modelling water quality in uk upland streams using high … · 2014-11-18 · nerc duress project...
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Modelling water quality in UK upland streams using high-frequency observations
Tim Jones, Nick A Chappell
Session 4: Water quality responses to environmental change?
12th British Hydrological Society National Symposium, University of Birmingham
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OVERVIEW: • High–frequency water quality
monitoring in upland Wales • Novel continuous-time transfer
function modelling of H+, DOC & NO3-N from rainfall
• Developing understanding of the relationship between catchment hydrological functioning & bio-geochemical response
• Fundamental importance of sub-daily to sub-hourly observations
Overview of the presentation
Nant Rhesfa (LI7) stream at Llyn Brianne basin
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NERC DURESS project
Diversity in Upland Rivers for Ecosystem Service Sustainability
One of 4 UK national projects under NERC Biodiversity & Ecosystem Service Sustainability
Affect of water quality (& quantity) variables on aquatic biodiversity & visa versa
upland streams with/without affects of conifer plantation – resultant impact on ecosystem service delivery
Component of NERC DURESS
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Necessarily inter-disciplinary project
Research / academic institutions e.g.: Practitioners in water sector (in Wales) e.g.:
Component of NERC DURESS
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4 identically instrumented micro-basins Llyn Brianne Upland Wales, UK Typical upland land-uses
Improved moorland (x2) Conifer plantations (x2)
Experimental basins: upland Wales, UK
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Hydrometric station (LI6) Water quality station (LI7)
Monitoring stations
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High frequency (15-min continuous monitoring) using state-of-art water quality & hydrometric sensors e.g.
S::CAN spectrolyser (DOC, TOC, NO3-N, turbidity, colour)
High-quality, high-frequency monitoring
Accurate measurement of water quality variables now possible in situ (& cost effectively) following sensor advances (Hipsey & Arheimer 2013 IAHS Publ 361: 17-29)
Digital differential pH probes e.g., Hach DPS1
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High quality time-series of hydrochemistry
LI6
LI7
LI3
LI8
from Fig. 1
Jones, Chappell & Tych ES&T
(submitted)
DOC conc. time series
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To develop understanding of catchment hydrological functioning from water quality time-series…
...need high quality data & numerical tools capable of extracting dominant dynamics
Requires high quality time-series of hydrochemistry & robust numerical methods
NO3-N
NO
3-N
mg/
L x3
St
ream
flo
w L
/s /
20
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Numerical tools... Some water quality time-series (often non-conservative variables) are information rich (e.g., H+, DOC) - methods available to extract dynamics contained e.g.,
RIVC
Refined Instrumental Variable Continuous-time Box-Jenkins identification algorithm
Taylor, Pedregal, Young & Tych (2007) Environ. Model. Software 22: 797–814
Time-series analysis
CAPTAIN TOOLBOX
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Numerical tools...
RIVC applied within a
Data-Based Mechanistic philosophy
(1) Identification of many potential model structures, minimising prior assumptions about processes – that are often unknown (‘Data-Based’)
(2) Rejection of most using objective statistical & mathematical criteria
(incorporating ‘Principles of Parsimony’ via heuristic measures)
(3) Rejection of further models that have no physical (e.g., hydrological) interpretation (‘Mechanistic’) – giving models for testing against
independent observations (e.g., dynamics within component flow paths)
Data-Based Mechanistic modelling
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354 356 358 360 362 364 366 3680
0.1
0.2
0.3
0.4
0.5
0.6
Julian day since 1/1/12 (15-minute data)
Dis
charg
e (
mm
/15m
in)
Efficiency = 0.92815
observed
simulated
rainfall-streamflow models identified linear 2nd order CT-TFs*
*given high efficiency, no marked drop in YIC, no complex roots etc
e.g., LI3 basin
Rainfall - Streamflow model
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e.g., for LI3 basin (in winter)
Decomposition of 2nd-order rainfall-streamflow model to two parallel pathways
usually considered to have most robust physical interpretation (via Dynamic Response Characteristics, DRCs)
Rainfall - Streamflow model
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Uncertainty in DRCs (1000 Monte Carlo realisations) – allow DRC comparison
e.g., for LI3 basin
5.4 5.5 5.6 5.7 5.80
50
100Fast path realisations, mean:3.3e+002 hrs std:4.1
25 30 35 40 45 50 550
50
100
150Slow path realisations, mean:2e+003 hrs std:4.1
20 30 40 50 60 70 800
20
40
60
80
100
120
140Flows per path (%)
fast
slow
li3qmodel3_tj2.m (Fig. 70 & 73)
Rainfall - Streamflow model
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Comparison of model characteristics between catchments
e.g., TC (time constant)
TC Dominant runoff process associated Flow path reference 5 mins overland flow (infiltration-excess primarily) Chappell et al. (2006)
2.9 hrs shallow subsurface flow from a hillslope Chappell et al. (1990)
100 hrs fracture flow in Lower Devonian slate Chappell & Franks (1996); Birkinshaw & Webb (2010)
107 dys deep pathway through a chalk aquifer Ockenden & Chappell 2011
from Jones & Chappell (2014) Hydrology Research doi: 10.2166/nh.2014.155
Rainfall – Streamflow model
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Time constant (TC) of hydrometric responses are similar
Basins behave similarly despite land use e.g., presence of a slower 25-35 hr component (in winter response) Source?
