Scoping the impact of tidal and wave energy extraction on suspended sediment and the underwater light climate

Transcription

1 Scoping the impact of tidal and wave energy extraction on suspended sediment and the underwater light climate Michael Heath 1, Alessandro Sabatino 1, Natalia Serpetti 2, David McKee 1 & Rory O Hara Murray 3 1. Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK 2. Scottish Association for Marine Science, Oban, 3. Marine Scotland Science, Aberdeen EPSRC TeraWatt Project MASTS Annual Science Conference 2015

2 Light penetration into the sea is a key property of a marine ecosystem Euphotic zone Refuge from predators In coastal waters turbidity is due mainly to suspended particulate mineral material

3 Factors involved in determining suspended particulate material (SPM) concentrations Suspended sediment Grain size Fall velocity Flocculation/aggregation Hydrodynamics Vertical diffusivity Bed shear stress Current velocity profile Wave orbital velocities Potentially affected by energy extraction Seabed sediments Grain size composition Cohesion Consolidation Compaction Bedform architecture

4 Modelling SPM concentrations in the sea a 3-D problem (unlike bed-load transport) No analytical basis for many of the processes involved Shear thresholds for particle mobilisation in mixed sediments Sinking speed of roughly shaped particles and flocculation Sediment consolidation Heavy reliance on empirical parameterisations Implementation of SPM within 3-D hydrodynamic schemes is certainly possible e.g. MIKE 3, Delft-3D, POLCOMS, FVCOM But cumbersome and very computationally intensive. Parameter optimisation practically impossible even with big investment of resources. Hence many investigators have gone for 1-D vertical models assumes that most of the SPM arises from vertical processes not horizontal transport Cheaper and permits vastly more intensive model development, fitting to observed data, and sensitivity analysis. If we re only interested in concentrations, not horizontal transport, then we can focus on the vertical processes

5 TeraWatt 1-D model of suspended sediment concentration C z(t) = C b(t) z ω s C b(t) γ κ u a(t) SPM concentration at altitude z above the seabed at time t SPM concentration at a reference altitude 1m above the seabed at time t = * -(Rouse number) altitude Rouse number SPM concentration at a reference altitude 1m above the seabed at time t = Sinking speed C b(t) = α S ε β E (t) * Von Karman constant reference altitude concentration τ a(t) δ Proportionality Sediment mud Sediment = constant * content * erodibility * * bed shear velocity Time-lag averaged seabed shear stress

6 Field data to parameterise the model - Marine Scotland monitoring programme at Stonehaven CTD/turbidity data: 1min intervals over 4 days (1 site) Daily intervals over a week (1 site) Monthly intervals over 1 year (7 sites) Weekly intervals over 3.5 years (2 sites)

7 Using the field data to parameterise the model Hydrodynamics MIKE 3 FM tidal current speed MIKE 21 SW wave height/period/direction Validated against Aberdeen tide gauge, RCM data and Firth of Forth Wavenet buoy data -> Bed shear stress at 30min intervals, 3.5 years, at each sampling site (HR Wallingford algorithm) Fitted values and s.e. of 9 parameters in the model Model turbidity derived for every sampled location/date/time/depth Numerical optimization Seabed Surveys Bathymetry Median grain size & mud content Turbidity profile sampling Total 34,313 observations Calibration subset 12,044 data Validation subset 22,269 data

8 Modelled and observed vertical profiles of turbidity Median, and 5 th 95 th centiles of turbidity at 0.5m altitude intervals at each site

9 Main sampling site, time series of depth averaged current speed and significant wave height

10 Modelled and observed turbidity at two depth horizons at the main sampling site

11 Sensitivity to seabed sediment properties Turbidity increases with seabed mud content Strong seasonal effect of sediment erodibility

12 Exploring the sensitivity to energy extraction Annual average tidal power at the main site = 20.5 KW.m -1 Annual average wave power = 7.4 KW.m -1 Scenario 1 Extract 3.7 KW.m -1 as wave energy = 29% reduction in wave height Scenario 2 Extract 3.7 KW.m -1 as tidal energy = 6.5% reduction in depth averaged current speed Scenario 3 Attenuate the depth averaged current speed by 50% = removal of 87.5% of power Question: Would we be able to detect the changes in turbidity that these extractions cause in the model, given the fit of the model to the real-world observations?

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14 Summary We ve built and parameterised a model that can be used to scope the sensitivity of SPM (turbidity) to changes in current and wave conditions. We applied the model to east coast Scotland because that s where we have a large set of data There are no known in-situ measurements of turbidity in the PFOW that we could use to parameterise the model for that region But there are satellite remote sensing data

15 Satellite images processed to reveal sea surface suspended sediment concentrations Winter Summer 02/01/ /08/2007 Time series of satellite-derived SPM at Stonehaven match really well with the in-situ turbidity data Maybe we can use satellitederived SPM from PFOW to calibrate our model

16 Realistic expectations from SPM models Individual device effects such as in this image are due to increased turbulence in the wake, not changes in mean flow. Our model cannot address this Landsat image of sediment sea surface suspended sediment in the Thames Estuary showing wake effects associated with wind turbine pylons Our model is more suited to addressing changes in the larger scale mean flow by the array as a whole