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

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 211 ◽  
Author(s):  
Sandeep Kumar Patakamuri ◽  
Krishnaveni Muthiah ◽  
Venkataramana Sridhar
Keyword(s):  
Time Series ◽  
Serial Correlation ◽  
Andhra Pradesh ◽  
Statistical Tests ◽  
Monsoon Season ◽  
Rainfall Data ◽  
Change Points ◽  
Northeast Monsoon ◽  
Study Results ◽  
Independent Series

The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. 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. NonParametric 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 six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast 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 results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.

scholarly journals Análise de não Homogeneidades de Séries de Vazão de Captações de Nascentes na Bacia Hidrográfica do Rio Gramame, PB, Brasil

2020 ◽  
Vol 13 (6) ◽  
pp. 2896
Author(s):  
Adriana Moura Martins ◽  
Hamilcar José Almeida Filgueira ◽  
Azamor Cirne de Azevedo Filho ◽  
Tarciso Cabral da Silva ◽  
Marcelo Henriques Da Silva Júnior
Keyword(s):  
Time Series ◽  
River Basin ◽  
Statistical Tests ◽  
Local Communities ◽  
Rainfall Data ◽  
South Coast ◽  
Change Points ◽  
Data Series ◽  
The South ◽  
Perennial Springs

A bacia hidrográfica do rio Gramame, no litoral sul paraibano, apresenta diversas nascentes perenes de água com vazões significativas que atendem a comunidades locais para diversos usos. Este trabalho teve como objetivo analisar quatro séries de vazões de captações de nascentes na região sudoeste da bacia e de dados pluviométricos, quanto à sua homogeneidade, entre os anos de 2010 e 2013. A questão motivadora da análise foi a suposta diminuição das vazões de captação das nascentes por consequência da construção de estradas e desmatamentos em áreas do entorno dessas nascentes. Para a análise da homogeneidade das séries, foram empregados testes estatísticos para determinação dos possíveis pontos de ruptura e de verificação da estacionariedade. Foi constatado que houve ruptura em todas as séries de vazões analisadas.  Analysis of non-homogeneities of time series of flow in sources in the Gramame River basin, Paraíba State, Brazil A B S T R A C TThe Gramame river basin on the south coast of Paraiba State, has several perennial springs with significant flows that serve local communities for various uses. However, the construction of roads, in areas around the springs, and recent deforestation indicated to have caused the decrease in flows captured from sources in the basin. This work aimed at analyzing four data series of flows captured from sources in the southwestern basin and the rainfall data series searching to verify their homogeneity, between the years 2010 and 2013. To analyze the homogeneity of the series, statistical tests were used to find significant change points and to verify the stationarity. It was found that rupture occurred in all series of flow analyzed.Keywords: flow from springs, hydrometeorological time series, groundwater.

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.

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 Change Point Enhanced Anomaly Detection for IoT Time Series Data

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1633
Author(s):  
Elena-Simona Apostol ◽  
Ciprian-Octavian Truică ◽  
Florin Pop ◽  
Christian Esposito
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Change Point ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Change Point Detection ◽  
Change Points ◽  
Series Data ◽  
Prediction And Forecasting ◽  
Point Detection

Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis in order to extract meaningful information and statistics. Anomaly detection is an important part of time series analysis, improving the quality of further analysis, such as prediction and forecasting. Thus, detecting sudden change points with normal behavior and using them to discriminate between abnormal behavior, i.e., outliers, is a crucial step used to minimize the false positive rate and to build accurate machine learning models for prediction and forecasting. In this paper, we propose a rule-based decision system that enhances anomaly detection in multivariate time series using change point detection. Our architecture uses a pipeline that automatically manages to detect real anomalies and remove the false positives introduced by change points. We employ both traditional and deep learning unsupervised algorithms, in total, five anomaly detection and five change point detection algorithms. Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point. In our experiments, we use a large real-world dataset containing multivariate time series about water consumption collected from smart meters. As an evaluation metric, we use Mean Absolute Error (MAE). The low MAE values show that the algorithms accurately determine anomalies and change points. The experimental results strengthen our assumption that anomaly detection can be improved by determining and removing change points as well as validates the correctness of our proposed rules in real-world scenarios. Furthermore, the proposed rule-based decision support systems enable users to make informed decisions regarding the status of the water distribution network and perform effectively predictive and proactive maintenance.

scholarly journals Synoptic Preconditions for Extreme Flooding during the Summer Asian Monsoon in the Mumbai Area

2014 ◽  
Vol 15 (1) ◽  
pp. 229-242 ◽  
Author(s):  
Marco Lomazzi ◽  
Dara Entekhabi ◽  
Joaquim G. Pinto ◽  
Giorgio Roth ◽  
Roberto Rudari
Keyword(s):  
Time Series ◽  
South Asian ◽  
Asian Monsoon ◽  
Indian Subcontinent ◽  
Monsoon Season ◽  
Value Added ◽  
South Asian Monsoon ◽  
Flood Events ◽  
The South ◽  
Synoptic Conditions

Abstract The summer monsoon season is an important hydrometeorological feature of the Indian subcontinent and it has significant socioeconomic impacts. This study is aimed at understanding the processes associated with the occurrence of catastrophic flood events. The study has two novel features that add to the existing body of knowledge about the South Asian monsoon: 1) it combines traditional hydrometeorological observations (rain gauge measurements) with unconventional data (media and state historical records of reported flooding) to produce value-added century-long time series of potential flood events and 2) it identifies the larger regional synoptic conditions leading to days with flood potential in the time series. The promise of mining unconventional data to extend hydrometeorological records is demonstrated in this study. The synoptic evolution of flooding events in the western-central coast of India and the densely populated Mumbai area are shown to correspond to active monsoon periods with embedded low pressure centers and have far-upstream influences from the western edge of the Indian Ocean basin. The coastal processes along the Arabian Peninsula where the currents interact with the continental shelf are found to be key features of extremes during the South Asian monsoon.

Classification of landslide activity based on spaceborne interferometric SAR at the Moselle Valley, Germany

2021 ◽  
Author(s):  
Andre C. Kalia
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Statistical Tests ◽  
Spatial Density ◽  
Deformation Pattern ◽  
Visual Interpretation ◽  
Landslide Activity ◽  
Spatial Coverage ◽  
Interferometric Sar

<p>Landslide activity is an important information for landslide hazard assessment. However, an information gap regarding up to date landslide activity is often present. Advanced differential interferometric SAR processing techniques (A-DInSAR), e.g. Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) are able to measure surface displacements with high precision, large spatial coverage and high spatial sampling density. Although the huge amount of measurement points is clearly an improvement, the practical usage is mainly based on visual interpretation. This is time-consuming, subjective and error prone due to e.g. outliers. The motivation of this work is to increase the automatization with respect to the information extraction regarding landslide activity.</p><p>This study focuses on the spatial density of multiple PSI/SBAS results and a post-processing workflow to semi-automatically detect active landslides. The proposed detection of active landslides is based on the detection of Active Deformation Areas (ADA) and a subsequent classification of the time series. The detection of ADA consists of a filtering of the A-DInSAR data, a velocity threshold and a spatial clustering algorithm (Barra et al., 2017). The classification of the A-DInSAR time series uses a conditional sequence of statistical tests to classify the time series into a-priori defined deformation patterns (Berti et al., 2013). Field investigations and thematic data verify the plausibility of the results. Subsequently the classification results are combined to provide a layer consisting of ADA including information regarding the deformation pattern through time.</p>

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