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Militino, Ana, Mehdi Moradi, and M. Ugarte. "On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data." Remote Sensing 12, no. 6 (March 21, 2020): 1008. http://dx.doi.org/10.3390/rs12061008.
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Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.
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Ishak, Elias, and Ataur Rahman. "Examination of Changes in Flood Data in Australia." Water 11, no. 8 (August 20, 2019): 1734. http://dx.doi.org/10.3390/w11081734.
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This study performs a simultaneous evaluation of gradual and abrupt changes in Australian annual maximum (AM) flood data using a modified Mann–Kendall and Pettitt change-point detection test. The results show that AM flood data in eastern Australia is dominated by downward trends. Depending on the significance level and study period under consideration, about 8% to 33% of stations are characterised by significant trends, where over 85% of detected significant trends are downward. Furthermore, the change-point analysis shows that the percentages of stations experiencing one abrupt change in the mean or in the direction of the trend are in the range of 8% to 33%, of which over 50% occurred in 1991, with a mode in 1995. Prominent resemblance between the monotonic trend and change-point analysis results is also noticed, in which a negative shift in the mean is observed at catchments that exhibited downward trends, and a positive shift in the mean is observed in the case of upward trends. Trend analysis of the segmented AM flood series based on their corresponding date indicates an absence of a significant trend, which may be attributed to the false detection of trends when the AM flood data are characterised by a shift in its mean.
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Alashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (February 21, 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.002.
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Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.
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Alashan, Sadık. "Can innovative trend analysis identify trend change points?" Brilliant Engineering 1, no. 3 (February 21, 2020): 6–15. http://dx.doi.org/10.36937/ben.2020.003.02.
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Trends in temperature series are the main cause of climate change. Because solar energy directs hydro-meteorological events and increasing variations in this resource change the balance between events such as evaporation, wind, and rainfall. There are many methods for calculating trends in a time series such as Mann-Kendall, Sen's slope estimator, Spearman's rho, linear regression and the new Sen innovative trend analysis (ITA). In addition, Mann-Kendall's variant, the sequential Mann Kendall, has been developed to identify trend change points; however, it is sensitive to related data as specified by some researchers. Şen_ITA is a new trend detection method and does not require independent and normally distributed time series, but has never been used to detect trend change points. In the literature, multiple, half-time and multi-durations ITA methods are used to calculate partial trends in a time series without identifying trend change points. In this study, trend change points are detected using the Şen_ITA method and named ITA_TCP. This approach may allow researchers to identify trend change points in a time series. Diyarbakır (Turkey) is selected as a study area, and ITA_TCP has detected trends and trends change points in monthly average temperatures. Although ITA detects only a significant upward trend in August, given the 95% statistical significance level, ITA_TCP shows three upward trends in June, July and August, and a decreasing trend in September. Critical trend slope values are obtained using the bootstrap method, which does not require the normal distribution assumption.
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Wehbe, Youssef, and Marouane Temimi. "A Remote Sensing-Based Assessment of Water Resources in the Arabian Peninsula." Remote Sensing 13, no. 2 (January 13, 2021): 247. http://dx.doi.org/10.3390/rs13020247.
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A better understanding of the spatiotemporal distribution of water resources is crucial for the sustainable development of hyper-arid regions. Here, we focus on the Arabian Peninsula (AP) and use remotely sensed data to (i) analyze the local climatology of total water storage (TWS), precipitation, and soil moisture; (ii) characterize their temporal variability and spatial distribution; and (iii) infer recent trends and change points within their time series. Remote sensing data for TWS, precipitation, and soil moisture are obtained from the Gravity Recovery and Climate Experiment (GRACE), the Tropical Rainfall Measuring Mission (TRMM), and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), respectively. The study relies on trend analysis, the modified Mann–Kendall test, and change point detection statistics. We first derive 10-year (2002–2011) seasonal averages from each of the datasets and intercompare their spatial organization. In the absence of large-scale in situ data, we then compare trends from GRACE TWS retrievals to in situ groundwater observations locally over the subdomain of the United Arab Emirates (UAE). TWS anomalies vary between −6.2 to 3.2 cm/month and −6.8 to −0.3 cm/month during the winter and summer periods, respectively. Trend analysis shows decreasing precipitation trends (−2.3 × 10−4 mm/day) spatially aligned with decreasing soil moisture trends (−1.5 × 10−4 g/cm3/month) over the southern part of the AP, whereas the highest decreasing TWS trends (−8.6 × 10−2 cm/month) are recorded over areas of excessive groundwater extraction in the northern AP. Interestingly, change point detection reveals increasing precipitation trends pre- and post-change point breaks over the entire AP region. Significant spatial dependencies are observed between TRMM and GRACE change points, particularly over Yemen during 2010, revealing the dominant impact of climatic changes on TWS depletion.
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Vaman, H. J., and K. Suresh Chandra. "OPTIMAL CHANGE-POINT DETECTION IN TREND MODELS WITH INTEGRATED MOVING AVERAGE ERRORS." Sequential Analysis 21, no. 1-2 (May 20, 2002): 99–107. http://dx.doi.org/10.1081/sqa-120004175.
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Ray, Litan Kumar, Narendra Kumar Goel, and Manohar Arora. "Trend analysis and change point detection of temperature over parts of India." Theoretical and Applied Climatology 138, no. 1-2 (February 23, 2019): 153–67. http://dx.doi.org/10.1007/s00704-019-02819-7.
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Sherwood, Steven C. "Simultaneous Detection of Climate Change and Observing Biases in a Network with Incomplete Sampling." Journal of Climate 20, no. 15 (August 1, 2007): 4047–62. http://dx.doi.org/10.1175/jcli4215.1.
