Development of an Objective Low Flow Identification Method Using Breakpoint Analysis

Raczynski, K., & Dyer, J. (2022). Development of an Objective Low Flow Identification Method Using Breakpoint Analysis. Water. 14(14), 2212. DOI:10.3390/w14142212.

first_page settings Order Article Reprints Open AccessArticle Development of an Objective Low Flow Identification Method Using Breakpoint Analysis by Krzysztof Raczyński 1,* [ORCID] and Jamie Dyer 1,2 [ORCID] 1 Northern Gulf Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, USA 2 Department of Geosciences, Mississippi State University, 200A Hilbun Hall, Starkville, MS 39762, USA * Author to whom correspondence should be addressed. Water 2022, 14(14), 2212; https://doi.org/10.3390/w14142212 Received: 22 May 2022 / Revised: 8 July 2022 / Accepted: 11 July 2022 / Published: 13 July 2022 (This article belongs to the Special Issue Assessing Hydrological Drought in a Climate Change: Methods and Measures) Download Browse Figures Versions Notes 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.