Penny JohnesAquatic Environments Research Centre,

University of Reading

Johnes, P. J. (2007) Uncertainties in riverine P load estimation: impact of load estimation methodology, sampling frequency, baseflow index and catchment

population density. Journal of Hydrology, 332, 1-2, 241-258.

Why do we need to control nutrient loading from land to water?

The role of nutrients in regulating ecosystem health

Consumption of PON and POP by filter feeders – zooplankton

and planktivorous fish

Uptake of SRP, NO3-, NO2-, NH3, NH4+ and LMW DON & DOP by plants and macro-algae (Seaweed)

Uptake of SRP, DOP, NO3-, NO2-, NH3, NH4+ and DON by phytoplankton

HMW DON, DOP, PON and POP as a substrate for microbial decomposition and assimilation

All forms of N and P would need to be controlled under WFD

The role of nutrients in regulating ecosystem health

Controlling nutrient flux to European waters under the WFD: the scale of the challenge Whole ecosystem restoration to achieve ‘Good Ecological Status’ under the

WFD is likely to require control of both N and P loading on European waters

Control of N and P flux to European waters will require consideration of a much wider range of sources, practices, and pathways than has previously been considered

Control of N and P flux to European waters will need to address inorganic, organic particulate and soluble nutrient flux from land-based and atmospheric sources

There will be significant time-lags in systems scale response to mitigation measures in some waters, notably those with a high baseflow index and N surplus in groundwater stores and/or significant P stores in soils and sediments

Routine water quality monitoring will need to provide robust and reliable data describing the transport of all forms of N and P delivered to water bodies from all catchment source areas if it is to provide the essential underpinning for implementation of the EU WFD

What happens when we try to manipulate nutrient enriched ecosystems based on an incomplete science?

The economic and environmental impact of reliance on low frequency, partial fraction data from routine water quality monitoring programmes

Bosherston Lily Pools, Wales

Barton Broad, Norfolk

Pembrokeshire Coast National Park

Norfolk BroadsNational Park

Restoration case studies from two important UK stonewort areas

Charophycea

The challenge

• Increased N and P loading to the water column, sustaining increased algal productivity

• Loss of marginal reed beds through wind stress on reed bedsweakened by N enrichment

• Loss of submergent plant communityexcept in sheltered bays, including nationally rare Charophyte species

• Abundant young roach community feeding on equally abundant Daphnia, feeding on abundant algal community

• Green and blue-green algal blooms

• Sediment, PP and N delivery fromdiffuse catchment sources

• P loading from point sources

• Infilling of the lake, compromisingnavigation rights in Barton Broad

• P enrichment of lake sediments, leading to substantial internal P loading to the water column

Measure Barton Broad Bosherston Lily Pools

Routine monitoring of point sources Daily determination of TP and SRP loads discharged to the lake

Weekly determination of TP and SRP loads discharged to the lake

Routine monitoring of lake and inflowing streams

Weekly determination of SRP, TP, Nr in the main inflow and lake

Monthly determination of SRP and Nr in the main inflow and lake

Diversion of STW to Sea Yes. N. Walsham STW, 1980 Yes. Stackpole STW, 1984

P stripping at STW Yes, at 4 STW No other main STW in catchment

Sediment trapping No Yes. Excavation of sedimentation lagoons and old fish ponds

Sediment dredging Yes. 350,000 m3 Yes. 15,000 m3 to Summer 2008l

‘Weed’ cutting No Yes

Biomanipulation Yes No

On farm reduction of N loss from agricultural land

No Some improvements to slurry handling on NT land

On farm reduction of P loss from agricultural land

None to 31 December 2008 Some improvements to slurry handling on NT land

NVZ New from 1 Jan 2009 appeals allowed up to 31 March 2009

New from 1 Jan 2009 appeals allowed up to 31 March 2009

Ecological targets met? Partially, with ongoing costsOnly in biomanipulated areas

No, but further decline halted (?)in sensitive Charophyte areas

Costs to date € 7 million € 2 million

Reliance on routine WQ monitoring for load estimation: problems and challenges under the WFD

How accurate are our estimates of ‘observed’ nutrient loading?