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Abstract All instrumental climate records are affected by instrumentation changes and variations in sampling over time. While much attention has been paid to the problem of detecting “change points” in time series, little has been paid to the statistical properties of climate signals that result after adjusting (“homogenizing”) the data—or to the effects of the irregular sampling and serial correlation exhibited by real climate records. These issues were examined here by simulating multistation datasets. Simple homogenization methods, which remove apparent artifacts and then calculate trends, tended to remove some of the real signal. That problem became severe when change-point times were not known a priori, leading to significant underestimation of real and/or artificial trends. A key cause is false detection of change points, even with nominally strict significance testing, due to serial correlation in the data. One conclusion is that trends in previously homogenized radiosonde datasets should be viewed with caution. Two-phase regression reduced but did not resolve this problem. A new approach is proposed in which trends, change points, and natural variability are estimated simultaneously. This is accomplished here for the case of incomplete data from a fixed station network by an adaptation of the “iterative universal Kriging” method, which converges to maximum-likelihood parameters by iterative imputation of missing values. With careful implementation this method’s trend estimates had low random errors and were nearly unbiased in these tests. It is argued that error-free detection of change points is neither realistic nor necessary, and that success should be measured instead by the integrity of climate signals.
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Alhathloul, Saleh H., Abdul A. Khan, and Ashok K. Mishra. "Trend analysis and change point detection of annual and seasonal horizontal visibility trends in Saudi Arabia." Theoretical and Applied Climatology 144, no. 1-2 (January 24, 2021): 127–46. http://dx.doi.org/10.1007/s00704-021-03533-z.
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Nguyen, Khanh Ninh, Annarosa Quarello, Olivier Bock, and Emilie Lebarbier. "Sensitivity of Change-Point Detection and Trend Estimates to GNSS IWV Time Series Properties." Atmosphere 12, no. 9 (August 26, 2021): 1102. http://dx.doi.org/10.3390/atmos12091102.
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This study investigates the sensitivity of the GNSSseg segmentation method to change in: GNSS data processing method, length of time series (17 to 25 years), auxiliary data used in the integrated water vapor (IWV) conversion, and reference time series used in the segmentation (ERA-Interim versus ERA5). Two GNSS data sets (IGS repro1 and CODE REPRO2015), representative of the first and second IGS reprocessing, were compared. Significant differences were found in the number and positions of detected change-points due to different a priori ZHD models, antenna/radome calibrations, and mapping functions. The more recent models used in the CODE solution have reduced noise and allow the segmentation to detect smaller offsets. Similarly, the more recent reanalysis ERA5 has reduced representativeness errors, improved quality compared to ERA-Interim, and achieves higher sensitivity of the segmentation. Only 45–50% of the detected change-points are similar between the two GNSS data sets or between the two reanalyses, compared to 70–80% when the length of the time series or the auxiliary data are changed. About 35% of the change-points are validated with respect to metadata. The uncertainty in the homogenized trends is estimated to be around 0.01–0.02 kg m−2 year−1.
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Ilori, Oluwaseun W., and Vincent O. Ajayi. "Change Detection and Trend Analysis of Future Temperature and Rainfall over West Africa." Earth Systems and Environment 4, no. 3 (August 30, 2020): 493–512. http://dx.doi.org/10.1007/s41748-020-00174-6.
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Abstract This paper examined future trends with change detection in temperature and rainfall over three agro-climatic zones of West Africa. Historical (1961–2000) and projection (2020–2099) data of ensemble-mean of six RCMs that dynamically downscaled five GCMs that participated in CMIP5 obtained from Co-Ordinated Regional Climate Downscaling Experiment (CORDEX) were used. Standard normal homogeneity, Buishand’s, Pettitt’s, and Mann–Kendall test were used for change point detection and trend analysis at 5% significant level. Inter-annual anomaly and projected change in the seasonal cycle relative to historical mean were investigated. The ensemble-mean evaluation performed for the historical period (1961–2000) using CRU dataset revealed that the change point occurred in rainfall and temperature series in the 1970s and 1980s, while a significant increasing trend is observed in temperature in all climatic zones. Change-point detection test projects rainfall series to be homogeneous as significant change point is expected to occur in temperature for all zones under RCP4.5 and RCP8.5 for near (2020–2059) and far-future (2060–2099). For the near-future, an increase in the mean temperature between 0.5–1.30 ℃ and 0.19–1.67 ℃ is projected to occur under RCP4.5 and RCP8.5 respectively. Projected relative change in seasonal cycle shows that winter months may witness increase in rainfall amounts under RCP4.5 but significantly dry under RCP8.5 in near and far-future as temperature is expected to become warmer in all months. Rainfall anomaly projects the Sahel to have a reduced amount of rainfall compared to other zones as temperature anomaly reveals a continuous increase in all the zones under the two RCPs. The results of this study show that climate change will intensify in West Africa in the future.
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Salehi, Somayeh, Majid Dehghani, Sayed M. Mortazavi, and Vijay P. Singh. "Trend analysis and change point detection of seasonal and annual precipitation in Iran." International Journal of Climatology 40, no. 1 (July 16, 2019): 308–23. http://dx.doi.org/10.1002/joc.6211.
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Sonali, P., and D. Nagesh Kumar. "Spatio-temporal variability of temperature and potential evapotranspiration over India." Journal of Water and Climate Change 7, no. 4 (April 25, 2016): 810–22. http://dx.doi.org/10.2166/wcc.2016.230.
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Worldwide, major changes in the climate are expected due to global warming, which leads to temperature variations. To assess the climate change impact on the hydrological cycle, a spatio-temporal change detection study of potential evapotranspiration (PET) along with maximum and minimum temperatures (Tmax and Tmin) over India have been performed for the second half of the 20th century (1950–2005) both at monthly and seasonal scale. From the observed monthly climatology of PET over India, high values of PET are envisioned during the months of March, April, May and June. Temperature is one of the significant factors in explaining changes in PET. Hence seasonal correlations of PET with Tmax and Tmin were analyzed using Spearman rank correlation. Correlation of PET with Tmax was found to be higher compared to that with Tmin. Seasonal variability of trend at each grid point over India was studied for Tmax, Tmin and PET separately. Trend Free Pre-Whitening and Modified Mann Kendall approaches, which consider the effect of serial correlation, were employed for the trend detection analysis. A significant trend was observed in Tmin compared to Tmax and PET. Significant upward trends in Tmax, Tmin and PET were observed over most of the grid points in the interior peninsular region.
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Yaman, Barbaros, and Mertol Ertuğrul. "Change-point detection and trend analysis in monthly, seasonal and annual air temperature and precipitation series in Bartın province in the western Black Sea region of Turkey." Geology, Geophysics and Environment 46, no. 3 (January 19, 2021): 223. http://dx.doi.org/10.7494/geol.2020.46.3.223.
