 1152 1270 1270 1207 1963 849 683 Flow (mm) 378 160 89 89 53 144 321 622 709 1462 419 134 SS (t/ha) ------0.36 - - 0.02 TRP (kg/ha) ------0.54 - - 0.08 TP (kg/ha) 1.5 0.32 0.25 0.46 0.28 0.22 0.09 0.74 1.42 - - 0.12 PP (kg/ha) 0.97 0.22 0.05 0.17 0.05 0.04 0.04 0.40

SUP (kg/ha) 0.19 0.07 0.06 0.09 0.06 0.06 0.04 0.25 - - - - SRP (kg/ha) 0.34 0.03 0.14 0.17 0.17 0.12 0.01 0.07 - - - -

NO3-N kg/ha) 5.07 1.77 9.99 11.8 5.00 8.56 1.76 6.34 14.6 - - 8.7

NH4-N (kg/ha) 0.33 0.07 0.15 0.19 0.07 0.1 0.09 0.43 - - - - DON (kg./ha) 4.42 1.3 1.7 2.54 1.03 1.08 0.67 5.03 - - - - PON (kg/ha) 0.34 0.76 0.36 0.79 0.25 0.21 0.21 1.36 - - - - TN (kg/ha) 11.9 3.91 12.5 15.7 6.41 9.89 2.77 13.3 - - - - 2013 Rain (mm) 824 824 914 914 1053 1053 910 910 1184 1528 762 633 Flow (mm) 458 275 278 278 199 271 841 621 719 1099 421 236 SS (t/ha) ------0.3 - 0.3 0.05 TRP (kg/ha) ------0.54 - 1.29 0.15 TP (kg/ha) 0.52 0.18 0.19 3.84 0.34 0.52 0.27 0.73 1.35 - 2 0.21 PP (kg/ha) 0.26 0.11 0.09 3.1 0.18 0.13 0.09 0.36 - - - - SUP (kg/ha) 0.10 0.05 0.04 0.17 0.05 0.12 0.09 0.28 - - - - SRP (kg/ha) 0.15 0.02 0.05 0.43 0.12 0.27 0.05 0.07 - - - -

NO3-N kg/ha) 1.85 1.99 5.96 23.3 5.08 17.9 3.18 4.66 12.4 - 14.8 16.7

NH4-N (kg/ha) 0.45 0.04 0.05 0.26 0.10 0.22 0.19 0.24 - - - - DON (kg./ha) 3.53 1.14 1.36 7.16 1.66 4.7 2.27 6.86 - - - - PON (kg/ha) 0.37 0.4 0.29 10.3 0.38 0.38 0.46 1.38 - - - - TN (kg/ha) 6.23 3.67 7.79 41.3 7.25 23.4 6.58 13.2 - - - -

Note: Uncertainty analysis undertaken on the SS data derived from turbidity sensors in the Hampshire Avon and Tamar indicated these were not reliable. They are not reported. In the Eden, only flow data for the Eden-Dacre site are reliable enough to be reported.

217

Table A2.7: Comparison of transfer of pollutants by flow type across the DTC sub-catchments for the 2013 hydrological year

DTC Hampshire Avon Tamar Eden Wensum catchment Sub- Sem @ Sem @ Ebble Ebble Wylye @ Wylye @ Caudworthy Neet Morland Dacre Pow Blackwater catchment Priors Cools upstream downstream Kingston Brixton Water Drain % Flow High 63 26.2 26.2 60 32.4 32 57 53 49 48 36 48 Mid 36 73.5 78.7 39 66.6 67 42 46 50 51 63 50 Low 1 0.3 0.1 1 1.0 1 1 1 1 1 0.1 1 % SS High 23 7.6 - 45 34.9 37 45 38 94 - 49 68 Mid 67 80 - 54 64.3 59 55 61 6 - 51 32 Low 10 12.4 - 1 0.8 4 0 1 0.1 - 0 1 % TP High 33 6.7 0 80 23.2 19 16 59 79 - 44 53 Mid 67 92.0 99.6 20 74.8 80 83 40 20 - 55 45 Low 0 1.3 0.4 0 1.9 1 1 1 1 - 1 2 % PP High 41.3 5.0 0 81.7 35.1 16.6 6.2 71.2 - - - - Mid 58.6 93.6 99.9 18.2 63.5 83.0 93.2 28.5 - - - - Low 0.1 1.4 0.1 0.1 1.4 0.4 0.6 0.3 - - - - % TRP High ------73 - 43 48 Mid ------27 - 57 50 Low ------0.3 - 0.2 2 % SUP High 41.6 8.6 0 49.8 6.3 17.9 12.8 49.5 - - - - Mid 58.3 90.3 99.1 49.8 90.6 80.4 85.5 49.5 - - - - Low 0.1 1.2 0.9 0.4 3.1 1.8 1.7 1.0- - - - - % SRP High 33 12.2 0 73 15.0 14 20 49 - - - - 218

Mid 67 86.5 99.6 27 82.9 54 80 50 - - - - Low 0 1.2 0.4 0 2.1 32 0 1 - - - - DTC Hampshire Avon Tamar Eden Wensum catchment Sub- Sem @ Sem @ Ebble Ebble Wylye @ Wylye @ Caudworthy Neet Morland Dacre Pow Blackwater catchment Priors Cools upstream downstream Kingston Brixton Water Drain

% NO3 High 36 5.9 0 58 9.7 21 24 50 45 - 35 54 Mid 64 93.2 99.4 42 8.8 78 76 49 54 - 65 45 Low 0 0.9 0.6 0 2.5 1 0 1 1 - 0.1 1

% NH4 High 13.0 4.6 0 56.5 17.3 27.2 9.0 39.8 - - - - Mid 86.9 94.5 99.2 42.4 81.0 72.4 90.4 59.6 - - - - Low 0 1.0 1.0 0.8 1.7 0.4 0.6 0.6 - - - - % DON High 38.0 94 0 60.9 12.6 20.5 10.3 48.9 - - - - Mid 61.9 89.3 99.3 38.7 85.0 78.6 88.0 50.4 - - - - Low 0.1 1.3 0.7 0.4 2.4 1.0 1.7 0.7 - - - - % PON High 36.2 4.0 0 82.5 35.2 18.8 15.0 67.7 - - - - Mid 63.8 94.4 99.7 17.4 63.1 80.6 84.8 32.1 - - - - Low 0 1.5 0.1 0.3 1.7 0.5 0.2 0.2 - - - - % TN High 37.0 6.6 0 63.9 11.4 20.4 24.1 51.3 - - - - Mid 63.0 92.4 99.4 35.8 85.5 78.4 75.1 48.0 - - - - Low 0 1.0 0.6 0.3 3.1 1.2 0.8 0.7 - - - - Note: % means the percentage of that variable transported during high, mid and low flow periods. For some sites turbidity/sediment calibrations are not robust enough for sediment yield calculations. No data are available for the Eden-Pow for the 2012 hydrological year as monitoring was not initiated until February 2012. For the Eden-Dacre only flow data are currently of good enough quality for analysis.

219

Table A2.8a: Estimated seasonal loads of nitrogen species and phosphorus fractions at the River Sem sub-catchments, Hampshire Avon DTC

Monitoring Monitoring site Prior's Farm Cools Cottage Year Spring Summer Autumn Winter Spring Summer Autumn Winter 2012 Nitrate (kg/ha) 1.36 0.81 0.31 1.88 0.55 0.31 0.51 0.44 Ammonium (kg/ha) 0.10 0.05 0.01 0.07 0.02 0.01 0.01 0.02 DON (kg/ha) 1.23 1.24 0.31 0.80 0.45 0.27 0.37 0.23 PON (kg/ha) 0.62 0.62 0.08 0.28 0.27 0.15 0.15 0.16 TN (kg/ha) 3.35 2.74 0.71 3.03 1.30 0.73 1.03 0.84 SRP (kg/ha) 0.08 0.10 0.03 0.04 0.01 0.01 0.01 0.01 SUP (kg/ha) 0.05 0.05 0.01 0.04 0.02 0.01 0.01 0.02 PP (kg/ha) 0.27 0.38 0.06 0.11 0.07 0.04 0.04 0.05 TP (kg/ha) 0.40 0.52 0.10 0.19 0.10 0.06 0.06 0.08 2013 Nitrate (kg/ha) 0.44 0.04 0.12 0.44 0.57 0.52 0.27 0.64 Ammonium (kg/ha) 0.07 0.00 0.05 0.22 0.01 0.01 0.01 0.01 DON (kg/ha) 0.57 0.04 0.20 1.18 0.30 0.36 0.23 0.25 PON (kg/ha) 0.07 0.00 0.02 0.16 0.08 0.21 0.04 0.07 TN (kg/ha) 1.17 0.09 0.34 2.00 1.06 1.11 0.55 0.97 SRP (kg/ha) 0.01 0.00 0.01 0.08 0.00 0.01 0.01 0.01 SUP (kg/ha) 0.02 0.00 0.01 0.04 0.01 0.02 0.01 0.01 PP (kg/ha) 0.05 0.00 0.02 0.09 0.02 0.06 0.01 0.02 TP (kg/ha) 0.08 0.01 0.03 0.20 0.04 0.08 0.02 0.04

220

Table A2.8b: Estimated seasonal loads of nitrogen species and phosphorus fractions at the River Ebble sub-catchments, Hampshire Avon DTC

Monitoring Monitoring site Upstream Downstream Year Spring Summer Autumn Winter Spring Summer Autumn Winter 2012 Nitrate (kg/ha) 0.39 3.83 0.82 -- 1.97 4.04 0.24 -- Ammonium (kg/ha) 0.00 0.06 0.01 -- 0.01 0.08 0.00 -- DON (kg/ha) 0.07 0.61 0.25 -- 0.39 0.86 0.07 -- PON (kg/ha) 0.09 0.12 0.01 -- 0.04 0.30 0.01 -- TN (kg/ha) 0.56 4.74 1.09 -- 2.41 5.46 0.32 -- SRP (kg/ha) 0.00 0.06 0.01 -- 0.01 0.07 0.00 -- SUP (kg/ha) 0.00 0.02 0.01 -- 0.01 0.03 0.00 -- PP (kg/ha) 0.00 0.02 0.00 -- 0.01 0.07 0.00 -- TP (kg/ha) 0.01 0.10 0.01 -- 0.03 0.17 0.01 -- 2013 Nitrate (kg/ha) 2.47 0.13 1.80 0.77 4.17 0.09 4.08 10.53 Ammonium (kg/ha) 0.02 0.00 0.02 0.00 0.04 0.00 0.04 0.12 DON (kg/ha) 0.46 0.03 0.56 0.18 1.08 0.03 1.50 3.34 PON (kg/ha) 0.04 0.00 0.03 0.16 0.32 0.01 3.20 5.40 TN (kg/ha) 3.08 0.16 2.42 1.11 5.79 0.14 8.85 19.39 SRP (kg/ha) 0.01 0.00 0.03 0.01 0.03 0.00 0.09 0.23 SUP (kg/ha) 0.02 0.00 0.02 0.00 0.03 0.00 0.04 0.07 PP (kg/ha) 0.01 0.00 0.01 0.05 0.10 0.00 1.03 1.58 TP (kg/ha) 0.04 0.00 0.05 0.06 0.20 0.00 1.16 1.97

