Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

USGS Publications Warehouse

Camp, Richard J.; Brinck, Kevin W.; Gorresen, P.M.; Paxton, Eben H.

2016-01-01

Estimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.

NASA standard: Trend analysis techniques

NASA Technical Reports Server (NTRS)

1988-01-01

This Standard presents descriptive and analytical techniques for NASA trend analysis applications. Trend analysis is applicable in all organizational elements of NASA connected with, or supporting, developmental/operational programs. Use of this Standard is not mandatory; however, it should be consulted for any data analysis activity requiring the identification or interpretation of trends. Trend Analysis is neither a precise term nor a circumscribed methodology, but rather connotes, generally, quantitative analysis of time-series data. For NASA activities, the appropriate and applicable techniques include descriptive and graphical statistics, and the fitting or modeling of data by linear, quadratic, and exponential models. Usually, but not always, the data is time-series in nature. Concepts such as autocorrelation and techniques such as Box-Jenkins time-series analysis would only rarely apply and are not included in this Standard. The document presents the basic ideas needed for qualitative and quantitative assessment of trends, together with relevant examples. A list of references provides additional sources of information.

Eurodelta-Trends, a Multi-Model Experiment of Air Quality Hindcast in Europe over 1990-2010. Experiment Design and Key Findings

NASA Astrophysics Data System (ADS)

Colette, A.; Ciarelli, G.; Otero, N.; Theobald, M.; Solberg, S.; Andersson, C.; Couvidat, F.; Manders-Groot, A.; Mar, K. A.; Mircea, M.; Pay, M. T.; Raffort, V.; Tsyro, S.; Cuvelier, K.; Adani, M.; Bessagnet, B.; Bergstrom, R.; Briganti, G.; Cappelletti, A.; D'isidoro, M.; Fagerli, H.; Ojha, N.; Roustan, Y.; Vivanco, M. G.

2017-12-01

The Eurodelta-Trends multi-model chemistry-transport experiment has been designed to better understand the evolution of air pollution and its drivers for the period 1990-2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional scale air quality. The experiment is designed in three tiers with increasing degree of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000 and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions and (iii) meteorology complements it. The most demanding tier consists in two complete time series from 1990 to 2010, simulated using either time varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and six models have completed the 21-year trend simulations. The modelling results are publicly available for further use by the scientific community. We assess the skill of the models in capturing observed air pollution trends for the 1990-2010 time period. The average particulate matter relative trends are well captured by the models, even if they display the usual lower bias in reproducing absolute levels. Ozone trends are also well reproduced, yet slightly overestimated in the 1990s. The attribution study emphasizes the efficiency of mitigation measures in reducing air pollution over Europe, although a strong impact of long range transport is pointed out for ozone trends. Meteorological variability is also an important factor in some regions of Europe. The results of the first health and ecosystem impact studies impacts building upon a regional scale multi-model ensemble over a 20yr time period will also be presented.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Trend Estimation and Regression Analysis in Climatological Time Series: An Application of Structural Time Series Models and the Kalman Filter.

NASA Astrophysics Data System (ADS)

Visser, H.; Molenaar, J.

1995-05-01

The detection of trends in climatological data has become central to the discussion on climate change due to the enhanced greenhouse effect. To prove detection, a method is needed (i) to make inferences on significant rises or declines in trends, (ii) to take into account natural variability in climate series, and (iii) to compare output from GCMs with the trends in observed climate data. To meet these requirements, flexible mathematical tools are needed. A structural time series model is proposed with which a stochastic trend, a deterministic trend, and regression coefficients can be estimated simultaneously. The stochastic trend component is described using the class of ARIMA models. The regression component is assumed to be linear. However, the regression coefficients corresponding with the explanatory variables may be time dependent to validate this assumption. The mathematical technique used to estimate this trend-regression model is the Kaiman filter. The main features of the filter are discussed.Examples of trend estimation are given using annual mean temperatures at a single station in the Netherlands (1706-1990) and annual mean temperatures at Northern Hemisphere land stations (1851-1990). The inclusion of explanatory variables is shown by regressing the latter temperature series on four variables: Southern Oscillation index (SOI), volcanic dust index (VDI), sunspot numbers (SSN), and a simulated temperature signal, induced by increasing greenhouse gases (GHG). In all analyses, the influence of SSN on global temperatures is found to be negligible. The correlations between temperatures and SOI and VDI appear to be negative. For SOI, this correlation is significant, but for VDI it is not, probably because of a lack of volcanic eruptions during the sample period. The relation between temperatures and GHG is positive, which is in agreement with the hypothesis of a warming climate because of increasing levels of greenhouse gases. The prediction performance of the model is rather poor, and possible explanations are discussed.

Long-term forecasting of internet backbone traffic.

PubMed

Papagiannaki, Konstantina; Taft, Nina; Zhang, Zhi-Li; Diot, Christophe

2005-09-01

We introduce a methodology to predict when and where link additions/upgrades have to take place in an Internet protocol (IP) backbone network. Using simple network management protocol (SNMP) statistics, collected continuously since 1999, we compute aggregate demand between any two adjacent points of presence (PoPs) and look at its evolution at time scales larger than 1 h. We show that IP backbone traffic exhibits visible long term trends, strong periodicities, and variability at multiple time scales. Our methodology relies on the wavelet multiresolution analysis (MRA) and linear time series models. Using wavelet MRA, we smooth the collected measurements until we identify the overall long-term trend. The fluctuations around the obtained trend are further analyzed at multiple time scales. We show that the largest amount of variability in the original signal is due to its fluctuations at the 12-h time scale. We model inter-PoP aggregate demand as a multiple linear regression model, consisting of the two identified components. We show that this model accounts for 98% of the total energy in the original signal, while explaining 90% of its variance. Weekly approximations of those components can be accurately modeled with low-order autoregressive integrated moving average (ARIMA) models. We show that forecasting the long term trend and the fluctuations of the traffic at the 12-h time scale yields accurate estimates for at least 6 months in the future.

Sea-Level Trend Uncertainty With Pacific Climatic Variability and Temporally-Correlated Noise

NASA Astrophysics Data System (ADS)

Royston, Sam; Watson, Christopher S.; Legrésy, Benoît; King, Matt A.; Church, John A.; Bos, Machiel S.

2018-03-01

Recent studies have identified climatic drivers of the east-west see-saw of Pacific Ocean satellite altimetry era sea level trends and a number of sea-level trend and acceleration assessments attempt to account for this. We investigate the effect of Pacific climate variability, together with temporally-correlated noise, on linear trend error estimates and determine new time-of-emergence (ToE) estimates across the Indian and Pacific Oceans. Sea-level trend studies often advocate the use of auto-regressive (AR) noise models to adequately assess formal uncertainties, yet sea level often exhibits colored but non-AR(1) noise. Standard error estimates are over- or under-estimated by an AR(1) model for much of the Indo-Pacific sea level. Allowing for PDO and ENSO variability in the trend estimate only reduces standard errors across the tropics and we find noise characteristics are largely unaffected. Of importance for trend and acceleration detection studies, formal error estimates remain on average up to 1.6 times those from an AR(1) model for long-duration tide gauge data. There is an even chance that the observed trend from the satellite altimetry era exceeds the noise in patches of the tropical Pacific and Indian Oceans and the south-west and north-east Pacific gyres. By including climate indices in the trend analysis, the time it takes for the observed linear sea-level trend to emerge from the noise reduces by up to 2 decades.

Attribution of trends in global vegetation greenness from 1982 to 2011

NASA Astrophysics Data System (ADS)

Zhu, Z.; Xu, L.; Bi, J.; Myneni, R.; Knyazikhin, Y.

2012-12-01

Time series of remotely sensed vegetation indices data provide evidence of changes in terrestrial vegetation activity over the past decades in the world. However, it is difficult to attribute cause-and-effect to vegetation trends because variations in vegetation productivity are driven by various factors. This study investigated changes in global vegetation productivity first, and then attributed the global natural vegetation with greening trend. Growing season integrated normalized difference vegetation index (GSI NDVI) derived from the new GIMMS NDVI3g dataset (1982-2011was analyzed. A combined time series analysis model, which was developed from simper linear trend model (SLT), autoregressive integrated moving average model (ARIMA) and Vogelsang's t-PST model shows that productivity of all vegetation types except deciduous broadleaf forest predominantly showed increasing trends through the 30-year period. The evolution of changes in productivity in the last decade was also investigated. Area of greening vegetation monotonically increased through the last decade, and both the browning and no change area monotonically decreased. To attribute the predominant increase trend of productivity of global natural vegetation, trends of eight climate time series datasets (three temperature, three precipitation and two radiation datasets) were analyzed. The attribution of trends in global vegetation greenness was summarized as relaxation of climatic constraints, fertilization and other unknown reasons. Result shows that nearly all the productivity increase of global natural vegetation was driven by relaxation of climatic constraints and fertilization, which play equally important role in driving global vegetation greenness.; Area fraction and productivity change fraction of IGBP vegetation land cover classes showing statistically significant (10% level) trend in GSI NDVIt;

Random forest meteorological normalisation models for Swiss PM10 trend analysis

NASA Astrophysics Data System (ADS)

Grange, Stuart K.; Carslaw, David C.; Lewis, Alastair C.; Boleti, Eirini; Hueglin, Christoph

2018-05-01

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil-Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between -0.09 and -1.16 µg m-3 yr-1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at -0.77 µg m-3 yr-1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations.

0.1 Trend analysis of δ18O composition of precipitation in Germany: Combining Mann-Kendall trend test and ARIMA models to correct for higher order serial correlation

NASA Astrophysics Data System (ADS)

Klaus, Julian; Pan Chun, Kwok; Stumpp, Christine

2015-04-01

Spatio-temporal dynamics of stable oxygen (18O) and hydrogen (2H) isotopes in precipitation can be used as proxies for changing hydro-meteorological and regional and global climate patterns. While spatial patterns and distributions gained much attention in recent years the temporal trends in stable isotope time series are rarely investigated and our understanding of them is still limited. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here we make use of an extensive data set of stable isotope in German precipitation. In this study we investigate temporal trends of δ18O in precipitation at 17 observation station in Germany between 1978 and 2009. For that we test different approaches for proper trend detection, accounting for first and higher order serial correlation. We test if significant trends in the isotope time series based on different models can be observed. We apply the Mann-Kendall trend tests on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models which account for first and higher order serial correlations. With the approach we can also account for the effects of temperature, precipitation amount on the trend. Further we investigate the role of geographic parameters on isotope trends. To benchmark our proposed approach, the ARIMA results are compared to a trend-free prewhiting (TFPW) procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we explore whether higher order serial correlations in isotope series affects our trend results. The results show that three out of the 17 stations have significant changes when higher order autocorrelation are adjusted, and four stations show a significant trend when temperature and precipitation effects are considered. Significant trends in the isotope time series are generally observed at low elevation stations (≤315 m a.s.l.). Higher order autoregressive processes are important in the isotope time series analysis. Our results show that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative.

How well do CMIP5 climate simulations replicate historical trends and patterns of droughts?

DOE PAGES

Nasrollahi, Nasrin; AghaKouchak, Amir; Cheng, Linyin; ...

2015-04-26

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variabilitymore » in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. As a result, pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models.« less

VizieR Online Data Catalog: Fermi/GBM GRB time-resolved spectral catalog (Yu+, 2016)

NASA Astrophysics Data System (ADS)

Yu, H.-F.; Preece, R. D.; Greiner, J.; Bhat, P. N.; Bissaldi, E.; Briggs, M. S.; Cleveland, W. H.; Connaughton, V.; Goldstein, A.; von Kienlin; A.; Kouveliotou, C.; Mailyan, B.; Meegan, C. A.; Paciesas, W. S.; Rau, A.; Roberts, O. J.; Veres, P.; Wilson-Hodge, C.; Zhang, B.-B.; van Eerten, H. J.

2016-01-01

Time-resolved spectral analysis results of BEST models: for each spectrum GRB name using the Fermi GBM trigger designation, spectrum number within individual burst, start time Tstart and end time Tstop for the time bin, BEST model, best-fit parameters of the BEST model, value of CSTAT per degrees of freedom, 10keV-1MeV photon and energy flux are given. Ep evolutionary trends: for each burst GRB name, number of spectra with Ep, Spearman's Rank Correlation Coefficients between Ep_ and photon flux and 90%, 95%, and 99% confidence intervals, Spearman's Rank Correlation Coefficients between Ep and energy flux and 90%, 95%, and 99% confidence intervals, Spearman's Rank Correlation Coefficient between Ep and time and 90%, 95%, and 99% confidence intervals, trends as determined by computer for 90%, 95%, and 99% confidence intervals, trends as determined by human eyes are given. (2 data files).