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Studies associated with climate change and variability are of great importance at both the global and local scale in the global climate crisis. In this study, change-point detection and trend analysis were carried out on mean, maximum, minimum air temperatures and total precipitation based on monthly, seasonal and annual scale in Bartın province located in the western Black Sea Region of Turkey. For this aim, 4-different homogenei-ty tests (von Neumann test, Pettitt test, Buishand range test and standard normal homogeneity test) for change-point detection, Modified Mann–Kendall test and Şen’s innovative trend test for trend analysis, and Sen’s slope test for the magnitude estimation of trends were used. According to the test results, the summer temperatures in particular show increasing trends at the 0.001 significance level. Mean maximum temperature in August, mean minimum temperature in June and August, and mean temperature in July and August are in increasing trend at the 0.001 significance level. Over a 51 year period (1965–2015) in Bartın province, the highest rate of change per decade in air temperatures is in August (0.55°C for Tmax, 0.46°C for Tmin and 0.43°C for Tmean) based on Sen’s slope. However, the study showed that apart from October precipitation, there is no significant trend in monthly, seasonal and annual precipitation in Bartın. Increasing trends in mentioned climate variables are also visually very clear and strong in Şen’s innovative trend method, and they comply with the statistical results. As a result, the study revealed some evidence that temperatures will increase in the future in Bartın and its environs.
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Bhaduri, Moinak, Dhruva Rangan, and Anurag Balaji. "Change detection in non-stationary Hawkes processes through sequential testing." ITM Web of Conferences 36 (2021): 01005. http://dx.doi.org/10.1051/itmconf/20213601005.
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Detecting changes in an incoming data flow is immensely crucial for understanding inherent dependencies, formulating new or adapting existing policies, and anticipating further changes. Distinct modeling constructs have triggered varied ways of detecting such changes, almost every one of which gives in to certain shortcomings. Parametric models based on time series objects, for instance, work well under distributional assumptions or when change detection in specific properties - such as mean, variance, trend, etc. are of interest. Others rely heavily on the “at most one change-point” assumption, and implementing binary segmentation to discover subsequent changes comes at a hefty computational cost. This work offers an alternative that remains both versatile and untethered to such stifling constraints. Detection is done through a sequence of tests with variations to certain trend permuted statistics. We study non-stationary Hawkes patterns which, with an underlying stochastic intensity, imply a natural branching process structure. Our proposals are shown to estimate changes efficiently in both the immigrant and the offspring intensity without sounding too many false positives. Comparisons with established competitors reveal smaller Hausdorff-based estimation errors, desirable inferential properties such as asymptotic consistency and narrower bootstrapped margins. Four real data sets on NASDAQ price movements, crude oil prices, tsunami occurrences, and COVID-19 infections have been analyzed. Forecasting methods are also touched upon.
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Amiri, Amirhossein, and Samaneh Zolfaghari. "Estimation of Change Point in Two-Stage Processes Subject to Step Change and Linear Trend." International Journal of Reliability, Quality and Safety Engineering 23, no. 02 (April 2016): 1650007. http://dx.doi.org/10.1142/s0218539316500078.
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When a control chart signals, it shows the process parameters have changed due to assignable cause(s). However, control chart signal is not the real time of a change in the process. Knowing the real time of change would simplify the detection and elimination of the assignable causes of variation. In this paper, a two-stage process is considered when the mean values of quality characteristics are changed under step shift and linear drift. First, a control chart based on the discriminant analysis (DA) is utilized to monitor the process. Then, when the out-of-control signal is received, the maximum likelihood estimator (MLE) based on the DA statistics, and clustering approach based on Mahalanobis distance of residuals are developed to estimate the real time of the change. The performances of the proposed estimators under different shifts are evaluated through numerical examples and a real case. The results indicate the better performance of the clustering approach rather than the MLE in most cases under both step shift and drift.
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Neubauer, Jirí, and Vítezslav Veselý. "Detection of multiple changes in mean by sparse parameter estimation." Nonlinear Analysis: Modelling and Control 18, no. 2 (April 25, 2013): 177–90. http://dx.doi.org/10.15388/na.18.2.14021.
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The contribution is focused on detection of multiple changes in the mean in a onedimensional stochastic process by sparse parameter estimation from an overparametrized model. The authors’ approach to change point detection differs entirely from standard statistical techniques. A stochastic process residing in a bounded interval with changes in the mean is estimated using dictionary (a family of functions, the so-called atoms, which are overcomplete in the sense of being nearly linearly dependent) and consisting of Heaviside functions. Among all possible representations of the process we want to find a sparse one utilizing a significantly reduced number of atoms. This problem can be solved by ℓ1-minimization. The basis pursuit algorithm is used to get sparse parameter estimates. In this contribution the authors calculate empirical probability of successful change point detection as a function depending on the number of change points and the level of standard deviation of additive white noise of the stochastic process. The empirical probability was computed by simulations where locations of change points were chosen randomly from uniform distribution. The authors’ approach is compared with LASSO algorithm, ℓ1 trend filtering and selected statistical methods. Such probability decreases with increasing number of change points and/or standard deviation of white noise. The proposed method was applied on the time series of nuclear magnetic response during the drilling of a well.
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Pranuthi, G. "Trend and Change Point Detection of Precipitation in Urbanizing Districts of Uttarakhand in India." Indian Journal of Science and Technology 7, no. 10 (October 20, 2014): 1573–82. http://dx.doi.org/10.17485/ijst/2014/v7i10.20.
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Bates, Bryson C., Richard E. Chandler, and Adrian W. Bowman. "Trend estimation and change point detection in individual climatic series using flexible regression methods." Journal of Geophysical Research: Atmospheres 117, no. D16 (August 22, 2012): n/a. http://dx.doi.org/10.1029/2011jd017077.
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Das, Jayanta, Tapash Mandal, and Piu Saha. "Spatio-temporal trend and change point detection of winter temperature of North Bengal, India." Spatial Information Research 27, no. 4 (January 19, 2019): 411–24. http://dx.doi.org/10.1007/s41324-019-00241-9.
