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

在过去的五十年里，地表水和地下水水文计算模型变得越来越复杂。基于物理的集成水文模型与数以百万计的节点，现在定期部署洪水预报和预测，气候变化调查，土地利用，土地覆盖，管理实践或气候变化的景观敏感性评估。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。虽然多产，这些模型仍然遭受严重的缺陷，在他们的能力，以代表一些常见的加拿大景观和现象，如湿地在北方平原的阿尔伯塔，融化的永久冻土水文在西北地区，和地下水-地表水的相互作用，在农业安大略。共同的障碍是我们无法映射我们的过程中产生的理解，通过小规模的实地研究，以了解更大的现象发生在流域尺度，特别是当横向水运动是一个控制因素。我们的阻碍，我们无法测量临界量在现场使用的水文模型和我们有限的理解如何算法描述物理现象的规模感兴趣的，特别是与有限的地下数据的存在。许多现有的模型可以适合观测的径流，但往往是错误的原因。

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.