Statistical analysis of strait time index and a simple model for trend and trend reversal

NASA Astrophysics Data System (ADS)

Chen, Kan; Jayaprakash, C.

2003-06-01

We analyze the daily closing prices of the Strait Time Index (STI) as well as the individual stocks traded in Singapore's stock market from 1988 to 2001. We find that the Hurst exponent is approximately 0.6 for both the STI and individual stocks, while the normal correlation functions show the random walk exponent of 0.5. We also investigate the conditional average of the price change in an interval of length T given the price change in the previous interval. We find strong correlations for price changes larger than a threshold value proportional to T; this indicates that there is no uniform crossover to Gaussian behavior. A simple model based on short-time trend and trend reversal is constructed. We show that the model exhibits statistical properties and market swings similar to those of the real market.

Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology

NASA Technical Reports Server (NTRS)

Forkel, Matthias; Carvalhais, Nuno; Verbesselt, Jan; Mahecha, Miguel D.; Neigh, Christopher S.R.; Reichstein, Markus

2013-01-01

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy.

Version 8 SBUV Ozone Profile Trends Compared with Trends from a Zonally Averaged Chemical Model

NASA Technical Reports Server (NTRS)

Rosenfield, Joan E.; Frith, Stacey; Stolarski, Richard

2004-01-01

Linear regression trends for the years 1979-2003 were computed using the new Version 8 merged Solar Backscatter Ultraviolet (SBUV) data set of ozone profiles. These trends were compared to trends computed using ozone profiles from the Goddard Space Flight Center (GSFC) zonally averaged coupled model. Observed and modeled annual trends between 50 N and 50 S were a maximum in the higher latitudes of the upper stratosphere, with southern hemisphere (SH) trends greater than northern hemisphere (NH) trends. The observed upper stratospheric maximum annual trend is -5.5 +/- 0.9 % per decade (1 sigma) at 47.5 S and -3.8 +/- 0.5 % per decade at 47.5 N, to be compared with the modeled trends of -4.5 +/- 0.3 % per decade in the SH and -4.0 +/- 0.2% per decade in the NH. Both observed and modeled trends are most negative in winter and least negative in summer, although the modeled seasonal difference is less than observed. Model trends are shown to be greatest in winter due to a repartitioning of chlorine species and the increasing abundance of chlorine with time. The model results show that trend differences can occur depending on whether ozone profiles are in mixing ratio or number density coordinates, and on whether they are recorded on pressure or altitude levels.

Influenza forecasting with Google Flu Trends.

PubMed

Dugas, Andrea Freyer; Jalalpour, Mehdi; Gel, Yulia; Levin, Scott; Torcaso, Fred; Igusa, Takeru; Rothman, Richard E

2013-01-01

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nasrollahi, Nasrin; AghaKouchak, Amir; Cheng, Linyin

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variabilitymore » in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. As a result, pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models.« less

Assessing spatiotemporal changes in forest carbon turnover times in observational data and models

NASA Astrophysics Data System (ADS)

Yu, K.; Smith, W. K.; Trugman, A. T.; van Mantgem, P.; Peng, C.; Condit, R.; Anderegg, W.

2017-12-01

Forests influence global carbon and water cycles, biophysical land-atmosphere feedbacks, and atmospheric composition. The capacity of forests to sequester atmospheric CO2 in a changing climate depends not only on the response of carbon uptake (i.e., gross primary productivity) but also on the simultaneous change in carbon residence time. However, changes in carbon residence with climate change are uncertain, impacting the accuracy of predictions of future terrestrial carbon cycle dynamics. Here, we use long-term forest inventory data representative of tropical, temperate, and boreal forests; satellite-based estimates of net primary productivity and vegetation carbon stock; and six models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to investigate spatiotemporal trends in carbon residence time and its relation to climate. Forest inventory and satellite-based estimates of carbon residence time show a pervasive decreasing trend across global forests. In contrast, the CMIP5 models diverge in predicting historical and future trends in carbon residence time. Divergence across CMIP5 models indicate carbon turnover times are not well constrained by observations, which likely contributes to large variability in future carbon cycle projections.

Zika pandemic online trends, incidence and health risk communication: a time trend study.

PubMed

Adebayo, Gbenga; Neumark, Yehuda; Gesser-Edelsburg, Anat; Abu Ahmad, Wiessam; Levine, Hagai

2017-01-01

We aimed to describe the online search trends of Zika and examine their association with Zika incidence, assess the content of Zika-related press releases issued by leading health authorities and examine the association between online trends and press release timing. Using Google Trends, the 1 May 2015 to 30 May 2016 online trends of Zika and associated search terms were studied globally and in the five countries with the highest numbers of suspected cases. Correlations were then examined between online trends and Zika incidence in these countries. All Zika-related press releases issued by WHO/Pan America Health Organization (PAHO) and Centers for Disease Control and Prevention (CDC) during the study period were assessed for transparency, uncertainty and audience segmentation. Witte's Extended Parallel Process Model was applied to assess self-efficacy, response efficacy, susceptibility and severity. AutoRegressive Integrated Moving Average with an eXogenous predictor variable (ARIMAX) (p,d,q) regression modelling was used to quantify the association between online trends and the timing of press releases. Globally, Zika online search trends were low until the beginning of 2016, when interest rose steeply. Strong correlations (r=0.748-0.922; p<0.001) were observed between online trends and the number of suspected Zika cases in four of the five countries studied. Compared with press releases issued by WHO/PAHO, CDC press releases were significantly more likely to provide contact details and links to other resources, include figures/graphs, be risk-advisory in nature and be more readable and briefer. ARIMAX modelling results indicate that online trends preceded by 1 week press releases by WHO (stationary-R 2 =0.345; p<0.001) and CDC (stationary-R 2 =0.318; p=0.014). These results suggest that online trends can aid in pandemic surveillance. Identification of shortcomings in the content and timing of Zika press releases can help guide health communication efforts in the current pandemic and future public health emergencies.

Multiple long-term trends and trend reversals dominate environmental conditions in a man-made freshwater reservoir.

PubMed

Znachor, Petr; Nedoma, Jiří; Hejzlar, Josef; Seďa, Jaromír; Kopáček, Jiří; Boukal, David; Mrkvička, Tomáš

2018-05-15

Man-made reservoirs are common across the world and provide a wide range of ecological services. Environmental conditions in riverine reservoirs are affected by the changing climate, catchment-wide processes and manipulations with the water level, and water abstraction from the reservoir. Long-term trends of environmental conditions in reservoirs thus reflect a wider range of drivers in comparison to lakes, which makes the understanding of reservoir dynamics more challenging. We analysed a 32-year time series of 36 environmental variables characterising weather, land use in the catchment, reservoir hydrochemistry, hydrology and light availability in the small, canyon-shaped Římov Reservoir in the Czech Republic to detect underlying trends, trend reversals and regime shifts. To do so, we fitted linear and piecewise linear regression and a regime shift model to the time series of mean annual values of each variable and to principal components produced by Principal Component Analysis. Models were weighted and ranked using Akaike information criterion and the model selection approach. Most environmental variables exhibited temporal changes that included time-varying trends and trend reversals. For instance, dissolved organic carbon showed a linear increasing trend while nitrate concentration or conductivity exemplified trend reversal. All trend reversals and cessations of temporal trends in reservoir hydrochemistry (except total phosphorus concentrations) occurred in the late 1980s and during 1990s as a consequence of dramatic socioeconomic changes. After a series of heavy rains in the late 1990s, an administrative decision to increase the flood-retention volume of the reservoir resulted in a significant regime shift in reservoir hydraulic conditions in 1999. Our analyses also highlight the utility of the model selection framework, based on relatively simple extensions of linear regression, to describe temporal trends in reservoir characteristics. This approach can provide a solid basis for a better understanding of processes in freshwater reservoirs. Copyright © 2017 Elsevier B.V. All rights reserved.

A hybrid-domain approach for modeling climate data time series

NASA Astrophysics Data System (ADS)

Wen, Qiuzi H.; Wang, Xiaolan L.; Wong, Augustine

2011-09-01

In order to model climate data time series that often contain periodic variations, trends, and sudden changes in mean (mean shifts, mostly artificial), this study proposes a hybrid-domain (HD) algorithm, which incorporates a time domain test and a newly developed frequency domain test through an iterative procedure that is analogue to the well known backfitting algorithm. A two-phase competition procedure is developed to address the confounding issue between modeling periodic variations and mean shifts. A variety of distinctive features of climate data time series, including trends, periodic variations, mean shifts, and a dependent noise structure, can be modeled in tandem using the HD algorithm. This is particularly important for homogenization of climate data from a low density observing network in which reference series are not available to help preserve climatic trends and long-term periodic variations, preventing them from being mistaken as artificial shifts. The HD algorithm is also powerful in estimating trend and periodicity in a homogeneous data time series (i.e., in the absence of any mean shift). The performance of the HD algorithm (in terms of false alarm rate and hit rate in detecting shifts/cycles, and estimation accuracy) is assessed via a simulation study. Its power is further illustrated through its application to a few climate data time series.

Trends in Classroom Observation Scores

PubMed Central

Lockwood, J. R.; McCaffrey, Daniel F.

2014-01-01

Observations and ratings of classroom teaching and interactions collected over time are susceptible to trends in both the quality of instruction and rater behavior. These trends have potential implications for inferences about teaching and for study design. We use scores on the Classroom Assessment Scoring System–Secondary (CLASS-S) protocol from 458 middle school teachers over a 2-year period to study changes over time in (a) the average quality of teaching for the population of teachers, (b) the average severity of the population of raters, and (c) the severity of individual raters. To obtain these estimates and assess them in the context of other factors that contribute to the variability in scores, we develop an augmented G study model that is broadly applicable for modeling sources of variability in classroom observation ratings data collected over time. In our data, we found that trends in teaching quality were small. Rater drift was very large during raters’ initial days of observation and persisted throughout nearly 2 years of scoring. Raters did not converge to a common level of severity; using our model we estimate that variability among raters actually increases over the course of the study. Variance decompositions based on the model find that trends are a modest source of variance relative to overall rater effects, rater errors on specific lessons, and residual error. The discussion provides possible explanations for trends and rater divergence as well as implications for designs collecting ratings over time. PMID:29795823

Trends in Classroom Observation Scores.

PubMed

Casabianca, Jodi M; Lockwood, J R; McCaffrey, Daniel F

2015-04-01

Observations and ratings of classroom teaching and interactions collected over time are susceptible to trends in both the quality of instruction and rater behavior. These trends have potential implications for inferences about teaching and for study design. We use scores on the Classroom Assessment Scoring System-Secondary (CLASS-S) protocol from 458 middle school teachers over a 2-year period to study changes over time in (a) the average quality of teaching for the population of teachers, (b) the average severity of the population of raters, and (c) the severity of individual raters. To obtain these estimates and assess them in the context of other factors that contribute to the variability in scores, we develop an augmented G study model that is broadly applicable for modeling sources of variability in classroom observation ratings data collected over time. In our data, we found that trends in teaching quality were small. Rater drift was very large during raters' initial days of observation and persisted throughout nearly 2 years of scoring. Raters did not converge to a common level of severity; using our model we estimate that variability among raters actually increases over the course of the study. Variance decompositions based on the model find that trends are a modest source of variance relative to overall rater effects, rater errors on specific lessons, and residual error. The discussion provides possible explanations for trends and rater divergence as well as implications for designs collecting ratings over time.

Bi-phasic trends in mercury concentrations in blood of Wisconsin common loons during 1992–2010

USGS Publications Warehouse

Meyer, Michael W.; Rasmussen, Paul W.; Watras, Carl J.; Fevold, Brick M.; Kenow, Kevin P.

2011-01-01

Wisconsin Department of Natural Resources (WDNR) assessed the ecological risk of mercury (Hg) in aquatic systems by monitoring common loon (Gavia immer) population dynamics and blood Hg concentrations. We report temporal trends in blood Hg concentrations based on 334 samples collected from adults recaptured in subsequent years (resampled 2-9 times) and from 421 blood samples of chicks collected at lakes resampled 2-8 times 1992-2010.. Temporal trends were identified with generalized additive mixed effects models (GAMMs) and mixed effects models to account for the potential lack of independence among observations from the same loon or same lake. Trend analyses indicated that Hg concentrations in the blood of Wisconsin loons declined over the period 1992-2000, and increased during 2002-2010, but not to the level observed in the early 1990s. The best fitting linear mixed effects model included separate trends for the two time periods. The estimated trend in Hg concentration among the adult loon population during 1992-2000 was -2.6% per year and the estimated trend during 2002-2010 was +1.8% per year; chick blood Hg concentrations decreased by -6.5% per year during 1992-2000, but increased 1.8% per year during 2002-2010. This bi-phasic pattern is similar to trends observed for concentrations of methylmercury (meHg) and SO4 in lake water of a well studied seepage lake (Little Rock Lake, Vilas County) within our study area. A cause-effect relationship between these independent trends is hypothesized.

