chapter 14 - project 2e - University of Waikato · accumulation produced by GNOME were compared to a verification database, which was produced using data on observed oil accumulation

RENA Environmental Recovery Monitoring Programme 397 | P a g e

Project 2e - Understanding the role of estuarine-shelf

exchange in controlling the hydrodynamic dispersal

and mixing of oil Hannah Jones, Karin Bryan, Julia C. Mullarney, Willem De Lange

Executive summary

Oil-spill dispersal over the month following the Rena grounding was hindcast using a combination of

hydrodynamic and oil-dispersal modelling. Tidally-driven currents were modelled on the Bay of

Plenty shelf and in Tauranga Harbour using three-dimensional hydrodynamic models Delft3D and

ELCOM, respectively. Hydrodynamics were calibrated and verified using observations from the Bay

of Plenty Regional Council, the Port of Tauranga and past studies by the University of Waikato.

Water level was well predicted by both models (with a mean absolute error of 0.08 m) and currents

were predicted well within the Harbour and less well on the shelf. The modelled currents were used

as forcing for the NOAA oil-spill model GNOME, along with observed wind speed and direction

collected at the Tauranga Airport meteorological station.

Oil was predicted to reach the shoreline on October 13th (8 days after the grounding), which was

consistent with SCAT surveys. Hot spots of oil accumulation occurred on the northwest side of

islands and headlands, as the oil dispersed southward along the coast, also consistent with SCAT

surveys. Hot spots of oil were also predicted along the open coast beaches, but were not very well

aligned with observations, which could be because the surf-zone and rip-current circulation was not

represented in this implementation of the model. Accumulation of oil was also predicted around Mt

Maunganui, which was caused by eddies forming on the tidal jet depositing oil onto the headland.

Within the Harbour, the highest accumulation of oil was predicted in Pilot Bay, which was caused by

the anti-clockwise eddy inside the harbour. These predictions were qualitatively supported by

observations from the SCAT surveys.

Model predictions were sensitive to assumptions about the timing and volume of oil released during

the grounding, which was difficult to measure with any accuracy. The dispersal of oil (both observed

and modelled) is highly dependent on the prevailing wind patterns, and more accurate prediction of

oil dispersal and accumulation would require better observations of local wind patterns.

Nevertheless, the qualitative comparison of predictions with observations indicated that the GNOME

modelling approach was an effective low-cost tool. Moreover, Delft3D and GNOME are both open

source and freely available software packages.


1. Introduction

On the 5th of October, 2011, the 47,000 tonne container ship “Rena” ran aground on the Astrolabe

Reef on the approach to Tauranga Harbour. There were 1,733 tonnes of oil on board the Rena, of

which 1,300 tonnes have been recovered as of May 4th 2012 (source: Maritime NZ). During the

week after the grounding, 350 tonnes of heavy (Bunker C) fuel oil were released onto the shelf,

where the oil was subjected to tidal currents, wind-induced currents, and shelf currents, which both

advected and dispersed the oil, with a large fraction stranded on Waihi Beach, Matakana Island,

Mount Maunganui Beach, Pāpāmoa Beach, and lesser amounts being dispersed farther afield. A

small fraction entered Tauranga Harbour and Maketū Estuary. A key characteristic of the oil

accumulation that was found was that it was not evenly dispersed, but rather oil was concentrated

in “hot spots” by the hydrodynamic processes.

One of the tools that we use to assess the potential extent of a spill is numerical oil spill modelling.

This approach allows mobilisation of response teams and clean-up equipment at the right spatial

scale to match the scale of the problem. A number of institutions provided oil-spill response forecast

modelling immediately following the grounding, led mainly by MetOcean Solutions, followed by

NIWA and ASR Ltd. Such models have two components, a hydrodynamic driver and a particle

tracking model (potentially with some ability to model oil decomposition during transport). These

models can provide widely varying predictions depending on how the wind and coastal currents are

incorporated; areas around the mouths of estuaries, where estuarine water interacts with the shelf,

are particularly hard to model. Although it is relatively straightforward to provide a quick prediction

of dispersal, to provide a correct prediction requires careful calibration and verification against

observations. Ideally, a portion of the groundwork should be undertaken in advance of a spill, as part

of a hazard management programme. Given that the Port of Tauranga operations are highly-

successful, and likely to continue to grow, some of the Rena recovery plan should be focussed

toward a better oil spill hazard management plan for the region, underpinned by verified

hydrodynamic modelling.

Study Aim and Objectives

The aim of this work is to undertake dispersal modelling of the area stretching from the northern

end of Matakana Island to the entrance of the Maketū harbour, and including the southern basin of

Tauranga Harbour and the Maketū Estuary, and to verify the results against information on oil

recovery collected by Maritime New Zealand.

This aim is divided into 4 sub-objectives:

� Work with summer student from the “Debris Dispersal Modelling Proposal (PI: de Lange,

hereafter caller ‘the Debris Project’)” to collect and collate measures of oil accumulation

into a spatial map, in collaboration with Maritime New Zealand and other parties that

were instrumental in the clean-up operation.

� Undertake shelf dispersal modelling, and compare the results against patterns of coastal

accumulation. Investigate the processes responsible for “hot spots” of accumulation.

� Undertake estuarine dispersal modelling using an entrance point source, and compare

against patterns of estuarine accumulation.

� Assist the MSc student from the Debris Project to run a number of scenarios to see how

generalizable the results from the Rena grounding would be to a grounding in the

vicinity of the main navigation channel.

This report summarises the oil spill mapping and the dispersal modelling. The MSc project, which will

investigate scenarios, has just started and is due for completion in 2015, when the student finishes

their thesis.


2. Methods

Hydrodynamic models were used to simulate currents on the open coast (shelf) and inside Tauranga

Harbour. The shelf modelling was undertaken using Delft3D and the estuarine modelling using

ELCOM. The output from these models was then used as forcing in a particle tracking model

(GNOME) to simulate oil dispersal following the Rena grounding. Maps of simulated coastal oil

accumulation produced by GNOME were compared to a verification database, which was produced

using data on observed oil accumulation provided by Maritime NZ and collated by Laura Hines

(summer student from the Debris Project).

2.1 Study area

The container vessel Rena ran aground on Astrolabe reef (c. 7 km north of Mōtītī Island, and c. 24

km northeast of Mt. Maunganui) on approach to the Port of Tauranga on 5th October 2011 (Figure

1). Approximately 350 tonnes of heavy fuel oil leaked from the vessel over the following days and

weeks, a portion of which was deposited on Mōtītī Island, Matakana Island and on the coastline

between Mt. Maunganui and Maketū Estuary. A small amount of oil entered Tauranga Harbour and

Maketū Estuary (Maritime NZ, 2011). The Bay of Plenty coastline is a low-energy wave environment,

sheltered from the prevailing westerly and south-westerly swell that affects other areas of New

Zealand (Heath, 1985). Tides (at Tauranga) range between 1.62 m for spring tides and 1.24 m for

neap tides, and tidal currents on the shelf are typically stronger than any other flow component

(Heath 1985).

Figure 1: Western Bay of Plenty coastline. The site of the Rena grounding (Astrolabe reef) is marked with a red star.

k

176°40'0"E176°30'0"E176°20'0"E176°10'0"E176°0'0"E

37°2

0'0"

S37

°30'

0"S

37°4

0'0"

S37

°50'

0"S

±

0 5 10 15 202.5Kilometers

Motiti Island

Mayor Island

Mt. Maunganui

Papamoa

Maketu

Tauranga

Matakana Island

Waihi Beach

Astrolabe reef

Pukehina


2.2 ELCOM

2.2.1 ELCOM model description and set-up

ELCOM (Estuary, Lake and Coastal Ocean Model) is a three-dimensional hydrodynamic model used

to simulate velocity, temperature and salinity distribution in water bodies. ELCOM has been

developed by the Centre for Water Research at the University of Western Australia, and is based on

the unsteady, viscous Navier-Stokes equations for incompressible flow (Hodges and Dallimore 2011).

