Predicting algal growth under climate change in the upper ... · modelling (RCM)) used as part of...
Transcript of Predicting algal growth under climate change in the upper ... · modelling (RCM)) used as part of...
Predicting algal growth
under climate change in
the upper Thames
Mike Hutchins, CEH Wallingford
(plus Richard Williams, Christel
Prudhomme, Sue Crooks)
Changes in the Thames by 2080
• Brought about by economic and social
change but in particular climate change...
– Slower flowing
– Warmer
– Have more sunlight hours
– Have higher nutrient concentrations if only
due to less in stream dilution
• These are better environmental conditions
for phytoplankton blooms; and will favour
potentially-toxic Cyanobacteria species
... Defra policy interest
• How and when will climate change have a
discernible and significant impact on water
quality?
• Commissioning of a case study
demonstrating modelling tools and
datasets for assessing these changes:
three Lake District lakes
Yorkshire Ouse (focus on River Ure)
Upper River Thames
Model chain of three main components
• Climate data from Hadley Centre’s 11-member
ensemble projection (from regional climate
modelling (RCM)) used as part of the UKCP09
scenarios. The 11 members represent a range of
model parameterisations reflecting uncertainty. All
use the SRES A1B emission scenario.
• Future Flows Hydrology (FFH) dataset. Derived via
rainfall-runoff modelling under an EA project to
provide a UK-wide consistent set of future daily
river flows.
• Water quality predictions using QUESTOR, a semi-
empirical, process-based model of river networks
QUESTOR river quality model (Thames)
CEH weekly water quality (2009 - )
Upstream QUESTOR boundary
Tidal limit
Major urban areas outside London
LONDON
Model inputs: (1) Flow
and quality data in (a)
tributaries (b) effluents
from sewage works,
(2) Solar radiation
Represents biochemical interactions in
the river channel environment; and
energy balance for water temperature
Wallingford
Eynsham
Blooms likely in long slow-flowing rivers
...with sufficient light, nutrients and temperature to thrive. All
these variables used in hydrological modelling at daily resolution
of chlorophyll-a, and dissolved oxygen (DO) impacts.
Wallingford
(92 km downstream)
Effect of increasing residence time
Chlorophyll-a content of
different types of
phytoplankton is known,
making it a useful
surrogate for biomass
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40
0 25 50 75 100
up
pe
r q
uar
tile
ch
l-a
(µg
/L)
distance downstream (km)
River Thames (2009-10)
CEH Thames Initiative data
QUESTOR model
QUESTOR calibrated in 2009-10 (e.g. Eynsham)
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Jan-2009 Apr-2009 Jul-2009 Oct-2009 Jan-2010 Apr-2010 Jul-2010 Oct-2010 Jan-2011
Nit
roge
n: m
g N
O3-N
/L
Ph
osp
ho
rus:
µg
SR
P/L
-25-20-15-10-50510152025
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20
40
60
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Wat
er
tem
p (
oC
)
Flo
w (m
3s-1
)
Is model simulating physical/chemical parameters well?
For algae, good summer flow/temp simulation is critical
Temp
Flow
N
P
Simulated
Observed
Model performance at Abingdon in 2009
Phytoplankton
biomass (mg chl-a/L)
Limitation due to light
Nutrients are in excess
High flows wash phytoplankton out of system
An unexplained mid- to late- summer suppression of phytoplankton is apparent
1. However, large variations
observed between years. Far
more phytoplankton in 2009.
So a 2009-10 model is a
compromise
2. Best fitting year-specific
models perform much
better. They are identical,
apart from having different
grazing rates
Bar charts of
upper quartile
chl-a at Wallingford
Invasive zebra mussels are abundant in the Thames. We
assume that there are good and bad years for grazers but
we don’t know why? Over-winter flow/temperature
regimes. Interactions higher up food chain
Comparing 2009 & 2010: simulated blooms similar
Model evaluation and future priorities
• Environmental variables well represented. Can
identify suitable temperature-controlled growth rates
for a mixed phytoplankton population. All optimised
models have doubling rates of 48 h (+/- ~ 6 h)
• By altering year-specific death rates, model can
represent magnitudes of blooms year-on-year.
• Remaining gaps in understanding:
– controls on over-winter survival of phytoplankton grazers
– reasons for late-summer phytoplankton suppression
– water quality response to extreme events
– how will nutrient concentrations change in the future?