A spurious warming trend in the NMME equatorial Pacific SST hindcasts

NASA Astrophysics Data System (ADS)

Shin, Chul-Su; Huang, Bohua

2017-06-01

Using seasonal hindcasts of six different models participating in the North American Multimodel Ensemble project, the trend of the predicted sea surface temperature (SST) in the tropical Pacific for 1982-2014 at each lead month and its temporal evolution with respect to the lead month are investigated for all individual models. Since the coupled models are initialized with the observed ocean, atmosphere, land states from observation-based reanalysis, some of them using their own data assimilation process, one would expect that the observed SST trend is reasonably well captured in their seasonal predictions. However, although the observed SST features a weak-cooling trend for the 33-year period with La Niña-like spatial pattern in the tropical central-eastern Pacific all year round, it is demonstrated that all models having a time-dependent realistic concentration of greenhouse gases (GHG) display a warming trend in the equatorial Pacific that amplifies as the lead-time increases. In addition, these models' behaviors are nearly independent of the starting month of the hindcasts although the growth rates of the trend vary with the lead month. This key characteristic of the forecasted SST trend in the equatorial Pacific is also identified in the NCAR CCSM3 hindcasts that have the GHG concentration for a fixed year. This suggests that a global warming forcing may not play a significant role in generating the spurious warming trend of the coupled models' SST hindcasts in the tropical Pacific. This model SST trend in the tropical central-eastern Pacific, which is opposite to the observed one, causes a developing El Niño-like warming bias in the forecasted SST with its peak in boreal winter. Its implications for seasonal prediction are discussed.

Comparison of two regression-based approaches for determining nutrient and sediment fluxes and trends in the Chesapeake Bay watershed

USGS Publications Warehouse

Moyer, Douglas; Hirsch, Robert M.; Hyer, Kenneth

2012-01-01

Nutrient and sediment fluxes and changes in fluxes over time are key indicators that water resource managers can use to assess the progress being made in improving the structure and function of the Chesapeake Bay ecosystem. The U.S. Geological Survey collects annual nutrient (nitrogen and phosphorus) and sediment flux data and computes trends that describe the extent to which water-quality conditions are changing within the major Chesapeake Bay tributaries. Two regression-based approaches were compared for estimating annual nutrient and sediment fluxes and for characterizing how these annual fluxes are changing over time. The two regression models compared are the traditionally used ESTIMATOR and the newly developed Weighted Regression on Time, Discharge, and Season (WRTDS). The model comparison focused on answering three questions: (1) What are the differences between the functional form and construction of each model? (2) Which model produces estimates of flux with the greatest accuracy and least amount of bias? (3) How different would the historical estimates of annual flux be if WRTDS had been used instead of ESTIMATOR? One additional point of comparison between the two models is how each model determines trends in annual flux once the year-to-year variations in discharge have been determined. All comparisons were made using total nitrogen, nitrate, total phosphorus, orthophosphorus, and suspended-sediment concentration data collected at the nine U.S. Geological Survey River Input Monitoring stations located on the Susquehanna, Potomac, James, Rappahannock, Appomattox, Pamunkey, Mattaponi, Patuxent, and Choptank Rivers in the Chesapeake Bay watershed. Two model characteristics that uniquely distinguish ESTIMATOR and WRTDS are the fundamental model form and the determination of model coefficients. ESTIMATOR and WRTDS both predict water-quality constituent concentration by developing a linear relation between the natural logarithm of observed constituent concentration and three explanatory variables—the natural log of discharge, time, and season. ESTIMATOR uses two additional explanatory variables—the square of the log of discharge and time-squared. Both models determine coefficients for variables for a series of estimation windows. ESTIMATOR establishes variable coefficients for a series of 9-year moving windows; all observed constituent concentration data within the 9-year window are used to establish each coefficient. Conversely, WRTDS establishes variable coefficients for each combination of discharge and time using only observed concentration data that are similar in time, season, and discharge to the day being estimated. As a result of these distinguishing characteristics, ESTIMATOR reproduces concentration-discharge relations that are closely approximated by a quadratic or linear function with respect to both the log of discharge and time. Conversely, the linear model form of WRTDS coupled with extensive model windowing for each combination of discharge and time allows WRTDS to reproduce observed concentration-discharge relations that are more sinuous in form. Another distinction between ESTIMATOR and WRTDS is the reporting of uncertainty associated with the model estimates of flux and trend. ESTIMATOR quantifies the standard error of prediction associated with the determination of flux and trends. The standard error of prediction enables the determination of the 95-percent confidence intervals for flux and trend as well as the ability to test whether the reported trend is significantly different from zero (where zero equals no trend). Conversely, WRTDS is unable to propagate error through the many (over 5,000) models for unique combinations of flow and time to determine a total standard error. As a result, WRTDS flux estimates are not reported with confidence intervals and a level of significance is not determined for flow-normalized fluxes. The differences between ESTIMATOR and WRTDS, with regard to model form and determination of model coefficients, have an influence on the determination of nutrient and sediment fluxes and associated changes in flux over time as a result of management activities. The comparison between the model estimates of flux and trend was made for combinations of five water-quality constituents at nine River Input Monitoring stations. The major findings with regard to nutrient and sediment fluxes are as follows: (1)WRTDS produced estimates of flux for all combinations that were more accurate, based on reduction in root mean squared error, than flux estimates from ESTIMATOR; (2) for 67 percent of the combinations, WRTDS and ESTIMATOR both produced estimates of flux that were minimally biased compared to observed fluxes(flux bias = tendency to over or underpredict flux observations); however, for 33 percent of the combinations, WRTDS produced estimates of flux that were considerably less biased (by at least 10 percent) than flux estimates from ESTIMATOR; (3) the average percent difference in annual fluxes generated by ESTIMATOR and WRTDS was less than 10 percent at 80 percent of the combinations; and (4) the greatest differences related to flux bias and annual fluxes all occurred for combinations where the pattern in observed concentration-discharge relation was sinuous (two points of inflection) rather than linear or quadratic (zero or one point of inflection). The major findings with regard to trends are as follows: (1) both models produce water-quality trends that have factored in the year-to-year variations in flow; (2) trends in water-quality condition are represented by ESTIMATOR as a trend in flow-adjusted concentration and by WRTDS as a flow normalized flux; (3) for 67 percent of the combinations with trend estimates, the WRTDS trends in flow-normalized flux are in the same direction and magnitude to the ESTIMATOR trends in flow-adjusted concentration, and at the remaining 33 percent the differences in trend magnitude and direction are related to fundamental differences between concentration and flux; and (4) the majority (85 percent) of the total nitrogen, nitrate, and orthophosphorus combinations exhibited long-term (1985 to 2010) trends in WRTDS flow-normalized flux that indicate improvement or reduction in associated flux and the majority (83 percent) of the total phosphorus (from 1985 to 2010) and suspended sediment (from 2001 to 2010) combinations exhibited trends in WRTDS flow-normalized flux that indicate degradation or increases in the flux delivered.

Hawaiian forest bird trends: using log-linear models to assess long-term trends is supported by model diagnostics and assumptions (reply to Freed and Cann 2013)

USGS Publications Warehouse

Camp, Richard J.; Pratt, Thane K.; Gorresen, P. Marcos; Woodworth, Bethany L.; Jeffrey, John J.

2014-01-01

Freed and Cann (2013) criticized our use of linear models to assess trends in the status of Hawaiian forest birds through time (Camp et al. 2009a, 2009b, 2010) by questioning our sampling scheme, whether we met model assumptions, and whether we ignored short-term changes in the population time series. In the present paper, we address these concerns and reiterate that our results do not support the position of Freed and Cann (2013) that the forest birds in the Hakalau Forest National Wildlife Refuge (NWR) are declining, or that the federally listed endangered birds are showing signs of imminent collapse. On the contrary, our data indicate that the 21-year long-term trends for native birds in Hakalau Forest NWR are stable to increasing, especially in areas that have received active management.

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

NASA Astrophysics Data System (ADS)

Quan, Jinling; Zhan, Wenfeng; Chen, Yunhao; Wang, Mengjie; Wang, Jinfei

2016-03-01

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62 K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (~ -0.086 K/yr, p < 0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (~0.084 K/yr before and ~0.245 K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p < 0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.022 ± 0.006 K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

Evaluation of CMIP5 Ability to Reproduce 20th Century Regional Trends in Surface Air Temperature and Precipitation over CONUS

NASA Astrophysics Data System (ADS)

Lee, J.; Waliser, D. E.; Lee, H.; Loikith, P. C.; Kunkel, K.

2017-12-01

Monitoring temporal changes in key climate variables, such as surface air temperature and precipitation, is an integral part of the ongoing efforts of the United States National Climate Assessment (NCA). Climate models participating in CMIP5 provide future trends for four different emissions scenarios. In order to have confidence in the future projections of surface air temperature and precipitation, it is crucial to evaluate the ability of CMIP5 models to reproduce observed trends for three different time periods (1895-1939, 1940-1979, and 1980-2005). Towards this goal, trends in surface air temperature and precipitation obtained from the NOAA nClimGrid 5 km gridded station observation-based product are compared during all three time periods to the 206 CMIP5 historical simulations from 48 unique GCMs and their multi-model ensemble (MME) for NCA-defined climate regions during summer (JJA) and winter (DJF). This evaluation quantitatively examines the biases of simulated trends of the spatially averaged temperature and precipitation in the NCA climate regions. The CMIP5 MME reproduces historical surface air temperature trends for JJA for all time period and all regions, except the Northern Great Plains from 1895-1939 and Southeast during 1980-2005. Likewise, for DJF, the MME reproduces historical surface air temperature trends across all time periods over all regions except the Southeast from 1895-1939 and the Midwest during 1940-1979. The Regional Climate Model Evaluation System (RCMES), an analysis tool which supports the NCA by providing access to data and tools for regional climate model validation, facilitates the comparisons between the models and observation. The RCMES Toolkit is designed to assist in the analysis of climate variables and the procedure of the evaluation of climate projection models to support the decision-making processes. This tool is used in conjunction with the above analysis and results will be presented to demonstrate its capability to access observation and model datasets, calculate evaluation metrics, and visualize the results. Several other examples of the RCMES capabilities can be found at https://rcmes.jpl.nasa.gov.

Time-series analysis of delta13C from tree rings. I. Time trends and autocorrelation.

PubMed

Monserud, R A; Marshall, J D

2001-09-01

Univariate time-series analyses were conducted on stable carbon isotope ratios obtained from tree-ring cellulose. We looked for the presence and structure of autocorrelation. Significant autocorrelation violates the statistical independence assumption and biases hypothesis tests. Its presence would indicate the existence of lagged physiological effects that persist for longer than the current year. We analyzed data from 28 trees (60-85 years old; mean = 73 years) of western white pine (Pinus monticola Dougl.), ponderosa pine (Pinus ponderosa Laws.), and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. glauca) growing in northern Idaho. Material was obtained by the stem analysis method from rings laid down in the upper portion of the crown throughout each tree's life. The sampling protocol minimized variation caused by changing light regimes within each tree. Autoregressive moving average (ARMA) models were used to describe the autocorrelation structure over time. Three time series were analyzed for each tree: the stable carbon isotope ratio (delta(13)C); discrimination (delta); and the difference between ambient and internal CO(2) concentrations (c(a) - c(i)). The effect of converting from ring cellulose to whole-leaf tissue did not affect the analysis because it was almost completely removed by the detrending that precedes time-series analysis. A simple linear or quadratic model adequately described the time trend. The residuals from the trend had a constant mean and variance, thus ensuring stationarity, a requirement for autocorrelation analysis. The trend over time for c(a) - c(i) was particularly strong (R(2) = 0.29-0.84). Autoregressive moving average analyses of the residuals from these trends indicated that two-thirds of the individual tree series contained significant autocorrelation, whereas the remaining third were random (white noise) over time. We were unable to distinguish between individuals with and without significant autocorrelation beforehand. Significant ARMA models were all of low order, with either first- or second-order (i.e., lagged 1 or 2 years, respectively) models performing well. A simple autoregressive (AR(1)), model was the most common. The most useful generalization was that the same ARMA model holds for each of the three series (delta(13)C, delta, c(a) - c(i)) for an individual tree, if the time trend has been properly removed for each series. The mean series for the two pine species were described by first-order ARMA models (1-year lags), whereas the Douglas-fir mean series were described by second-order models (2-year lags) with negligible first-order effects. Apparently, the process of constructing a mean time series for a species preserves an underlying signal related to delta(13)C while canceling some of the random individual tree variation. Furthermore, the best model for the overall mean series (e.g., for a species) cannot be inferred from a consensus of the individual tree model forms, nor can its parameters be estimated reliably from the mean of the individual tree parameters. Because two-thirds of the individual tree time series contained significant autocorrelation, the normal assumption of a random structure over time is unwarranted, even after accounting for the time trend. The residuals of an appropriate ARMA model satisfy the independence assumption, and can be used to make hypothesis tests.

