Historical Series Data Research Articles

Hydrocarbon pollution in underwater sinkholes of the Mexican Caribbean caused by tourism and asphalt: Historical data series and cluster analysis

In this study historical data sets from 2012, 2013, 2014 and 2015 of hydrocarbon pollution in sinkholes of Quintana Roo are presented for two vacational seasons: “High” (July) and “Low” (October). Anthracene, Benzo[a]pyrene and Pyrene were found to be the main hydrocarbon contaminants and these poly aromatics hydrocarbons are strongly related to pollution by asphalt. Tourism contributes to hydrocarbon pollution in the sinkholes of Quintana Roo in two main ways: runoff of fuel from cars and, principally, from annual asphalting. Additionally, in this paper we conduct a cluster analysis to classify the pollution grade of the sinkholes. Clustering can be used to observe and interpret the cause of vacational seasonal pollution produced by tourism.

Modelling the spatial dependence of the rainfall erosivity index in the Brazilian semiarid region

Abstract: The objective of this work was to model the spatial dependence and to map the rainfall erosivity index (EI30) in the semiarid region of Brazil. Registers of monthly erosivity from 210 rainfall stations were used, with daily time series equal to or greater than 15 years. Based on the values of the EI30, a spatial dependence model was made by adjusting the semivariogram. From the semivariogram models, erosivity isoline maps were generated with a kriging interpolator. According to the historical data series, the maximum monthly average value of the EI30 was observed in March, and the annual value ranged from 1,439 to 5,864 MJ mm ha-1 per year, classified as low and moderate, respectively. The highest EI30 values were obtained in the northern and southern extremes of the semiarid region. Average spatial dependence was observed for rainfall erosivity, in most months, especially with the spherical semivariogram model. The range of erosivity varied from 62 to 1,508 km for the monthly EI30 and was of approximately 1,046 km for the annual one. The applied model, with the validation of the semivariograms using the jackknife test, allows the spatialization of the EI30 for the semiarid region of Brazil.

Long-term power-to-gas potential from wind and solar power: A country analysis for Italy

Challenges related to variability of renewable energy sources (RES) recently arose in many countries and several solutions based on energy storage were proposed; among them, a promising option is Power-to-Gas (P2G), able to recover excess and unbalanced electrical energy. In this work, an assessment of long-term P2G potential is performed on a country scale, based on the analysis of electrical system historical data series, rescaled in order to consider the evolution of load and installed wind and solar capacity. In a long-term perspective, it is assumed the complete exploitation of the technical potential of the RES, which represents an upper deployment boundary with current technology. Once satisfied the electric load, residual energy to the P2G system and hydrogen production are calculated on a hourly basis; P2G installed capacity is a consequence of the assumed target on minimum operation on a yearly basis. The Italian case is analyzed, evidencing that the recovered excess energy from RES could substitute nearly 5% of current natural gas consumption or about 7% of national fuel consumption when used for hydrogen mobility. A range of options and a sensitivity analysis on assumptions is presented, showing scenarios with up to 200 GW of installed RES and a 50% additional load with respect to current one. In addition, the extension of the model to a zonal grid structure evidences the impact of transmission lines saturation that may increase gas production up to 50%. Results are compared with the German case, considered in a previous work, evidencing differences due to the diverse energy production mix.

O analfabetismo no Brasil: lições da história

The article discusses the problem of illiteracy in Brazil, seeking to reveal their historical and social roots. It starts from an analysis of the data that leaves an optimistic reading on the gradual fall of the indices. Neither can the fall be considered significant, when one observes the absolute number of illiterates and the trends of the historical series of data, nor is possible attribute this reduction to the results of the public policies. By making a historical study, one can see that economic, political and social relations end up determining the conditions in which the supply of schooling will take place, what persons will have access to it, what will be its possibility of progress, among other aspects. At the same time, educational structures have been at the mercy of the social and economic context, failing to seek an organization that could offers real development opportunities to all students.