221

Table A2.8c: Estimated seasonal loads of nitrogen species and phosphorus fractions at the River Wylye sub-catchments, Hampshire Avon DTC

Monitoring Monitoring site Kingston Deverill Brixton Deverill Year Spring Summer Autumn Winter Spring Summer Autumn Winter 2012 Nitrate (kg/ha) 1.65 1.31 0.69 -- 1.93 4.00 1.41 1.13 Ammonium (kg/ha) 0.01 0.02 0.01 -- 0.01 0.04 0.02 0.02 DON (kg/ha) 0.31 0.27 0.17 -- 0.24 0.46 0.16 0.17 PON (kg/ha) 0.04 0.08 0.02 -- 0.04 0.09 0.03 0.03 TN (kg/ha) 2.04 1.69 0.89 -- 2.21 4.77 1.37 1.41 SRP (kg/ha) 0.05 0.05 0.01 -- 0.02 0.06 0.03 0.02 SUP (kg/ha) 0.01 0.02 0.00 -- 0.01 0.03 0.01 0.01 PP (kg/ha) 0.01 0.02 0.00 -- 0.00 0.01 0.01 0.00 TP (kg/ha) 0.07 0.09 0.02 -- 0.04 0.10 0.05 0.03 2013 Nitrate (kg/ha) 1.58 1.26 0.90 1.39 3.49 0.85 4.00 9.24 Ammonium (kg/ha) 0.05 0.01 0.01 0.03 0.02 0.01 0.06 0.14 DON (kg/ha) 0.64 0.30 0.23 0.49 0.68 0.20 1.18 2.56 PON (kg/ha) 0.22 0.02 0.03 0.08 0.09 0.02 0.13 0.15 TN (kg/ha) 2.48 1.62 1.17 2.00 4.46 1.07 5.44 12.09 SRP (kg/ha) 0.04 0.01 0.01 0.07 0.04 0.01 0.08 0.15 SUP (kg/ha) 0.01 0.01 0.01 0.02 0.02 0.01 0.04 0.06 PP (kg/ha) 0.10 0.01 0.01 0.04 0.01 0.01 0.06 0.06 TP (kg/ha) 0.15 0.04 0.03 0.14 0.08 0.02 0.17 0.27

222

Table A2.8d: Estimated seasonal loads of nitrogen species and phosphorus fractions in the Caudworthy Water and Neet sub-catchments, Tamar DTC

Monitoring Monitoring site Caudworthy Water Neet Year Spring Summer Autumn Winter Spring Summer Autumn Winter 2012 Nitrate (kg/ha) 0.33 0.35 0.21 0.27 0.16 2.05 0.85 2.64 Ammonium (kg/ha) 0.02 0.01 0.00 0.04 0.00 0.08 0.07 0.23 DON (kg/ha) 0.13 0.16 0.10 0.03 0.08 2.17 0.89 1.36 PON (kg/ha) 0.08 0.03 0.00 0.01 0.03 0.46 0.35 0.38 TN (kg/ha) 0.55 0.55 0.34 0.35 0.29 4.82 2.18 4.61 SRP (kg/ha) 0.00 0.00 0.00 0.00 0.00 0.03 0.02 0.02 SUP (kg/ha) 0.01 0.01 0.00 0.01 0.01 0.09 0.05 0.08 PP (kg/ha) 0.02 0.00 0.00 0.00 0.01 0.15 0.10 0.11 TP (kg/ha) 0.03 0.01 0.01 0.01 0.01 0.27 0.16 0.21 2013 Nitrate (kg/ha) 1.63 0.17 0.56 0.81 0.44 0.26 3.92 0.91 Ammonium (kg/ha) 0.05 0.01 0.05 0.06 0.02 0.01 0.17 0.06 DON (kg/ha) 0.93 0.25 0.52 0.58 0.71 0.53 4.92 1.55 PON (kg/ha) 0.13 0.02 0.13 0.20 0.09 0.11 1.31 0.12 TN (kg/ha) 2.99 0.44 1.26 1.65 1.29 0.92 10.32 2.63 SRP (kg/ha) 0.01 0.00 0.01 0.02 0.01 0.01 0.05 0.01 SUP (kg/ha) 0.03 0.01 0.02 0.02 0.03 0.02 0.20 0.06 PP (kg/ha) 0.02 0.00 0.02 0.04 0.02 0.03 0.33 0.05 TP (kg/ha) 0.08 0.02 0.06 0.08 0.06 0.06 0.59 0.12

223

Table A2.8e: Estimated seasonal loads of nitrate, suspended sediment and phosphorus fractions in Morland and Pow outlet monitoring locations in the Eden DTC

Monitoring Monitoring site Morland Pow Year Oct-Dec Jan-Mar Apr-Jun Jul-Sep Oct-Dec Jan-Mar Apr-Jun Jul-Sep 2012 Nitrate (kg/ha) 3.2 3.8 2.7 5.1 SS (kg/ha) 140.0 45.6 82.7 88.5 TP (kg/ha) 0.41 0.35 0.17 0.49 TRP (kg/ha) 0.17 0.12 0.06 0.19 2013 Nitrate (kg/ha) 2.3 2.3 2.6 5.2 1.2 2.6 3.7 7.2 SS (kg/ha) 181.3 49.1 44.1 27.7 183.7 41.9 64.8 2.2 TP (kg/ha) 0.15 0.17 0.3 0.71 0.08 0.36 0.37 1.18 TRP (kg/ha) 0.07 0.06 0.13 0.29 0.07 0.24 0.23 0.76

Table A2.8f: Estimated seasonal loads of nitrogen species and phosphorus fractions in Stinton Hall and Park Farm monitoring locations on the Blackwater, Wensum DTC

Monitoring Monitoring site Stinton Hall, Blackwater Park Farm, Blackwater Year Spring Summer Autumn Winter Spring Summer Autumn Winter 2012 Nitrate (kg/ha) 11.9 5.1 3.8 9.5 3.2 1.2 1.7 2.7 TP (kg/ha) 0.06 0.07 0.06 0.09 0.03 0.03 0.02 0.04 TRP (kg/ha) 0.05 0.05 0.05 0.07 0.02 0.02 0.01 0.03 2013 Nitrate (kg/ha) 1.8 1.5 22.6 11.9 1.7 0.9 7.3 6.8 TP (kg/ha) 0.03 0.03 0.19 0.1 0.03 0.02 0.09 0.08 TRP (kg/ha) 0.02 0.03 0.11 0.06 0.02 0.01 0.06 0.05

224

Note: in all tables below ‘Sem’ refers to the Sem at Priors Farm, ‘Ebble’ refers to the downstream site, ‘Wylye’ refers to the Wylye at Brixton Deverill Table A2.9a: Comparison of flow transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec 17 - 17 - 28 39 37 32 19 Jan-Mar 19 - 17 - 25 19 20 13 26 Apr-Jun 39 - 30 - 23 20 20 22 34 Jul-Sep 25 - 37 - 25 23 23 34 20 2013 Oct-Dec 57 37 42 63 51 52 51 58 43 Jan-Mar 38 52 43 30 38 21 19 24 37 Apr-Jun 4 10 10 6 7 13 16 12 13 Jul-Sep 0.3 0.3 5 1 4 13 15 6 7

Table A2.9b: Comparison of SS transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec N/A N/A N/A N/A N/A 39 - - 9 Jan-Mar N/A N/A N/A N/A N/A 13 - - 35 Apr-Jun N/A N/A N/A N/A N/A 23 - - 35 Jul-Sep N/A N/A N/A N/A N/A 25 - - 21 2013 Oct-Dec N/A N/A N/A N/A N/A 60 - 63 49 Jan-Mar N/A N/A N/A N/A N/A 16 - 14 37 Apr-Jun N/A N/A N/A N/A N/A 15 - 22 10 Jul-Sep N/A N/A N/A N/A N/A 9 - 1 4 Note: Uncertainty analysis undertake by the Hampshire Avon team indicated that derived SS loads from turbidity sensor data streams were not sufficiently robust to be reported

225

Table A2.9c: Comparison of Nitrate-N transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec 7 - 17 18 15 34 - - 19 Jan-Mar 43 - 13 23 46 18 - - 31 Apr-Jun 31 - 23 28 3 26 - - 36 Jul-Sep 19 - 47 31 36 21 - - 13 2013 Oct-Dec 12 22 23 18 71 42 - 49 43 Jan-Mar 42 56 53 25 16 21 - 25 41 Apr-Jun 42 22 20 52 8 18 - 18 10 Jul-Sep 4 0 5 5 5 18 - 8 6

Table A2.9d: Comparison of SRP transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec 12 - 23 17 25 - - - - Jan-Mar 16 - 15 17 35 - - - - Apr-Jun 32 - 15 33 1 - - - - Jul-Sep 40 - 46 33 39 - - - - 2013 Oct-Dec 9 26 29 26 15 - - - - Jan-Mar 78 65 55 38 42 - - - - Apr-Jun 10 8 13 33 21 - - - - Jul-Sep 3 0 3 3 21 - - - -

226

Table A2.9e: Comparison of TRP transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec - - - - - 36 - - 15 Jan-Mar - - - - - 12 - - 33 Apr-Jun - - - - - 22 - - 28 Jul-Sep - - - - - 31 - - 25 2013 Oct-Dec - - - - - 53 - 58 39 Jan-Mar - - - - - 24 - 18 37 Apr-Jun - - - - - 12 - 19 15 Jul-Sep - - - - - 12 - 5 10

Table A2.9f: Comparison of TP transfers (as %) by season in the DTC catchments

DTC catchment Hampshire Avon Tamar Eden Wensum Sub-catchment Sem Ebble Wylye Caudworthy Water Neet Morland Dacre Pow Blackwater Drain 2012 Oct-Dec 8 - 23 15 24 34 - - 15 Jan-Mar 16 - 14 15 32 12 - - 33 Apr-Jun 33 - 17 52 2 25 - - 29 Jul-Sep 43 - 45 19 41 29 - - 24 2013 Oct-Dec 8 35 32 25 71 53 - 59 40 Jan-Mar 51 59 50 34 15 23 - 19 37 Apr-Jun 21 6 14 34 7 13 - 18 14 Jul-Sep 21 0 4 7 7 11 - 4 8

227

Appendix 2.5 Analysis of Event Based Pollutant Transport in the Demonstration Test Catchments In the Eden-Morland and Eden-Pow sub-catchments in 2013, event transfers are responsible for 94% and 49% of sediment, 79% and 44% of TP, and 45% and 35% of nitrate respectively. For the Wensum sub- catchments, these figures are 68% for sediment, 53% for TP, and 54% for nitrate. The following section considers the nature of the event transfers in the Eden, Hampshire Avon and the Wensum DTC catchments, and uses these to infer process information about the sources and pathways of nitrate, sediment, and phosphorus transfer in these catchments.