Using Functional Data Analysis Models to Estimate Future Time Trends in Age-Specific Breast Cancer Mortality for the United States and England–Wales

PubMed Central

Erbas, Bircan; Akram, Muhammed; Gertig, Dorota M; English, Dallas; Hopper, John L.; Kavanagh, Anne M; Hyndman, Rob

2010-01-01

Background Mortality/incidence predictions are used for allocating public health resources and should accurately reflect age-related changes through time. We present a new forecasting model for estimating future trends in age-related breast cancer mortality for the United States and England–Wales. Methods We used functional data analysis techniques both to model breast cancer mortality-age relationships in the United States from 1950 through 2001 and England–Wales from 1950 through 2003 and to estimate 20-year predictions using a new forecasting method. Results In the United States, trends for women aged 45 to 54 years have continued to decline since 1980. In contrast, trends in women aged 60 to 84 years increased in the 1980s and declined in the 1990s. For England–Wales, trends for women aged 45 to 74 years slightly increased before 1980, but declined thereafter. The greatest age-related changes for both regions were during the 1990s. For both the United States and England–Wales, trends are expected to decline and then stabilize, with the greatest decline in women aged 60 to 70 years. Forecasts suggest relatively stable trends for women older than 75 years. Conclusions Prediction of age-related changes in mortality/incidence can be used for planning and targeting programs for specific age groups. Currently, these models are being extended to incorporate other variables that may influence age-related changes in mortality/incidence trends. In their current form, these models will be most useful for modeling and projecting future trends of diseases for which there has been very little advancement in treatment and minimal cohort effects (eg. lethal cancers). PMID:20139657

The Trend-in-trend Research Design for Causal Inference.

PubMed

Ji, Xinyao; Small, Dylan S; Leonard, Charles E; Hennessy, Sean

2017-07-01

Cohort studies can be biased by unmeasured confounding. We propose a hybrid ecologic-epidemiologic design called the trend-in-trend design, which requires a strong time trend in exposure, but is unbiased unless there are unmeasured factors affecting outcome for which there are time trends in prevalence that are correlated with time trends in exposure across strata with different exposure trends. Thus, the conditions under which the trend-in-trend study is biased are a subset of those under which a cohort study is biased. The trend-in-trend design first divides the study population into strata based on the cumulative probability of exposure given covariates, which effectively stratifies on time trend in exposure, provided there is a trend. Next, a covariates-free maximum likelihood model estimates the odds ratio (OR) using data on exposure prevalence and outcome frequency within cumulative probability of exposure strata, across multiple periods. In simulations, the trend-in-trend design produced ORs with negligible bias in the presence of unmeasured confounding. In empiric applications, trend-in-trend reproduced the known positive association between rofecoxib and myocardial infarction (observed OR: 1.2, 95% confidence interval: 1.1, 1.4), and known null associations between rofecoxib and severe hypoglycemia (OR = 1.1 [0.92, 1.3]) and nonvertebral fracture (OR = 0.84 [0.64, 1.1]). The trend-in-trend method may be useful in settings where there is a strong time trend in exposure, such as a newly approved drug or other medical intervention. See video abstract at, http://links.lww.com/EDE/B178.

An introduction to modeling longitudinal data with generalized additive models: applications to single-case designs.

PubMed

Sullivan, Kristynn J; Shadish, William R; Steiner, Peter M

2015-03-01

Single-case designs (SCDs) are short time series that assess intervention effects by measuring units repeatedly over time in both the presence and absence of treatment. This article introduces a statistical technique for analyzing SCD data that has not been much used in psychological and educational research: generalized additive models (GAMs). In parametric regression, the researcher must choose a functional form to impose on the data, for example, that trend over time is linear. GAMs reverse this process by letting the data inform the choice of functional form. In this article we review the problem that trend poses in SCDs, discuss how current SCD analytic methods approach trend, describe GAMs as a possible solution, suggest a GAM model testing procedure for examining the presence of trend in SCDs, present a small simulation to show the statistical properties of GAMs, and illustrate the procedure on 3 examples of different lengths. Results suggest that GAMs may be very useful both as a form of sensitivity analysis for checking the plausibility of assumptions about trend and as a primary data analysis strategy for testing treatment effects. We conclude with a discussion of some problems with GAMs and some future directions for research on the application of GAMs to SCDs. (c) 2015 APA, all rights reserved).

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

Interpreting space-based trends in carbon monoxide with multiple models

DOE PAGES

Strode, Sarah A.; Worden, Helen M.; Damon, Megan; ...

2016-06-10

Here, we use a series of chemical transport model and chemistry climate model simulations to investigate the observed negative trends in MOPITT CO over several regions of the world, and to examine the consistency of time-dependent emission inventories with observations. We also found that simulations driven by the MACCity inventory, used for the Chemistry Climate Modeling Initiative (CCMI), reproduce the negative trends in the CO column observed by MOPITT for 2000–2010 over the eastern United States and Europe. However, the simulations have positive trends over eastern China, in contrast to the negative trends observed by MOPITT. The model bias inmore » CO, after applying MOPITT averaging kernels, contributes to the model–observation discrepancy in the trend over eastern China. This demonstrates that biases in a model's average concentrations can influence the interpretation of the temporal trend compared to satellite observations. The total ozone column plays a role in determining the simulated tropospheric CO trends. A large positive anomaly in the simulated total ozone column in 2010 leads to a negative anomaly in OH and hence a positive anomaly in CO, contributing to the positive trend in simulated CO. Our results demonstrate that accurately simulating variability in the ozone column is important for simulating and interpreting trends in CO.« less

Interpreting space-based trends in carbon monoxide with multiple models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Strode, Sarah A.; Worden, Helen M.; Damon, Megan

Here, we use a series of chemical transport model and chemistry climate model simulations to investigate the observed negative trends in MOPITT CO over several regions of the world, and to examine the consistency of time-dependent emission inventories with observations. We also found that simulations driven by the MACCity inventory, used for the Chemistry Climate Modeling Initiative (CCMI), reproduce the negative trends in the CO column observed by MOPITT for 2000–2010 over the eastern United States and Europe. However, the simulations have positive trends over eastern China, in contrast to the negative trends observed by MOPITT. The model bias inmore » CO, after applying MOPITT averaging kernels, contributes to the model–observation discrepancy in the trend over eastern China. This demonstrates that biases in a model's average concentrations can influence the interpretation of the temporal trend compared to satellite observations. The total ozone column plays a role in determining the simulated tropospheric CO trends. A large positive anomaly in the simulated total ozone column in 2010 leads to a negative anomaly in OH and hence a positive anomaly in CO, contributing to the positive trend in simulated CO. Our results demonstrate that accurately simulating variability in the ozone column is important for simulating and interpreting trends in CO.« less

Temperature and ice layer trends in the summer middle atmosphere

NASA Astrophysics Data System (ADS)

Lübken, F.-J.; Berger, U.

2012-04-01

We present results from our LIMA model (Leibniz Institute Middle Atmosphere Model) which nicely reproduces mean conditions of the summer mesopause region and also mean characteristics of ice layers known as noctilucent clouds. LIMA nudges to ECMWF data in the troposphere and lower stratosphere which influences the background conditions in the mesosphere. We study temperature trends in the mesosphere at middle and polar latitudes and compared with temperature trends from satellites, lidar, and phase height observations. For the first time large observed temperature trends in the summer mesosphere can be reproduced and explained by a model. As will be shown, stratospheric ozone has a major impact on temperature trends in the summer mesosphere. The temperature trend is not uniform in time: it is moderate from 1961 (the beginning of our record) until the beginning of the 1980s. Thereafter, temperatures decrease much stronger until the mid 1990s. Thereafter, temperatures are nearly constant or even increase with time. As will be shown, trends in ozone and carbon dioxide explain most of this behavior. Ice layers in the summer mesosphere are very sensitive to background conditions and are therefore considered to be appropriate tracers for long term variations in the middle atmosphere. We use LIMA background conditions to determine ice layer characteristics in the mesopause region. We compare our results with measurements, for example with albedos from the SBUV satellites, and show that we can nicely reproduce observed trends. It turns out that temperature trends are positive (negative) in the upper (lower) part of the ice layer regime. This complicates an interpretation of NLC long term variations in terms of temperature trends.

Zika pandemic online trends, incidence and health risk communication: a time trend study

PubMed Central

Neumark, Yehuda; Gesser-Edelsburg, Anat; Abu Ahmad, Wiessam

2017-01-01

Objectives We aimed to describe the online search trends of Zika and examine their association with Zika incidence, assess the content of Zika-related press releases issued by leading health authorities and examine the association between online trends and press release timing. Design Using Google Trends, the 1 May 2015 to 30 May 2016 online trends of Zika and associated search terms were studied globally and in the five countries with the highest numbers of suspected cases. Correlations were then examined between online trends and Zika incidence in these countries. All Zika-related press releases issued by WHO/Pan America Health Organization (PAHO) and Centers for Disease Control and Prevention (CDC) during the study period were assessed for transparency, uncertainty and audience segmentation. Witte's Extended Parallel Process Model was applied to assess self-efficacy, response efficacy, susceptibility and severity. AutoRegressive Integrated Moving Average with an eXogenous predictor variable (ARIMAX) (p,d,q) regression modelling was used to quantify the association between online trends and the timing of press releases. Results Globally, Zika online search trends were low until the beginning of 2016, when interest rose steeply. Strong correlations (r=0.748–0.922; p<0.001) were observed between online trends and the number of suspected Zika cases in four of the five countries studied. Compared with press releases issued by WHO/PAHO, CDC press releases were significantly more likely to provide contact details and links to other resources, include figures/graphs, be risk-advisory in nature and be more readable and briefer. ARIMAX modelling results indicate that online trends preceded by 1 week press releases by WHO (stationary-R2=0.345; p<0.001) and CDC (stationary-R2=0.318; p=0.014). Conclusions These results suggest that online trends can aid in pandemic surveillance. Identification of shortcomings in the content and timing of Zika press releases can help guide health communication efforts in the current pandemic and future public health emergencies. PMID:29082006

Estimates of Zenith Total Delay trends from GPS reprocessing with autoregressive process

NASA Astrophysics Data System (ADS)

Klos, Anna; Hunegnaw, Addisu; Teferle, Felix Norman; Ebuy Abraha, Kibrom; Ahmed, Furqan; Bogusz, Janusz

2017-04-01

Nowadays, near real-time Zenith Total Delay (ZTD) estimates from Global Positioning System (GPS) observations are routinely assimilated into numerical weather prediction (NWP) models to improve the reliability of forecasts. On the other hand, ZTD time series derived from homogeneously re-processed GPS observations over long periods have the potential to improve our understanding of climate change on various temporal and spatial scales. With such time series only recently reaching somewhat adequate time spans, the application of GPS-derived ZTD estimates to climate monitoring is still to be developed further. In this research, we examine the character of noise in ZTD time series for 1995-2015 in order to estimate more realistic magnitudes of trend and its uncertainty than would be the case if the stochastic properties are not taken into account. Furthermore, the hourly sampled, homogeneously re-processed and carefully homogenized ZTD time series from over 700 globally distributed stations were classified into five major climate zones. We found that the amplitudes of annual signals reach values of 10-150 mm with minimum values for the polar and Alpine zones. The amplitudes of daily signals were estimated to be 0-12 mm with maximum values found for the dry zone. We examined seven different noise models for the residual ZTD time series after modelling all known periodicities. This identified a combination of white plus autoregressive process of fourth order to be optimal to match all changes in power of the ZTD data. When the stochastic properties are neglected, ie. a pure white noise model is employed, only 11 from 120 trends were insignificant. Using the optimum noise model more than half of the 120 examined trends became insignificant. We show that the uncertainty of ZTD trends is underestimated by a factor of 3-12 when the stochastic properties of the ZTD time series are ignored and we conclude that it is essential to properly model the noise characteristics of such time series when interpretations in terms of climate change are to be performed.

