Oceanographic drivers of bleaching in the GBR: Hazard maps for 2016 - 2017 (NESP TWQ 4.2, AIMS)

This dataset explores a new approach to predict coral bleaching events. It uses a temperature anomaly map to create a spatially dynamic temperature threshold for the calculation of degree heating weeks (DHW) instead of using a static constant. The dynamic threshold was used to classifies map areas with low, medium or high risk of coral bleaching for years 2016 and 2017.Methods:Understanding that the combination of several variables could provide better explanatory value than each individual variable by itself, we used a classification tree prediction model (Breiman et al, 1984) to select the relevant variables and determine the threshold values for each of them as the best prediction solution for the bleaching category. Using the data from 2016 and 2017 aerial bleaching surveys at specific reefs, we derived the corresponding anomaly values and paired them with the estimated bleaching response. The classification tree algorithm will select the values of the variables that produce the most efficient partition of the data into the bleaching categories. The algorithm was trained using a randomly selected sample of 80% of the survey locations (training set), and the remaining 20% was used for validation of the results (test set). The accuracy of the classification system was calculated comparing the predicted bleaching category of the test set and comparing it with the observed bleaching category.Using a recursive partition approach we were able to create a system that correctly classified more than 66% of the reef bleaching conditions. The importance of the variables in the classification procedure according to the number of splits attributed to that variable is DHWmax anomaly > MHW count anomaly > Proportion of the mixed water column > PAR anomaly > Upwelling anomaly > MHW duration anomaly. Having a DHWmax anomaly of 4.4 °C-week above the expected climatological value and 0.3 °C above the expected value for the upwelling anomaly are the conditions linked to a severe bleaching in any reef. No or mild bleaching occurs when DHWmax anomaly was below 4.4 °C-week, and the water column was mostly stratified.Format:The data is in geoTIFF format.CRS: EPSG:4326 - WGS 84 - GeographicReferences:eReefs THREDDS cataloguehttps://thredds.ereefs.aims.gov.au/thredds/NOAA Coral Reef Watch Daily 5km Satellite Coral Bleaching Heat Stress Monitoring Products (Version 3.1)https://coralreefwatch.noaa.gov/product/5km/index.php#data_accessDataset References:Beaman, R.J. 2017. High-resolution depth model for the Great Barrier Reef - 30 m. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/5a207b36022d2Simpson, J. H., Tett, P. B., Argote-Espinoza, M. L., Edwards, A., Jones, K. J., and Savidge, G. (1982). Mixing and phytoplankton growth around an island in a stratified sea. Continental Shelf Research 1, 15–31. doi:10.1016/0278-4343(82)90030-9.Steven AD, Baird ME, Brinkman R, Car NJ, Cox SJ, Herzfeld M, Hodge J, Jones E, King E, Margvelashvili N, Robillot C. eReefs: an operational information system for managing the Great Barrier Reef. Journal of Operational Oceanography. 2019 Nov 20;12(sup2):S12-28.Liu, G., Heron, S., Eakin, C., Muller-Karger, F., Vega-Rodriguez, M., Guild, L., et al. (2014). Reef-Scale Thermal Stress Monitoring of Coral Ecosystems: New 5-km Global Products from NOAA Coral Reef Watch. Remote Sensing 6, 11579–11606. Doi:10.3390/rs61111579.Liu, G., Strong, A. E., and Skirving, W. (2003). Remote sensing of sea surface temperatures during 2002 Barrier Reef coral bleaching. Eos, Transactions American Geophysical Union 84, 137–141. Doi:10.1029/2003EO150001.Breiman L., Friedman J. H., Olshen R. A., and Stone, C. J. (1984) Classification and Regression Trees. Wadsworth.Benazzouz, A., Mordane, S., Orbi, A., Chagdali, M., Hilmi, K., Atillah, A., et al. (2014). An improved coastal upwelling index from sea surface temperature using satellite-based approach – The case of the Canary Current upwelling system. Continental Shelf Research 81, 38–54. Doi:10.1016/j.csr.2014.03.012.Data Location:This dataset is filed in the eAtlas enduring data repository at: data\custodian\2018-2021-NESP-TWQ-4\4.2_Oceanographic-drivers-of-bleaching