Figure A2.5 shows an event which occurred between 25th and 29th April 2012 for three sub-catchments of three DTC focus catchments (the Tamar is not included here because of a lack of high-resolution nutrient data). This event was the first event generated after the 2012 drought in all three catchments, and marked the transition from an extreme dry period to a wet spring and summer. Comparison of high-resolution event monitoring data across the DTC sub-catchments using this April 2012 event as an example illustrates how event responses may differ across UK catchments:

 In both the Hampshire Avon-Wylye and Wensum-Blackwater Drain sub-catchments, two streamflow discharge peaks occurred in response to bimodal event rainfall, while only one significant (>Q10) peak occurred in the Eden-Morland catchment. Soils had already begun to wet up in the Wylye and the Blackwater Drain sub-catchments in response to low intensity rainfall over the previous weeks, and flow pathways would quickly have become operational, while flow connectivity would have been low in the Morland sub-catchment as conditions were extremely dry prior to this event. The flashy Morland response is typical of the Eden (0-2 hr delay in peak discharge from peak rainfall), with streamflow peaking and falling sharply and rapidly in response to peak rainfall (Figure A2.6), while flow peaks were attenuated in the more groundwater-dominated Wylye and Blackwater.

 Nitrate dilution occurred in response to increased discharge in this event in the Wylye as the low-nitrate concentration rainwater diluted the high-nitrate concentration baseflow in this groundwater-dominated chalk catchment. In the Blackwater drain, an initial dilution in nitrate concentration was followed by a delayed peak which occurred after peak discharge. Although this is a typical nitrate response observed in streamflow during the ‘post-fertilisation window’, other types of nitrate responses did occur in events in the Wensum sub-catchment. No nitrate data were available for this event for the Eden-Morland catchment.

 Large total phosphorus peaks were generated in response to rainfall in each catchment, with the Eden and Wensum both showing concentrations peaked sharply as rapidly responding event pathways transported eroded sediment and associated particulate P sources to the stream. The TP peak in the Wylye was more attenuated, illustrating the importance of slower flow pathways in transferring P through the catchment to the stream. In the Blackwater, TP and TRP responses were extremely similar, indicating that both particulate and soluble forms of phosphorus had similar mobilisation mechanisms and pathways. This is in contrast to the Eden-Morland catchment, where the particulate fraction dominated the TP signal at peak TP concentration, followed by an increase in the contribution from TRP on the falling limb of the hydrograph, indicating slower, sub-surface pathways actively transporting TRP forms from land to stream

228

Hampshire Avon - Wylye

Wensum - Blackwater Drain

Eden – Morland

b) Hampshire Avon - Wylye

Wensum - Blackwater Drain

Eden – Morland

Figure A2.5: Event responses to rainfall for three DTC sub-catchments for an event which occurred across all DTC focus catchments in April 2012 for a) streamflow and b) pollutants

229

Figure A2.6: Time lapse images of a typical storm event in the Eden-Dacre catchment show how rapidly streamflow level and turbidity can respond to rainfall. The time interval between each image is one hour

This event is discussed in more detail in Outram et al. (2014). The findings of this paper highlight the acute storm-dependent transfers of nutrients in all three catchments which were exacerbated by drought conditions preceding the storms. The spectrum of pollution sources and pathways reported represents the scale of the challenge to environmental managers tasked with mitigating diffuse pollution, particularly when weather patterns vary greatly year to year

A2.5.1 Storm Events in the Eden Figure A2.7 shows typical event responses for the Eden-Morland sub-catchment, during both winter and summer. In 2012, more than 80% of the sediment transferred through the catchment outlet was transferred in just half of the 33 significant rainfall events recorded, and over 10% of the sediment was transferred in just two of these events. Similarly, over 30% total phosphorus was transferred in just four rainfall events. For the Eden-Pow catchment (Figure A2.8), runoff is less flashy than in Morland, although it still exhibited a typical Eden-type response as streamflow rose and fell rapidly in response to rainfall in both winter and summer.

Nitrate responses in both the Morland and Pow sub-catchments differed seasonally, with dilution of nitrate concentrations being the typical response to winter rainfall (Figure A2.7a and Figure A2.8a), when runoff pathways low in nitrate (particularly surface runoff but also rapidly responding drainflow) entered the stream. For both the Morland and Pow sub-catchments, a seasonal shift in the pattern of event nitrate transfers occurred, and in late spring/summer, nitrate peaked sharply in response to rainfall and remained attenuated before falling gradually to an elevated baseflow level. The nitrate response was coincident with the flow response, and is likely to represent surface runoff and preferential drainflow sources, sustained by baseflow drainflow inputs.

The majority of TP transported in Morland was particulate in both winter and summer (Figure A2.7), with much lower concentrations in the TRP fraction. Differences in response between TP and TRP in terms of timing and attenuation of peaks and responsiveness to rainfall inputs suggests that different pathways contributed both particulate and ‘dissolved’ (reactive, available) P to the stream, with slower flow pathways such as drainflow and soil throughflow contributing a smaller proportion of dissolved P. A much greater proportion of TP was in the form TRP in the Pow sub-catchment than in Morland, with the timing of TP and TRP peaks being similar, and with the particulate fraction only being important during winter high stormflow peaks (Figure A2.8a).

230

Figure A2.7: Response of nitrate, turbidity, total phosphorus (TP) and total reactive phosphorus (TRP) to rainfall for the Eden-Morland sub-catchment showing a) typical winter nitrate dilution, and b) summer nitrate peaks

231

Figure A2.8: Response of nitrate, turbidity, total phosphorus (TP) and total reactive phosphorus (TRP) to rainfall for the Eden-Pow sub-catchment showing a) typical winter nitrate dilution, and b) summer nitrate peaks

232

A2.5.2 Storm Events in the Wensum In the Wensum DTC sub-catchment, repetitive patterns in the nitrate concentration response to flow were observed throughout the year. This is largely because of the importance of groundwater in nitrate transport, which changed throughout the year according to the amount of water in the deep and shallow aquifers. When the field drains were active during the winter months, this represented a quick flow pathway for nitrate, particularly when soils were saturated, as nitrate is a dissolved nitrogen fraction. By contrast, in the summer, soils were drier and groundwater levels were lower, so rainfall largely resulted in storage and not runoff. Antecedent conditions and fertiliser application rates also play a role.

Autumn first flush At the start of each hydrological year, the first few rainfall events tended to result only in nitrate dilution as runoff with low nitrate concentrations flowed into the stream. After a few successive events, when groundwater recharge had begun to occur and shallow groundwater pathways were active, a typical autumn ‘first flush’ of nitrate was observed, where the peak concentration of nitrate tended to arrive around 24 hours after peak discharge. Although some of this nitrate was from the groundwater itself, the concentration of nitrate in the groundwater could not account for the total nitrate recorded in the stream. Therefore, most of it was likely to be as a result of elevated shallow groundwater interacting with the upper soil layers which were high in nitrate from mineralisation of organic matter over the summer and residual fertiliser. The event shown in Figure A2.9a is typical, where peak nitrate concentrations were around 7 mg N L-1 and remained elevated for several days. In the example shown here, the turbidity, TP and TRP responses were minimal due to previous events resulting in temporary exhaustion of sediment and sediment-bound P sources.

Winter elevated dilution After several autumn flushing events at the start of each hydrological year, the background concentrations of nitrate in January to February remained elevated at around 7 mg N L-1, as opposed to less than 5 mg N L-1 before autumn rainfall. At this point in the year the field drains became active in the western part of the catchment. Field drain flow typically had nitrate concentrations in the winter months of 10-20 mg N L-1 during baseflow, which could be even higher during rainfall events. Any rainfall occurring during January to February tended to produce a nitrate dilution response in streamflow (Figure A2.9b), as low-nitrate event water from the surface temporarily diluted the nitrate signal from the field drains. In this example, TP, TRP and turbidity again had a very minimal response which is likely to be due to further source limitation of sediment from previous events.

Post-fertiliser application peaks Following several nitrate dilution events in January to February, the background concentration of nitrate in the Blackwater Drain reduced in concentration. However, data collected in spring between 2012 and 2014 showed that events occurring during the ‘fertiliser application window’ between March and May tended to result in large peaks of nitrate during events (Figure A2.10a). Peak nitrate concentrations during events of 15 mg N L-1 during this time were not uncommon, and peaks could exceed 20 mg N L-1 (the upper measurement limit) for several days, with peak nitrate occurring around a day after peak discharge. The nitrate peaks observed here were likely to include runoff from multiple sources including excess fertiliser. After initial dilution due to increasing stream discharge, nitrate concentrations started to rise with an early peak of turbidity and phosphorus. In this arable catchment, nitrate sources as a result of fertilisation can reach the stream quite rapidly in soil water (throughflow) and drainflow pathways when field drains are active and soil saturation is high. At this time of the year, when groundwater recharge had been occurring throughout the winter, groundwater was at its maximum level for the hydrological year, and inflow of groundwater rich in nitrate to the stream was also likely. 233

Figure A2.9: Response of streamflow discharge, nitrate, turbidity, total phosphorus (TP) and total reactive phosphorus (TRP) to rainfall for the Wensum-Blackwater Drain sub-catchment in a) October 2012, showing typical autumn flushing of nitrate, and b) February 2013, showing typical late-winter dilution of nitrate

234

Figure A2.10: Response of nitrate, turbidity total phosphorus (TP) and total reactive phosphorus (TRP) to rainfall for the Wensum-Blackwater Drain sub-catchment in a) March 2013, showing a typical post- fertilisation nitrate peak, and b) August 2013, showing typical summer dilution of nitrate followed by a delayed peak

235

In this particular event, turbidity, TP and TRP all showed a marked response to rainfall as discharge was high indicating the availability of pollutant transport pathways, and the previous months had been very dry and allowed a build-up of rapidly-mobilised sediment and phosphorus sources. In this event it was clear that turbidity, TP and TRP all responded rapidly to the onset of rainfall, with all three parameters peaking before peak discharge. Concentrations of P and sediment quickly receded to pre-event levels due to source exhaustion, in contrast to nitrate where concentrations remained elevated for several days as sources continued to be flushed through the catchment.

Summer dilution In the summer months, any rainfall events that occurred often resulted in a minimal discharge response as the majority of runoff was evaporated or stored in the catchment as soils were well below field capacity, and the groundwater level was at its lowest so groundwater inflow to the stream did not occur. Nitrate concentrations in the field drains fell after the end of the fertiliser application window, and a dilution in nitrate concentration occurred as low-nitrate concentration event water entered the stream (Figure A2.10b). Sediment and phosphorus responses to summer events varied, with larger peaks observed after drier antecedent periods. Only a limited number of field drains were running at this time as there was little connectivity with shallow groundwater.

No seasonality has been observed in the TP and TRP concentration response, which appeared to be more dependent on antecedent conditions. TP and TRP appeared to be mobilised by events throughout the year as even a small rainfall event in summer could result in a relatively large phosphorus peak, although the largest peaks tended to occur between December and March. However, if several events occurred in quick succession, both forms of phosphorus could become exhausted, with peak concentrations and loadings decreasing with each successive event, even if discharge volume was higher in later events. Generally, the phosphorus load during an event was proportional to discharge volume, but there were exceptions to this. When an event occured after a dry period of weeks to months, the TP and TRP loads generated during the event were disproportionately large in comparison to the flow volume. By contrast, after several successive events, the TP and TRP loads generated were disproportionately small to flow volume. This suggests that in the Blackwater there was a build-up of phosphorus during dry antecedent periods which is easily and rapidly mobilised by the next rainfall event. These sources, probably topsoils, road debris, and stream banks, were easily exhausted by successive events.

A2.5.3 Soil Sampling and Soil Texture Mapping in the Wensum Soil sampling and soil texture mapping were completed on nine fields in mini-catchment A within the Wensum Blackwater sub-catchment, with the purpose of understanding the variation in soil types and for determining the locations of porous pots and soil moisture probes. The nine fields cover an area of 143 ha and are the focus of the mitigation measure trials in the Wensum DTC.