In this study, ELCOM was run with a 30 second time step between 1st October 2011 and 31st October

2011. This simulation includes the period of time during which most of the oil was lost from Rena,

and was observed to strand on beaches or enter the estuary (MaritimeNZ 2011). As the simulation

began from a cold start (i.e. flat water level and no currents) there were 4 days of model “spin up”

time before the Rena grounding on the 5th October 2011, when results were extracted for use in the

particle tracking model (GNOME).

The ELCOM model set-up used in this study, along with extensive calibration and validation, is

described in detail in Tay et al. (in press.). The ELCOM bathymetric grid (75 x 75 m resolution) was

constructed from a number of sources, including University of Waikato echo-sounder surveys, Port

of Tauranga hydrographic soundings, and navy fairsheets (Figure 2). The ELCOM model domain

includes the southern basin of Tauranga Harbour, an area of c. 95 x 106 m2 at mean sea level.

Although the southern basin is connected to the northern basin of the harbour, there is little or no

exchange of water between them (Tay et al. in press. and references therein). ELCOM model vertical

layer thickness (n = 12) varied between 0.5 m at the surface to 5 m at the seabed.

Figure 2: Bathymetry grid for Tauranga Harbour ELCOM model including the location of water level recorders and ADCP.

Water levels along the open ocean boundaries (outside the southern entrance) were derived from

NIWA’s tidal model (http://www.niwa.co.nz/services/online-services/tide-forecaster). Water

temperature for the open boundaries was derived from measured water temperature from Bay of

Plenty Regional Council’s wave buoy, which is 13 km offshore (37.411°S, 176.371°E). Salinities for the

open boundaries were derived from offshore transect data collected in October 2003, assuming that

offshore salinity was the same in October 2003 as in October 2011. Freshwater inflows into the

harbour are the Wairoa River, Waipapa River, Waimapu Stream, Kopurereroa Stream, Te Puna

Stream, Wainui River and Aongatete River, which were prescribed using BoPRC continuous flow data

(where available) and design hydrographs. Hourly meteorological data (i.e. rainfall, wind speed and

direction, solar radiation, air temperature, relative humidity and cloud cover) for Tauranga airport

were acquired from NIWA’s Cliflo service (National Institute of Water and Atmospheric Research

National Climate database, http://cliflow.niwa.co.nz).


2.2.2 ELCOM validation

The ELCOM model has already been calibrated and subject to extensive validation (Tay et al. in

press.). However, as a further validation exercise, data were extracted from model output for

comparison with field data collected in, and outside, the harbour. Continuous measurements of tidal

elevation were provided by BOPRC and Port of Tauranga for three stations inside the harbour

(Ōmokoroa, Sulphur Point and Tug Berth) and by Port of Tauranga for one station outside of the

harbour (A Beacon). ADCP current meter data collected in the harbour entrance were also provided

by Port of Tauranga. The model error was represented by model performance statistics; the

coefficient of determination (R2) and the mean absolute error (MAE).

2.3 Delft3D

2.3.1 Delft3D model description and set-up

Delft3D-FLOW, developed by Deltares (The Netherlands), is a hydrodynamic simulation programme

that calculates non-steady flow from tidal and meteorological forcing on a rectilinear or curvilinear

boundary fitted grid. Delft3D-FLOW may be implemented in 3D, with the vertical grid defined using

the sigma (σ) co-ordinate approach, or in 2D using a depth-averaged approach (2DH) (Deltares,

2011). The model has been extensively validated (e.g. Elias et al. 2000; Lesser et al. 2004).

In this study, the Delft3D model domain includes both the northern and southern basins of Tauranga

harbour. The grid extends c. 25 km offshore (to a water depth of around 70 – 90 m) and c. 61 km

along shore from Waihi to Maketū. The grid resolution is 200 x 200 m in the horizontal and the grid

is rotated at an angle of 50° to the North-South axis (Figure 3). Bathymetry was derived from various

sources, such as echo-soundings and nautical charts (Kwoll, 2010). Preliminary simulations indicated

improved simulation of surface velocities with a 3D model, compared to a depth-averaged model.

The number and thickness of the layers in the 3D model was determined based on the need for fine

resolution near the bed (to resolve the logarithmic profile of the horizontal velocity components in

the vertical) and near the surface. Based on these considerations (and the need for the layer

thickness to have a smooth distribution) the 3D model has 16 σ-layers, with layers thicknesses (bed)

1.5, 2, 2.8, 4, 5.6, 7.8, 11, 15.3, 15.3, 11, 7.8, 5.6, 4, 2.8, 2, and 1.5 (surface) % of total depth.

Figure 3: Bathymetry grid for Delft model. Open boundaries indicated with solid black line.


Hydrodynamic simulations using the Delft3D was forced at the open boundaries using the tidal

constituents M2, N2, S2, K1, MU2, O1, and L2. Time series of water levels at the four corners of the

open boundaries were derived from the NIWA tidal model (http://www.niwa.co.nz/services/online-

services/tide-forecaster). The tidal constituents were extracted from the water levels using the tidal

harmonic analysis package, “t_tide” (Pawlowicz et al. 2002). Preliminary simulations with the open

boundaries forced by water levels were found to produce instabilities (eddies and areas of very high

flow) caused by reflections at the boundaries. To overcome this, the flow was forced using

astronomic Riemann boundary conditions for all tidal constituents at the open boundaries. Riemann

boundary conditions are based on a linearised Riemann invariant:

�� = � + �� , (Units m s-1)

where, U is velocity, η is sea surface elevation, g is acceleration due to gravity, and h is water depth.

This type of boundary reduces reflection of obliquely incident waves and is non-reflective for

outgoing waves normal to the boundary (e.g. Mullarney et al. 2008). Free slip (no shear stress)

conditions were applied at the closed boundaries, and the vertical velocity profile at the boundaries

was a logarithmic function of the water depth. Wind forcing was not applied to the Delft3D

hydrodynamic model as wind forcing was applied in the particle tracking model, GNOME.

2.3.2 Delft3D calibration

The simulation began on 1st October 2011 from a “cold start” (i.e. uniform water levels, no currents)

and ran for 30 days with a 60 s time step. To reduce numerical instabilities in the initial adjustment

period, the boundary forcing was gradually applied over a smoothing period of 60 min. The model

was calibrated using water level and current data provided by BOPRC and Port of Tauranga (i.e. sites

at A Beacon, Sulphur Point, Tug Berth, Ōmokoroa and the harbour entrance ADCP) by adjusting the

roughness coefficient, threshold depth, and horizontal eddy viscosity.

Preliminary simulations with a 2DH model were used to estimate the Chézy coefficient for the 3D

model. In the 2DH model the shear stress at the bed is assumed to be given by a quadratic friction

law,

�� = �� ,

where U is the magnitude of the depth-averaged horizontal velocity and C2D is the 2D-Chézy

coefficient (m ½ s-1). In Delft3D the Chézy coefficient can be prescribed as a uniform or spatially

varying value or determined using a Manning’s or White Colebrook’s formulation (Deltares, 2012).

In the 3D model a quadratic bed stress formulation using the velocity just above the bed is used to

calculate shear stress,

�� =��

�� ,

where ub is the magnitude of the horizontal velocity in the first layer above the bed and C3D is the 3D-

Chézy coefficient (m ½ s-1). This formulation assumes that the first grid point above the bed is within

the logarithmic boundary layer. Thus, the vertical layer structure must be configured to allow

simulation of a log layer. The 3D-Chézy coefficient can be estimated from the 2D-Chézy coefficient,

firstly by conversion of C2D to the bed roughness length (z0).

�� = � × �� !− #+$�� × %√'(),


where H is the total water depth (m), k is the von Karman constant (0.41) and C2D is the 2D-Chézy

coefficient (m ½ s-1). Then, the 3D-Chézy coefficient (C3D) can be calculated from z0 (bed roughness

length) as

�� =√% × ln $1 +�..×/012 ' ,

where σb is the depth of the computational layer at the bed and k is the von Karman constant.

