Māui Field – South Taranaki Basin

Quantitative Spill Modelling Study

Prepared by: Prepared for:

RPS ERM

Suite E1, Level 4 Level 1 140 Bundall Road 60 Leichhardt St Bundall QLD 4217 Spring Hill QLD 4000

T: +61 7 5574 1112 T: +61 7 3839 8393 F: +61 8 9211 1122 F: +61 7 3839 8381 E: [email protected] E: [email protected] W: www.erm.com Client Manager: Nathan Benfer Report Number: MAQ0624J Version / Date: Rev 1 / 19 September 2017

rpsgroup.com.au Māui Field – South Taranaki Basin Quantitative Spill Modelling Study

IMPORTANT NOTE Apart from fair dealing for the purposes of private study, research, criticism, or review as permitted under the Copyright Act, no part of this report, its attachments or appendices may be reproduced by any process without the written consent of RPS Australia West Pty Ltd (“RPS” or “we”). All enquiries should be directed to RPS. We have prepared this report for Shell Taranaki Limited (“Client”) for the specific purpose for which it is supplied (“Purpose”). This report is strictly limited to the Purpose including the facts and matters stated within it and is not to be used, directly or indirectly, for any other application, purpose, use or matter. In preparing this report RPS has made certain assumptions. We have assumed that all information and documents provided to us by the Client or as a result of a specific request or enquiry were complete, accurate and up-to-date. Where we have obtained information from a government register or database, we have assumed that the information is accurate. Where an assumption has been made, we have not made any independent investigations with respect to the matters the subject of that assumption. As such we would not be aware of any reason if any of the assumptions were incorrect. This report is presented without the assumption of a duty of care to any other person (“Third Party”) (other than the Client). The report may not contain sufficient information for the purposes of a Third Party or for other uses. Without the prior written consent of RPS: (a) this report may not be relied on by a Third Party; and (b) RPS will not be liable to a Third Party for any loss, damage, liability or claim arising out of or incidental to a Third- Party publishing, using or relying on the facts, content, opinions or subject matter contained in this report. If a Third Party uses or relies on the facts, content, opinions or subject matter contained in this report with or without the consent of RPS, RPS disclaims all risk from any loss, damage, claim or liability arising directly or indirectly, and incurred by any third party, from the use of or reliance on this report. In this note, a reference to loss and damage includes past and prospective economic loss, loss of profits, damage to property, injury to any person (including death) costs and expenses incurred in taking measures to prevent, mitigate or rectify any harm, loss of opportunity, legal costs, compensation, interest and any other direct, indirect, consequential or financial or other loss. This report has been issued to the client under the agreed schedule and budgetary requirements and contains confidential information that is intended only for use by the client and is not for public circulation, publication, nor any third-party use without the approval of the client. Readers should understand that modelling is predictive in nature and while this report is based on information from sources that RPS considers reliable, the accuracy and completeness of said information cannot be guaranteed. Therefore, RPS, its directors, and employees accept no liability for the result of any action taken or not taken on the basis of the information given in this report, nor for any negligent misstatements, errors, and omissions. This report was compiled with consideration for the specified client's objectives, situation, and needs. Those acting upon such information without first consulting RPS, do so entirely at their own risk.

Document Status

Version Purpose of Document Origin Review Review Date N. Benfer Draft Issued for internal review N. Benfer 15 September 2017 J. Parker Rev0 Issued to client for review J. Parker N. Benfer 18 September 2017 Rev 1 Issued to client for review J. Parker N. Benfer 19 September 2017

Approval for Issue

Name Signature Date N. Benfer 19 September 2017

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Contents

EXECUTIVE SUMMARY ...... VII Background ...... vii Methodology ...... vii Marine Diesel Properties ...... vii Modelling Results - A surface release of 200 m3 marine diesel oil at MPB ...... viii 1.0 INTRODUCTION ...... 9 1.1 Project Background ...... 9 2.0 SCOPE OF WORK...... 11 3.0 REGIONAL CURRENTS ...... 12 3.2 Tidal Currents ...... 14 3.2.1 Grid Setup ...... 14 3.2.2 Tidal Conditions ...... 17 3.2.3 Surface Elevation Validation ...... 18 3.3 Ocean Currents ...... 24 3.4 Currents at the Release Site ...... 25 4.0 WIND DATA ...... 27 5.0 SURFACE WATER TEMPERATURE AND SALINITY ...... 30 6.0 HYDROCARBON SPILL MODEL – SIMAP ...... 32 6.1 Stochastic Modelling ...... 32 6.2 Sea-surface, Shoreline and In-Water Thresholds ...... 33 6.2.1 Sea-Surface and Shoreline Exposure Thresholds ...... 33 6.2.2 Water Column Exposure Thresholds ...... 36 6.3 Exposure Calculation ...... 39 7.0 HYDROCARBON PROPERTIES ...... 41 8.0 MODEL SETTINGS AND ASSUMPTIONS ...... 43 9.0 INTERPRETING MODEL OUTPUT AND RESULTS ...... 44 9.1 Stochastic Analysis ...... 44 9.2 Deterministic Analysis ...... 45 10.0 RESULTS - SURFACE RELEASE OF 200 M3 MARINE DIESEL OIL AT MPB ...... 46 10.1 Stochastic Analysis ...... 46 10.1.1 Sea Surface Exposure ...... 46 10.2 Deterministic Analysis ...... 62 10.2.1 Summer Season ...... 62 10.2.2 Winter Season ...... 64 11.0 REFERENCES ...... 66

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Tables

Table 1 Location of the Māui B (MPB) production platform used as release sites for the hydrocarbon spill modelling study...... 9 Table 2 Statistical comparison between the observed and HYDROMAP predicted surface elevations data from the 1st to 31st July 2014...... 20 Table 3 Predicted average and maximum surface current speed adjacent MPB. The data was derived by combining the HYCOM ocean data and HYDROMAP tidal data from 2009-2013 (inclusive)...... 25 Table 4 Predicted average and maximum winds for the wind station adjacent to MPB. Data derived from CFSR hindcast model from 2009-2013 (inclusive)...... 28 Table 5 Monthly average sea-surface temperature and salinity near the release sites...... 30 Table 6 : The Bonn Agreement Oil Appearance Code ...... 34 Table 7 Classifications of zones of potential surface exposure...... 34 Table 8 Classifications of thresholds used to assess zones of shoreline contact...... 35 Table 9 Entrained hydrocarbon threshold values applied as part of the modelling study ...... 37 Table 10 : Physical characteristics of Marine Diesel Oil...... 41 Table 11 : Boiling point ranges of Marine Diesel Oil...... 41 Table 12 : Summary of the hydrocarbon spill model settings used in this assessment...... 43 Table 13 Maximum distances from the release site to zones of potential sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB...... 47

Figures

Figure 1 Location Māui B (MPB) production platform within the Māui gas field, located in the South Taranaki Basin, offshore New Zealand North Island, used as release sites for the hydrocarbon spill modelling study. 10 Figure 2 Schematic showing the oceanic current circulation surrounding New Zealand. (Image source: Brodie, 1960) ...... 13 Figure 3 Map showing the extents of the grid (upper image) and the finer grid resolution (nested cells) along the coastline and islands closer to the study site (lower image)...... 15 Figure 4 Bathymetry grid used to define the depths throughout the model domain, and the location of the release sites (in orange)...... 16 Figure 5 Screenshot of the predicted tidal currents in the region. Note the density of the arrows vary with the grid resolution, particularly along the coastline and around the islands. Colour of each arrow indicates current speed in m/s. Every second vector is shown to improve figure clarity...... 17 Figure 6 Location of the nine tide stations around New Zealand used to validate the tidal model...... 19 Figure 7 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Auckland (top), Bluff (middle) and Lyttelton (bottom), between the 1st and the 31st of July 2014...... 21 Figure 8 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Napier (top), Nelson (middle) and Picton (bottom), between the 1st and the 31st of July 2014...... 22 Figure 9 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Port Taranaki (top), Wellington (middle) and Westport (bottom), between the 1st and the 31st of July 2014...... 23 Figure 10 Snapshot of the predicted HYCOM ocean surface current for the 1st July 2013. Colour of individual arrows indicate current speed (m/s)...... 24 Figure 11 Predicted monthly surface current rose plots adjacent to MPB. Data was derived by combining the