What impact do infrequent sampling and reliance on partial nutrient fractions have on our perceptions of the origins and scale of the nutrient loading problem?

What is the nature of the uncertainty associated with infrequent or partial observational data?

Accuracy Reproducibility Reliability

How do we take appropriate note of the different relationships which develop between land management and nutrient export?

How can we account for these differences in space and time in the design of our monitoring programmes?

How can we take account of observational uncertainty within a modelling uncertainty framework?

Using high frequency data sets to determine the nature of the uncertainties associated with reliance of partial, low frequency environmental data

• How ‘wrong’ might we be?

• How certain can we be in deploying management measures to ‘restore’ ecosystem health under the WFD?

Results from a numerical experiment based on artificial decimation of high frequency researc foh databases

0.00

0.05

0.10

0.15

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0.25

0.30

0.35

0.40

0.45

01/0

4/19

98

01/0

5/19

98

01/0

6/19

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01/0

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00

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2/20

00

01/0

3/20

00

Phosphorus Fractions (mgl-1) SRP Phosphorus Fractions (mgl-1) SUPPhosphorus Fractions (mgl-1) TDP Phosphorus Fractions (mgl-1) PPPhosphorus Fractions (mgl-1) TP

0

5

10

15

20

25

30

35

40 Suspended sediment (mg l-1) Mean discharge x 10 (m3 s-1)

Suspended sediment, P fractionation & discharge, River Lambourn at Boxford, 1998-2000

0.000.200.400.600.801.001.201.401.60

01/0

4/19

98

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1/19

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1/20

00

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2/20

00

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3/20

00

Phosphorus fractions (mgl-1) SRP Phosphorus fractions (mgl-1) SUPPhosphorus fractions (mgl-1) TDP Phosphorus fractions (mgl-1) PPPhosphorus fractions (mgl-1) TP

0

100

200

300

400

500

600

700 Suspended Sediment (mg l-1) Mean Discharge x 20 (m3 s-1)

Suspended sediment, P fractionation & discharge, River Enborne at Brimpton, 1998-2000

River Basin Major geology Years of record Daily variables

Windrush at RissingtonWindrush at Worsham

Jurassic limestone (Oolite), Lias Clay

87/88, 88/8987/88, 88/89

TP, TDP, Q, SS, N species(TDP, PP weekly)

River Cole Site ARiver Cole Site BRiver Cole Site D

Cornbrash, Oxford Clay

95/9695/9695/96

TP, TDP, Q, SS(SRP, DHP, PP weekly)

Lambourn UpLambourn Down

Upper Chalk 95/96, 96/97, 98/99, 99/00 95/96, 96/97, 98/99, 99/00

TP, TDP, Q, SS, N species(SRP, DHP, PP weekly)

Enborne UpEnborne Down

Tertiary deposits mostly London Clay

98/99, 99/0098/99, 99/00

T, TDP, Q, SS(SRP, DHP, PP weekly)

Winterbourne Stream Upper Chalk 95/96, 96/97 TP, TDP, Q, SS, N species(SRP, DHP, PP weekly)

ChitterneEbbleNadderSemEast AvonWest Avon

Chalk, Greensand, mixed

02/0303/04

02/03, 03/0403/0402/03

02/03, 03/04

TP, Q, SS(TDP, PP weekly)

Wye at ErwoodFromeGarron BrookStretford BrookWorm Brook

Mixed 02/0302/03, 03/0402/03, 03/04

03/0403/04

TP, Q, SS(TDP, PP weekly)

Ant at Swafield BridgeAnt at Honing LockAnt at Hunsett Mill

Norwich & Red Crags 99/0099/0099/00

TP, Q, SS, (TDP, SRP, DHP, PP weekly)

