Historical Series Data Research Articles

Influencia de una estrategia de alerta multidisciplinaria en la mortalidad por endocarditis infecciosa izquierda

Introduction and ObjectivesMortality from left-sided infective endocarditis remains very high. The aim of this study was to assess the impact of a multidisciplinary alert strategy (AMULTEI), based on clinical, echocardiographic and microbiological findings, implemented in 2008 in a tertiary hospital. MethodsCohort study comparing our historical data series (1996-2007) with the number of patients diagnosed with left-sided endocarditis from 2008-2011 (AMULTEI). ResultsThe AMULTEI cohort included 72 patients who were compared with 155 patients in the historical cohort. AMULTEI patients were significantly older (62.5 vs 57.9 years in the historical cohort; P=.047) and had higher comorbidity (Charlson index, 3.33 vs 2.58 in the historical cohort; P=.023). There was also a trend toward more enterococcal etiology in the AMULTEI group (20.8% vs 11.6% in the historical cohort; P=.067). In the AMULTEI group, early surgery was more frequently performed (48.6% vs 23.2%; P<.001) during hospitalization, the incidence of septic shock was significantly lower (9.7% vs 24.5%; P=.009) and there was a trend toward reductions in neurological complications (19.4% vs 29.0%; P=.25) and severe heart failure (12.5% vs 18.7%; P=.24). In-hospital mortality and mortality during the first month of follow-up were significantly lower in the AMULTEI group (16.7% vs 36.1%; P=.003). ConclusionsDespite the trend toward older age and more comorbidity measured by the Charlson index, early mortality was significantly lower in patients treated with the AMULTEI strategy.Full English text available from:www.revespcardiol.org/en

Influence of a Multidisciplinary Alert Strategy on Mortality Due to Left-sided Infective Endocarditis

Introduction and ObjectivesMortality from left-sided infective endocarditis remains very high. The aim of this study was to assess the impact of a multidisciplinary alert strategy (AMULTEI), based on clinical, echocardiographic and microbiological findings, implemented in 2008 in a tertiary hospital. MethodsCohort study comparing our historical data series (1996-2007) with the number of patients diagnosed with left-sided endocarditis from 2008-2011 (AMULTEI). ResultsThe AMULTEI cohort included 72 patients who were compared with 155 patients in the historical cohort. AMULTEI patients were significantly older (62.5 vs 57.9 years in the historical cohort; P=.047) and had higher comorbidity (Charlson index, 3.33 vs 2.58 in the historical cohort; P=.023). There was also a trend toward more enterococcal etiology in the AMULTEI group (20.8% vs 11.6% in the historical cohort; P=.067). In the AMULTEI group, early surgery was more frequently performed (48.6% vs 23.2%; P<.001) during hospitalization, the incidence of septic shock was significantly lower (9.7% vs 24.5%; P=.009) and there was a trend toward reductions in neurological complications (19.4% vs 29.0%; P=.25) and severe heart failure (12.5% vs 18.7%; P=.24). In-hospital mortality and mortality during the first month of follow-up were significantly lower in the AMULTEI group (16.7% vs 36.1%; P=.003). ConclusionsDespite the trend toward older age and more comorbidity measured by the Charlson index, early mortality was significantly lower in patients treated with the AMULTEI strategy.

The Sectoral Adaptive Capacity Index of Hungarian Road Transport

The understanding of different adaptive capacities is a prerequisite for targeting interventions aiming to reduce the adverse effects of climate change. Indicators and indices are common tools in this process, but their construction is fraught with many uncertainties. This paper aims to investigate the application of the Adaptive Capacity Index applied to the road transport sector. Authors sought to describe the effects of climate change on road transport and have modeled its adaptive capacity as a probability function of a combination of variables. The authors have designed and created the Sectoral Adaptive Capacity Index for the Hungarian transport sector, and investigated the trends in the historical data series of this indicator.