As with the ELCOM model, Delft3D model performance was statistically assessed.

2.3.3 Delft3D validation

Parameters resulting from the Delft3D model calibration were fixed for a further month-long

simulation for the purposes of model validation. Water level and current data collected at five sites

inside and outside the harbour between 10th May and 10th June 2006, described in detail in Spiers et

al. (2009), were used for model validation. Briefly, tidal elevation, current velocity and direction

were measured at three sites outside (C1, C2 and C3) and one site inside (C5) Tauranga Harbour.

Station C4 was not used as it was found to contain erroneous data (Spiers et al. 2009). C1 was at 27

m water depth, outside of the influence of the ebb jet, C2 was at 11 m water depth in the pathway

of the ebb jet and C3 was at 6 m water depth on the ebb-tidal delta. C5 was inside the harbour, in

the western channel, at 9m water depth (Figure 4).

Figure 4: Map of current meter locations taken from Spiers et al., 2009.


2.4 GNOME

2.4.1 GNOME model description and setup

GNOME (General NOAA Oil Modeling Environment) is an oil spill trajectory model that simulates the

movement of oil due to winds, currents and spreading (NOAA 2012). GNOME is freely available and

may be run in “standard mode”, an automated mode used for spill planning, and “diagnostic mode”,

designed to accurately model real spills. In standard mode GNOME uses “location files” (pre-

packaged tide and current data) to estimate the trajectory of spills. The spill trajectory includes both

a “best guess” and a “minimum regret” solution that takes into account the uncertainty in the model

inputs. Location files are not available for the Bay of Plenty coastline, and the aim of this study was

to model oil dispersal after the Rena grounding in October 2011, requiring GNOME to be run in

diagnostic mode. Diagnostic mode requires input for shoreline and hydrodynamic information, and

has additional capabilities compared to standard mode, such as control over the degree of

uncertainty used in the minimum regret estimates.

GNOME is a two-dimensional Eulerian/Lagrangian model that requires three major inputs, maps,

movers (current, wind or diffusion) and spills. For this study, output from the hydrodynamic models,

ELCOM and Delft3D, was used as input in GNOME as a current mover. Currently, GNOME can only

use 2D currents (although NOAA is planning on adding capability to use 3D output from

hydrodynamic models). Therefore, surface flows from the ELCOM and Delft3D models were used as

input to GNOME.

2.4.1 Map files

Maps were downloaded for Western Bay of Plenty coastline from the GOODS’ (GNOME Online

Oceanographic Data Server) map generator tool (http://gnome.orr.noaa.gov/goods). This tool

produces shoreline data in a boundary file atlas (BNA) format. BNA maps use vector shorelines which

are rasterized by GNOME into a land and water bitmap in order to track the oil beaching (NOAA

2012).

2.4.2 Movers

Movers in GNOME include currents, wind and diffusion. The movement of the oil is calculated from

the u (east-west) and v (north-south) velocity components, summed for all the movers at each time

step, using a 1st-order Runge-Kutta method. Decreasing the model time step (default is 15 minutes)

may provide accuracy comparable to that using a more complex higher-order method (NOAA 2012).

GNOME accepts various grids and formats for current data. For this study, the hydrodynamic model

(ELCOM and Delft3D) data were extracted and written to NetCDF files for importing into GNOME.

Although both the ELCOM and Delft models use a rectangular grid, the rotation of the Delft grid

caused problems when trying to load Delft currents into GNOME. To overcome this, Delft currents

were loaded as a curvilinear grid, which required that a land mask was also defined in the NetCDF

file.

GNOME allows for wind movers to be defined as constant, time-dependent or time and space

dependent. Time-dependent wind data were available for the simulation period for Tauranga

airport. These data were loaded directly into GNOME for simulations using currents derived from

Delft3D (i.e. for the shelf) and applied spatially uniformly over the entire domain. For simulations

using currents from the harbour model ELCOM (which is forced by tides, freshwater inflows and

meteorological data) the wind mover was not activated in GNOME.

GNOME calculates diffusion of the oil (random spreading) using a simple random walk based on a

diffusion value set in the model. The diffusion value represents the horizontal eddy diffusivity in the

water, which can range from c. 0.1 m2 s-1 to 100 m2 s-1. When GNOME is used in forecast mode this


value can be adjusted based on over-flight data. In this study, the diffusion value was adjusted

within the ranges defined above to improve model accuracy when compared with maps of observed

coastal oil accumulation (see Section 0).

2.4.3 Spills

Oil spills in GNOME can be modelled as up to 10,000 lagrangian elements (LEs), each of which have

parameters assigned including location, release time, age, pollutant type, and status. The status of

each LE may be floating, beached, evaporated or off the map, and a mass balance summary provided

in GNOME shows the proportion of the total spill in each category throughout the simulation.

In this study, the Rena spill was initialised using data from Maritime NZ that described the timing and

amount of oil spilt from the vessel. The Rena grounded on Astrolabe reef (37.540°S 176.425°E) at

2:00 am on 5th October 2011, and approximately 350 tonnes of oil leaked from the ship (typically at

low tide) between then and late October. The majority of the oil likely leaked from the vessel

between 10th and 12th October, when weather conditions were poor (winds speeds typically 5 – 8 m

s-1, with north to north-easterly direction).

2.4.4 GNOME calibration

There are a number of parameters relating to processes such as diffusion, windage or uncertainty in

the input data, which may be adjusted during GNOME model calibration. When run in forecast

mode, GNOME input data should be constantly updated and the model re-run and re-calibrated at

least once a day (NOAA, 2012). When running GNOME in hindcast mode (as in this study) there may

be more certainty related to the input data (which is based on measured data, such as wind, or

calibrated hydrodynamic models), compared to, for example, wind forecasts. However, model

parameters must still be calibrated using observed data.

GNOME simulations for Tauranga Harbour (i.e. using ELCOM model output) and the shelf (i.e. using

Delft3D model output) began on 5th October 2011 (when the Rena grounded and oil was first

observed to be leaking from the ship). The model was calibrated using maps depicting oil observed

in the water and on the shoreline by adjusting the diffusion, windage, refloat half-life and model

time step.

Diffusion in GNOME has been described above in Section 0. Windage is the movement of oil by the

wind, which is typically about 3% of the wind speed (NOAA, 2012). The default value in GNOME is a

random value taken from between 1% and 4%, with a default persistence (the amount of time

before the random value is reset) of 15 minutes. Refloating in GNOME is determined by the refloat

half-life, which is defined as the number of hours in which half of the oil that has beached is

removed by an offshore wind, diffusion or raised water levels (such as an incoming tide). The

adhesiveness of oil to a shoreline is dependent on factors such as oil type, sediment porosity, and

the type, and density of vegetation.

Additional parameters describe uncertainty in the wind, current and diffusion movers, and thus

control the output for the minimum regret LEs. For wind, parameters are used to describe how

much the wind speed and direction are likely to be in error. For currents, parameters describe the

percentage of current velocity the uncertainty spans for both the north-south and east-west current

directions.

Output from the GNOME shelf model was used (when verified with information from Maritime NZ)

to describe an entrance point source for Tauranga Harbour GNOME simulations for the harbour used

ELCOM model output as a current mover, and results were compared with maps of oil accumulation

in the estuary.


2.4.5 Verification of GNOME output with observed data

Data on oil dispersal and accumulation following the Rena grounding was obtained from Maritime

New Zealand in the form of SCAT (Shore Clean-up Assessment Techniques) survey reports and over-

flight observations. Data were extracted and compiled to summarise critical information (where

available), such as the survey/oil location, the beach zone surveyed (e.g., lower, middle, or upper

intertidal), the amount of oil observed (e.g., as percentage cover on the shoreline), and oil character

(e.g., tar balls, tar patties).

