Trends In Series Research Articles

Exploring recent groundwater level changes using Innovative Trend Analysis (ITA) technique over three districts of Jharkhand, India

Groundwater is the largest store of available freshwater in the world. With the increasing impact of climate change and human activities, assessment of variations in groundwater levels is essential. This study presents the trend of groundwater levels in three districts (Purbi Singhbhum, Ranchi and Saraikela) of Jharkhand State, India. The depth to groundwater level (DGWL) data from 24 wells over the three districts for 1996–2018 is collected for both pre-monsoon and post-monsoon seasons. The conventional non-parametric trend analysis techniques (e.g., Mann-Kendall, Sen's Slope, Spearman's Rho tests) have several restrictive assumptions, such as Gaussian distributed time series and absence of serial correlation. Innovative Trend Analysis (ITA) technique does not have these restrictive assumptions and therefore, can be effectively used to detect trends in a time series of shorter length. Moreover, ITA is a slope-based technique that provides visual interpretations as well as the statistical significance of trends in a time series, thereby comprehensively acquiring the benefits of multiple conventional methods. Therefore, ITA is employed in this study to investigate the trends in groundwater levels. The results reveal a significantly increasing trend of DGWL for 17 sites in pre-monsoon and 14 sites in post-monsoon seasons. On the other hand, only four and six stations showed negative trends during pre- and post-monsoon seasons, respectively. The increasing DGWL trends at a majority of the sites imply a serious decline in the groundwater levels over the years. This study will be helpful to water resources managers to identify the causes of the declining groundwater levels, take necessary actions to control them, and efficiently manage groundwater at local levels.

Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh.

Accurate predictive time series modelling is important in public health planning and response during the emergence of a novel pandemic. Therefore, the aims of the study are three-fold: (a) to model the overall trend of COVID-19 confirmed cases and deaths in Bangladesh; (b) to generate a short-term forecast of 8 weeks of COVID-19 cases and deaths; (c) to compare the predictive accuracy of the Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost) for precise modelling of non-linear features and seasonal trends of the time series. The data were collected from the onset of the epidemic in Bangladesh from the Directorate General of Health Service (DGHS) and Institute of Epidemiology, Disease Control and Research (IEDCR). The daily confirmed cases and deaths of COVID-19 of 633 days in Bangladesh were divided into several training and test sets. The ARIMA and XGBoost models were established using those training data, and the test sets were used to evaluate each model's ability to forecast and finally averaged all the predictive performances to choose the best model. The predictive accuracy of the models was assessed using the mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The findings reveal the existence of a nonlinear trend and weekly seasonality in the dataset. The average error measures of the ARIMA model for both COVID-19 confirmed cases and deaths were lower than XGBoost model. Hence, in our study, the ARIMA model performed better than the XGBoost model in predicting COVID-19 confirmed cases and deaths in Bangladesh. The suggested prediction model might play a critical role in estimating the spread of a novel pandemic in Bangladesh and similar countries.

International Natural Gas Price Trends Prediction with Historical Prices and Related News

Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship and hurricanes can cause the jump in natural gas prices volatility. To address this issue, a deep learning model based on oil and gas news is proposed to predict natural gas price trends in this paper. In this model, news text embedding is conducted by BERT-Base, Uncased on natural gas-related news. Attention model is adopted to balance the weight of the news vector. Meanwhile, corresponding natural gas price embedding is conducted by a BiLSTM module. The Attention-weighted news vectors and price embedding are the inputs of the fused network with transformer is built. BiLSTM is used to extract used price information related with news features. Transformer is employed to capture time series trend of mixed features. Finally, the network achieves an accuracy as 79%, and the performance is better than most traditional machine learning algorithms.

Analysis of atmospheric ozone in Fenwei Plain based on remote sensing monitoring.

This study uses the daily product data of the concentration of ozone in the atmospheric column (ozone column concentration) collected by the Aura satellite's Ozone Monitoring Instrument (OMI), to evaluate the ozone pollution status of the Fenwei Plain in east-central China, by employing pixel-based spatial analysis, an θslope trend index, a Hurst index, and grey correlation. The following results were found. (1) The spatial distribution of ozone in the atmosphere of the Fenwei Plain was higher in the north and lower in the south, with high values appearing in Jinzhong, Lvliang, and other cities. (2) The changes in ozone column concentration periodically and seasonally in the Fenwei Plain. Seasonally, the ozone column concentration was highest in spring, followed by summer, winter, and autumn. (3) The pixel-based trend change of the ozone slope (θslope) indicated that the ozone concentration in the region was in a downward trend, while the long-term correlation of the time series trend Hurst index found that the region should expect to see a weak rebound in the ozone column concentration in the future, so that routine monitoring should be strengthened. (4) The present study on the factors influencing the ozone concentration found that the concentration is relatively closely related to temperature, air pressure, humidity, grain sowing area, highway mileage, and secondary industry.