Soil sampling was carried out several times throughout DTC phase 1. Firstly, soil sampling was carried out in the nine fields from 13-22 May 2013 by a UEA team (Table A2.10). Soil samples were collected by Dutch auger at three soil depths (0-30cm, 30-60cm, 60-90cm) at 12 sites in each field in a ‘W’ or ‘zigzag’ layout. Interest was in understanding the variability of soils in the nine fields and so the collected soil samples were not bulked. 324 soil samples in total were collected (i.e. 9 fields x 12 sites x 3 depths = 324; Figure A2.11). Moist, well-mixed samples were put into sealed, plastic bags and transferred back to the laboratory. Samples were then placed in a drying rack and allowed to air dry for ~seven days. Portions of homogenised (stones removed) and dried sample were put in sealed plastic bags for archiving and the remaining sample material was ground gently with mortar and pestle to pass U.S. No.10 (2 mm opening) sieve. The collected soils data were used for determining soil texture and soil organic matter. 236

Table A2.10 Summary of soil sampling date and measurements made during DTC phase 1 at Salle, Norfolk. May2013 Sep2013 Feb2013 May&Jul2014 Feb2015 Jun&Jul2015 UEA Team ADAS ADAS UEA team UEA team UEA team SMN 4 sites/field 4 sites/field 4 sites/field 4 sites/field 4 sites/field (NO3, NH4, Available N) 3 depths 3 depths Top soil 4 sites/field 4 sites/field 4 sites/field 4 sites/field (pH, P, K, Mg, OM) 12 sites/ field Soil texture 4 sites/field 3 depths Physical properties 12 sites/field 4 sites/field 4 sites/field (BD,Infil., Penet.)

Figure A2.11: Location of all soil sampling sites, May 2013 – July 2015

The second round of soil sampling was carried out by ADAS after the harvest between 27th August and 3rd September 2013. Expert farm knowledge of the fields identified that four soil types are generally present in each field, and so samples were collected for each soil type in each field. The selected sampling points within each soil type were chosen to correspond to the location of earlier soil sampling wherever possible. Samples were taken in two different ways, according to the measurement of interest. Samples for pH, K,

Mg, P, SO4 and organic matter measurement were taken in the topsoil (0-15cm) by hand auger at 12 locations within 2 metres around each site, and then the 12 samples were bulked to provide one representative sample for a given site (i.e. 9 fields x 4 sites = 36 samples). Samples for soil mineral nitrogen (SMN) measurement and soil texture were collected with a Hydrocare hydraulic drill in two concentric circles at a total of 12 locations within 10 metres around each site. Soil cores taken using the Hydrocare drill were divided into three depths (0-30cm, 30-60cm, 60-90cm) and the 12 samples for each depth were also

237 bulked to provide one representative sample for a given site (i.e. 9 fields x 4 sites x 3 depths = 108 samples for SMN and soil texture measurement). The samples were put in sealed plastic bags and sent to Natural Resource Management (NRM) laboratories for analysis.

The number of total soil samples collected in the nine measures fields for the first two sampling rounds combined is 432 samples (i.e. 9 fields x 16 sites x 3 depths = 432 samples). On 4th and 5th February 2014, soil samples were collected again by ADAS at four sites per field using the methods described above. Subsequently, three further soil sampling rounds (July 2014, February 2015, and July 2015) were completed by the UEA team, with topsoil (0-15 cm) samples only collected at the same four sites per field.

The particle size distributions as percent sand, silt and clay content for 144 samples at three soil depths are shown in Figure A2.12. Samples from topsoil (0-30 cm) are concentrated in a relatively small area in the triangle and most have clay loam, sandy loam, sandy silt loam and sandy clay loam soil texture, with only one clay soil texture sample and no sand soil texture. In contrast, samples collected deep in the soil profile (60-90cm) are distributed in a relatively large area of the triangle, with many clay and sand texture samples. This means that the texture of topsoil in the nine fields is more homogeneous than the deep soil layer. The greatest variations occur in the clay and sand fractions, particularly in the deep soil layer, with very little variation in the proportion of silt-sized material.

Figure A2.12: Soil particle size distribution at 0-30 cm depth (top left), 30-60 cm depth (top right), and 60- 90 cm depth (bottom) in samples collected for the nine measures fields at Salle, Norfolk

238

The spatial variation of soil fractions in the topsoil of the study area was visualised by using data for all three soil fractions. Data for each fraction were interpolated by kriging using ArcGIS10.1 to produce composite-banded, interpolated maps. The distribution of soil fractions in the topsoil (0-30 cm) are shown in Figure A2.13. Red colours indicate sand-dominated soil textures and blue colours represent soils with high clay content. Sheds Field and the southern part of Moor Hall Field show a reddish colour indicating a high sand content. The eastern part of Swanhills field and the north eastern part of Gatehouse Hyrne field possess a relatively high silt content. In contrast, Potash field and the western part of Gatehouse Hyrne field have a bluish colour, indicating soils with a high clay content.

The results based on soil texture analysis compare well with an independent set of electrical conductivity scan results (not shown). The electrical conductivity of soils changes depending on porosity and soil moisture content. Sands generally exhibit low conductivity, silts have a medium conductivity, and clays have a high conductivity. Consequently, electrical conductivity correlates strongly to soil particle size and texture. Low electrical conductivity zones were found to be located in areas with high sand content and high electrical conductivity contours coincided with areas having a high clay content.

Figure A2.13: Distribution of soil fractions in the 0-30 cm soil depth at Salle, Norfolk

239

A2.5.4 Soil Moisture Monitoring in the Wensum Ten sets of soil moisture probes were first installed in January 2013 for the purpose of determining soil conditions, for example infiltration behaviour and field capacity conditions, in the nine mitigation measures fields at Salle. The Adcon GPRS telemetry system enables soil moisture at intervals of 10 cm to a depth of 90 cm to be recorded at 15-minute intervals using Adcon SM1 capacitance-based measurement probes. Temperature is also measured at three depths (15, 45 and 75 cm). Profiles of soil moisture development over time can be used to calculate water drainage and evapotranspiration rates and also downward fluxes of dissolved solutes such as nitrate where porous pot data exist.

An example of the type of soil moisture data collected is shown in Figure A2.14 for probe 50413 installed in Dunkirk field at Manor Farm, Salle, in mini-catchment A. The data are for the period February to early July 2013. The green line represents soil moisture at 30 cm depth and shows a number of peaks in response to rainfall events. The peaks in February and March reach maximum, or field capacity, values for the clay loam soil at this location of about 56% moisture content. Following the winter, there is a gradual drying trend at 30 cm depth that becomes more pronounced from mid-April, and especially following the late May peak in soil moisture content. The soil moisture content at greater depth remains saturated for longer with the record at 60 cm depth (dark blue line) only responding to drying at the start of June. The record at 90 cm depth shows no response to drying by the start of July (light blue line). The data recorded on 3rd July give soil moisture contents of 31.9%, 54.4% and 59.0% at depths of 30 cm, 60 cm and 90 cm, respectively. The red line shows the temperature recorded at 75 cm depth and displays a gradual warming from about 4oC in February to a value of 13.0oC on 3rd July 2013.

Figure A2.14: Record of soil moisture content at 30 cm (green line), 60 cm (dark blue line) and 90 cm (light blue line) depth at Manor Farm, Salle, from February to early July 2013. The red line represents soil temperature at 75 cm depth. The soil moisture data range from 32-59% and the temperature data range from 4-13oC 240

Soil moisture conditions matter to farmers when deciding when to undertake field operations, as wet weather delays drilling at the start of the autumn and in the spring. After a wet spell there is always the temptation to get on the land and drill before it is really suitable, which can risk inferior yields compared with waiting for better soil conditions. As an example, data from Manor Farm, Salle, demonstrate the impact of leaving the soil just another day (Figure A2.15) or preferably two days before wheeled or tracked vehicles run over it. The figure shows rapid drying of the soil at 30-50 cm depth between 5th February and 6th-7th February 2013, despite little change in the moisture status of the shallower layers. As farm equipment has become heavier, this type of information has added significance in attempting to avoid compaction in the deeper soil layers.

Figure A2.15: Soil moisture status (expressed as a scale frequency unit or percent volume) at Manor Farm, Salle, 3-8 February 2013

A2.5.5 High-Temporal Resolution Fluvial Sediment Source Fingerprinting with Bayesian Uncertainty Analysis in the Wensum We have been working to addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimising analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter has been tackled by using direct X-ray fluorescence (XRF) and diffuse reflectance infrared Fourier transform (DRIFT) spectroscopic analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non-destructively monitored at high- temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterisation of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60- and 120-minute resolution for the duration of numerous precipitation events.

An example application of this geochemical fingerprinting procedure is presented in Figure A2.16 for a succession of precipitation events in November 2012. This reveals that as successive rainfall events passed 241 across the catchment, concentrations of organic carbon and major clay mineral-associated elements within SPM increased, whilst concentrations of calcium declined. Modelling these geochemical trends with a Bayesian sediment mixing model revealed that each passing precipitation front is associated with a peak in topsoil and road verge sediment contributions and a decline in the importance of subsurface sediment contributions. This indicates that the antecedent wetness of the catchment, combined with these rainfall volumes, led to an increase in saturation excess surface runoff and hence greater land-to-river transfer of topsoil and road verge sediments. These results correspond well with visual observations of sediment-laden road runoff, emanating from a few critical source areas, discharging into the stream channel during these precipitation events. The methodology we have developed demonstrates how combining Bayesian mixing models with the direct spectroscopic analysis of SPM-covered filter papers can produce high-temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level.