From the SCAT data, two metrics were calculated for each day in October following the grounding: i)

the average oil percentage cover (averaged across the intertidal zones for each location surveyed

and averaged across all locations surveyed) and ii) the maximum oil percentage cover. Data on the

quantity of oil removed from the beaches was insufficient to estimate the actual amount (in tonnes)

of oil accumulated on the shoreline, thus it was not possible to quantitatively compare GNOME

output (tonnes of oil accumulated on the shoreline) with SCAT data (percentage cover of oil on the

shoreline). However, GNOME output and SCAT data were compared to assess model accuracy with

respect to the timing and location of oil accumulation.

Several maps of oil dispersal (in the water) and accumulation (on the shoreline) were also provided

by Maritime NZ that summarised the SCAT and over-flight observations. These maps were compared

with GNOME model output.


3. Results and discussion

3.1 ELCOM

3.1.1. ELCOM modelled water level and tidal variations

The Tauranga Harbour ELCOM model had already been subject to extensive calibration and

validation (Tay et al. in press.), and we used the calibrated model parameters obtained in their

study. For the period 1st October – 31st October 2011 modelled and measured water levels for the

four stations (A Beacon, Tug Berth, Sulphur Point and Ōmokoroa) were in good agreement (R2

between 0.95 – 0.98 and MAE between 0.07 – 0.09 m for all locations;table 23). Time series

comparisons of measured and modelled water levels for 5 days are provided in Figure 5.

Tidal constituents were extracted from the modelled and measured water levels using a freeware

tidal harmonic analysis package, “t_tide” (Pawlowicz et al. 2002). The M2 tide dominated both the

modelled and measured tides (c. 0.7 m), with the N2, S2 and K1 tides contributing c. 0.15, 0.10 and

0.05 m to the remaining signal, respectively. Hence, the ELCOM model predicted tidal amplitudes

well. The M2 tide was slightly under-predicted, by between c. 0.02 and 0.08 m, the N2 and S2 tides

were under-predicted by up to 0.04 m, and the K1 tide was over-predicted by 0.01 m (table 2). The

model predicted phases well (i.e. within 3 degrees) for the M2 tide at all sites. The model tended to

overpredict the phase of the N2 and S2 tides near the harbour entrance (by between 3 and 10

degrees), and for the K1 and O1 tides the model tended to under-predict the phase (by c. 10

degrees) in the inner harbour (i.e. at Ōmokoroa).

Table 1: Statistical comparison of measured and ELCOM modelled water levels. R2 is the coefficient of determination and MAE is the mean

absolute error.

Water levels

Site R2 MAE (m)

A Beacon 0.983 0.075

Tug Berth 0.977 0.074

Sulphur Point 0.978 0.083

Ōmokoroa 0.954 0.094

Table 2: Statistical comparison of measured and ELCOM modelled tidal amplitude and phase.

Tidal constituents

Site M2

amp

error

(m)

M2

phase

error

(deg)

N2

amp

error

(m)

N2

phase

error

(deg)

S2

amp

error

(m)

S2

phase

error

(deg)

K1

amp

error

(m)

K1

phase

error

(deg)

O1

amp

error

(m)

O1

phase

error

(deg)

A Beacon -0.02 -1.16 0.03 6.03 -0.02 5.09 0.02 7.02 0.00 -0.29

Tug Berth -0.02 2.92 0.03 10.06 -0.02 11.58 0.02 8.37 0.00 4.35

Sulphur Point -0.06 3.23 0.01 9.08 -0.01 7.79 0.02 -8.07 0.01 -4.07

Ōmokoroa -0.08 -2.28 -0.01 0.82 -0.01 0.24 0.00 -11.21 0.01 -15.70


Figure 5: Measured and modelled (ELCOM) water levels for sites inside (Tug Berth, Sulphur Point and Ōmokoroa) and outside (A Beacon)

Tauranga Harbour.

3.1.2 ELCOM modelled currents

Measured current velocities from the Port of Tauranga ADCP were depth-averaged from 0.5 to 7 m

depth (Lance Wood, pers. comm.) and so are not directly comparable to either the surface or depth-

averaged ELCOM output (as the total depth at the ADCP site is approximately 18m). Unsurprisingly

therefore, the depth-averaged modelled velocities were consistently lower than the measured

velocities (mean measured 1.1 m s-1, mean modelled 0.7 m s-1, MAE 0.43 m s-1). However, peak tidal

velocities (depth-averaged) for the harbour entrance have been reported previously to be 1.8 m s-1

in Vennell (2006) and 1.7 m s-1 in Spiers et al. (2009), which compares well with the peak velocity in

the depth-averaged model output (1.6 m s-1).

The ELCOM surface sheet velocities were in better agreement with the ADCP velocities (mean

measured 1.1 m s-1, mean modelled 1.24 m s-1, MAE 0.20 m s-1), although velocities were sometimes

over-predicted by the model (by c. 0.2 m s-1; table 3, figure 6). The peak velocity from ELCOM surface

output was 2.7 m s-1, which is slightly greater than the peak measured velocity of 2.5 m s-1.

Modelled current directions compared well with measured data from the Port of Tauranga ADCP,

with better agreement between measured and modelled data on the flood tide (MAE 16 degrees) ,

than on the ebb (MAE 37 degrees) (Table 4, Figure 7). The model also captured the high ebb tide

velocities, compared to the flood (1.48 m s-1 at the ebb cf. 1.10 m s-1 at the flood, for the measured

data, and 1.54 m s-1 at the ebb cf. 1.16 m s-1 at the flood, for the model).

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1A Beacon

Wat

er le

vel (

m)

Date

Measured water levelModelled (ELCOM) water level

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Tug Berth

Wat

er le

vel (

m)

Date

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Sulphur Point

Wat

er le

vel (

m)

Date05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Omokoroa

Wat

er le

vel (

m)

Date


Table 3: Statistical comparison of measured and modelled (ELCOM) current velocities at Port of Tauranga ADCP site. R2 is the coefficient of

determination, MAE is the mean absolute error, Meanobs is the mean of the ADCP measurements, Meanmod is the mean of the model

output.

Current velocity

R2 MAE (m s

-1) Meanobs (m s

-1) Meanmod (m s

-1)

ELCOM (depth-averaged) 0.91 0.43 1.1 0.7

ELCOM (surface) 0.87 0.20 1.1 1.24

Figure 6: Measured and modelled (ELCOM depth averaged and surface) current velocity at the harbour entrance (Port of Tauranga ADCP

site).

Figure 7: Measured (blue circles) and modelled (ELCOM surface; red circles) current velocity (m s

-1) and direction (degrees) at the harbour

entrance.

05/10 06/10 07/10 08/10 09/10 10/100

0.5

1

1.5

2

2.5V

eloc

ity (m

/s)

Date

Measured VelocityModelled (ELCOM-depth averaged) VelocityModelled (ELCOM-surface) Velocity


Table 4: Statistical comparison of measured and modelled (ELCOM surface) current speed and direction at Port of Tauranga ADCP site.

MAE is the mean absolute error, Meanobs is the mean of the measurements and Meanmod is the mean of the model output.

Speed (m s-1

) Direction (degrees)

Meanobs Meanmod MAE Meanobs Meanmod MAE

Total 1.10 1.24 0.20 - - 26.70

Flood 1.10 1.16 0.17 145.77 161.66 16.19

Ebb 1.48 1.54 0.21 8.00 330.72 37.12

3.2 Delft3D

3.2.1 Delft modelled water level and tidal variations

The Delft3D model was calibrated by adjusting the bottom roughness coefficient, threshold depth,

and horizontal eddy viscosity. Final calibrated parameters are detailed in table 5.

Estimation of the 3D-Chézy coefficient from the 2DH-Chézy coefficient did not result in good

agreement between measured and modelled data, requiring further 3D model calibration. The final

calibrated 3D-Chézy coefficient was spatially variable, ranging from 80 m1/2 s-1 (shallow water) to 40

m1/2 s-1 (deep water), corresponding to a roughness length (z0) that increased with depth from c.

