scholarly journals Cellular time series: a data structure for spatio-temporal analysis and management of geoscience information

2019 ◽  
Vol 21 (6) ◽  
pp. 999-1013
Author(s):  
Sina Nabaei ◽  
Bahram Saghafian
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Temporal Analysis ◽  
Change Point Detection ◽  
Trend Test ◽  
Homogeneity Test ◽  
Monthly Precipitation ◽  
Detection Techniques ◽  
Standard Normal Homogeneity Test ◽  
Spatio Temporal

Abstract Geoscientists are continuously confronted by difficulties involved in handling varieties of data formats. Configuration of data only in time or space domains leads to the use of multiple stand-alone software in the spatio-temporal analysis which is a time-consuming approach. In this paper, the concept of cellular time series (CTS) and three types of meta data are introduced to improve the handling of CTS in the spatio-temporal analysis. The data structure was designed via Python programming language; however, the structure could also be implemented by other languages (e.g., R and MATLAB). We used this concept in the hydro-meteorological discipline. In our application, CTS of monthly precipitation was generated by employing data of 102 stations across Iran. The non-parametric Mann–Kendall trend test and change point detection techniques, including Pettitt's test, standard normal homogeneity test, and the Buishand range test were applied on the generated CTS. Results revealed a negative annual trend in the eastern parts, as well as being sporadically spread over the southern and western parts of the country. Furthermore, the year 1998 was detected as a significant change year in the eastern and southern regions of Iran. The proposed structure may be used by geoscientists and data providers for straightforward simultaneous spatio-temporal analysis.

scholarly journals Time Series Analysis of Monthly and Annual Precipitation in The State of Texas Using High-Resolution Radar Products

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 982
Author(s):  
Dawit Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
Southern Oscillation ◽  
Stage Iv ◽  
The State ◽  
Trend Test ◽  
Homogeneity Test ◽  
Monthly Precipitation ◽  
Slope Test ◽  
Standard Normal Homogeneity Test ◽  
Significant Difference ◽  
Before And After

Precipitation is the main source for replenishing groundwater stored in aquifers for a myriad of beneficial purposes, especially in arid and semi-arid regions. A significant portion of the municipal and agricultural water demand is satisfied through groundwater withdrawals in Texas. These withdrawals have to be monitored and regulated to be in balance with the recharge amount from precipitation in order to ensure water security. The main goal of this study is to understand the spatio-temporal variability of precipitation in the 21st century using high spatial resolution stage-IV radar data over the state of Texas and examine some climatic controls behind this variability. The results will shed light on the trends of precipitation and hence will contribute to improving water resources management strategies and policies. Pettit’s test and Standard Normal Homogeneity Test (SNHT), tools for detecting change-point in the monthly precipitation, suggested change-points have occurred across the state around the years 2013 and 2014. The test for the homogeneity of the data before and after 2013 revealed that, in over 64% of the state, the precipitation means were significantly different. The Panhandle region (northern part) is the only part of the state that did not show a significant difference in the mean precipitation before and after 2013. Theil-Sen’s slope test, Correlated Seasonal Mann-Kendall Test, and Cox and Stuart Trend Test all indicated that there were no significant trends in the monthly precipitation after 2013 in over 98% of the area of the state. Texas precipitation was found to be influenced significantly by the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). A significant correlation in more than 82% and 60% of the state was found with ENSO at two-month and with PDO at four-month lag, respectively.

scholarly journals A Historical Analysis of Rainfall Trend in the Olifants Basin in South Africa

2016 ◽  
Vol 5 (1) ◽  
pp. 129 ◽  
Author(s):  
C. Gyamfi ◽  
J. M. Ndambuki ◽  
R. W. Salim
Keyword(s):  
South African ◽  
Historical Analysis ◽  
Climate Change Impacts ◽  
Test Methods ◽  
Annual Rainfall ◽  
Trend Test ◽  
Homogeneity Test ◽  
Rainfall Trend ◽  
Font Size ◽  
Spatio Temporal