242

Figure A2.16: Time-series plots for three consecutive precipitation events in November 2012, showing changing SPM geochemistry (% by weight) and sediment source contributions at 120-minute intervals over a 118-hour period. Shading around geochemical parameters represents instrumental precision (2 standard deviations) based on 46 repeat analyses of a control sample. Light and dark shading around median source apportionment estimates represent the 95% and 50% Bayesian credible intervals respectively

243

Appendix 2.6 Wensum Ecological Data

Total N mg l-1 Diatom sampling Total P (mg l-1) averaged Site code Diatom EQR (NO +NO +NH ) averaged date 3 2 4 over preceding 21 days over preceding 21 days

Wen-A 17/05/2011 0.81 Wen-A 25/07/2011 1 2.85 Wen-A 02/11/2011 0.5 3.55 0.193 Wen-A 25/05/2012 0.49 14.17 0.052 Wen-A 12/07/2012 0.65 13.43 0.066 Wen-A 03/10/2012 0.54 5.15 0.134 Wen-A 22/04/2013 0.33 13.11 0.040 Wen-A 24/07/2013 0.58 4.85 0.085 Wen-A 20/09/2013 0.56 3.36 0.186 Wen-B 17/05/2011 1 Wen-B 25/07/2011 1 7.43 Wen-B 02/11/2011 0.53 8.13 0.110 Wen-B 25/05/2012 0.78 12.57 0.068 Wen-B 12/07/2012 0.57 7.50 0.071 Wen-B 03/10/2012 0.67 7.46 0.126 Wen-B 22/04/2013 0.89 9.92 0.066 Wen-B 24/07/2013 0.65 9.11 0.048 Wen-B 20/09/2013 0.63 0.095 Wen-C 17/05/2011 0.67 Wen-C 25/07/2011 0.75 5.63 Wen-C 02/11/2011 0.45 5.98 0.147 Wen-C 25/05/2012 0.5 0.095 Wen-C 16/07/2012 0.26 4.36 0.169 Wen-C 03/10/2012 0.43 4.41 0.242 Wen-C 22/04/2013 0.74 0.069 Wen-C 24/07/2013 0.65 6.69 0.098 Wen-C 20/09/2013 0.65 6.05 0.150 Wen-D 17/05/2011 0.76 Wen-D 26/07/2011 0.89 6.69 Wen-D 02/11/2011 0.48 4.43 0.056 Wen-D 25/05/2012 0.43 0.058 Wen-D 16/07/2012 0.44 5.17 0.062 Wen-D 03/10/2012 0.47 6.86 0.064 Wen-D 22/04/2013 0.63 6.77 0.04 Wen-D 24/07/2013 0.5 5.16 0.054 Wen-D 20/09/2013 0.55 6.39 0.059 Wen-E 17/05/2011 0.78 Wen-E 25/07/2011 0.94 4.20 Wen-E 02/11/2011 0.57 4.54 0.091 Wen-E 25/05/2012 0.67 13.14 0.036 Wen-E 12/07/2012 0.19 9.45 0.07 Wen-E 03/10/2012 0.51 5.53 0.088 Wen-E 22/04/2013 0.54 12.05 0.048 Wen-E 24/07/2013 0.57 5.66 0.086 Wen-E 20/09/2013 0.55 4.81 0.114 Wen-F 17/05/2011 0.68 Wen-F 26/07/2011 0.73 4.87 Wen-F 02/11/2011 0.55 4.79 0.053 Wen-F 31/05/2012 0.36 5.17 0.078 Wen-F 16/07/2012 0.36 5.09 0.072 Wen-F 03/10/2012 0.28 5.32 0.088 Wen-F 22/04/2013 0.52 9.53 0.042 Wen-F 24/07/2013 0.4 5.84 0.098 Wen-F 20/09/2013 0.55 5.10 0.102

244

Total N mg l-1 Total P (mg l-1) Macrophyte Site code FINAL EQR (NO +NO +NH ) average average Jan 1st to sampling date 3 2 4 Jan 1st to sampling date sampling date

Wen-A Wen-A 24/07/2011 0.39 2.85 0.040 Wen-A Wen-A Wen-A 12/07/2012 0.17 13.43 0.072 Wen-A Wen-A Wen-A 25/07/2013 0.33 4.85 0.078 Wen-A Wen-B Wen-B 24/07/2011 0.36 7.43 0.057 Wen-B Wen-B Wen-B 12/07/2012 0.13 7.50 0.083 Wen-B Wen-B Wen-B 24/07/2013 0.18 9.11 0.068 Wen-B Wen-C Wen-C 25/07/2011 0.24 5.63 0.121 Wen-C Wen-C Wen-C 16/07/2012 0.2 4.36 0.120 Wen-C Wen-C Wen-C 24/07/2013 0.25 6.69 0.090 Wen-C Wen-D Wen-D 26/07/2011 0.27 6.69 0.096 Wen-D Wen-D Wen-D 16/07/2012 0.43 5.17 0.081 Wen-D Wen-D Wen-D 24/07/2013 0.26 5.16 0.060 Wen-D Wen-E Wen-E 25/07/2011 0.39 4.20 0.063 Wen-E Wen-E Wen-E 16/07/2012 0.41 9.45 0.069 Wen-E Wen-E Wen-E 24/07/2013 0.4 5.66 0.088 Wen-E Wen-F Wen-F 26/07/2011 0.53 4.87 0.095 Wen-F Wen-F Wen-F 16/07/2012 0.56 5.09 0.088 Wen-F Wen-F Wen-F 24/07/2013 0.53 5.84 0.094 Wen-F

245

Appendix 3.1 Faecal Indicator Organisms Background to Faecal Indicator Organism Issues Faecal indicator organisms (FIOs) are the key faecal derived pollutant from livestock, and a major cause of water quality failure in the UK under the WFD. The empirical evidence to show the relative significance of FIOs derived from direct inputs resulting from livestock access to water courses versus diffuse sources is poor in the UK. Stream bank and drinking bays are measures to restrict both direct and indirect livestock access to watercourses, thereby reducing the direct input of FIOs. Understanding the efficacy of such measures is important in order that policy can support the most appropriate measures for FIO reduction.

The DTC programme has included investigations into faecal indicator organism (FIO) contamination and farm-based BMPs designed to attenuate FIO flux from catchment systems. The WFD defines ‘protected areas’ in its Annex 4 which includes bathing waters and water used for harvesting of commercial species such as shellfish. Both types of water are regulated using FIOs, principally Escherichia coli and intestinal enterococci. Both E. coli and intestinal enterococci are excreted by humans and livestock. Indeed, in many livestock farming areas of the UK, the dominant loading is livestock faeces voided in-field, onto farm hard standing areas and whilst the stock is housed in winter. The WFD requires the design of a ‘programme of measures’ under Article 11 to ensure compliance of such ‘protected areas’ and this should include integrated application of diffuse and point-source control. The former includes farm based Best Management Plans (BMPs) and control of urban diffuse flux from surface waters draining urban surfaces and often containing cross-connections to foul drainage. FIO point sources, include treated effluents, which are generally disinfected using UV light in the UK to reduce bacterial concentration before discharge via sea outfalls, and intermittent discharges from combined sewage overflows, pumping station overflows and storm tank overflows. These intermittent discharges are rarely treated but the application of UV disinfection to intermittent discharges is growing in the UK after promising early results. The current UK picture and impact of FIO pollution at the main impacted resource sites is one of reducing pollution from the sewerage system, due to investments committed in successive AMP programmes by water and sewerage undertakers, but with considerable remaining attention devoted to control and reduction of intermittent discharges impacting coastal waters.

As the control of anthropogenic FIO loadings has progressed, a residual loading has remained at many sites. At the same time, the FIO-based standards for bathing waters have become more stringent following introduction of the 2006 Bathing Water Directive (Anon, 2006) which comes into force in 2015 following advice by the WHO (2003) which is based on UK DEFRA-funded epidemiological research (Kay et al., 1994: 2004). These current trends in policy and practise have reinforced the need to initiate research on farm- based livestock pollution generation and control.

One strand of this effort is seen in DEFRA DTC project WQ0203. This has three themes, namely:

i. literature evaluation of BMP efficacy world-wide with a scoping report in 2010 and a final literature update report due in 2015; ii. the practicality and attenuation effectiveness of integrated constructed wetlands (completed in Wales); iii. the utility of stream bank fencing (i.e. stock exclusion fencing) in reducing stream FIO loading (commenced in the Eden and on-going in the Tamar DTC catchments).

246

Methods Three bacteriophages were used in the experiment: a bacteriophage of Serratia marcescens (PJN1 isolated from sea water, host bacterium: S. marcescens SM24 (NCIMB 10644)), Enterobacter cloacae (phage and host, a wild strain of Ent. cloacae, isolated from sewage (Skilton and Wheeler, 1988)), and coliphages: MS2 (NCTC 12847, host bacterium: Escherichia coli (E. coli) K12 (NCTC 12846. Bacteriophage suspensions were prepared by growing each host bacterium in high nutrient medium (brain heart infusion (Oxoid), casamino acid (Difco) broth) (SCA, 2000) in a batch fermenter (L H Fermentation 500 series) and monitoring growth rate by the level of carbon dioxide in the effluent gas (ADC Gas Analysis, Hoddesdon). At an appropriate stage in the growth cycle, the fermenter was infected with the bacteriophage, followed by subsequent lysis. The lysate was cleaned by centrifugation (3600 x g for 0.5h at 4oC) and the supernatant shaken with 10% polyethylene glycol 6000. The resultant precipitate was concentrated by centrifugation as above, mixed with an equal volume of glycerol, and titrated using the agar overlay method outlined below to determine the tracer concentration. Concentrates were stored below -20oC until required.

Bacteriophage enumeration followed the double agar overlay method (Adams, 1959; Havelaar and Hogeboom, 1984; SCA, 2000). Host bacterium broth cultures were grown in the same high nutrient medium used for fermentation, incubated at 37°C overnight. 0.1 ml of host bacterium and 1 ml of sample were pipetted into 4 ml of molten semi-solid overlay (45°C) and plated onto blood agar base in 90 mm triple-vent Petri dishes. Overlays were allowed to set and the plates dried by partly removing the lids and allowing them to stand for 15 minutes. The lids were then replaced, the plates inverted and incubated (37°C for 15 ± 3 h). Following incubation, areas of lysis (plaques) were counted and reported as plaque forming units (pfu)/ml. Appropriate positive and negative controls were included with each batch of samples for each tracer.

Analysis of FIOs followed standard methods based on membrane filtration using 0.45 µm sterile cellulose nitrate membranes (Sartorius). Presumptive faecal coliform organisms were enumerated using membrane filtration as specified in SCA (2000). Membranes were incubated for 4 h at 30˚C, followed by 14 h at 44˚C (±0.5˚C). Enterococci were isolated using membrane enterococcus agar (MEA, Oxoid) by incubation for 4 h at 37˚C, followed by 44 h at 44˚C (±0.5˚C) (SCA, 2012). All maroon colonies were counted as presumptive enterococci SCA (2000, 2009, 2011, 2012). Results were reported as colony forming units (cfu)/100 ml. The lower limit of detection for the analyses was 9 cfu/100 ml. Appropriate positive and negative controls were included with each batch of samples for each FIO.

BMP Efficacy Literature Evaluation Table A2.10 and Table A2.11 show the latest update of the on-going literature evaluation. This combines the 2010 report for DEFRA (CREH, 2010) with later work completed for SEPA (CREH, 2012). In England, the Environment Agency is developing catchment flux modelling of FIOs (Burgess pers. Comm. 2014). This is a multivariate model which has refined FIO export coefficients (Kay et al., 2008a) and effluent concentrations (Kay et al., 2008b) together with attenuation estimates derived from ADAS (2011).

The ADAS report used ‘expert’ judgement for estimates of FIO attenuation through available BMPs because of the lack of empirical data in this area. Figures 27 and 28 in this report go some way to provide such estimates with a literature-derived empirical evidence base. It is worth noting the potential beneficial impacts of slurry and FYM storage in reducing FIOs by 99.99% in Figure 27. This BMP is worthy of further attention and it implies a technically feasible measure with high attenuation efficacy for FIOs spread to land as part of normal fertilization practices in UK livestock farming areas.

The preliminary results of FIO-specific measures are presented in Section 3.