Downdating high-resolution population density maps using sealed surface cover time series

Many countries in Europe and North America see their natural and agricultural landscapes being replaced by a fragmented, sprawled landscape. Spatially detailed modelling of changes in land use, population and transport could help to forecast the impact of scenarios aimed at mitigating the process of urban sprawl. A common problem with land-use change models however, is the lack of historical data for proper model calibration. In this paper we describe an approach for developing historical population density maps by downdating a recent high-resolution population density raster, using a time series of sealed surface data and historical census data as an input. In the proposed approach, we hypothesise a local relationship between increasing population densities and increasing sealed surface fraction estimates, the latter obtained from remote sensing imagery. We apply the method to Flanders, Belgium, a region where population growth and improved transport networks led to a diffuse urban expansion, with ribbon development along many roads and a strong fragmentation of open space. The resulting population and sealed surface maps provide interesting data on the urban sprawl phenomenon in the past decades. By computing a densification index we observe that most urban areas witness a recent population density increase while in several rural areas the built-up area per inhabitant is still growing. The downdated time series of population maps obtained in this study will be used to set up a historical calibration for an activity-based cellular automata model for Flanders and Brussels which, among other data, needs high-resolution population maps.

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

This study presents a system day-ahead hourly market clearing price forecasting tool for the day-ahead (DA) market and a system DA hourly marginal price forecasting tool for the real-time market of the Turkish electric market (TEM). These forecasting tools are developed based on artificial neural networks (ANNs). A series of historical price data of the TEM are utilized to model and optimize the ANN structure and to develop the ANN-based price forecasting tool. The methodology used to select the optimum ANN architecture provides the minimum daily mean absolute percentage error for both day-ahead market prices in the TEM. Performances of the proposed ANN model and the multiple linear regression model in forecasting the day-ahead hourly market clearing price are compared. The proposed ANN model is modified using volatility analysis and the Bienayme-Chebyshev inequality in order to forecast system marginal prices probabilistically within a lower and an upper boundary.

Estimativa do fluxo de calor no solo a partir da temperatura do solo em São Paulo, SP

O fluxo de calor no solo é um dos componentes do balanço de energia, que, junto com calor latente e sensível e os processos biológicos, é resultado da partição da energia líquida disponível. Esse índice geralmente é obtido a partir de medições com fluxímetros, termômetros de solo, ou ainda, estimado por satélites ambientais, segundo metodologias específicas. Neste trabalho, utilizaram-se medições de temperatura do solo a 0 e 20 cm de profundidade para calcular o fluxo de calor no solo por meio do método de DECICO (1967). As medições foram realizadas em uma área de Latossolo Vermelho-Amarelo no Parque Estadual Fontes do Ipiranga (PEFI), importante remanescente preservada da Mata Atlântica na Região Metropolitana de São Paulo, que dispõe de uma importante série histórica de dados climáticos. Os resultados indicam que o fluxo de calor médio é de 3,69 W m-2 ou 2,83% da energia líquida disponível, semelhante a áreas de Mata Atlântica e Floresta Amazônica.

Should we Sample a time Series more Frequently?: Decision Support via Multirate Spectrum Estimation

SummarySuppose that we have a historical time series with samples taken at a slow rate, e.g. quarterly. The paper proposes a new method to answer the question: is it worth sampling the series at a faster rate, e.g. monthly? Our contention is that classical time series methods are designed to analyse a series at a single and given sampling rate with the consequence that analysts are not often encouraged to think carefully about what an appropriate sampling rate might be. To answer the sampling rate question we propose a novel Bayesian method that incorporates the historical series, cost information and small amounts of pilot data sampled at the faster rate. The heart of our method is a new Bayesian spectral estimation technique that is capable of coherently using data sampled at multiple rates and is demonstrated to have superior practical performance compared with alternatives. Additionally, we introduce a method for hindcasting historical data at the faster rate. A freeware R package, regspec, is available that implements our methods. We illustrate our work by using official statistics time series including the UK consumer price index and counts of UK residents travelling abroad, but our methods are general and apply to any situation where time series data are collected.

Skill Assessment of HF Radar–Derived Products for Lagrangian Simulations in the Bay of Biscay

AbstractSince January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly high-spatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period (2009–12). The skills of the different HF radar products are evaluated within a 48-h window. The mean distances between real trajectories and their radar-derived counterparts range from 4 to 5 km for real-time and forecast currents after 12 hours of simulations. The forecast model improves persistence (i.e., the simulations obtained by using the last available OMA fields as a constant variable) after 6 hours of simulation and improves the estimation of trajectories up to 28% after 48 hours. The performance of the forecast is observed to be variable in space and time, related to the different ocean processes governing the local ocean circulation.

