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An Objective Time‐Series‐Analysis Method for Rainfall‐Runoff Event Identification
Author(s) -
Giani G.,
Tarasova L.,
Woods R. A.,
RicoRamirez M. A.
Publication year - 2022
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2021wr031283
Subject(s) - baseflow , streamflow , surface runoff , identification (biology) , environmental science , time series , event (particle physics) , series (stratigraphy) , temporal resolution , precipitation , computer science , statistics , data mining , meteorology , mathematics , machine learning , geography , geology , drainage basin , ecology , paleontology , botany , physics , cartography , quantum mechanics , biology
Methodologies for rainfall‐runoff event identification from continuous time series suffer from significant subjectivity. In particular, whether they initiate the identification from rainfall or from the streamflow timeseries, they usually require baseflow separation and they need substantial modifications and parameters’ recalibration when changing temporal resolution of the data. Therefore, here we propose a novel objective methodology for event identification that is easily transferable across sites and temporal resolutions, without having to make subjective choices and adjust multiple parameters. The proposed method to identify rainfall‐runoff events is based on a time series analysis technique that simultaneously considers rainfall and streamflow time series and does not make any a priori assumptions about baseflow separation. The novel method allows also to produce a baseflow separation a posteriori by connecting the delimiters of identified streamflow events. Moreover, the proposed method can be applied at any time resolution as long as the resolution is high enough to capture the time delay between precipitation and runoff response. When comparing the results between the proposed and the traditional baseflow‐based event identification approach, we observe a good agreement in terms of event properties both at hourly and daily scale (correlation of runoff ratios between the two methods equal to 0.78 [daily data] and 0.84 [hourly data]). The analysis comparing hourly and daily event identifications with the proposed method reveals also that the novel method produces coherent events across different temporal resolutions (correlation of runoff ratios between daily and hourly data equal to 0.71).