Uncertainty estimation with bias-correction for flow series based on rating curve

Summary Streamflow discharge constitutes one of the fundamental data required to perform water balance studies and develop hydrological models. A rating curve, designed based on a series of concurrent stage and discharge measurements at a gauging location, provides a way to generate complete discharge time series with a reasonable quality if sufficient measurement points are available. However, the associated uncertainty is frequently not available even though it has a significant impact on hydrological modelling. In this paper, we identify the discrepancy of the hydrographers’ rating curves used to derive the historical discharge data series and proposed a modification by bias correction which is also in the form of power function as the traditional rating curve. In order to obtain the uncertainty estimation, we propose a further both-side Box–Cox transformation to stabilize the regression residuals as close to the normal distribution as possible, so that a proper uncertainty can be attached for the whole discharge series in the ensemble generation. We demonstrate the proposed method by applying it to the gauging stations in the Flinders and Gilbert rivers in north-west Queensland, Australia.

An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

Abstract. Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e.g. water stage in rivers) or agricultural (e.g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett–Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett–Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett–Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period.

Derivation of Intertemporal Targets for Large Pumped Hydro Energy Storage With Stochastic Optimization

This paper models large pumped hydro energy storage in a future power system where variable generation, primarily in the form of wind generation, is the dominant source of power generation. The research question posed is how to formulate day-ahead and week-ahead reservoir targets for pumped hydro energy storage in the context of wind forecast uncertainty. The innovation in the work is the use of historical wind data series and wind forecasts to derive a management strategy for the operation of large PHES using stochastic optimization that outperforms current methods in power systems with significant wind generation. This approach derives intertemporal targets for large pumped hydro energy storage that reduce overall system costs when compared to targets derived using a conventional method.

Potential of utilization of rain water excess for irrigation of green roofs in Mato Grosso, Brasil

The aim of this study was to identify the relation between the evapotranspirometer demand and the supply of water from local rainfall, evaluating the possibility of using water excess for irrigation of Green Roofs in the State of Mato Grosso, in Brazil. The study was done using a series of historical data provided by the National Institute of Meteorology (INMET - Instituto Nacional de Meteorologia) which has official climatological stations in 12 cities and regions of the State. The evapotranspiration values were obtained by the Penman-Monteith method and by the Climatic Water Balance (CWB) by the Thornthwaite and Mather method using Available Water Capacity (AWC) of 12mm. With the CWB the excess and deficit were calculated, which were used for the estimative of the volume and area of a reservoir as a function of a collector area of a roof of 100m² and the volume of supplementary water for irrigation. With the obtained results, it was found that in most investigated regions of the State the use of green roofs is not compromised by the water deficiency. On the other hand, the use of a reservoir to accumulate the rain water excess may be impractical, because it requires a considerable area for installation and also because of the high cost of the land.

Modeling hydrological impacts of climate change in different climatic zones

PurposeRecent advances in hydrological impact studies point that the response of specific catchments to climate change scenario using a single model approach is questionable. This study was aimed at investigating the impact of climate change on three river basins in China, Ethiopia and Norway using WASMOD and HBV hydrological models.Design/methodology/approachFirst, hydrological models' parameters were determined using current hydro‐climatic data inputs. Second, the historical time series of climatic data was adjusted according to the climate change scenarios. Third, the hydrological characteristics of the catchments under the adjusted climatic conditions were simulated using the calibrated hydrological models. Finally, comparisons of the model simulations of the current and possible future hydrological characteristics were performed. Responses were evaluated in terms of runoff, actual evapotranspiration and soil moisture change for incremental precipitation and temperature change scenarios.FindingsFrom the results obtained, it can be inferred that two equally well calibrated models gave different hydrological response to hypothetical climatic scenarios. The authors' findings support the concern that climate change analysis using lumped hydrological models may lead to unreliable conclusions.Practical implicationsExtrapolation of driving forces (temperature and precipitation) beyond the range of parameter calibration yields unreliable response. It is beyond the scope of this study to reduce this model ambiguity, but reduction of uncertainty is a challenge for further research.Originality/valueThe research was conducted based on the primary time series data using the existing two hydrological models to test the magnitude differences one can expect when using different hydrological models to simulate hydrological response of climate changes in different climate zones.