<span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">A set of homogeneity test methods and the Mann-Kendall trend test were applied on historical rainfall records of the Olifants Basin to detect changes in rainfall pattern under a changing climate. In total, historical rainfall records from 13 stations obtained from the South African Weather Service (SAWS) and the Department of Water Affairs (DWA) spanning the period 1975-2013 were used in the analysis. Results of the study indicate an insignificant declining rainfall trend in the Olifants Basin with a mean annual rainfall of 664 mm. Rainfall in the basin exhibits spatio–temporal variation with coefficient of variation of 24%. Inter-annual and seasonal variability is dominant in the records examined. Changes observed in rainfall over the years were therefore concluded to have resulted due to climate change impacts.</span>

scholarly journals Spatio-Temporal Analysis of Heating and Cooling Degree-Days Over Iran

2021 ◽  
Author(s):  
Amin Sadeqi ◽  
Hossein Tabari ◽  
Yagob Dinpashoh
Keyword(s):  
Time Series ◽  
Energy Demand ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Degree Days ◽  
Heating And Cooling ◽  
Cooling Degree Days ◽  
Spatio Temporal ◽  
Heating Degree Days ◽  
Cooling Energy Demand

Abstract Climate change affects the energy demand in different sectors of the society. To investigate this possible impact, in this research, temporal trends and change points in heating degree-days (HDD), cooling degree-days (CDD), and their simultaneous combination (HDD+CDD) were analysed for a 60-year period (1960-2019) in Iran. The results show that less than 20% of the study stations had significant trends (either upward or downward) in HDD time series, while more than 80% of the stations had significant increasing trends in CDD and HDD+CDD time series. Abrupt changes in HDD time series mostly occurred in the early 1980s, but those in CDD time series were mostly observed in the 1990s. The cooling energy demand in Iran has dramatically increased as CDD values have raised up from 690 ºC-days to 1010 ºC-days in the last 60 years. HDD, however, almost remained constant in the same period. The results suggest that if global warming continues with the current pace, cooling energy demand in the residential sector will considerably increase in the future, calling for a change in residential energy consumption policies.

scholarly journals A Novel Approach for the Detection of Inhomogeneities Affecting Climate Time Series

2012 ◽  
Vol 51 (2) ◽  
pp. 317-326 ◽  
Author(s):  
Andrea Toreti ◽  
Franz G. Kuglitsch ◽  
Elena Xoplaki ◽  
Jürg Luterbacher
Keyword(s):  
Time Series ◽  
Markov Models ◽  
Winter Precipitation ◽  
Homogeneity Test ◽  
Temperature And Precipitation ◽  
Novel Approach ◽  
Standard Normal Homogeneity Test ◽  
Standard Normal ◽  
Set Up ◽  
Climate Time Series

AbstractSudden changes caused by nonclimatic factors (inhomogeneities) usually affect instrumental time series of climate variables. To perform robust climate analyses based on observations, a proper identification of such changes is necessary. Here, an approach (named the “GAHMDI” method, after its components and purpose) that is based on a genetic algorithm and hidden Markov models is proposed for detection of inhomogeneities caused by changes in the mean and variance. Simulated series and a case study (winter precipitation from a weather station located in Milan, Italy) are set up to compare GAHMDI with existing methodologies and to highlight its features. For the identification of a single changepoint, GAHMDI performs similarly to other methods (e.g., standard normal homogeneity test). However, for the identification of multiple inhomogeneities and changes in variance, GAHMDI returns better results than three widespread methods by avoiding overdetection. For future applications and research in the homogenization of climate datasets (temperature and precipitation) the use of GAHMDI is encouraged, preferably in combination with another detection procedure (e.g., the method of Caussinus and Mestre) when metadata are not available. Since GAHMDI is developed in the generic context of time series segmentation, it can be applied to series of generic variables—for instance, those related to economics, biology, and informatics.

scholarly journals Change Point Detection in Time Series Using Higher-Order Statistics: A Heuristic Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Constantinos S. Hilas ◽  
Ioannis T. Rekanos ◽  
Paris Ast. Mastorocostas
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Order Statistics ◽  
Heuristic Method ◽  
Interrupted Time Series ◽  
Higher Order ◽  
Change Point Detection ◽  
Higher Order Statistics ◽  
Real World Data ◽  
Detection Techniques