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HYDROMAP tidal currents and HYCOM ocean currents for 2009 – 2013. The colour key shows the current speed (m/s), the compass direction provides the current direction flowing TOWARDS and the length of the wedge gives the percentage of the record for a particular speed and direction combination...... 26 Figure 12 Spatial resolution of the CFSR modelled wind data used as input into the hydrocarbon spill model.28 Figure 13 Predicted monthly wind rose distributions from 2009–2013 (inclusive), for the wind station adjacent to MPB. The colour key shows the wind magnitude, the compass direction provides the direction FROM and the length of the wedge gives the percentage of the record for a particular speed and direction combination.29 Figure 14 Monthly average sea temperature and salinity profiles in adjacent waters to MPB...... 31 Figure 15 : Photographs showing the difference between hydrocarbon colour and thickness on the sea- surface (source: adapted from OilSpillSolutions.org)...... 34 Figure 16 Zoomed out view of the shorelines used for reporting shoreline contact to New Zealand mainland (North Island and South Island) and smaller surrounding islands...... 36 Figure 17 Sensitive resources classified as marine reserves used for reporting exposure...... 38 Figure 18 Sensitive resource classified as a marine mammal sanctuary used for reporting exposure from in- water exposure...... 38 Figure 19 Example time series plot of concentration of entrained hydrocarbons (top) at a receptor, and exposure (bottom) with no depuration (blue) and with depuration (green)...... 40 Figure 20 Weathering of marine diesel oil under three static wind conditions. The results are based on a 200 m3 spill of marine diesel oil released over 6 hours, tracked for 20 days...... 42 Figure 21 Potential zones of sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 48 Figure 22 Potential zones of sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions...... 49 Figure 23 Probability of sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 50 Figure 24 Probability of sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions...... 51 Figure 25 Probability of sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 52 Figure 26 Probability of sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions...... 53 Figure 27 Probability of sea-surface exposure (above high exposure >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 54 Figure 28 Probability of sea-surface exposure (above high exposure >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions...... 55 Figure 29 Minimum time before sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 56 Figure 30 Minimum time before sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 57

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Figure 31 Minimum time before sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 58 Figure 32 Minimum time before sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 59 Figure 33 Minimum time before sea-surface exposure (above high exposure or >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions...... 60 Figure 34 Minimum time before sea-surface exposure (above high exposure or >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions...... 61 Figure 35 : Zones of potential sea-surface exposure resulting from the identified single spill trajectory during summer conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB starting on the 1 pm 25th October 2010...... 62 Figure 36 : Predicted weathering and fates graph for the identified single spill trajectory during summer conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB...... 63 Figure 37 : Zones of potential sea-surface exposure resulting from the identified single spill trajectory during winter conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB starting on the 4 pm 25th April 2013...... 64 Figure 38 : Predicted weathering and fates graph for the identified single spill trajectory during winter conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB...... 65 Figure 39 Location of the four wind stations used to validate the CFSR wind model...... 72 Figure 40 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Auckland Aerodrome. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots)...... 73 Figure 41 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at New Plymouth. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots)...... 74 Figure 42 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Invercargill Airport. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots)...... 75 Figure 43 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Christchurch Aerodrome. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots)...... 76 Figure 44 Map showing the actual drifter track (indicated by the black circles), the model predicted trajectory of the drifter (white zone) and the centre line of the predicted trajectory of the drifter (black line) over a 48 hour period starting on 17 October 2012. The MPA and MPB are indicated as red circles to the south...... 78

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Executive Summary

Background

Shell Taranaki Limited (Shell Taranaki) is the operator of the Māui gas field in the Taranaki Basin on the west coast of New Zealand’s North Island. The Māui B (MPB) production platform (Table 1 and Figure 1) is situated approximately 48 km southwest of the mainland in water depth of approximately 108 m.

RPS APASA was commissioned to undertake a hydrocarbon spill modelling assessment that will satisfy requirements regarding environmental impact assessment.

The results for each production platform were presented on a seasonal basis (summer (September to February) and winter (March to August)).

The spill modelling was performed using an advanced three-dimensional trajectory and fates model, SIMAP (Spill Impact Mapping Analysis Program). The SIMAP model calculates the movement and weathering of hydrocarbons over time, based on the prevailing wind and current conditions and the physical and chemical properties.

Methodology

The modelling study was carried out in several stages. Firstly, a five year current dataset (2009–2013) that includes the combined influence of three-dimensional ocean and tidal currents was developed. Secondly, the currents, spatial winds and marine diesel oil (MDO) properties were used as inputs in the three-dimensional oil spill model (SIMAP) to simulate the drift, spread, weathering and fate of the spilled hydrocarbons.

As spills can occur during any set of wind and current conditions, modelling was conducted using a stochastic (random or non-deterministic) approach, which involved running 100 spill simulations per season using the same release information (spill volume, duration and composition of the hydrocarbon), though different start times. This ensured that each simulation was subjected to different wind and current conditions and, in turn, movement and weathering of the MDO. Once all the simulations were run, the model combined the results to determine the risk and potential exposure to the surrounding waters and contact to the shorelines and specified sensitive resources. This totalled 200 spill trajectories for the entire assessment.

Marine Diesel Properties

Marine diesel oil (MDO) has an API of 37.6, density of 829.1 kg/m3 (at 15 ºC) and a low viscosity of 4.0 cP at 25ºC, classifying it as a Group II oil according to the International Tankers Owners Pollution Federation (ITOPF, 2014) classifications. Marine diesel oil is characterised by a large mixture (95%) of low and semi- to low-volatiles and contains 5% persistent hydrocarbons. It is important to note that some heavy components contained in marine diesel oil have a strong tendency to physically entrain into the upper water column in the

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presence of moderate winds (i.e. >12 knots) and breaking waves, but can re-float to the surface if these energies abate.

Modelling Results - A surface release of 200 m3 marine diesel oil at MPB ▪ Zones of low sea surface exposure (0.5 g/m2) were predicted to extend to a 99th percentile maximum distance of 74 km and 127 km east-southeast for the summer and winter seasons, respectively. ▪ Moderate (10 g/m2) and high (25 g/m2) oil exposure was limited to within 26 km of the release site for both seasons. ▪ Oil above the minimum reporting threshold (0.5 g/m2) was not predicted to persist on the sea surface beyond 10 days. ▪ No shoreline contact above the minimum reporting threshold (100 g/m2) was predicted for either season assessed. ▪ No zones of exposure to entrained hydrocarbons at levels exceeding the minimum threshold of 67,200 ppb-hrs were observed for either season assessed.

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1.0 Introduction

1.1 Project Background

RPS APASA was commissioned to undertake a hydrocarbon spill modelling assessment that will satisfy requirements regarding environmental impact assessment.

The spill modelling was performed using an advanced three-dimensional trajectory and fates model, SIMAP (Spill Impact Mapping Analysis Program). The SIMAP model calculates the transport, spreading, entrainment and evaporation of spilled hydrocarbons over time, based on the prevailing wind and current conditions and the physical and chemical properties.

The results for each production platform were presented on a seasonal basis (summer (September to February) and winter (March to August)).

Table 1 Location of the Māui B (MPB) production platform used as release sites for the hydrocarbon spill modelling study. Release Site Latitude Longitude Water Depth (m) MPB 39° 38’ 43"S 173° 18' 56"E 108

The SIMAP system, the methods and analysis presented herein use modelling algorithms which have been anonymously peer reviewed and published in international journals. Further, Asia-Pacific ASA warrants that this work meets and exceeds the ASTM Standard F2067-13 “Standard Practice for Development and Use of Oil Spill Models”.