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Wang, Xiaolan L. "Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change." Journal of Atmospheric and Oceanic Technology 25, no. 3 (March 1, 2008): 368–84. http://dx.doi.org/10.1175/2007jtecha982.1.
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Abstract In this study, a penalized maximal F test (PMFT) is proposed for detecting undocumented mean shifts that are not accompanied by any sudden change in the linear trend of time series. PMFT aims to even out the uneven distribution of false alarm rate and detection power of the corresponding unpenalized maximal F test that is based on a common-trend two-phase regression model (TPR3). The performance of PMFT is compared with that of TPR3 using Monte Carlo simulations and real climate data series. It is shown that, due to the effect of unequal sample sizes, the false alarm rate of TPR3 has a W-shaped distribution, with much higher than specified values for points near the ends of the series and lower values for points between either of the ends and the middle of the series. Consequently, for a mean shift of certain magnitude, TPR3 would detect it with a lower-than-specified level of confidence and hence more easily when it occurs near the ends of the series than somewhere between either of the ends and the middle of the series; it would mistakenly declare many more changepoints near the ends of a homogeneous series. These undesirable features of TPR3 are diminished in PMFT by using an empirical penalty function to take into account the relative position of each point being tested. As a result, PMFT has a notably higher power of detection; its false alarm rate and effective level of confidence are very close to the nominal level, basically evenly distributed across all possible candidate changepoints. The improvement in hit rate can be more than 10% for detecting small shifts (Δ ≤ σ, where σ is the noise standard deviation).
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Ďurigová, Mária, Kamila Hlavčová, and Jana Poórová. "Detection of Changes in Hydrological Time Series During Recent Decades." Slovak Journal of Civil Engineering 28, no. 2 (June 1, 2020): 56–62. http://dx.doi.org/10.2478/sjce-2020-0016.
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AbstractAn analysis of a hydrological time-series data offers the possibility of detecting changes that have arisen due to climate change or change in land use. This paper deals with the detection of changes in the hydrological time data series. The trend analysis was applied at 58 stage-discharge gauging stations that are located throughout Slovakia, with the measurement period from 1962 to 2017. The Mann-Kendall test show a declining trends in the summer and a few rising trends in the winter in discharges. In the town of Banská Bystrica at a station on the Hron River, decades of discharges, air temperatures, and precipitation totals were analyzed. The five decades from the 1960s to the 2000s were used. The hydrological time data series were also analyzed by the Pettitt’s test, which is used to detect change points. The decadal analysis at the Banská Bystrica station shows an increase in the air temperature but insignificant changes in discharges and precipitation. Pettitt’s test identified many change points in the 1990s in the air temperature.
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Li, Guofang, Xinyi Xiang, and Caixiu Guo. "Analysis of Nonstationary Change of Annual Maximum Level Records in the Yangtze River Estuary." Advances in Meteorology 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7205723.
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Under the impact of climate change and human activities, the stationarity of hydrometeorological extreme value series has been losing in many regions, which makes occurrence rules of hydrometeorological extreme events more complicated. In this study, the efficiencies of trend test methods such as Spearman rank correlation test and Mann-Kendall test, as well as the efficiencies of change-point test methods such as moving T test, moving rank sum test, Pettitt test, and sequential Mann-Kendall test were analyzed quantitatively through Monte Carlo simulation. Five representative level stations in the Yangtze River estuary were selected, and the methods listed above were used in the trend and change-point detection of the annual maximum tidal level records in the period of 1950–2008. It was found that obvious rising tendency existed in the annual maximum tidal level series for all these 5 stations, and year 1980 (for 3 stations) and year 1979 (for 2 stations) were statistically significant change-points. Two subseries were divided with the change-point as the dividing point for all these actual series in the stations. Frequency analyses were carried out, respectively, for all of the subseries, and the impact of nonstationary changes in annual maximum tidal levels on probability distribution was evaluated quantitatively.
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Animashaun, I. M., P. G. Oguntunde, A. S. Akinwumiju, and O. O. Olubanjo. "Rainfall Analysis over the Niger Central Hydrological Area, Nigeria: Variability, Trend, and Change point detection." Scientific African 8 (July 2020): e00419. http://dx.doi.org/10.1016/j.sciaf.2020.e00419.
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Gao, Zhenguo, Zuofeng Shang, Pang Du, and John L. Robertson. "Variance Change Point Detection Under a Smoothly-Changing Mean Trend with Application to Liver Procurement." Journal of the American Statistical Association 114, no. 526 (July 11, 2018): 773–81. http://dx.doi.org/10.1080/01621459.2018.1442341.
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Suhaila, Jamaludin, and Zulkifli Yusop. "Trend analysis and change point detection of annual and seasonal temperature series in Peninsular Malaysia." Meteorology and Atmospheric Physics 130, no. 5 (June 7, 2017): 565–81. http://dx.doi.org/10.1007/s00703-017-0537-6.
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Chang, Qingrui, Chi Zhang, Song Zhang, and Binquan Li. "Streamflow and Sediment Declines in a Loess Hill and Gully Landform Basin Due to Climate Variability and Anthropogenic Activities." Water 11, no. 11 (November 9, 2019): 2352. http://dx.doi.org/10.3390/w11112352.
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Streamflow and sediment runoff are important indicators for the changes in hydrological processes. In the context of environmental changes, decreases in both streamflow and sediment (especially in the flood season) are often observed in most of the tributaries of the middle Yellow River in China’s Loess Plateau. Understanding the effect of human activities could be useful for the management of soil and water conservation (SWC) and new constructions. In this paper, changes in streamflow and sediment during the flood season (June–September) of the 1966–2017 period in a typical loess hill and gully landform basin were analyzed. Basin-wide rainfall of the flood season decreased nonsignificantly with an average rate of −0.6 mm/flood season for the whole study period by using the trend-free pre-whitening based Mann–Kendall trend test, while the decreasing rate was weakened on the time scale. A remarkable warming trend (1985–1999) and two decreasing trends (1966–1984 and 2000–2017) were observed, and the overall increasing trend could be found in air temperature series with a rate of 0.01 °C/flood season during the study period. Statistical models were developed to describe the rainfall-runoff and rainfall-sediment processes in the pre-impact period (when the hydrological series was stationary). Furthermore, the relative effects of climate variability and human activities on hydrological changes were quantified. Results proved the dominant role of human activities (versus climate variability) on the reductions of both streamflow and sediment load. The relative contribution of human activities to streamflow decrease was 84.6% during the post-impact period 1995–2017, while the contributions were 48.8% and 80.1% for two post-impact periods (1982–1996 and 1997–2017), respectively, to the reduction of sediment load. Besides, the effect of the exclusion of anomalous streamflow or sediment events on change-point detection was also analyzed. It indicated that the anomalous events affect the detection of change points and should be given full consideration in order to decide whether to remove them in the change-point detection. Otherwise, the full series with anomalous samples will completely affect the attribution results of hydrological changes. We also suggest that large-scale SWC measures with different construction quality and operational life could intercept and relieve most floods and high sediment concentration processes, but may amplify the peaks of streamflow and sediment when the interception capacities are exceeded under the condition of extreme rainstorm events.
