[论文解读] A calibration framework for high-resolution hydrological models using a multiresolution and heterogeneous strategy

Ruochen Sun, Felipe Hernández+2 more|arXiv (Cornell University)|Apr 6, 2020

Hydrology and Watershed Management Studies参考文献 94被引用 6

一句话总结

本文提出了一种用于高分辨率水文模型的多分辨率异构校准框架，通过按敏感性分组参数并跨多次优化运行迭代细化搜索范围，提升了参数估计性能。该方法减少了等效性问题，增强了参数估计的现实性，并在包含134个参数的双模型实验中，相比传统方法实现了更优的计算效率。

ABSTRACT

Increasing spatial and temporal resolution of numerical models continues to propel progress in hydrological sciences, but, at the same time, it has strained the ability of modern automatic calibration methods to produce realistic model parameter combinations for these models. This paper presents a new reliable and fast automatic calibration framework to address this issue. In essence, the proposed framework, adopting a divide and conquer strategy, first partitions the parameters into groups of different resolutions based on their sensitivity or importance, in which the most sensitive parameters are prioritized with highest resolution in parameter search space, while the least sensitive ones are explored with the coarsest resolution at beginning. This is followed by an optimization based iterative calibration procedure consisting of a series of sub-tasks or runs. Between consecutive runs, the setup configuration is heterogeneous with parameter search ranges and resolutions varying among groups. At the completion of each sub-task, the parameter ranges within each group are systematically refined from their previously estimated ranges which are initially based on a priori information. Parameters attain stable convergence progressively with each run. A comparison of this new calibration framework with a traditional optimization-based approach was performed using a quasi-synthetic double-model setup experiment to calibrate 134 parameters and two well-known distributed hydrological models: the Variable Infiltration Capacity (VIC) model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The results demonstrate statistically that the proposed framework can better mitigate equifinality problem, yields more realistic model parameter estimates, and is computationally more efficient.

研究动机与目标

• 解决高分辨率水文模型在大规模参数空间下的校准挑战。
• 减少由高模型复杂度和分辨率引起的参数估计等效性问题。
• 提升分布式水文模型自动校准的计算效率。
• 开发一种系统性、迭代式的校准策略，根据参数敏感性自适应调整参数搜索分辨率。
• 通过动态范围优化，提升模型参数的现实性与收敛性。

提出的方法

• 该框架根据参数的敏感性或重要性将模型参数划分为若干组，并为每组分配不同的分辨率水平。
• 采用分而治之的策略，优先对最敏感的参数使用最高分辨率的搜索空间。
• 通过迭代优化过程执行一系列子任务，各次运行中采用异构配置，按组别调整参数搜索范围与分辨率。
• 每次运行后，基于先前估计结果与先验信息，系统性地细化参数搜索范围。
• 该过程实现参数的逐步收敛，高敏感性参数在序列中更早稳定。
• 该方法在VIC与DHSVM模型的准合成双模型设置下进行评估，校准134个参数。

实验结果

研究问题

• RQ1 多分辨率与异构校准框架能否减少高分辨率水文模型中的等效性问题？
• RQ2 与传统优化方法相比，该框架在参数估计现实性方面有何提升？
• RQ3 该框架在大规模水文模型校准中能多大程度提升计算效率？
• RQ4 基于敏感性的参数分组如何影响校准过程中的收敛性与稳定性？
• RQ5 在迭代运行中实施动态范围优化，能否带来更稳健、更可靠的参数估计？

主要发现

• 与基于传统优化的校准方法相比，所提出框架显著缓解了等效性问题。
• 通过基于敏感性与迭代反馈系统性地细化搜索范围，该方法生成了更具现实性的模型参数估计。
• 该方法实现了更优的计算效率，显著减少了高分辨率模型校准所需的时间与资源。
• 由于采用更高分辨率搜索，高度敏感参数在迭代过程中更可靠且更早收敛。
• 各次运行中采用的异构配置实现了对参数空间的自适应探索，提升了整体校准性能。
• 统计比较结果证实，该框架在校准VIC与DHSVM模型中134个参数时，兼具更高的准确度与效率。