247

Table A2.10: Typical rates of FIO attenuation (expressed to nearest log10) associated with livestock faeces and manure/slurry

ATTENUATION (%) 99.9999 99.999 99.99 99.9 99 90 0

Log10 6 5 4 3 2 1 0 FRESH FAECES Fresh

Voided in watercourse

Voided on land: mean attenuation with 3–276 * fresh daily input over 3 months (118) days

STORED FAECES Fresh

FYM: mean attenuation with fresh daily 2–32 * input over 3 months (8) days

Slurry: mean attenuation with fresh daily 8–613 * input over 3 months (69) days

FYM APPLIC’N (3 mth accumul’n) As applied

Broadcast: mean attenuation after 1 month with no further application

Incorporation: mean attenuation after 1 mth with no further applic’n

SLURRY APPLIC’N (3 mth accum) As applied

Broadcast: mean attenuation after 1 month with no further application

Injection: mean attenuation after 1 mth * Range (and median) time for 4 log10 attenuation with with no further applic’n no fresh input 248

Table A2.11: Typical rates of FIO/pathogen attenuation (expressed to nearest log10) in runoff from yards and agricultural land as result of specific measures

ATTENUATION (%) 99.999 99.99 99.9 99 90 0

Log10 5 4 3 2 1 0

DIRTY WATER TREATMENT Untreated

Pond Maximum Median reported

Constructed wetland Maximum Median reported

WOODCHIP CORRAL Untreated

Effluent from corral Maximum Median reported

GRASSED SWALE Untreated

Runoff from trackways and fields* Median

VBS (inc. RIPARIAN BUFFER STRIP) Untreated

Surface runoff from fields Maximum Median reported

* Based on sediment interception studies, not FIOs

249

Tamar DTC faecal indicator organisms (FIOs) methods Three bacteriophages were used in the experiment: a bacteriophage of Serratia marcescens (PJN1 isolated from sea water, host bacterium: S. marcescens SM24 (NCIMB 10644)), Enterobacter cloacae (phage and host, a wild strain of Ent. cloacae, isolated from sewage (Skilton and Wheeler, 1988)), and coliphages: MS2 (NCTC 12847, host bacterium: Escherichia coli (E. coli) K12 (NCTC 12846. Bacteriophage suspensions were prepared by growing each host bacterium in high nutrient medium (brain heart infusion (Oxoid), casamino acid (Difco) broth) (SCA, 2000) in a batch fermenter (L H Fermentation 500 series) and monitoring growth rate by the level of carbon dioxide in the effluent gas (ADC Gas Analysis, Hoddesdon). At an appropriate stage in the growth cycle, the fermenter was infected with the bacteriophage, followed by subsequent lysis. The lysate was cleaned by centrifugation (3600 x g for 0.5h at 4oC) and the supernatant shaken with 10% polyethylene glycol 6000. The resultant precipitate was concentrated by centrifugation as above, mixed with an equal volume of glycerol, and titrated using the agar overlay method outlined below to determine the tracer concentration. Concentrates were stored below -20oC until required.

250

Appendix 3.2: Avon DTC Engagement with Farmers and Advisors Table A3.1: Details of farmer engagement Caudworthy Catchment

Farm Date Duration Purpose Outcome Comments Higher 03/4/2013ˣ All day Liaise WCRT/John Sample Meeting with farmers where direct access to their land Whiteley; Crowther/Farmers/catchment sites is required for sample collection and specific works are Mr Uglow walk over/visit ALL sample revised required for FIO studies – specifically Crowther fencing sites study (before and after) Slyddon; 03/4/2013 All day Liaise WCRT/John Meeting with farmer to identify access to land for Mr Crowther/Farmers sample collection. Petherrick ˣ Meeting with WCRT advisor

Table A3.2: Details of farmer engagement in the Priors sub-catchment, Avon DTC

Farm Date Duration Purpose Outcome Comments Coleman’s 08/01/2013* All day Catchment walk Introduction Identified that some works have been carried out e.g. yard roofing, to Mr Green guttering.. Hayes 08/01/2013 All day Catchment walk Contact Mr Negotiations for track work begin; 3 quotes based on ADAS report Stiles ‘Outline Specifications Surface Water Diversions and Track Works Hayes, Farm Prepared August 2012. Quotes: BRAD ltd, £78,000 + VAT; SAMPSON Contractors ltd, £23,333 +VAT; Philip SIDFORD ltd, £43,110 +VAT. Quotes reviewed by ADAS, Sidford’s considered to have the ‘measure of the job’ allowed for thicker concrete. Sampson’s on the right lines, but lighter specifications. BRAD ‘way out on costs, yet thinner concrete than Sidford’s – 15% more expensive’. Negotiates from the position that ‘we’ could fund Sampson’s work. Farmer concern re specification – specifically lower concrete spec. 150 mm C35 vs 200 mm C40.

251

Priors 08/01/2013 All day Catchment walk No meeting Hayes 9/01/2013 Correspondence, Outlines initial thoughts and requirement for Sidford’s to amend their letter quote. Hayes 27/3/2013 All day (90 Negotiation of Agree further Discussion focusses on initial negotiation – from 2012 when monies from minute works to be funded meeting CSF were on the table for track works. Monies returned not spent! So meeting) some frustration on Mr Stiles Page re the length of time the process is taking. He needs to see ‘us’ up our offer of £25 k. Agrees to further meeting the following week. Hayes 4/4/2013 All day (60 Negotiation of Agree further Discussion focusses on revised offer to fund up to £30 k. Agreement minute works to be funded meeting reached for a further meeting with representative from Sidford’s being meeting) present Hayes 9/4/2013 All day Negotiation of Revised Discussion focuses on works agreement that we will fund up to £38 k. A (120minute works to be funded quote for revised quote is required. meeting) works Hayes 24/4/2013 All day (30 Agreement for Revised quote accepted – works to commence week of 24th June 2013: minute works to begin £36,400 +VAT. meeting) Hayes 24/6/2013 All Day Contractor starts Regular visits Meet with contractors and agree regular meetings over course of works work ca. 4 weeks Hayes 3/7/2013 All Day Progress Meet with contractors – issues of heat and laying concrete – early morning and late evening; excess use of water for damping down! Hayes 10/7/2013 All Day Progress Meet with contractor – discussion re design and placement of V notch weirs. Three weirs, constructed from railway sleeper damns with central ‘V’ notch insert. V notch plates ordered. Hayes 19/7/2013 All Day Works completed Final site meeting with contractor – works completed. Hayes 25/7/2013 All Day ‘V’ Notch plate V Notch and stage boards installed: Water level sensors ordered HOBO installation. loggers/ISCO samplers – in consultation with farmer – no external power agreement re solar panel installation. Hayes 11/10/2013 All Day Installation of water 10 week lead time for ALL four loggers to be delivered. level sensors Hayes 21/11/2012 All Day Discussion with Mr Data collection and discussion with Mr Stiles to organise a visit for the Stiles re DTC visit DTC’s to visit and talk around the installation of the works on his farm. *Meeting with CSF officer 252

Appendix 4.1 Pesticide Analysis in the Wensum Catchment The river Wensum catchment in Norfolk is extensively farmed and has a number of significant diffuse pollution pressures. As a consequence of the general catchment issues a partnership project was established in 2012 to undertake a survey of pesticides and herbicides at 20 river locations in the Wensum DTC focus catchment (Figure A4.1). The work was supported financially by Anglian Water Services (AWS) and the EA, with the main objective of assessing the spatial variability of pesticides across the catchment using a semi-quantitative mass-scan analysis approach. The main substance of concern for AWS in the catchment is metaldehyde, a widely used molluscicide detected above drinking water standards in the water supply intake at Heigham on the River Wensum just upstream of Norwich. Currently this substance is difficult to remove using standard water treatment methods.

Figure A4.1: GC-MS mass scan determined metaldehyde showing approximate average concentrations for all eight sampling rounds

In autumn 2012 when sampling took place, the normal application of crop treatments was delayed in the Wensum catchment because of the prevailing very wet soil conditions and the consequent delay in the harvest. Daily rainfall totals and river flows at Swanton Morley in the central part of the catchment are illustrated in Figure A4.2. It is evident from this figure that the timing of sampling is important in relation antecedent weather conditions. The first sampling round on 12 September 2012 took place after a two- week period with little rain and prior to expected autumn use of pesticides and herbicides in the catchment. Hence, this round is a good indicator of background and baseflow contributions. The second sampling round took place on 26 September after two days of rainfall (total 10 mm), and just after a minor mean daily hydrograph peak of 2.4 m3s-1 recorded at Swanton Morley. Other sampling rounds coincident with high river flows occurred on 2 November (mean daily flow 4.0 m3s-1) and 28 November (mean daily flow 16.9 m3s-1).

253

Figure A4.2: Rainfall and mean daily flows at Swanton Morley alongside pesticide sampling dates

The main findings of the pesticide monitoring are as follows:  Nearly a quarter of mass scan amenable detections in 159 samples were for organic compounds present at concentrations above the drinking water standard of 0.1 mg l-1 (Table A4.1); -1  Approximate total GC amenable pesticides and herbicides exceeded the 0.1 mg l drinking water standard in 126 of the 159 samples analysed;  Five key pesticides accounted for 90% of those pesticides detected in the scans: metaldehyde, metazachlor, dimethenimid, flufenecet and propizamide (Figure A4.3);  Metaldehyde was ubiquitous across the 20 sites sampled in the Wensum (see Figure A4.1). Nineteen out of the 20 sites recorded at least one incidence of metaldehyde concentrations in excess of approximately 0.5 mg l-1 and there were 33 sampling site results when the approximate concentration of metaldehyde exceeded 1 mg l-1;  Compounds detected varied at sites over time with high concentrations of pesticides tending to be associated with higher flow and recent rainfall events;  Under low flow conditions the most common herbicide detected was dichlrobenzamide;  The four key sewage-related compounds were caffeine, bisphenol, gabapentine and carbamazipine (pharmaceutical drugs).

254

Table A4.1: Summary of results of GC-MS mass scan amenable compounds detected in 156 of 159 samples submitted for analysis. A total of 78 separate compounds were detected although 10 of these were detected only once at the detection limit of 0.01 mg l-1

Detection concentration Number of detections Percentage of total detections >1 mg l-1 48 4.0 >0.5 mg l-1 116 9.5 >0.1 (Drinking water standard) 282 23.2 >=0.02 mg l-1 909 74.7 Total detections above 0.01 mg l-1 1216 Number of samples 159 Non-detections 3 Missed samples 1

Figure A4.3: Main GC-MS mass scan amenable detections of agricultural pesticides above 0.01 mg l-1

255

9 Demonstration Test Catchment Programme Staff

Adrian Collins Role: Lead-PI, Hampshire Avon and Tamar DTCs Adrian (Adie) was lead-PI on the Hampshire Avon and Tamar DTCs during phase 1 as well as lead-PI on a national DTC project assessing on-farm sediment mitigation options. He wrote the DTC farm business baseline survey applied nationally across the platform. He is a now Principal Scientist in the Sustainable Soils and Grassland Systems Department of Rothamsted Research-North Wyke, having previously been the Head of Water in the Soils, Agriculture and Water team at ADAS. He has worked for over 20 years researching diffuse pollution from agriculture or additional sectors and its mitigation, focussing specifically on landscape or catchment scale studies and national scale extrapolation.

Adie is Visiting Professor at the University of Southampton, Visiting Professor at the Chinese Academy of Sciences, Vice President of the International Commission on Continental Erosion (ICCE) of the International Association of Hydrological Sciences (IAHS), a member of the UK Committee for National and International Hydrology and a Steering Group member for the EU SEDNET initiative. He is also a member of the National Scientific Advisory Panel for the Salmon and Trout Association. In phase 2, Adie will continue to be lead-PI on the Hampshire Avon and Tamar components of the national platform.