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Nema, Manish K., Deepak Khare, Jan Adamowski, and Surendra K. Chandniha. "Spatio-temporal analysis of rainfall trends in Chhattisgarh State, Central India over the last 115 years." Journal of Water and Land Development 36, no. 1 (March 1, 2018): 117–28. http://dx.doi.org/10.2478/jwld-2018-0012.
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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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Gebremicael, Tesfay G., Yasir A. Mohamed, Pieter v. Zaag, and Eyasu Y. Hagos. "Temporal and spatial changes of rainfall and streamflow in the Upper Tekezē–Atbara river basin, Ethiopia." Hydrology and Earth System Sciences 21, no. 4 (April 19, 2017): 2127–42. http://dx.doi.org/10.5194/hess-21-2127-2017.
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Abstract. The Upper Tekezē–Atbara river sub-basin, part of the Nile Basin, is characterized by high temporal and spatial variability of rainfall and streamflow. In spite of its importance for sustainable water use and food security, the changing patterns of streamflow and its association with climate change is not well understood. This study aims to improve the understanding of the linkages between rainfall and streamflow trends and identify possible drivers of streamflow variabilities in the basin. Trend analyses and change-point detections of rainfall and streamflow were analysed using Mann–Kendall and Pettitt tests, respectively, using data records for 21 rainfall and 9 streamflow stations. The nature of changes and linkages between rainfall and streamflow were carefully examined for monthly, seasonal and annual flows, as well as indicators of hydrologic alteration (IHA). The trend and change-point analyses found that 19 of the tested 21 rainfall stations did not show statistically significant changes. In contrast, trend analyses on the streamflow showed both significant increasing and decreasing patterns. A decreasing trend in the dry season (October to February), short season (March to May), main rainy season (June to September) and annual totals is dominant in six out of the nine stations. Only one out of nine gauging stations experienced significant increasing flow in the dry and short rainy seasons, attributed to the construction of Tekezē hydropower dam upstream this station in 2009. Overall, streamflow trends and change-point timings were found to be inconsistent among the stations. Changes in streamflow without significant change in rainfall suggests factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the basin. Further studies are needed to verify and quantify the hydrological changes shown in statistical tests by identifying the physical mechanisms behind those changes. The findings from this study are useful as a prerequisite for studying the effects of catchment management dynamics on the hydrological variabilities in the basin.
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Itoh, N., and N. Marwan. "An extended singular spectrum transformation (SST) for the investigation of Kenyan precipitation data." Nonlinear Processes in Geophysics 20, no. 4 (July 12, 2013): 467–81. http://dx.doi.org/10.5194/npg-20-467-2013.
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Abstract. In this paper a change-point detection method is proposed by extending the singular spectrum transformation (SST) developed as one of the capabilities of singular spectrum analysis (SSA). The method uncovers change points related with trends and periodicities. The potential of the proposed method is demonstrated by analysing simple model time series including linear functions and sine functions as well as real world data (precipitation data in Kenya). A statistical test of the results is proposed based on a Monte Carlo simulation with surrogate methods. As a result, the successful estimation of change points as inherent properties in the representative time series of both trend and harmonics is shown. With regards to the application, we find change points in the precipitation data of Kenyan towns (Nakuru, Naivasha, Narok, and Kisumu) which coincide with the variability of the Indian Ocean Dipole (IOD) suggesting its impact of extreme climate in East Africa.
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Palaniswami, Supriya, and Krishnaveni Muthiah. "Change Point Detection and Trend Analysis of Rainfall and Temperature Series over the Vellar River Basin." Polish Journal of Environmental Studies 27, no. 4 (March 30, 2018): 1673–81. http://dx.doi.org/10.15244/pjoes/77080.
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Beaulieu, C., S. A. Henson, J. L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp. "Factors challenging our ability to detect long-term trends in ocean chlorophyll." Biogeosciences Discussions 9, no. 11 (November 20, 2012): 16419–56. http://dx.doi.org/10.5194/bgd-9-16419-2012.
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Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean Land Colour Instrument (OLCI) in 2013 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
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Kok Yang, Chang, Fam Pei Shan, and Tay Lea Tien. "Climate change detection in penang island using deterministic interpolation methods." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 1 (July 1, 2020): 412. http://dx.doi.org/10.11591/ijeecs.v19.i1.pp412-419.
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This paper presents detection of climate change in Penang Island by using precipitation data based on interpolation technique. Climate change brings about vast and everlasting effects on all living creatures on the Earth. These effects are especially detrimental towards heritage sites, landscapes and businesses based in Penang Island, Malaysia. This study focuses mainly on investigating the indication of climate change in Penang Island over the period of 2003-2018 by utilising sound application procedures of proven analysis methods. Two deterministic interpolation methods are used to produce new estimation points based on the precipitation data to enrich the monitoring network of rainfall stations in Penang Island. Monthly and monthly-average precipitation maps for Penang Island are produced by using inverse distance weighting interpolation method. Results reveal that seven out of twelve months of a year show increasing precipitation trends over the period of study and March is the only month that shows a decreasing trend in precipitation. Monthly-average precipitation in Penang Island also displays a gradual trend of precipitation increase over the period of study, further conforming the finding of monthly precipitation increase over the period of study. The finding of this study provides insight for local agriculturists and ministry to make better decision in response to climate change in Penang.