Note that the modelling does not take into consideration any of the spill prevention, mitigation and response capabilities that Shell Taranaki propose to have in place during operations. The modelling makes no allowance for intervention following a spill to reduce volumes and/or prevent hydrocarbons from reaching sensitive areas.

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2.0 Scope of Work

The scope of work included the following components: (1) Generate five years of three-dimensional currents (2009 to 2013) that include the combined influence of ocean and tidal currents. (2) Use spatial wind data, current data and the hydrocarbon (MDO) characteristics as input into the three dimensional hydrocarbon spill model, SIMAP to model the movement, spreading, weathering and shoreline exposure by the hydrocarbon over time; (3) Use SIMAP’s stochastic model (also known as a probability model) to determine the probability of exposure to particular areas (or sensitive resources) on a seasonal basis. This involved running 100 randomly selected single trajectories per season, each having the same spill information (spill volume, duration and composition of hydrocarbons) but varying the start time based on the defined seasons. This ensured that each spill trajectory was subjected to varying wind and current conditions; (4) For each scenario and season, combine the 100 simulations into a single stochastic output; (5) Calculate the probability of exposure to the sea-surface and shoreline (if any) for a defined threshold thickness; (6) Calculate the in-water exposure from entrained hydrocarbons; and (7) Review the seasonal results and if there is shoreline contact identify the single spill trajectory per season with the highest volume ashore or if no shoreline contact identify the single spill trajectory per season with the largest area of moderate sea surface exposure.

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3.0 Regional Currents

An extensive review of the ocean circulation surrounding New Zealand, which describes the ocean currents in the region, is provided by Heath (1985). The study goes on to describe there are two main surface water masses which surround New Zealand, which include the Subtropical and Subantarctic Surface waters. The Subtropical waters predominately originate from an extension of the East Australian Current (EAC), which is a western boundary current that flows from the South Equatorial Current (SEC), and down the eastern coast of Australia. Typically the EAC carries warm waters to the south, before splitting off into the Tasman Sea approximately in line with Sydney (Coleman, 1984) and carrying the warmer waters eastwards towards New Zealand (Heath, 1985). The Subantarctic Water tends to flow northwards along the eastern side of the South Island, originating from the Circumpolar Current south of New Zealand.

The oceanic currents in the vicinity of the Taranaki Basin are predominately influenced by the typically eastward flowing D’Urville Current and the northward flowing Westland Current. The D’Urville Current consists of warm saline water that flows eastwards into the Cook Strait from the west-northwest (Brodie, 1960; Heath, 1985). The Westland Current has been observed to flow predominately northwards along the west coast of the South Island, where it then mixes with the D’Urville in the vicinity of the Cook Strait. An extension of the Westland current has been observed to extend further northwards along the western coast of the North Island. Previous work by Bowman et al., (1983) indicate that strong non-tidal flows through the Cook Strait may be influenced strongly by the prevailing winds, as shown by a pair of drifters which were observed to travel from the Taranaki Bight through the Cook Strait in a south easterly direction, with strong prevailing winds from the northwest.

This trend of ocean current circulation as outlined above was evident in the currents used for this study, with non-tidal easterly flows evident through Cook Strait, and a northerly flow along the western coast of the South and North Islands depicted Figure 10. Further, the eastward blowing wind evident in Figure 12, results in the eastward flowing non-tidal currents shown in Figure 10, which is supported by the findings of Bowman et al., (1983).

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Figure 2 Schematic showing the oceanic current circulation surrounding New Zealand. (Image source: Brodie, 1960)

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3.2 Tidal Currents

The effects of tides were generated using RPS ASA’s advanced ocean/coastal model, HYDROMAP. The HYDROMAP model has been thoroughly tested and verified through field measurements throughout the world over the past 26 years (Isaji and Spaulding, 1984; Isaji et al., 2001; Zigic et al., 2003). In fact, HYDROMAP tidal current data has been used as input for the OILMAP hydrocarbon spill modelling system, which forms part of the Incident Management System (IMS) operated by MNZ (Maritime New Zealand).

HYDROMAP employs a sophisticated sub-gridding strategy, which supports up to six levels of spatial resolution, halving the grid cell size as each level of resolution is employed. The sub-gridding allows for higher resolution of currents within areas of greater bathymetric and coastline complexity, and/or of particular interest to a study.

The numerical solution methodology follows that of Davies (1977a and 1977b) with further developments for model efficiency by Owen (1980) and Gordon (1982). A more detailed presentation of the model can be found in Isaji and Spaulding (1984) and Isaji et al. (2001).

3.2.1 Grid Setup

HYDROMAP was set-up on a domain that extended 2,286 km (east-west) by 3,120 km (north-south). The domain was subdivided horizontally into a grid with 4 levels of resolution. The resolution of the primary level was set at 8 km. The resolution of the first, second third and fourth levels were 4 km, 2 km, 1 km, and 500 m respectively. The finer grids were allocated in a step-wise fashion to more accurately resolve flows along the coastline, around islands and over more complex bathymetry. Figure 3 shows the tidal model grid domain, which extends over the whole of the New Zealand region from 156.5°E to 179.83°E longitude and 27.83°S to 56°S latitude.

A combination of datasets was used to describe the shape of the seabed within the high resolution grid. Depths for the region were extracted from nautical charts and the SRTM30_PLUS dataset (see Becker et al., 2009), which provides a 30-arc second, or approximately 1 km, resolution (refer Figure 4). The minimum, average and maximum depths across the gridded region were 2 m, 1,210 m and 5,987 m, respectively.

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Figure 3 Map showing the extents of the grid (upper image) and the finer grid resolution (nested cells) along the coastline and islands closer to the study site (lower image).

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Figure 4 Bathymetry grid used to define the depths throughout the model domain, and the location of the release sites (in orange).

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A screen shot of the predicted tidal vectors in the region is shown in Figure 5. The current speeds are indicated by the coloured speed contours, with blue representing weaker current speeds (< 0.5 m/s), increasing to red representing strong current speeds (> 2 m/s). The current vectors also vary in size depending on currents speed, with larger vectors indicating stronger current speeds and smaller vectors representing weaker current speeds. The vectors in Figure 5 indicate strong tidal currents exist in Cook Strait, between North and South Island, whilst weaker tidal currents are evident in the deeper offshore regions. The variable spatial resolution of the tidal model is indicated by the density of current vectors shown in Figure 5, where areas of greater coastline or bathymetric complexity were resolved with higher spatial resolution. Note that only every second current vector is shown in the figure to improve clarity.

Figure 5 Screenshot of the predicted tidal currents in the region. Note the density of the arrows vary with the grid resolution, particularly along the coastline and around the islands. Colour of each arrow indicates current speed in m/s. Every second vector is shown to improve figure clarity.

3.2.2 Tidal Conditions

The ocean boundary data for the regional model was obtained from satellite measured altimetry data (TOPEX/Poseidon 7.2) which provided estimates of the eight dominant tidal constituents at a horizontal scale of approximately 0.25 degrees. The eight major tidal constituents used were K2, S2, M2, N2, K1, P1, O1 and Q1. Using the tidal data, surface heights were firstly calculated along the open boundaries, at each time step in the model.

The Topex-Poseidon satellite data is produced and quality controlled by NASA (National Aeronautics and Space Administration). The satellites, equipped with two highly accurate altimeters, capable of taking sea level measurements accurate to less than ± 5 cm, measured oceanic surface elevations

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(and the resultant tides) for over 13 years (1992–2005). In total these satellites carried out 62,000 orbits of the planet. The Topex-Poseidon tidal data has been widely used amongst the oceanographic community, being the subject of more than 2,100 research publications (e.g. Andersen, 1995; Ludicone et al., 1998; Matsumoto et al., 47,9120; Kostianoy et al., 2003; Yaremchuk and Tangdong, 2004; Qiu and Chen 2010). As such the Topex/Poseidon tidal data is considered suitably accurate for this study.