Analysis of Climate Variability in a Time Series of Precipitation and Temperature Data: A Case Study in Cartagena de Indias, Colombia

Anthropogenic climate change is a global trend, hitherto incontrovertible, causing immense social and economic damage. Although the this is evident at the global level, at the local level, there is still debate about the most appropriate analyses to support this fact. This debate is particularly relevant in developing countries, such as Colombia, where there is a significant lack of data at the local level that require analysis and interpretation. Consequently, studies are often superficially conducted to support climate change theory at the local level. However, such studies are then used to design hydraulic infrastructure, with potential catastrophic errors for human and environmental health. In this study, we sought evidence of climate change through an analysis of a series of data on temperature (maximum, mean and minimum), as well as total annual and maximum rainfall in 24 h registered at the Rafael Nuñez Airport station in the city of Cartagena, Colombia, from 1941 to 2015. The hypotheses of homogeneity, trend, stationarity and non-stationarity were analyzed. Problems of non-homogeneity and the presence of periodicity in the analyzed series were found, showing a trend and apparent non-stationarity in the original series. This could be associated with the effects of climate change. In this case, no correlation was found between temperatures and rainfall. Spectral analysis was performed for all series, and residual series were generated by extracting the harmonics of greatest significance. It was found that the series data generated from the third harmonic are generally stationary and without trend. Therefore, the trend and non-stationarity of the original series are due to problems of non-homogeneity and periodicity in the series. In the results of the stationarity test conducted according to the Phillips–Perron criterion, all series were non-stationary. For the two additional criteria of stationarity tests, 40% were shown to be stationary, and 60% were non-stationary. Specifically, non-homogeneity problems and apparent trends associated with climate change could have negative implications for the design of drainage systems.

Trends in Health Care Access/Experiences: Differential Gains across Sexuality and Sex Intersections before and after Marriage Equality.

Background: Sexual minority adults experience several health care access inequities compared to their heterosexual peers; such inequities may be affected by LGBTQ+ legislation, such as the 2015 national marriage equality ruling. Methods: Using population-based data (n = 28,463) from the Association of American Medical Colleges biannual Consumer Survey of Health Care Access, we calculated trend ratios (TR) for indicators of health care access (e.g., insurance coverage, delaying or forgoing care due to cost) and satisfaction (e.g., general satisfaction, being mistreated due to sexual orientation) from 2013 to 2018 across sexuality and sex. We also tested for changes in trends related to the 2015 marriage equality ruling using interrupted time series trend interactions (TRInt). Results: The largest increases in access were observed in gay men (TR = 2.42, 95% CI 1.28, 4.57). Bisexual men had decreases in access over this period (TR = 0.47, 95% CI 0.22, 0.99). Only gay men had a significant increase in the health care access trend after U.S. national marriage equality (TRInt = 5.59, 95% CI 2.00, 9.18), while other sexual minority groups did not. Conclusions: We found that trends in health care access and satisfaction varied significantly across sexualities and sex. Our findings highlight important disparities in how federal marriage equality has benefited sexual minority groups.

Southern Alps equilibrium line altitudes: four decades of observations show coherent glacier–climate responses and a rising snowline trend

AbstractAn end of summer snowline (EOSS) photographic dataset for Aotearoa New Zealand contains over four decades of equilibrium line altitude (ELA) observations for more than 50 index glaciers. This dataset provides an opportunity to create a climatological ELA reference series that has several applications. Our work screened out EOSS sites that had low temporal coverage and also removed limited observations when the official survey did not take place. Snowline data from 41 of 50 glaciers in the EOSS dataset were retained and included in a normalised master snowline series that spans 1977–2020. Application of the regionally representative normalised master snowline series in monthly and seasonally resolved climate response function analyses showed consistently strong relationships with austral warm-season temperatures for land-based stations west of the Southern Alps and the central Tasman Sea. There is a trend towards higher regional snowlines since the 1990s that has been steepening in recent decades. If contemporary decadal normalised master snowline series trends are maintained, the average Southern Alps snowline elevation will be displaced at least 200 m higher than normal by the 2025–2034 decade. More frequent extremely high snowlines are expected to drive more extreme cumulative mass-balance losses that will reduce the glacierised area of Aotearoa New Zealand.