Identifying trends in climate: an application to the cenozoic

NASA Astrophysics Data System (ADS)

Richards, Gordon R.

1998-05-01

The recent literature on trending in climate has raised several issues, whether trends should be modeled as deterministic or stochastic, whether trends are nonlinear, and the relative merits of statistical models versus models based on physics. This article models trending since the late Cretaceous. This 68 million-year interval is selected because the reliability of tests for trending is critically dependent on the length of time spanned by the data. Two main hypotheses are tested, that the trend has been caused primarily by CO2 forcing, and that it reflects a variety of forcing factors which can be approximated by statistical methods. The CO2 data is obtained from model simulations. Several widely-used statistical models are found to be inadequate. ARIMA methods parameterize too much of the short-term variation, and do not identify low frequency movements. Further, the unit root in the ARIMA process does not predict the long-term path of temperature. Spectral methods also have little ability to predict temperature at long horizons. Instead, the statistical trend is estimated using a nonlinear smoothing filter. Both of these paradigms make it possible to model climate as a cointegrated process, in which temperature can wander quite far from the trend path in the intermediate term, but converges back over longer horizons. Comparing the forecasting properties of the two trend models demonstrates that the optimal forecasting model includes CO2 forcing and a parametric representation of the nonlinear variability in climate.

Time series analysis and mortality model of dog bite victims presented for treatment at a referral clinic for rabies exposure in Monrovia, Liberia, 2010-2013.

PubMed

Olarinmoye, Ayodeji O; Ojo, Johnson F; Fasunla, Ayotunde J; Ishola, Olayinka O; Dakinah, Fahnboah G; Mulbah, Charles K; Al-Hezaimi, Khalid; Olugasa, Babasola O

2017-08-01

We developed time trend model, determined treatment outcome and estimated annual human deaths among dog bite victims (DBVs) from 2010 to 2013 in Monrovia, Liberia. Data obtained from clinic records included victim's age, gender and site of bite marks, site name of residence of rabies-exposed patients, promptness of care sought, initial treatment and post-exposure-prophylaxis (PEP) compliance. We computed DBV time-trend plot, seasonal index and year 2014 case forecast. Associated annual human death (AHD) was estimated using a standardized decision tree model. Of the 775 DBVs enlisted, care seeking time was within 24h of injury in 328 (42.32%) DBVs. Victim's residential location, site of bite mark, and time dependent variables were significantly associated with treatment outcome (p< 0.05). The equation X^ t =28.278-0.365t models the trend of DBVs. The high (n=705, 90.97%) defaulted PEP and average 155 AHD from rabies implied urgent need for policy formulation on national programme for rabies prevention in Liberia. Copyright © 2017 Elsevier Ltd. All rights reserved.

Numerical and Qualitative Contrasts of Two Statistical Models ...

EPA Pesticide Factsheets

Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and products. This study provided an empirical and qualitative comparison of both models using 29 years of data for two discrete time series of chlorophyll-a (chl-a) in the Patuxent River estuary. Empirical descriptions of each model were based on predictive performance against the observed data, ability to reproduce flow-normalized trends with simulated data, and comparisons of performance with validation datasets. Between-model differences were apparent but minor and both models had comparable abilities to remove flow effects from simulated time series. Both models similarly predicted observations for missing data with different characteristics. Trends from each model revealed distinct mainstem influences of the Chesapeake Bay with both models predicting a roughly 65% increase in chl-a over time in the lower estuary, whereas flow-normalized predictions for the upper estuary showed a more dynamic pattern, with a nearly 100% increase in chl-a in the last 10 years. Qualitative comparisons highlighted important differences in the statistical structure, available products, and characteristics of the data and desired analysis. This manuscript describes a quantitative comparison of two recently-

Geochemical modeling of magma mixing and magma reservoir volumes during early episodes of Kīlauea Volcano's Pu`u `Ō`ō eruption

NASA Astrophysics Data System (ADS)

Shamberger, Patrick J.; Garcia, Michael O.

2007-02-01

Geochemical modeling of magma mixing allows for evaluation of volumes of magma storage reservoirs and magma plumbing configurations. A new analytical expression is derived for a simple two-component box-mixing model describing the proportions of mixing components in erupted lavas as a function of time. Four versions of this model are applied to a mixing trend spanning episodes 3 31 of Kilauea Volcano’s Puu Oo eruption, each testing different constraints on magma reservoir input and output fluxes. Unknown parameters (e.g., magma reservoir influx rate, initial reservoir volume) are optimized for each model using a non-linear least squares technique to fit model trends to geochemical time-series data. The modeled mixing trend closely reproduces the observed compositional trend. The two models that match measured lava effusion rates have constant magma input and output fluxes and suggest a large pre-mixing magma reservoir (46±2 and 49±1 million m3), with little or no volume change over time. This volume is much larger than a previous estimate for the shallow, dike-shaped magma reservoir under the Puu Oo vent, which grew from ˜3 to ˜10 12 million m3. These volumetric differences are interpreted as indicating that mixing occurred first in a larger, deeper reservoir before the magma was injected into the overlying smaller reservoir.

Predicting Rehabilitation Success Rate Trends among Ethnic Minorities Served by State Vocational Rehabilitation Agencies: A National Time Series Forecast Model Demonstration Study

ERIC Educational Resources Information Center

Moore, Corey L.; Wang, Ningning; Washington, Janique Tynez

2017-01-01

Purpose: This study assessed and demonstrated the efficacy of two select empirical forecast models (i.e., autoregressive integrated moving average [ARIMA] model vs. grey model [GM]) in accurately predicting state vocational rehabilitation agency (SVRA) rehabilitation success rate trends across six different racial and ethnic population cohorts…

Trend assessment: applications for hydrology and climate research

NASA Astrophysics Data System (ADS)

Kallache, M.; Rust, H. W.; Kropp, J.

2005-02-01

The assessment of trends in climatology and hydrology still is a matter of debate. Capturing typical properties of time series, like trends, is highly relevant for the discussion of potential impacts of global warming or flood occurrences. It provides indicators for the separation of anthropogenic signals and natural forcing factors by distinguishing between deterministic trends and stochastic variability. In this contribution river run-off data from gauges in Southern Germany are analysed regarding their trend behaviour by combining a deterministic trend component and a stochastic model part in a semi-parametric approach. In this way the trade-off between trend and autocorrelation structure can be considered explicitly. A test for a significant trend is introduced via three steps: First, a stochastic fractional ARIMA model, which is able to reproduce short-term as well as long-term correlations, is fitted to the empirical data. In a second step, wavelet analysis is used to separate the variability of small and large time-scales assuming that the trend component is part of the latter. Finally, a comparison of the overall variability to that restricted to small scales results in a test for a trend. The extraction of the large-scale behaviour by wavelet analysis provides a clue concerning the shape of the trend.

Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.

Analysis and generation of groundwater concentration time series

NASA Astrophysics Data System (ADS)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

Trend analysis of salt load and evaluation of the frequency of water-quality measurements for the Gunnison, the Colorado, and the Dolores rivers in Colorado and Utah

USGS Publications Warehouse

Kircher, J.E.; Dinicola, Richard S.; Middelburg, R.F.

1984-01-01

Monthly values were computed for water-quality constituents at four streamflow gaging stations in the Upper Colorado River basin for the determination of trends. Seasonal regression and seasonal Kendall trend analysis techniques were applied to two monthly data sets at each station site for four different time periods. A recently developed method for determining optimal water-discharge data-collection frequency was also applied to the monthly water-quality data. Trend analysis results varied with each monthly load computational method, period of record, and trend detection model used. No conclusions could be reached regarding which computational method was best to use in trend analysis. Time-period selection for analysis was found to be important with regard to intended use of the results. Seasonal Kendall procedures were found to be applicable to most data sets. Seasonal regression models were more difficult to apply and were sometimes of questionable validity; however, those results were more informative than seasonal Kendall results. The best model to use depends upon the characteristics of the data and the amount of trend information needed. The measurement-frequency optimization method had potential for application to water-quality data, but refinements are needed. (USGS)

When at what scale will trends in European mean and heavy precipitation emerge

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-04-01

A multi-model ensemble of regional climate projections for Europe is employed to investigate how the time of emergence (TOE) for seasonal sums and maxima of daily precipitation depends on spatial scale. The TOE is redefined for emergence from internal variability only, the spread of the TOE due to imperfect climate model formulation is used as a measure of uncertainty in the TOE itself. Thereby the TOE becomes a fundamentally limiting time scale and translates into a minimum spatial scale on which robust conclusions can be drawn about precipitation trends. Thus also minimum temporal and spatial scales for adaptation planning are given. In northern Europe, positive winter trends in mean and heavy precipitation, in southwestern and southeastern Europe summer trends in mean precipitation emerge already within the next decades. Yet across wide areas, especially for heavy summer precipitation, the local trend emerges only late in the 21st century or later. For precipitation averaged to larger scales, the trend in general emerges earlier. Douglas Maraun, When at what scale will trends in European mean and heavy precipitation emerge? Env. Res. Lett., in press, 2013.

A correction method for systematic error in (1)H-NMR time-course data validated through stochastic cell culture simulation.

PubMed

Sokolenko, Stanislav; Aucoin, Marc G

2015-09-04

The growing ubiquity of metabolomic techniques has facilitated high frequency time-course data collection for an increasing number of applications. While the concentration trends of individual metabolites can be modeled with common curve fitting techniques, a more accurate representation of the data needs to consider effects that act on more than one metabolite in a given sample. To this end, we present a simple algorithm that uses nonparametric smoothing carried out on all observed metabolites at once to identify and correct systematic error from dilution effects. In addition, we develop a simulation of metabolite concentration time-course trends to supplement available data and explore algorithm performance. Although we focus on nuclear magnetic resonance (NMR) analysis in the context of cell culture, a number of possible extensions are discussed. Realistic metabolic data was successfully simulated using a 4-step process. Starting with a set of metabolite concentration time-courses from a metabolomic experiment, each time-course was classified as either increasing, decreasing, concave, or approximately constant. Trend shapes were simulated from generic functions corresponding to each classification. The resulting shapes were then scaled to simulated compound concentrations. Finally, the scaled trends were perturbed using a combination of random and systematic errors. To detect systematic errors, a nonparametric fit was applied to each trend and percent deviations calculated at every timepoint. Systematic errors could be identified at time-points where the median percent deviation exceeded a threshold value, determined by the choice of smoothing model and the number of observed trends. Regardless of model, increasing the number of observations over a time-course resulted in more accurate error estimates, although the improvement was not particularly large between 10 and 20 samples per trend. The presented algorithm was able to identify systematic errors as small as 2.5 % under a wide range of conditions. Both the simulation framework and error correction method represent examples of time-course analysis that can be applied to further developments in (1)H-NMR methodology and the more general application of quantitative metabolomics.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990-2010

NASA Astrophysics Data System (ADS)

Colette, Augustin; Andersson, Camilla; Manders, Astrid; Mar, Kathleen; Mircea, Mihaela; Pay, Maria-Teresa; Raffort, Valentin; Tsyro, Svetlana; Cuvelier, Cornelius; Adani, Mario; Bessagnet, Bertrand; Bergström, Robert; Briganti, Gino; Butler, Tim; Cappelletti, Andrea; Couvidat, Florian; D'Isidoro, Massimo; Doumbia, Thierno; Fagerli, Hilde; Granier, Claire; Heyes, Chris; Klimont, Zig; Ojha, Narendra; Otero, Noelia; Schaap, Martijn; Sindelarova, Katarina; Stegehuis, Annemiek I.; Roustan, Yelva; Vautard, Robert; van Meijgaard, Erik; Garcia Vivanco, Marta; Wind, Peter

2017-09-01

The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990-2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have - to date - completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990-2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

An at-site flood estimation method in the context of nonstationarity II. Statistical analysis of floods in Quebec

NASA Astrophysics Data System (ADS)

Gado, Tamer A.; Nguyen, Van-Thanh-Van

2016-04-01

This paper, the second of a two-part paper, investigates the nonstationary behaviour of flood peaks in Quebec (Canada) by analyzing the annual maximum flow series (AMS) available for the common 1966-2001 period from a network of 32 watersheds. Temporal trends in the mean of flood peaks were examined by the nonparametric Mann-Kendall test. The significance of the detected trends over the whole province is also assessed by a bootstrap test that preserves the cross-correlation structure of the network. Furthermore, The LM-NS method (introduced in the first part) is used to parametrically model the AMS, investigating its applicability to real data, to account for temporal trends in the moments of the time series. In this study two probability distributions (GEV & Gumbel) were selected to model four different types of time-varying moments of the historical time series considered, comprising eight competing models. The selected models are: two stationary models (GEV0 & Gumbel0), two nonstationary models in the mean as a linear function of time (GEV1 & Gumbel1), two nonstationary models in the mean as a parabolic function of time (GEV2 & Gumbel2), and two nonstationary models in the mean and the log standard deviation as linear functions of time (GEV11 & Gumbel11). The eight models were applied to flood data available for each watershed and their performance was compared to identify the best model for each location. The comparative methodology involves two phases: (1) a descriptive ability based on likelihood-based optimality criteria such as the Bayesian Information Criterion (BIC) and the deviance statistic; and (2) a predictive ability based on the residual bootstrap. According to the Mann-Kendall test and the LM-NS method, a quarter of the analyzed stations show significant trends in the AMS. All of the significant trends are negative, indicating decreasing flood magnitudes in Quebec. It was found that the LM-NS method could provide accurate flood estimates in the context of nonstationarity. The results have indicated the importance of taking into consideration the nonstationary behaviour of the flood series in order to improve the quality of flood estimation. The results also provided a general impression on the possible impacts of climate change on flood estimation in the Quebec province.