Spiking Neural Networks for Crop Yield Estimation Based on Spatiotemporal Analysis of Image Time Series

This paper presents spiking neural networks (SNNs) for remote sensing spatiotemporal analysis of image time series, which make use of the highly parallel and low-power-consuming neuromorphic hardware platforms possible. This paper illustrates this concept with the introduction of the first SNN computational model for crop yield estimation from normalized difference vegetation index image time series. It presents the development and testing of a methodological framework which utilizes the spatial accumulation of time series of Moderate Resolution Imaging Spectroradiometer 250-m resolution data and historical crop yield data to train an SNN to make timely prediction of crop yield. The research work also includes an analysis on the optimum number of features needed to optimize the results from our experimental data set. The proposed approach was applied to estimate the winter wheat ( Triticum aestivum L. ) yield in Shandong province, one of the main winter-wheat-growing regions of China. Our method was able to predict the yield around six weeks before harvest with a very high accuracy. Our methodology provided an average accuracy of 95.64%, with an average error of prediction of 0.236 t/ha and correlation coefficient of 0.801 based on a nine-feature model.

Health risks associated to vulnerability of water and sanitation systems in face of climate extreme events in a West African medium-sized city

Introduction: The frequency of extreme weather events associated to climate change like floods have increased in West Africa over the past several years. This study undertook an assessment of the risk factors and vulnerabilities related to water and sanitation systems in the face of repeated flooding events within a West African mid-sized city. Methods: A multidisciplinary (household and geographical) survey was undertaken after the rainy season, covering the whole city of Korhogo in Cote d’Ivoire and targeted 600 households. GPS devices were used to geo-locate major environmental factors including wells and other potential breeding sites for water-vectors. The identified risks have been analyzed considering historical climate data series for temperature and rainfall (1971 – 2001) and the potential consequential projections for 2020, 2030 and 2050. Results: For the Korhogo area, the climate projections would indicate an increase in the mean temperatures of 0.44°C, 1.13°C and 1.96°C for 2020, 2050 and 2080, respectively. The associated maximum temperature increases projected are 0.31°C, 1.36°C and 1.17°C in 2020, 2050 and 2080 and for rainfall the projections indicated an increase of 24%, 29% to 33% and 12% to 28% for same horizons. We found 347 wells in the area and 325 stagnant wastewater points; 60% of households have their own wells in the yard, 63% report using water from wells for drinking purposes, 90% have latrines in the yard, very often located at less than 15 meters from the well, 15% of a total of 429 water points yielded a positive result when screened for presence of Anopheles larvae. Conclusions: Considering the assumed projections of increasing rainfall with a possible continued deficit in the drainage systems in small and mid-sized cities, the flood occurrence is expected to increase and the particular weaknesses of the water and sanitation systems will expose vulnerable groups to more risks to water-related and vector-borne diseases.

Recent Climate Trends and Drought Behavioral Assessment Based on Precipitation and Temperature Data Series in the Songhua River Basin of China

Precise analysis of spatiotemporal trends of temperature, precipitation and meteorological droughts plays a key role in the sustainable management of water resources in the given region. This study first aims to detect the long-term climate (monthly/seasonally and annually) trends from the historical temperature and precipitation data series by applying Spearmen’s Rho and Mann-Kendall test at 5 % significant level. The measurements of both climate variables for a total period of 49 years (1965–2013) were collected from the 11 different meteorological stations located in the Songhua River basin of China. Secondly, the two well-known meteorological drought indices including the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) were applied on normalize data to detect the drought hazards at 3, 6, 9 and 12 month time scale in the study area. The analysis of monthly precipitation showed significant (p < 0.05) increasing trends during the winter (November and December months) season. Similarly, the results of seasonal and annual air temperature showed a significant increase from 1 °C to 1.5 °C for the past 49 years in the basin. According to the Sen’s slope estimator, the rate of increment in seasonal temperature slope (0.26 °C/season) and precipitation (9.02 mm/season) were greater than annual air temperature (0.04 °C/year) and precipitation (1.36 mm/year). By comparing the results of SPI and RDI indices showed good performance at 9 (r = 0.96, p < 0.01) and 12 (r = 0.99, p < 0.01) month drought analysis. However, the yearly drought analysis at over all stations indicated that a 20 years were under dry conditions in entire study area during 49 years. We found the extreme dry and wet conditions in the study region were prevailing during the years of 2001 and 2007, and 1994 and 2013, respectively. Overall, the analysis and quantifications of this study provides a mechanism for the policy makers to mitigate the impact of extreme climate and drought conditions in order to improve local water resources management in the region.

