Hydrological consistency in modeling - Improving model structure and parameter estimation with temporal diagnostic analyses

集水区模型再现了集水区的水文过程及其时间动态。将模型视为与应用研究相反的学习工具，这些模型用于改进对过程及其在模型中的表示的理解。在一致的模型概念中，水文过程在模型结构中得到适当的表示。每个模型参数用于相关过程，其主导周期与时间过程模式一致。时间诊断分析在高时间分辨率下研究模型结构与现实世界过程的关系。在这个项目中，主要过程的时间变化是通过对每个模型时间步的参数敏感性进行时间分析来检测的。参数敏感性的时间分析与总流量和相应的过程有关，以识别时间过程模式。结合模型性能的时间分析，检测出模型性能差阶段的主导参数。在此基础上，推导了模型部件的失效。时间诊断信息也包含在模型校准中。对于参数值的估计，只使用该参数的高灵敏度周期。本项目旨在基于主导过程的时间模式，开发新的方法来实现模型结构和校准的水文一致性。主要研究目标是：1)通过分析主要模型参数与相应水文过程的时间变化，实现时间过程模式的水文一致性；2)通过结合参数敏感性和模型性能的时间分析来检测结构模型失效；3)通过开发一种基于高参数周期的创新校准方法来估计模型参数敏感度。

Catchment models reproduce the hydrological processes and their temporal dynamic in a catchment. Seeing models as learning tools in contrast to application studies, these models are used for an improved understanding of processes and their representation in models. In a consistent model concept, the hydrological processes are appropriately represented in the model structure. Each model parameter is used for the related process and its dominant periods coincide with the temporal process patterns. Temporal diagnostic analyses investigate the relation of model structure and real-world processes in a high temporal resolution. In this project, temporal variations in dominant processes are detected by a temporal analysis of parameter sensitivity for each model time step. The temporal analysis of parameter sensitivities is related to total discharge and to the corresponding process to identify temporal process patterns. In combination with a temporal analysis of model performance, dominant parameters in phases of poor model performance are detected. Based on this, failures in model components are derived. Temporal diagnostic information is also included in the model calibration. For the estimation of the parameter values, only the periods of high sensitivity of this parameter are used.This project aims to develop new methods to achieve hydrologic consistency in model structure and calibration based on the temporal patterns of dominant processes. The main research objectives aim to achieve hydrologic consistency1) in the temporal process patterns by analyzing the temporal variations in dominant model parameters in relation to the corresponding hydrological process2) in the model structure by detecting structural model failures with a joined temporal analysis of parameter sensitivity and model performance3) in the estimation of model parameters by developing a new innovative calibration method based on periods of high parameter sensitivity.