Location of catchments with daily TP and Q records used in load estimation analysis

Rank by population

density

Rank by baseflow index

Rank by catchment size

East AvonFromeSemCole

Ant at Hunsett MillWest Avon

LambournWinterbourne

EbbleChitterne

Windrush at WorshamWindrush at Rissington

Wye at ErwoodWindrush at Worsham

NadderLambourn

Windrush at RissingtonEnborne

Ant at Honing LockStretford BrookAnt at Swafield

EbbleEnborne

Windrush at WorshamChitterneNadder

Windrush at RissingtonLambourn

Ant at Hunsett MillAnt at Honing Lock

Ant at Swafield BridgeNadderFrome

Garron BrookStretford Brook

Worm BrookEast AvonWest Avon

EbbleGarron Brook

ColeAnt at Hunsett Mill

East AvonWest Avon

FromeChitterne

Worm BrookStretford Brook

Wye at ErwoodGarron Brook

Winterbourne StreamWorm Brook

SemCole

EnborneWye at Erwood

WinterbourneAnt at Honing Lock

Ant at SwafieldSem

High

Low

Large

Small

Sampling frequencies analysed:sub-daily records generated through artificial decimation of the observed daily records

Daily sampling 39 records x 8 methods

Stratified sampling (structure by day of week and Q) Top 10% flows sampled daily, remainder sampled

weekly 39 records x 8 methods x 7 replicates

Weekly sampling (structured by day of week) 39 records x 8 methods x 7 replicates

Monthly sampling (structure by day of month) 39 records x 8 methods x 30 replicates

Load estimation equations(after Dolan et al., 1981; Kronvang et al., 1996; Webb, et al., 2000)

( )

( )

ei

bii

q

lq

r

i

ii

ri

pii

ii

ii

in

nCF

aQC

q

Sn

lqS

nCF

QQ

in

QCin

KLoad

QnC

in

KLoad

QCin

KLoad

nQC

in

KLoad

nQ

in

nC

in

KLoad

101

12

11

11

3

1

1

1

1

1

11

2

2

∑

∑

∑

∑

∑

∑

∑∑

=

=

=

+

+

=

=

==

=

=

==

=

=

=

=

=

samples ofnumber n)s (m dischargemonthly meanQ

)l (mg ionconcentratmonthly meanC

)s (m samples between intervalfor discharge meanQ

)s (m record of periodfor discharge meanQ

)s (m sampling of timeat discharge ousinstantaneQi

)l (mg samples individual with associated ionconcentratousinstantaneC

recordofperiodofaccounttaketofactorconversionK

1-3m

1-m

1-3pi

1-3r

1-3

1i

==

=

=

=

=

=

=−

( )

( )

)log()log(

.1

1

..1

1

1

222

1

2

eiii

n

iiq

n

iiilq

CCe

qnQn

S

lqnCQn

S

−=

−

−=

−

−=

∑

∑

=

=

Where:1.

2.

3.

4.

5.

6.

7.

8.

Where:

Interpolation methods

Beale’s Ratio estimator

Extrapolation methodsLog-log rating

‘Smearing estimate’ Where:Log-log estimate of concentration ismultiplied by CF2 to give ‘smeared’ estimate of concentration

Gives a correction factor CF3 representing the ratio between mean measured loads and mean actual flow. Influence of CF3 decreases as n increases

Evaluation criteria Bias (B)

Difference between the ‘True’ load and the mean of the distribution of the estimates

Standard deviation (S) Reflects the precision of the estimate

RMSE Root Mean Square Error, where:

RMSE = √(B2 + S2)

Load estimates generated by equations (as % of ‘True’ load), based on daily sampling

0.0

100.0

200.0

Equation 1 Equation 2 Equation 3 Equation 4 Equation 5 Equation 6 Equation 7 Equation 8

