Abstract
Characterisation of rapid fluctuations of flow and stage
Power production by hydro-electric facilities according to rapidly varying demand on the electricity market (hydropeaking) may lead to unnatural fluctuations of flow and water level in rivers downstream of power plant outlets. We developed a method for automated analysis of time series in order to quantify these rapid fluctuations with the purpose of assessing impacts on the river ecosystem. Parameters of three categories were chosen: 1. Maximum and minimum of an increase/decrease, flow ratio (magnitude) 2. Average and maximal rate of increase/decrease, point of time, duration (scale of time) 3. Count of increase/decrease (frequency). The data processing comprises data reading, correction and preparation of the time series (outliers/error elimination, time shift and leap year, interpolation, smoothing), parameter calculation, statistical description and graphic illustration. The rapid fluctuations are identified by establishing river and data specific thresholds for the rate of change in flow and water level. The computational tool was demonstrated with the flow and stage time series of a gaging station in the Norwegian river Nidelva.
Power production by hydro-electric facilities according to rapidly varying demand on the electricity market (hydropeaking) may lead to unnatural fluctuations of flow and water level in rivers downstream of power plant outlets. We developed a method for automated analysis of time series in order to quantify these rapid fluctuations with the purpose of assessing impacts on the river ecosystem. Parameters of three categories were chosen: 1. Maximum and minimum of an increase/decrease, flow ratio (magnitude) 2. Average and maximal rate of increase/decrease, point of time, duration (scale of time) 3. Count of increase/decrease (frequency). The data processing comprises data reading, correction and preparation of the time series (outliers/error elimination, time shift and leap year, interpolation, smoothing), parameter calculation, statistical description and graphic illustration. The rapid fluctuations are identified by establishing river and data specific thresholds for the rate of change in flow and water level. The computational tool was demonstrated with the flow and stage time series of a gaging station in the Norwegian river Nidelva.
