Evaluating how Storm Events Impact Phosphorus and Sediment ...€¦ · Land use throughout Europe is dominated by farmland, with Britain hosting 75% of its land to agriculture (18.3million

Evaluating how Storm Events

Impact Phosphorus and

Sediment Fluxes to the

River Wensum

ENV 3A91-Undergraduate Final Year

Independent Project

Benedict Orchard

3887189

Project Supervisor: Professor Kevin Hiscock

Student Number: 3887189 2

Abstract

Phosphorus and sediment are pollutants known for their adverse affects on ecological and chemical

status of receiving water. Storm events enhance the mobilisation of these pollutants allowing for

high-resolution temporal monitoring of their impact to watercourses. Two events were monitored in

order to evaluate this impact, on the Blackwater sub-catchment of the River Wensum, Norfolk. A

further evaluation was carried out on the impact of subsurface drainage on pollutant fluxes.

Collaborating with the wider Wensum Demonstration Test Catchment (DTC) project, high

specification monitoring stations allowed for the capture of water samples every 30 minutes across

12 hours. These were analysed for phosphorus and sediment concentrations, from which fluxes

were calculated.

Trends in the high-resolution data indicated the presence of subsurface drainage through large time

lags between peak rainfall intensity and peak concentrations (average= 4hours). The complex nature

of phosphorus was apparent from the influence of varying characteristics at each monitoring site.

Intense arable farms produced high Soluble Reactive Phosphorus (PO43-

) concentrations, while

pastoral influences related to the largest Total Suspended Sediment (TSS) fluxes, and sandy soils

saw high correlations between Total Particulate Phosphorus (TPP) and Total Phosphorus (TP).

Storm events increased pollutant fluxes compared to their baseline values (TP: +172%, TSS:

+346%) while, contrary to much literature, fluxes from sites with subsurface drainage showed to

have higher fluxes than those without (TP: +80%). This may owe to intense fertiliser regimes at

these sites, overpowering soil uptake capacity. Better understanding of these results will arise from

monitoring more events over a larger temporal scale. As the DTC programme ages enhancements in

methodologies, data sets and analysis will surely follow.

Keywords: High-resolution monitoring, subsurface drainage, diffuse pollution, mitigation options


Contents

1 Introduction ................................................................................................................................. 5

1.1 Phosphorus and its Importance .............................................................................................. 5

1.2 Value of Events in Pollutant Monitoring ............................................................................... 5

1.3 Relevance of Subsurface Drainage for Future Agricultural Management ............................ 6

1.4 Scope of Project ..................................................................................................................... 7

1.4.1 Project Rationale ............................................................................................................ 8

1.4.2 Aims ............................................................................................................................... 8

1.4.3 Hypotheses ..................................................................................................................... 8

2 Location ....................................................................................................................................... 9

2.1 The Wensum DTC Programme ............................................................................................. 9

2.2 The Blackwater Catchment ................................................................................................... 9

2.2.1 Subsurface Drainage Network ..................................................................................... 12

3 Methodologies ............................................................................................................................ 13

3.1 Data Collection .................................................................................................................... 13

3.2 Monitoring Stations ............................................................................................................. 13

3.3 Laboratory Work ................................................................................................................. 14

3.4 Field Data ............................................................................................................................ 15

3.4.1 Rain gauges .................................................................................................................. 15

3.4.2 Stream Cross Section ................................................................................................... 15

3.5 Estimating the Stream Discharge without gauging equipment ........................................... 16

3.5.1 Considerations with the Data Manipulation ................................................................. 17

3.6 Analysis of the methodologies ............................................................................................ 18

4 Results ........................................................................................................................................ 19

4.1 Rainfall ................................................................................................................................ 19

4.2 Hydrographs ........................................................................................................................ 21

4.3 Concentrations ..................................................................................................................... 23

4.3.1 Baseline ........................................................................................................................ 23

4.3.2 16th

July 2011 ............................................................................................................... 26

4.3.3 4th

November 2011 ....................................................................................................... 29

4.4 Fluxes................................................................................................................................... 30

5 Discussion ................................................................................................................................... 32

5.1 Concentration Trends .......................................................................................................... 32

5.1.1 Seasonal Differences .................................................................................................... 33

5.2 Flux Trends .......................................................................................................................... 33

5.2.1 Impact of Events ........................................................................................................... 33


5.2.2 Impact of Subsurface Drainage .................................................................................... 34

5.2.3 Cross Correlations ........................................................................................................ 34

5.3 Literature Comparisons ....................................................................................................... 35

5.4 Data Reliability .................................................................................................................... 37

5.5 Best Management Practices ................................................................................................. 37

5.6 Climate Change ................................................................................................................... 38

5.7 Future Research ................................................................................................................... 39

6 Conclusion ................................................................................................................................. 40

7 Acknowledgements ................................................................................................................... 41

8 References .................................................................................................................................. 42

9 Appendices ................................................................................................................................. 47

9.1 Appendix 1 – Total flux values and percentage change ...................................................... 47

9.2 Appendix 2 – Calculating flux from discharge ................................................................... 48

9.3 Appendix 3 – Relationship of various phosphorus determinations ..................................... 49

9.4 Appendix 4 – Project Proposal ............................................................................................ 50

9.5 Appendix 5 – Dissertation Progress report (18th

October 2011) ......................................... 53


1 Introduction

1.1 Phosphorus and its Importance

Land use throughout Europe is dominated by farmland, with Britain hosting 75% of its land to

agriculture (18.3million ha). Post 1945 a 77% reduction in farm labour and a fourfold increase in

yield identified the intensification of agriculture in Britain (Robinson & Sutherland, 2002). This

intensification, along with expanding population, gives rise to increased phosphorus runoff to

receiving waters. Between the 1970s and 1980s phosphorus fertiliser use increased by 65%, leading

to strong emphasis on their reduction since 1990. Yet to date they remain over 20% higher than post

war (DEFRA, 2011). Increasing fertiliser use coupled with inputs from livestock excreta, soil

erosion and runoff from impervious surfaces boosts the enrichment of phosphorus in rivers (Withers

& Jarvie, 2008). The current riverine fluxes of Total Phosphorus (TP) are 20Tg P a-1

(Edwards &

Withers, 2008). Phosphorus is a limiting nutrient to freshwater ecosystems and an increase in

concentration of bio-available forms increases the size and population of autotrophic organisms,

such as algae (Stutter & Lumsdon, 2008). This leads to a reduction in light penetration, increasing

incidences of surface algal scum and water deoxygenating leading to fish kills (Withers & Jarvie,

2008). Despite advances in knowledge and mitigation options for phosphorus, concentrations in

both soluble and particulate form are regularly above environmental limits in base flow (Granger et

al. 2010). Emphasis into sediment reduction is apparent due to the close correlations between Total

Suspended Solids (TSS) and phosphorus within literature (Jarvie et al, 2002), owing to sediments

potential as a transportation vessel.

Withers & Jarvie (2008) state the complexity between supply, ambient concentrations and

ecological response of phosphorus is very high. Yet government legislation, such as European

Water Framework Directive (WFD), puts pressure on the need to understand surface water

pollutants. Pronouncing that “Member States shall protect and enhance all artificial and heavily

modified bodies of water, with the aim of achieving good ecological potential and good surface

water chemical status at the latest 15 years from the date of entry into force of this Directive”

(European Parliament, 2000). This directive initiated research papers into the effects of many

pollutants, continuing today, such as Neal et al. (2006). He argues the influence of TSS and Total

Particulate Phosphorus (TPP) is of critical concern to the ecological vitality of riverine systems in

agricultural areas, given the remit and timeframe of the WFD.

1.2 Value of Events in Pollutant Monitoring

Rainwater is the highest form of nutrient, pollutant, soil and vegetation transport from agricultural

land. Sediment and phosphorus become mobilised and transport along surface and subsurface

hydrological pathways, when rainwater passes (Withers & Jarvie, 2008). Intense rainfall from


storms (referred to now on as events), are therefore useful in monitoring pollutant delivery. Event

monitoring provides opportunity to obtain clear high temporal resolution trends due to the rapid

fluctuation in intensity, which can identify land characteristics such as sources and delivery

pathways. This level of detail may be missed by lower resolution rainfall monitoring, leading to

miscalculations on pollutant flux (Granger et al. 2010). A study by Dalzell et al. (2011) indicated

that on average, 90% of total annual flow occurred during just 15% of the time, indicating that large

volumes of water, mobilized during an event, is the dominant factor controlling export from the

agricultural fields.

The high temporal trends available allow for better understanding of the hydrological response of a

catchment and therefore pollutant transfer mechanisms. Granger et al. (2010) argues that significant

increases in flux from events are a cause for concern towards environmental legislation. Monitoring

events is therefore mandatory in order to improve environmental status and inform those who are

more closely impacted, such as farmers, of the issues facing them.

1.3 Relevance of Subsurface Drainage for Future Agricultural Management

Subsurface drainage is a practice most common to more intensive agriculture, used to “increase

crop production by improving soil trafficability and by protecting the crop from high water tables

during the growing season” (Istok & Kling, 1983). Introducing or improving subsurface drainage

reduces overland flow and shifts the possible pathways for entry, thereby reducing sediment and

phosphorus losses from farm fields and improving water quality (Dalzell et al. 2011; Turtola &

Paajanen, 1995). Istok & Kling (1983) identifies a 65% reduction in watershed runoff and a 55%

reduction in sediment yield through subsurface drainage, while Turtola & Paajanen (1995) indicates

a 17% reduction, Bengtson et al. (1988) finds a 30% reduction and Schwab et al. (1980) a 40%

reduction, all in sediment yield. Bengtson et al. (1988) and Schwab et al. (1980) have noted similar

results for phosphorus loss to water, with reductions in Total Phosphorus (TP) of 36% and 45%,

respectively. Total Dissolved Phosphorus (TDP) and TPP have also seen reductions of 25% and

16% respectively (Turtola & Paajanen, 1995).

We have known about the influence of soil infiltration capacity on the erosion process for some

time, however not until Bottcher et al. (1981) was the recognition of drainage as an erosion control

practice brought about. They reported, “A 17ha field with a complete subsurface drainage system

and restricted surface runoff had significantly reduced water and sediment losses as compared to the

more normal drainage situation”. Turtola & Paajanen (1995) agrees by stating, subsurface drainage

might be a cost effective means of reducing eutrophication, if farmers consider the environmental

benefits or disadvantages rather than just agronomic ones. Currently it remains poorly understood

how subsurface drainage may influence the quantity and quality of nutrient export from

agriculturally dominated landscapes (Dalzell et al. 2011), however literature has provided


indication that there is benefit to gain from evaluating subsurface drainage as a benefactor to

sediment and phosphorus fluxes (Istok & Kling, 1983; Bengtson et al. 1988). In addition research

suggests subsurface drainage would also benefit pesticides (Brown & Beinum, 2009), Dissolved

Organic Matter, (Dalzell et al. 2011) and Nitrogen (Turtola & Paajanen, 1995). Despite those

studies, no action has been taken to benefit waterways.