% d

evia

tion

of lo

ad fr

om 't

rue'

mea

n

Ant at Honing 99-00

Ant at Hunsett 99-00

Ant at Sw afield 99-00

Chitterne 02-03

East Avon 03-04

Ebble 03-04

Enborne at Brimpton Dow n 98-99

Enborne at Brimpton Dow n 99-00

Enborne at Brimpton Up 98-99

Enborne at Brimpton Up 99-00

Frome 02-03

Frome 03-04

Garron Brook 02-03

Garron Brook 03-04

Lambourn at Boxford Dow n 95-96

Lambourn at Boxford Dow n 96-97

Lambourn at Boxford Dow n 98-99

Lambourn at Boxford Dow n 99-00

Lambourn at Boxford Up 95-96

Lambourn at Boxford Up 96-97

Lambourn at Boxford Up 98-99

Lambourn at Boxford Up 99-00

Nadder at Wilton 02-03

Nadder at Wilton 03-04

River Cole Site A 95-96

River Cole Site B 95-96

River Cole Site D 95-96

Sem 03-04

Stretford Brook 03-04

West Avon 02-03

West Avon 03-04

Windrush at Rissington 87-88

Windrush at Rissington 88-89

Windrush at Worsham 87-88

Windrush at Worsham 88-89

Winterbourne at Honeybottom 95-96

Winterbourne at Honeybottom 96-97

Worm Brook 03-04

Wye at Erw ood 02-03

0.0

1.0

2.0

3.0

4.0

Ant at

Honing

99-00

Ant at

Hunse

tt 99-0

0

Ant at

Swafield

99-00

Chitter

ne 02

-03

East A

von 0

3-04

Ebble

03-04

Enborn

e at B

rimpto

n Dow

n 98-9

9

Enborn

e at B

rimpto

n Dow

n 99-0

0

Enborn

e at B

rimpto

n Up 9

8-99

Enborn

e at B

rimpto

n Up 9

9-00

Frome 0

2-03

Frome 0

3-04

Garron

Broo

k 02-0

3

Garron

Broo

k 03-0

4

Lambo

urn at

Box

ford D

own 9

5-96

Lambo

urn at

Box

ford D

own 9

6-97

Lambo

urn at

Box

ford D

own 9

8-99

Lambo

urn at

Box

ford D

own 9

9-00

Lambo

urn at

Box

ford U

p 95-9

6

Lambo

urn at

Box

ford U

p 96-9

7

Lambo

urn at

Box

ford U

p 98-9

9

Lambo

urn at

Box

ford U

p 99-0

0

Nadde

r at W

ilton 0

2-03

Nadde

r at W

ilton 0

3-04

River C

ole S

ite A

95-96

River C

ole S

ite B

95-96

River C

ole S

ite D

95-96

Sem 03

-04

Stretfo

rd Broo

k 03-0

4

Wes

t Avo

n 02-0

3

Wes

t Avo

n 03-0

4

Wind

rush a

t Riss

ington

87-88

Wind

rush a

t Riss

ington

88-89

Wind

rush a

t Wors

ham 87

-88

Wind

rush a

t Wors

ham 88

-89

Wint

erbou

rne at

Hon

eybo

ttom 95

-96

Wint

erbou

rne at

Hon

eybo

ttom 96

-97

Worm

Broo

k 03-0

4

Wye

at E

rwoo

d 02-0

3

Catchment

TP lo

ad e

stim

ate

(kg/

ha)

Max TP (+1sd)Min TP (-1sd)TP load (TRUE)

Imprecision as an indicator of uncertainty in TP load estimates based on daily data

