Large-scale water quality modeling to screen for risks in Canadian and global freshwater bodies using a high-resolution river routing approach

使用高分辨率河流路线方法进行大规模水质建模，以筛查加拿大和全球淡水体的风险

• 批准号: RGPIN-2019-04541
• 负责人: Lehner, Bernhard
• 金额: $ 3.72万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714718
• 关键词: Large; scale; water; quality; modeling

Protecting the quality of our surface water resources is of paramount importance to preserve the health and safety of humans and aquatic ecosystems that depend on them. To successfully maintain the ecosystem services of rivers and lakes, including the supply of drinking water, we need to be able to monitor and predict critical water quality characteristics that sustain aquatic habitats and biodiversity, such as water temperature and sediment concentrations; and we need to understand the sources, fate and distribution of potentially harmful substances affecting them. This importance has been internationally recognized by nominating the provision of clean water as a central Sustainable Development Goal. Since water quality in the river system is sensitive to both climate change and anthropogenic activities, and effects can accumulate or propagate over long distances along the river network, there is an urgent need to better understand relationships between surface water and human perturbations at large scales, i.e. for entire basins, countries, and the world. My proposed research program over the next 5 years aims to enhance global hydrological modeling schemes in order to analyze current and future water quality trends that put Canadian and global freshwater resources at risk. I will focus on three specific research frontiers: (a) I will create a modeling approach to estimate water temperature regimes for all rivers and lakes globally at high spatial resolution, based on combining global air temperature with a flow routing scheme, taking glaciers, snow, and groundwater into account. (b) I will develop a global modeling framework to estimate sediment transport and its possible changes at high spatial resolution. And (c) I will complete the development of a chemical fate model that is capable of estimating concentrations of emerging contaminants in our waterways, such as down-the-drain household chemicals, pharmaceuticals, industrial pollutants, microplastics, or nanoparticals, covering all of Canada and ultimately the world. For the proposed research program, my students will blend state-of-the-art methodologies from Geographic Information Science (GIS) and Remote Sensing. The seamless global data that I have produced over the last decade in my research lab, including high-resolution maps of 8.5 million river reaches and 1.4 million lakes, combined with new modeling techniques will allow for first-time studies at pan-Canadian and global scales that have previously been unachievable. The main goal is to develop a model and scenario framework capable of screening for regions at risk, in particular at critical locations such as drinking water intakes. I anticipate broad applicability of my results for strategic freshwater planning and integrated river basin management, including assessments of the effects of domestic or industrial water use, agricultural land use practices, reservoir operation, and climate change on downstream water quality.

保护地表水资源的质量对于维护人类和依赖它们的水生生态系统的健康和安全至关重要。为了成功地维持河流和湖泊的生态系统服务，包括饮用水的供应，我们需要能够监测和预测维持水生生境和生物多样性的关键水质特征，如水温和沉积物浓度;我们需要了解影响它们的潜在有害物质的来源、归宿和分布。这一重要性得到了国际社会的认可，将提供清洁水作为可持续发展的核心目标。由于河流系统中的水质对气候变化和人类活动都很敏感，并且影响可以沿着河流网络长距离积累或传播，因此迫切需要更好地了解地表水与大尺度人类扰动之间的关系，即整个流域，国家和世界。 我提出的研究计划在未来5年旨在加强全球水文建模方案，以分析当前和未来的水质趋势，把加拿大和全球淡水资源处于危险之中。我将专注于三个具体的研究前沿：（a）我将创建一个建模方法，以高空间分辨率估计全球所有河流和湖泊的水温状况，基于将全球气温与流量路由方案相结合，将冰川，雪和地下水考虑在内。(b)我将开发一个全球建模框架，以高空间分辨率估计沉积物输运及其可能的变化。以及（c）我将完成一个化学归宿模型的开发，该模型能够估计我们水道中新出现的污染物的浓度，例如下水道家用化学品，药品，工业污染物，微塑料或纳米颗粒，覆盖整个加拿大并最终覆盖世界。 对于拟议的研究计划，我的学生将融合国家的最先进的方法，从地理信息科学（GIS）和遥感。我在过去十年中在我的研究实验室中制作的无缝全球数据，包括850万条河流和140万个湖泊的高分辨率地图，结合新的建模技术，将允许首次在泛加拿大和全球范围内进行研究，这在以前是无法实现的。主要目标是开发一个能够筛查风险区域的模型和情景框架，特别是在饮用水取水口等关键地点。我预计我的战略淡水规划和综合河流流域管理，包括家庭或工业用水，农业土地利用的做法，水库运行和气候变化对下游水质的影响评估的结果的广泛适用性。