– what will be the impact of population growth, and changes
to management/treatment of water resources and waste?
What are impacts of flooding on water quality?
Wallingford – Dec 2012
July 2007 floods resulted in low DO (Oxford – Reading)
Many potential sources of uncertainty
Had-RM3 Perturbed Physics Ensemble Climate Model
Bias correction
Downscaling
Air temp PE
Rainfall-runoff Model
(CLASSIC, CERF)
Rainfall
Regression
Solar radiation
Attenuation by
trees and in
water column
Donate and scale
flows to unmodelled
tribuaries Pollutant
loads from
tributaries
(and STWs)
Photosynthetically
active radiation Water temp Flow
Water Quality Model (QUESTOR)
Phytoplankton biomass (chl-a) nutrients DO BOD
5
Key sources
to isolate
4
1
3 2
Uncertainty due to hydrological modelling
I. A baseline QUESTOR model, set up using
all available flow data (2009-10)
II. Re-run QUESTOR replacing observed flows
in un-modelled tributaries with observed
flows donated and scaled from the modelled
tributaries.
III. Re-run again, also replacing observations
with modelled flows (where possible)
Only 5 of the 11 gauged tributaries were
modelled under the FFH project - so, 3 runs:
Errors due to donating (II) & modelling (III) flows
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Flow DO Temp
Run I
Run II
Run III
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Flow DO Temp
Run I
Run II
Run III
Eynsham Wallingford
Nash-Sutcliffe goodness-of-fit values (y-axis): impacts only small
How well is extreme water quality modelled using climate drivers?
DO BOD Temp Chl-a
Run I (2009-10) 0 0 0 27
30 year RCM/FFH 7.1-21.7 0.2-4.3 3.7-17.5 23.2-44.7
Run I (2009-10) 0 33 1.5 99.5
30 year RCM/FFH 1.7-9.2 13.0-36.9 1.7-17.4 75.9-103.0
Eynsham
Wallingford
• For RCM, 1961-90 is taken as a standard period indicative of
present day. RCM (and FFH) do not reproduce “real weather”.
• Days per year when undesirable thresholds exceeded (WFD-
relevant conditions: DO < 6 mg L-1, BOD > 4 mg L-1, Temp >
25 ºC, Chl-a > 0.03 mg L-1):
• When using climate model drivers the frequency of incidence
of extreme conditions is probably overestimated. Why?
Water quality is most vulnerable at low flows in summer
Flow Q95 (m3s-1) Eynsham Days Lock
1961-90 observed 1.17 3.36
2009-10 observed 1.44 4.02
Run I (2009-10) 2.12 4.11
Run II (2009-10) 0.68 3.06
Run III (2009-10) 1.33 3.47
30 year RCM/FFH 0.10-0.78 1.18-2.51
• Lowest flows are underestimated when using RCM/FFH
• Analysis of RCM outputs and climate records suggest
the highest air temperatures simulated by the models are
unfeasibly extreme.
• Climate model drivers suggest even in present day
conditions the Thames above Oxford is vulnerable to
drying out. This is not realistic.
Summary results
1. The increase
represents the future
2040-69 situation
relative to present day.
2. The bar represents
the mean of changes
seen from the 11
applications of the
model chain
3. Error bars represent
the maximum and
minimum change.
• Changes in drivers by the 2040-69 period (Wallingford): + 3-5 ºC 90th percentile (i.e. summer) air temperature
+ 4-10% solar radiation (70th percentile)
- 25% Q95 flow i.e. summer low flow (range: +7.3 to -41.3)
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DO BOD Temp chl-a
Incr
ease
in d
ays
per
yea
r
Wallingford
-5
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DO BOD Temp chl-a
Incr
ease
in d
ays
per
yea
r
Eynsham
Threshold values:DO = 6 mg/LBOD = 4 mg/LTemp = 25 Cchl-a = 0.03 mg/L
• Whilst simulations derived from RCM applications
appear reliable across the inter-quartile range (and to a
large degree to 5th and 95th percentile levels), the most
extreme conditions are not simulated reliably.
• The future projections should not be presented as
absolute indicators of water quality, rather as a change
relative to present day conditions.
• Accelerated phytoplankton growth in future will lead to
more limitation (including self-shading) and greater risk
of blooms crashing, leading to possible DO sags.
• Uncertainty in model chain:
Conclusions
Climate
modelling
Water quality
modelling
Hydrological
modelling