Biotinidase activity is affected by both seasonal temperature and filter collection cards

This study set out to examine pre-analytical factors affecting the frequency of positive results in newborn screening for biotinidase deficiency. This investigation was prompted by an increase in the annual screen positive rate for biotinidase deficiency in Ontario from 2.65x10−4 in 2016 to 6.57x10−4 in 2017.Season and trend decomposition was used to separate seasonality from an underlying trend in the time series of biotindase activity measurements for the period 2014–01-12 to 2019–07-27 (n = 798,770). This analysis revealed a marked seasonal effect (winter = median + ⩽ 17 MRU, summer = mean - ⩽20 MRU) and a non-linear negative trend. Seasonal temperature was correlated with biotinidase results (Pearson’s r = 0.79) but not with the observed negative trend (Pearson’s r = 0.0025). Time series analysis of biotinidase results grouped by print lot of filter paper revealed that recently printed filter paper cards inhibit biotinidase and that this inhibition resolved over time. This study demonstrates that biotindase activity is inhibited by both increased seasonal temperature and collection on newly printed filter cards.

Accuracy, stability, and continuity of AVHRR, SeaWiFS, MODIS, and VIIRS deep blue long-term land aerosol retrieval in Asia

The deep blue (DB) aerosol algorithm applied to four satellite instruments, AVHRR, SeaWiFS, MODIS, and VIIRS, produced a long-term aerosol data set since 1989. This study first evaluated and compared the accuracy, stability, and continuity of four DB aerosol optical depth (AOD) products in Asia using AErosol RObotic NETwork measurements. Then, the regional AOD spatial distributions, coverages, and series trends are analyzed. The results show that VIIRS DB has the highest accuracy and stability, with an expected error (EE, ±(0.05 + 20%)) of 76.59% and stability of approximately 0.027 per decade. The performance of MODIS DB is slightly worse than that of VIIRS. However, their AOD pattern, coverage, and trend are comparable. The performance of AVHRR (EE = 58.10%) and the stability of SeaWiFS (0.093 per decade) are less good. Therefore, SeaWiFS DB data should be used with caution for trend analysis. The AOD accuracy and coverage together determine the AOD pattern and the continuity of multi-sensor data. In addition to consistent algorithm accuracy, it is necessary to consider the influences in sensor sampling and inappropriate-pixel screening schemes in the joint multi-sensor analysis. Encouragingly, although multiple DB products have different AOD averages of regional series, their changing trends are consistent. Error analysis shows that the AOD bias characteristic is different in different surface conditions. This indicates that the surface reflectance estimated by the DB algorithm using different techniques is divergent, which may be the direction for the improvement of the algorithm.

A Positive Climatic Trend in the Global Offshore Wind Power

The climatic variation of offshore wind energy has a close relationship with the long-term plan of energy utilization. However, the work on this aspect is scarce and mainly focuses on the variation of wind power density (WPD). There is little research on the climatic trends of effective wind speed occurrence (EWSO) and occurrence of energy level greater than 200 W/m2 (rich level occurrence, RLO), which are directly related to the available rate and richness of wind energy. Based on the ERA-Interim wind product from the ECMWF, this study calculated the climatic trends of series of key factors of wind energy in the global oceans, including the WPD, EWSO, and RLO. The results show that the wind energy exhibits a positive trend globally for the past 36 years, with overall annual increasing trends in WPD, EWSO, and RLO, of 0.698 (W/m2)/yr, 0.076%/yr, and 0.090%/yr separately. The annual trend exhibits evident regional differences. The areas with significant increasing trends are mainly distributed in the mid- low-latitude waters of global oceans and part of the southern hemisphere westerlies. The annual increasing trend of WPD is strongest in the southern westerlies, especially in the extratropical South Pacific (ETSP), of about 1.64 (W/m2)/yr. The annual increasing trends of EWSO and RLO are strongest in the tropical waters, especially the tropical Pacific Ocean (TPO), of 0.17%/yr and 0.19%/yr separately. The annual and seasonal WPD, EWSO, and RLO in most global oceans have significant increasing trends or no significant variation, meaning that the wind energy trends are rich or stable, which is beneficial for energy development. The climatic trends of wind energy are dominated by different time periods. There is no evident abrupt change of wind energy in the extratropical waters globally and tropical Atlantic Ocean (TAO). The abrupt periods of wind energy in the TIO and TPO occurred in the end of the 20th century and the beginning of the 21st century. The wind energy of the South China Sea, Arabian Sea, and the Bay of Bengal and nino3 index share a common period of approximately 5 years. The offshore wind energy was controlled by an oscillating phenomenon.