Deep soil pipes path Drift (C) path Rock-fracture path
Shows areas gauged in 1980s (LI8 basin smaller)
LI3
LI6
LI8 LI7
5.5 hrs(45%) 33 hrs (55%)
2.4 hrs (45%) 25 hrs (55%)
3.3 hrs (43%) 36 hrs (57%)
3.7 hrs (30%) 35 hrs (70%)
Compare rainfall-streamflow response between nearby basins
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LI3
LI6
LI8 LI7
2.5 hrs(55%) 22 hrs (45%)
2.3 hrs (36%) 13 hrs (64%)
3.4 hrs (32%) 18 hrs (68%)
7.5 hrs (57%) 321 hrs (43%)
Rainfall-H+ load models identified: linear 2nd order CT-TFs optimal
Residence times of H+ load response to rainfall (TC) Faster response than rainfall-streamflow
‘Exhaustion’ or ion exchange effect?
except mature conifer (LI8)
Compare rainfall-streamflow response with rainfall-H+ response
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Compare rainfall-streamflow response with rainfall-H+ response
Also shown in model simulated data
e.g.,
Identified components of
Rainfall-streamflow (__)
Rainfall-H+ load (- -)
LI3 models
from Jones & Chappell (2014) Hydrology Research doi:
10.2166/nh.2014.155
H+ path exhausted faster than hydrometric response
path (LI3, LI6, LI7) – flow path implications?
FAST
SLOW
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Simulated DOC load from rainfall
observed DOC
simulated DOC
from Jones, Chappell & Tych
(2014) ES&T submitted
Obtain similar linear 2nd order CT-TF models for rainfall-DOC load
402 404 406 408 410 412 4140
1
2
mm
/15m
in (a)
402 404 406 408 410 412 4140
0.5
1
kg/1
5m
in (b)
402 404 406 408 410 412 4140
0.5
1
kg/1
5m
in (c)
402 404 406 408 410 412 4140
0.5
1
kg/1
5m
in (d)
402 404 406 408 410 412 4140
0.5
1
kg/1
5m
in
Julian day from 1/1/12
(e)
LI3
LI6
LI7
LI8
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Observe & compare hydrometric & water quality response of component paths
How realistic are component path
proportions & dynamics
identified from whole-basin
responses?
Next phase
of independent observation &
modelling of component flow
paths
e.g., hydrometric & H+ response in natural soil pipes, fracture flow, soil pathways
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400 405 410 4150
50
100
150
200
250
Julian day since 1/1/12
Q L
/s
H
+ u
eq/L
x5
Discharge
H+
Key message:
interpretation only possible if can avoid under-sampling water quality time-series
e.g. LI3 Rainfall-streamflow fast TC = 5.13 hrs
Rainfall-H+ load fast TC = 2.50 hrs
Time Constants (TCs) from li3q8.m
Minimum sampling for H+ load
Nyquist-Shannon: fast TC/2 = 75 mins
Young (2010 BHS)*: fast TC/6 = 25 mins
*Young, P. 2010. The estimation of continuous-time rainfall-flow models for flood risk management. In: Role of Hydrology in Managing Consequences of a
Changing Global Environment. BHS, Newcastle
15-min observations
Interpretation only plausible if sampling has been sufficent given the dynamics
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Lancaster University 11th July 2013
Thank you - any questions?
Further information www.lancaster.ac.uk/lec/sites/duress [email protected] [email protected]
12th British Hydrological Society National Symposium, University of Birmingham