0.0001 m to 0.002 m. These values of z0 are consistent with those provided in the literature, for un-

rippled mud/sand and rippled sand (e.g. Soulsby 1997).

Table 5: Calibrated model parameters for the Delft3D model.

Parameter Value Units

Threshold depth 0.02 m

Horizontal eddy viscosity 1 m2 s

-1

Vertical eddy viscosity 0 m2 s

-1

Turbulence model k-ε

Number of layers 16

Thickness of layers 1.5, 2, 2.8, 4, 5.6, 7.8, 11, 15.3, 15.3, 11, 7.8, 5.6, 4, 2.8, 2, 1.5 %

Chézy roughness coefficient Spatially variable (80 – 40) m1/2

s-1

The Delft3D modelled and measured water levels for the four stations used in model calibration (A

Beacon, Tug Berth, Sulphur Point and Ōmokoroa) were in good agreement (R2 0.97 – 0.98, MAE 0.07

– 0.08 m). Model performance for the validation period (R2 0.96 – 0.97, MAE 0.07 – 0.08 m), was

similar to that achieved for the calibration period (Table 6). Time series comparisons of measured

and modelled water levels are provided in Figure 8 and Figure 9.

For the calibration period, the Delft3D model predicted tidal amplitudes to within 0.03 m and

predicted tidal phases for the major tidal constituents (M2, N2 and S2) at sites outside the harbour

and near the harbour entrance to within c. 5 degrees. However, the model tended to under-predict

the phase in the inner harbour (i.e. at Ōmokoroa) by c. 6 - 7 degrees ( table 6).


Table 6 This discrepancy may be partly owing to the location of the Ōmokoroa water level recorder

in a small channel that is not well represented in the Delft3D model bathymetry.

For the validation period, the Delft3D model predicted tidal amplitudes to within 0.04 m and tidal

phases for the major tidal constituents to within c. 5 degrees (i.e. similar performance to the

calibration period; table 6).


Table 6: Statistical comparison of measured and Delft3D modelled water levels. R2 is the coefficient of determination, and MAE is the

mean absolute error.

Model performance statistics for water levels

Model and site Calibration or Validation R2 MAE (m)

A Beacon Calibration 0.982 0.072

Tug Berth Calibration 0.971 0.078

Sulphur Point Calibration 0.974 0.082

Ōmokoroa Calibration 0.965 0.082

C1 Validation 0.968 0.077




Table 7: Statistical comparison of measured and Delft3D modelled tidal amplitude and phase.

Tidal constituents

Model and

site

Calibration

or

Validation

M2

amp

error

(m)

M2

phase

error

(deg)

N2

amp

error

(m)

N2

phase

error

(deg)

S2

amp

error

(m)

S2

phase

error

(deg)

K1

amp

error

(m)

K1

phase

error

(deg)

O1

amp

error

(m)

O1

phase

error

(deg)

A Beacon Calibration -0.01 -1.42 -0.01 1.29 0.01 4.77 -0.01 3.67 -0.01 8.26

Tug Berth Calibration 0.03 -5.64 0.01 -3.69 0.02 1.19 -0.01 0.39 -0.01 9.93

Sulphur Point Calibration -0.01 -4.53 -0.01 -3.82 0.03 -1.24 -0.01 -15.46 0.01 2.89

Ōmokoroa Calibration -0.01 -6.73 -0.01 -6.56 0.03 -6.36 -0.01 -14.68 0.01 17.02

C1 Validation -0.04 -4.23 0.01 1.95 0.03 -0.28 -0.02 -11.77 0.01 13.48

C2 Validation -0.03 -3.89 0.02 2.45 0.03 0.79 -0.02 -13.48 0.01 15.23

C3 Validation -0.02 -4.25 0.02 1.91 0.03 -0.37 -0.02 -13.61 0.01 16.54

C5 Validation 0.00 -6.20 0.03 1.34 0.03 -2.45 -0.02 -14.42 0.01 16.61

Figure 8: Measured and modelled (Delft3D) water levels for calibration sites inside (Tug Berth, Sulphur Point and Ōmokoroa) and outside

(A Beacon) Tauranga Harbour.

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1A Beacon

Wat

er le

vel (

m)

Date

Measured water levelModelled (Delft3D) water level

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Tug Berth

Wat

er le

vel (

m)

Date

05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Sulphur Point

Wat

er le

vel (

m)

Date05/10 06/10 07/10 08/10 09/10 10/10-1

-0.5

0

0.5

1Omokoroa

Wat

er le

vel (

m)

Date


Figure 9: Measured and modelled (Delft3D) water levels for validation sites outside (C1, C2, C3) and inside (C5) Tauranga Harbour.

3.2.2 Delft modelled currents

For the calibration period, modelled current velocities for the Delft3D model were consistently lower

than those measured at the Port of Tauranga ADCP site (MAE 0.41 m s-1; Figure 10; table 8).

Modelled current directions compared well with measured data however, with better agreement

between measured and modelled data on the flood tide (MAE 18 degrees), than on the ebb (MAE 30

degrees) (Table 9, Figure 12). The model also captured the pattern of higher ebb tide velocities,

compared to flood, although the velocities were consistently under-predicted.

Velocities at validation sites C1, C2 and C3 were also consistently lower than measured velocities

(MAE 0.04 to 0.10 m s-1, 52 to 67 % of the average measured current velocity). Velocities were better

predicted at site C5, however (MAE 0.10 m s-1, 22 % of the average measured current velocity; table

30). As with the ADCP (calibration) site, the model captured the pattern of higher ebb tide velocities

at sites inside and near the harbour entrance (i.e. C2, C3 and C5; Table 9). Modelled current

direction compared very well with measured data at site C5 (MAE ebb tide 3.5 degrees and MAE

flood tide 12.8 degrees), with reasonable agreement between measured and modelled data for sites

C1, C2 and C3 (MAE c. 10 – 14 degrees).

20/05 21/05 22/05 23/05 24/05 25/05-1

-0.5

0

0.5

1C1

Wat

er le

vel (

m)

Date20/05 21/05 22/05 23/05 24/05 25/05-1

-0.5

0

0.5

1C2

Wat

er le

vel (

m)

Date

20/05 21/05 22/05 23/05 24/05 25/05-1

-0.5

0

0.5

1C3

Wat

er le

vel (

m)

Date20/05 21/05 22/05 23/05 24/05 25/05

-1

-0.5

0

0.5

1C5

Wat

er le

vel (

m)

Date

Measured water levelModelled (Delft3D) water level


Table 8: Statistical comparison of measured and modelled (Delft3D) current velocities at calibration and validation sites. R2 is the

coefficient of determination, MAE is the mean absolute error, Meanobs is the mean of the measurements, Meanmod is the mean of the

model output. The Delft3D model output is from the appropriate model layer corresponding to the measured depth.

Model performance statistics for current velocity

Site Measurement

depth (m)

Calibration / Validation R2 MAE

(m s-1

)

Meanobs

(m s-1

)

Meanmod

(m s-1

)

ADCP Surface Calibration 0.83 0.41 1.10 0.72

C1 27.1 Validation 0.00 0.04 0.06 0.02

C2 10.2 Validation 0.40 0.10 0.19 0.10

C3 5.8 Validation 0.02 0.10 0.16 0.06

C5 9.5 Validation 0.75 0.10 0.45 0.41

Figure 10: Measured and modelled (Delft3D) current velocity at the mouth of the harbour (ADCP site).

05/10 06/10 07/10 08/10 09/10 10/100

0.5

1

1.5

2

2.5

Vel

ocity

(m/s

)

Date

Measured VelocityModelled (Delft3D-surface) Velocity


Figure 11: Measured (blue solid lines) and modelled (red dashed lines Delft3D) current velocity at validation sites outside (C1, C2, C3) and

inside (C5) Tauranga Harbour.

Figure 12: Measured (blue circles) and modelled (Delft3D- surface; red circles) current velocity (m s

-1) and direction (degrees) at the

harbour entrance (ADCP site).

