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首页> 外文期刊>Journal of Environmental Management >Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications
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Dynamic partitioning of tropical Indian Ocean surface waters using ocean colour data - management and modelling applications

机译：使用海洋彩色数据 - 管理和建模应用的热带印度洋地表水的动态分配

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Over the past few decades, partitioning of the surface ocean into ecologically-meaningful spatial domains has been approached using a range of data types, with the aim of improving our understanding of open ocean processes, supporting marine management decisions and constraining coupled ocean-biogeochemical models. The simplest partitioning method, which could provide low-latency information for managers at low cost, remains a purely optical classification based on ocean colour remote sensing. The question is whether such a simple approach has value. Here, the efficacy of optical classifications in constraining physical variables that modulate the epipelagic environment is tested for the tropical Indian Ocean, with a focus on the Chagos marine protected area (MPA). Using remote sensing data, it was found that optical classes corresponded to distinctive ranges of wind speed, wind stress curl, sea surface temperature, sea surface slope, sea surface height anomaly and geostrophic currents (Kruskal-Wallis and post-hoc Tukey honestly significantly different tests, a = 0.01). Between-class differences were significant for a set of sub-domains that resolved zonal and meridional gradients across the MPA and Seychelles-Chagos Thermocline Ridge, whereas between-domain differences were only significant for the north-south gradient (PERMANOVA, a = 0.01). A preliminary test of between-class differences in surface CO_2 concentrations from the Orbiting Carbon Observatory-2 demonstrated a small decrease in mean pCO_2 with increasing chlorophyll (chl), from 418 to 398 ppm. Simple optical class maps therefore provide an overview of growth conditions, the spatial distribution of resources - from which habitat fragmentation metrics can be calculated, and carbon sequestration potential. Within the 17 year study period, biotic variables were found to have decreased at up to 0.025%a~(-1) for all optical classes, which is slower than reported elsewhere (Mann-Kendall-Sen regression,α = 0.01). Within the MPA, positive Indian Ocean Dipole conditions and negative Southern Oscillation Indices were weakly associated with decreasing chl, fluorescence line height (FLH), eddy kinetic energy, easterly wind stress and wind stress curl, and with increasing FLH/chl, sea surface temperature, SSH gradients and northerly wind stress, consistent with reduced surface mixing and increased stratification. The optical partitioning scheme described here can be applied in Google Earth Engine to support management decisions at daily or monthly scales, and potential applications are discussed.
机译：在过去的几十年里，已经使用一系列数据类型接近了地面海洋进入生态有意义的空间域的分区，目的是提高我们对开放海洋流程的理解，支持海洋管理决策和约束耦合海洋生物地球化学模型。最简单的分区方法，其可以以低成本提供管理者的低延迟信息，仍然是基于海洋遥感的纯光学分类。问题是这种简单的方法是否具有价值。这里，对热带印度洋测试了调节eBipelagic环境的系统变量的光学分类的功效，专注于夹层海洋保护区（MPA）。使用遥感数据，发现光学类别与风速，风力卷曲，海表面温度，海面坡，海表面高度异常和地球节电流的独特范围相对应（Kruskal-Wallis和Hoc Tukey诚实地显着差异测试，a = 0.01）。阶级之间的差异对于一组亚域来说是显着的，这对于在MPA和Seychelles-Chagos Thermocline Ridge之间解决了地位和子午线梯度的一组子域，而在南北梯度（alperova，a = 0.01）之间只有很大的域差异。从碳碳观察台2的表面CO_2浓度的级别差异的初步试验表现出平均pCO_2的小降低，其叶绿素（CHL）增加418至398ppm。因此，简单的光学类地图提供了增长条件的概述，资源的空间分布 - 可以计算栖息地分段度量和碳封存电位。在17年的研究期内，发现所有光学类别的生物变量在最高可达0.025％A〜（-1）中，这比其他地方报告慢（Mann-Kendall-sen回归，α= 0.01）。在MPA内，正印度洋偶极子条件和负南部振荡指数与减少CHL，荧光线高度（FLH），涡流动能，复活风应力和风力应力卷曲弱相关，并随着脓疱/ CHL，海表面温度的增加，SSH梯度和北风压力，与表面混合减小和分层增加一致。这里描述的光学分区方案可以应用于Google地球发动机，以支持日常或每月尺度的管理决策，并且讨论了潜在的应用。

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