Analysing deaths and confirmed cases of COVID-19 pandemic by analytical approaches.

In this work, the time series of growth rates regarding confirmed cases and deaths of COVID-19 for several sampled countries are investigated via an introduction of an orthonormal basis. This basis, which is served as the feature benchmark, reveals the hidden features of COVID-19 via the magnitude of Fourier coefficients. These coefficients are ranked in the form of ranking vectors for all the sampled countries. Based on these and Manhattan metric, we then perform spectral clustering to categorise the countries. Unlike the classical cosine similarity analysis which, relatively speaking, is a composite index and hard to identify the features of the categorised countries, spectral analysis delves into the internal structures or dynamical trend of the time series. This research shows there is no single feature that dominates the trend of the growth rates. It also reveals that results from the spectral analysis are different from the ones of cosine similarity. In the end, some approximated values of the confirmed cases and deaths are also calculated by the spectral analysis.

Spatio-temporal analysis of precipitation, temperature and drought from 1985 to 2020 in Penang, Malaysia

AbstractThis study aims to analyze the spatial and temporal variability of precipitation, temperature and drought in Penang from 1985 to 2020. The trend and magnitude changes in climate series were tested using the Mann–Kendall test and Sens’ slope methods. A significant increasing trend of annual precipitation was detected in the middle and northern parts of Penang mainland by 21.38 and 35.01 mm/decade. Annual mean maximum temperature increased significantly on Penang Island and the middle of Penang mainland at the rates of 0.25 and 0.34 °C/decade, while annual mean minimum temperature increased significantly for all stations from 0.36 to 0.52 °C/decade, showing increases of warm nights in the past few years. Extreme drought events (SPI-3 > −2) over Penang were mostly occurred during the strong El Niño years, e.g. 1988–1989, 1997–1998, 2010–2011 and 2014–2016. The number of drought events is higher during the 2001–2010 (4–11 events) period as compared to the 1991–2000 (6–9 events) and 2011–2020 (7–8 events). Interestingly, the 2011–2020 period tends to have a shorter drought duration, but more intense droughts have been observed, particularly on Penang Island and the northern and southern parts of Penang mainland.

Demographics and Seasonality of Retinal Detachment, Retinal Breaks, and Posterior Vitreous Detachment from the Intelligent Research in Sight Registry.

PurposeTo investigate the incidence, seasonal variation, and differences among age, sex, and race for rhegmatogenous retinal detachment (RRD) repair, retinal break (RB) treatment, and posterior vitreous detachment (PVD) in the Intelligent Research in Sight (IRIS) Registry.DesignRetrospective database study.ParticipantsPatients in the IRIS Registry who underwent RRD repair, RB treatment, or cataract surgery (CS) based on Current Procedural Terminology codes and PVD diagnosis based on International Classification of Diseases, Ninth and Tenth Revision, codes.MethodsDaily incidence rates were defined as the ratio of patients who underwent RRD repair or RB treatment and patients with a diagnosis of PVD to the total number of patients followed on a given day within the IRIS Registry. The CS group was included as a comparison for seasonal variation. Rates were stratified by decade of life, sex, and race.Main Outcome MeasuresTime series trends for incidence rates of RRD, RB, and PVD.ResultsA total of 7 115 774 patients received a diagnosis of incident PVD, 237 646 patients underwent RRD repair, and 359 022 patients underwent RB treatment. Also included were 5 940 448 patients who underwent CS. The mean daily incidence for RRD repair, RB treatment, PVD diagnosis, and CS were 0.46 per 100 000 patients, 0.70 per 100 000 patients, 13.90 per 100 000 patients, and 11.80 per 100 000 patients, respectively. Men showed higher incidence of RRD repair and RB treatment than women, whereas women showed higher incidence of PVD diagnosis. Rhegmatogenous retinal detachment incidence was higher in White people compared with other races. Seasonal decreases in PVD, RB treatment, RRD repair, and CS corresponded to national holidays, with larger decreases in winter months. Kaplan-Meier estimates showed that RRD repair and RB treatment typically occurred within 60 days of PVD diagnosis.ConclusionsWithin the IRIS Registry, the highest incidence of RRD was in the 6th and 7th decade of life. There was a higher incidence of RRD repair and RB treatment in men compared with women. The seasonal variation associated with national holidays was less pronounced for RRD repair and RB treatment.