TP load estimates ranked by population density

0.0

1.0

2.0

3.0

4.0

Worm

Broo

k 03-0

4

Wint

erbou

rne at

Hon

eybo

ttom 95

-96

Wint

erbou

rne at

Hon

eybo

ttom 96

-97

Garron

Broo

k 02-0

3

Garron

Broo

k 03-0

4

Wye

at E

rwoo

d 02-0

3

Lambo

urn at

Box

ford D

own 9

5-96

Lambo

urn at

Box

ford D

own 9

6-97

Lambo

urn at

Box

ford D

own 9

8-99

Lambo

urn at

Box

ford D

own 9

9-00

Lambo

urn at

Box

ford U

p 95-9

6

Lambo

urn at

Box

ford U

p 96-9

7

Lambo

urn at

Box

ford U

p 98-9

9

Lambo

urn at

Box

ford U

p 99-0

0

Wind

rush a

t Riss

ington

87-88

Wind

rush a

t Riss

ington

88-89

Nadde

r at W

ilton 0

2-03

Nadde

r at W

ilton 0

3-04

Chitter

ne 02

-03

Wind

rush a

t Wors

ham 87

-88

Wind

rush a

t Wors

ham 88

-89

Enborn

e at B

rimpto

n Dow

n 98-9

9

Enborn

e at B

rimpto

n Dow

n 99-0

0

Enborn

e at B

rimpto

n Up 9

8-99

Enborn

e at B

rimpto

n Up 9

9-00

Ebble

03-04

Ant at

Swafield

99-00

Ant at

Honing

99-00

Stretfo

rd Broo

k 03-0

4

Wes

t Avo

n 02-0

3

Wes

t Avo

n 03-0

4

Ant at

Hunse

tt 99-0

0

River C

ole S

ite A

95-96

River C

ole S

ite B

95-96

River C

ole S

ite D

95-96

Sem 03

-04

Frome 0

2-03

Frome 0

3-04

East A

von 0

3-04

Catchment

TP lo

ad e

stim

ate

(kg/

ha)

Max TP (+1sd)

Min TP (-1sd)

TP load (TRUE)

TP load estimates ranked by Base Flow Index

0.0

1.0

2.0

3.0

4.0

Wye

at E

rwoo

d 02-0

3

Enborn

e at B

rimpto

n Dow

n 98-9

9

Enborn

e at B

rimpto

n Dow

n 99-0

0

Enborn

e at B

rimpto

n Up 9

8-99

Enborn

e at B

rimpto

n Up 9

9-00

River C

ole S

ite A

95-96

River C

ole S

ite B

95-96

River C

ole S

ite D

95-96

Sem 03

-04

Stretfo

rd Broo

k 03-0

4

Worm

Broo

k 03-0

4

Frome 0

2-03

Frome 0

3-04

Garron

Broo

k 02-0

3

Garron

Broo

k 03-0

4

East A

von 0

3-04

Wes

t Avo

n 02-0

3

Wes

t Avo

n 03-0

4

Ant at

Honing

99-00

Ant at

Hunse

tt 99-0

0

Ant at

Swafield

99-00

Nadde

r at W

ilton 0

2-03

Nadde

r at W

ilton 0

3-04

Wind

rush a

t Riss

ington

87-88

Wind

rush a

t Riss

ington

88-89

Wind

rush a

t Wors

ham 87

-88

Wind

rush a

t Wors

ham 88

-89

Ebble

03-04

Chitter

ne 02

-03

Lambo

urn at

Box

ford D

own 9

5-96

Lambo

urn at

Box

ford D

own 9

6-97

Lambo

urn at

Box

ford D

own 9

8-99

Lambo

urn at

Box

ford D

own 9

9-00

Lambo

urn at

Box

ford U

p 95-9

6

Lambo

urn at

Box

ford U

p 96-9

7

Lambo

urn at

Box

ford U

p 98-9

9

Lambo

urn at

Box

ford U

p 99-0

0

Wint

erbou

rne at

Hon

eybo

ttom 95

-96

Wint

erbou

rne at

Hon

eybo

ttom 96

-97

Catchment

TP lo

ad e

stim

ate

(kg/

ha)

Max TP (+1sd)

Min TP (-1sd)

TP load (TRUE)

RMSE of load estimates

0

100

200

1 2 3 4 5 6 7Equation

RM

SE

(as

% o

f Tru

e lo

ad)

1 2 3 4 5 6 7 8

Impact of sampling frequency on bias and precision of load estimates (shown as % of true load)

METHOD 5Equation 5

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Deviation of the TP load estimate from the true load, based on 1 daily sampling record, 7 stratified sampling replicates, 7 weekly sampling replications and 30 monthly sampling replicates for each river basin.

