ERA Acute will also be implemented as a new ERA methodology for the Norwegian continental shelf. ERA Acute is a recently developed Oil Spill Risk Assessment (OSRA) methodology for quantifying the impacts and risks of oil spills (Environmental Risk Assessment, ERA).
Why the Need for a New Methodology?
Some available models for use in a risk assessment process are oil spill trajectory models. Guideline for Oil Spill Risk Assessment and Response Planning for Offshore Installations” (IPIECA-IOGP2013).
Methodology, Model and Software
Basic Concepts of ERA Acute
- Four Compartments
- ERA Acute Uses Continuous Risk Functions
- Two Main Steps — Three Levels of Detail
- Introducing the Resource Damage Factor
Chapter 2 is devoted to the application of ERA acute results for environmental risk management purposes. In the first step (A), ERA Acute methodology uses inputs from the oil spill trajectory model and the VEC data to calculate impacts in each grid cell for each oil drift simulation (see Fig.1.6).
Inputs Needed for ERA Acute
Oil Spill Trajectory Modelling
Many of the input parameters to ERA Acute from oil drift modeling are averaged over the spill simulation time steps, i.e. For the shoreline and seabed compartments, the accumulated oil mass at the end of the simulation is recorded and given as output of the oil drift model, which in turn is used as input to ERA Acute to quantify the impact in these compartments.
Valued Ecosystem Components
For a population, each grid cell will have a value representing the fraction of the population present in that cell. The distribution of a VEC population within an area is sensitive to the quality of data, which directly affects the accuracy of the impact calculations.
Model Input Parameters
For all ERA Acute parameters, default values are proposed based on an assessment of available scientific knowledge. However, parameter values related to VEC and the specific population in question should be estimated, tested and, if necessary, adjusted and validated for the specific VEC population.
From Spill Scenario to Case and from Damage to Risk
For each scenario, several simulations are performed (with equal probability contribution within the scenario), giving results in cells. Since oil and gas activities can have many different spill scenarios, it is also relevant to show the individual risk for each spill scenario or the aggregated risk of the DSHA or the case.
What Can ERA Acute Results Be Used for?
The DSHA then consists of one or more spill scenarios, defined in ERA Acute as a spill situation characterized by all scenario properties where the rate and duration vary between scenarios. The results are usually viewed from a case perspective and, for example, broken down into scenarios to illustrate which of the spill scenarios contributes the most to the environmental risk (Figure 1.5).
Model Sensitivity and Uncertainty Issues
Introduction
With regard to oil drift simulations, VEC data is entered as input data for ERA Acute. The ERA Acute software tool is built to enable and provide use of the methodology and results.
ERA Acute Usage Areas
In many areas, VEC data may be limited or less detailed, in which case ERA Acute offers the ability to perform more conservative calculations. The illustrations of impacts and risks used here are for illustration purposes only and are made using ERA Acute software.
Environmental Risk Screening
All oil spill parameters can be mapped in the ERA Acute Screening Assessment. Assuming that certain species with particular sensitivity to oil spills (level A.1) are present in the spill area, the ERA Acute screening can also estimate the expected mortality of such species in that area (Figure 2.1, left).
Damage and Risk Assessment
The example in Fig.2.5 shows a breakdown of the possibilities for submitting the ERA Acute results. Use an oil drift model that provides a modern calculation of oil in the sediment, corrected for the substrate type (TOC content).
Risk Mitigation and Net Environmental Bene fi t Assessments
Setting up the Case and Input to Exposure Calculations
Each combination of rate and duration is run as a set of multiple simulations in modeling the oil spill trajectory, given as input to ERA Acute for exposure calculation. The oil spill trajectory data is exported to the same grid as used for the VEC data, and the link between the two data types is the cell ID.
Impact and Restoration Modelling
Step A: Impact Modelling
VEC data and the additional input data required for the exposure calculations are described for each compartment in the sections below. Level A.1: If VEC data is omitted, ERA Acute assumes sensitive sources are present in all cells in the analysis area (N =1, ref Eq. 3.1), so the impact depends on exposure and lethality calculations for each cell.
Step B: Impact Duration Modelling
Tier A.3: Fraction of VEC population present in the cell, increasing to N =1 (100%) across all cells for sea surface and water column, length of coastal VEC type for coastline or area of seabed habitat. However, ERA Acute offers the possibility, if there is more knowledge, to let the user form an expert opinion about the distribution between these parameters in the input, for example if a threshold for recovery is known.
The Two Steps Together and the Resource Damage
Recovery time (trec), the sum of the three time factors is the total time from spill to recovered VEC. Much of the research literature on restoration following historic spills does not distinguish between the lag and restoration phases (see reference lists in background reports).
Surface Compartment Calculations
Impact Modelling
Time Factors and Recovery Modelling
Shoreline Compartment
Impact Modelling
The total effect for each ESI classification is then given by the total length (L) for all grid cells where the thickness is above the lethal threshold (TH).
Time Factors and Recovery Modelling
44 3 An overview of the acute ERA model Table 3.2 Lag times in shoreline types classified by energy level and key oil characteristics. The time to recovery of benthic invertebrates to 99% of the function/pre-spill situation is shown in Table 3.3 (Brude et al.2015).
