Moving trend analysis methodology for hydro-meteorology time series dynamic assessment

Abstract

Abstract Temporal hydro-meteorological time series have different components, such as the deterministic (periodicity, trend, jump) and stochastic (uncertainty, statistical, probabilistic) parts that are important for practical applications and prediction in water resources management studies. For many years, stochastic components were assumed to be stationary in order to reliably implement stochastic modelling procedures. In the last 30 years, there are many publications in the literature due to global warming and accordingly, climate change, which exhibits non-stationary behaviors in hydro-meteorology time series records. Oftentimes, classical trend analyzes cover the entire recording time with a single holistic straight-line trend and slope. Such an approach does not provide information on trend evolutionary development at shorter times over the entire record length. This paper proposes a methodology for identifying local finite length trends in a systematic way that moves dynamically over a series of short time frames for internal trend evolution developments and interpretations. 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 computational results and time series internal structural development with key comments. The moving trend method is similar to the classical moving average methodology with one important difference that instead of arithmetic averages and their horizontal lines, a series of local trend are given over the recording period with increasing or decreasing partial trends. 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.