Equation 1

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 2

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 3

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 4

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency%

of t

rue

mea

n

MaxMeanMin

Equation 5

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 6

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 7

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Equation 8

0

100

200

300

400

500

Daily Stratified Weekly Monthly

Sampling frequency

% o

f tru

e m

ean

MaxMeanMin

Data ranked by baseflow index

and from daily to monthly sampling frequency

Conclusions on load estimation methodology selection

Where daily data are available, methods which estimate load based on paired instantaneous Q and concentration (C) data are the most precise and least biased

As sampling frequency decreases, methods taking account of the ratio of observed flows to mean annual flow return the lowest RMSE

Methods estimating total annual load from the mean of the observed concentrations and flows are not reliable for TP load estimation (in lowland UK rivers)

There is no single methodology which is equally appropriate for all contaminants in all systems. The selection of methodology must be tailored to the specific conditions of the catchment under study

Recommended procedures for Total P load estimation in lowland clay and permeable catchments

Baseflow index

Population density

Stratified sampling

Weekly sampling

Monthly sampling

HighModerate

Low

Low Moderate

High

2, 7, 82, 7, 82, 7, 8

2, 3, 533

333

Conclusions on uncertainties associated with ‘observed’ load estimates for nutrient budget studies and diffuse pollution modelling Uncertainty in loading estimates increases as sampling frequency decreases Both under- and over-estimates may be generated of up to 2 orders of magnitude

where monthly data are all that are available Uncertainty increases at all sampling frequencies as:

Population density increases Baseflow index decreases, and River regime becomes more extreme

For baseflow dominated systems, sampling at less than daily sampling frequency returns a relatively low RMSE TP loads calculated from infrequent sampling programmes for these systems may be viewed

as reasonably reliable indicators of riverine TP loading. This may be used to constrain model parametric uncertainty

For systems with significant quickflow hydrological response, and those with a substantial point source P loading, sampling at less than daily sampling frequency will return highly uncertain estimates of observed load. For modelling applications in such systems, multiple parameter sets and/or models may be

fitted to the observational band: no one parameter set or model may be claimed as optimal, unless observational uncertainty can be constrained through the deployment of additional resource in the sampling programme

In systems where daily sampling has taken place it may be possible to set error bars around load estimates, based on the range of loading estimates generated by different load estimation techniques

Temporal reliability

Do the models we are building allow the empirical relationships to change over time?

Climate change Changes in N fixation rates in response to nutrient

enrichment? Non-linear relationships in nutrient flux from landscape

sources in unpolluted and super-saturated systems

Inter-annual reach scale variations in flow controls on nutrient transport and speciation (after Prior and Johnes, 2002; Evans and Johnes, 2004: River Lambourn at Boxford)

Variable1994-1997

UpstreamMean conc.

(mg l-1)

DownstreamMean conc.

(mg l-1)

QSRPSUPTDPPPTPSS

1.24 m3 s-1

0.0200.0300.0500.0890.13915.1

1.24 m3 s-1

0.0440.0560.1000.1200.2205.66

Variable1998-2000

Upstream Mean conc.

(mg l-1)

DownstreamMean conc.

