scholarly journals Variations of the Precipitation Extremes over the Guangdong-Hong Kong-Macao Greater Bay Area in China

2021 ◽  
Author(s):  
Xianru Li ◽  
Zhigang Wei ◽  
Huan Wang ◽  
Li Ma ◽  
Shitong Guo
Keyword(s):  
Hong Kong ◽  
Change Point ◽  
Abrupt Change ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Change Points ◽  
China Meteorological Administration ◽  
Annual Variations ◽  
Intensity Index ◽  
Bay Area

Abstract By using the gridded 0.25°×0.25° observation dataset of CN05.1 provided by the China Meteorological Administration, this study investigates the variations of the nine precipitation extreme indices over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China in the period from 1961 to 2018. Based on trends and inter-annual variations, the nine kinds of extreme precipitation are classified into four categories: the category 1 is the very wet days (R95P), the extremely wet days (R99P), the maximum 1-day precipitation amount (RX1day) and the maximum 5-day precipitation amount (RX5day). The category 2 is the number of heavy precipitation days (R10day), the number of very heavy precipitation days (R20day) and the simple daily intensity index (SDII). The category 3 and 4 is the consecutive wet days (CWDday) and the consecutive dry days (CDDday), respectively. For the extreme precipitations in the category 1, the abrupt change point from less to more values occurs in 1991 in summer. Three abrupt change points, from less to more in 1972 and 2009, and from more to less in 1994 occur in spring. For the extreme precipitations in the category 2, the abrupt change point from less to more values occurs in 1993 in summer. Three abrupt change points, from less to more in 1965 and 2010, and from more to less in 1990 occur in spring. Annually and seasonally, the abrupt changes occur in early 2010s for CWDday which has clearly been more and for CDDday which has clearly been less. In addition, CWDday occurs abrupt change points from less to more in 1966 and from more to less in1983 in spring. The variations of these extreme precipitations have significant periodic oscillations of 3–5 years, quasi-8 years or 8–14 years. During 1961–1994, 1995–2009 and 2010–2018 three stages, the changes of the annual and most seasonal R95P, R99P, R10day, R20day and SDII are consistent with those of precipitation. The values in the latter stage are increasing compared with those in the former stage. The changes of RX1day, RX5day, CWDday and CDDday have their own characteristics.

The changes in Mulberry-based fish ponds in the Guangdong-Hong Kong-Macao Greater Bay Area over the past 40 years

2021 ◽  
Author(s):  
Wenxin Zhang ◽  
Zihao Cheng ◽  
Xianfeng Liu ◽  
Gangte Lin ◽  
Junan He ◽  
...  
Keyword(s):  
Hong Kong ◽  
Water Environment ◽  
Fish Ponds ◽  
Visual Interpretation ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Industrial Transfer ◽  
Intensity Index ◽  
Bay Area ◽  
Water Intensity

<p>Mulberry-based fish ponds are representative traditional eco-agriculture in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Investigations about the changes in such ponds and their relevant water environment under the background of rapid urbanization can provide a reference for the protection and development of these ponds. Using the Landsat images obtained after 1986, this study employed supervised classification and visual interpretation approaches and water intensity index as well as calculating synthesized index to identify the spatial patterns of changes in Mulberry-based fish ponds in the GBA. The results indicated that the year of 2013 was the inflection point of fish pond changes, which can also be proved by calculating synthesized index. The causes to the changes in fish ponds were further explored from four aspects: land use change, industrial transfer, government guidance and financial motives.</p>

scholarly journals Testing reliability of the spatial Hurst exponent method for detecting a change point

2021 ◽  
Author(s):  
Uday Pratap Singh ◽  
Ashok Kumar Mittal
Keyword(s):  
Dynamical System ◽  
Change Point ◽  
Hurst Exponent ◽  
Abrupt Change ◽  
Climate Dynamics ◽  
Change Points ◽  
Fluctuation Analysis ◽  
Multiple Variables ◽  
Spatio Temporal ◽  
Hurst Exponents

Abstract The reliability of using abrupt changes in the spatial Hurst exponent for identifying temporal points of abrupt change in climate dynamics is explored. If a spatio-temporal dynamical system undergoes an abrupt change at a particular time, the time series of spatial Hurst exponent obtained from the data of any variable of the system should also show an abrupt change at that time. As expected, spatial Hurst exponents for each of the two variables of a model spatio-temporal system – a globally coupled map lattice based on the Burgers' chaotic map – showed abrupt change at the same time that a parameter of the system was changed. This method was applied for the identification of change points in climate dynamics using the NCEP/NCAR data on air temperature, pressure and relative humidity variables. Different abrupt change points in spatial Hurst exponents were detected for the data of these different variables. That suggests, for a dynamical system, change point detected using the two-dimensional detrended fluctuation analysis method on a single variable alone is insufficient to comment about the abrupt change in the system dynamics and should be based on multiple variables of the dynamical system.