3.2.3 Surface Elevation Validation

To ensure that the tidal predictions were accurate, predicted surface elevations were compared to observed surface elevation data at nine locations around the New Zealand coastline (shown in Figure 6).

Figure 7 to Figure 9 illustrate a comparison of the HYDROMAP predicted surface elevations and the observed surface elevations for each of the nine locations between the 1st and 31st of July 2014. As shown on the graphs, the HYDROMAP model accurately reproduced the phase and amplitudes of the sea surface elevation fluctuations (due to tides) throughout the spring and neap tidal cycles.

To additionally quantify the performance of the model predicted tidal elevations, two statistical measures were implemented to compare the predicted versus observed surface elevations for the nine tide stations. The two statistical measures include; the Mean Absolute Error (MAE) (Willmott, 1982, Willmott and Matsuura, 2005); and the Index of Agreement (IOA) (Willmott, 1981). The results of this analysis are shown in Table 2.

The following gives a brief description of the IOA and the MAE, and the equations used to calculate them.

The MAE is the average of the absolute values of the difference between the model predicted and observed surface elevations. It is a more natural measure of the average error (Willmott and Matsuura, 2005) and more readily understood as it produces a measure which is in the units of the variable being measured (in this case; surface elevation, which was measured in metres). It gives an indication of the average errors that could be expected, in terms of metres for surface elevation. Refer to Equation 1 below.

Eq. 1 Where: N = Number of observations

Pi = Model predicted surface elevation

Oi = Observed surface elevation The Index of Agreement in contrast, gives a non-dimensional measure of model accuracy or performance. A perfect agreement between the model predicted and observed surface elevations exists if the index gives an agreement value of 1, and complete disagreement between model and observed surface elevations will produce an index measure of 0 (Wilmott, 1981). Willmott et al (1985) also suggests that values larger than 0.5 may represent good model performance. The IOA is determined by Equation 2 below.

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Eq. 2

Where: Xmodel = Model predicted surface elevation

Xobs = Observed surface elevation As indicated above, an IOA closer to one; and a MAE closer to zero represent better model performance. The IOA values in Table 2 include a maximum of 1.00 and a minimum of 0.99 across all 9 sites, this represents remarkable model performance when replicating the surface elevations due to tidal influences. Additionally, the MAE values were on average 0.09 m (9 cm) which again indicates the tidal model performed very well throughout the model domain.

Figure 6 Location of the nine tide stations around New Zealand used to validate the tidal model.

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Table 2 Statistical comparison between the observed and HYDROMAP predicted surface elevations data from the 1st to 31st July 2014. Tide Station IOA MAE (m) Auckland (North Island) 0.99 0.10 Bluff (South Island) 0.99 0.09 Lyttelton (South Island) 1.00 0.07 Napier (North Island) 0.99 0.09 Nelson (South Island) 0.99 0.18 Picton (South Island) 0.99 0.05 Port Taranaki (North Island) 0.99 0.11 Wellington (North Island) 0.99 0.05 Westport (South Island) 0.99 0.11 Average 0.99 0.09

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Figure 7 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Auckland (top), Bluff (middle) and Lyttelton (bottom), between the 1st and the 31st of July 2014.

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Figure 8 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Napier (top), Nelson (middle) and Picton (bottom), between the 1st and the 31st of July 2014.

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Figure 9 Comparison between predicted (red line) and observed (blue line) surface elevation variation at Port Taranaki (top), Wellington (middle) and Westport (bottom), between the 1st and the 31st of July 2014.

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3.3 Ocean Currents

Data describing the flow of ocean currents was obtained from HYCOM (Hybrid Coordinate Ocean Model, (Chassignet et al., 2007), which is operated by the HYCOM Consortium, sponsored by the Global Ocean Data Assimilation Experiment (GODAE). HYCOM is a data-assimilative, three- dimensional ocean model that is run as a hindcast (for a past period), assimilating time-varying observations of sea-surface height, sea-surface temperature and in-situ temperature and salinity measurements (Chassignet et al., 2009). The HYCOM predictions for drift currents are produced at a horizontal spatial resolution of approximately 8.25 km (1/12th of a degree) over the region, at a frequency of once per day. HYCOM uses isopycnal layers in the open, stratified ocean, but uses the layered continuity equation to make a dynamically smooth transition to a terrain•following coordinate in shallow coastal regions, and to z•level coordinates in the mixed layer and/or unstratified seas. For this study, the HYCOM hindcast currents were obtained for the years 2009 to 2013 (inclusive). Figure 10 shows an example modelled surface ocean currents (HYCOM) for the region on the 1st July 2013.

Figure 10 Snapshot of the predicted HYCOM ocean surface current for the 1st July 2013. Colour of individual arrows indicate current speed (m/s).

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3.4 Currents at the Release Site

Table 3 displays the average and maximum combined surface current speeds (ocean plus tides) adjacent MPB. Figure 11 shows the monthly surface current rose distributions adjacent to MPB.

Note the convention for defining current direction is the direction the current flows towards, which is used to reference current direction throughout this report. Each branch of the rose represents the currents flowing to that direction, with north to the top of the diagram. Sixteen directions are used. The branches are divided into segments of different colour, which represent the current speed ranges for each direction. Speed intervals of 0.2 m/s are used in these current roses. The length of each coloured segment is relative to the proportion of currents flowing within the corresponding speed and direction.

The data showed that the surface current speeds and directions were variable between the months, though with a predominant flow towards the south-southeast and north. The maximum and average surface current speeds were 1.44 m/s 0.24 m/s, respectively.

Table 3 Predicted average and maximum surface current speed adjacent MPB. The data was derived by combining the HYCOM ocean data and HYDROMAP tidal data from 2009-2013 (inclusive). Average Maximum Month current speed current General Direction (m/s) speed (m/s) January 0.24 1.17 Northeast to Southeast February 0.20 0.96 Northeast to Southeast March 0.23 1.37 Northwest and Southeast April 0.25 1.44 Southeast May 0.26 0.91 Southeast June 0.25 1.04 Variable July 0.24 1.10 Northwest to Northeast August 0.23 1.21 South September 0.27 1.32 Southeast October 0.25 1.00 Northeast to southeast November 0.23 1.07 Northeast December 0.25 0.94 Southeast Minimum 0.20 0.91 Maximum 0.27 1.44

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Figure 11 Predicted monthly surface current rose plots adjacent to MPB. Data was derived by combining the HYDROMAP tidal currents and HYCOM ocean currents for 2009 – 2013. The colour key shows the current speed (m/s), the compass direction provides the current direction flowing TOWARDS and the length of the wedge gives the percentage of the record for a particular speed and direction combination.

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4.0 Wind Data

Spatial wind data was sourced from the National Centre for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR; see Saha et al., 2010). CFSR wind model is a fully coupled, data-assimilative model hindcast representing the interaction between the earth’s oceans, land and atmosphere. The gridded wind data output is available at ¼ of a degree resolution and 1-hourly time intervals.

The CFSR wind data for the years 2009–2013 (inclusive) was extracted for the same extent as the current data for input into the hydrocarbon spill model. Figure 12 shows the spatial resolution of the wind data. Table 4 shows the monthly average and maximum winds derived from the wind station adjacent to MPB. Figure 13 shows the monthly wind rose distributions derived from the CFSR data for the wind station adjacent to MPB.

Note that the atmospheric convention for defining wind direction, that is, the direction the wind blows from, is used to reference wind direction throughout this report. Each branch of the rose represents wind coming from that direction, with north to the top of the diagram. Sixteen directions are used. The branches are divided into segments of different colour, which represent wind speed ranges from that direction. Speed ranges of 6 knots are used in these wind roses. The length of each segment within a branch is proportional to the frequency of winds blowing within the corresponding range of speeds from that direction.

