Development of an Objective Low Flow Identification Method Using Breakpoint Analysis

Abstract

Low flow events (a.k.a. streamflow drought) are described as episodes where stream flows are lower or equal to a specified minimum threshold level. This threshold is usually predefined at the methodological stage of a study and is generally applied as a chosen flow percentile, determined from a flow duration curve (FDC). Unfortunately, many available methods for choosing both the percentile and FDCs result in a large range of potential thresholds, which reduces the ability to statistically compare the results from the different methods while also losing the natural character of the phenomenon. The aim of this work is to introduce a new approach for low flow threshold calculation through the application of an objective approach using breakpoint analysis. This method allows for the identification of an environmental moment of river transition, from atmospheric feed flows to base flow, which characterizes the moment at the beginning of the hydrological drought. The method allows for not only the capture of the genesis of a low flow event but, above all, unifies the approach toward threshold levels and completely excludes the impact of the subjective researcher’s decisions, which occur at the methodological stage when selecting the threshold criteria or when choosing a respective percentile. In addition, the method can be successfully used in datasets characterized by a high level of discretization, such as numerical model data, where the subsurface runoff component is not described in sufficient detail. Results of this work show that the objective identification method is better able to capture the occurrence of a low flow event, improving the ability to identify hydrologic drought conditions. The proposed method is published together with the Python module objective_thresholds for broad use in other studies.

Quantifying patterns of streamflow peaks over the southeastern United States using a long‐term retrospective data set

“…Like low flow conditions, significant changes in FDC shape indicates a potential change in local conditions that corresponds to changes in flow distribution in the river channel. In the case of low flows, such a change in the lower range of the FDC indicates a moment of change from precipitation‐based supplementation to groundwater‐based supplementation of runoff, indicating the onset of hydrologic drought (Raczyński & Dyer, 2022). In the upper range of the FDC this level might indicate bank‐level flows or extreme precipitation conditions, relating to increased runoff due to complete saturation of the soil or reaching the limit of catchment retention abilities.…”

“…To objectively determine the threshold ( Q obj ) at each node, the Fisher‐Jenks natural breakpoint algorithm is used over the upper range (35%) of the FDC (Raczyński & Dyer, 2022). The flow at a specific node is considered a SFP when its value exceeds the value of Q obj defined for that specific node.…”

“…Unlike in the case of observational data sets, the modelled data are limited by the parametrization of the model based on estimated environmental conditions. Although this issue is observed more often in low flow values (Raczyński & Dyer, 2022), it might occur within higher flow ranges if unfavourable modelling conditions occur. To avoid such scenarios and exclude uncertainty associated with subjective researcher decision on threshold percentile level (Pan & Rahman, 2022), a breakpoint approach was used to generate thresholds for each streamflow node separately.…”

Assessing Hydrological Drought in a Climate Change: Methods and Measures

“…Another series of articles analyzed river discharge events in different countries. After a critical overview of the percentiles and flow duration curve (FDC) use, Raczy ński and Dyer [6] introduced a new methodology to compute the low flow threshold (LFT) based on the change point detection. They indicate that the new algorithm has the advantage of objectively identifying the beginning of the low flow events, unifying the approaches for selecting the threshold levels.…”