Distribution of skates and sharks in the North Sea: 112 years of change

How have North Sea skate and shark assemblages changed since the early 20th century when bottom trawling became widespread, whilst their environment became increasingly impacted by fishing, climate change, habitat degradation and other anthropogenic pressures? This article examines long-term changes in the distribution and occurrence of the elasmobranch assemblage of the southern North Sea, based on extensive historical time series (1902-2013) of fishery-independent survey data. In general, larger species (thornback ray, tope, spurdog) exhibited long-term declines, and the largest (common skate complex) became locally extirpated (as did angelshark). Smaller species increased (spotted and starry ray, lesser-spotted dogfish) as did smooth-hound, likely benefiting from greater resilience to fishing and/or climate change. This indicates a fundamental shift from historical dominance of larger, commercially valuable species to current prevalence of smaller, more productive species often of low commercial value. In recent years, however, some trends have reversed, with the (cold-water associated) starry ray now declining and thornback ray increasing. This shift may be attributed to (i) fishing, including mechanised beam trawling introduced in the 1960s-1970s, and historical target fisheries for elasmobranchs; (ii) climate change, currently favouring warm-water above cold-water species; and (iii) habitat loss, including potential degradation of coastal and outer estuarine nursery habitats. The same anthropogenic pressures, here documented to have impacted North Sea elasmobranchs over the past century, are likewise impacting shelf seas worldwide and may increase in the future; therefore, parallel changes in elasmobranch communities in other regions are to be expected.

The long-run performance of the New Zealand stock markets: 1899-2013

Purpose – The aim of this paper is to construct a historical index for the New Zealand stock markets going back to 1899. From these historical returns, the authors can extract the average capital gains and dividend yield. It also allows them to provide an estimate for the equity risk premium (ERP). Design/methodology/approach – The authors collect stock-level data (prices, dividends, etc.) from quote records that are kept at the National Library in Wellington. From the stock-level data, the authors compute a value-weighted market index over the period 1899-2013. Findings – Over the period 1899-2013, the arithmetic mean of equity returns is 10.82 per cent p.a., with a standard deviation of 20.09 per cent. The New Zealand equity market had 92 years of positive returns and 23 years of negative returns during the sample period. The 10-year government bond yield, over the entire period, has an arithmetic mean return of 5.75 per cent. The ERP, on average, is 5.07 per cent. Originality/value – The authors collect the longest available historical data series for the New Zealand equity market. They document statistical properties as well as the long-term ERP over the entire sample period of 115 years and several subperiods. The ERP is a key input in corporate/project valuation.

How Meat Demand Elasticities Vary with Price, Income, and Product Category

As US beef and pork prices approached record high levels in 2014, industry analysts expressed surprised at consumer response. Because the relative price swings have occurred only recently, traditional approaches to demand analysis that rely on historical data series may be less useful than is typically the case. Employing one of the largest and longest‐running choice experiments, we analyze data on 110,295 choices made by 12,255 consumers observed over a year‐long time period coinciding with historically high meat prices. Our findings reveal nonlinear demands for meat products, with demand being more inelastic at higher prices. Ground beef, steak, and pork chop demands are more sensitive to changes in chicken breast price than the reverse. Moreover, cross‐price elasticities between disaggregate meat products shrink as prices rise. Consumers' incomes significantly affect demand inter‐relationships. Higher income consumers are more likely to choose steak and chicken breasts and are less likely to choose ground beef, chicken wings, and deli ham than are lower income consumers. High‐income consumers tend to be less responsive to own‐price changes and more responsive to cross‐price changes than lower income consumers. This analysis provides estimates of structural demand parameters that help explain current meat expenditure patterns, and the results have implications for the assumption of linearity often invoked in policy analyses.