The hindcast CFSR wind data indicated that the winds across the region were relatively strong (maximum monthly mean of 18.5 knots; maximum of 49.5 knots) all year round with predominant directions from either Southwest or Southeast (see Figure 13).

A detailed comparison of the CFSR model data and measured winds are given in Appendix 1.

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Table 4 Predicted average and maximum winds for the wind station adjacent to MPB. Data derived from CFSR hindcast model from 2009-2013 (inclusive). Average wind Maximum wind Month General Direction (knots) (knots) January 16.1 44.8 Southwest February 13.7 36.2 Southwest and Southeast March 15.8 49.0 Southwest and Southeast April 16.4 48.8 Southwest and Southeast May 17.3 41.5 Southwest and Southeast June 17.6 49.5 Southwest and Southeast July 17.9 46.0 Southwest and Southeast August 15.8 41.0 Southwest and Southeast September 18.5 45.3 Southwest October 17.9 49.2 Southwest November 16.2 39.0 Southwest December 15.4 41.4 Southwest Minimum 13.7 36.2 Maximum 18.5 49.5

Figure 12 Spatial resolution of the CFSR modelled wind data used as input into the hydrocarbon spill model.

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Figure 13 Predicted monthly wind rose distributions from 2009–2013 (inclusive), for the wind station adjacent to MPB. The colour key shows the wind magnitude, the compass direction provides the direction FROM and the length of the wedge gives the percentage of the record for a particular speed and direction combination.

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5.0 Surface Water Temperature and Salinity

The monthly temperature and water profiles of the water column near the release sites was obtained from the World Ocean Atlas 2013 database produced by the National Oceanographic Data Centre (National Oceanic and Atmospheric Administration) and its co-located World Data Center for Oceanography (see Levitus et al., 2013).

Monthly average sea-surface temperatures near the release sites were found to vary over the course of the year from a minimum of 13.6°C (July) to a maximum of 18.8°C (March) (refer to Table 5).

Monthly average salinity of the upper water column near the release sites varied from a minimum of 34.7 psu (August) to a maximum of 35.4 psu (September) (refer to Table 5).

To accurately represent the sea temperature and salinity throughout the whole water column the modelling used monthly average sea temperature and salinity profiles, as presented in Table 5.

These parameters were used as factors to inform the weathering, movement and evaporative loss of MDO on the sea surface and in the water column.

Table 5 Monthly average sea-surface temperature and salinity near the release sites. Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Temperature (°C) 16.6 18.6 18.8 17.3 16.0 14.8 13.6 13.8 14.3 13.4 15.2 16.4

Salinity (psu) 35.1 35.3 35.1 35.0 35.3 35.3 35.2 34.7 35.4 35.0 35.1 35.0

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Figure 14 Monthly average sea temperature and salinity profiles in adjacent waters to MPB.

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6.0 Hydrocarbon Spill Model – SIMAP

The hydrocarbon spill modelling was performed using SIMAP. SIMAP is designed to simulate the fate and effects of spilled hydrocarbons for both the surface and subsurface releases (Spaulding et al., 1994; French et al., 1999; French-McCay, 2003; French-McCay, 2004; French-McCay et al., 2004; Spaulding, Kolluru, Anderson, and Howlett, 1994).

The SIMAP trajectory model separately calculates the movement of the material that: (i) is on the water surface (as surface slicks), (ii) in the water column (as either entrained whole oil droplets or dissolved hydrocarbon), (iii) has stranded on shorelines, or (iv) that has precipitated out of the water column onto the seabed. The model calculates the transport of surface slicks from the combined forces exerted by surface currents and wind acting on the hydrocarbon. Transport of entrained hydrocarbon (hydrocarbon that is below the water surface) is calculated using the currents only.

6.1 Stochastic Modelling

The SIMAP model may be used to simulate the fate of a single hydrocarbon spill at a specified time and therefore under a given set of time-varying winds and currents. This is the general approach for an exercise or known spill event.

As spills can occur during any set of wind and current conditions, SIMAP’s stochastic model was used to quantify the probability of exposure to the sea-surface, in-water and shoreline contacts for a hypothetical spill scenario over a 5-year period. The model runs many single spill trajectories (e.g. 100 per location and season) using the same spill information (i.e. release location, spill volume, duration and hydrocarbon type) but varies the start time, and in turn, the prevailing wind and current conditions. This approach ensures that the predicted transport and weathering of a hydrocarbon slick is subjected to range of current and wind conditions.

During each spill trajectory, the model records the grid cells exposed to hydrocarbons, as well as the time elapsed. Once all of the spill trajectories have been run, the model then combines the results from the individual simulations to determine the following: ▪ Probability of exposure to the sea-surface ▪ Minimum time before sea-surface exposure ▪ Maximum exposure (or load) observed on the sea-surface ▪ Probability of exposure to any shorelines ▪ Probability of exposure to individual shorelines ▪ Maximum volume of hydrocarbon that may contact shorelines from a single simulation within a scenario ▪ Maximum load that an individual shoreline may experience ▪ Probability of exposure from entrained hydrocarbons in the water column; and ▪ Maximum exposure from entrained hydrocarbons observed in the water column. The stochastic model output does not represent the extent of any one spill trajectory (which would be significantly smaller) but rather provides a summary of all trajectories run for the scenario.

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For this assessment, one hundred spill trajectories were run and combined into a single stochastic output for each season. This totalled 200 spill trajectories for the entire assessment.

6.2 Sea-surface, Shoreline and In-Water Thresholds

6.2.1 Sea-Surface and Shoreline Exposure Thresholds

Sea-Surface

The SIMAP model is able to track hydrocarbons on the sea surface to levels that are lower than biologically significant or visible to the naked eye. Therefore, reporting thresholds have been specified (based on the scientific literature) to control the recording of “contact” or “exposure” to locations at meaningful levels only.

To better assess the potential for sea-surface contact, each spill was tracked to a minimum of 0.5 g/m2, which equates approximately to an average thickness of ~0.5 μm. Hydrocarbon of this thickness is described as a silvery to rainbow sheen in appearance, according to the Bonn Agreement Oil Appearance Code (Bonn Agreement, 2006) (Table 6) and is also considered the practical limit of observing hydrocarbon in the marine environment (AMSA, 2013). Exposure to the sea-surface to hydrocarbon of this concentration is considered low, as there would only be a visual impact and no ecological impact.

Ecological impact has been estimated to occur at 10 g/m2 (~10 µm) according to French et al (1996) and French-McCay (2009) (see references therein) as this level of oiling has been observed to mortally impact some birds and other wildlife associated with the water surface. The 10 g/m2 threshold has been selected to define the zone of potential moderate exposure.

Scholten et al. (1996) and Koops et al. (2004) indicated that at a concentration of surface hydrocarbon 25 g/m2 or greater would be harmful for all birds that contact the slick. Exposure to hydrocarbon concentrations above this threshold is used to define the zone of potential high exposure.

A summary of the classifications of zones of potential surface exposure used in this study is presented in Table 7.

Figure 15 are photographs showing the difference in appearance between a silvery sheen, rainbow sheen and the metallic sheen.

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Table 6 : The Bonn Agreement Oil Appearance Code Layer Thickness Code Description Appearance Litres per km2 Interval (μm) 1 Sheen (silvery/grey) 0.04 – 0.30 40 – 300 2 Rainbow 0.30 – 5.0 300 – 5,000 3 Metallic 5.0 – 50 5,000 – 50,000 4 Discontinuous True Oil Colour 50 – 47,912 50,000 – 47,912,000 5 Continuous True Oil Colour 47,912 –> 47,912,000 –>

Figure 15 : Photographs showing the difference between hydrocarbon colour and thickness on the sea- surface (source: adapted from OilSpillSolutions.org).

