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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636399
• 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.

近五十年来，地表水和地下水水文的计算模型变得越来越复杂。具有数百万节点的基于物理的综合水文模型现在经常用于洪水预报和预测、气候变化调查以及土地利用、土地覆盖、管理实践或气候变化对景观敏感性的评估。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。虽然成果丰富，但这些模型在描述加拿大一些常见景观和现象方面仍然存在严重缺陷，例如阿尔伯塔北部平原的湿地、西北地区永久冻土的融化、安大略省农业地区地下水-地表水的相互作用。常见的障碍是我们无法将我们通过小规模实地研究产生的过程理解映射到对流域尺度上发生的更大现象的理解，特别是当横向水运动是一个控制因素时。我们无法在现场测量用于水文模型的临界量，而且我们对如何在感兴趣的尺度上用算法描述物理现象的理解有限，特别是在有限的地下数据存在的情况下，我们受到了阻碍。许多现有的模型可以适合于对水流的观测，但往往是出于错误的原因。