End-to-end cyberinfrastructure to support data-model integration in water connectivity studies

支持水连通性研究中数据模型集成的端到端网络基础设施

• 批准号: RTI-2020-00085
• 负责人: Ali, Genevieve
• 金额: $ 4.17万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Research Tools and Instruments
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=690738
• 关键词: End; end; cyberinfrastructure; support; data

Hydrologic (or water) connectivity describes the movement of water between pairs of locations and has important implications for process understanding and watershed management. However, most hydrologic models do not explicitly depict connectivity, resulting in considerable knowledge gaps. Two emerging trends in the fields of hydrology and remote sensing are creating exciting, but largely untapped, opportunities in connectivity science. First, increased availability of data from optical and radar satellite sensors is transforming the way in which environmental dynamics are studied; most notably, dynamic surface water processes can now be observed at unprecedented spatial and temporal resolution. Second, agent-based models (ABMs) are commonly used in ecology and are powerful tools to simulate interactions between individual elements in an ecosystem. Some hydrologists have suggested that data-driven ABMs might be more efficient than equation-based models (EBMs) to simulate small-scale interactions leading to water connectivity, but that hypothesis has yet to be fully tested. The integration of remote sensing data into EBMs and ABMs has the potential to reveal formerly unknown connectivity dynamics, thus contributing to a more complete understanding of watershed processes and their management. However, multi-sensor satellite data processing and the application of EBMs and ABMs require significant computing power and data storage capacity, which is the motivation for this funding application. ******An end-to-end cyberinfrastructure will be developed to support data-model integration in water connectivity studies across agricultural and wetland-dominated watersheds. The cyberinfrastructure will comprise two multi-core and high memory computing servers, one network-attached data storage system, and one high-performance workstation. The servers will allow the processing of multi-sensor satellite images, the computation of connectivity metrics, and their integration into EBMs and ABMs for advanced water connectivity modelling. The network-attached data storage system will be configured to enable frequent data back-ups, with built-in redundancy in case of accidental data losses. Lastly, the high-performance workstation will serve as a link between the two computing servers, and as an interface for stakeholders to test the connectivity models and offer feedback based on specific watershed management objectives or targets. The research enabled by this cyberinfrastructure will be unprecedented in terms of volume of data processed and model complexity: it will not only address key questions relevant to connectivity science, but it will also help prioritize how, when and where corrective action should be taken within watersheds by providing spatially and temporally explicit information on water movements.

水文（或水）连通性描述了水在成对地点之间的运动，对过程理解和流域管理具有重要意义。然而，大多数水文模型没有明确描述连通性，导致相当大的知识空白。水文学和遥感领域的两个新兴趋势正在为连通性科学创造令人兴奋但在很大程度上尚未开发的机会。首先，光学和雷达卫星传感器提供的数据越来越多，正在改变研究环境动力学的方式；最值得注意的是，现在可以以前所未有的空间和时间分辨率观察到动态地表水过程。其次，基于主体的模型（ABMs）在生态学中被广泛使用，是模拟生态系统中单个元素之间相互作用的强大工具。一些水文学家提出，数据驱动的ABMs可能比基于方程的模型（EBMs）更有效地模拟导致水连通性的小规模相互作用，但这一假设尚未得到充分验证。将遥感数据整合到流域生态系统和流域生态系统中，有可能揭示以前未知的连通性动态，从而有助于更全面地了解流域过程及其管理。然而，多传感器卫星数据处理以及EBMs和ABMs的应用需要显著的计算能力和数据存储能力，这是该资助应用的动机。******将开发端到端网络基础设施，以支持跨农业和湿地为主的流域的水连通性研究中的数据模型集成。网络基础设施将包括两台多核高内存计算服务器、一台联网数据存储系统和一台高性能工作站。这些服务器将允许处理多传感器卫星图像，计算连通性指标，并将其集成到ebm和ABMs中，以进行高级水连通性建模。网络连接的数据存储系统将被配置为能够频繁备份数据，并内置冗余，以防意外数据丢失。最后，高性能工作站将作为两个计算服务器之间的链接，并作为利益相关者测试连接模型的接口，并根据特定的流域管理目标或指标提供反馈。在处理的数据量和模型复杂性方面，这种网络基础设施所支持的研究将是前所未有的：它不仅将解决与连通性科学相关的关键问题，而且还将通过提供有关水运动的空间和时间明确信息，帮助确定在流域内采取纠正措施的方式、时间和地点。