Table 7 Classifications of zones of potential surface exposure. Hydrocarbon Potential level of Concentration exposure (g/m2) 0.5 - 10 Low 10 - 25 Moderate > 25 High

Shoreline

There are many different types of shorelines, ranging from cliffs, rocky beaches, sandy beaches, mud flats and mangroves, and each of these will influence the volume of hydrocarbon that could be stranded ashore and its thickness before the shoreline saturation point occurs. For instance, a sandy beach may allow hydrocarbon to percolate through the sand, thus increasing its ability to hold more hydrocarbon ashore over tidal cycles and various wave actions than an equivalent area of water; hence hydrocarbon can increase in thickness onshore over time. A sandy beach shoreline was assumed as the default shoreline type for the modelling herein, as it allows for the highest carrying capacity of MDO (of the available open/exposed shoreline types). Hence the results contained herein would be indicative of a worst case scenario, which allows for the highest volume of MDO may be stranded on the shoreline (compared to other shoreline types, such as exposed rocky shores).

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Studies indicate that of those species that occupy the intertidal zone, intertidal invertebrates are among some of the most sensitive to oiling (French-McCay, 2009) for a list of supporting references). Hydrocarbon thickness thresholds for intertidal invertebrates have been developed (French-McCay, 2009) based on Owens and Sergy, 1994 definition of hydrocarbon thickness onshore; “stain/film” as <100 g/m2, hydrocarbon “coat” as 100–1,000 g/m2, and hydrocarbon “cover” as 1,000-10,000 g/m2. A threshold of 100 g/m2 hydrocarbon thickness would be enough to coat the animal and likely impact its survival and reproductive capacity, while stain (<100 g/m2) would be less likely to have an effect French-McCay, 2009. Thus 100 g/m2 (approximately equivalent to 100 µm) is considered the lethal threshold for invertebrates living on hard substrates (rocky, artificial/man-made, rip-rap, etc.) and sediments (mud, silt, sand or gravel) in intertidal habitats.

Similar thresholds (100 g/m2) have been derived for shorebirds and wildlife on or along the shore (furbearing aquatic mammals and marine reptiles) based on studies assessing sub-lethal and lethal impacts (see French-McCay, 2009).These thresholds have been used in previous environmental risk assessment studies (French-McCay et al., 2004, French-McCay et al., 2011, 2012, French-McCay, 2003 including the U.S. NOAA (National Oceanic and Atmospheric Administration, 2013). The 100 g/m2 threshold is also recommended in the Australian Maritime Safety Authority’s (AMSA) foreshore assessment guide1 as the acceptable minimum thickness that does not inhibit the potential for recovery and is best remediated by natural coastal processes alone (AMSA, 2007).

More than 1,000 g/m2 of hydrocarbon during the growing season would be required to impact marsh plants significantly, according to observations by Lin and Mendelssohn, (1996). Similar thresholds have been found in studies assessing hydrocarbon impacts on mangroves (Grant et al., 1993 and Suprayogi and Murray, 1999). Thus 1,000 g/m2 is representative of higher level ecological impacts (i.e. ecosystem based impacts).

The following thresholds have therefore been derived to classify zones of shoreline contact based on ecological effects: 100-1,000 g/m2 and >1,000 g/m2 (see Table 8).

Table 8 Classifications of thresholds used to assess zones of shoreline contact.

Shoreline threshold (µm or g/m2) 100-1,000 >1,000

To facilitate the interpretation of the results and calculation of statistics, the mainland was broken down in to smaller sections. Figure 16 shows the New Zealand coastline divided into sections of shoreline and individual islands based on the coastline borders of regional councils.

1 Recommended for shoreline types including sandy beach, boulder shorelines, pebble shorelines, rock platforms and industry facility structures.

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Figure 16 Zoomed out view of the shorelines used for reporting shoreline contact to New Zealand mainland (North Island and South Island) and smaller surrounding islands.

6.2.2 Water Column Exposure Thresholds

Entrained Hydrocarbons

Considering that entrained hydrocarbon has undergone processes analogous to weathering and/or water-washing (i.e. many of the toxic soluble hydrocarbons have been removed through evaporation and/or dissolution), its toxicity is representative of true ‘dispersed hydrocarbon’ phase impacts. OSPAR (2012) has published predicted no effect concentrations (PNEC) for ‘dispersed hydrocarbon’ in produced formation water (PFW) discharges. Dispersed hydrocarbon in PFW discharges are small, discrete droplets suspended in the discharged water which are very similar to insoluble dispersed hydrocarbon droplets formed from subsea releases. In essence, the hydrocarbon has been partitioned (naturally separated) from gas/hydrocarbon/water mixture by solubility (water washing) and vapour pressure (evaporation) based on the individual hydrocarbon chemical properties.

The OSPAR PNEC for PFW is 70 ppb for protection of 95% of species, based on biomarker testing (i.e. whole organism responses) to total hydrocarbons (THC) by Smit et al. (2009). This PNEC represents an acceptable long term chronic exposure level from continuous point source discharges in the North Sea, which is one of the most concentrated areas in the world for hydrocarbon and gas production. Appropriate threshold values can be extrapolated from the NOECs examined in Smit et al. (2009) based on effects ranging from oxidative stress to impacts on growth, reproduction and survival and are represented by: 7 µg/l (7 ppb) (for 1% affected fraction of species), 70.5 µg/l (70 ppb) (for 5% affected fraction of species) and 804 µg/l (804 ppb) (for 50% affected fraction of species). Utilising methodologies contained in ANZECC (47,9120), which is based upon USEPA Guidelines, PNECs can be back-calculated to determine LC50 values by applying a factor of 100 to the PNEC values. This approach is supported by assessment factor criteria contained within the European Chemicals Agency

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(2008) and the OECD Existing Chemicals Programme 2002 (OECD, 2002). Employing these criteria, the following conservative threshold values for entrained hydrocarbons are applied:

▪ LC50 (99% species protection): 700 µg/l (ppb)

▪ LC50 (95% species protection): 7,050 µg/l (ppb); and

▪ LC50 (50% species protection): 80,400 µg/l (ppb).

The final exposure thresholds adopt a continuous exposure criterion of 96 hours.

Table 9 provides a summary of the entrained hydrocarbon threshold values used to define different levels of potential exposure in the modelling study.

Figure 17 and Figure 18 display the marine reserves and marine mammal sanctuary within the north island of New Zealand.

Table 9 Entrained hydrocarbon threshold values applied as part of the modelling study Threshold value for Equivalent exposure of Range of sensitive Potential entrained hydrocarbon entrained species potentially level of concentrations for a 96 hydrocarbons over impacted from acute exposure hour LC50 (ppb) 96 hrs (ppb-hrs) exposure Very sensitive species 700 67,200 Low (99th percentile) Average sensitive species 7,050 676,800 Moderate (95th percentile) Tolerant sensitive species 80,400 7,718,400 High (50th percentile)

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Figure 17 Sensitive resources classified as marine reserves used for reporting exposure.

Figure 18 Sensitive resource classified as a marine mammal sanctuary used for reporting exposure from in-water exposure.

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6.3 Exposure Calculation

The thresholds used for the in-water concentrations of entrained hydrocarbons were described as an exposure over time, rather than an instantaneous peak value. The exposure is expressed as a concentration multiplied by the number of hours exposed at that concentration, in the units of parts per billion multiplied by hours (ppb.hrs).

There are two important and opposing mechanisms that will affect the cumulative exposure. The rate of uptake, due to the exposure concentration and the duration of exposure, and the rate of removal due to the ability of the organism to expel or metabolise hydrocarbons; a process referred to as depuration. Calculation for these natural removal processes are important so as to avoid falsely forecasting impacts by only allowing for the uptake of hydrocarbons, particularly over long duration release simulations.

The uptake of entrained hydrocarbons was calculated over time for each model grid cell by addition of the concentrations calculated at each subsequent time step, multiplied by the time interval (typically hourly). Depuration was calculated by applying an exponential decay function to the previously accumulated exposure.

A review of the literature describing the observed rates of depuration of hydrocarbons indicates that the reduction of concentration follows an exponential decay. For sub-lethal concentrations, depuration rates will be faster with increased concentration and then decrease as concentrations approach zero. Hence, depuration of the concentrations in a cell over each time step was calculated by applying an exponential decay function to the tissue concentration calculated by uptake.

