Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment

Zekâi Şen ()
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Zekâi Şen: İstanbul Medipol University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 11, No 23, 4415-4429

Abstract: Abstract In the last 30 years, there are many publications in the literature due to global warming and climate change impacts exhibiting non-stationary behaviors in hydro-meteorology time series records especially in the forms of increasing or decreasing trends. The conventional trend analyzes cover the entire recording time with a single straight-line trend and slope. These methods do not provide information about up and down partial moving trends evolution at shorter durations along the entire record length. This paper proposes a dynamic methodology for identifying such evolutionary finite duration moving trend method (MTM) identifications and interpretations. The purpose of choosing MTM was to investigate the dynamic partial trend evolution over the recording period so that dry (decreasing trend) and wet (increasing trend) segments could be objectively identified and these trends could assist in water resources management in the study area. The moving trend analysis is like the classical moving average methodology with one important digression that instead of arithmetic averages and their horizontal line representations, a series of finite duration successive increasing and decreasing trends are identified over a given hydro-meteorology time series record. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important implications. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.

Keywords: Analysis; Discharge; Evolution; Hydro-meteorology; Moving; Partial; Trend (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03872-2

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