How important is accounting for serial correlation and field significance in trend detection of extreme rainfall occurrences? 

2021 ◽  
Author(s):  
Stefano Farris ◽  
Roberto Deidda ◽  
Francesco Viola ◽  
Giuseppe Mascaro
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Statistical Tests ◽  
Extreme Rainfall ◽  
Nonparametric Tests ◽  
Precipitation Extremes ◽  
Trend Detection ◽  
Monte Carlo Experiments ◽  
Precipitation Records

<p>A number of studies have shown that the ability of statistical tests to detect trends in hydrologic extremes is negatively affected by (i) the presence of autocorrelation in the time series, and (ii) field significance. Here, we investigate these two issues and evaluate the power of several trend tests using time series of frequencies (or counts) of precipitation extremes from long-term (100 years) precipitation records of 1087 gauges of the Global Historical Climate Network database. For this aim, we design several Monte Carlo experiments based on simulations of random count time series with different levels of autocorrelation and trend. We find the following. (1) The observed records are consistent with the hypothesis of autocorrelation induced by the presence of trends, indicating that the existence of serial correlation does not significantly affect trend detection. (2) Tests based on the linear and Poisson regressions are more powerful that nonparametric tests, such as Mann Kendall. (3) Accounting for field significance improves the interpretation of the results by limiting the rejection of the false null hypothesis. We then use these results to investigate the presence of trends in the observed records. We find that, depending on the quantiles used to define the frequency of precipitation extremes, 34-47% of the selected gages exhibit a statistically significant trend, of which 70-80% are positive and located mainly in United States and Northern Europe. The significant negative trends are mostly located in Southern Australia.</p>

scholarly journals Statistical regularization for trend detection: an integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

2020 ◽  
Vol 20 (16) ◽  
pp. 9915-9938
Author(s):  
Kai-Lan Chang ◽  
Owen R. Cooper ◽  
Audrey Gaudel ◽  
Irina Petropavlovskikh ◽  
Valérie Thouret
Keyword(s):  
Time Series ◽  
Tropospheric Ozone ◽  
Sampling Frequency ◽  
Basic Trend ◽  
Trend Detection ◽  
Percentage Error ◽  
Statistical Regularization ◽  
Vertical Correlation ◽  
Accurate Quantification

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the short-lived anomalies in the time series resulting from ozone's high temporal variability. To enhance trend detection, we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured variation through a statistical regularization (i.e., a roughness penalty) by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from (1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China throughout 1994–2017 and (2) NOAA GML's (Global Monitoring Laboratory) ozonesonde records at Hilo, Hawaii, (1982–2018) and Trinidad Head, California (1998–2018). We illustrate the ability of this technique to detect a consistent trend estimate and its effectiveness in reducing the associated uncertainty in the profile data due to the low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation, we found that a typical sampling strategy (i.e. four profiles per month) might result in 7 % of sampled trends falling outside the 2σ uncertainty interval derived from the full dataset with an associated 10 % of mean absolute percentage error. Based on a series of sensitivity studies, we determined optimal sampling frequencies for (1) basic trend detection and (2) accurate quantification of the trend. When applying the integrated fit method, we find that a typical sampling frequency of four profiles per month is adequate for basic trend detection; however, accurate quantification of the trend requires 14 profiles per month. Accurate trend quantification can be achieved with only 10 profiles per month if a regular sampling frequency is applied. In contrast, the standard separated fit method, which ignores the vertical correlation between pressure surfaces, requires 8 profiles per month for basic trend detection and 18 profiles per month for accurate trend quantification. While our method improves trend detection from sparse datasets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

NONPARAMETRIC TESTS FOR TREND DETECTION IN WATER QUALITY TIME SERIES

1988 ◽  
Vol 24 (3) ◽  
pp. 545-556 ◽  
Author(s):  
David Berryman ◽  
Bernard Bobée ◽  
Daniel Cluis ◽  
John Haemmerli
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Nonparametric Tests ◽  
Trend Detection

scholarly journals Statistical regularization for trend detection: An integrated approach for detecting long-term trends from sparse tropospheric ozone profiles