Changes in the level of a time series are usually attributed to an intervention that affects its temporal evolution. The resulting time series are referred to as interrupted time series and may be used to identify the events that caused the intervention and to quantify their impact. In the present paper, a heuristic method for level change detection in time series is presented. The method uses higher-order statistics, namely, the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series and the time instance when it has happened. The technique is straightforwardly applicable to the detection of outliers in time series and promises to have several applications. The method is tested with both simulated and real-world data and is compared to other popular change detection techniques.

scholarly journals G-STC-M Spatio-Temporal Analysis Method for Archaeological Sites

2021 ◽  
Vol 10 (5) ◽  
pp. 312
Author(s):  
Jing Cui ◽  
Yanrong Liu ◽  
Junling Sun ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Qing Dynasty ◽  
Tang Dynasty ◽  
Temporal Analysis ◽  
Space Time ◽  
Test Method ◽  
Archaeological Sites ◽  
Trend Test ◽  
Analysis Method ◽  
The Qing Dynasty ◽  
Spatio Temporal

Based on the significant hotspots analysis method (Getis-Ord Gi* significance statistics), space-time cube model (STC) and the Mann–Kendall trend test method, this paper proposes a G-STC-M spatio-temporal analysis method based on Archaeological Sites. This method can integrate spatio-temporal data variable analysis and the space-time cube model to explore the spatio-temporal distribution of Archaeological Sites. The G-STC-M method was used to conduct time slice analysis on the data of Archaeological Sites in the study area, and the spatio-temporal variation characteristics of Archaeological Sites in East China from the Tang Dynasty to the Qing Dynasty were discussed. The distribution of Archaeological Sites has temporal hotspots and spatial hotspots. Temporally, the distribution of Archaeological Sites showed a gradual increasing trend, and the number of Archaeological Sites reached the maximum in the Qing Dynasty. Spatially, the hotspots of Archaeological Sites are mainly distributed in Jiangsu (30°~33° N, 118°~121° E) and Anhui (29°~31° N, 117°~119° E) and the central region of Zhejiang (28°~31° N, 118°~121° E). Temporally and spatially, the distribution of Archaeological Sites is mainly centered in Shanghai (30°~32° N, 121°~122° E), spreading to the southern region.

scholarly journals Spatio-temporal analysis of rainfall trends in Chhattisgarh State, Central India over the last 115 years

2018 ◽  
Vol 36 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Manish K. Nema ◽  
Deepak Khare ◽  
Jan Adamowski ◽  
Surendra K. Chandniha
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Water Resources Management ◽  
Change Point ◽  
Temporal Analysis ◽  
Temporal Variations ◽  
Change Point Detection ◽  
Resources Management ◽  
Sustainable Water Resources Management ◽  
Spatio Temporal

AbstractA quantitative and qualitative understanding of the anticipated climate-change-driven multi-scale spatio-temporal shifts in precipitation and attendant river flows is crucial to the development of water resources management approaches capable of sustaining and even improving the ecological and socioeconomic viability of rain-fed agricultural regions. A set of homogeneity tests for change point detection, non-parametric trend tests, and the Sen’s slope estimator were applied to long-term gridded rainfall records of 27 newly formed districts in Chhattisgarh State, India. Illustrating the impacts of climate change, an analysis of spatial variability, multi-temporal (monthly, seasonal, annual) trends and inter-annual variations in rainfall over the last 115 years (1901–2015 mean 1360 mm·y−1) showed an overall decline in rainfall, with 1961 being a change point year (i.e., shift from rising to declining trend) for most districts in Chhattisgarh. Spatio-temporal variations in rainfall within the state of Chhattisgarh showed a coefficient of variation of 19.77%. Strong inter-annual and seasonal variability in regional rainfall were noted. These rainfall trend analyses may help predict future climate scenarios and thereby allow planning of effective and sustainable water resources management for the region.

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