Arash Amir Mohammad, Fryirs Kirstie, Ralph Timothy J
School of Natural Sciences, Macquarie University, North Ryde, NSW, Australia.
PLoS One. 2025 Jan 10;20(1):e0315796. doi: 10.1371/journal.pone.0315796. eCollection 2025.
The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flow and flood hydrology at reach and catchment scales. However, the analysis of individual hydrographs is time consuming, making the analysis of hundreds or thousands of them unachievable. A method or protocol is needed to ensure that the datasets being generated, and the metrics produced, have been consistently derived and validated. In this lab protocol, we present workflows in Python for extracting flow hydrographs with any available temporal resolution from any Open Access or publicly available gauging station records. The workflow identifies morphologically-defined flow and flood types (i.e. in-channel fresh, high flow and overbank flood) and uses them to classify hydrographs. It then calculates several at-a-station and upstream-to-downstream hydrograph shape metrics including kurtosis, skewness, peak hydrograph stage, peak arrival time, rate-of-rise, peak-to-peak travel time, flood wave celerity, flood peak attenuation, and flood wave attenuation index. Some metrics require GIS-derived data, such as catchment area and upstream-to-downstream channel distance between gauges. The output dataset provides quantified hydrograph shape metrics which can be used to track changes in flow and flood hydrographs over time, or to characterise the flow and flood hydrology of catchments and regions. The workflows are flexible enough to allow for additional hydrograph shape indicators to be added or swapped out, or to use a different hydrograph classification method that suits local conditions. The protocol could be considered a change detection tool to identify where changes in hydrology are occurring and where to target more sophisticated modelling exercises to explain the changes detected. We demonstrate the workflow using 117 Open Access gauging station records that are available for coastal rivers of New South Wales (NSW), Australia.
流量过程线的形态特征包含了在河段和流域尺度上理解、监测和评估流量及洪水水文学变化的关键信息。然而，对单个过程线进行分析耗时费力，使得对成百上千条过程线进行分析变得难以实现。因此，需要一种方法或流程来确保所生成的数据集以及所产生的指标是经过一致推导和验证的。在本实验流程中，我们展示了使用Python的工作流程，可从任何开放获取或公开可用的测量站记录中提取具有任意时间分辨率的流量过程线。该工作流程识别形态学定义的流量和洪水类型（即河道内新鲜水流、高流量和漫滩洪水），并以此对过程线进行分类。然后，它会计算几个站级和上下游过程线形态指标，包括峰度、偏度、过程线峰值水位、峰值到达时间、上升速率、峰峰传播时间、洪水波速、洪水峰值衰减以及洪水波衰减指数。有些指标需要地理信息系统（GIS）导出的数据，如集水面积和测量站之间的上下游河道距离。输出数据集提供了量化的过程线形态指标，可用于跟踪流量和洪水过程线随时间的变化，或描述流域和区域的流量及洪水水文学特征。这些工作流程足够灵活，允许添加或替换其他过程线形态指标，或使用适合当地情况的不同过程线分类方法。该流程可被视为一种变化检测工具，用于识别水文变化发生的位置，以及确定针对哪些地方进行更复杂的建模工作来解释检测到的变化。我们使用澳大利亚新南威尔士州（NSW）沿海河流的117条开放获取测量站记录来演示该工作流程。