Assessment of long term spatio-temporal variability in temperature over the Kalaburgi District of Karnataka, India

In recent days observed extreme variations in the climate and weather events are increasingly being recognized as key aspects of climate change. ln this study we assessed the spatial-temporal variability of minimum and maximum temperature over the Kalaburgi district of Karnataka, India for the period 1981-2018, using gridded data with 0.5-degree resolution obtained from National Aeronautics and Space Administration prediction of worldwide energy resource (NASA POWER) project. Trend detection and quantification in the temperature evaluated using the non-parametric Mann-Kendall (MK) test and Sen’s slope estimator. The overall average of minimum and maximum temperature data and Sen,s estimate showed the increasing trend in series for the last 38 years. The highest average minimum temperature observed is densly distributed over the Jewargi with a range of 24.74 0C to 26.68 0C and all other places found sparsely distributed with range 19.98 0C to 23.50 0C. ln the overall, the spatial distribution analysis results observed the densely distribution of maximum temperature in AIand, Afzalpur, Jewargi and part of Kalaburgi with range of 32.88 0C to 33.99 0C. The average maximum temperature exhibited the increasing trend linearly and Sen's slope also estimated increasing trend in the study area.

Spatiotemporal analysis of droughts characteristics and drivers in the Omo-Gibe River basin, Ethiopia

BackgroundDrought is one of the leading destructive natural disasters adversely affecting natural resources and livelihoods. Thus, this study evaluated the spatial and temporal meteorological drought characteristics and their drivers in the Omo-Gibe River basin (OGRB) from 1981 to 2017. The drought analysis used the standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) with 4- and 12-month timescales. The Mann–Kendall (MK), Sen's slope estimator, and Pettit test were used to evaluate the trend and change points of the time series. Pearson correlation was used to examine the teleconnection between large-scale global climate signals with the basin's seasonal and annual drought indices.ResultsAccordingly, extreme and severe drought events were observed in 1988, 2000, and 2009. In the basin, prolonged drought events were recorded from 2000 to 2015. The statistically significant (P < 0.05) increasing trend of seasonal and annual drought events was observed in all basin parts. However, more drought events distribution was exhibited in the south than in the north and central parts. The Sawla station (southern part) showed a higher drought frequency, ranging from 18.18 to 20.36%. The maximum intensity and peak drought events were observed in this sub-basin, with SPEI reaching − 2.27 and − 4.89, respectively. The global indices NINO3.4, SOI, and DMI are drivers for triggering the meteorological droughts in OGRB.ConclusionsSubstantial warming and erratic rainfall have made OGRB vulnerable to drought events. The intensification of droughts in the basin has also been recorded in humid parts of the basin which has a significant adverse effect on the water availability of down streams. This indicates that the observed drought intensity can increase the water deficit and other natural resources degradation. Therefore, this study provides essential information on drought characteristics for decision-makers to plan appropriate strategies for early warning systems to adapt and mitigate drought hazards in the basin.

A Trend Analysis of Climatic Variables in the Karimganj District of Assam, India

Objectives: The main goal of this research is to analyse the trends of climatic variables in the Karimganj district of Assam. The study examines the trend of seasonal and annual temperature maximum, temperature minimum, rainfall and relative humidity for the period 1981-2020. Methods: A non-parametric Mann-Kendall (MK) technique has been employed to establish statistically significant trend in the series. The magnitude of a time series trend has been evaluated by Sen’s slope test. Trend lines and time series graphs are used for better understanding of trends. Findings: It has been found that annual and seasonal temperature maximum (Tmax) have a significant negative trend over the period except post monsoon season. There is no significant trend in the annual temperature minimum (Tmin) series but seasonal mean temperature minimum is showing a significant positive trend at different levels of significance during all four seasons. Except winter season, annual rainfall series and all other seasonal average rainfall series are showing significant positive trends. The seasonal averages and annual relative humidity are showing statistically significant positive trends over the period. Novelty: The study is unique and innovative for Karimganj district of Assam to best of our knowledge. From the analysis, the study claims that climate change is occurring in the district. To combat the impact of climate change, participation of people in adaptations practices and government intervention in the form of mitigation policies are necessary. JEL Classification: Q50, Q54 Keywords: Karimgani; climate change; rainfall; temperature; relative humidity; Assam

