A Hierarchical and Patch Dynamics Paradigm for Modeling Complex, 3D Groundwater Systems Across Multiple Scales

0523107 Li这项工作将通过添加基于PEST的多尺度逆建模模块来补充NSF Award 0430987。 通过该奖项开发的新的“前向”层次斑块动态建模能力将通过增加一个重要的多尺度逆建模组件来补充，这将大大提高我们识别和表征复杂的地下系统和地表水界面通量的能力，以促进系统地使用来自不同来源的多尺度昂贵的现场信息。分层逆环境将基于流行的逆建模引擎- PEST -一个通用的，模型独立的非线性参数估计程序。PEST程序将被改编并无缝嵌入到分层建模环境中。它将动态地与整个模型层次结构进行通信，避免了对离线处理、I/O和文件存储的需要。 分层逆环境将允许跨尺度的任何组合的一个或多个斑块内的流动和/或运输过程的集成的、同时的校准。为了校准复杂的系统，用户将交互地和图形地通知PEST将以什么尺度调整哪些参数/输入值，以及贴片和尺度相关的校准目标（例如，压头、通量、浓度等）。然后，PEST将控制分层建模系统，在调整参数值的同时，根据需要多次运行该系统，直到模型输出与相应的现场或实验室测量值之间的差异在加权最小二乘意义上尽可能小。该程序将提供一个分层环境，以图形方式显示最佳参数值、与最佳参数值相关的不确定性估计、最佳拟合模型结果、模型-测量残差以及与最佳参数和残差集相关的一套统计数据。

0523107LiThis work would complement NSF Award 0430987 by adding a PEST-Based Module for Multi-Scale Inverse Modeling. The new "forward" hierarchical patch dynamic modeling capability developed through that award would by complemented by adding an important multi-scale inverse modeling component that would significantly enhance our ability to identify and characterize complex subsurface systems and fluxes at surface water interfaces so as to facilitate systematic use of expensive field information across multiple scales from disparate sources. The hierarchical inverse environment will be based on the popular inverse modeling engine - PEST - a general, model independent nonlinear parameter estimation program. The PEST program will be adapted and seamlessly embedded in the hierarchical modeling environment. It will communicate with the entire model hierarchy dynamically, obviating the need for offline processing, I/O, and file storage. The hierarchical inverse environment will allow integrated, simultaneous calibration of flow and/or transport processes within one or more patches across any combination of scales. To calibrate a complex system, a user will inform PEST interactively and graphically which parameters/input values at what scales will be adjusted and the patch and scale dependent calibration targets (e.g., heads, fluxes, concentration, etc.). PEST will then take control of the hierarchical modeling system, running it as many times as it needs to while adjusting parameter values until the discrepancies between model outputs and corresponding field or laboratory measurements are as small as possible in the weighted least squares sense. The program will provide a hierarchical environment to graphically display optimal parameter values, an estimate of the uncertainty associated with optimal parameter values, best-fit model outcomes, model-to-measurement residuals, and a suite of statistics related to the optimal parameter and residual sets.