Measuring and modelling hydrological parameter fields: strategies for sampling and integrating fine-scale surface and subsurface data in a spatial simulation framework

测量水文系统中精细尺度空间异质性或“斑块性”的重要性和难度已得到充分认识，特别是对于像湿地这样的复杂系统。我的研究计划的目标是a)揭示现场采样策略及其对描述空间复杂水文系统的有效性之间的关系，通过b)开发和方法来确定湿地的地下水力导电性场。拟议的研究将使用“混合模式”方法，将理论模拟实验与实验室和现场调查紧密结合起来，以驱动参数发现模型。这项工作将使用现场数据采集系统、地理空间和地球物理整合工具，以及我的研究小组在过去三年中开发的建模框架。我将开发从GPR信号中估计电导率场和水力导电性场的方法，并在“沙盒”实验室环境以及Fletcher Fen和Point Pelee Marsh进行验证。与该计划面向实地的方面平行，我将使用模拟实验来评估采样策略和各种测量方法对我们表征湿地系统的能力的影响。这些调查使用从我们的现场数据中得出的空间、时间和测量误差分布的估计，从我们的ECO-COSM模拟框架中实施的代表性水文模型中得出具有不同程度测量不确定性的数据集。该研究有望揭示数据采样模式与自然系统中运行过程之间基本时空相互作用的重要见解。这项工作将导致许多应用发展，包括描述湿地的新的非侵入性方法和更好地量化复杂自然系统的采样设计方法。

The importance and the difficulty of measuring fine-scale spatial heterogeneity or 'patchiness' in hydrological systems is well recognized, especially for complex systems like wetlands. The goal of my research program is to a) uncover the relationship between field sampling strategies and their effectiveness for describing spatially complex hydrological systems, by example of b) developing and methods to determine subsurface hydraulic conductivity fields in wetlands. The proposed study will use a 'mixed-mode' approach, tightly integrating theoretical simulation experiments with lab and field investigations to drive parameter discovery models. The work will use a mixture of field data acquisition systems, geospatial and geophysical integration tools, and modelling frameworks developed by my research group over the past three years. I will develop methods to estimate electrical and hydraulic conductivity fields from GPR signals, validated in a 'sandbox' lab environment and in Fletcher Fen and Point Pelee Marsh. Parallel to the field-oriented aspects of the program, I will use simulation experiments to evaluate the effects that sampling strategies and varied measurement methods have on our ability to characterize wetland systems. These investigations use estimates of spatial, temporal, and measurement error distributions drawn from our field data to derive datasets with varying degrees of measurement uncertainty from representative hydrologic models implemented in our ECO-COSM simulation framework. The research is expected to reveal important insights into the fundamental spatial and temporal interactions between patterns of data sampling and the processes operative in natural systems. The work will lead to many applied developments including new, non-invasive methods for describing wetlands and sampling design methods that better quantify complex natural systems.