Following a trend with an exponential moving average: Analytical results for a Gaussian model

NASA Astrophysics Data System (ADS)

Grebenkov, Denis S.; Serror, Jeremy

2014-01-01

We investigate how price variations of a stock are transformed into profits and losses (P&Ls) of a trend following strategy. In the frame of a Gaussian model, we derive the probability distribution of P&Ls and analyze its moments (mean, variance, skewness and kurtosis) and asymptotic behavior (quantiles). We show that the asymmetry of the distribution (with often small losses and less frequent but significant profits) is reminiscent to trend following strategies and less dependent on peculiarities of price variations. At short times, trend following strategies admit larger losses than one may anticipate from standard Gaussian estimates, while smaller losses are ensured at longer times. Simple explicit formulas characterizing the distribution of P&Ls illustrate the basic mechanisms of momentum trading, while general matrix representations can be applied to arbitrary Gaussian models. We also compute explicitly annualized risk adjusted P&L and strategy turnover to account for transaction costs. We deduce the trend following optimal timescale and its dependence on both auto-correlation level and transaction costs. Theoretical results are illustrated on the Dow Jones index.

AR(p) -based detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Alvarez-Ramirez, J.; Rodriguez, E.

2018-07-01

Autoregressive models are commonly used for modeling time-series from nature, economics and finance. This work explored simple autoregressive AR(p) models to remove long-term trends in detrended fluctuation analysis (DFA). Crude oil prices and bitcoin exchange rate were considered, with the former corresponding to a mature market and the latter to an emergent market. Results showed that AR(p) -based DFA performs similar to traditional DFA. However, the former DFA provides information on stability of long-term trends, which is valuable for understanding and quantifying the dynamics of complex time series from financial systems.

Epidemiologic contributions to recent cancer trends among HIV-infected people in the United States.

PubMed

Robbins, Hilary A; Shiels, Meredith S; Pfeiffer, Ruth M; Engels, Eric A

2014-03-27

HIV-infected people have elevated risk for some cancers. Changing incidence of these cancers over time may reflect changes in three factors: HIV population demographic structure (e.g. age distribution), general population (background) cancer rates, and HIV-associated relative risks. We assessed the contributions of these factors to time trends for 10 cancers during 1996-2010. Population-based registry linkage study. We applied Poisson models to data from the U.S. HIV/AIDS Cancer Match Study to estimate annual percentage changes (APCs) in incidence rates of AIDS-defining cancers [ADCs: Kaposi sarcoma, non-Hodgkin lymphoma (NHL), and cervical cancer] and seven non-AIDS-defining cancers (NADCs). We evaluated HIV-infected cancer trends with and without adjustment for demographics, trends in background rates, and trends in standardized incidence ratios (SIRs, to capture relative risk). Cancer rates among HIV-infected people rose over time for anal (APC 3.8%), liver (8.5%), and prostate (9.8%) cancers, but declined for Kaposi sarcoma (1996-2000: -29.3%; 2000-2010: -7.8%), NHL (1996-2003: -15.7%; 2003-2010: -5.5%), cervical cancer (-11.1%), Hodgkin lymphoma (-4.0%), and lung cancer (-2.8%). Breast and colorectal cancer incidence did not change over time. Based on comparison to adjusted models, changing demographics contributed to trends for Kaposi sarcoma and breast, colorectal, liver, lung, and prostate cancers (all P < 0.01). Trends in background rates were notable for liver (APC 5.6%) and lung (-3.2%) cancers. SIRs declined for ADCs, Hodgkin lymphoma (APC -3.2%), and lung cancer (-4.4%). Demographic shifts influenced several cancer trends among HIV-infected individuals. Falling relative risks largely explained ADC declines, while background incidence contributed to some NADC trends.

Estimating trends in the global mean temperature record

NASA Astrophysics Data System (ADS)

Poppick, Andrew; Moyer, Elisabeth J.; Stein, Michael L.

2017-06-01

Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

Time-trend analysis and developing a forecasting model for the prevalence of multiple sclerosis in Kohgiluyeh and Boyer-Ahmad Province, southwest of Iran.

PubMed

Mousavizadeh, A; Dastoorpoor, M; Naimi, E; Dohrabpour, K

2018-01-01

This study was designed and implemented to assess the current situation and to estimate the time trend of multiple sclerosis (MS), as well as to explain potential factors associated with such a trend. This longitudinal study was carried out based on analysis of the data from the monitoring and treatment surveillance system for 421 patients with MS in Kohgiluyeh and Boyer-Ahmad Province, Iran, from 1990 to 2015. To this end, curve estimation approach was used to investigate the changes in prevalence and incidence of the disease, and univariate time series model analysis was applied in order to estimate the disease incidence in the next 10 years. The mean and standard deviation of age were 29.78 and 8.5 years at the time of diagnosis, and the mean and 95% confidence interval of age were 29.18 (28.86-30.77) and 29.68 (28.06-31.30) at the time of diagnosis for women and men, respectively. The sex ratio (males to females) was estimated as 3.3, and the prevalence of the disease was estimated as 60.14 in 100,000 people. The diagram of the 35-year trend of the disease indicated three distinct patterns with a tendency to increase in recent years. The prevalence and incidence trend of the disease in the study population is consistent with regional and global changes. Climatic and environmental factors such as extreme weather changes, dust particles, expansion of the application of new industrial materials, and regional wars with potential use of banned weapons are among the issues that may, in part, be able to justify the global and regional changes of the disease. Predictive models indicate a growing trend of the disease, highlighting the need for more regular monitoring of the disease trend in upcoming years. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Are infant mortality rate declines exponential? The general pattern of 20th century infant mortality rate decline

PubMed Central

Bishai, David; Opuni, Marjorie

2009-01-01

Background Time trends in infant mortality for the 20th century show a curvilinear pattern that most demographers have assumed to be approximately exponential. Virtually all cross-country comparisons and time series analyses of infant mortality have studied the logarithm of infant mortality to account for the curvilinear time trend. However, there is no evidence that the log transform is the best fit for infant mortality time trends. Methods We use maximum likelihood methods to determine the best transformation to fit time trends in infant mortality reduction in the 20th century and to assess the importance of the proper transformation in identifying the relationship between infant mortality and gross domestic product (GDP) per capita. We apply the Box Cox transform to infant mortality rate (IMR) time series from 18 countries to identify the best fitting value of lambda for each country and for the pooled sample. For each country, we test the value of λ against the null that λ = 0 (logarithmic model) and against the null that λ = 1 (linear model). We then demonstrate the importance of selecting the proper transformation by comparing regressions of ln(IMR) on same year GDP per capita against Box Cox transformed models. Results Based on chi-squared test statistics, infant mortality decline is best described as an exponential decline only for the United States. For the remaining 17 countries we study, IMR decline is neither best modelled as logarithmic nor as a linear process. Imposing a logarithmic transform on IMR can lead to bias in fitting the relationship between IMR and GDP per capita. Conclusion The assumption that IMR declines are exponential is enshrined in the Preston curve and in nearly all cross-country as well as time series analyses of IMR data since Preston's 1975 paper, but this assumption is seldom correct. Statistical analyses of IMR trends should assess the robustness of findings to transformations other than the log transform. PMID:19698144

An autocatalytic network model for stock markets

NASA Astrophysics Data System (ADS)

Caetano, Marco Antonio Leonel; Yoneyama, Takashi

2015-02-01

The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.

Trends of atmospheric circulation during singular hot days in Europe

NASA Astrophysics Data System (ADS)

Jézéquel, Aglaé; Cattiaux, Julien; Naveau, Philippe; Radanovics, Sabine; Ribes, Aurélien; Vautard, Robert; Vrac, Mathieu; Yiou, Pascal

2018-05-01

The influence of climate change on mid-latitudes atmospheric circulation is still very uncertain. The large internal variability makes it difficult to extract any statistically significant signal regarding the evolution of the circulation. Here we propose a methodology to calculate dynamical trends tailored to the circulation of specific days by computing the evolution of the distances between the circulation of the day of interest and the other days of the time series. We compute these dynamical trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single model (CESM), in order to account for different sources of uncertainty. While we find a positive trend for most models for 2003, we cannot conclude for 2010 since the models disagree on the trend estimates.

Statistical approach to the analysis of olive long-term pollen season trends in southern Spain.

PubMed

García-Mozo, H; Yaezel, L; Oteros, J; Galán, C

2014-03-01

Analysis of long-term airborne pollen counts makes it possible not only to chart pollen-season trends but also to track changing patterns in flowering phenology. Changes in higher plant response over a long interval are considered among the most valuable bioindicators of climate change impact. Phenological-trend models can also provide information regarding crop production and pollen-allergen emission. The interest of this information makes essential the election of the statistical analysis for time series study. We analysed trends and variations in the olive flowering season over a 30-year period (1982-2011) in southern Europe (Córdoba, Spain), focussing on: annual Pollen Index (PI); Pollen Season Start (PSS), Peak Date (PD), Pollen Season End (PSE) and Pollen Season Duration (PSD). Apart from the traditional Linear Regression analysis, a Seasonal-Trend Decomposition procedure based on Loess (STL) and an ARIMA model were performed. Linear regression results indicated a trend toward delayed PSE and earlier PSS and PD, probably influenced by the rise in temperature. These changes are provoking longer flowering periods in the study area. The use of the STL technique provided a clearer picture of phenological behaviour. Data decomposition on pollination dynamics enabled the trend toward an alternate bearing cycle to be distinguished from the influence of other stochastic fluctuations. Results pointed to show a rising trend in pollen production. With a view toward forecasting future phenological trends, ARIMA models were constructed to predict PSD, PSS and PI until 2016. Projections displayed a better goodness of fit than those derived from linear regression. Findings suggest that olive reproductive cycle is changing considerably over the last 30years due to climate change. Further conclusions are that STL improves the effectiveness of traditional linear regression in trend analysis, and ARIMA models can provide reliable trend projections for future years taking into account the internal fluctuations in time series. Copyright © 2013 Elsevier B.V. All rights reserved.

Model under-representation of decadal Pacific trade wind trends and its link to tropical Atlantic bias

NASA Astrophysics Data System (ADS)

Kajtar, Jules B.; Santoso, Agus; McGregor, Shayne; England, Matthew H.; Baillie, Zak

2018-02-01

The strengthening of the Pacific trade winds in recent decades has been unmatched in the observational record stretching back to the early twentieth century. This wind strengthening has been connected with numerous climate-related phenomena, including accelerated sea-level rise in the western Pacific, alterations to Indo-Pacific ocean currents, increased ocean heat uptake, and a slow-down in the rate of global-mean surface warming. Here we show that models in the Coupled Model Intercomparison Project phase 5 underestimate the observed range of decadal trends in the Pacific trade winds, despite capturing the range in decadal sea surface temperature (SST) variability. Analysis of observational data suggests that tropical Atlantic SST contributes considerably to the Pacific trade wind trends, whereas the Atlantic feedback in coupled models is muted. Atmosphere-only simulations forced by observed SST are capable of recovering the time-variation and the magnitude of the trade wind trends. Hence, we explore whether it is the biases in the mean or in the anomalous SST patterns that are responsible for the under-representation in fully coupled models. Over interannual time-scales, we find that model biases in the patterns of Atlantic SST anomalies are the strongest source of error in the precipitation and atmospheric circulation response. In contrast, on decadal time-scales, the magnitude of the model biases in Atlantic mean SST are directly linked with the trade wind variability response.