Projected Changes of Precipitation Characteristics Depend on Downscaling Method and Training Data: MACA versus LOCA Using the U.S. Northeast as an Example

2020 ◽  
Vol 21 (12) ◽  
pp. 2739-2758
Author(s):  
Guiling Wang ◽  
Christine J. Kirchhoff ◽  
Anji Seth ◽  
John T. Abatzoglou ◽  
Ben Livneh ◽  
...  
Keyword(s):  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Training Data ◽  
Temporal Aggregation ◽  
Intensity Index ◽  
Precipitation Characteristics ◽  
Projected Changes ◽  
And Training ◽  
The U.S

AbstractThis study compares projected changes of precipitation characteristics in the U.S. Northeast in two analog-based climate downscaling products, Multivariate Adaptive Constructed Analogs (MACA) and Localized Constructed Analogs (LOCA). The level of similarity or differences between the two products varies with the type of precipitation metrics. For the total precipitation amount, the two products project significant annual increases that are similar in magnitude, spatial pattern, and seasonal distribution, with the largest increases in winter and spring. For the overall precipitation intensity or temporal aggregation of heavy precipitation (e.g., number of days with more than one inch of precipitation, the simple intensity index, and the fraction of annual precipitation accounted for by heavy events), both products project significant increases across the region with strong model consensus; the magnitude of absolute increases are similar between the two products, but the relative increases are larger in LOCA due to an underestimation of heavy precipitation in LOCA’s training data. For precipitation extremes such as the annual maximum 1-day precipitation, both products project significant increases in the long-term mean, but the magnitude of both the absolute and relative changes are much smaller in LOCA than in MACA, indicating that the extreme precipitation differences in the training data are amplified in future projections as a result of the analog-based downscaling algorithms. The two products differ the most in the intensity and frequency of rare extremes (e.g., 1-in-20-years events) for which MACA projects significant increases while the LOCA-projected changes are inconclusive over much of the study area.

CHANGE POINT ANALYSIS: A STATISTICAL APPROACH TO DETECT POTENTIAL ABRUPT CHANGE

2017 ◽  
Vol 79 (5) ◽  
Author(s):  
Siti Nur Afiqah Mohd Arif ◽  
Mohamad Farhan Mohamad Mohsin ◽  
Azuraliza Abu Bakar ◽  
Abdul Razak Hamdan ◽  
Sharifah Mastura Syed Abdullah
Keyword(s):  
Relative Humidity ◽  
Change Point ◽  
Abrupt Change ◽  
Carbon Dioxide Concentration ◽  
Change Points ◽  
Change Point Analysis ◽  
Total Rainfall ◽  
Essential Information ◽  
Point Analysis ◽  
Annual Means

Change-point analysis has proven to be an efficient tool in understanding the essential information contained in meteorological data, such as rainfall, ozone level, and carbon dioxide concentration. In this study, change-point analysis was used to discover potential significant changes in the annual means of total rainfall, temperature and relative humidity from 25 years of Malaysian climate data. Two methods, the CUSUM and bootstrap, were used in the analysis, where the CUSUM was used to analyze the data trends and patterns and bootstrapping was used to calculate the occurrence of change points based on the confidence level. The results of the analysis showed that potential abrupt shifts seem to have taken place in 1999, 2001 and 2002 with respect to the annual means for relative humidity, temperature and total rainfall, respectively. These identified change points will be further analyzed as potential candidates of abrupt change by extending the proposed method in a future study.

scholarly journals Bayesian Changepoint Analysis of the Annual Maximum of Daily and Subdaily Precipitation over South Korea

2009 ◽  
Vol 22 (24) ◽  
pp. 6741-6757 ◽  
Author(s):  
Chansoo Kim ◽  
Myoung-Seok Suh ◽  
Ki-Ok Hong
Keyword(s):  
South Korea ◽  
Change Point ◽  
Posterior Probability ◽  
Bayes Factor ◽  
Precipitation Amount ◽  
Heavy Precipitation ◽  
Annual Maximum ◽  
Changepoint Analysis ◽  
Time Periods ◽  
Highest Posterior Density

Abstract Bayesian changepoint analysis is applied to detect a change point in the 30-year (1976–2005) time series of the area-averaged annual maximum precipitation (A3MP) for the six accumulated time periods (1, 3, 6, 12, 24, and 48 h) over South Korea. Using noninformative priors, Bayesian model selection is performed by posterior probability through the Bayes factor, and the exact Bayes estimators of the parameters and unknown change point for the selected change model are obtained. To investigate the significance of the mean differences in the six A3MP between before and after the change point, posterior probability and 90% highest posterior density credible intervals are examined. The results show that a single change occurred around 1997 in the A3MP without regard to the accumulated time periods over South Korea. This is strongly consistent with the abrupt increases in the intensity and frequency of heavy precipitation after 1997. The A3MP after the change point (1997) significantly increased more than 15% compared with the A3MP before the change point. The intensification of A3MP resulted in a great increase of the annual total precipitation (about +18%), especially a greater increase of the heavy precipitation amount (+51%) and frequency (+48%) over South Korea.

scholarly journals Patterns of the Network of Cross-Border University Research Collaboration in the Guangdong-Hong Kong-Macau Greater Bay Area

2020 ◽  
Vol 12 (17) ◽  
pp. 6846
Author(s):  
Jinyuan Ma ◽  
Fan Jiang ◽  
Liujian Gu ◽  
Xiang Zheng ◽  
Xiao Lin ◽  
...  
Keyword(s):  
Social Sciences ◽  
Hong Kong ◽  
Research Collaboration ◽  
Citation Impact ◽  
High Quality ◽  
High Quality Research ◽  
Cross Border ◽  
Bay Area ◽  
Research Areas ◽  
Industrial Distribution

This study analyzes the patterns of university co-authorship networks in the Guangdong-Hong Kong-Macau Greater Bay Area. It also examines the quality and subject distribution of co-authored articles within these networks. Social network analysis is used to outline the structure and evolution of the networks that have produced co-authored articles at universities in the Greater Bay Area from 2014 to 2018, at both regional and institutional levels. Field-weighted citation impact (FWCI) is used to analyze the quality and citation impact of co-authored articles in different subject fields. The findings of the study reveal that university co-authorship networks in the Greater Bay Area are still dispersed, and their disciplinary development is unbalanced. The study also finds that, while the research areas covered by high-quality co-authored articles fit the strategic needs of technological innovation and industrial distribution in the Greater Bay Area, high-quality research collaboration in the humanities and social sciences is insufficient.

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.

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