Estudo da suscetibilidade à inundação com base em análise geomorfológica, bacia hidrográfica do Arroio Santa Isabel, região costeira do Rio Grande do Sul, Brasil

As inundações causam prejuízos em todo o mundo, afetando principalmente a população que ocupa áreas próximas aos leitos dos rios. Considerando a diversidade de condicionantes das inundações, este estudo buscou o entendimento da inter-relação de fatores geomorfológicos, climatológicos e antrópicos para mapear e categorizar a suscetibilidade à inundação da bacia hidrográfica do arroio Santa Isabel, tributário do rio Camaquã, sul do Rio Grande do Sul, região com frequentes inundações. A análise dos mapas geológico, geomorfológico, hipsométrico, clinográfico, cobertura vegetal e usos da terra, com o apoio do levantamento de uma longa série histórica de pluviosidade (46 anos) e de imagens referentes aos períodos dos eventos de inundação captadas pelo sensor Landsat 5, permitiram categorizar cinco níveis de suscetibilidade à inundação na referida bacia. Os fatores determinantes para essa categorização foram: amplitude do relevo e declividade na planície de inundação e no seu entorno imediato e as interven- ções antrópicas diretas e indiretas nessa planície, com destaque para supressão da vegetação e para as alterações nos canais (construção de barragens). Ainda foi considerada a influência exercida pelo rio Camaquã na porção jusante da bacia. A bacia apresenta suscetibilidade média a muito forte em 75% do sistema fluvial. A metodologia utiliza uma abordagem integrada de dados de fácil acesso e mostrou-se satisfatória à análise das inundações, servindo de base para o ordenamento territorial.

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

The MeteoMet project – metrology for meteorology: challenges and results

ABSTRACTThe study describes significant outcomes of the ‘Metrology for Meteorology’ project, MeteoMet, which is an attempt to bridge the meteorological and metrological communities. The concept of traceability, an idea used in both fields but with a subtle difference in meaning, is at the heart of the project. For meteorology, a traceable measurement is the one that can be traced back to a particular instrument, time and location. From a metrological perspective, traceability further implies that the measurement can be traced back to a primary realization of the quantity being measured in terms of the base units of the International System of Units, the SI. These two perspectives reflect long‐standing differences in culture and practice and this project – and this study – represents only the first step towards better communication between the two communities.The 3 year MeteoMet project was funded by the European Metrology Research Program (EMRP) and involved 18 European National Metrological Institutes, 3 universities and 35 collaborating stakeholders including national meteorology organizations, research institutes, universities, associations and instrument companies.The project brought a metrological perspective to several long‐standing measurement problems in meteorology and climatology, varying from conventional ground‐based measurements to those made in the upper atmosphere. It included development and testing of novel instrumentation as well as improved calibration procedures and facilities, instrument intercomparison under realistic conditions and best practice dissemination. Additionally, the validation of historical temperature data series with respect to measurement uncertainties and a methodology for recalculation of the values were included.

Determination of droughts and high floods of the Bermejo River (Argentina) based on documentary evidence (17th to 20th century)

This study reconstructs a series of droughts and high flow volumes of the Bermejo River from the 17th to 20th century based on a content analysis of historic documentary evidence, which is calibrated with instrumental climate data. The historic data series shows an increase in the frequency of extraordinarily high waters beginning in the 19th century and a significant decrease in extreme droughts beginning in 1890. The data are compared to variations in the Mendoza River for the same period, which show that there was a long-standing lack of correlation between the rivers.

An Integrated Procedure to Determine a Reference Station Network for Evaluating and Adjusting Urban Bias in Surface Air Temperature Data

AbstractTrends in surface air temperature (SAT) are a critical indicator for climate change at varied spatial scales. Because of urbanization effects, however, the current SAT records of many urban stations can hardly meet the demands of the studies. Evaluation and adjustment of the urbanization effects on the SAT trends are needed, which requires an objective selection of reference (rural) stations. Based on the station history information from all meteorological stations with long-term records in mainland China, an integrated procedure for determining the reference SAT stations has been developed and is applied in forming a network of reference SAT stations. Historical data from the network are used to assess the urbanization effects on the long-term SAT trends of the stations of the national Reference Climate Network and Basic Meteorological Network (RCN+BMN or national stations), which had been used most frequently in studies of regional climate change throughout the country. This paper describes in detail the integrated procedure and the assessment results of urbanization effects on the SAT trends of the national stations applying the data from the reference station network determined using the procedure. The results showed a highly significant urbanization effect of 0.074°C (10 yr)−1 and urbanization contribution of 24.9% for the national stations of mainland China during the time period 1961–2004, which compared well to results that were reported in previous studies by the authors using the predecessor of the present reference network and the reference stations selected but when applying other methods. The authors are thus confident that the SAT data from the updated China reference station network as reported in this paper best represented the baseline SAT trends nationwide and could be used for evaluating and adjusting the urban biases in the historical data series of the SAT from different observational networks.