Characterizing uncertainties in recent trends of global terrestrial net primary production through ensemble modeling

NASA Astrophysics Data System (ADS)

Wang, W.; Hashimoto, H.; Ganguly, S.; Votava, P.; Nemani, R. R.; Myneni, R. B.

2010-12-01

Large uncertainties exist in our understanding of the trends and variability in global net primary production (NPP) and its controls. This study attempts to address this question through a multi-model ensemble experiment. In particular, we drive ecosystem models including CASA, LPJ, Biome-BGC, TOPS-BGC, and BEAMS with a long-term climate dataset (i.e., CRU-NCEP) to estimate global NPP from 1901 to 2009 at a spatial resolution of 0.5 x 0.5 degree. We calculate the trends of simulated NPP during different time periods and test their sensitivities to climate variables of solar radiation, air temperature, precipitation, vapor pressure deficit (VPD), and atmospheric CO2 levels. The results indicate a large diversity among the simulated NPP trends over the past 50 years, ranging from nearly no trend to an increasing trend of ~0.1 PgC/yr. Spatial patterns of the NPP generally show positive trends in boreal forests, induced mainly by increasing temperatures in these regions; they also show negative trends in the tropics, although the spatial patterns are more diverse. These diverse trends result from different climatic sensitivities of NPP among the tested models. Depending the ecological processes (e.g., photosynthesis or respiration) a model emphasizes, it can be more or less responsive to changes in solar radiation, temperatures, water, or atmospheric CO2 levels. Overall, these results highlight the limit of current ecosystem models in simulating NPP, which cannot be easily observed. They suggest that the traditional single-model approach is not ideal for characterizing trends and variability in global carbon cycling.

Projection of landfill stabilization period by time series analysis of leachate quality and transformation trends of VOCs.

PubMed

Sizirici, Banu; Tansel, Berrin

2010-01-01

The purpose of this study was to evaluate suitability of using the time series analysis for selected leachate quantity and quality parameters to forecast the duration of post closure period of a closed landfill. Selected leachate quality parameters (i.e., sodium, chloride, iron, bicarbonate, total dissolved solids (TDS), and ammonium as N) and volatile organic compounds (VOCs) (i.e., vinyl chloride, 1,4-dichlorobenzene, chlorobenzene, benzene, toluene, ethyl benzene, xylenes, total BTEX) were analyzed by the time series multiplicative decomposition model to estimate the projected levels of the parameters. These parameters were selected based on their detection levels and consistency of detection in leachate samples. In addition, VOCs detected in leachate and their chemical transformations were considered in view of the decomposition stage of the landfill. Projected leachate quality trends were analyzed and compared with the maximum contaminant level (MCL) for the respective parameters. Conditions that lead to specific trends (i.e., increasing, decreasing, or steady) and interactions of leachate quality parameters were evaluated. Decreasing trends were projected for leachate quantity, concentrations of sodium, chloride, TDS, ammonia as N, vinyl chloride, 1,4-dichlorobenzene, benzene, toluene, ethyl benzene, xylenes, and total BTEX. Increasing trends were projected for concentrations of iron, bicarbonate, and chlorobenzene. Anaerobic conditions in landfill provide favorable conditions for corrosion of iron resulting in higher concentrations over time. Bicarbonate formation as a byproduct of bacterial respiration during waste decomposition and the lime rock cap system of the landfill contribute to the increasing levels of bicarbonate in leachate. Chlorobenzene is produced during anaerobic biodegradation of 1,4-dichlorobenzene, hence, the increasing trend of chlorobenzene may be due to the declining trend of 1,4-dichlorobenzene. The time series multiplicative decomposition model in general provides an adequate forecast for future planning purposes for the parameters monitored in leachate. The model projections for 1,4-dichlorobenzene were relatively less accurate in comparison to the projections for vinyl chloride and chlorobenzene. Based on the trends observed, future monitoring needs for the selected leachate parameters were identified.

Trends in stratospheric ozone profiles using functional mixed models

NASA Astrophysics Data System (ADS)

Park, A. Y.; Guillas, S.; Petropavlovskikh, I.

2013-05-01

This paper is devoted to the modeling of altitude-dependent patterns of ozone variations over time. Umkher ozone profiles (quarter of Umkehr layer) from 1978 to 2011 are investigated at two locations: Boulder (USA) and Arosa (Switzerland). The study consists of two statistical stages. First we approximate ozone profiles employing an appropriate basis. To capture primary modes of ozone variations without losing essential information, a functional principal component analysis is performed as it penalizes roughness of the function and smooths excessive variations in the shape of the ozone profiles. As a result, data driven basis functions are obtained. Secondly we estimate the effects of covariates - month, year (trend), quasi biennial oscillation, the Solar cycle, arctic oscillation and the El Niño/Southern Oscillation cycle - on the principal component scores of ozone profiles over time using generalized additive models. The effects are smooth functions of the covariates, and are represented by knot-based regression cubic splines. Finally we employ generalized additive mixed effects models incorporating a more complex error structure that reflects the observed seasonality in the data. The analysis provides more accurate estimates of influences and trends, together with enhanced uncertainty quantification. We are able to capture fine variations in the time evolution of the profiles such as the semi-annual oscillation. We conclude by showing the trends by altitude over Boulder. The strongly declining trends over 2003-2011 for altitudes of 32-64 hPa show that stratospheric ozone is not yet fully recovering.

Behavior-over-time graphs: assessing perceived trends in healthy eating and active living environments and behaviors across 49 communities.

PubMed

Hoehner, Christine M; Sabounchi, Nasim S; Brennan, Laura K; Hovmand, Peter; Kemner, Allison

2015-01-01

In the evaluation of the Healthy Kids, Healthy Communities initiative, investigators implemented Group Model Building (GMB) to promote systems thinking at the community level. As part of the GMB sessions held in each community partnership, participants created behavior-over-time graphs (BOTGs) to characterize their perceptions of changes over time related to policies, environments, collaborations, and social determinants in their community related to healthy eating, active living, and childhood obesity. To describe the process of coding BOTGs and their trends. Descriptive study of trends among BOTGs from 11 domains (eg, active living environments, social determinants of health, funding) and relevant categories and subcategories based on the graphed variables. In addition, BOTGs were distinguished by whether the variables were positively (eg, access to healthy foods) or negatively (eg, screen time) associated with health. The GMB sessions were held in 49 community partnerships across the United States. Participants in the GMB sessions (n = 590; n = 5-21 per session) included key individuals engaged in or impacted by the policy, system, or environmental changes occurring in the community. Thirty codes were developed to describe the direction (increasing, decreasing, stable) and shape (linear, reinforcing, balancing, or oscillating) of trends from 1660 graphs. The patterns of trends varied by domain. For example, among variables positively associated with health, the prevalence of reinforcing increasing trends was highest for active living and healthy eating environments (37.4% and 29.3%, respectively), partnership and community capacity (38.8%), and policies (30.2%). Examination of trends of specific variables suggested both convergence (eg, for cost of healthy foods) and divergence (eg, for farmers' markets) of trends across partnerships. Behavior-over-time graphs provide a unique data source for understanding community-level trends and, when combined with causal maps and computer modeling, can yield insights about prevention strategies to address childhood obesity.

Consistent response of vegetation dynamics to recent climate change in tropical mountain regions.

PubMed

Krishnaswamy, Jagdish; John, Robert; Joseph, Shijo

2014-01-01

Global climate change has emerged as a major driver of ecosystem change. Here, we present evidence for globally consistent responses in vegetation dynamics to recent climate change in the world's mountain ecosystems located in the pan-tropical belt (30°N-30°S). We analyzed decadal-scale trends and seasonal cycles of vegetation greenness using monthly time series of satellite greenness (Normalized Difference Vegetation Index) and climate data for the period 1982-2006 for 47 mountain protected areas in five biodiversity hotspots. The time series of annual maximum NDVI for each of five continental regions shows mild greening trends followed by reversal to stronger browning trends around the mid-1990s. During the same period we found increasing trends in temperature but only marginal change in precipitation. The amplitude of the annual greenness cycle increased with time, and was strongly associated with the observed increase in temperature amplitude. We applied dynamic models with time-dependent regression parameters to study the time evolution of NDVI-climate relationships. We found that the relationship between vegetation greenness and temperature weakened over time or was negative. Such loss of positive temperature sensitivity has been documented in other regions as a response to temperature-induced moisture stress. We also used dynamic models to extract the trends in vegetation greenness that remain after accounting for the effects of temperature and precipitation. We found residual browning and greening trends in all regions, which indicate that factors other than temperature and precipitation also influence vegetation dynamics. Browning rates became progressively weaker with increase in elevation as indicated by quantile regression models. Tropical mountain vegetation is considered sensitive to climatic changes, so these consistent vegetation responses across widespread regions indicate persistent global-scale effects of climate warming and associated moisture stresses. © 2013 John Wiley & Sons Ltd.

Bayesian change point analysis of abundance trends for pelagic fishes in the upper San Francisco Estuary.

PubMed

Thomson, James R; Kimmerer, Wim J; Brown, Larry R; Newman, Ken B; Mac Nally, Ralph; Bennett, William A; Feyrer, Frederick; Fleishman, Erica

2010-07-01

We examined trends in abundance of four pelagic fish species (delta smelt, longfin smelt, striped bass, and threadfin shad) in the upper San Francisco Estuary, California, USA, over 40 years using Bayesian change point models. Change point models identify times of abrupt or unusual changes in absolute abundance (step changes) or in rates of change in abundance (trend changes). We coupled Bayesian model selection with linear regression splines to identify biotic or abiotic covariates with the strongest associations with abundances of each species. We then refitted change point models conditional on the selected covariates to explore whether those covariates could explain statistical trends or change points in species abundances. We also fitted a multispecies change point model that identified change points common to all species. All models included hierarchical structures to model data uncertainties, including observation errors and missing covariate values. There were step declines in abundances of all four species in the early 2000s, with a likely common decline in 2002. Abiotic variables, including water clarity, position of the 2 per thousand isohaline (X2), and the volume of freshwater exported from the estuary, explained some variation in species' abundances over the time series, but no selected covariates could explain statistically the post-2000 change points for any species.

Trends in ecosystem water use efficiency are potentially amplified by plasticity in plant functional traits

NASA Astrophysics Data System (ADS)

Mastrotheodoros, Theodoros; Pappas, Christoforos; Molnar, Peter; Burlando, Paolo; Keenan, Trevor F.; Gentine, Pierre; Fatichi, Simone

2017-04-01

Increasing atmospheric carbon dioxide concentrations stimulate photosynthesis and reduce stomatal conductance, modifying plant water use efficiency. We analyzed eddy covariance flux tower observations from 20 forested ecosystems across the Northern Hemisphere. For these sites, a previous study showed an increase in inherent water use efficiency (IWUE) five times greater than expectations. We used an updated dataset and robust uncertainty quantification to analyze these contemporary trends in IWUE. We found that IWUE increased in the last 15-20 years by roughly 1.4% yr-1, which is less than previously reported, but still 2.8 times greater than theoretical expectations. Numerical simulations by means of an ecosystem model based on temporally static plant functional traits (i.e. model parameters) do not reproduce this increase. We tested the hypothesis that the observed increase in IWUE could be attributed to changes in plant functional traits, potentially triggered by environmental changes. Simulation results accounting for trait plasticity (i.e. by changing model parameters such as specific leaf area and maximum Rubisco capacity) match the observed trends in IWUE, with an increase in both leaf internal CO2 concentration and gross ecosystem production (GEP), and with a negligible trend in evapotranspiration (ET). This supports the hypothesis that changes in plant functional traits of about 1.0% yr-1 can explain the observed IWUE trends and are consistent with observed trends of GEP and ET at larger scales. Our results highlight that at decadal or longer time scales trait plasticity can considerably influence the water, carbon and energy fluxes with implications for both the monitoring of temporal changes in plant traits and their representation in Earth system models.

Models for forecasting hospital bed requirements in the acute sector.

PubMed Central

Farmer, R D; Emami, J

1990-01-01

STUDY OBJECTIVE--The aim was to evaluate the current approach to forecasting hospital bed requirements. DESIGN--The study was a time series and regression analysis. The time series for mean duration of stay for general surgery in the age group 15-44 years (1969-1982) was used in the evaluation of different methods of forecasting future values of mean duration of stay and its subsequent use in the formation of hospital bed requirements. RESULTS--It has been suggested that the simple trend fitting approach suffers from model specification error and imposes unjustified restrictions on the data. Time series approach (Box-Jenkins method) was shown to be a more appropriate way of modelling the data. CONCLUSION--The simple trend fitting approach is inferior to the time series approach in modelling hospital bed requirements. PMID:2277253

Global trends in ocean phytoplankton: a new assessment using revised ocean colour data.