20/05 21/05 22/05 23/05 24/05 25/050

0.2

0.4

0.6

0.8

1C1

Vel

ocity

(m

/s)

Date

Measured velocityModelled (Delft3D) velocity

20/05 21/05 22/05 23/05 24/05 25/050

0.2

0.4

0.6

0.8

1C2

Vel

ocity

(m

/s)

Date

20/05 21/05 22/05 23/05 24/05 25/050

0.2

0.4

0.6

0.8

1C3

Vel

ocity

(m

/s)

Date20/05 21/05 22/05 23/05 24/05 25/050

0.2

0.4

0.6

0.8

1C5

Vel

ocity

(m

/s)

Date


Figure 13: Measured (blue circles) and modelled (Delft3D; red circles) current velocity (m s

-1) and direction (degrees) at validation sites

outside (C1, C2 and C3) and inside (C5) Tauranga Harbour.

Table 9: Statistical comparison of measured and modelled (Delft3D) current speed and direction. MAE is the mean absolute error, Meanobs

is the mean of the measurements and Meanmod is the mean of the model output.

Speed (m s-1

) Direction (degrees)

Site Calibration /

Validation

Tide Meanobs Meanmod MAE Meanobs Meanmod MAE

ADCP Calibration Flood 1.10 0.72 0.33 145.77 163.88 18.11

Ebb 1.48 0.85 0.61 8.00 337.19 30.48

C1 Validation Flood 0.05 0.02 0.04 240.41 239.51 13.70

Ebb 0.02 0.02 0.01 66.28 64.48 9.13


Ebb 0.29 0.14 0.22 20.71 21.33 11.17


Ebb 0.23 0.09 0.21 334.05 335.48 10.86


Ebb 0.55 0.50 0.08 88.02 87.11 3.52


A Delft3D model with a finer resolution around Tauranga harbour entrance and tidal channels would

likely improve the accuracy of the predicted current velocities. The Tauranga Harbour ELCOM model

had a finer grid resolution (75 x 75 m cf. 200 x 200 m in Delft3D) and better resolved bathymetry and

had better agreement between measured and modelled velocities. Likewise, a Delft3D model of the

Tauranga Harbour southern entrance area (Spiers et al. 2009) achieved a MAE of less than 20% of

measured velocities (using the same dataset as used for model validation in this study), by increasing

grid resolution around the entrance to 2 x 2 m. However, such a reduction in grid size requires a

large reduction in the model time step, (3 seconds cf. 60 seconds in this study), which leads to a

much larger increase in model run times; simulations covering large areas and timespans are thus

rendered largely impractical. In future implementations, further consideration could be given to

using a variable grid resolution and more finely resolved bathymetry for areas near harbour

entrances, whilst retaining a coarser grid resolution offshore.

The Delft3D model was forced by tides only, so discrepancies between measured and modelled

velocities may be attributable to wind or swell conditions. This omission may be particularly relevant

at sites away from the influence of harbour entrances or tidal channels where tidal forcing may not

be the major driver of current velocities, such as at site C1. Although the Bay of Plenty coastline is a

typically a low-energy wave environment (Heath 1985), it is exposed to swell from the north and

north-easterly direction that may affect surface currents. Inclusion of, or coupling to, a wave model

would increase the complexity (and run times) of the hydrodynamic model and was outside the

scope of this study, but may improve model performance. However, preliminary simulations with

wind forcing included in the Delft3D model did not improve model fit, despite the inclusion in the

model of finely resolved vertical layers near the surface (Section 0). Wind forcing was instead

included in the GNOME particle tracking model, which included parameters for windage and

uncertainty, and this inclusion led to improved predictions of oil dispersal and accumulation.

3.3 GNOME

3.3.1 GNOME modelled oil accumulation and dispersal

The GNOME models were calibrated by adjusting parameters associated with diffusion, refloat half-

life, model time step, uncertainty and windage. Final calibrated parameters are detailed in Table 10.

Mud has a lower refloat half-life than sand; the refloat half-life was set at 12 hours for the shelf

model, and 6 hrs for the harbour model to reflect the more muddy environment in the estuary

(Dancuk, 2009). Along- and cross- current uncertainty values were increased for the shelf model

(compared to the harbour model) due to the under-prediction of current speed by the Delft3D

model.

As the Delft3D model under predicted tidal currents around the harbour entrance, combining the

ELCOM and Delft3D currents in one GNOME model led to large differences in modelled velocities at

the interface between the two hydrodynamic models. Instead, output from the GNOME shelf model

using Delft3D currents was used to prescribe the entrance point source conditions (i.e. timing,

amount) for the GNOME harbour model using ELCOM currents. The GNOME shelf model indicated

that the oil entered Tauranga harbour (through the southern entrance) from a north-easterly

direction between 10th and 13th October. The direction changed to north-westerly from 13th to 14th

October, and consequently the oil mostly began to move offshore, and more oil did not enter the

harbour until 30th October. These model results are consistent with SCAT surveys which indicated

presence of oil inside Tauranga harbour by 17th October (Figure 22).


Table 10: Calibrated model parameters for the GNOME models

Parameter Value Units

GNOME shelf model (using Delft3D currents)

Diffusion 1 m2 s

-1

Diffusion uncertainty 1

Refloat half-life 12 Hours

Time step 0.17 Hours

Windage 1-4 %

Wind speed scale 2 m s-1

Wind angle scale 0.4 Radians

Along current uncertainty 25 %

Cross current uncertainty 25 %

GNOME harbour model (using ELCOM currents)

Diffusion 1 m2 s

-1

Diffusion uncertainty 1

Refloat half-life 6 Hours

Time step 0.17 Hours

Along current uncertainty 5 %

Cross current uncertainty 5 %

3.3.2 Comparison with SCAT data

GNOME shelf model output generally compared well with observations. The amount of oil beached

(the best estimate, i.e. not including the uncertainty solution) was output from the model every 6

hours and visually compared with SCAT report data (Figure 14 and Figure 15).

The GNOME shelf model predicted that oil would first reach the shoreline on 11th October, and by

13th October large amounts of oil (c. 60 – 90 tonnes) would have accumulated. Although it was not

possible to estimate the actual amount of oil that did accumulate on the shoreline based on SCAT

reports (which recorded estimates of percentage cover) the observations do indicate that the

maximum oiling did occur on the 13th October, as predicted by the model. However, the model did

not predict the small amount of oil observed on the shoreline on the 10th October. This difference

may be due to the uncertainty around parameterising the release of oil from the Rena. Reports are

somewhat vague and conflicting about the timing and amount of oil lost from the vessel, and

inaccurate initialisation of the oil spill in GNOME will affect the accuracy of the simulation results.

Further peaks in oil accumulation on 15 - 16th October and 19th – 20th October captured by the model

also largely matched observations of maximum and average oil coverage on the shoreline (Figure 14

and Figure 15). The model appears to over-predict the oil beached on 19th – 20th October, but SCAT

reports indicate that there was beach clean-up (i.e. removal of oil) in progress before and around

this time. It was not possible to take removal of oil from beaches into account with the GNOME

model (and there was little data available on the actual amount of oil removed from the beaches

during clean-up operations). Therefore, removal of oil from the beaches during clean-up operations

may account for the over-prediction of oil accumulation around 19th – 20th October.


Figure 14: Amount of oil beached throughout the GNOME shelf model simulation (5

th October – 30

th October 2011).

Figure 15: Maximum (A) and average (B) oil coverage on shoreline (%) as assessed by SCAT surveys 7

th – 31

st October 2011 (Data source:

Maritime NZ).


3.3.3 Comparison with SCAT and over-flight maps

Immediately after the Rena grounding (6th to 8th October 2011) the GNOME shelf model predicted

the oil would disperse to the north and east (Figure 16), which is consistent with over-flight

observations (Figure 17).

