Predicting algal growth under climate change in the upper Thames Mike Hutchins, CEH Wallingford

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1 Predicting algal growth under climate change in the upper Thames Mike Hutchins, CEH Wallingford (plus Richard Williams, Christel Prudhomme, Sue Crooks)

2 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

3 ... 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

4 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 semiempirical, process-based model of river networks

5 QUESTOR river quality model (Thames) Model inputs: (1) Flow and quality data in (a) tributaries (b) effluents from sewage works, (2) Solar radiation Upstream QUESTOR boundary CEH weekly water quality ( ) Major urban areas outside London Eynsham Wallingford LONDON Represents biochemical interactions in the river channel environment; and energy balance for water temperature Tidal limit

6 upper quartile chl-a (µg/l) 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 River Thames ( ) CEH Thames Initiative data QUESTOR model distance downstream (km) Effect of increasing residence time Wallingford (92 km downstream) Chlorophyll-a content of different types of phytoplankton is known, making it a useful surrogate for biomass

7 Phosphorus: µg SRP/L Nitrogen: mg NO 3 -N/L Flow (m 3 s -1 ) Water temp ( o C) QUESTOR calibrated in (e.g. Eynsham) Is model simulating physical/chemical parameters well? For algae, good summer flow/temp simulation is critical N P Simulated Observed Temp Flow 0 0 Jan-2009 Apr-2009 Jul-2009 Oct-2009 Jan-2010 Apr-2010 Jul-2010 Oct-2010 Jan

8 Model performance at Abingdon in 2009 Limitation due to light Nutrients are in excess Phytoplankton biomass (mg chl-a/l) High flows wash phytoplankton out of system An unexplained mid- to late- summer suppression of phytoplankton is apparent

9 Comparing 2009 & 2010: simulated blooms similar 1. However, large variations observed between years. Far more phytoplankton in So a 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

10 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?

11 What are impacts of flooding on water quality? Wallingford Dec 2012 July 2007 floods resulted in low DO (Oxford Reading)

12 Many potential sources of uncertainty 5 Had-RM3 Perturbed Physics Ensemble Climate Model Rainfall PE Air temp Solar radiation Bias correction Downscaling Key sources to isolate 4 3 Rainfall-runoff Model (CLASSIC, CERF) Donate and scale flows to unmodelled tribuaries Flow Regression Water temp Attenuation by trees and in water column Photosynthetically active radiation 2 Pollutant loads from tributaries (and STWs) 1 Water Quality Model (QUESTOR) DO BOD nutrients Phytoplankton biomass (chl-a)

13 Uncertainty due to hydrological modelling Only 5 of the 11 gauged tributaries were modelled under the FFH project - so, 3 runs: I. A baseline QUESTOR model, set up using all available flow data ( ) 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)

14 Errors due to donating (II) & modelling (III) flows 1 1 Run I Run I Run II Run II 0.8 Run III 0.8 Run III Flow DO Temp Flow DO Temp Eynsham Wallingford Nash-Sutcliffe goodness-of-fit values (y-axis): impacts only small

15 How well is extreme water quality modelled using climate drivers? For RCM, 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 (WFDrelevant conditions: DO < 6 mg L -1, BOD > 4 mg L -1, Temp > 25 ºC, Chl-a > 0.03 mg L -1 ): Eynsham Wallingford DO BOD Temp Chl-a Run I ( ) year RCM/FFH Run I ( ) year RCM/FFH When using climate model drivers the frequency of incidence of extreme conditions is probably overestimated. Why?

16 Water quality is most vulnerable at low flows in summer Flow Q95 (m 3 s -1 ) Eynsham Days Lock observed observed Run I ( ) Run II ( ) Run III ( ) year RCM/FFH 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.

17 Increase in days per year Increase in days per year Summary results Changes in drivers by the period (Wallingford): ºC 90 th percentile (i.e. summer) air temperature % solar radiation (70 th percentile) - 25% Q95 flow i.e. summer low flow (range: +7.3 to -41.3) Eynsham Threshold values: DO = 6 mg/l BOD = 4 mg/l Temp = 25 C chl-a = 0.03 mg/l DO BOD Temp chl-a Wallingford DO BOD Temp chl-a 1. The increase represents the future 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.

18 Conclusions Whilst simulations derived from RCM applications appear reliable across the inter-quartile range (and to a large degree to 5 th and 95 th 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: Climate modelling Water quality modelling Hydrological modelling