The overall aim of further research into subsurface drainage is to support its inclusion into the

Environmental Stewardship (ES) scheme, for which there is currently no related scheme. The ES is

a UK and European Government funded scheme, aimed at providing farmers with schemes that see

them “looking after England‟s countryside – the wildlife, landscapes, historic features and natural

resources (soils and water) – and providing new opportunities for public access in some cases”,

while improving their farming practices and earning a financial reward (NE, 2010).

1.4 Scope of Project

This study aims to analyse and evaluate the impact an event has on sediment and phosphorus fluxes

and whether the presence of subsurface drainage reduces those fluxes, both supported by past and

present literature. It will draw on data from the multi-million pound Wensum Demonstration Test

Catchment (DTC), managed by the University of East Anglia.

Two events, occurring on 16th

July and 4th

November 2011, were monitored allowing interpretation

of the mobilisation, transport and fluxes of five phosphorus determinants and TSS from the

Blackwater sub-catchment of the River Wensum. A baseline period was also monitored over 23

weeks, enabling the comparison of fluxes leading to an understanding of the holistic impact of an

event on agricultural land. Comparing sites where the dominating flow is from subsurface drainage

with those that are characterised by overland flow, will allow for evaluation of whether subsurface

drainage reduces the fluxes.

Within the literature, there are studies that evaluate the trends of a suite of pollutants (e.g. Granger

et al. 2010) as they provide a holistic approach to advising on mitigation measures. However, this

study and those of Brown & Beinum (2009) and Dalzell et al. (2011) focus on the sources,

behaviours and transport of individual pollutants to obtain information on associated mechanisms

and processes involved. Along with TSS this study measures TP as it provides complete phosphorus

flux. It however does not indicate the fraction that is available for biological uptake, leading to

monitoring Total Reactive Phosphorus (TRP) and Soluble Reactive Phosphorus (referred to as

PO43-

) as well. TDP is also measured because it includes the Soluble Un-reactive Phosphorus

fraction. Finally, TPP is measured indicating phosphorus that attaches to particulate matter, such as

soil or algae, due to the difficulty of transporting phosphorus on its own. This influences the high


correlations between TP and TSS. Figure 9.2 in Appendix 3 presents a flow diagram for the

methodologies to obtain the different determinations.

1.4.1 Project Rationale

Current literature contains a multitudinous amount of research in pollutant runoff, event monitoring

and subsurface drainage into context. The sections above state their individual importance; yet

linking them together places this study at the forefront of current research. Using a small sub-

catchment, this study aims to link its findings and evaluations into an array of research within a

wider scale project. Along with the given evaluations, there is an emphasis on mitigating

phosphorus fluxes to receiving water, as despite large amounts of work there are currently few

dependable measures. Therefore, some mitigation options are recommended. To emphasise the need

for mitigation, quantitative projections demonstrating the impact climate change is having on event

frequency, are provided.

1.4.2 Aims

Evaluate the impact of an event on pollutant flux

Evaluate the effect subsurface drainage has on pollutant flux

Examine some initial solutions for mitigation options to improve water quality

Identify the projections for event frequency from a changing climate.

1.4.3 Hypotheses

1. Events cause increased pollutant fluxes, over base flux, from agricultural catchments

2. Subsurface drainage reduces pollutant flux from agricultural catchments.


2 Location

2.1 The Wensum DTC Programme

This study is linked to the ongoing research of the Wensum DTC project, run by the University of

East Anglia (UEA) and Government funded. The project is set up to test if it is possible to “cost

effectively reduce the impact of agricultural diffuse water pollution on ecological function while

maintaining food security through the implementation of multiple on-farm measures” (Wensum

DTCa, www.wensumalliance.org.uk).

The focus is across the whole River Wensum catchment, which sources from 80km West of

Norwich, UK, between the villages of Colkirk and Whissonsett, ending up joining the River Yare

just south of Norwich (Figure 2.1). The river is of high interest due to the 71km stretch designated a

Site of Special Scientific Interest (SSSI) and a Special Area of Conservation (SAC) under the EU

Habitats Directive. The Wensum catchment covers approximately 1000km2, completely dominated

by cretaceous chalk bedrock and receives just 620mm annual rainfall on average1 (Met Office,

www.metoffice.gov.uk). The catchment hosts as a habitat to over 100 plant species and rich

invertebrate fauna; however 99.4% of its habitats are seen are „unfavourable and declining‟ mainly

due to sediments and diffuse water pollution (Wensum DTCb, www.wensumalliance.org.uk).

The DTC project uses the Blackwater sub-catchment for more detailed analysis. This study uses the

specialist equipment within the Blackwater sub-catchment to obtain its results. A smaller scale

study is preferred owing to the complexity behind the sources, sinks and transformations of

phosphorus, with larger scale approaches often losing the resolution required to understand the

ecological relevance (Withers & Jarvie, 2008)

2.2 The Blackwater Catchment

The Blackwater catchment is one of the larger sub-catchments within the Wensum covering

19.7km2, and sits on the North East edge of the Wensum catchment. It has a moderate bifurcation

ratio2 due to many first order streams within a small area feeding fewer higher order streams,

resulting in a good response to rainfall events. The sub-catchment is subdivided into a further six

sub-catchments (Figure 2.1). Each of the sub-catchments (A-F) has differing characteristics; in size,

land use, soil type, land management and more (Table 2.1), providing the ability to compare and

contrast results.

1 Based on Marham weather station

2 Bifurcation ratio is the ratio of the number of streams of any order to the number of streams of the next

highest order

http://www.wensumalliance.org.uk/


Figure 2.1 – Blackwater catchment and sub-catchments with monitoring equipment set-up (and location within whole Wensum catchment)

Norwich

Fakenham

Kings Lynn

Dereham Swaffham


Sub-

catchment

Sub-catchment size Land use Soil type Drainage Topography

Swanhills-

A

Large (5.4km2) Arable farming; consisting mainly of winter

wheat (by monitoring station), winter rape,

sugar beet and permanent set aside towards

upper part of catchment

Seasonally wet

deep loam to

clay

Yes Gradient from ~60m above sea

level in the west to ~40m above

sea level in the east. Stream

channel about 10m lower than

surrounding land

Swanhills-

B

Small (1.3km2) Arable farming including winter wheat (by

monitoring station), upper catchment

includes sugar beet and small amount of

deciduous forest near source

Deep loam with

sandy influence

Yes Same gradient as Swanhills-A

except moving from North West

to South East

Brake

Hills-C

Medium (3.5km2) Deciduous forest and grassland used by

pastoral farming practices (monitoring station

directly downstream of cow field). Small

amount of arable farming by source of one

tributary.

Deep loam soil

with presence of

sand and gravel

No Mainly flat at ~300m above sea

level with some high spots at

~50m. Stream channel ~10m

lower than surrounding land

Black

Bridge-D

Large (6.6km2) Monitoring station in deciduous forest at

confluence of two streams. Both streams

have large deciduous and coniferous forest

influence with rest arable farming. One

sourced next to urban standings (Cawston

village)

Deep loam soil

with small urban

standings.

Monitoring

station on sandy

soil and gravel

No Gradient of low in the south

west (~30m above sea level) to

high in the east and north (~50m

above sea level). Some higher

points in the centre ~60m above

sea level

Stinton

Hall Farm-

E

Very small (0.43km2)

Large flow weighted size

(7.1km2)

Receives flow from Swanhills-A

and B

Monitoring station next to grassland, but sub-

catchment mainly arable farming with small

amount of deciduous forest, also includes

grass and permanent set aside in catchment

Deep loam soil No

(Indirectly

Yes)

Uniformly flat for the majority

~30m above sea level

Park Farm-

F

Medium (2.4km2)

Very large flow weighted size

(19.7km2)

Receives flow from all sub-

catchments

Monitoring station next to grassland which is

large dominance in this sub-catchment, but

also contains deciduous forest and arable

farming with some small urban standings

(farm houses and manors)

Mostly deep

loam with sandy

influence.

Monitoring

station on

seasonally wet

deep sand

No Uniformly flat for the majority

~20m above sea level

Table 2.1 – Sub-catchment characteristics


2.2.1 Subsurface Drainage Network

To understand the extent of sub-surface drainage within the sub-catchments, a GIS map was created

as part of the Wensum DTC (Figure 2.2). This outlined that Swanhills-A has an extensive drainage

network across its sub-catchment. From this, we would expect to see the biggest difference in flux

response, under the assumption of discharge dominated by the presence of subsurface drainage.

Swanhills-A shall be used to evaluate the impact of drainage on fluxes. Although Swanhills-B does

contain drainage, the network is much smaller, in the upper reaches of the sub-catchment. This will

be help identify the effect on flux is dependent on amount of drainage or not.

Figure 2.2 – Network of sub-surface drainage systems (at field scale) within the Blackwater

catchment (Staff of Wensum DTC, 2011)


3 Methodologies

This section describes the project set up, made available to me to support my project, and

methodologies deployed to obtain results.

3.1 Data Collection

The data for this project was collected from three monitoring periods. This includes a baseline

period spanning 23 weeks from April until September 2011. Over this period 35 samples were

taken, but only 31 analysed, due to omitting four results as they were coherent with event rainfall

(greater than 5mm day-1

). Another dataset comprised of monitoring all six catchments, for 12hours

at 60-minute intervals, during an event on 16th

July. The last dataset was from an event on 4th

November monitoring for 12 hours at 30-minute intervals at only three catchments, due to

maintenance by the Wensum DTC team that was out of my control. Data collection for events was

severely limited due to a particularly dry autumn. This natural problem had not been accounted for

when determining the methodology.