The model predicted that oil would first reach Mt. Maunganui on 11th October and that by 13th

October the oiling would extend from Mt. Maunganui to Pāpāmoa (Figure 18). This prediction is

consistent with reports from Maritime NZ that indicate oiling first occurred at Mt. Maunganui and

heavy oiling was observed at Pāpāmoa on 13th October (MaritimeNZ 2011). Furthermore, over-flight

observations from the 13th October are consistent with GNOME output that shows oil in the water

extending from the Rena south-west towards the Mt. Maunganui/Pāpāmoa coastline (Figure 19).

The model predicted significant oiling on the mainland coastline and on the west coast of Mōtītī

Island by 19th October (Figure 20), which is largely in agreement with SCAT oiling summary charts

from this time (Figure 22). It should be noted that there is variability evident even in observed data;

for example, there are differences in oiling extent and amount from two SCAT summary charts for

17th October (Figure 22).

At the end of the model simulation (30th October) the amount of oil present on the shoreline had

decreased (compared to peaks earlier in October) but had increased in extent to include Tauranga

and Maketū harbours, most of Mōtītī Island and parts of the shoreline east of Maketū (Figure 21).

The model did not capture oiling on Matakana Island as part of the best estimate forecast solution,

but the uncertainty solution did indicate that oiling was possible from Waihi to Pukehina, which is

largely consistent with SCAT reports/charts for oil extent at the end of October and beginning of

November (Figure 23 and Figure 24). The model predicts oiling on the shoreline east of Maketū

estuary (e.g., at Pukehina beach), but SCAT reports indicate very little or no oil was observed there.

However, removal of oil from the shoreline between Mt. Maunganui and Maketū during clean-up

operations may have prevented remobilisation of oil and subsequent oiling on beaches south-east of

Maketū.

Figure 16: GNOME shelf model output (best estimate forecast and uncertainty solutions) for 6

th, 7

th and 8

th October 2011.

k

±

0 4 8 12 162Kilometers

GNOME06/10/2011 - 08/10/2011

k RenaForecast 08/10/2011 17:20

floatingUncertainty 08/10/2011 17:20

floatingForecast 07/10/2011 17:20


floatingForecast 06/10/2011 17:20


floating


Figure 17: Over-flight observations of oil extent from Rena on 6

th, 7

th and 8

th October 2011 (source: Maritime NZ).

Figure 18: GNOME shelf model output (best estimate forecast solution) for 21:20 on 11

th October 2011 (orange circles; oil has just reached

the shoreline at Mt. Maunganui) and for 06:20 on 13th

October 2011 (purple circles; oiling extends from Mt. Maunganui to Pāpāmoa).

k

±


GNOME11/10/2011 - 13/10/2011

k RenaForecast 13/10/2011 06:20

BeachedFloating

Forecast 11/10/2011 21:20BeachedFloating


Figure 19: Over-flight observations of oil extent from Rena on 13


Figure 20: GNOME shelf model output (forecast = black circles and uncertainty solution = red circles) for 18:20 on 19

th October 2011.

k

±


GNOME19/10/2011

k Rena

Forecast 19/10/2011 18:20Beached

Floating

Uncertainty 19/10/2011 18:20Beached

Floating


Figure 21: GNOME shelf model output (forecast = black circles and uncertainty solution = red circles) for 21:20 on 30

th October 2011.

k

±


GNOME30/10/2011

k Rena

Forecast 30/10/2011 21:20Beached

Floating

Uncertainty 30/10/2011 21:20Beached

Floating


Figure 22: SCAT oiling summaries for 17



Figure 23: SCAT oiling summary for 22

nd – 27


Figure 24: SCAT oiling summary for 4

th November 2011 (source: Maritime NZ).


Tauranga Harbour and Maketū estuary

The GNOME shelf model indicated that oil entered Tauranga Harbour between the 10th and 14th

October 2011. Output from the GNOME model for Tauranga Harbour is consistent with SCAT data

that indicates by the 17th October oil had accumulated on the shoreline inside the harbour entrance

near Mt. Maunganui, near Matua and Ōtūmoetai, with small amounts also present on the southern

shoreline of Matakana Island (Figure 25 and Figure 26).

The GNOME shelf model indicated that the oil took longer to reach Maketū estuary than Tauranga

Harbour; oil first arrived at the entrance on 13th October, with significant oiling of Ōkūrei Point (the

headland outside of Maketū estuary) around 18th – 24th October. The model indicated that a small

amount of oil entered the Maketū estuary, and beached just inside the harbour entrance, which is

consistent with SCAT reports (Figure 27 and Figure 28).

Figure 25: GNOME harbour model output (best estimate forecast solution = black circles, uncertainty solution = red circles) for 18:20 on

17th

October 2011 (beached oil only).

Figure 26: SCAT oiling summaries for Tauranga Harbour on 17



Figure 27: GNOME shelf model output for Maketū estuary area (best estimate forecast solution = black circles, uncertainty solution = red

circles) for 18:20 on 24th

October 2011 (beached oil only).

Figure 28: SCAT oiling summary for Maketū on 24



3.4 Fate of released oil

3.4.1 Shelf model

The amount of oil released, floating, beached, evaporated and dispersed, and that which had moved

out of the modelled area (“off map”) was output from the model every 6 hours for the entire

simulation (Figure 29). The majority of the oil over most of the simulation remained floating;

although a significant proportion (100 tonnes or c. 35 % of that released) had evaporated or

dispersed within a few days. The amount of oil beached on the shoreline averaged c. 10 % of the

total released over the entire simulation (i.e. 5th – 30th October), although this peaked at 30 % at

three and ten days after release. By the end of the simulation (30th October 2011) c. 40 % of the oil

had travelled outside of the modelled area (mostly to the north and east). The amount of oil

entering Tauranga Harbour and Maketū estuary was relatively small (< 5 - 10 tonnes).

Figure 29: Amount of oil released from Rena, floating, beached on shoreline, evaporated and dispersed or “off map” (i.e. outside of

modelled area) throughout the duration of the GNOME shelf model simulation (5th

October – 30th

October 2011).

The oil that did beach on the shoreline was not evenly dispersed, with “hot spots” of accumulation

evident in the model output, specifically at Pāpāmoa beach, parts of the western shoreline of Mōtītī

Island, and Ōkūrei Point. The model only includes the effect of winds and tides, and does not

include the effect of waves and surf-zone currents. Surfzone rip currents are common along the

Pāpāmoa coast, and these would trap oil within their recirculation systems. Mōtītī Island provides a

shallow barrier from the influence of waves, and waves would refract on the shallow topography

landward of Mōtītī (Figure 3), so that the wave-induced drift currents and the alongshore currents

would converge behind this island, explaining the heavier accumulation here (e.g. Figure 22).

Moderate accumulation on the northwest side of Mōtītī and also on Ōkūrei Point are predicted by

the model, and are due to the north-easterly winds advecting the oil southward.

3.4.2 Tauranga harbour model

For the Tauranga harbour GNOME model, the amount of oil released, floating, beached, evaporated

and dispersed was output from the model every 6 hours for the entire simulation (Figure 30). As for

the shelf model, the majority of oil remained floating, and a large amount (c. 25 %) evaporated or

dispersed. However, on average c. 35 % of the oil beached on the shoreline and the amount floating

and beached changed as oil was stranded on tidal flats during low tide and re-floated at high tide. Of

the oil that entered the harbour, the model predicted that the larger percentage would remain

floating, so the booms that were deployed within the harbour during the clean-up scheme were

essential to trapping this floating oil.


Figure 30: Amount of oil released (at Tauranga harbour southern entrance), floating, beached on shoreline, evaporated and dispersed

throughout the GNOME harbour model simulation (5th

October – 30th

October 2011). (There is no removal of oil included in the GNOME

modelling).

