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Three-dimensional daily hindcast of oil concentrations and oil mass estimates from the far-field modeling of a deepwater oil spill scenario in the Cuban Continental Shelf, using the Connectivity Modeling System on a probabilistic approach

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DataONE2020-02-23 更新2024-06-08 收录
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The dataset contains the numerical results of probabilistic forecasts for possible oil spills in the Cuban Western Continental Shelf and two animation figures (*.gif) of model results. The origin of possible oil spills scenarios is deep wells proposed to operate in and around Cabo San Antonio. Oil dispersal and concentrations were simulated using the latest updated version of the oil application of the Connectivity Modeling System (CMS) or oil-CMS. In this version, the specified hydrocarbon pseudo-components are in the same droplet. The post-processing analysis yielded 4-D spatiotemporal data of the oil concentrations and oil mass on a regular horizontal and vertical grid, as well as the time evolution of the horizontally cumulative oil mass for a period of about 6 months. In the present oil spill scenario, a deep-sea blowout is modeled at 22.08N, 85.10W, the oil droplets are released at 1222m depth, or 300m above the hypothetical oil well, in similarity to Deepwater Horizon disaster in 2010. 3000 oil droplets were released every 2 hours for 87 days, equivalent to a total of 3132000 oil droplets released during the simulation. Initial droplet sizes were determined at random by the CMS in the range of 1-500 micron. Each oil droplet contained three (3) pseudo-components accounting for the differential oil density as follows: 10% of light oil with the density of 800kg/m^3, 75% of the oil with 840 kg/m^3, and 15% of heavy oil with 950 kg/m^3 density. The half-life decay rates of oil fractions were 30 days, 40 days, and 180 days, respectively. The surface evaporation half-life was set to 250 hours; horizontal diffusion was set to 10 m^2/s. Ocean hydrodynamic forcing for the CMS model was coming from the HYbrid Coordinate Ocean Model (HYCOM) for the Gulf of Mexico region on a 0.04-deg. horizontal grid and 40 vertical levels from the surface to 5500m. HYCOM provided daily average 3-D momentum, temperature and salinity forcing fields to the CMS model. The surface wind drift parameterization used surface winds and wind stresses from the 0.5-degree Navy Operational Global Atmospheric Prediction System (NOGAPS). The transport and evolution of the oil droplets were tracked by the oil-CMS model during the 167 days of the simulation, recording each particle’s horizontal position, depth, diameter, and density into the model output file every 2 hours. Model data had to be post-processed to obtain oil concentrations estimates. The post-processing algorithm took into account the total amount of oil spilled during the 87-day incident as estimated from the reports (730000 tons). Results from the recent laboratory deep-pressure oil experiment and from the observational studies in post-Deepwater Horizon disaster were used to adopt presumed initial droplet size distribution (DSD) for the cases of untreated oil and for the oil treated with a subsea injection of chemical dispersants. The post-processing algorithm then utilized the change-of-variable technique for the probability density functions to obtain an oil mass distribution from a known DSD. A scaling factor was further determined to obtain a representative particle mass and then the volumetric concentration in water. Two postprocessing options were assumed with different initial DSD, corresponding to the untreated oil, and oil treated with subsea dispersant injection (SSDI) that shifts the modal peak in DSD to a smaller droplet. This dataset supports the publications: Paris, C. B., Vaz, A. C., Berenshtein, I., Perlin, N., Faillettaz, R., Aman, Z. M., & Murawski, S. A. (2019). Simulating Deep Oil Spills Beyond the Gulf of Mexico. Scenarios and Responses to Future Deep Oil Spills, 315–336. doi:10.1007/978-3-030-12963-7_19; and Paris, C. B., Murawski, S. A., Olascoaga, M. J., Vaz, A. C., Berenshtein, I., Miron, P., & Beron-Vera, F. J. (2019). Connectivity of the Gulf of Mexico Continental Shelf Fish Populations and Implications of Simulated Oil Spills. Scenarios and Responses to Future Deep Oil Spills, 369–389. doi:10.1007/978-3-030-12963-7_22.

