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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
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0.45
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4/19
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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
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1/19
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2/19
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01/0
1/20
00
01/0
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
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
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