本数据集探索了一种预测珊瑚白化事件的全新方法。其采用温度距平图构建空间动态温度阈值，用于计算热积周（Degree Heating Weeks, DHW），摒弃了传统静态恒定阈值的使用方式。该动态阈值被用于对2016年与2017年的珊瑚白化风险等级进行划分，将地图区域分为低、中、高风险三类。

研究方法：
鉴于多变量组合相比单一变量具备更优的解释价值，本研究采用分类树预测模型（Breiman等，1984）筛选相关变量，并确定各变量的阈值，以此作为珊瑚白化等级的最优预测方案。本研究利用2016年与2017年特定珊瑚礁的航空白化调查数据，推导得到对应的距平值，并将其与估算得到的白化响应结果进行配对。分类树算法将筛选出可将数据最高效地划分为不同白化等级的变量取值。该算法以随机选取的80%调查点位作为训练集进行训练，剩余20%的点位则作为测试集用于结果验证。本研究通过对比测试集的预测白化等级与实际观测得到的白化等级，计算得到该分类系统的准确率。

通过递归分区法，本研究构建的分类系统可对超过66%的珊瑚礁白化状况实现正确分类。根据分类过程中各变量对应的节点分裂次数，各变量的重要性排序为：最大热积周距平（DHWmax anomaly）> 海洋热浪（Marine Heat Wave, MHW）次数距平 > 混合水柱占比 > 光合有效辐射（Photosynthetically Active Radiation, PAR）距平 > 上升流距平（Upwelling anomaly）> 海洋热浪持续时间距平（MHW duration anomaly）。

当最大热积周距平较气候基准值偏高4.4 ℃·周，且上升流距平较基准值偏高0.3 ℃时，任意珊瑚礁均可能发生重度白化。若最大热积周距平低于4.4 ℃·周，且水体柱层大部分处于分层状态，则不会发生白化或仅出现轻度白化。

数据格式：数据采用geoTIFF格式。
坐标参考系（CRS）：EPSG:4326 - WGS 84 - 地理坐标系

数据来源参考文献：
1. eReefs THREDDS目录：https://thredds.ereefs.aims.gov.au/thredds/
2. 美国国家海洋和大气管理局（NOAA）珊瑚礁观测站每日5公里分辨率卫星珊瑚白化热应力监测产品（版本3.1）：https://coralreefwatch.noaa.gov/product/5km/index.php#data_access

数据集参考文献：
[1] Beaman RJ. 2017. 大堡礁高精度水深模型（30米）[R]. 堪培拉：澳大利亚地球科学局. https://dx.doi.org/10.4225/25/5a207b36022d2
[2] Simpson JH, Tett PB, Argote-Espinoza ML, Edwards A, Jones KJ, Savidge G. 1982. 分层海域岛屿周边的混合作用与浮游植物生长[J]. 大陆架研究, 1:15-31. https://doi.org/10.1016/0278-4343(82)90030-9
[3] Steven AD, Baird ME, Brinkman R, Car NJ, Cox SJ, Herzfeld M, Hodge J, Jones E, King E, Margvelashvili N, Robillot C. eReefs：大堡礁业务化管理信息系统[J]. 业务海洋学杂志, 2019 Nov 20;12(sup2):S12-28.
[4] Liu G, Heron S, Eakin C, Muller-Karger F, Vega-Rodriguez M, Guild L, et al. 2014. 珊瑚生态系统的礁尺度热应力监测：来自NOAA珊瑚礁观测站的新型5公里全球产品[J]. 遥感, 6:11579-11606. https://doi.org/10.3390/rs61111579
[5] Liu G, Strong AE, Skirving W. 2003. 2002年大堡礁珊瑚白化期间的海表温度遥感监测[J]. 美国地球物理联合会汇刊, 84:137-141. https://doi.org/10.1029/2003EO150001
[6] Breiman L, Friedman JH, Olshen RA, Stone CJ. 1984. 分类与回归树[M]. Wadsworth.
[7] Benazzouz A, Mordane S, Orbi A, Chagdali M, Hilmi K, Atillah A, et al. 2014. 基于卫星遥感方法改进的沿海上升流指数——以加那利寒流上升流系统为例[J]. 大陆架研究, 81:38-54. https://doi.org/10.1016/j.csr.2014.03.012

数据存储位置：本数据集存储于eAtlas永久数据仓库中，路径为：datacustodian2018-2021-NESP-TWQ-44.2_Oceanographic-drivers-of-bleaching