TENDÊNCIA DO NÚMERO DE DIAS DE CHUVA NO ESTADO DO MATO GROSSO

Rainfall is one of the most important variables of the hydrological cycle,making its constraints, as the Number of Days with Rain (NDR), of greatimportance in climatological studies. In order to analyze the monthlyand annual trend of Number of Days with Rain in the state of MatoGrosso, we performed a study of its extreme variability and its possiblecorrelation with the El Nino Southern Oscillation (ENSO) phenomenaof moderate or heavy intensity. We used monthly and annual averagesof the NDR of the historical series of rainfall data obtained from theNational Hydrometeorological Network, the National Water Agency andthe Geological Survey of Brazil. It was used the historical series of 30years (1977-2006). For the statistic treatment we calculated the temporalaverages of the number of days with rainfall of 120 rainfall stationsdistributed in the state of Mato Grosso. It was observed that only themonth of March tended to increase the NDR for the historical series studied, leaving the other months and the annual general average withdecreasing trend on NDR in the state of Mato Grosso. Results are presentedin graphical trends of NDR for each month and year, correlatedwith the intensity of ENSO, and also in the monthly histogram with theaverage of 30 years and for each one of the three decades.

Assessing the impacts of changing land cover and climate on Hokersar wetland in Indian Himalayas

Monitoring the spatiotemporal changes in wetlands and assessing their causal factors is critical for developing robust strategies for the conservation and restoration of these ecologically important ecosystems. In this study, the spatiotemporal changes in the land cover system within a Himalayan wetland and its catchment were assessed and correlated using a time series of satellite, historical, and field data. Significant changes in the spatial extent, water depth, and the land system of the Hokersar wetland were observed from the spatiotemporal analysis of the data from 1969 to 2008. The wetland area has shrunk from 18.75 km2 in 1969 to 13 km2 in 2008 with drastic reduction in the water depth of the wetland. The marshy lands, habitat of the migratory birds, have shrunk from 16.3 km2 in 1969 to 5.62 km2 in 2008 and have been colonized by various other land cover types. The land system and water extent changes within the wetland were related to the spatiotemporal changes in the land cover and hydrometeorological variables at the catchment scale. Significant changes in the forest cover (88.33–55.78 km2), settlement (4.63–15.35 km2), and water bodies (1.75–0.51 km2) were observed in the catchment. It is concluded that the urbanization, deforestation, changes in the hydrologic and climatic conditions, and other land system changes observed in the catchment are the main causes responsible for the depleting wetland extent, water depth, and biodiversity by adversely influencing the hydrologic erosion and other land surface processes in the catchment. All these causes and effects are manifest in the form of deterioration of the water quality, water quantity, the biodiversity changes, and the decreasing migratory bird population in the wetland.

Hubungan Panjang Seri Data Historik Terhadap Kualitas Data Hasil Prediksi Pada Penerapan Model Rantai Markov Untuk Peramalan Aliran Sungai

Markov Chain Model is a stochastic model for forecasting the river flow which in his analysis always involves a long series of historical data. In most studies the method is still highly theoretical and not fully applicable significantly due to the limited data in the field.This study is an attempt to optimize the application of Markov Chain Model for its functionality extensively to extrapolate data streams. The scope of this research is basically conducted a study on the relationship between the length of the historical flow data series with data quality prediction results. By knowing these characteristics, the error correction of analysis results can be expected due to data limitations, so that the Markov Chain Model can be widely applied to optimization of waterworks operations.Results for the Konto River and River showed that the prediction of flow Kwayangan next year with Markov chain models tend to give better results than the results of forecasting by conventional methods are widely applied. Markov model is good enough to predict the river flow has low flow fluctuations, but for a river flow fluctuated sharply less than satisfactory results. The length of data series ranges from 15 to 20 of the optimal inputs to produce a minimum error rate prediction. Accuracy of prediction result is not determined by the length of the input data series, but is determined by the nature of statistical data. Value of lag-1 correlation coefficient are large and small skewness coefficient of the historical data tends to give a satisfactory prediction results.Key words: river flow, data, prediktion, markov model.