Observed rates of depuration show significant variation for different soluble hydrocarbons and different organisms, varying from a few days to a few weeks (Solbakken et al., 1984). For this study, the decay coefficient was set so that the exposure would fall to 1% of an initial concentration over 1 week, given no further exposure.

Cumulative exposures at each time step were then compared to threshold exposures and any location where the exposure thresholds were ever exceeded during any simulation was mapped.

To illustrate the effect of allowing for depuration of hydrocarbons over time, an example time-series plot of concentration and exposure at a receptor location is presented in Figure 19. The time-series of concentration shows intermittent contacts to hydrocarbons with gaps between contacts, of the order of ~5 days. Such an outcome might be expected, for example, from variation in the position of hydrocarbon plumes resulting from variations in the current field during an ongoing discharge. The lower panel shows the calculated exposure if depuration is not considered (blue line) and the exposure where an exponential depuration rate is allowed for, assuming a time-scale of 7 days for tissue concentrations to reduce to 1% of a starting concentration. The horizontal black lines designate exposure thresholds.

In the case where depuration was ignored (blue line), the calculated exposure simply increases with addition of each hydrocarbon concentration, and remains constant during times where there is no contact to hydrocarbons. In the case where depuration is allowed for (green line), exposure decreases exponentially between exposures. The maximum exposure occurs around day 50, where there was a prolonged (~5 days) exposure to a high concentration. The threshold exposure is not exceeded by the intermittent exposures to low concentrations but is exceeded at around day 48 when higher concentrations occurred for a longer duration.

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Figure 19 Example time series plot of concentration of entrained hydrocarbons (top) at a receptor, and exposure (bottom) with no depuration (blue) and with depuration (green).

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7.0 Hydrocarbon Properties

Marine diesel oil (MDO) was used for the surface release scenario from a loss of vessel containment. Marine diesel oil has an API of 37.6, density of 829 kg/m3 (at 15 ºC) and a low viscosity of 4.0 cP at 25ºC (refer to Figure 20), classifying it as a Group II oil according to the International Tankers Owners Pollution Federation (ITOPF, 2014) classifications. Marine diesel oil is characterised by a large mixture (95%) of low and semi- to low-volatiles and contains 5% persistent hydrocarbons (refer to Table 11). It is important to note that some heavy components contained in marine diesel oil have a strong tendency to physically entrain into the upper water column in the presence of moderate winds (i.e. >12 knots) and breaking waves, but can re-float to the surface if these energies abate.

Figure 20 illustrates the weathering graphs of 200 m3 surface release marine diesel oil under 3 static wind conditions. The graphs illustrate greater persistence of MDO on the sea surface with decreasing wind speeds, which correlates to increasing volumes of MDO occurring in the water column with increasing wind speeds. Additionally, evaporative losses over the 20 day period were greatest during the 5 knot wind conditions when the occurrence of MDO on the sea surface was greatest.

Table 10 : Physical characteristics of Marine Diesel Oil. Characteristic Marine Diesel Oil Density (kg/m3) 829.1 at 25oC API 37.60 Dynamic viscosity (cP) 4.0 at 25oC Pour Point (ºC) -14 ITOPF Oil Property Category Group II

Table 11 : Boiling point ranges of Marine Diesel Oil. Low Volatiles Semi-volatiles Residual Characteristic volatiles (%) (%) (%) (%)

Boiling point (°C) <180 180 – 265 265 – 380 >380

Marine Diesel Oil 6.0 34.6 54.4 5.0 Non-persistent Persistent

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Figure 20 Weathering of marine diesel oil under three static wind conditions. The results are based on a 200 m3 spill of marine diesel oil released over 6 hours, tracked for 20 days.

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8.0 Model Settings and Assumptions

The hydrocarbon spill modelling study was conducted to assess the risk and potential exposure to the surrounding waters and contact to the shorelines from the following hypothetical scenario: ▪ Scenario– A 200 m3 surface release of marine diesel over 6 hours from MPB The assessment was completed on annual basis for 2009-2013. Table 12 provides a summary of the hydrocarbon spill model settings and assumptions.

Table 12 : Summary of the hydrocarbon spill model settings used in this assessment. Parameter Scenario Scenario description Vessel Release Release Location MPB Number of randomly 200 selected spill start times for (i.e. 100 simulations per season) the scenario Hydrocarbon Type Marine Diesel Oil Spill Volume (m3) 200 Release Type Surface Release duration (hourss) 6 Simulation length (days) 20 Summer (September to February) Seasons assessed Winter (March to August) Surface hydrocarbon 0.5 g/m2, 10 g/m2, 25 g/m2 concentration thresholds Shoreline load threshold 100 g/m2, 1,000 g/m2

Entrained hydrocarbon 67,200 (700 ppb x 96 hrs, potential low exposure) dosages to assess the 676,800 (7,050 ppb x 96 hrs, potential moderate exposure) potential exposure (ppb-hrs) 7,718,400 (7,718,400 ppb x 96 hrs, potential high exposure)

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9.0 Interpreting Model Output and Results

The results from the modelling study are presented in a number of tables and figures, which aim to provide an understanding of both the predicted sea surface exposure, shoreline contact and in-water exposure for each season.

9.1 Stochastic Analysis

The stochastic results provide a summary based on the collective behaviour of all 100 spill trajectories per season. Results are presented as both figures/maps and tabular form.

The statistics and figures are based on the following principles: ▪ Potential zones of exposure (surface oil, entrained hydrocarbons) – A summary plot of the maximum concentration (surface) or dosage (in-water) that occurred within a grid cell from all simulations/trajectories. ▪ Greatest distance travelled by a trajectory – The furthest point from the release site that surface oil was observed in the model at a given threshold. ▪ Probability of exposure/contact (surface oil, shoreline oil, entrained hydrocarbon) – The proportion of individual simulations/trajectories that impacted a grid cell, or defined receptor, at a given threshold (surface, shoreline or subsea). ▪ Minimum time before oil exposure – The minimum time for a grid cell, or defined receptor, to record exposure at a specific threshold (surface or shoreline). ▪ Maximum potential shoreline loading - The maximum loading within a shoreline grid cell within a defined receptor. ▪ Average potential shoreline loading - The average loading within each shoreline grid cell, within a predefined receptor. Note, only non-zero values are considered. ▪ Maximum volume of oil ashore – The maximum volume of oil to come ashore on a defined receptor from a single simulation/trajectory. ▪ Average volume of oil ashore – The average volume of oil ashore, across all simulations/trajectories, for a defined receptor. Note, only non-zero values are considered. ▪ Maximum length of shoreline contacted – The maximum length of shoreline where shoreline accumulation exceeds a specified threshold, for defined receptors, or all shorelines. ▪ Average length of shoreline contacted by oil – The average length of shoreline, across all simulations/trajectories, where shoreline accumulation exceeds a specified threshold, for defined receptors, or all shorelines. Note, only non-zero values are considered.

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9.2 Deterministic Analysis

Single spill trajectories are presented in the results section to display the potential weathering, movement and extent of surface oil from a single spill, as opposed to the cumulative effect of all 100 single spill trajectories. It is important to also provide an analysis of single spill trajectories, which is useful to understand how a single spill may unfold.

The single spill trajectories are identified based on largest volume of oil to come ashore, if there is no shoreline contact then a trajectory is determined by the largest sea surface swept area at the moderate threshold (10 g/m2).

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10.0 Results - Surface Release of 200 m3 Marine Diesel Oil at MPB

This scenario examined a 200 m3 surface release of marine diesel oil over 6 hours that may result from a hypothetical vessel collision incident at MPB. One hundred spill trajectories were simulated for each season and tracked for a period of 20 days.

The stochastic analysis is presented in Section 10.1 and the deterministic analysis is presented in Section 10.2. The deterministic analysis contains a single spill trajectory from each season that had largest sea surface swept area at the moderate threshold (10 g/m2).