Application of improved seasonal GM(1,1) model based on HP filter for runoff prediction in Xiangjiang River.

Runoff forecasting is essential for the reasonable use of regional water resources, flood prevention, and mitigation, as well as the development of ecological civilization. Runoff is influenced by the intersection of many factors, and the change process is extremely complex, showing significant stochasticity, nonlinearity, and heterogeneity, making traditional prediction models less adaptable. The Hodrick-Prescott filter (HP filter) is a well-established signal separation method. The traditional GM(1,1) model is capable of portraying the growth trend of the time series but cannot capture the periodic characteristics of the time series. Therefore, a novel coupled prediction model-HPF-GM(1,1) model is proposed in this study and applied to the runoff prediction of the Zhuzhou section of Xiangjiang River in Hunan Province. This model enables to separate seasonal factors from non-seasonal factors in the runoff time series, and introduce seasonal factors based on the traditional GM(1,1) model, which solves the challenge that the traditional GM(1,1) model is unable to predict seasonal time series. The results show that the HPF-GM(1,1) model has a mean relative error of 4.82%, a root mean square error of 7.44, and a Nash efficiency coefficient of 0.93, which is better than the traditional GM(1,1) model, the DGGM(1,1) model and the SGM(1,1) model of prediction accuracy. Obviously, the HP filter provides a new approach to data pre-processing of runoff series and the proposed HPF-GM(1,1)-coupled model extends new ideas for nonlinear runoff prediction.

Meaningful Trend in Climate Time Series: A Discussion Based On Linear and Smoothing Techniques for Drought Analysis in Taiwan

Finding significant trends in hydroclimate time series has been deemed an essential task in numerous studies. Despite the existence of various trend detection methods, statistical significance is mostly examined for linear trends and related to the meaningfulness of the found trends. We wish to draw attention to a more general definition of meaningful trends by cross-referencing not only linear but also smoothing techniques. We apply linear regression (LR) and two smoothing techniques based on regularized minimal-energy tensor-product B-splines (RMTB) to the trend detection of standardized precipitation index (SPI) series over Taiwan. LR and both RMTB-based methods identify an overall upward (wetting) trend in the SPI series across the time scales in Taiwan from 1960 to 2019. However, if dividing the entire time series into the earlier (1960–1989) and later (1990–2019) sub-series, we find that some downward (drying) trends at varied time scales migrate from southcentral–southwestern to eastern regions. Among these significant trends, we have more confidence in the recent drying trend over eastern Taiwan since all the methods show trend patterns in highest similarity. We also argue that LR should be used with great caution, unless linearity in data series and independence and normality in residuals can be assured.

Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO.

Analysis of the Evolution of Climatic and Hydrological Variables in the Tagus River Basin, Spain

During the second half of the 20th century, several Spanish rivers experienced a decrease in the availability of water resources which coincided with an increase in human water demands. This situation is expected to be exacerbated by climate change. This study analyses the evolution of annual streamflow in 16 sub-basins of the Tagus River basin (Spain) during the 1950–2010 period and its relationship with selected variables. Our main objective is to characterize changes in in-stream flows and to identify what factors could have contributed to them. First, we used non-parametric tests to detect trends in the hydro-climatic series. Then, we analyzed changes in the runoff coefficient and applied regression-based techniques to detect anthropic drivers that could have influenced the observed trends. The analysis revealed a general decreasing trend in streamflow and an increasing trend in air temperature, while trends in precipitation are less clear. Residuals from regression models indicate that the evolution of several non-climatic factors is likely to have influenced the decline in streamflow. Our results suggest that the combination of the expansion of forested areas (a 60% increase from 1950 to 2010) and irrigated land (a 400% increase since 1950) could have played an important role in the reduction of streamflow in the Tagus basin.