Chuvas na região Centro-Oeste e no Estado do Tocantins: análise histórica e tendência futura

Meteorological phenomena are the result of climatic variations which can be observed during a series in order to detect trends or changes in hydrometeorological time series. Aiming to analyze the rainfall and the future trend of rainfall in the Midwest and the state of Tocantins, a study was carried out with linear regression and measures of central tendency and dispersion of rainfall from rain. We used 261 rainfall stations with 30 years of data. We used the average monthly and annual precipitation series of historical rainfall data obtained by the National Hydrometeorology, submitted to a preliminary analysis. The statistic calculated the mean, median and standard deviation of rainfall time needed to check the parameters, and observed the dispersion of the sample. There is a downward trend in average rainfall between 1977 and 2006 for the Midwest region and the State of Tocantins, in the order of 6.5%. Only the months of March and April showed growth of precipitation. Throughout this paper presents the temporal distribution, linear regression analysis and statistics, future trends, and monthly and annual variation of rainfall from the historical average for the Midwest region and the State of Tocantins.

Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

Century-Scale Responses of Ecosystem Carbon Storage and Flux to Multiple Environmental Changes in the Southern United States

Terrestrial ecosystems in the southern United States (SUS) have experienced a complex set of changes in climate, atmospheric CO2 concentration, tropospheric ozone (O3), nitrogen (N) deposition, and land-use and land-cover change (LULCC) during the past century. Although each of these factors has received attention for its alterations on ecosystem carbon (C) dynamics, their combined effects and relative contributions are still not well understood. By using the Dynamic Land Ecosystem Model (DLEM) in combination with spatially explicit, long-term historical data series on multiple environmental factors, we examined the century-scale responses of ecosystem C storage and flux to multiple environmental changes in the SUS. The results indicated that multiple environmental changes shifted SUS ecosystems from a C source of 1.20 ± 0.56 Pg (1 Pg = 1015 g) during the period 1895 to 1950, to a C sink of 2.00 ± 0.94 Pg during the period 1951 to 2007. Over the entire period spanning 1895–2007, SUS ecosystems were a net C sink of 0.80 ± 0.38 Pg. The C sink was primarily due to an increase in the vegetation C pool, whereas the soil C pool decreased during the study period. The spatiotemporal changes of C storage were caused by changes in multiple environmental factors. Among the five factors examined (climate, LULCC, N deposition, atmospheric CO2, and tropospheric O3), elevated atmospheric CO2 concentration was the largest contributor to C sequestration, followed by N deposition. LULCC, climate, and tropospheric O3 concentration contributed to C losses during the study period. The SUS ecosystem C sink was largely the result of interactive effects among multiple environmental factors, particularly atmospheric N input and atmospheric CO2.

Converging to a National Lynching Database: Recent Developments and the Way Forward

In tandem with a recent surge in interest in lynching in the United States in the late 19th and early 20th centuries, researchers in a number of fields have begun to use lynching data in new ways for a wide range of empirical investigations. A limited number of historical national lynching data series are available, have well-known flaws, and are nonetheless used. This article analyzes and compares these series, summarizes recent efforts to address their shortcomings, and identifies extensions that could aid in the construction a national database of confirmed lynching victims, whose broader applications are just beginning to be explored.

Prototype for monitoring and forecasting fall foliage coloration in real time from satellite data

While determining vegetation phenology from the time series of historical satellite data has been widely investigated throughout the last decade, little effort has been devoted to real-time monitoring and short-term forecasting. The latter is more important for numerical weather modeling, ecosystem forecasting, forest and crop management, and health risk warning. In this study we developed a prototype approach for the real-time monitoring and short-term forecasting of fall foliage status (including low coloration, moderate coloration, near-peak coloration, peak coloration, and post-peak coloration) using temporal satellite observations. The algorithm combined the climatology of vegetation phenology and temporally available satellite observations to establish a set of potential temporal trajectories of foliage development at a given time. These trajectories were used to identify foliage coloration phases in real time, to predict the occurrence of future phenological events, and, furthermore, to analyze the uncertainty of monitoring and forecasting. With an increase in satellite observations, monitoring and forecasting were continuously updated. The approach developed was tested using MODIS (Moderate Resolution Imaging Spectroradiometer) data at a spatial resolution of 500m across northeastern North America and evaluated using field measurements at the Harvard Forests of the northeastern United States and standard MODIS foliage coloration phases. The results indicate that short-term forecasting can be well implemented in more than half a month before the occurrence of a foliage phase, and that the accuracy of the real-time monitoring of both near-peak-coloration and peak-coloration occurrence is less than 5 days in most mixed forests and deciduous forests.