3.2 Monitoring Stations

Being part of the Wensum DTC project meant utilising their state of the art technology and data to

collect reliable water samples. Monitoring stations at the mouth of each sub-catchment (Figure 2.1)

house the main pieces of equipment, which analyse the water for varying parameters. There are

differences between the six monitoring stations, with four (A-D) classified as „small‟ monitoring

stations and two (E&F) „large‟. They all house, a multi-parameter sonde (YSI Incorporated,

www.ysi.com), an automatic water sampler (Teledyne ISCO 3700) and a telemetry system (Meteor

Communications, www.telemetry-data.com) (Figure 3.1b). The sonde takes high-resolution (every

30-minute) measurements of temperature [°C], conductivity [uS/cm], Dissolved Oxygen [%, mg/l],

pH, ammonium [mg/l], turbidity [NTU], and chlorophyll [ug/l]. Stage [m] is also monitored via a

pressure transducer. The „large‟ stations provide greater analysis as in addition they house four

other pieces of equipment, to measure: TP and TRP [mg P/l], Nitrate [mg N/l], Ammonia [mg/l]

and flow [m3s

-1] (Figure 3.1a).

Event monitoring uses the automatic water sampler, as they collect high temporal resolution water

samples to see the progression of an event. Containing 24 one-litre bottles they sample every 30

minutes for 12 hours, and are set off remotely via text message to allow for quick response to

changing weather. Due to the automation, any process error would have been reduced. The baseline

samples were not automatic, instead collected by manual grab samples in one-litre bottles, on a

weekly to bi-weekly basis. After collection, all samples are analysed in the laboratory (Section 3.3).


Figure 3.1 – Equipment housed within a) the large monitoring stations (E&F), and b) at the

small monitoring stations (A-D) (Wensum DTC staff, 2011)

3.3 Laboratory Work

Each of the samples from the sub-catchments was analysed by the ENV Analytical laboratory team

at UEA. After receiving the samples they were thoroughly mixed, divided into two 500ml bottles

(one for TSS and one for all phosphorus determinants), then stored at 4°C until the analysis below

was required; when they would be brought back to room temperature (Analytical Laboratory,

2011a).

Total Phosphorus:

50ml of an unfiltered water sample was acidified with Sulphuric acid, and Potassium Persulphate

added, before digestion in an autoclave at 121°C for 20 minutes. This turns all the un-reactive

complexes, such as polyphosphates and those attached to metals, into complexes reactive with the

reagent. Afterwards Ammonium Molybdate solution, Ascorbic acid and Potassium Antimonyl

Tartrate solution were added to form a molybdenum blue complex. Finally, UV absorbance was

measured, at 885nm using 4cm cells, where the concentration was calculated by converting

absorbance using a calibration graph (Analytical Laboratory, 2011b).

Total Reactive Phosphorus:

For this analysis, unfiltered water is applied through the same method as TP, apart from no initial

acidification or digestion. This means only the already reactive complexes are measured (Analytical

Laboratory, 2011d).

Total Dissolved Phosphorus:

This analysis uses the same methodology as TP, except by using a filtered water sample. This

measures all the already dissolved complexes, but due to acidifying and digesting the sample, no

distinction between reactive and un-reactive complexes is available (Analytical Laboratory, 2011c).

Pump

YSI multi-

parameter

sonde

ISCO automatic

water sampler

Solar panels for

battery

Meteor

telemetry

unit

Battery

a) b)


Dissolved Reactive Phosphorus (PO43-

):

This analysis uses the same methodology as TRP, however using a filtered sample, resulting in

measuring only dissolved and reactive complexes through just the colorimetric UV absorbance

analysis.

Total Particulate Phosphorus:

This is calculated by taking the difference between TP and TDP, and identifies all complexes not

fine enough to be filtered.

Total Suspended Solids:

This analysis uses 500ml of a well-mixed sample that is filtered through a standard glass fibre filter.

The residue retained on the filter is dried to constant weight at 103-105°C. TSS is then calculated

using Equation 3.1 (Analytical Laboratory, 2011e).

Where: A= weight of the washed filter (g)

B= weight of filter + residue (g)

C= volume of sample filtered (ml)

3.4 Field Data

3.4.1 Rain gauges

Across the Blackwater catchment there are three rain gauges (Figure 2.1) recording data via

telemetry to an online database (Timeview Telemetry, www.timeview2.net). For all monitoring

periods, one gauge was used to represent rainfall across the whole catchment, as the variation

between all three, for a given event, was only 1.5%.

3.4.2 Stream Cross Section

Unplanned within the initial methodology, and due to technical errors within the set up of the

automatic sensors, I measured the cross section of the stream next to monitoring station E. I was

initially planning to use the automatic discharge reading from sites E and F, and then applying

concentration data directly to these values to calculate flux. Unfortunately, over time these sensors

started giving underestimations due to a change in the calibration of the rating curve. Measuring the

cross section involved recording the depth profiles every 10cm (Figure 3.2). This meant discharge

values could be obtained through data manipulation (Section 3.5).

Note: The proposed best-fit area fits as closely to the actual measurements as possible; however,

there are over and under estimations. The large anomaly 2.2m from the left bank is believed

to be an anomaly, where the depth was measured in a manmade dip deeper than the

streambed.

eq. 3.1


Figure 3.2 – Depth profile of stream at Stinton Hall Farm-E [m] with calculations for cross

sectional area [m2] and wetted perimeter [m] based on a best-fit (blue shading)

3.5 Estimating the Stream Discharge without gauging equipment

The measured depth profile of the stream, along with the stage measurements from the pressure

transducer provided the necessary information to estimate discharge in all six sub-catchments.

Firstly, by applying the data to Manning‟s Equation (Equation 3.2) the flow potential of the river

was calculated. This equation relates velocity to stage (Hiscock, 2005).

, the hydraulic radius, was calculated by dividing the cross sectional area [m2] of the stream by

the wetted perimeter [m] (Figure 3.2). is the parameter that changes with rainfall so the „best fit‟

area and perimeter were used to develop an equation to calculate as stage changed. , the slope of

the stream over 200m, was collected via moving a GPS3 device through the stream; capturing

elevation. The slope was identified as 0.0014 by plotting elevation data with distance. , the

roughness co-efficient of the streambed, is estimated from look up tables; defined by the channel

type. I used a value of 0.035 based on a “standard natural stream or river in stable condition”

(Wilson, 1990). Applying these values to Manning‟s Equation resulted in a calculated value for

[ms-1

]. To turn this into discharge [m3s

-1] the velocity was multiplied by the cross sectional area

[m2].

This discharge however only applies to the site where measurements were taken (Stinton Hall

Farm-E). The estimation of discharge at the un-gauged sites is based on sub-catchment area.

3 GPS= Global Positioning System

eq. 3.2

Facing downstream

Wat

er

de

pth

(m

)

Distance from left bank (m)


Assuming that discharge increases in a linear fashion according to catchment area (derived from

DTM4), discharge at each un-gauged site was calculated. This methodology, derived from work by

Jarvie et al. (2002), is frequently used because of its simplicity and pragmatic response to data

scarcity.

3.5.1 Considerations with the Data Manipulation

Before interpreting the manipulated data, it is important to acknowledge some „considerations‟ with

the methodology.

Manning‟s equation only provides discharge potential. Stinton Hall Farm-E has a bridge

downstream of the depth profile, which could be obstructing flow and therefore the calculated

discharge is an overestimate.

Variations with the inputs could see differences in calculated values. Table 3.1 provides a

summary of the sensitivity between the two most highly variable parameters within

Manning‟s Equation . It indicates a big difference between values signifying that

results must be taken as potential rather than certain.

Roughness Co-efficient (

Natural stream

channel, flowing

smoothly in clean

conditions-0.030

Standard natural

stream or river in

stable condition-

0.035

River with shallows

and meanders and

noticeable aquatic

growth-0.045

River or stream

with rocks and

stones, shallow

and weedy-0.06

Wate

r S

lop

e (

)

Measured

+10%

(0.00154) 0.373241 0.319921 0.248828 0.186621

Measured

(0.0014) 0.355872 0.305033 0.237248 0.177936

Measured

-10%

(0.00126) 0.337609 0.28938 0.225073 0.168805

Table 3.1 –Variations in velocity, when differing slope and roughness co-efficient values are

applied to Manning’s Equation

Basing discharge on catchment size is the best option available after the unplanned technical

issues however, it is important to note that with a groundwater-fed river system, the

uncertainty is higher. In these systems, discharge is likely to be associated with the

distribution of springs and groundwater flow pathways, rather than the area of the surface

catchment drained (Jarvie et al. 2002).

4 DTM= Digital Terrain Modelling


This method also means only total discharges can be calculated at each site, other than E, as

transcribing higher resolution temporal changes from catchment to another is invalid.

3.6 Analysis of the methodologies

Set out here are uncertainties and „considerations‟ with the methodologies presented above. The

biggest setback to obtaining results was the natural, unplanned, unexpectedly dry autumn. Others

include:

the change in calibration of the rating curve that resulted in the use of Manning‟s Equation to

calculate discharge (Section 3.5.1. for „considerations‟ with this)

the set up of Wensum DTC and its apparatus are in early running stages, resulting in

unplanned interferences, needing solutions. For example, the original apparatus to measure

stage were often silted over and required cleaning

difficulty when measuring the depth profile of the stream from trying to define the streambed,

as the build up of silt can sit 5cm above the streambed. An assumption was made, for

consistency, that the top of the sediment was the bed of the stream. This may have resulted in

an under estimation of the depth of water flow

the best-fit shape of the stream profile was applied for ease of calculation, but would hold

uncertainty. This is noted.

Anomalous results from the laboratory analysis are possible. This could occur in the collection or

storage stage of the samples; however, it is more likely that they would be from interferences

through other compounds within the water. Examples are Arsenates and Silicates giving positive

interferences in the colorimetric analysis, and Nitrites and Hexavalent Chromium interfering to give

low analytical results (Analytical Laboratory, 2011bcd). When analysing TSS, the filtration

apparatus, filter material, pre-washing and drying temperature have been shown to affect the results.

Samples high in TSS are also known to have positive interferences (Analytical Laboratory, 2011e).


4 Results

This section displays results of various parameters from the three monitoring periods.

4.1 Rainfall

The recorded rainfall provides important information on scale of an event or seasonal/annual rates.

Figure 4.1a shows the variability in rainfall intensity [mm day-1

] across the whole 163 day baseline

period, with corresponding 31 sample dates in red. Between April and September there was very

low rainfall, especially in spring, with most of rain falling in summer. Total rainfall was only

242.4mm over the period, just 1.5mm day-1

, which is very low. Noted from the varying rainfall was

the linked response in stream stage, which ranged from minimum of 0.170m to maximum of

0.275m. The average was 0.189m.