(mg l-1)

QSRPSUPTDPPPTPSS

1.36 m3 s-1

0.0870.0620.1490.0330.1839.23

1.36 m3 s-1

0.0710.0510.1220.0220.1447.97

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

01/10

/1995

01/01

/1996

01/04

/1996

01/07

/1996

01/10

/1996

01/01

/1997

01/04

/1997

01/07

/1997

01/10

/1997

01/01

/1998

01/04

/1998

01/07

/1998

01/10

/1998

01/01

/1999

01/04

/1999

01/07

/1999

01/10

/1999

01/01

/2000

SRP (mg-1)

TP (mg-1)

Q/10 m3 s-1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

01/10

/1995

01/01

/1996

01/04

/1996

01/07

/1996

01/10

/1996

01/01

/1997

01/04

/1997

01/07

/1997

01/10

/1997

01/01

/1998

01/04

/1998

01/07

/1998

01/10

/1998

01/01

/1999

01/04

/1999

01/07

/1999

01/10

/1999

01/01

/2000

Q/10 m3 s-1

SRP (mg/l)

Total P (mg/l)

TDP:TP ratios in sites with daily observations of concentration and flow

0.000.100.200.300.400.500.600.700.800.901.00

Wint

erbou

rne S

tream

95-96

Wint

erbou

rne S

tream

96-97

Lambo

urn D

own 9

5-96

Lambo

urn D

own 9

6-97

Lambo

urn D

own 9

8-99

Lambo

urn D

own 9

9-00

Lambo

urn U

p 95-9

6

Lambo

urn U

p 96-9

7

Lambo

urn U

p 98-9

9

Lambo

urn U

p 99-0

0

Wind

rush a

t Riss

ington

87-88

Wind

rush a

t Riss

ington

88-89

Wind

rush a

t Wors

ham 87

-88

Wind

rush a

t Wors

ham 88

-89

Enborn

e Dow

n 98-9

9

Enborn

e Dow

n 99-0

0

Enborn

e Up 9

8-00

Enborn

e Up 9

9-00

River C

ole S

ite A

95-96

River C

ole S

ite B

95-96

River C

ole S

ite D

95-96

% o

f TP

load

as

TDP

TDP:TP (concentration) TDP:TP inst. Load)

High Baseflow index ModerateLow Population density High

Conclusions on uncertainties associated with temporal instability in flow:contaminant relationships

Relationships between flow and TP and other constituent fractions are not stable from one year to the next, even in baseflow-dominated systems.

The use of extrapolation methods to reconstruct TP loads based on flow for years with infrequent TP observations is not recommended, unless flow stratification of the record is used.

There is a need for further analysis of this nature to determine the relative uncertainties in load estimation for other nutrient fractions

There is a further need to extend this analysis to incorporate records from upland, forested and other non-agricultural systems

How do we deal with observational uncertainty in order to properly underpin implementation of WFD?

The way forward

Sensor development for key indicator variables to support holistic interpretation of landscape scale nutrient flux behaviour

Sensor network deployment to provide high temporal and spatial resolution background data for in-depth studies at key locations within catchments

Development of demonstration test catchment networks to assess the temporal context for catchment scale response to diffuse source mitigation at an appropriate scale for management

Examples of recent, ongoing and future UK investment in sensor network deployment Investment in the development of novel sensor technologies:

Example: EPSRC Programmes: Novel Sensor Technologies for Environmental Monitoring and Modelling (€5 M to date) Lab based development of sensors for inorganic nutrient fractions and key

physical drivers

Investment in the deployment of novel sensor technologies and new sensor networks to address environmental science questions: Example 1: NERC Programme: Networks of Sensors - Demonstration

High Resolution Networks research programme (€12 M), 2011-2014 Focus is on identifying additional benefits of new sensor technologies to

address key science questions Example 2: Defra Programme: Demonstration Test Catchments (€10 M

to date with further planned investment), 2010-2014 Focus is on using novel sensor networks for inorganic nutrient fractions and

physical drivers combined with traditional interval sampling for full nutrient speciation and geochemical analyses to assess the chemical response to catchment mitigation measures