“Hot spots” of accumulation were also evident in Tauranga Harbour, particularly inside the harbour

entrance near Mt. Maunganui, with smaller amounts at the southern tip of Matakana Island, the

shoreline near Matua, Ōtūmoetai and Sulphur Point. Tidal residual currents cause a net circulation

across the flood tidal delta and a clockwise back-eddy behind Mount Maunganui. This back-eddy

would concentrate oil at the north end of Pilot Bay. The model also shows that the main entrance

tidal jet is oriented more northward, toward Panepane Point. As the tide floods out of the Harbour,

two eddies are formed on either side of the jet, on the seaward side of the entrance. Because of the

influence of the Mount, and the northerly orientation of the tidal jet, the anti-clockwise eddy on the

Mount side of the jet is much stronger than on the other side (Figure 30). This eddy recirculates oil in

the entrance channel, and may cause it to be trapped and concentrated around the edge of the

Mount (particularly where the SCAT maps indicate ‘heavy’ deposition. The GNOME model also

predicted deposition within the Port of Tauranga, which was not reported in the SCAT maps.

However, it is not clear whether SCAT surveys were conducted within the Port. It is also possible that

the wharf structure did not easily allow accumulation of oil.


Figure 31: Tidal residual circulation within the harbour showing how backeddies might concentrate material at the northend of Pilot Bay,

and along Matua. Taken from Tay et al., in press. The black circles and arrows indicate the location of observations used for model

calibration.


4. Conclusions

Currents in the Bay of Plenty and within Tauranga Harbour were modelled using the state-of-the-art

3-D hydrodynamic models Delft3D (developed by Deltares in the Netherlands) and ELCOM

(developed by the University of Western Australia), and the model results were used as input to the

NOAA oil spill model ‘GNOME’. The Delft3D model was driven with tidal currents only, and wind was

included in the GNOME input conditions. This combination provided a better result than modelling

the wind and tides within Delft3D, because of the better formulation of wind effects on oil within

GNOME. Model output was calibrated and verified against water level and current meter

observations collected by the Port of Tauranga, Bay of Plenty Regional Council and from previous

studies conducted by the University of Waikato.

Results from the Bay of Plenty model show that oil first reached the shore on the 10th of October, 5

days after the grounding, which is consistent with assessments by SCAT surveys. The oil was

predicted to arrive on the shore in pulses, which were governed by the tidal influence on the release

of oil from the ship, and also by the strong dependence of the oil dispersal on the strength and

direction of the wind. The delayed land-fall of the oil was due to the off-shore wind that occurred

during the time of grounding. Two weeks after the grounding, the majority of the oil had spread

southward along the coast, with hot spots of accumulation on the northwest side of islands (e.g.

Mōtītī) and headlands (e.g. Ōkūrei Point), also consistent with SCAT surveys. Hot spots of

accumulation along the open coast beaches (e.g. Pāpāmoa) were not well predicted due to the lack

of representation of wave and surfzone conditions within the model. Although the Delft3D

modelling package can model rip-current circulation, these models are very much under

development.

Water in the lower part of Tauranga Harbour (around the Port), is extremely well flushed (Tay et al.,

in press) due to the shallow depth and strong tidal currents, and when oil reached the entrance of

the Harbour, it was transported into the Harbour within a tidal cycle. The main areas of

accumulation were in the Pilot Bay region, where back-eddies concentrated the oil. The narrow

entrances of subestuaries (e.g. Waikareao Estuary and Stella Passage), restricted the oil movement

into the upper reaches. The majority of the oil in the estuary was predicted to remain floating for

weeks justifying an emphasis on deployment booms as a clean-up strategy.

The greatest limitation in this study is the estimates made of the time of the release of oil from the

Rena. The wind and tidal patterns are constantly changing on the shelf, and a slightly different

release time can significantly change the predicted accumulation. Although the tidal currents are

reasonably well predicted, the wind and wave-driven currents are much more difficult to predict,

and very little validation data exists on the shelf. Only a very short (c. a month) deployment from

locations that were chosen for a specific investigation around the ebb-tidal delta were available.

Finally, the SCAT reports only provided qualitative information on the accumulation of oil, which

could not be statistically compared with the model results (which provided predictions of tonnes of

accumulation).

We have four possible recommendations for future hazard management around our ports.

� It would be useful to pre-run scenarios of the dispersal patterns that would occur with the

most common summer and winter weather patterns from various potential grounding

locations, and develop a hazard map which can be used to organise response teams and

resources (e.g. booms) quickly in the event of another spill.

� It would be very useful to install a HF RADAR system which could be used for operational

forecasting of currents around the harbour entrance. Such systems provide surface current


patterns in real-time, over kilometre scales, which can be used to train forecasting models to

provide highly-accurate results.

� It would significantly improve our national operational oceanography capability by investing in

operational shelf modellers at a national level (and not relying on this capability development

to occur largely in private consultancies) and by insisting that such capability is based on open-

source models and freely (and easily) available observations so that uptake of technology is

maximised. We should also agree on a common open-source model so that bathymetries and

boundary files can easily be exchanged between working groups. At the moment we have a

number of working groups replicating core wave, tide and current forecasting capability

because of lack of free information exchange. This is wasting valuable resources that could be

used to produce highly-effective (and cost effective) hazard management. Production of shelf-

oceanographic forecasts should be a national priority and should be managed by key

performance indicators, such as producing a forecast to a specified degree of accuracy within

a specific timeframe following an event.

� New Zealand has a scarcity of observational buoys in its waters. Such real time observations

and data assimilative models are imperative in order to make accurate real time modelling

forecasts. Such operational forecasts are needed in many situations, from oil spill response to

monitoring of harmful algal blooms to search and rescue operations. Further, by including

measurements from all aspects of the marine environment - not only physical measurements

(wave, current and temperature and salinity) but also biogeochemical and optical water

quality measurements - such buoys provide a window to longer-term oceanic changes.

Deployment of a number of buoys in key locations and free access to the data would bring

New Zealand into line with other nations would greatly enhance our capability for marine

environmental prediction. Deploying and maintaining such an observational system is complex

and would require multi-organisational cooperation between all interested parties such as

government, regional councils, Crown Research Institutes and Universities.


5. Recommendations

The combination of 3D-hydrodynamic modelling and GNOME modelling was a useful, relatively low

cost way to successfully predict the extent of oil dispersal after an oil spill, particularly if the wind

was incorporated in GNOME rather than in the hydrodynamic model. Better prediction could result

if it were possible to more accurately measure the release of oil from the ship.

The main limitations of the modelling were not the design of the models themselves, but the

availability of observations to drive and calibrate the models. Much of the work developing and

calibrating the models can be done prior to an event. This would be easier to accomplish if all groups

working in and around the harbour could adopt a common (and open-source) modelling framework

so that each new component of work could build on the previous work. For example, the US Army

Corps of Engineers (USACE) has adopted the open-source Delft3D modelling system as their

community model for near shore and coastal modelling. Thus groups working at university and

government labs all around the US can easily interact and exchange modelling information, and

better serve the USACE goal of improved coastal hazard management.

It would also be useful to have spatial measurements of surface currents around the main navigation

channel such as could be provided by an HF Radar system. Observations of the offshore wind

conditions would provide an excellent complement to the weather station at Tauranga Airport.

Finally, deployment of observational buoys in New Zealand waters, which provide physical and

biogeochemical measurements, would greatly enhance the capability of predictive models.


6. Acknowledgements

We would like to acknowledge the support of Maritime New Zealand in providing access to data. Dr

Christian Winter and Eva Kwoll from the University of Bremen provided Delft3D model grids and set

up. Laura Hines collated the oil spill response information from Maritime New Zealand as part of her

summer school project. Dr Kyle Spiers (former student) provided current meter data for around the

entrance, and the Port of Tauranga provided water level and current meter data. Bay of Plenty

Regional Council provided water level data from inside Tauranga Harbour.


7. Bibliography

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Spiers, K.C., Healy, T.R., and Winter, C. (2009) Ebb-Jet Dynamics and Transient Eddy Formation at

Tauranga Harbour: Implications for Entrance Channel Shoaling. Journal of Coastal Research

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Documents

chapter 14 - project 2e - University of Waikato · accumulation produced by GNOME were compared to a verification database, which was produced using data on observed oil accumulation