The time series of total rainfall across the first event (16/07/2011) is shown in Figure 4.1b. The

front of the event starts at approximately 10am. The intensity quickly increases to its maximum of

4.2mmhr-1

within one hour, in turn causing a sharp increase in volume of 12.4mm in just over 4

hours. This four-hour period encapsulates 92.5% of the total rainfall that day (13.4mm) in just 17%

of the day, similar to results by Dalzell et al. (2011) for discharge in an event. The rainfall here

correlates very highly with stage (r2=0.98), indicating a rapid runoff response from the catchment.

The time series of the second event (04/11/2011) is shown by Figure 4.1c. Monitoring started at

midnight; however, the onset of rain had already commenced by this point, meaning the intensity

increased quickly. A lower magnitude than the first event, of 2-3 mmhr-1

, lasted for 3.5hours

resulting in 8.6mm falling. This was followed by a five-hour quiet period (0.6mm) and concluded

with a small secondary event of 2mm across two hours. Coherent with the first event, the most

intense period encapsulated 75.4% of total rainfall (11.4mm) in just 14% of the day. The correlation

between rainfall and stage here was not as strong as the first event (r2= 0.56).


Figure 4.1 – a) Baseline rainfall intensity [mm day-1

], b) Total rainfall [mm] for event on

16/07/2011, and c) Total rainfall [mm] for the event on 4/11/2011

a)

c)

b)


4.2 Hydrographs

The following figures identify the stream hydrographs for all monitoring periods, which help

identify the catchments response to rainfall and any hydrological processes within the catchment.

Identifying responses such as shape, peak and time lag from events or annually, helps understand

the sub-catchment response. With annual trends, it is easier to associate larger influences such as

snowmelt, flash flooding or natural seasonal trends.

Note: due to the methodological considerations explained in Section 3.51, discharge data is only

shown for Stinton Hall Farm-E, where it was collected.

Figure 4.2a shows discharge and rainfall across the baseline period, identifying a reasonably

uniform trend (range=0.03m3s

-1). This is likely due to the permeable nature of the calcareous

limestone bedrock, where a large proportion of the discharge owes to base flow through the

catchment. The small peaks however are due to the quick responding small fields, which feed many

first order streams, saturating until surface flow occurs.

The response to the July event is a typical „bell shape‟ for discharge, whereby it begins increasing

from the onset of rainfall (Figure 4.2b). Discharge steadily increases until a rapid change that sees

discharge increase by >50%, in two hours. This change is indicative of the two different sub-

catchments (Swanhills-A and B) that feed Stinton Hall Farm-E, responding at different rates. The

initial slower increase is from overland flow directly within catchment E or indirectly from

Swanhills-B. Within Swanhills-A most of the rain would however infiltrate the land, filling the

subsurface drainage. When saturation is reached after lag time of ~3.5 hours from initial increase,

drain flow commences seeing more flow in a shorter period, causing a sharper increase. The lag

time between peak rainfall and peak discharge is approximately six hours, followed by a recession

time of approximately four hours where discharge starts to level off revealing a higher base flow,

due to groundwater influence. The overall discharge across the whole day was 7100m3.

The hydrograph presented in Figure 4.2c for the November event shows a similar shape to the first,

however the lag time between peak rainfall and peak discharge is only around four hours. This

could be owing to a quicker response of the subsurface drainage; lag time ~2.5 hours after initial

increase. The land is more saturated at this time of year and without crops, the rain has less

interception and an easier route to the drainage. Overall discharge here is greater than in July at

approximately 9200m3 with the recession period a bit longer, approximately six hours. These are

likely due to greater volumes of water through the drainage network at this time of year.


Figure 4.2 – Hydrograph showing discharge [m

3s

-1] with rainfall [mm hr

-1] for a) baseline, b)

16th

July, and c) 4th

November

a)

c)

b)


4.3 Concentrations

Concentrations obtained from the lab identify temporal trends in pollutant transfer from events. This data can help inform us of hydrological response,

and aid in understanding the influence of certain catchment qualities and characteristics.

4.3.1 Baseline

a) b)


Figure 4.3 – Concentrations of phosphorus and sediment recorded across the baseline period from a) Swanhills-A, b) Swanhills-B, c) Brake

Hills-C, d) Black Bridge-D, e) Stinton Hall Farm-E, and f) Park Farm-F

c) d)

e) f)


Figure 4.3a shows the baseline changes in pollutant concentrations at Swanhills-A. Noted is a

relatively calm trend (30-60 ug l-1

) until mid June when all phosphorus determinants, except TPP,

increases. These continue to fluctuate slightly, but increasing overall to ~ 90-ug l-1

, until baseline

monitoring ceases. There are similar scale concentrations at Swanhills-B for the beginning of the

monitoring period however; these stay constant for the whole period with even less variation in the

data over time (Figure 4.3b).

Brake Hills-C (Figure 4.3c) shows widely fluctuating concentrations with up to ~95ug l-1

range. In

general, the concentrations from this catchment are higher than the other catchments (max

TP≈200ug l-1

). TRP and TPP have the highest correlation to TP, yet TPP has the lowest

concentrations.

Like Swanhills-A and B, Black Bridge-D is also arable farming yet it has slightly higher

concentrations (~ 60-ug l-1

on average) with greater fluctuations (Figure 4.3d). These fluctuations

are due to much quicker and sharper responses to rain. There is a very large peak at the beginning of

May, likely a unique cause due to its magnitude and its high correlation with TSS and TPP. The

sediment values are the highest across this catchment than any other.

Stinton Hall Farm-E and Park Farm-F are both slightly different in their characteristics as they

receive most of their discharge from other sub-catchments. Stinton Hall Farm-E (Figure 4.3e)

mimics the low and smooth pollutant levels received from Swanhills-A and B. The high levels of

TRP and PO43-

are also transferred and correlate to the only minor peaks noted.

Park Farm-F (Figure 4.3f) can be the hardest to interpret as it is influenced by sub-catchments E, C

and D. Concentrations here are second highest to Brake Hills-C, due to greater input from the other

large sub-catchments. Trends of the other sub-catchments are present, such as the large fluctuations

from Brake Hills-C and the large peak in TP, PP and TRP from Black Bridge-D. There is also an

overall decrease in concentrations, similar to Black Bridge-D suggesting this has the largest

influence.


4.3.2 16th

July 2011

a) b)

c) d)


Figure 4.4 – Concentrations of phosphorus and sediment recorded during the event on 16th

July 2011 from a) Swanhills-A, b) Swanhills-B, c)

Brake Hills-C, d) Black Bridge-D, e) Stinton Hall Farm-E, and f) Park Farm-F

e) f)


Figure 4.4a-f shows the trends in pollutant concentrations from the 16th

July event, most notably

indicating a response very different to the baseline. Concentrations at Swanhills-A increase with

time as more water passes through the catchment. The peak concentration occurs at around 16:00

(Figure 4.4a), correlating highly with a four hour time lag on rainfall intensity (r2=0.77). This

identifies the presence of subsurface drainage as water infiltrates and fills up enough to start

flowing. The peak data also correlates almost perfectly with discharge (r2=0.98) suggesting transfer

is dependent on the discharge rather than immediate overland flow.

There is a more varied response at Swanhills-B, making it harder to identify trends (Figure 4.4b).

Overall, there seems to be a response to the event about one hour after the onset of rain. This

quicker response and more sporadic results are the cause of the mixed practices on the land such as

only small drainage network, so more overland flow. Overall concentrations are lower than other

catchments, because of the smaller area.

Brake Hills-C, shown in Figure 4.4c, has the most pronounced response. Almost immediately after

the onset of the rain a large peak in both TP and PP (~ 1600-ug l-1

) occurs, but is not sustained,

dying down again immediately. A smaller secondary peak occurs approximately three hours after

which again immediately dies down to the sustained base level of ~400 ug l-1

; the highest of all sub-

catchments.

The response of Black Bridge-D is an increase in concentrations (of ~ 80-ug l-1

) immediately after

the onset of rain, indicating predominant surface flow. It is more sustained (lasting approximately

six hours) than the other sub-catchments (Figure 4.4d). TPP is closely correlated to TP, as in the

baseline data.

Stinton Hall Farm-E (Figure 4.4e), identifies two peaks across the event period. The first occurs at

around 12pm correlating highest (r2=0.6) with a one hour time lag to peak rainfall. The second is

smaller and correlates with the peak discharge relating to the differing responses of the feeding sub-

catchments.

Park Farm-F again shows a mixed response, with its concentration peak in between that of rainfall

intensity and discharge (Figure 4.4f). Increases in TP begin as the onset of rain commences with

TPP the strongest correlation influencing the steady increase until peak concentrations occur in all

determinants with TP≈150 ug l-1

.


4.3.3 4th

November 2011

Figure 4.5 – Concentrations of phosphorus and sediment recorded during the event on 4

th November 2011 from a) Swanhills-A, b) Black

Bridge-D, and c) Stinton Hall Farm-E

a) b)

c)


Figures 4.5a-c show the resonse of catchments A, D and E to a winter event. Swanhills-A (Figure

4.5a) is very hard to read with varied results. Unfotunately it is hard to determine which results are

true due to interferences with the experimental analysis. This caused results such as higher TRP

values than TP. If we try to remove the errors qualitively and lower the trend then high TRP and

PO43-

within the catchment follows previous sub-catchment trends.

Similar trends are noted for Black Bridge-D from the second event again revealing a more sustained

response (Figure 4.5b). Concentrations for this sub-catchment are the lowest from this

event.Highest correlation occurs with rainfall as increases are noted with the onset of rain. TPP still

influences TP the most, with PO43-

remaining low. The response is however flashier than previous.

Stinton Hall Farm-E shows high concentrations still defined by a double peak (Figure 4.5c). The

first, not long after the onset of rain shows aquick response, with the second much larger and highly

correlated with discharge. High TRP, TDP and PO43-

values are present but with TPP correlating

the closest to TP. The first peak causes a doubling in concentration while the second sees a five fold

increase to a max of ~430 ug l-1

in TP.

4.4 Fluxes

Flux data uses the catchment discharge to provide holistic values of land output from an event, and

its relative meaning to base flow. Fluxes provide clear outputs for literature comparisons and testing

hypotheses. Table 4.1 summarises total fluxes5.