10.1 Stochastic Analysis

There is no section in this report for shoreline contact or exposure from entrained hydrocarbons, as there were no exceedances of relevant minimum thresholds.

Each spill trajectory was tracked to a minimum threshold thickness of 0.5 g/m2 (low exposure) on the sea- surface.

10.1.1 Sea Surface Exposure

Figure 21 and Figure 22 show the zones of potential low (0.5-10 g/m2), moderate (10-25 g/m2) and high (>25 g/m2) sea-surface exposure for the summer and winter season, respectively. Table 13 summarises the extent (maximum distance and 99th percentile distance) of each zone of sea-surface exposure for summer and winter.

The zones of low sea surface exposure (0.5 g/m2) were predicted to extend to a 99th percentile maximum distance of 74 km and 127 km east-southeast for the summer and winter seasons, respectively. The absolute maximum distance travelled was 195 km and 198 km during summer and winter respectively, which were predicted to occur as isolated patches of low sea surface exposure near Otako in the South Taranaki Bight.

Zones of moderate (10 g/m2) and high (25 g/m2) oil exposure were observed much closer to the release site, with both thresholds remaining within 26 km of the release site.

Figure 23 to Figure 28 present the probability of sea-surface exposure reported for the low (0.5-10 g/m2), moderate (10-25 g/m2) and high (>25 g/m2) exposure thresholds, for the summer and winter seasons. The predicted minimum time before sea-surface exposure for each threshold and season is presented in Figure 29 to Figure 34.

The direction travelled by trajectories was more variable during summer whereas during winter there was an observable trend along a southeast-northwest axis. Low oil on the sea surface was not predicted to persist on the sea surface longer than 10 days.

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Table 13 Maximum distances from the release site to zones of potential sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB.

Zones of potential sea-surface exposure Season Distance and direction Low Moderate High (0.5–10 g/m2) (10–25 g/m2) (>25 g/m2)

Max. distance from release site (km) 195 24 21

Max. distance from release site (km) 74 22 19 Summer (99th percentile) East- Direction East-southeast East-southeast southeast Max. distance from release site (km) 198 26 17 Max. distance from release site (km) Winter 127 25 16 (99th percentile) Direction Southeast South-southeast Southeast

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Figure 21 Potential zones of sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 22 Potential zones of sea-surface exposure, in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions.

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Figure 23 Probability of sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 24 Probability of sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions.

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Figure 25 Probability of sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 26 Probability of sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions.

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Figure 27 Probability of sea-surface exposure (above high exposure >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 28 Probability of sea-surface exposure (above high exposure >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions.

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Figure 29 Minimum time before sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 30 Minimum time before sea-surface exposure (above low exposure or >0.5 g/m2 and <10 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 32 Minimum time before sea-surface exposure (above moderate exposure or >10 g/m2 and <25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 33 Minimum time before sea-surface exposure (above high exposure or >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during summer (September to February) conditions.

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Figure 34 Minimum time before sea-surface exposure (above high exposure or >25 g/m2), in the event of a 200 m3 surface release of MDO over 6 hours at MPB. The results were calculated from 100 spill trajectories during winter (March to August) conditions.

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10.2 Deterministic Analysis

10.2.1 Summer Season

Figure 35 shows the predicted sea-surface exposure zones for the selected single spill trajectory. The trajectory was identified based on largest sea surface swept area at the moderate threshold (10 g/m2). Note there was no predicted shoreline contact or zones of entrained hydrocarbons above relevant minimum thresholds.

Oil on the sea surface, above the low threshold (0.5 g/m2), was predicted travel southeast from the release site for the entire simulation. Low oil travelled a maximum distance of 29 km from the MPB before falling below the minimum threshold.

Figure 36 displays the fates and weathering graph for the corresponding spill trajectory. The graph indicates the rapid evaporation of the volatile components over the release period (initial 6 hours). Strong winds are predicted to entrained a significant proportion in the water column throughout the simulation which decays slowly over time.

Figure 35 : Zones of potential sea-surface exposure resulting from the identified single spill trajectory during summer conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB starting on the 1 pm 25th October 2010.

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Figure 36 : Predicted weathering and fates graph for the identified single spill trajectory during summer conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB.

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10.2.2 Winter Season

Figure 37 shows the predicted sea-surface exposure zones for the selected single spill trajectory. The trajectory was identified based on largest sea surface swept area at the moderate threshold (10 g/m2). Note there was no predicted shoreline contact or zones of entrained hydrocarbons above relevant minimum thresholds.

Upon release sea surface oil initially travelled southeast of the release site, but shifted south. Sea surface oil was predicted to travel up to 30 km from the release site, at which point it fell below the minimum reporting threshold (0.5 g/m2).

Figure 38 displays the fates and weathering graph for the corresponding spill trajectory. The graph indicates the rapid evaporation of the volatile components over the release period (initial 6 hours). Oil on the sea surface decreases over the first 2 days, as oil is entrained in the water column. Throughout the remaining simulation the entrained oil decays.

Figure 37 : Zones of potential sea-surface exposure resulting from the identified single spill trajectory during winter conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB starting on the 4 pm 25th April 2013.

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Figure 38 : Predicted weathering and fates graph for the identified single spill trajectory during winter conditions. Results are based on a 200 m3 surface release of MDO over 6 hours at MPB.

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Appendix 1 Wind Data Comparison

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The location of four wind stations used to validate the CFSR wind model are shown in Figure 39 below. Figure 40 to Figure 43 show time series comparisons of the modelled CFSR winds with measured winds at the four locations around New Zealand (2012-2013 years shown for clarity).

Shown in each figure are comparisons for wind speed (in knots), wind direction (in degrees from), zonal wind velocity (east-west component, in knots), and the meridional wind velocity (north-south component, in knots).

The graphs show that the modelled winds are in very good agreement with the measured winds throughout the 2-year period, and were able to accurately represent the fluctuations in both wind speed and direction.

Figure 39 Location of the four wind stations used to validate the CFSR wind model.

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Figure 40 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Auckland Aerodrome. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots).

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Figure 41 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at New Plymouth. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots).

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Figure 42 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Invercargill Airport. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots).

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Figure 43 Timeseries comparison of measured winds (green) and CFSR modelled winds (blue) at Christchurch Aerodrome. Shown are Wind speed (knots), Wind direction (degrees from), Wind velocity (E-W component, in knots), and Wind velocity (N-S component, in knots).

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Appendix 2 Drifter Comparison

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A comparison of the modelled currents (HYCOM ocean currents plus HYDROMAP tidal currents) and the model winds (CFSR) was carried out to determine the ability for the currents and winds to simulate the trajectory of a Surface Velocity Program (SVP) drifter, which passed by the MPA and MPB in October 2012 (Drifter data source NOAA, 2014).

The drifter was observed to travel in an east to north easterly direction, towards the coastline at a speed of approximately 1.5 km/h (~0.8 knots) (refer to Figure 44). Once it neared the shoreline (approximately 36 hours later) it drifted parallel to the shore in a north to north easterly direction before becoming beached on the shoreline approximately 10 km southwest of Port Taranaki.

The model predicted trajectory was carried out using a stochastic particle trajectory model, forced by the model currents and winds as outlined above. The start location was chosen as it was nearby to both MPA and MPB. The model results show a very good agreement with the drifter trajectory over the 48 hours it took to drift from a location in proximity to the two platforms (17 October 2012), to when it reached the shore and became stranded (19 October 2012). Both the direction and length of drift was well replicated by the model, thus indicating the accuracy of the forecast currents and winds, and in turn confirming their suitability for use in this study.

Figure 44 Map showing the actual drifter track (indicated by the black circles), the model predicted trajectory of the drifter (white zone) and the centre line of the predicted trajectory of the drifter (black line) over a 48 hour period starting on 17 October 2012. The MPA and MPB are indicated as red circles to the south.

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