2020 ◽  
Author(s):  
Kai-Lan Chang ◽  
Owen R Cooper ◽  
Audrey Gaudel ◽  
Irina Petropavlovskikh ◽  
Valerie Thouret
Keyword(s):  
Time Series ◽  
Tropospheric Ozone ◽  
Sampling Frequency ◽  
Trend Detection ◽  
Percentage Error ◽  
Full Data ◽  
Uncertainty Interval ◽  
Data Set ◽  
Statistical Regularization

Abstract. Detecting a tropospheric ozone trend from sparsely sampled ozonesonde profiles (typically once per week) is challenging due to the noise in the time series resulting from ozone's high temporal variability. To enhance trend detection we have developed a sophisticated statistical approach that utilizes a geoadditive model to assess ozone variability across a time series of vertical profiles. Treating the profile time series as a set of individual time series on discrete pressure surfaces, a class of smoothing spline ANOVA (analysis of variance) models is used for the purpose of jointly modeling multiple correlated time series (on separate pressure surfaces) by their associated seasonal and interannual variabilities. This integrated fit method filters out the unstructured noise through a statistical regularization (i.e. a roughness penalty), by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from 1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China, and 2) NOAA GMD's (Global Monitoring Division) ozonesonde records at Hilo, Hawaii and Trinidad Head, California. We illustrate the ability of this technique to detect a consistent trend estimate, and its effectiveness for reducing the associated uncertainty in the noisy profile data due to low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection. When ignoring the vertical correlation we found that a typical sampling strategy of 4 profiles-per-month results in 7 % of sampled trends falling outside the 2-sigma uncertainty interval derived from the full data set, with associated 10 % of mean absolute percentage error. We determined that an optimal sampling frequency is 14 profiles per month when using the integrated fit method for calculating trends; when the integrated fit method is not applied, the sampling frequency had to be increased to 18 profiles per month to achieve the same result. While our method improves trend detection from sparse data sets, the key to substantially reducing the uncertainty is to increase the sampling frequency.

TESTING FOR DISTRIBUTIONAL CHANGE IN TIME SERIES

2001 ◽  
Vol 17 (1) ◽  
pp. 156-187 ◽  
Author(s):  
Atsushi Inoue
Keyword(s):  
Time Series ◽  
A Priori ◽  
Nuisance Parameter ◽  
Nonparametric Tests ◽  
Local Power ◽  
Finite Sample ◽  
Null Distributions ◽  
Monte Carlo Experiments ◽  
Simulation Based ◽  
Finite Sample Properties

This paper proposes nonparametric tests of change in the distribution function of a time series. The limiting null distributions of the test statistics depend on a nuisance parameter, and critical values cannot be tabulated a priori. To circumvent this problem, a new simulation-based statistical method is developed. The validity of our simulation procedure is established in terms of size, local power, and test consistency. The finite-sample properties of the proposed tests are evaluated in a set of Monte Carlo experiments, and the distributional stability in financial markets is examined.

scholarly journals Long Term Homogeneity, Trend and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India

2019 ◽  
Author(s):  
Sandeep Kumar Patakamuri ◽  
Krishnaveni Muthiah ◽  
Venkataramana Sridhar
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Andhra Pradesh ◽  
Water Resource Management ◽  
Statistical Tests ◽  
Monsoon Season ◽  
Rainfall Data ◽  
Change Points ◽  
South West Monsoon ◽  
Independent Series

Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the sub-district level and aggregated to monthly, annual and seasonal rainfall totals and the number of rainy days. The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. Non-Parametric Mann-Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann-Kendall tests (Pre-Whitening, Trend Free Pre-Whitening, Bias Corrected Pre-Whitening and two variants of Variance Correction Approaches) were applied. A significant increasing summer rainfall trend is observed in 6 out of 27 stations. Significant decreasing trends are observed at two stations in the south-west monsoon season and at two stations in the north-east monsoon season. To identify the trend change-points in the time series, distribution-free Cumulative SUM test and sequential Mann-Kendall tests were applied. Two open-source library packages were developed in R language namely, ‘modifiedmk’ and ‘trendchange’ to implement the statistical tests mentioned in this paper. The study will benefit water resource management, drought mitigation, socio-economic development and sustainable agricultural planning in the region.