The baseline period presents the minimum, maximum and average fluxes from the 31 samples. The

standard deviation is also presented indicating the scale of deviation from the average; being largest

at Black Bridge-D and lowest at Swanhills-B. Differences between catchments are apparent, for

example baseline TSS flux is lowest at the smallest sub-catchment (Stinton Hall Farm-B), and

highest at the largest sub-catchment (Park Farm-F). The highest phosphorus flux is Brake Hills-C,

for both baseline and events. The contribution of the varying phosphorus determinant towards TP

fluxes is defined by catchment characteristics. Swanhills-B shows reasonably equal contributions,

whereas Brake Hills-C has TPP as a clear defining contributor. The apparent defining contributors

are TDP, TRP and PO43-

at sub-catchments A, B and E, and TPP and TRP at sub-catchments C, D

and F.

Generally, an increase in flux between baseline and events is noted. It is most noticeable at Brake

Hills-C, where increases in TPP reach 443%, and Swanhills-A which shows increases in all

phosphorus determinants of over 100% (PO43-

=203%) (Appendix 1). There are some exceptions to

this, albeit giving reductions much smaller, and therefore less significant, than the increases. These

5 Values to 2 significant figures as laboratory error varies from 1-0.01ug l

-1 and stage data has error of

±0.001m


are not taken as certain. Swanhills-B for example sees decreases in all phosphorus determinants, but

increasing TSS, while Stinton Hall Farm-E sees the opposite. Black Bridge-D and Park Farm-F

show decreases in TSS of 45% and 39% respectively with phosphorus values showing similar

results. It is important to note these decreases regardless of their significance.

Table 4.1 – Pollutant fluxes across the three monitoring periods

6 Average baseline flux indicates the average of the baseline proportioned to the equivalent length

of a storm; enabling comparison

*Laboratory analytical results likely to have been interfered with

Sub-

catchment

Substance Baseline6 16

th July 4

th November

Minimum

Flux

[ x10-4

kg ha-1

]

Maximum

Flux

[ x10-4

kg ha-1

]

Standard

Deviation

[x10-4

]

Average

Flux

[x10-4

kg ha-1

]

Total Event

Flux

[x10-4

kg ha-1

]

Total Event

Flux

[x10-4

kg ha-1

]

Swanhills-

A

TP 1.2 7.6 1.7 3.4 9.0 11*

TPP 0.28 3.2 0.77 1.3 3.4 0.95*

TDP 0.27 7.1 1.8 2.1 6.2 11*

TRP 0.20 7.6 1.6 2.4 5.3 20*

PO43-

0.26 7.1 1.8 1.8 5.6 11*

TSS 83 2100 460 440 670 600

Swanhills-

B

TP 1.8 7.5 1.3 3.3 3.0 –

TPP 0.0 4.1 0.78 0.88 1.1 –

TDP 0.33 5.0 0.98 2.4 2.1 –

TRP 1.3 4.5 0.97 2.5 1.5 –

PO43-

1.4 4.1 0.80 2.3 1.6 –

TSS 95 1400 260 280 640 –

Brake

Hills-C

TP 7.3 14 1.5 10 28 –

TPP 1.8 6.5 1.4 3.9 22 –

TDP 3.3 8.6 1.2 6.2 6.2 –

TRP 6.5 13 1.5 8.8 15 –

PO43-

3.9 8.3 1.1 5.7 5.6 –

TSS 60 660 160 370 1700 –

Black

Bridge-D

TP 2.8 22 3.5 5.6 5.0 3.1

TPP 1.1 20 3.6 3.8 3.3 2.2

TDP 0.26 2.5 0.49 1.9 1.7 0.89

TRP 1.6 16 2.7 4.1 4.0 1.6

PO43-

1.2 2.0 0.18 1.7 1.4 0.51

TSS 220 4300 920 710 390 280

Stinton

Hall Farm-

E

TP 1.5 9.0 1.5 3.1 4.9 8.8

TPP 0.0 5.8 1.1 1.3 1.6 2.1

TDP 0.0 3.8 0.83 1.9 3.0 6.7

TRP 0.79 6.9 1.3 2.3 3.0 8.5

PO43-

0.45 3.4 0.68 1.7 2.5 5.4

TSS 26 1900 340 270 210 400

Park Farm-

F

TP 3.0 18 2.8 5.8 4.6 –

TPP 0.84 16 2.8 4.0 2.5 –

TDP 0.59 2.4 0.38 1.8 2.1 –

TRP 1.7 11 1.9 4.4 2.5 –

PO43-

1.1 2.0 0.18 1.6 1.6 –

TSS 180 4100 760 790 500 –


5 Discussion

5.1 Concentration Trends

Trends at Swanhills-A show clear influences from the presence of subsurface drainage in both

baseline and event monitoring (Figures 4.3a and 4.4a). Across the baseline, the low and flat trend

attributes to drainage as interception between field and receiving water. Spring 2011 was

exceptionally dry, but was followed by some summer rain. This began saturating the land resulting

in a rising water table and drain flow. Phosphorus is more susceptible to movement in soils that

have become water logged (Campbell & Edwards, 2001) so the rain brings increased movement of

pollutants through the drains. During an event, a scaled down version of this is noted seen by the

time lag from rainfall onset, as drains fill up. Possible adsorption of PO43-

and filtering of TSS and

TPP may be occurring as water percolates soil, due to higher TDP and low particulates (Withers &

Jarvie, 2008). Similarities in characteristics of Swanhills-B to Swanhills-A translate to the

concentrations; however the smaller area and less drainage results in smaller variations in baseline

data (Figure 4.3b). The event data fluctuates greatly, with a dip in TP and TPP at 15:18 due to an

anomaly from analytical errors (Figure 4.4b). Other fluctuations may be from interferences of

Aluminium, Iron, clays and Calcium Carbonate affecting the portion of freely available phosphorus;

more apparent in arable farms (Campbell & Edwards, 2001).

Baseline fluctuations of phosphorus at Brake Hills-C (Figure 4.3c) owes to the quick overland flow

noted along with high masses of livestock wastes intermittently picked up. Event data (Figure 4.4c)

shows a higher resolution trend of these fluctuations. The first peak produces the quickest and

largest concentration response with high TPP contributing. This suggests high bank erosion,

exaggerated by livestock, where TSS carries natural phosphorus within the soil along with

particulates of livestock excreta. The magnitude implies a build up in the preceding drier periods.

Pollutants from further upstream, possibly diluting on the way, may cause the lower secondary

peak.

The lack of subsurface drainage and greater susceptibility to rain at Black Bridge-D is noted by the

sharper concentration in baseline responses (Figure 4.3d). The quicker responses determine flow to

be predominantly overland and highly correlating TPP with TP indicates preferential phosphorus

movement with soils, sediments and metals (Campbell & Edwards, 2001). The event response here

is the most prolonged (Figure 4.4d). The immediate response, from overland flow, is exacerbated

by the sandy soil present, enabling greater susceptibility of phosphorus transfer (Campbell &

Edwards, 2001). This large catchment encloses two streams which confluence at the monitoring

station, resulting in runoff from more fields from farther away and staggered pollutant arrivals.


Characteristics of Stinton Hall Farm-E present in both baseline and event trends are the dominant

grassland, enabling stronger binding with soil and compaction from above, resulting in low TPP

and sediment (Figures 4.3e and 4.4e). High correlations with PO43-

, TDP and TRP are also

consistent, relating to characteristics of the Swanhills-A and B feeding this site such as heavy

fertiliser application. Another influence of these catchments is the double concentration peak.

Figure 4.4b shows Swanhills-B responding quicker, implying it contributes to the first peak and is

larger because of the adjacent contribution from the small, rapidly responding, catchment E itself.

The second is therefore the arrival of drain discharge from Swanhills-A, post saturation.

5.1.1 Seasonal Differences

The responses of the second event identify simliarties and differences from seasonal changes.

Similarities are more apparent in overall responses, such as the sustained response at Black Bridge-

D (Figure 4.5b) and the double peak at Stinton Hall Farm-E (Figure 4.5c). Differences are seen at

Black Bridge-D, where the second event response is flashier due to increased transportation rate

from the cultivated sandy soil. In addition, there are lower concentrations in sediment from

decreasing sediment fluxes as the wet season progresses, with initial loadings transporting the

largest mass. The double peak from catchment E this time has a larger second peak. This time,

Swanhills-A has slower but larger concentration responses due to phosphorus and sediment settled

inside the drains from previous precipitaion. This is noteable in Figure 4.5a from highly correlating

TPP with peaks.

5.2 Flux Trends

By studying the flux data (Table 4.1), it is possible to carry out comparisons between holistic

consequences of rainfall on varying sub-catchments. Assessing the hypotheses meant comparing

fluxes from individual sub-catchments across different monitoring periods, and fluxes from

different sub-catchments across individual monitoring period. Identified, are apparent changes with

some much more significant than others. What we only see rarely is a change in magnitude. This

would add significance to the comparisons made especially given the large standard deviations of

the fluxes. This is indicative of how varied the daily flux can be. Although average values provide a

representation of the catchment, there are infrequent and anomalous characteristics that escape our

knowledge. Therefore, the analysis of flux data is theorised around all changes carrying

significance.

5.2.1 Impact of Events

Comparisons between baseline and event data, within a catchment, have identified definitive

increases with events from all catchments except D and F. This demonstrates the high susceptibility

of sediment and phosphorus transfer with increasing river discharge, supporting findings by Jarvie

et al. (2002) who claims the “greater erosive and transport capacity of intense rainstorms, is the

main pathway of sediment to receiving water”. Similarities between event impact on each sub-


catchment exists, such as pollutant sources (i.e. bank erosion or fertiliser), however differences

occur in specific influences that in turn affects the scale flux increase. For example, Brake Hills-C

has the largest increase in both TP and TSS, of 172% and 346% respectively. This owes to the

livestock presence increasing bank erosion and waste inputs. Swanhills-A however increases in TP

and TSS by 165% and 57% respectively, due to intense fertiliser application. This sees greater

increases in phosphorus fluxes than sediment, opposing to Brake Hills-C.

5.2.2 Impact of Subsurface Drainage

Comparing the presence of subsurface drainage at Swanhills-A with Black Bridge-D has identified

results contrary to those expected. During events fluxes of TP at Swanhills-A are 80% higher.

Explaining this may arise from understanding why Black Bridge-D has lower than expected fluxes.

Here the flow is mainly overland flow, present in two forms; Hortonian overland flow (where soil

saturates from infiltrating rainwater), which is hard to acheive or saturation overland flow (where a

rising water table causes soil saturation). However, given the event was mid-summer, the water

table would not have been high enough to cause much flow. It also shows lower TRP and PO43-

than Swanhills-A, indicating less fertiliser use.

Alternately, the contrary fluxes may be because Swanhills-A is exerting larger than expected fluxes.