本数据集包含古巴西部大陆架可能发生的石油泄漏的概率预报数值结果,以及两组*.gif格式的模型结果动画文件。本次模拟的潜在石油泄漏情景源点为计划在圣安东尼奥角(Cabo San Antonio)及其周边海域作业的深水井。研究采用最新更新版的连通性模拟系统(Connectivity Modeling System, CMS)石油模块(oil-CMS)对石油的扩散过程与浓度分布进行模拟,该版本中指定的烃类假组分共存于同一油滴中。后处理分析得到了基于规则水平与垂直网格的石油浓度与石油质量四维时空数据,同时得到了约6个月时长内的水平累积石油质量随时间的演化过程。本研究中的石油泄漏情景模拟了一处位于22.08°N、85.10°W的深海井喷事故,油滴释放深度为1222米,即位于假想油井上方300米处,模拟设计参照2010年深水地平线(Deepwater Horizon)事故。每2小时释放3000个油滴,持续87天,模拟期间总计释放3132000个油滴。油滴初始粒径由CMS在1~500微米范围内随机生成,每个油滴包含3种假组分以表征石油的密度差异:10%为密度800kg/m³的轻质油,75%为密度840kg/m³的常规原油,15%为密度950kg/m³的重质油。各油组分的降解半衰期分别为30天、40天与180天;表面蒸发半衰期设置为250小时;水平扩散系数设为10 m²/s。CMS模型的海洋水动力强迫数据来自墨西哥湾海域的混合坐标海洋模型(HYbrid Coordinate Ocean Model, HYCOM),该数据集采用0.04°的水平网格分辨率,垂直方向共40层,深度范围从海面至5500米,HYCOM每日为CMS模型提供三维动量、温度与盐度的平均强迫场。表面风漂流参数化方案采用分辨率为0.5°的海军作战全球大气预报系统(Navy Operational Global Atmospheric Prediction System, NOGAPS)提供的表面风速与风应力数据。在167天的模拟周期内,oil-CMS模型对油滴的运移与演化过程进行追踪,每2小时将每个粒子的水平位置、深度、粒径与密度记录至模型输出文件中。需对模型原始数据进行后处理以得到石油浓度估算结果,后处理算法考虑了报告中估算的87天泄漏事件总溢油量(730000吨)。研究采用近期深海压力石油实验室实验及深水地平线事故后观测研究的结果,确定了未处理石油与海底注入化学分散剂处理后的石油的初始油滴粒径分布(Drop Size Distribution, DSD)。后处理算法利用概率密度函数的变量替换技术,基于已知的DSD得到石油质量分布;进一步确定缩放因子以得到代表性粒子质量,进而计算得到水中的体积浓度。本研究设置了两种后处理情景,对应不同的初始油滴粒径分布:分别为未处理石油,以及采用海底分散剂注入(Subsea Dispersant Injection, SSDI)处理的石油——后者可使DSD的模态峰值向更小粒径偏移。本数据集支持以下两篇学术出版物:Paris, C. B., Vaz, A. C., Berenshtein, I., Perlin, N., Faillettaz, R., Aman, Z. M., & Murawski, S. A. (2019). Simulating Deep Oil Spills Beyond the Gulf of Mexico. Scenarios and Responses to Future Deep Oil Spills, 315–336. doi:10.1007/978-3-030-12963-7_19; 以及 Paris, C. B., Murawski, S. A., Olascoaga, M. J., Vaz, A. C., Berenshtein, I., Miron, P., & Beron-Vera, F. J. (2019). Connectivity of the Gulf of Mexico Continental Shelf Fish Populations and Implications of Simulated Oil Spills. Scenarios and Responses to Future Deep Oil Spills, 369–389. doi:10.1007/978-3-030-12963-7_22.

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2020-02-23
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