Water Column Compartment
Impact Modelling
Each component group has an LC50 value and at each time step (in each cell) the corresponding potential lethality of the mixture is calculated by a modification of Eq.3.11 (French-McCay2002). The uptake rate is related to the size of the organism (Hendriks et al. 2001) and the lipophilic properties of the compounds, which is related to the octanol/water partition constant (Log Kow).
Time Factors and Recovery Modelling
Both modeled and satellite oil drift data were used in the study for part of the sea surface. 62 4 Testing and validation against historical spills Table 4.1 Performance limits used to evaluate the performance of biological impact models for seabirds, marine mammals, sea turtles, and the coast in ERA Acute for the Deepwater Horizon oil spill case.
Sea fl oor Compartment Functions
Impact Modelling
The seabed is subdivided into hard bottom and soft bottom (sediment) and feeding modes are used to determine the exposure pathway(s) for species groups on several types of soft sediment substrate (Stephansen et al.2015). For deposit feeders that absorb sediment particles, the separation between THCsed and gut water exposure (THCIing) is determined using calculated bioconcentration factors (BCF) to determine biota-to-sediment accumulation factors (BSAF) (Kraaij et al.2002; (Klif) Klima-og11ktoret2020).
Time Factors and Recovery Modelling
Seven feeding modes are defined based on biological criteria, assigned to four essential combinations of exposure modes: exposure through the water column (WC) (epifauna) or IW (infauna) and either of these by ingestion (Ing) for sediment feeders. If accurate data can be found for the distribution of community feeding modes, community VEC can be assigned by combining the community feeding mode fractions that contribute to the calculation (see Stephansen et al. 2015).
Summarizing Impacts in Cells to Scenarios and DSHAs
Available at: https://norskoljeoggass.no/globalassets/dokumenter/miljo/era-acute/report-1-era-acute-dam age-and-restoration-2006.pdf. Performance limits were set for evaluation of the ERA Acute results based on the ranges of effect and recovery estimates reported in the post-spill assessments.
Method of Validation Against Historic Spills
- Analysis Areas
- Construction of Performance Boundaries
- Reconstruction of the Oil Spills in the Analysis Areas
- Reconstruction of Resource Data in the Analysis Areas
A single simulation was run for the DHOS case to obtain concentration of oil in the sediment. Three methods were used to estimate VEC densities and construct resource datasets used in the ERA Acute modeling.
Results of the Validation
Oil Drift
The modeled data (without the use of oil spill response measurements) cover a larger area, but with more variable oil coverage in cells and significantly shorter exposure times. Modeled data show the highest amount of beach oil in areas classified as "heaviest oiling" (cf. Nixon et al.2016), but also predict beaching in areas with no oil observed in the NRDA.
Acute Mortality in the Surface Compartment
This is mainly due to a significantly longer oil exposure time in the satellite data than in the modeled oil drift data. The estimated mortality with ERA Acute for cetaceans in the GOM was significantly underestimated compared to the field assessment and performance limits.
Impact in the Shoreline Compartment
For ESI 4 (Coarse Sand Beaches) and ESI 9 (Tidal Protected Areas), the acute ERA numbers are lower than the impacts reported in the surveys, although the impact investigated for this type of ESI is limited to only a few km. For ESI 8 (Protected Rocky Shores) and ESI 10 (Wetlands), the ERA Acute numbers are between the HM and HML survey numbers.
Discussion of the Validation
The modeled oil displacement used as input for the validation study does not include the oil spill response. Garshelis DL (1997) Sea otter mortality estimated from carcasses collected after the Exxon Valdez oil spill.
Sensitivity Testing and Uncertainty Handling
Abstract The uncertainty assessment and sensitivity testing of the functions and parameters used in ERA Acute serve two functions. For ERA Acute there are model uncertainties pertaining to both the structure (model uncertainty) and the numerical parameters used (epistemic uncertainties).
Methods Used in Sensitivity Testing
Deterministic Testing
Stochastic Testing
The use of these statistical methods in ERA Acute sensitivity testing is described in more detail in the project reports by Bjørgesæter and Damsgaard-Jensen (2018) and Stephansen and Bjørgesæter (2017). The results from the Spearman correlation coefficient analysis are presented in the test reports by Bjørgesæter and Damsgaard-Jensen (2018) for surface, water column and coastal sections and Stephansen and Bjørgesæter (2017) for the seabed space.
Example from Surface Compartment
The result is a sensitivity index for each input parameter in the formula, which is the fraction of the variation in the output value that can be attributed to the various parameters. Population loss is more sensitive to the change in the oil spill impact parameters than to the change in the two model parameters.
Uncertainty Scoring of the Parameters
Surface Compartment
Use best practice for oil drift simulation setup to ensure comparable and reliable statistics predictions. Based on current knowledge, reducing Th from 10 to 2 µm increases the impact by a factor of about 2.0–2.5, depending on the VEC distribution and the distance to the release point.
Water Column Compartment
Shoreline Compartment
Cov Uses oil drift model that uses a modern calculation of oil coverage above the threshold on the surface with best practice settings. Uses oil drift model that uses a modern beach mass calculation, with best practice settings.
Sea fl oor Compartment