Withers & Jarvie (2008) report how subsurface drainage provides a degree of connectivity that

would otherwise not exist; delivering large phosphorus concentrations in small amounts of storm

water during more ecologically sensitive periods (summer). Swanhills-A contains drainage to help

with soil infiltration in the clayey soil, prone to surface runoff, however an event could overpower

the soils infiltration capacity, especially if the drainage systems are old or blocked. No soil has an

infinite capacity to absorb phosphorus (Campbell & Edwards, 2001).

The exception to the above argument, in agreement with literature, lies in the baseline fluxes, where

Black Bridge-D has 60% greater fluxes in both phosphorus and sediment than Swanhills-A. Black

Bridge-D may promote high runoff over the long term from the extremely low phosphorus

adsorption capacity of sandy soil (Campbell & Edwards, 2001). The, suspected anomalous, large

baseline peaks in TP and TPP may however have obscured this result. Large standard deviations

(Table 4.1) support this. It could be from sediment dumping, field ploughing or stream/lake

dredging.

5.2.3 Cross Correlations

Phosphorus concentrations within rivers are subject to variations according to changing source

contributions and within-river processes (Jarvie et al. 2002). Further flux analysis can therefore

arise from relationships between phosphorus and other compounds, which can act as tracers, to

separate these different factors.


Boron is a tracer of phosphorus sourced from sewage effluent, containing household and industrial

detergents, where the chemically un-reactive species borate is present (Jarvie et al. 2002). The

relationship between Boron and PO43-

is very scattered in all sub-catchments with no positive

gradients. This is coherent with the area as no sewage treatment works sit in the area.

Trapping and releasing fine sediment plays an important role in controlling phosphorus

concentrations and sediment dynamics and relates to characteristics of chalk streams (Jarvie et al.

2002). Across the sub-catchments, TPP shows the best correlation with TP, indicating the

movement of sediment with phosphorus. Correlations between TP and TSS are positive but highly

scattered identifying similar responses in phosphorus and sediment, suggesting a high proportion of

TP derives from particulate material. The highest correlation is at Black Bridge-D (r2=0.89)

suggesting the sandy soil acted as a more efficient phosphorus transporter, whereas a smaller

correlation is seen at Swanhills-A, where the fertiliser contributes to phosphorus fluxes through soil

erosion, bank slumping or, when there is a very low correlation, incidental loss7 (Withers & Jarvie,

2008).

Livestock provides an entry route of phosphorus to water, as noted at Brake Hills-C, and relates to

the presence of Nitrogen (Granger et al. 2011). The highest correlation between Total Nitrogen

(TN) and TP is at Park Farm-F (r2=0.3) with Brake Hills-C and Black Bridge-D following

(r2=0.15). This is unexpected due to Brake Hills-C characteristics, but the correlation at Park Farm-

F may be improved by the influence of overland flow from impervious surfaces, as suggested by

Tunney et al. (2007).

Jarvie et al. (2002) claims chalk streams derive some of their flow from groundwater. This

contribution is apparent within the Blackwater catchment with positive, but scattered, correlations

between flow [m3s

-1] and Alkalinity [mg CaCO3 l

-1] (best r

2=0.2). The mixing between non-

polluted groundwater and surface water may cause lower than expected fluxes.

5.3 Literature Comparisons

Comparing total fluxes of this study with those found in similar studies assesses where this study

sits within the scientific community, while identifying any contrasting ideas. Difficulties arise

immediately due to a variety of possible parameters for use; most common is varying scale. This

results in making assumptions, such scaling the results to match fluxes presented over years or

seasons. Although this is possible, caution is taken as these may give over or under estimations.

Table 5.1 is a summary of total fluxes within the literature; appropriately scaled.

7 Recent fertiliser applications washed off as insufficient time to bond with soil particles


Scale Pollutant Flux

(kg ha-1

)

Study characteristics Reference

Annual TP 0.24 Gelbæk catchment, Denmark (6.1 ha) Grant et al.

1996 TPP 0.1

Annual TSS 109 Cherwell catchment, Oxfordshire (9400 ha) Neal et al.

2006 TPP 0.25

TDP 0.57

Annual TSS 1170 Johnstone River catchment, Queensland,

Australia (160200 ha)

Hunter &

Walton,

2008 TP 2.2

TPP 1.8

Annual TSS 57.7 Blackwater catchment, Norfolk (1970 ha) This study

TP 0.42

TPP 0.3

TDP 0.13

Event TP 0.0085 Tuross River catchment, Sydney, Australia

(160500 ha). Event = 78.6mm rain

Drewry et

al. 2009 TSS 5.7

Event TP 0.0072 Leary Weber ditch catchment, Indiana, USA

(8.1 ha). Event = 29.1mm rain

Vidon &

Cuadra,

2011

Event TSS 2.3 Den Brook Catchment, Devon (48 ha).

Event = 14.6mm rain

Granger et

al. 2010 TPP 0.02

TDP 0.017

Event TSS 0.05 Blackwater catchment, Norfolk (1970 ha).

Event = 13.3mm rain

This study

TP 0.00046

TPP 0.00025

TDP 0.00021

Table 5.1 – Literature summary of national and international catchment fluxes

Fluxes within the literature notably show some degree of variation. Some of this variation arises

from the great complexity in measuring phosphorus fluxes. This complex molecule has multiple

parameters, including water temperature, soil moisture and pH, cover crop and clay content, that in

turn affect transformations, processes and bio-available phosphorus (Campbell et al, in; Ritter,

2001). This provides confidence that these variations are not significant enough. Given the total

annual phosphorus fluxes are within the same magnitude, they are in agreement with this study.

Significant variation occurs however in annual sediment fluxes and event pollutant fluxes. This

study found lower fluxes than the literature, in some cases up to a couple orders of magnitude.

Using catchment characteristics within literature it is possible to see that discharge strongly

influences flux, illustrated by Jarvie et al. (2002) and Neal et al. (2006) who recorded similar

concentrations to this study, but with larger total fluxes. Characteristics affecting discharge include

stream order, with higher orders (such as Drewry et al. 2009) meaning more tributaries feeding

larger volumes of water to give greater discharges. This study has mostly first order streams and

only a fourth-order principle stream, yet the effect was portrayed with the largest discharge seen at


Park Farm-F. Another link is seen between total rainfall and increased fluxes, owing not only to an

increased discharge, but also providing a route for greater pollutant mobilisation.

Other links include location of study and related annual rainfall as identified by this study, with the

lowest fluxes and average annual rainfall of 620mm, and Hunter & Walton (2008) who recorded the

highest fluxes with an average annual rainfall of 3545mm. They also found sediment fluxes two

magnitudes higher than this study. This may instead relate to land use, which was dominated by

rainforests. These yielded >41% of the sediment flux. The catchment also contains sugar cane and

banana plantations, known for causing severe soil disturbance, in this instance yielding a flux 3-4

times greater than their land coverage. Land use also affects results by Granger et al. (2010) where

predominant grassland sustaining sheep and cattle produces higher pollutant fluxes, from a smaller

catchment. Neal et al. (2006) reports that catchments with better flow management and those that

are fed by reservoirs, ponds and canals are more likely to have higher fluxes. They also report on

the impact bedrock permeability has on flux, stating that highly permeable catchments, such as

chalk, have reduced fluxes as runoff is slowed. Research by Jarvie et al. (2002) agrees, claiming

chalk streams have low TSS and TPP because they derive most of their flow from groundwater.

5.4 Data Reliability

This study relied on extreme weather conditions to provide its results. However, the anomalously

dry autumn noted across East Anglia proved detrimental to the data set and reliability of findings.

Had more data been available analysis would have been extended and trends further supported,

maybe revealing new conclusions. Technical errors within the original methodology that lead to

data interpretation limited interpretation, such that high temporal discharges could not be

transcribed from one sub-catchment to another. Noted elsewhere in the literature is the importance

of high resolution monitoring in providing mitigation measures (Granger et al. 2010). They report

that, sampling water every 60 minutes leads to over (+25%) and under estimations (-35%) of

pollutants, compared to 30 minute sampling. Repeatability issues arose from defining the onset of

an event. Weather forecasts provided by UEA (Weather Quest, www.weatherquest.co.uk) identified

intense rain events, however were not always reliable resulting in consistency issues with data

collection.

5.5 Best Management Practices

Mitigation options, known as Best Management Practices (BMPs), are the most effective and

practicable means of controlling pollutants at high environmental levels (Mostaghimi et al. 2001).

This study evaluated the theory behind sub-surface drainage reducing pollutant fluxes to receiving

water (Dalzell et al. 2011; and Turtola & Paajanen, 1995). Results contradicting this however do

not support its use as a BMP; instead agrees with less common literature such as Riise et al. (2004)

who found no difference in pesticide flux between surface and drain flow. It can however be


difficult to mitigate pollution as management practices that ensure consistent crop productivity will

often influence the physical and biological factors responsible for production and export of nutrients

as well.

Introducing BMPs revolves around three routes; source control; slowing pathways and transport;

and protecting receptors. Source control generally implies use restrictions. Brown & Beinum (2009)

suggest halting pesticide use on vulnerable land, such as heavy clays, because their research

indicates positive relationships between clay content and pesticide flux, implying surface flow.

Other fieldwork from them argues for mitigation through restricting fertilizer application based on

soil and weather conditions. They found large pesticide losses from; very wet soil applications

owing to immanent drain flow and from very dry clay soils where extensive cracking influenced

rapid transport. Conservation tillage, a widely used practice in America, reduces sediment fluxes by

ploughing only the necessary parts of the field, giving up to 90% reductions (Mostaghimi et al.

2001). A variation on this practice includes application banding of herbicides, ensuring fertiliser

application only where required, which saw 50-66% reductions by Brown & Beinum (2009).

Soil management practices help transportation of sediment particles. Walker et al (1999) identified

that as particle size of a clay soil increased so did the adsorption equilibrium of the herbicide

Isoproturon. Brown et al. (1999) proved this when he found that a fine top soil reduced sediment

flux by 30%. Alternatively, Harris & Catt (1999) claim plugging cracks in soil with sealants reduces

infiltration and macropore flow, while Mostaghimi et al. (2001) introduces the concept of sediment

detention structures to trap polluted water through gravity.

Edge-of-field changes such as stream fencing, terraces, vegetated waterways and rotational grazing

of livestock all provide protection to the watercourse (Mostaghimi et al. 2001). Buffer zones

however show great potential as Gharabaghi et al. (2006) found 5m of buffer strip effectively

removed up to 75-95% of the sediment flux, by using grasses and vegetation to filter the solution.

