Discussion of “Time-Series Analyses of Water-Quality Data”

1967 ◽  
Vol 93 (6) ◽  
pp. 264-268
Author(s):  
Kyatsandra Seetharamiah
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Data ◽  
Water Quality Data ◽  
Time Series Analyses

scholarly journals Evaluation of statistical methods for quantifying fractal scaling in water-quality time series with irregular sampling

2018 ◽  
Vol 22 (2) ◽  
pp. 1175-1192 ◽  
Author(s):  
Qian Zhang ◽  
Ciaran J. Harman ◽  
James W. Kirchner
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Irregular Sampling ◽  
Quality Data ◽  
Sampled Data ◽  
Spectral Slope ◽  
Water Quality Data ◽  
Fractal Scaling ◽  
Wide Range ◽  
Irregularly Sampled Data

Abstract. River water-quality time series often exhibit fractal scaling, which here refers to autocorrelation that decays as a power law over some range of scales. Fractal scaling presents challenges to the identification of deterministic trends because (1) fractal scaling has the potential to lead to false inference about the statistical significance of trends and (2) the abundance of irregularly spaced data in water-quality monitoring networks complicates efforts to quantify fractal scaling. Traditional methods for estimating fractal scaling – in the form of spectral slope (β) or other equivalent scaling parameters (e.g., Hurst exponent) – are generally inapplicable to irregularly sampled data. Here we consider two types of estimation approaches for irregularly sampled data and evaluate their performance using synthetic time series. These time series were generated such that (1) they exhibit a wide range of prescribed fractal scaling behaviors, ranging from white noise (β  =  0) to Brown noise (β  =  2) and (2) their sampling gap intervals mimic the sampling irregularity (as quantified by both the skewness and mean of gap-interval lengths) in real water-quality data. The results suggest that none of the existing methods fully account for the effects of sampling irregularity on β estimation. First, the results illustrate the danger of using interpolation for gap filling when examining autocorrelation, as the interpolation methods consistently underestimate or overestimate β under a wide range of prescribed β values and gap distributions. Second, the widely used Lomb–Scargle spectral method also consistently underestimates β. A previously published modified form, using only the lowest 5 % of the frequencies for spectral slope estimation, has very poor precision, although the overall bias is small. Third, a recent wavelet-based method, coupled with an aliasing filter, generally has the smallest bias and root-mean-squared error among all methods for a wide range of prescribed β values and gap distributions. The aliasing method, however, does not itself account for sampling irregularity, and this introduces some bias in the result. Nonetheless, the wavelet method is recommended for estimating β in irregular time series until improved methods are developed. Finally, all methods' performances depend strongly on the sampling irregularity, highlighting that the accuracy and precision of each method are data specific. Accurately quantifying the strength of fractal scaling in irregular water-quality time series remains an unresolved challenge for the hydrologic community and for other disciplines that must grapple with irregular sampling.

scholarly journals Time series modeling of biochemical oxygen demand at the upstream catchment of Feitsui Reservoir, Taiwan

2016 ◽  
Vol 47 (5) ◽  
pp. 1069-1085 ◽  
Author(s):  
Yung-Chia Chiu ◽  
Chih-Wei Chiang ◽  
Tsung-Yu Lee
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Biochemical Oxygen Demand ◽  
Oxygen Demand ◽  
Quality Data ◽  
Series Data ◽  
Water Quality Data ◽  
Inference System ◽  
Feitsui Reservoir ◽  
Upstream Catchment

The adaptive neuro fuzzy inference system (ANFIS) has been proposed to model the time series of water quality data in this study. The biochemical oxygen demand data collected at the upstream catchment of Feitsui Reservoir in Taiwan for more than 20 years are selected as the target water quality variable. The classical statistical technique of the Box-Jenkins method is applied for the selection of appropriate input variables and data pre-processing of using differencing is implemented during the model development. The time series data obtained by ANFIS models are compared to those obtained by autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs). The results show that the ANFIS model identified at each sampling station is superior to the respective ARIMA and ANN models. The R values at all sampling stations of the training and testing datasets are 0.83–0.98 and 0.81–0.89, respectively, except at Huang-ju-pi-liao station. ANFIS models can provide accurate predictions for complex hydrological processes, and can be extended to other areas to improve the understanding of river pollution trends. The procedure of input selection and the pre-processing of input data proposed in this study can stimulate the usage of ANFIS in other related studies.