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ZARENISTANAK, MOHAMMAD, AMIT G. DHORDE, and R. H. KRIPALANI. "Trend analysis and change point detection of annual and seasonal precipitation and temperature series over southwest Iran." Journal of Earth System Science 123, no. 2 (March 2014): 281–95. http://dx.doi.org/10.1007/s12040-013-0395-7.
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Getahun, Yitea Seneshaw, Ming-Hsu Li, and Iam-Fei Pun. "Trend and change-point detection analyses of rainfall and temperature over the Awash River basin of Ethiopia." Heliyon 7, no. 9 (September 2021): e08024. http://dx.doi.org/10.1016/j.heliyon.2021.e08024.
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Khadka, Neha, Cibele Teixeira Pinto, and Larry Leigh. "Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery." Remote Sensing 13, no. 11 (May 25, 2021): 2079. http://dx.doi.org/10.3390/rs13112079.
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The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of-Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS’ stability.
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Bruyndonckx, Robin, Samuel Coenen, Niels Adriaenssens, Ann Versporten, Dominique L. Monnet, Herman Goossens, Geert Molenberghs, et al. "Analysing the trend over time of antibiotic consumption in the community: a tutorial on the detection of common change-points." Journal of Antimicrobial Chemotherapy 76, Supplement_2 (July 1, 2021): ii79—ii85. http://dx.doi.org/10.1093/jac/dkab180.
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Abstract Objectives This tutorial describes and illustrates statistical methods to detect time trends possibly including abrupt changes (referred to as change-points) in the consumption of antibiotics in the community. Methods For the period 1997–2017, data on consumption of antibacterials for systemic use (ATC group J01) in the community, aggregated at the level of the active substance, were collected using the WHO ATC/DDD methodology and expressed in DDD (ATC/DDD index 2019) per 1000 inhabitants per day. Trends over time and presence of common change-points were studied through a set of non-linear mixed models. Results After a thorough description of the set of models used to assess the time trend and presence of common change-points herein, the methodology was applied to the consumption of antibacterials for systemic use (ATC J01) in 25 EU/European Economic Area (EEA) countries. The best fit was obtained for a model including two change-points: one in the first quarter of 2004 and one in the last quarter of 2008. Conclusions Allowing for the inclusion of common change-points improved model fit. Individual countries investigating changes in their antibiotic consumption pattern can use this tutorial to analyse their country data.
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Mekonnen, Dagnenet Fenta, Zheng Duan, Tom Rientjes, and Markus Disse. "Analysis of combined and isolated effects of land-use and land-cover changes and climate change on the upper Blue Nile River basin's streamflow." Hydrology and Earth System Sciences 22, no. 12 (November 30, 2018): 6187–207. http://dx.doi.org/10.5194/hess-22-6187-2018.
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Abstract. Understanding responses by changes in land use and land cover (LULC) and climate over the past decades on streamflow in the upper Blue Nile River basin is important for water management and water resource planning in the Nile basin at large. This study assesses the long-term trends of rainfall and streamflow and analyses the responses of steamflow to changes in LULC and climate in the upper Blue Nile River basin. Findings of the Mann–Kendall (MK) test indicate statistically insignificant increasing trends for basin-wide annual, monthly, and long rainy-season rainfall but no trend for the daily, short rainy-season, and dry season rainfall. The Pettitt test did not detect any jump point in basin-wide rainfall series, except for daily time series rainfall. The findings of the MK test for daily, monthly, annual, and seasonal streamflow showed a statistically significant increasing trend. Landsat satellite images for 1973, 1985, 1995, and 2010 were used for LULC change-detection analysis. The LULC change-detection findings indicate increases in cultivated land and decreases in forest coverage prior to 1995, but forest area increases after 1995 with the area of cultivated land that decreased. Statistically, forest coverage changed from 17.4 % to 14.4%, by 12.2 %, and by 15.6 %, while cultivated land changed from 62.9 % to 65.6 %, by 67.5 %, and by 63.9 % from 1973 to 1985, in 1995, and in 2010, respectively. Results of hydrological modelling indicate that mean annual streamflow increased by 16.9 % between the 1970s and 2000s due to the combined effects of LULC and climate change. Findings on the effects of LULC change on only streamflow indicate that surface runoff and base flow are affected and are attributed to the 5.1 % reduction in forest coverage and a 4.6 % increase in cultivated land areas. The effects of climate change only revealed that the increased rainfall intensity and number of extreme rainfall events from 1971 to 2010 significantly affected the surface runoff and base flow. Hydrological impacts by climate change are more significant as compared to the impacts of LULC change for streamflow of the upper Blue Nile River basin.
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Beaulieu, C., S. A. Henson, Jorge L. Sarmiento, J. P. Dunne, S. C. Doney, R. R. Rykaczewski, and L. Bopp. "Factors challenging our ability to detect long-term trends in ocean chlorophyll." Biogeosciences 10, no. 4 (April 23, 2013): 2711–24. http://dx.doi.org/10.5194/bg-10-2711-2013.
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Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean and Land Colour Instrument (OLCI) expected in 2014 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.
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Higginbottom, Thomas P., and Elias Symeonakis. "Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data." Remote Sensing 12, no. 11 (June 11, 2020): 1894. http://dx.doi.org/10.3390/rs12111894.
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Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.
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Chakraborty, D., S. Saha, R. K. Singh, B. K. Sethy, A. Kumar, U. S. Saikia, S. K. Das, et al. "Trend Analysis and Change Point Detection of Mean Air Temperature: A Spatio-Temporal Perspective of North-Eastern India." Environmental Processes 4, no. 4 (September 6, 2017): 937–57. http://dx.doi.org/10.1007/s40710-017-0263-6.
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Kliestik, Tomas, Katarina Valaskova, Elvira Nica, Maria Kovacova, and George Lazaroiu. "Advanced methods of earnings management: monotonic trends and change-points under spotlight in the Visegrad countries." Oeconomia Copernicana 11, no. 2 (June 22, 2020): 371–400. http://dx.doi.org/10.24136/oc.2020.016.