More natural and permanent options exist with Riparian buffer zones, providing ecological benefit

as well, or constructed wetlands that are now more popular due to their natural value as a pollutant

remover (Mostaghimi et al. 2001).

5.6 Climate Change

The climate is changing, therefore so is the environment and the rivers within. An increased

prevalence of droughts and extreme summer low flows will cause base flow dilution reductions and

eventually more anoxic zones, while higher air temperatures will affect biological activity through

warmer streams (Withers & Jarvie, 2008). A changing climate is also projected to increase the

magnitude and frequency of events. Using „climate change projection‟ models, provided by UK


Climate Projections 2009 (UKCP09), the degree of change is quantified and summarised in Table

5.2. An event is defined as >10mm precipitation for one day.

Period Emissions

Scenario

% change from baseline period (1961 – 1990)8

Winter

(DJF)

Low 24.2

Medium 33.1

High 39.5

Spring

(MAM)

Low 9.0

Medium 11.4

High 13.3

Summer

(JJA)

Low -8.4

Medium -17.0

High -11.4

Autumn

(SON)

Low 3.5

Medium 4.0

High 3.7

Annual Low 5.3

Medium 5.3

High 8.4

Table 5.2 – UKCP09 projections for change in event frequency in 2050s over the Blackwater

sub-catchment (UKCP09, http://ukclimateprojections.defra.gov.uk)

Table 5.2 identifies alarming annual increases of 8.4%, while the change in extremities throughout

the seasons is even greater. Summer months are projected to witness 17% fewer events while the

opposite in winter is much greater with increases in events of up to >39%. These projections

support the continued research into impacts of events and more importantly the relevant BMPs.

5.7 Future Research

This research finds relevance within water management parties, such as water industries and

farmers. Future research should continue to promote the need for mitigation of water pollution and

endeavour to influence Government legislation or practices, such as new incentives within ES.

However, research not only needs to appeal to those in power but also stakeholders, e.g. farmers,

where liaising will be crucial to ensure a willingness to commit to the cause. Advances in

methodologies will help the cause. These include adding in financial calculations allowing farmers

to apply themselves to the implications in an agronomic and economic sense with the aim of

securing their support for BMPs within ES. Identifying event thresholds will see improvements to

data collection. Through in situ stream analysis, such as change in stage or discharge, sampling

would automatically commence ensuring complete event capture throughout years to come. Using

thresholds will support the extension of this study to larger temporal scales, e.g. annual, enabling

further pollution analysis. The Wensum DTC programme intends to achieve this.

8 50% probability level; indicating projections are equally likely to happen as not


6 Conclusion

This study aimed to evaluate both the impact of events on pollutant transfer and whether subsurface

drainage reduces this transfer. It used six „characteristic varying‟ sub-catchments within the

Blackwater Catchment of the River Wensum.

Analysis of the high-resolution pollutant concentrations highlighted two main conclusions. First is

the delayed response in pollutant transport, after the onset of rain, in those catchments with

subsurface drainage (average time lag of 4hours). This delay is noticed during baseline monitoring,

with drained catchments showing more stable trends, as opposed to flashy results from overland

flow. Secondly, the elicit complexity of phosphorus is very apparent across differing catchment

characteristics. Relationships have been drawn such as; sandy soil catchments see the quickest and

more sustained responses, and pastoral catchments have the highest pollutant values especially TPP.

Analysis of the total fluxes concluded that events have a positive impact on pollutant transfer over

baseline data. In agreement with literature, I have therefore accepted the first hypothesis. The main

conclusions were in line with concentration data suggesting fluxes are dependent on catchment

characteristics. The largest increase was seen at Brake Hills-C (TPP= +443%, TSS= +346%) where

the defining characteristic is livestock, and the build up of faeces and soil during dry periods saw

significant transport with rain. Swanhills-A indicated large increases in PO43-

of +203%, where

intense farming has led to mass fertiliser application. This leads to a continuously large transfer as

previously settled pollutants in the drainage network wash through with future events. The literature

that derived the second hypothesis was however unsupported by this study. No reduction in

pollutant flux was seen from the sub-catchments where discharge is drainage dominated, therefore

rejecting the second hypothesis. This could be due to greater than expected infiltration, in dry

weather, causing lower than expected overland flow fluxes. Alternatively, drainage provides extra

interconnectivity for pollutant transfer, which would otherwise not exist during an event, causing

higher than expected fluxes from drained land.

The adverse effects that pollutants entail on receiving waters is a continuing issue, which has seen

hard hitting legislation such as the WFD requiring improvements in ecological potential and

chemical status of surface water within the next three years (European Parliament, 2000). Although

hindered by an unusually dry autumn, this research is one of the first specified studies to use the set

up of a DTC project to map the issue. The DTC projects act as pathways to stakeholder

engagement; necessary for quicker and more efficient applications of mitigation options, of which

ES plays a crucial role. As the DTC programmes age, the technology and data sets collected will

follow, lending to improved results and enhanced analysis. Linking this with advancements in

methodologies will ensure the most efficient way of tackling water pollution.


7 Acknowledgements

My thanks go to Professor Kevin Hiscock for his continued help as project supervisor, aiding with

development, resources and information for this project. Thanks to Dr Tobias Krueger who helped

with the data collection and theory. Many thanks to Ms Liz Rix and the ENV analytical laboratory

team who carried out all the analysis on the water samples. Finally thank you to all the members of

the Wensum DTC programme for their help and guidance.


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9 Appendices

9.1 Appendix 1 – Total flux values and percentage change

Table 9.1 highlights the relative change in total mass of flux for ease of comparison.

Sub-catchment Substance Average Baseline Flux

16th July Flux 4th November Flux

kg kg % change from baseline

kg % change from baseline

Swanhills-A TP 0.18 0.48 +167 0.59 +228

TPP 0.071 0.18 +154 0.051 -28

TDP 0.11 0.33 +200 0.59 +436

TRP 0.13 0.28 +115 1.1 +746

PO4 0.099 0.30 +203 0.58 +486

TSS 23 36 +57 32 +39

Swanhills-B TP 0.044 0.04 -9 – –

TPP 0.012 0.015 +25 – –

TDP 0.032 0.028 -13 – –

TRP 0.033 0.021 -36 – –

PO4 0.031 0.021 -32 – –

TSS 3.7 8.6 +132 – –

Brake Hills-C TP 0.36 0.98 +172 – –

TPP 0.14 0.76 +443 – –

TDP 0.22 0.22 0 – –

TRP 0.31 0.51 +65 – –

PO4 0.2 0.2 0 – –

TSS 13 58 +346 – –

Black Bridge-D TP 0.37 0.33 -11 0.21 -43

TPP 0.25 0.22 -12 0.15 -40

TDP 0.12 0.11 -8 0.059 -51

TRP 0.27 0.27 0 0.11 -59

PO4 0.11 0.09 -18 0.033 -70

TSS 47 26 -45 19 -60

Stinton Hall Farm-E

TP 0.22 0.35 +59 0.63 +186

TPP 0.094 0.11 +17 0.15 +60

TDP 0.13 0.22 +69 0.48 +269

TRP 0.16 0.22 +38 0.61 +281

PO4 0.12 0.18 +50 0.39 +225

TSS 19 15 -21 29 +53

Park Farm-F TP 1.2 0.91 -24 – –

TPP 0.79 0.49 -38 – –

TDP 0.36 0.41 14 – –

TRP 0.87 0.5 -43 – –

PO4 0.31 0.31 0 – –

TSS 160 98 -39 – –

Table 9.1 – Total pollutant flux for baseline and storm monitoring, with percentage change


9.2 Appendix 2 – Calculating flux from discharge

Detailed steps from discharge to flux are provided (Figure 9.1) to help the reader if they require

an understanding of parameters and unit considerations to apply when calculating flux in common

units [kg ha-1

].

Where: C = concentration [varying units given]

Q = discharge [m3s

-1]

F = flux [varying units given]

A = area of catchment [hectares]

T = length of monitoring period [seconds]

Figure 9.1 – Detailed five-step process from concentration [ug l-1

] to flux [kg ha-1

]

Step 1

Step 2

Step 4

Step 5

Step 3

Divide by 1000 for , another 1000 for and another 1000 for

Multiply by 1000 for (As 1 = 1000 litres)

Multiply by discharge for flux in

Divide by area for flux per unit area

Multiply by period of monitoring (in this case a

storm event of 12 hours) to get


9.3 Appendix 3 – Relationship of various phosphorus determinations

Figure 9.2 is to provide further knowledge and a visualisation of how to obtain different phosphorus

determinants in the laboratory.

Figure 9.2 – Flow chart of relationships between various phosphorus determinations (DoE,

1980)


9.4 Appendix 4 – Project Proposal

Dissertation Proposal

Supervisor: Kevin Hiscock

Background/Rationale

Concentrations of phosphorus in both soluble and particulate form increase with an increasing

discharge from an agricultural catchment, and are often known to be above environmental limits in

base flow (Granger et al. 2010). Phosphorus is of significant environmental importance as it is often

the limiting nutrient for river algae. Under heavy loads of this pollutant, algae thrive and grow in

mass enormously causing severe problems to the flora and fauna of the river, including fish deaths.

This term is referred to as Eutrophication and is a cause for concern with governments, resulting in

the ongoing introduction of legislation and policy aimed at improving water (e.g. European Union

Water Framework Directive). This legislation often proves a challenge to water companies who

then have to improve their effluent discharge. These are termed point sources, however in

agricultural areas studies have shown that pollutants are usually derived from diffuse sources. Due

to the nature of the high correlation of phosphorus with rainfall, and hence storm events, it is

necessary to monitor these at a high temporal resolution to get any type of decent analysis and not

over or under estimate input. High resolution monitoring should therefore be carried out of known

different catchment inputs, so that comparisons can be made of the final outcomes with what would

be expected. This will result in the possibility of introducing any mitigation techniques.

Hypothesis/question

Storm events will dramatically impact the concentrations of both Suspended Sediment and

Phosphorus within the Blackwater catchment of the River Wensum.

The Study System

Correlating my sampling with that of the Wensum Alliance project means I can use some of

their longer term data to help me with the

analyses. For this reason, I will be taking

sampling locations from within the Blackwater

area of the catchment, which is a focal point for

the Alliance. There will be three different

catchments; arable farmland, pastoral farmland,

and sewage works, which all correspond to

sampling points used by the Alliance.