scholarly journals GRQA: Global River Water Quality Archive

2021 ◽  
Author(s):  
Holger Virro ◽  
Giuseppe Amatulli ◽  
Alexander Kmoch ◽  
Longzhu Shen ◽  
Evelyn Uuemaa
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Measurement ◽  
Observation Time ◽  
Quality Analysis ◽  
Quality Data ◽  
Observation Data ◽  
Water Quality Data ◽  
Spatial Coverage ◽  
Global Datasets

Abstract. A major problem related to global water quality analysis and modelling has been the lack of available good quality and consistent water quality measurement datasets with a global spatial coverage. Current study aims to contribute into improving the global datasets on water quality by aggregating and harmonizing five national, continental and global datasets: CESI, GEMSTAT, GLORICH, WATERBASE and WQP. The GRQA compilation involved converting observation data from the five sources into a common format and harmonizing the corresponding metadata, flagging outliers, calculating time series characteristics and detecting duplicate observations from sources with a spatial overlap. The final dataset extends the spatial and temporal coverage of previously available water quality data and contains 42 parameters and over 16 million measurements around the globe covering the 1898–2020 time period. Metadata in the form of statistical tables, maps and figures are provided along with observation time series. The GRQA dataset, supplementary metadata and figures are available for download on the DataCite and OpenAire enabled repository of the University of Tartu, DataDOI, http://dx.doi.org/10.23673/re-273 (Virro et al., 2021).

Main knowledge gaps in critical zone processes and behaviour: Extracting information from water quality time-series data and models outputs

2020 ◽  
Author(s):  
Zahra Thomas ◽  
Ophélie Fovet ◽  
Qian Zhang ◽  
Channa Rajanayaka ◽  
Christian Zammit ◽  
...  
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Monitoring Network ◽  
Water Quality Monitoring ◽  
Critical Zone ◽  
Quality Monitoring ◽  
Quality Data ◽  
Knowledge Gaps ◽  
Water Quality Data ◽  
Insight Into

<p>In the last few decades, the degradation of water quality and resulting regulations, such as the European Water Framework Directive, the United States Clean Water Act, and the New Zealand Resource Management Act 1991 have promoted water quality monitoring in terms of parameter richness, spatial density and high temporal resolution. Long-term catchment observatories have been strengthened to gain insight into hydrological and biogeochemical processes. New technologies have been developed and deployed to collect more in situ water quality data at higher frequencies. Thus, water quality monitoring around the world has produced a large amount of data from research catchments but also from national monitoring networks. Despite these efforts, water quality data are highly heterogeneous in terms of targeted parameters, measurement methods, sampling frequencies. Also, accessibility to water samples differ from each hydrological compartment (stream, groundwater, soil water and precipitation). Among water quality time-series, higher sampling frequencies are available for stream water where monitoring is relatively easy to carry out generating a high amount of data. However, groundwater data are rare since monitoring and access is relatively difficult. Also, the aim of monitoring network evolved with time. In fact, networks are usually established for a specific purpose which is changing with time and the questions the network is trying to answer? This raise the issue of spatial and temporal flexibility- multi purpose network and the use of network to support model development which could be seen as a “theoretical” monitoring network.</p><p>The objective of this talk is to present a review of methods used for analysing temporal water quality signals and models outputs, based on a panel of examples from few but densely monitored environmental research observatories. Such infrastructures also give an insight into critical zone (CZ) research that help to build a transdisciplinary community to identify the main knowledge gaps in CZ processes and behaviour.</p>

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