Penny Johnes Role: Joint PI, Hampshire Avon and Tamar DTCs Penny is currently Professor of Biogeochemistry at the University of Bristol, which she joined on 1st January 2014. Previously she was Professor of Freshwater Science and Director of the Aquatic Environments Research Centre at the University of Reading. Penny is an environmental scientist by background who has worked on the biogeochemistry of aquatic systems and the impacts of food production and environmental change on the quality of inland and coastal waters for the past 25 years. She has provided advice to a range of UK Government and international agencies on the nature and scale of nutrient enrichment in waters, the consequences of this enrichment for ecosystem health, and the most effective strategies for the control of nutrient flux from land based sources to waters. She is currently a member of the UNECE Task Force for Reactive Nitrogen and its Expert Panel on Nitrogen Budgets, a member of the NERC Pool of Chairs, and the IAHS International Commission on Water Quality.

In the DTC programme, Penny is one of 5 Joint-PIs on the Hampshire Avon and Tamar DTCs, with specific responsibility for leading the team undertaking the laboratory analysis of samples collected across the DTC sites, advising on the field instrumentation, the sample collection and handling protocols, and the development and implementation of Quality Assurance protocols and Quality Control procedures for all samples collected across this part of the platform.

Ian Foster Role: Mitigation measures Ian is a graduate of King’s College London (BSc) and the University of Exeter (PhD). For more than 40 years he has worked on issues of water quality, erosion, land degradation and reservoir sedimentation in many parts of the world including the UK, the circum-Mediterranean area, the Middle East and South Africa. He has expertise in catchment monitoring, sediment source tracing and in reconstructing the history of erosion from sediments accumulating in lakes and reservoirs. At Northampton he runs gamma spectrometry, environmental magnetism and particle size laboratories. Ian has attracted research and consultancy income from a number of UK organisations including NERC, Defra, the Environment Agency and numerous 256 commercial clients. He has published over 120 scientific papers in international peer reviewed journals and currently supervises PhD students working on topics including sediment quality issues in the River Ravensbourne (London), sediment pressures in the Rivers Nene and Rother, UK, agricultural mitigation options for sediment control and sediment-associated radioactivity in UK catchments. He is currently a visiting Professor at the University of Westminster (London) and Rhodes University (South Africa) where he has been researching erosion and land degradation for the last decade.

Matilda Biddulph Role: PhD Research Student Matilda (Mattie) is in the final year of her PhD at the University of Northampton, under a studentship funded in part by the university and in part by Defra. She is working in conjunction with the Hampshire Avon DTC. Prior to this she completed her undergraduate degree in Physical Geography (BSc) at Durham University, and a Masters by Research in Hydrological river response during storms, as well as water quality with relation to the survival of Freshwater Pearl Mussels in the River Esk, North Yorkshire. Her PhD focuses on creating a toolkit of methods that can test the efficacy of mitigation options, which have been identified for reducing agricultural pollution of sediment and contaminants. These methods must be affordable, sustainable, replicable and efficient in order for them to be an effective use for monitoring the mitigation options. To do this she has been monitoring various mitigation options across a number of sites from December 2012 and is due to finish fieldwork in February 2015.

Phil Haygarth Role: Co-lead of Eden DTC Phil is Professor of Soil and Water Science at Lancaster University with a research focus on this interface, focussing on 'Catchment Science’ issues, particularly related to phosphorus in soil and water quality. He led the EdenDTC team through phase 1, as well as leading UK Research Council Grants on potential effects of climate and land use change on nutrient emissions from soils to waters (NERC), and on understanding organic phosphorus mobility from soils to crops (BBSRC). Phil is an enthusiastic research-led teacher who contributes to undergraduate, masters and PhD teaching, and recently successfully led a bid to lead a national consortium of 8 UK organisations to form a National PhD Doctoral Training College on Soil Science. He has also just completed a role as President of the British Society of Soil Science.

Phil Barker Role: Co-lead of Eden DTC and lead of Eden DTC biomonitoring Phil addresses big environmental questions using the ecology and chemistry of microscopic organisms. His focus is on using diatoms and stable isotope methods to explore changes in climate, biogeochemical cycling and water quality changes. He has spent much of the last 25 years working in Africa investigating long term climate change from lake sediment archives. Parallel studies have been conducted in the Lake District examining water quality and ecological changes in lakes and rivers. Phil has published more than 100 papers and book chapters including six in Nature and Science. He currently investigating changes in the carbon cycle of freshwater ecosystems using carbon isotopes from contemporary biofilms in lakes and rivers and leads the biomonitoring for the Eden DTC. Phil is Deputy Director of Lancaster Environment Centre, a multidisciplinary department of Lancaster University that includes over 150 academic, research and support staff, 170 PhD’s, 100 PGTs and 700 undergraduates.

257

Nick Barber Role: Post-Doctoral Research Technician Nick started working on the Eden DTC project in late 2013, based in the Geography Department at Durham University. He completed his higher education at Newcastle University, including a Masters in Hydrology and a PhD in monitoring and management of sediment and nutrients in agricultural catchments. Much of this work was carried out in the Eden catchment so a good understanding of the hydrogeophysical characteristics of the area was gained prior to his current role on the team. He has also worked on Natural Flood Management schemes in Northumberland, so understands the importance of public engagement in catchment management.

Nick’s position on the DTC requires him to run the field-monitoring programme (as part of a three-person team), compile and quality control high-resolution water quality data, analyse and interpret data, and assist the implementation and assessment of on-farm mitigation measures.

Clare Benskin Role: Post-Doctoral Research Technician Clare has been working on the Eden DTC project since November 2010, based in the Lancaster Environment Centre at Lancaster University. She completed her higher education at The University of St Andrews, including a BSc in Animal Biology and an MLitt in IT in Arts. Subsequently, she completed a PhD at Lancaster University, using molecular techniques to assess bacterial communities in the avian gut.

Clare’s position on the DTC requires her to co-run the field-monitoring programme (as part of a three- person team), and assist the implementation and assessment of on-farm mitigation measures.

Dr Martin Blackwell Role: Co PI Avon component 2 Martin is a Soil Scientist at Rothamsted Research North Wyke which is part of the Department of Sustainable Soils and Grassland Systems. He has more than 20 years’ experience researching soil and water biogeochemistry, specializing in both phosphorus (P) and nitrogen (N) dynamics in grassland and wetland systems, nutrient transfers from soils to surface waters and diffuse agricultural pollution mitigation methods. He is on the committee of the Southwest England Soils Discussion Group and Associate Editor for the journal Grass and Forage Science. Key areas of interest include:

 Factors affecting critical P values in different soils 18  Stable isotope techniques for tracing phosphate (δ O-PO4) in soils, water and plants  The use of 31P-NMR analysis for the measurement of P compounds in soils and waters  The cycling and availability of organic P compounds in soils  The effects of patterns of soil drying and rewetting on nutrient availability and losses from soils  The role of buffer strips for the protection of surface waters from diffuse agricultural pollution  Ecosystem services delivered by agricultural and wetland ecosystems

Current projects include Rothamsted Research’s Institute Strategic Programme on ‘Delivering Sustainable Systems’ (focusing on P cycling and availability), a BBSRC responsive mode project titled ‘Organic P Utilization in Soils (OPUS)’, and the Defra funded ‘Demonstration Test catchments (DTC) Project’ and ‘Sustainable Intensification Platforms Project’.

258

Sean Burke Role: PI for Component 2 Dr Sean Burke’s expertise lies in surface water quality, hydrochemistry and resource protection through catchment management. His research interests have focused on the interdisciplinary catchment science that is needed to underpin catchment management for effective resource management. Along with DTCs much of Sean’s recent work has focused on protecting water resources from agricultural pollution and the development of cost effective measures to reduce its impact. This work has involved working with partners in China, Denmark, Holland and Germany. Previous work undertaken by Sean has centred on qualitative and quantitative surface water and groundwater risk assessment in the UK, Europe and South America. Mine water rebound/chemistry and predictive mine water quality modelling has also been a focal point of his research and he is an established international expert in this area. He has extensive experience of bringing together multidisciplinary teams need to develop the tools necessary for holistic catchment management and therefore the approach needed for resource protection.

Will Cleasby Role: Catchment Engagement Officer Will has worked for the Eden Rivers Trust for 6 years as the main point of contact with the farming community, carrying out riparian habitat restoration projects and delivering advice on nutrient management and mitigation against diffuse pollution from agriculture. His background and education is in agriculture, and along with other family members runs a mixed (mainly beef cattle) farm near Temple Sowerby in the Eden Valley.

Will was appointed in 2010 as the Catchment Engagement Officer for the Eden Demonstration Test Catchment project. His role within the project is to engage with landowners, farmers and local stakeholders in the Eden catchment to encourage support and participation in the DTC Project, and develop the local social framework required to support future aspects of the project.

Clare Deasy Role: Senior researcher Clare has worked at Lancaster University as a Senior Research Associate on a number of Defra-funded diffuse pollution mitigation projects (including MOPS1, MOPS2, and DTC). Clare has an MSc in Catchment Dynamics and Management from the University of Leeds, and a PhD in Hillslope Sediment and Phosphorus Transfer from Sheffield University. Since her PhD she has undertaken applied research into mitigation options to reduce diffuse pollution from agricultural land, working with a variety of catchment stakeholders to design and implement approaches such as in-field arable options and field wetlands. She project managed the MOPS1 and MOPS2 projects, and has been involved with the DTC project from its inception. She has worked on DTC phase 1 analysing and interpreting some the data and disseminating results, contributing to the design of the mitigation and monitoring approaches, and been part of the team writing the final report. In January 2014, Clare moved into a new role as ‘Catchment Advisor on the Water Framework Directive’ at Northumbrian Water, but still retains her academic interests in catchment processes and mitigation of diffuse pollution.

Chris Hodgson Role: Co Investigator Avon Component 2 Chris is an environmental microbiologist working at Rothamsted Research North Wyke, currently working on a Defra funded ‘Demonstration Test catchments (DTC) Project’ and industry collaborations looking at animal feed quality and feed additives. He is part of the team that developed “The Farm Crap App”: 259

A free mobile app designed to raise awareness amongst farmers and growers about the nutritive and economic value of manures and slurries.

Initially he was employed as a post-doctoral researcher on a Rural Economy and Land Use (RELU) project (RES-0224-25-0086), where he investigated the movement of FIOs from various farming systems within the Taw river catchment. He since worked on a variety of DEFRA funded projects which included; (WQ 0118) Understanding the behaviour of pollutants through cracking clay soils and (WQ 0111) Faecal indicator organism losses from farming systems (FIO-FARM).

He obtained a first class honours degree in Environmental Analysis in 1999 and followed this with a PhD investigating the microbiological performance of constructed wetlands to reduce diffuse pollution. After completing his PhD he spent a short but fruitful postdoctoral period, investigating biocidal formulations and biocidal wipes before moving to Devon and Rothamsted Research North Wyke in 2005.

Simon Johnson Role: Catchment stakeholder liaison Simon joined the Eden Rivers Trust in June 2009 from the Wild Trout Trust (WTT) where he was the Director for the last six years. At WTT he was responsible for the impressive growth of that organisation, delivering effective conservation and education projects related to wild trout throughout mainland Britain and Northern Ireland, as well as displaying impressive fund-raising skills.