scholarly journals Detecting trend on ecological river status – how to deal with short incomplete bioindicator time series? Methodological and operational issues

2018 ◽  
Vol 379 ◽  
pp. 169-174
Author(s):  
Flavie Cernesson ◽  
Marie-George Tournoud ◽  
Nathalie Lalande
Keyword(s):  
Time Series ◽  
Statistical Tests ◽  
Methodological Issue ◽  
Temporal Trends ◽  
Graphical Analysis ◽  
Point Of View ◽  
Trend Detection ◽  
Data Sets ◽  
Monitoring Networks ◽  
Operational Issues

Abstract. Among the various parameters monitored in river monitoring networks, bioindicators provide very informative data. Analysing time variations in bioindicator data is tricky for water managers because the data sets are often short, irregular, and non-normally distributed. It is then a challenging methodological issue for scientists, as it is in Saône basin (30 000 km2, France) where, between 1998 and 2010, among 812 IBGN (French macroinvertebrate bioindicator) monitoring stations, only 71 time series have got more than 10 data values and were studied here. Combining various analytical tools (three parametric and non-parametric statistical tests plus a graphical analysis), 45 IBGN time series were classified as stationary and 26 as non-stationary (only one of which showing a degradation). Series from sampling stations located within the same hydroecoregion showed similar trends, while river size classes seemed to be non-significant to explain temporal trends. So, from a methodological point of view, combining statistical tests and graphical analysis is a relevant option when striving to improve trend detection. Moreover, it was possible to propose a way to summarise series in order to analyse links between ecological river quality indicators and land use stressors.

scholarly journals Analyzing Trend and Variability of Rainfall in The Tafna Basin (Northwestern Algeria)

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 347 ◽  
Author(s):  
Hanane Bougara ◽  
Kamila Baba Hamed ◽  
Christian Borgemeister ◽  
Bernhard Tischbein ◽  
Navneet Kumar
Keyword(s):  
Time Series ◽  
Water Resource Management ◽  
Statistical Tests ◽  
Trend Detection ◽  
Detection Methods ◽  
Annual Rainfall ◽  
Rainfall Time Series ◽  
Friedman Test ◽  
Upward Trend ◽  
Increasing Trend

Northwest Algeria has experienced fluctuations in rainfall between the two decades 1940s and 1990s from positive to negative anomalies, which reflected a significant decline in rainfall during the mid-1970s. Therefore, further analyzing rainfall in this region is required for improving the strategies on water resource management. In this study, we complement previous studies by dealing with sub basins that were not previously addressed in Tafna basin (our study area located in Northwest Algeria), and by including additional statistical methods (Kruskal–Wallis test, Jonckheere-Terpstra test, and the Friedman test) that were not earlier reported on the large scale (Northwest Algeria). In order to analyse the homogeneity, trends, and stationarity in rainfall time series for nine rainfall stations over the period 1979–2011, we have used several statistical tests. The results showed an increasing trend for annual rainfall after the break detected in 2007 for Djbel Chouachi, Ouled Mimoun, Sidi Benkhala stations using Hubert, Pettitt, and Buishand tests. The Lee and Heghinian test has detected a break at the same year in 2007 for all stations except Sebdou, Beni Bahdel, and Hennaya stations, which have a break date in 1980. We have confirmed this increasing trend for rainfall with other trend detection methods such as Mann Kendall and Sen’s method that highlighted an upward trend for all the stations in the autumn season, which is mainly due to an increase in rainfall in September and October. On a monthly scale, the date of rupture is different from one station to another because the time series are not homogeneous. In addition, we have applied three tests enabling further results: (i) the Jonckheere-Terpstra test has detected an upward trend for two stations (Khemis and Hennaya), (ii) Friedman test has indicated the difference between the mean rank again with Khemis and Hennaya stations and the Merbeh station, (iii) according to the Kruskal-Wallis test, there have been no variance detected between all the rainfall stations. The increasing trend in rainfall may lead to a rise in stream flow and enhance potential floods risks in low-lying regions of the study area.

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