Figure 9.3 – Monthly monitoring regime of

phosphorus in a river, indicating how it avoids

over calculation from storm events


Design and Methodology

For further comparative measures, I hope to be

able to monitor the effect of at least two storm

events, if not three. I will set up ISCO pump

monitors, which are turned on remotely 30 minutes

before a storm event (after recommendation from

„Weatherquest‟) and measure the concentrations of

given pollutants at set intervals. My intervals will

be at every 30 minutes or less. This is because high temporal resolution monitoring requires short

intervals so that under or over estimation does not occur. Granger et al. 2010 found such variations

can be an over estimation of 35%, when using 60 minute monitoring over 30 minute monitoring. It

is only long term data sets, which should use low resolution monitoring as these will typically miss

the storm peaks and avoid miscalculation of annual averages (Figure 9.3). Using the concentration

data, along with flow data gathered from a valeport flow meter and interpolation of data from the

Alliance stations, we can monitor the effect a storm event in terms of its pollutant loadings. Using

these loadings, we can create chemographs (Figure 9.4) of concentration with discharge and

compare these to what would be expected from the three different catchments. To apportion the

extent of the input of the given catchment there are known compounds, which are associated with

the type of discharge. These are Boron for wastewater from sewage, the P:B ratio and NH3 for

pastoral farming, which is from animal excreta.

Data Summaries and Analyses

The aim is to follow the trend of the storm through change in load. Chemographs, or loading

graphs, provide a representation of the whole storm (discharge and concentration) and will be the

best chance to compare change in storm with change in input. A storm hydrograph of rain with time

will identify the trend of the storm, and basic overall correlation graphs between expected and

actual data will mean I am able to assess the impact of the storm and to what extent the catchment

types vary. Maps will show location variation and sampling points.

Relevance

High resolution monitoring needs to occur in order to verify any form of mitigation measures

suggested, and has been an important part of recent investigation, with studies from Scholefield et

al. (2005) whose hourly monitoring of riverine nutrients revealed diurnal patterns during base flow,

and Granger et al. (2009) who showed rapid increases of most riverine pollutants with a storm

hydrograph, apportioning phosphorus sources to agricultural manure and fertiliser.

This study will provide analysis of three Norfolk catchments, predominantly characterised by

flat, agricultural land, underlain with a calcareous chalk rock and a generally dry climate. This

Figure 9.4 – Chemographs of riverine pollutants


smaller data set will provide outputs to analyse and identify the true difference between land

management techniques, proving the hypothesis. The longer term, high resolution, larger Wensum

data set can be used for supporting data and to check similarity. Studying the variability of different

events for the same catchment will provide confidence to the argument.

References

Granger, S.J., Hawkins, J.M.B., Bol, R., White, S.M., Naden, P., Old, G., Bilotta, G.S., Brazier,

R.E., Macleod, C.J.A., and Haygarth, P.M., 2010, High temporal resolution monitoring of multiple

pollutant responses in drainage from an intensively managed grassland catchment caused by a

summer storm, Water, Air & Soil Pollution, 205, 377-393.

Scholefield, D., Le Goff, T., Braven, J., Ebdon, L., Long, T., and Butler, M., 2005, Concerted

diurnal patterns in riverine nutrient concentrations and physical conditions, Science of the Total

Environment, 344, 201– 210.

Planning Schedule

Figure 9.5 – Timeline of proposed work for project


9.5 Appendix 5 – Dissertation Progress report (18th

October 2011)

Work completed to date

To date I have not completed as much practical fieldwork, for my results, as I would have

hoped. Due to the anomaly in warm weather we experienced during September/October along with

a remarkably dry Autumn, I have not had a real chance to monitor an effective storm event.

I have however used data from an event back in July to start my results analysis where I have

created graphs for all sub-catchments (A – F) showing the following (see results section for

examples):

Rainfall (Total and intensity)

Concentration with time including rainfall with time (up to 5 hours time lag)

Scatter plots correlating concentration with total rainfall and intensity (up to 5 hours time

lag)

Flow data (based on relative area to F)

Flux data showing kg ha-1

of P and sediment released during the storm event (and a scale

up to kg ha-1

a-1

)

I have also done a substantive literature review to gain background into related topics to do

with my project. This has helped me begin drafting the introduction and background setting for my

report.

Methods used

For my results, I am using data, which is obtained by the Wensum DTC project run by UEA.

They have ISCO pump monitors set up at each of the six monitoring stations within the Blackwater

catchment of the River Wensum. These are remotely turned on by mobile text and contain 24

sampling bottle which can be set to sample the water up to every 30 minutes. These are then taken

back to the UEA laboratories where they are analysed to ascertain concentrations of phosphorus and

sediment.

To get flow data, which I can use with the concentration data to calculate flux, we very recently

decided that we would have to go out into the field during a storm event to use a flow meter

(Valeport BFM001-002). This is because the flow data that is recorded at sites E and F has been

shown to be invalid.

Further analysis

Further analysis that still needs to be undertaken is to monitor one more storm. From this, I can

carry out the same analysis as for the first storm and maybe also a scatter plot of flux with flow.

Once I have the flux data for a couple of storms, I can carry out comparisons between the results to


identify and trend differences, value differences and any anomalies. I will also compare between

sites A, B, E to C or D as these have quite differing set ups and can help me explain some of the

results and answer my hypothesis. I will then be able to compare the loss from storm events with

the application rates of fertiliser used based on the British Survey of Fertiliser Practice 2010 and

hopefully be able to forecast some financial calculations to the farmers based on loss of fertiliser.

Problems encountered

So far, I have unfortunately encountered quite a few problems. This mainly revolves around the

reliance on a good storm event to warrant setting off the pump monitors. I require a large enough

rainfall, which would result in, sustained increased flow in the river, reliant on allowing the sub-

catchments to wet up and provide drainage output (hopefully with mobilised phosphorus and

sediment). Unfortunately due to the unprecedentedly dry autumn we have not seen such an event as

of yet. I have also changed my proposal in a couple of ways. I am now not using my own set up of

pump monitors and sample analysis, due to the drastic time and money implications. I am instead

using the ones that had just been set up as part of the Wensum DTC project. This limited my ability

to place the pumps spatially where I wanted and also relied on the pump monitors to function

properly. However, this is where another issue has arisen in terms of the flow data not recording

properly, therefore being unreliable. We have therefore decided that come a storm event we will

need to go out into the field and manual record flow data with a flow meter within the sub-

catchments. I also decided to change the focus on the hypothesis and create a more answerable

hypothesis. Therefore, it has changed from:

Storm events will dramatically impact the concentrations of both Suspended Sediment and

Phosphorus within the Blackwater catchment of the River Wensum

to:

Sub-surface drainage has an impact on the concentrations and fluxes of both Suspended

Sediment and Phosphorus within the Blackwater catchment of the River Wensum

Preliminary results obtained

Figure 9.6 – 9.8 show examples of the charts I have created from the initial results of a storm

event on the 16th

July 2011.


Figure 9.6 – The loss of Phosphorus and Suspended Sediments including the total storm loss

in kg/ha and kg

Figure 9.7 – Flow over time of the storm at all 6 sub-catchments, including moving average

for each

Figure 9.8 – Concentration of phosphorus, suspended sediment and rainfall intensity with

time

07:46 08:16

09:16 10:16

11:16 12:16

13:16 14:16

15:16 16:16

17:16 18:16

19:16 20:16

0

0.00002

0.00004

0.00006

0.00008

0.0001

0.00012

0.00014

0.00016

0.00018

0.0002

Time (HH:MM)

Flu

x (k

g h

a-1)

Loss of Phosphorus and Suspended Solids from storm event at Swanhills-A on 16/07/2011

Total P

Total Particulate P

Total Dissolved P

Total Reactive P

PO4 (kg P)

Total Suspended Solids (000's)

Total loss = 0.000239 kg/ha = 0.1283 kg Total loss = 0.0000857 kg/ha = 0.0461 kg Total loss = 0.000176 kg/ha = 0.0944 kg Total loss = 0.000151 kg/ha = 0.0813 kg Total loss = 0.000157 kg/ha = 0.0842 kg Total loss = 0.0191 kg/ha = 10.3 kg

0

0.05

0.1

0.15

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:0…

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:0…

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Flo

w (

m3

s-1)

Time (HH:MM)

FLOW at all six sub-catchments (m3 s-1)

FLOW at F (m3 s-1) FLOW at E (m3 s-1) FLOW at D (m3 s-1) FLOW at A (m3 s-1) FLOW at C (m3 s-1) FLOW at B (m3 s-1) 2 per. Mov. Avg. (FLOW at F (m3 s-1)) 2 per. Mov. Avg. (FLOW at E (m3 s-1))

0

0.5

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Rai

nfa

ll In

ten

sity

(m

m/h

r)

Co

nce

ntr

atio

ns

Time (HH:MM)

Concentration data at Swanhills - A

Rainfall Intensity mm/hr

Total P ug/l

Total Particulate P ug/l

Total Dissolved P ug/l

Total Reactive P ug/l

PO4 ug P/l

Total Suspended Solids mg/l


Work plan

The remaining research will commence whenever the weather allows me to. I am constantly

keeping an eye on the „WeatherQuest‟ data. Once it looks like a good storm is due, we will go out

into the field to undertake sampling and flow gauging. These samples will be analysed ASAP and I

hope to have data from at least two storms by 3 weeks time. This is not ideal in terms of data

collection and I did hope to have my data almost all collected by now, however it has been

unfortunate and out of my control. I just hope that there will be some valid data to work with, in the

next 3 weeks otherwise I will have to use what I have by then.

Dissertation Structure

Abstract and keywords

Introduction

o Importance of phosphorus and sediment to water quality

o Importance of storm events in pollutant flux monitoring

o Importance of subsurface drainage for future agricultural management

o Aims and objectives of this project (including rationale, scope and originality)

Location

o My links to the Wensum DTC project and why the Wensum and Blackwater catchment

are significant

o Description and rationale behind focus on specific sub-catchments (A – F)

Method

o Description of set up of pump monitoring stations and how the storm event data is

captured

o Description of lab analysis

o Description of flow gauging

Results

o Graphs showing the rain data (total and intensity) and flow data (hydrographs) of storm

events

o Graphs showing the concentrations with time and rain of all catchments and all storms

o Graphs showing the flux data from all catchments

Interpretation

o Explanation of trends of concentration / flux data (between sub-catchments and storms)

o Best Management Practices

o Impact of climate change on storm events

o Further implications

Conclusion

References

Documents

Evaluating how Storm Events Impact Phosphorus and Sediment ...€¦ · Land use throughout Europe is dominated by farmland, with Britain hosting 75% of its land to agriculture (18.3million