Assimilating Surface Water and Ocean Topography(SWOT) Satellite and observational data for improved streamflow information

加拿大的陆地水文循环涉及流域内储存水（积雪、冰川、湖泊、湿地、土壤水分、地下水、永久冻土和河道储存）的复杂相互作用，这些水通过耦合的能量和物质交换连接在一起，产生流域径流和水蒸气进入大气。加拿大依赖水的经济所获得的社会经济效益与河流流量或水储存的可用性密切相关，无论是通过种植作物、生产水电、获得饮用水还是免受洪水侵袭的能力。鉴于我们对水的依赖，水资源供应的变化对国家安全和人身安全、经济繁荣和环境可持续性都有影响。除了水文循环的变化外，气候变化还部分表现为极端天气事件，如长时间的高温和低温、更频繁的风暴和强风以及降水状况的重大变化。这一现实需要一个可持续和具有成本效益的水文气象网络和预测系统。国际地表水和海洋地形（SWOT）水文团队已经为所有SWOT观测的河流开发了流量算法，但精度低于现场测量的精度，并建立了SWOT先验河流数据库（SWORD）以支持SWOT观测的发展。SWORD数据库的一个主要成果是提供固定的节点位置、河段边界、高分辨率河段中心线和附加的水文变量（宽度、坡度等）。以及全球30米宽及以上河流的一致拓扑系统。这项研究将以加拿大现有水文测量网络内拟议的SWORD业务卫星产品为基础，并确保与之兼容。这将包括改进地理空间情报，改进路由和改进流程表示，以部署到加拿大的大型河流流域。水文建模框架与SWORD数据库中使用的地理空间产品一致，允许水文和路由模型之间的交换以及SWORD的SWOT特性。结果将是数据集和模型不可知的工作流（例如，jupyter笔记本电脑），可以很容易地允许同化研究的SWOT决议，并随后在世界各地的应用程序中使用。该提案将寻求开发创新技术，通过开发一种综合方法，将SWOT数据应用于加拿大的水循环预测，以i）同化SWOT数据进行排放估算，ii）建立在当前的MESH建模和预测系统基础上，以更好地预测流域水文。这将导致一个新的SWOT同化战略的预测和水文模型的改进。

The terrestrial hydrological cycle in Canada involves a complex interplay of stored water (snowpack, glaciers, lakes, wetlands, soil moisture, groundwater, permafrost, and river channel storage) within basins that are linked by coupled energy and mass exchanges to generate basins streamflow and water vapour to the atmosphere. Socio-economic benefits to Canada's water dependent economy are intimately tied to the availability of river flow or water storage, be it through the ability to grow crops, produce hydropower, have access to drinking water, or to be safe from floods. Given our reliance on water, the impacts of changes to water availability have implications for national security and personal safety, economic prosperity, and environmental sustainability. In addition to the changes in the hydrological cycle, a changing climate manifests itself, in part, through extreme weather events such as prolonged periods of heat and cold, more frequent storms and severe winds, and significant changes to precipitation regimes. This reality requires a sustainable and cost-effective hydro-meteorological network and prediction system. The international Surface Water and Ocean Topography (SWOT) hydrology team has developed discharge algorithms for all SWOT-observed rivers, but at a lower accuracy than what is possible at an in-situ gauge and a SWOT a priori River Database (SWORD) was built to support the development of SWOT observations. A major outcome for the SWORD database is to provide fixed node locations, reach boundaries, and high-resolution reach centre lines and attached hydrologic variables (width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater. This research will build on, and ensure compatibility with, the SWORD operational satellite products being proposed within the exiting hydrometric network in Canada. This will including improved geospatial intelligence, improved routing, and improved process representations, for deployment to large river basins in Canada. The hydrological modelling framework is consistent with the geospatial products used in the SWORD database, allowing for interchange between the hydrology and routing models and the SWORD characterization for SWOT. The result will be datasets and model-agnostic workflows (e.g., jupyter notebooks) that can easily allow for assimilation studies at the SWOT resolution, and subsequently used in applications around the world. This proposal will seek to develop innovative techniques to apply SWOT data for water cycle prediction in Canada by developing a comprehensive approach to i) assimilate SWOT data for discharge estimation, and ii) build on the current MESH modelling and forecast systems to better predict river basin hydrology. This will result in a novel SWOT-assimilation strategy for forecasting and hydrological model improvements.