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Research background: Enterprises manage earnings in an effort to balance their profit fluctuations to provide increasingly consistent earnings in every reporting period. Earnings management is legal and very effective method of accounting techniques and may be used to obtain specific objectives of the enterprises involving the manipulation of accruals. Therefore, there is a need to analyze it in the context of group of countries, while the issue of their detection in the new ways appears.
Purpose of the article: The analysis of annual earnings before interest and taxes (EBIT) of 5,640 enterprises from the Visegrad Four during the period 2009–2018 confirms that the development of earnings management in these countries is not a randomness. Thus, the aim of this article is to determine the existence of positive trend in earnings management and to detect the change-point in its development for each Visegrad country.
Methods: Grubbs test, Mann-Kendall trend test and Buishand test were used as appropriate statistical methods. Mann-Kendall test identifies significant monotonic trend occurrence in earnings manipulation in every country. Buishand test indicates significant years, which divides the development of EBIT into two homogenous groups with individual central lines.
Findings & Value added: Based on the statistical analysis applied, we rejected randomness in the managing of earning, but we determined the trend of its increasing. The positive earnings manipulation was not homogenous in the analyzed period, however, a change-point was defined. Year 2014 was identified as a break-point for Slovak, Polish and Hungarian enterprises considering the earnings manipulation. Year 2013 was detected as a change-point in Czech enterprises. The methodical approach used may be very helpful for researchers from other countries to determine, detect and understand earnings management as well as for the investors to make decisions based on a specificities of an individual country.
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Krzyscin, J. W. "Change in ozone depletion rates beginning in the mid 1990s: trend analyses of the TOMS/ SBUV merged total ozone data, 1978-2003." Annales Geophysicae 24, no. 2 (March 23, 2006): 493–502. http://dx.doi.org/10.5194/angeo-24-493-2006.
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Abstract. Statistical analyses have been applied to the gridded monthly means of total ozone from combined TOMS and SBUV measurements (version 8 of the data) for the period 1978-2003. We focus on the detection of a change in the trend pattern by searching for a turnaround in the previous downward trend. The ozone time series have been examined separately for each grid point and season, taking into account the various descriptions of the trend term: double-linear, proportional to the index of the overall chlorine content in the stratosphere, and a smooth curve without an a priori defined shape (the output of the regression model). Standard explanatory variables representing physical and chemical processes known to influence the ozone distribution have been considered: Mg II index, QBO wind at 10 and 30 hPa, zonal wind anomalies at 50 hPa along the 60° north or 60° south circle, the index of the stratospheric aerosols loading in the NH or SH, and the tropopause pressure. The multivariate adaptive regression splines methodology is used to find an optimal set of the explanatory variables and shape of the trend curve. The statistical errors of the models' estimates have been calculated using block bootstrapping of the models' residuals. The results appear to be consistent among models using different formulations of the trend pattern. The 2003 level of total ozone after the removal of the variations due to the parameterized dynamical/chemical forcing on the ozone is still below the long-term (1978-2003) mean level over the extratropical regions. The deficit is ~2-5% in the NH and much larger in the SH and exhibits clear seasonal variability, ~15% in autumn, ~10% in winter, and ~-5% in spring and summer. The present total ozone level is higher beyond the tropics than that in the mid 1990s but it is too early to announce a beginning of the ozone recovery there because of the trend uncertainties, due to errors of the regression estimates for individual grid points and longitudinal variability of the trend pattern. A rigorous statistical test has shown the statistically significant turnaround for some grid points over the extratropical region and a deepening of the ozone negative trend has not been found for any grid point.
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Awty-Carroll, Katie, Pete Bunting, Andy Hardy, and Gemma Bell. "An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data." Remote Sensing 11, no. 23 (November 25, 2019): 2779. http://dx.doi.org/10.3390/rs11232779.
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Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work.
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Ge, Huimin, Hui Sun, and Ying Lu. "Research on Characteristics and Trends of Traffic Flow Based on Mixed Velocity Method and Background Difference Method." Mathematical Problems in Engineering 2020 (August 28, 2020): 1–9. http://dx.doi.org/10.1155/2020/8546479.
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This research is conducted on the characters and trends of traffic flow in highway maintenance work areas under typical maintenance work forms. In order to improve the safety of the highway maintenance work area, a data monitoring method based on the combination of mixed speed measurement and background difference method were developed. During the on-site detection, the starting point of the warning zone, the starting point of the upstream transition zone, the starting point of the working zone, the midpoint of the working zone, and the six speed measurement sections of the working zone were collected at the end point and the end zone. In the video detection, the background subtraction was used, and the morphological denoting method and the connected domain analysis method were used to retain the vehicle foreground. After analyzing the connection domain and removing the wrong target, the vehicle target area is extracted from research. The research finally obtained the traffic flow characteristics of the start point of the warning zone, the start point of the upstream transition zone, the start point of the work zone, the midpoint of the work zone, the end point of the work zone, and the end point of the downstream transition zone. The study also obtained the traffic volume and the change trend of headway. The combination of mixed velocity method and background difference method is helpful for data monitoring in typical highway maintenance work areas. The measured data results are helpful for studying the distribution characteristics and trends of traffic flow in typical highway maintenance work areas.
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Barraza, Veronica. "Detection of Trend Change-Point in Passive Microwave and Optical Time Series Using Bayesian Inference over the Dry Chaco Forest." Proceedings 1, no. 2 (November 14, 2016): 45. http://dx.doi.org/10.3390/ecsa-3-e012.
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Masiliūnas, Dainius, Nandin-Erdene Tsendbazar, Martin Herold, and Jan Verbesselt. "BFAST Lite: A Lightweight Break Detection Method for Time Series Analysis." Remote Sensing 13, no. 16 (August 21, 2021): 3308. http://dx.doi.org/10.3390/rs13163308.
