Scale issues in connected landscapes: Resolving emergent behaviour for improved hydrologic modelling

互联景观中的规模问题：解决紧急行为以改进水文建模

• 批准号: RGPIN-2017-03920
• 负责人: Craig, James
• 金额: $ 2.48万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 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=685358
• 关键词: Scale; issues; connected; landscapes; Resolving

Over the past fifty years, computational models of surface water and groundwater hydrology have become increasingly complex. Physically-based integrated hydrologic models with millions of nodes are now regularly deployed for flood forecasting and prediction, climate change investigation, and assessment of landscape sensitivity to changes in land use, land cover, management practices, or climate. These models are used to support critical management decisions about infrastructure, environmental practices, forestry, floodplain delineation, and land management in Canada and abroad. While prolific, these models still suffer from critical drawbacks in their ability to represent some common Canadian landscapes and phenomena, such as the wetlands in the Boreal plains of Alberta, thawing of permafrost hydrology in the Northwest Territories, and groundwater-surface water interactions in agricultural Ontario. The common impediment is our inability to map our process understanding generated through small-scale field studies to understanding of the larger phenomena occurring at the watershed scale, particularly when lateral water movement is a controlling factor. We are hampered by both our inability to measure critical quantities in the field for use in hydrologic models and by our limited understanding about how to algorithmically describe physical phenomena at scales of interest, especially with the presence of limited subsurface data. Many existing models can be fit to observations of streamflow, but often for the wrong reasons. ******The proposed research program focuses on the development of improved computational hydrology models to be used in support of water and energy resource management. The program links several ongoing and forthcoming projects aimed at improving our ability to model, understand, and predict the bulk behavior of watersheds while recognizing the limited availability of data. The primary objectives are to: (1) confront the critical issue of scale-dependence of physical processes in hydrologic models, using high-resolution models to identify controls on emergent watershed bulk behaviour; and (2) create, test, and apply novel, cost-efficient, and robust methods for simulating hydrologic phenomena at relevant scales for practical application. Advanced methods of model evaluation will be developed and used to test for and demonstrate improved model performance. By better depicting the physical phenomenon occurring in our watersheds at appropriate scales, these models and methods will advance our ability to manage Canada's water and energy resources.**

在过去的五十年里，地表水和地下水水文学的计算模型变得越来越复杂。现在，有数百万个节点的基于物理的综合水文模型被定期用于洪水预报和预报、气候变化调查以及评估景观对土地利用、土地覆盖、管理做法或气候变化的敏感性。这些模型用于支持加拿大和国外有关基础设施、环境实践、林业、洪泛区划定和土地管理的关键管理决策。虽然多产，但这些模型在描述加拿大一些常见的景观和现象方面仍然存在严重缺陷，例如阿尔伯塔省北部平原的湿地、西北地区的永久冻土水文融化以及安大略省农业地区的地下水-地表水相互作用。共同的障碍是我们无法将通过小规模实地研究产生的过程理解映射到对分水岭尺度上发生的更大现象的理解，特别是在横向水流是控制因素的情况下。我们既无法在实地测量用于水文模型的临界量，又由于我们对如何在感兴趣的尺度上以算法描述物理现象的了解有限，特别是在存在有限的地下数据的情况下。许多现有的模型可以适用于径流的观测，但往往是出于错误的原因。*拟议的研究计划侧重于开发改进的计算水文学模型，以支持水资源和能源资源管理。该计划将几个正在进行和即将进行的项目联系起来，这些项目旨在提高我们对流域整体行为进行建模、理解和预测的能力，同时认识到数据的有限可用性。其主要目标是：(1)解决水文模型中物理过程的尺度依赖性这一关键问题，使用高分辨率模型来确定对新出现的流域整体行为的控制；以及(2)为实际应用创造、测试和应用新的、经济有效的和健壮的方法来模拟相关尺度上的水文现象。将开发和使用先进的模型评估方法来测试和展示改进的模型性能。通过在适当的尺度上更好地描述我们的流域中发生的物理现象，这些模型和方法将提高我们管理加拿大水资源和能源资源的能力。