Jennine Jonczyk Role: Field programme co-ordination and the design and implement of mitigation measures Jennine is a catchment scientist based at Newcastle University. She completed her Masters in Environmental and Resource Assessment and her PhD in ‘processes leading to nutrient pollution at field and sub-catchment scale’ at Newcastle. Jennine has worked in the Proactive team at Newcastle since 2005 on projects using an earth systems engineering approach. Projects involved trialling mitigation ideas for attenuation of DWPA with multiple benefits (e.g. flood risk & biodiversity); mitigation and monitoring of DWPA at different catchment scales; and co-designing several catchment plans with stakeholders.

Jennine’s role within DTC, since 2010, has been centred on the co-ordination of the day-to-day tasks of running a field programme, collating and analysing data and finding solutions to issues as they arise. She took a lead in the design and the implementation of the mitigation measures being trialled within the Eden catchment. Jennine enjoys working across the wider stakeholder group and is especially keen on interpreting data on catchment function and translating it into practice.

Gareth Owen Role: PhD Research Student and Hydro-meteorological data manager Gareth has worked for a consultancy on a number of flood forecasting, flood warning and flood alleviation schemes, prior to which he worked in a number of GIS and data management roles. Through his MSc at Newcastle and his thesis in particular he gained hands on experience of the installation and maintenance of Hydro-meteorological equipment.

Since 2010, Gareth has worked for the Eden DTC as a field technician, based at Newcastle University, and was largely be responsible for the hydrology and meteorology equipment, primarily being involved in the

260 installation and maintenance of field equipment. He then commenced a PhD in 2012, continuing to support the field team and working with the hydro-meteorological equipment and data.

Gloria Periera Role: Lead on the Water Quality Analysis module Gloria is the head of the Centralised Chemistry Group at CEH Lancaster, comprising a team of 13 chemists that analyses levels of nutrients, metals, organic compounds and stable isotopes in environmental samples. I am also the team leader for the organics group, specialising in the analysis of persistent organic compounds, phytopigments, pharmaceuticals, phospolipid fatty acids, etc. She has been acting as lead on the Water Quality Analysis module for the Eden DTC.

Paul Quinn Role: Instrumentation and data management Paul joined the School of Civil Engineering and Geosciences at Newcastle in 1997, as part of the Water Resources Engineering Group. He is currently a Senior Lecturer in Catchment Hydrology, and his area of teaching revolves around hydrology (water quantity and quality), with a focus on Catchment Systems Engineering. His research interests focus on catchment instrumentation, GIS and modelling. The work includes the creation of a wide range of soft engineered interventions for diffuse pollution management and flood risk mitigation (including, ponds, wetlands, riparian and ditch management). Equally the work requires liaison with catchment stakeholders and regional and national policy bodies (such as Defra, NGO’s and The Environment Agency).

Sim Reaney Role: Lead of ‘Catchment Characterisation’ work package Sim has worked at Durham University since 2005 and is currently a lecturer in the department of Geography. Previously Sim was a RCUK research fellow in the Institute of Hazard, Risk and Resilience and also worked as a post-doctoral researcher on diffuse pollution. Sim has developed and applied a range of environmental models for understanding hydrological and biogeochemical issues. These models have included fully distributed catchment simulation modelling which has been used for understanding water quality under projected climate change for the UK water industry and the risk mapping of diffuse pollution sources areas at the catchment scale using the SCIMAP approach. Sim has been a co-investigator of the River Eden Demonstration Test Catchment with a focus on the spatial issues.

Maria Snell Role: Freshwater Ecologist and Biomonitoring Maria is a freshwater ecologist who interests are in exploring the factors that regulate the stability, structure and functioning of in-stream ecological networks in particular benthic diatom communities. Within Eden DTC Maria works on understanding fine-spatial high-temporal patterns benthic diatom biofilm community structure and ecological functional processes, such as chlorophyll-a production, to reveal timescales of response and physicochemical sensitivities of benthic ecosystems in headwaters which are important considerations for environmental monitoring and policy decision making. Maria also works closely with Environment Agency and Eden Rivers Trust to explore ecological monitoring concordance between diatom, macroinvertebrate, macrophyte and fish communities.

261

Ben Surridge Role: Lead of groundwater and mitigation measures work packages Ben is currently a Lecturer in Environmental Science at Lancaster University, with principal research interests in phosphorus biogeochemistry. His past research has focussed on geochemical and hydrological controls on phosphorus and nitrogen mobility in rivers, floodplains and hyporheic zones, employing field, laboratory and data modelling techniques. His current research is funded by NERC, BBSRC, EPSRC and Defra, and spans the development of stable isotope techniques to trace the sources and metabolism of phosphorus in the environment, the use of reactive industrial by-products to mitigate diffuse water pollution from agriculture, and the development of new technologies to determine plant available phosphorus concentrations in soils. Ben currently leads the groundwater and the mitigation measures workpackages in the EdenDTC project. He has published over 20 research papers in peer-reviewed international journals and contributed one invited book chapter.

Catherine Wearing Role: Water and Soil Analytical Laboratory Manager Catherine has worked at Lancaster University since 2014 and has previously worked as a researcher and in research support. Her research at the University of Leeds and Stirling focussed on water chemistry and hydrology in upland areas. In her current role, as the water and soil analytical laboratory manager, she analyses samples generated by the project.

Deborah Bellaby Role: Research Project Administrator

Kevin Hiscock Role: Principal Investigator (Natural Sciences) Kevin has over 30 years of experience advancing understanding of the natural and contaminant hydrochemistry of catchment systems both in the UK and overseas. Since joining UEA in 1989, Kevin has developed an international reputation in teaching and research in hydrology and hydrogeology with high- level knowledge of inorganic and stable isotope hydrochemistry, hydrological modelling and the impacts of climate and land use change on water resources. Current research is assessing the origins and fluxes of catchment nitrogen and phosphorus leaching losses and the indirect emissions of nitrous oxide, a potent greenhouse gas, in groundwater. Kevin leads the investigation of catchment physical and chemical processes in the Wensum DTC and is a member of the Defra Agricultural GHG Platform.

Andrew Lovett Role: Principal Investigator (Socio-economics) Andrew specialises in applications of geographical information systems (GIS) and statistical methods in the environmental sciences. He has authored or co-authored over 130 refereed journal articles or book chapters. Since joining UEA in 1990, Andrew has worked on a wide variety of multidisciplinary research projects and in recent years has focused particularly on aspects of landscape planning and visualisation, catchment management and environmental economics. Andrew leads the knowledge exchange and farm economic data analysis in the Wensum DTC.

262

Ros Boar Role: Principal Investigator (Ecological Analysis) Ros Boar’s research experience on the River Wensum dates back to 1991 when she was principal investigator for a National Rivers Authority-funded project on the effects of water resources management on the Rivers Bure, Wensum and Nar in north Norfolk. The project collated and interpreted historical and present-day data on the Wensum concluding that physical changes in river and channel morphology, resulting from land use change, and agriculture in particular, shape the biology of river communities. In the Wensum DTC, Ros acts as the link between the Environment Agency (which is collecting in-stream biological data) and the UEA team analysing the multi-variate datasets.

Faye Outram Role: Field Programme Manager and Data Manager Faye is a catchment scientist who specialises in nutrient enrichment in agricultural catchments. Her roles in the Wensum DTC project have included Field Programme Manager and Data Manager. As Field Programme Manager she managed the installation and upkeep of the in-situ monitoring network. As Data Manager she carries out Quality Assurance and Quality Control protocols on data collected from the monitoring network. Faye uses a variety of techniques to analyse and interpret high frequency hydrology, hydrochemistry and meteorology datasets. Particular areas of interest include the response of agricultural streams to rainfall events in terms of timing, concentration, loading of nitrogen and phosphorus and influencing factors such as antecedent conditions and flow pathways.

Steve Dugdale Role: Field Programme Manager and Database Manager Steve has GIS analysis skills and has applied these in a number of projects, including work on the National Ecosystem Assessment project. For the Wensum DTC, Steve maintains and services the monitoring kiosks on a weekly basis and organises and implements fieldwork activities to evaluate the effect of mitigation measures. Steve also manages the database relating to monitoring of the in-field mitigation measures.

Gilla Sünnenberg Role: GIS analyst Gilla researches GIS applications (particularly ESRI ArcGIS) in the environmental sciences and cartographic design. Gilla has extensive experience working with UK and international spatial databases, including Ordnance Survey products and land cover mapping, and is interested in the development of applications for use with Google Earth.

Trudie Dockerty Role: Knowledge Exchange and Website Manager Trudie is an environmental researcher interested in scenarios and stakeholder engagement for evaluating a range of environmental impacts. She has experience of questionnaire development and survey and recently completed a systematic literature review on the impacts of energy systems on ecosystem services with implications for energy futures. Trudie is interested in the communication of scientific information to the public, policy makers and practitioners and has experience of developing stakeholder networks, creation of a variety of information materials and, in her current role, development and maintenance of the Wensum DTC website.

263

Jenny Stevenson Role: Fieldwork Services Jenny is an experienced field technician used to working outdoors in all weathers and environments. She has worked on many different projects using a variety of field equipment and participated in many sampling programmes. Her regular role in the Wensum DTC project is to support the maintenance of the field equipment deployed in the catchment and to assist in the weekly grab and fortnightly drain sampling programmes.

Liz Rix Role: Laboratory Services Liz gained an MSc in Spectroscopy and Physical Analytical Chemistry in 2000 from the School of Chemical Sciences at UEA. Liz now manages the Analytical Facilities Laboratory in the School of Environmental Sciences and provides chemical analytical support to the Wensum DTC project. The analytical laboratories house a wide variety of 20 different analytical instruments and provide analytical services including method development and quality control to the Norwich Research Park and commercial enterprises.

John Brindle Role: Laboratory Services John is responsible for the safe operation of laboratory facilities in the School of Environmental Sciences in the areas of sedimentology, nanomaterials for energy storage and petrology, including sample preparation. For the Wensum DTC, John maintains and calibrates the YSI multi-parameter sondes, as well as assisting with field operations.

Richard Cooper Role: PhD student Richard has been a PhD student at UEA since 2011 specialising in fluvial geochemistry and catchment science. Richard’s primary research focus has been to advance methods for apportioning the sources of sediment in rivers by combining spectroscopy and stable isotope analysis with Bayesian mixing models. Richard has actively published articles on sediment source apportionment in the River Wensum catchment.

Emily Vrain Role: PhD student Emilie Vrain specialises in behavioural and attitudinal research within the agricultural industry. Her PhD focuses on farmer attitudes and behaviour towards mitigating diffuse water pollution and the mechanisms required to encourage uptake of measures. She has carried out a number of social science surveys with farmers and farm advisors in all the DTC catchments. Throughout her PhD, Emilie has produced several policy briefing documents to aid decision-making during the redesign of England’s agri-environment schemes.

Lister Noble Role: Farm Liaison Lister previously managed the long-term Cropvision precision farming project for Aventis France (now Bayer, a worldwide agricultural research and manufacturing company). Cropvision is a whole farm management package using an Internet-based agricultural decision-support system based on a simulation model of crop growth and development using satellite images. Lister has been a partner in the Unilever/Birds Eye Walls Sustainable Agriculture Project based at their Colworth Research Centre. For

264

Thames Water, Lister managed long-term farm-scale trials in East Anglia utilising bio-solids (treated sewage products) to investigate the effects of applications on crop production, crop quality, soil nutrients and soil structure. Lister currently acts as a knowledge-broker between the UEA team and the agricultural sector, providing agronomic advice and helping with farm access.

265