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BFAST Lite is a newly proposed unsupervised time series change detection algorithm that is derived from the original BFAST (Breaks for Additive Season and Trend) algorithm, focusing on improvements to speed and flexibility. The goal of the BFAST Lite algorithm is to aid the upscaling of BFAST for global land cover change detection. In this paper, we introduce and describe the algorithm and then compare its accuracy, speed and features with other algorithms in the BFAST family: BFAST and BFAST Monitor. We tested the three algorithms on an eleven-year-long time series of MODIS imagery, using a global reference dataset with over 30,000 point locations of land cover change to validate the results. We set the parameters of all algorithms to comparable values and analysed the algorithm accuracy over a range of time series ordered by the certainty of that the input time series has at least one abrupt break. To compare the algorithm accuracy, we analysed the time difference between the detected breaks and the reference data to obtain a confusion matrix and derived statistics from it. Lastly, we compared the processing speed of the algorithms using both the original R code as well as an optimised C++ implementation for each algorithm. The results showed that BFAST Lite has similar accuracy to BFAST but is significantly faster, more flexible and can handle missing values. Its ability to use alternative information criteria to select the number of breaks resulted in the best balance between the user’s and producer’s accuracy of detected changes of all the tested algorithms. Therefore, BFAST Lite is a useful addition to the BFAST family of unsupervised time series break detection algorithms, which can be used as an aid in narrowing down areas with changes for updating land cover maps, detecting disturbances or estimating magnitudes and rates of change over large areas.
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Souza, Eliana Amorim de, Anderson Fuentes Ferreira, Reagan Nzundu Boigny, Carlos Henrique Alencar, Jorg Heukelbach, Francisco Rogerlândio Martins-Melo, Jaqueline Caracas Barbosa, and Alberto Novaes Ramos Junior. "Leprosy and gender in Brazil: trends in an endemic area of the Northeast region, 2001–2014." Revista de Saúde Pública 52 (February 26, 2018): 20. http://dx.doi.org/10.11606/s1518-8787.2018052000335.
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OBJECTIVE: To analyze, stratifield by gender, trends of the new case leprosy detection rates in the general population and in children; of grade 2 disability, and of proportion of multibacillary cases, in the state of Bahia, Brazil from 2001 to 2014. METHODS: A time series study based on leprosy data from the National Information System for Notifiable Diseases. The time trend analysis included Poisson regression models by infection points (Joinpoint) stratified by gender. RESULTS: There was a total of 40,054 new leprosy cases with a downward trend of the overall detection rate (Average Annual Percent Change [AAPC = -0.4, 95%CI -2.8–1.9] and a nonsignificant increase in children under 15 years (AAPC = 0.2, 95%CI -3.9–4.5). The proportion of grade 2 disability among new cases increased significantly (AAPC = 4.0, 95%CI 1.3–6.8), as well as the proportion of multibacillary cases (AAPC = 2.2, 95%CI 0.1–4.3). Stratification by gender showed a downward trend of detection rates in females and no significant change in males; in females, there was a more pronounced upward trend of the proportion of multibacillary and grade 2 disability cases. CONCLUSIONS: Leprosy is still highly endemic in the state of Bahia, with active transmission, late diagnosis, and a probable hidden endemic. There are different gender patterns, indicating the importance of early diagnosis and prompt treatment, specifically in males without neglecting the situation among females.
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York, Ashley V., Karen E. Frey, Sadegh Jamali, and Sarah B. Das. "Change Points Detected in Decadal and Seasonal Trends of Outlet Glacier Terminus Positions across West Greenland." Remote Sensing 12, no. 21 (November 7, 2020): 3651. http://dx.doi.org/10.3390/rs12213651.
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We investigated the change in terminus position between 1985 and 2015 of 17 marine-terminating glaciers that drain into Disko and Uummannaq Bays, West Greenland, by manually digitizing over 5000 individual frontal positions from over 1200 Landsat images. We find that 15 of 17 glacier termini retreated over the study period, with ~80% of this retreat occurring since 2000. Increased frequency of Landsat observations since 2000 allowed for further investigation of the seasonal variability in terminus position. We identified 10 actively retreating glaciers based on a significant positive relationship between glaciers with cumulative retreat >300 m since 2000 and their average annual amplitude (seasonal range) in terminus position. Finally, using the Detecting Breakpoints and Estimating Segments in Trend (DBEST) program, we investigated whether the 2000–2015 trends in terminus position were explained by the occurrence of change points (significant trend transitions). Based on the change point analysis, we found that nine of 10 glaciers identified as actively retreating also underwent two or three periods of change, during which their terminus positions were characterized by increases in cumulative retreat. Previous literature suggests potential relationships between our identified change dates with anomalous ocean conditions, such as low sea ice concentration and high sea surface temperatures, and our change durations with individual fjord geometry.
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Morland, J., M. Collaud Coen, K. Hocke, P. Jeannet, and C. Mätzler. "Tropospheric water vapour above Switzerland over the last 12 years." Atmospheric Chemistry and Physics 9, no. 16 (August 19, 2009): 5975–88. http://dx.doi.org/10.5194/acp-9-5975-2009.
Full textAbstract:
Abstract. Integrated Water vapour (IWV) has been measured since 1994 by the TROWARA microwave radiometer in Bern, Switzerland. Homogenization techniques were used to identify and correct step changes in IWV related to instrument problems. IWV from radiosonde, GPS and sun photometer (SPM) was used in the homogenisation process as well as partial IWV columns between valley and mountain weather stations. The average IWV of the homogenised TROWARA time series was 14.4 mm over the 1996–2007 period, with maximum and minimum monthly average values of 22.4 mm and 8 mm occurring in August and January, respectively. A weak diurnal cycle in TROWARA IWV was detected with an amplitude of 0.32 mm, a maximum at 21:00 UT and a minimum at 11:00 UT. For 1996–2007, TROWARA trends were compared with those calculated from the Payerne radiosonde and the closest ECMWF grid point to Bern. Using least squares analysis, the IWV time series of radiosondes at Payerne, ECMWF, and TROWARA showed consistent positive trends from 1996 to 2007. The radiosondes measured an IWV trend of 0.45±0.29%/y, the TROWARA radiometer observed a trend of 0.39±0.44%/y, and ECMWF operational analysis gave a trend of 0.25±0.34%/y. Since IWV has a strong and variable annual cycle, a seasonal trend analysis (Mann-Kendall analysis) was also performed. The seasonal trends are stronger by a factor 10 or so compared to the full year trends above. The positive IWV trends of the summer months are partly compensated by the negative trends of the winter months. The strong seasonal trends of IWV on regional scale underline the necessity of long-term monitoring of IWV for detection,understanding, and forecast of climate change effects in the Alpine region.