Effect of spatial resolution on regionalization of hydrological model parameters

AbstractLack of availability of historical data series is one of the major hindrances in hydrological modelling. Regionalization of hydrological model parameters is one of the solutions to obtain the parameters for ungauged basins. Recently, lots of methodologies have been developed. They can be categorized as model calibration then fitting regression between model parameters and catchments characteristics, using some kind of transfer function. The aim of this study was to compare different regionalization methods as well as to look how the spatial resolution affects regionalization. In this study, a modified Lipschitz and monotony condition was used for regionalization. To identify the effect of the model resolution, the parameters of a distributed and semi‐distributed version of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model were regionalized. The study was conducted at the upper Neckar catchment of southwest Germany. It has been found that the combination of Lipschitz and monotony condition has performed reasonably. It has been seen that the distributed model structure has outperformed the semi‐distributed model structure. It shows under present data conditions that higher model resolution can describe processes of ungauged basins reasonably. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Use of Remote Sensing Data for Landslide Change Detection

Remote sensing techniques have been widely used since the 1990s in landslide research, deploying for this purpose different spatial and spectral resolution imagery. This research includes photo-interpretation and inventory of large landslides, determinant factors analysis, stereo-plotting of movements, and automatic detection by textural analysis. The potential or intrinsic factors of landslides include geological and morphological factors, while the external or triggering factors include earthquakes, climate, and hydrological and human activities (deforestation, the expansion of urban areas, and the increase of agricultural activity). In this paper, the variations of land use are analyzed using a historical series of aerial-photographic and satellite data (1988 – 2006). Land use affects the stability of landslides. In this paper the proposed model has been applied in the Montescagliso municipality (Basilicata, Southern Italy).

Leprosy decline in Amazonas State, Brazil

To analyse the leprosy case detection rates in Amazonas State, Brazil, by age group from 1980 to 2009. The historical data series of leprosy cases by age group from 1980 to 2009 were fitted as a function of time using Poisson regression models. Relative annual reduction in the detection rate (RAR) by age group was estimated as one minus the exponential of the estimated regression coefficient for time. To compare the regression coefficients, we used their 95% confidence interval. The relative annual reduction varied from 9% in the age group of 0-4 years to 1% in the age group of 60-69 years. There was a declining trend of the RAR in the younger age groups that disappeared after 29 years of age. The detection rate in people >29 years old declined very little over time, with no statistically significant difference between age groups. Our findings show a reduction in the infection risk in the last 30 years and a birth cohort effect: cohorts born in more recent years faced smaller risks of leprosy infection than older cohorts.

Necessidade de irrigação para o feijoeiro na região central do Rio Grande do Sul

A falta de conhecimento da probabilidade de ocorrência de adversidades climáticas representa um problema para o planejamento da produção do feijoeiro. Este estudo objetivou determinar os valores de lâmina de irrigação e do número de irrigações necessários para o feijão semeado em diferentes épocas, considerandose a capacidade de armazenamento de água disponível (CAD) dos principais solos da região Central do Rio Grande do Sul. A simulação do ciclo foi realizada utilizando-se a soma térmica, em dez épocas de semeadura (ES) e em cada ano da série histórica de dados meteorológicos, para o período entre 1968 e 2006. A irrigação foi simulada para 13 solos da região de abrangência da estação meteorológica de Santa Maria, que foram reunidos em seis grupos. Para simular a irrigação utilizaram-se quatro manejos: 0,8; 0,7; 0,6 e 0,5 os quais correspondem ao esgotamento máximo de 20, 30, 40 e 50% da CAD, respectivamente. A variação do conteúdo de água no solo foi determinada pelo balanço hídrico diário do solo, considerandose a irrigação. A maior exigência de irrigação ocorreu nas últimas ES do período da safra e a menor no início da safra e na safrinha. Para a probabilidade de 90% de ocorrência a lâmina de irrigação variou entre 32 e 143 mm e o número de irrigações entre 3 e 20, durante o ciclo de cultivo.