PubMed

Gregg, Watson W; Rousseaux, Cécile S; Franz, Bryan A

2017-01-01

A recent revision of the NASA global ocean colour record shows changes in global ocean chlorophyll trends. This new 18-year time series now includes three global satellite sensors, the Sea-viewing Wide Field of view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite (VIIRS). The major changes are radiometric drift correction, a new algorithm for chlorophyll, and a new sensor VIIRS. The new satellite data record shows no significant trend in global annual median chlorophyll from 1998 to 2015, in contrast to a statistically significant negative trend from 1998 to 2012 in the previous version. When revised satellite data are assimilated into a global ocean biogeochemical model, no trend is observed in global annual median chlorophyll. This is consistent with previous findings for the 1998-2012 time period using the previous processing version and only two sensors (SeaWiFS and MODIS). Detecting trends in ocean chlorophyll with satellites is sensitive to data processing options and radiometric drift correction. The assimilation of these data, however, reduces sensitivity to algorithms and radiometry, as well as the addition of a new sensor. This suggests the assimilation model has skill in detecting trends in global ocean colour. Using the assimilation model, spatial distributions of significant trends for the 18-year record (1998-2015) show recent decadal changes. Most notable are the North and Equatorial Indian Oceans basins, which exhibit a striking decline in chlorophyll. It is exemplified by declines in diatoms and chlorophytes, which in the model are large and intermediate size phytoplankton. This decline is partially compensated by significant increases in cyanobacteria, which represent very small phytoplankton. This suggests the beginning of a shift in phytoplankton composition in these tropical and subtropical Indian basins.

Detection of carbon monoxide trends in the presence of interannual variability

NASA Astrophysics Data System (ADS)

Strode, Sarah A.; Pawson, Steven

2013-11-01

in fossil fuel emissions are a major driver of changes in atmospheric CO, but detection of trends in CO from anthropogenic sources is complicated by the presence of large interannual variability (IAV) in biomass burning. We use a multiyear model simulation of CO with year-specific biomass burning to predict the number of years needed to detect the impact of changes in Asian anthropogenic emissions on downwind regions. Our study includes two cases for changing anthropogenic emissions: a stepwise change of 15% and a linear trend of 3% yr-1. We first examine how well the model reproduces the observed IAV of CO over the North Pacific, since this variability impacts the time needed to detect significant anthropogenic trends. The modeled IAV over the North Pacific correlates well with that seen from the Measurements of Pollution in the Troposphere (MOPITT) instrument but underestimates the magnitude of the variability. The model predicts that a 3% yr-1 trend in Asian anthropogenic emissions would lead to a statistically significant trend in CO surface concentration in the western United States within 12 years, and accounting for Siberian boreal biomass-burning emissions greatly reduces the number of years needed for trend detection. Combining the modeled trend with the observed MOPITT variability at 500 hPa, we estimate that the 3% yr-1 trend could be detectable in satellite observations over Asia in approximately a decade. Our predicted timescales for trend detection highlight the importance of long-term measurements of CO from satellites.

Artificial Neural Network versus Linear Models Forecasting Doha Stock Market

NASA Astrophysics Data System (ADS)

Yousif, Adil; Elfaki, Faiz

2017-12-01

The purpose of this study is to determine the instability of Doha stock market and develop forecasting models. Linear time series models are used and compared with a nonlinear Artificial Neural Network (ANN) namely Multilayer Perceptron (MLP) Technique. It aims to establish the best useful model based on daily and monthly data which are collected from Qatar exchange for the period starting from January 2007 to January 2015. Proposed models are for the general index of Qatar stock exchange and also for the usages in other several sectors. With the help of these models, Doha stock market index and other various sectors were predicted. The study was conducted by using various time series techniques to study and analyze data trend in producing appropriate results. After applying several models, such as: Quadratic trend model, double exponential smoothing model, and ARIMA, it was concluded that ARIMA (2,2) was the most suitable linear model for the daily general index. However, ANN model was found to be more accurate than time series models.

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012

PubMed Central

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

Background The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. Methods In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). Results The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Conclusion Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis. PMID:26901682

Time Series Modelling of Syphilis Incidence in China from 2005 to 2012.

PubMed

Zhang, Xingyu; Zhang, Tao; Pei, Jiao; Liu, Yuanyuan; Li, Xiaosong; Medrano-Gracia, Pau

2016-01-01

The infection rate of syphilis in China has increased dramatically in recent decades, becoming a serious public health concern. Early prediction of syphilis is therefore of great importance for heath planning and management. In this paper, we analyzed surveillance time series data for primary, secondary, tertiary, congenital and latent syphilis in mainland China from 2005 to 2012. Seasonality and long-term trend were explored with decomposition methods. Autoregressive integrated moving average (ARIMA) was used to fit a univariate time series model of syphilis incidence. A separate multi-variable time series for each syphilis type was also tested using an autoregressive integrated moving average model with exogenous variables (ARIMAX). The syphilis incidence rates have increased three-fold from 2005 to 2012. All syphilis time series showed strong seasonality and increasing long-term trend. Both ARIMA and ARIMAX models fitted and estimated syphilis incidence well. All univariate time series showed highest goodness-of-fit results with the ARIMA(0,0,1)×(0,1,1) model. Time series analysis was an effective tool for modelling the historical and future incidence of syphilis in China. The ARIMAX model showed superior performance than the ARIMA model for the modelling of syphilis incidence. Time series correlations existed between the models for primary, secondary, tertiary, congenital and latent syphilis.

Bayesian spatiotemporal crash frequency models with mixture components for space-time interactions.

PubMed

Cheng, Wen; Gill, Gurdiljot Singh; Zhang, Yongping; Cao, Zhong

2018-03-01

The traffic safety research has developed spatiotemporal models to explore the variations in the spatial pattern of crash risk over time. Many studies observed notable benefits associated with the inclusion of spatial and temporal correlation and their interactions. However, the safety literature lacks sufficient research for the comparison of different temporal treatments and their interaction with spatial component. This study developed four spatiotemporal models with varying complexity due to the different temporal treatments such as (I) linear time trend; (II) quadratic time trend; (III) Autoregressive-1 (AR-1); and (IV) time adjacency. Moreover, the study introduced a flexible two-component mixture for the space-time interaction which allows greater flexibility compared to the traditional linear space-time interaction. The mixture component allows the accommodation of global space-time interaction as well as the departures from the overall spatial and temporal risk patterns. This study performed a comprehensive assessment of mixture models based on the diverse criteria pertaining to goodness-of-fit, cross-validation and evaluation based on in-sample data for predictive accuracy of crash estimates. The assessment of model performance in terms of goodness-of-fit clearly established the superiority of the time-adjacency specification which was evidently more complex due to the addition of information borrowed from neighboring years, but this addition of parameters allowed significant advantage at posterior deviance which subsequently benefited overall fit to crash data. The Base models were also developed to study the comparison between the proposed mixture and traditional space-time components for each temporal model. The mixture models consistently outperformed the corresponding Base models due to the advantages of much lower deviance. For cross-validation comparison of predictive accuracy, linear time trend model was adjudged the best as it recorded the highest value of log pseudo marginal likelihood (LPML). Four other evaluation criteria were considered for typical validation using the same data for model development. Under each criterion, observed crash counts were compared with three types of data containing Bayesian estimated, normal predicted, and model replicated ones. The linear model again performed the best in most scenarios except one case of using model replicated data and two cases involving prediction without including random effects. These phenomena indicated the mediocre performance of linear trend when random effects were excluded for evaluation. This might be due to the flexible mixture space-time interaction which can efficiently absorb the residual variability escaping from the predictable part of the model. The comparison of Base and mixture models in terms of prediction accuracy further bolstered the superiority of the mixture models as the mixture ones generated more precise estimated crash counts across all four models, suggesting that the advantages associated with mixture component at model fit were transferable to prediction accuracy. Finally, the residual analysis demonstrated the consistently superior performance of random effect models which validates the importance of incorporating the correlation structures to account for unobserved heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Sensitivity of Antarctic sea ice to the Southern Annular Mode in coupled climate models

NASA Astrophysics Data System (ADS)

Holland, Marika M.; Landrum, Laura; Kostov, Yavor; Marshall, John

2017-09-01

We assess the sea ice response to Southern Annular Mode (SAM) anomalies for pre-industrial control simulations from the Coupled Model Intercomparison Project (CMIP5). Consistent with work by Ferreira et al. (J Clim 28:1206-1226, 2015. doi: 10.1175/JCLI-D-14-00313.1), the models generally simulate a two-timescale response to positive SAM anomalies, with an initial increase in ice followed by an eventual sea ice decline. However, the models differ in the cross-over time at which the change in ice response occurs, in the overall magnitude of the response, and in the spatial distribution of the response. Late twentieth century Antarctic sea ice trends in CMIP5 simulations are related in part to different modeled responses to SAM variability acting on different time-varying transient SAM conditions. This explains a significant fraction of the spread in simulated late twentieth century southern hemisphere sea ice extent trends across the model simulations. Applying the modeled sea ice response to SAM variability but driven by the observed record of SAM suggests that variations in the austral summer SAM, which has exhibited a significant positive trend, have driven a modest sea ice decrease. However, additional work is needed to narrow the considerable model uncertainty in the climate response to SAM variability and its implications for 20th-21st century trends.

Comparison of Mean Climate Trends in the Northern Hemisphere Between N.C.E.P. and Two Atmosphere-Ocean Model Forced Runs

NASA Technical Reports Server (NTRS)

Lucarini, Valerio; Russell, Gary L.; Hansen, James E. (Technical Monitor)

2002-01-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies Atmosphere-Ocean Model (AOM). The computed trends of surface pressure, surface temperature, 850, 500 and 200 mb geopotential heights and related temperatures of the model for the time frame 1960-2000 are compared to those obtained from the National Centers for Environmental Prediction observations. A spatial correlation analysis and mean value comparison are performed, showing good agreement. A brief general discussion about the statistics of trend detection is presented. The domain of interest is the Northern Hemisphere (NH) because of the higher reliability of both the model results and the observations. The accuracy that this AOM has in describing the observed regional and NH climate trends makes it reliable in forecasting future climate changes.

Casas Muertas and Oficina No. 1: internal migrations and malaria trends in Venezuela 1905-1945.

PubMed

Chaves, Luis Fernando

2007-06-01

To compare internal migration and temperature as factors behind the decreasing trend in malaria deaths observed in Venezuela from 1905 to 1945, linear autoregressive models are fitted to a historical dataset. The model that only incorporates internal migration is the one with the best fit. The decreasing trend in malaria deaths in Venezuela, from 1905 to 1945, is not explained by a trend in mean annual temperature, but it is associated with an increase in the proportion of population in the Capital District, during a time period when the area was the principal attractor of migrations within the country.

Model tropical Atlantic biases underpin diminished Pacific decadal variability

NASA Astrophysics Data System (ADS)

McGregor, Shayne; Stuecker, Malte F.; Kajtar, Jules B.; England, Matthew H.; Collins, Mat

2018-06-01

Pacific trade winds have displayed unprecedented strengthening in recent decades1. This strengthening has been associated with east Pacific sea surface cooling2 and the early twenty-first-century slowdown in global surface warming2,3, amongst a host of other substantial impacts4-9. Although some climate models produce the timing of these recently observed trends10, they all fail to produce the trend magnitude2,11,12. This may in part be related to the apparent model underrepresentation of low-frequency Pacific Ocean variability and decadal wind trends2,11-13 or be due to a misrepresentation of a forced response1,14-16 or a combination of both. An increasingly prominent connection between the Pacific and Atlantic basins has been identified as a key driver of this strengthening of the Pacific trade winds12,17-20. Here we use targeted climate model experiments to show that combining the recent Atlantic warming trend with the typical climate model bias leads to a substantially underestimated response for the Pacific Ocean wind and surface temperature. The underestimation largely stems from a reduction and eastward shift of the atmospheric heating response to the tropical Atlantic warming trend. This result suggests that the recent Pacific trends and model decadal variability may be better captured by models with improved mean-state climatologies.

Physical characteristics and evolutionary trends of continental rifts

NASA Technical Reports Server (NTRS)

Ramberg, I. B.; Morgan, P.

1984-01-01

Rifts may be defined as zones beneath which the entire lithosphere has ruptured in extension. They are widespread and occur in a variety of tectonic settings, and range up to 2,600 m.y. in age. The object of this review is to highlight characteristic features of modern and ancient rifts, to emphasize differences and similarities in order to help characterize evolutionary trends, to identify physical conditions favorable for initiation as well as termination of rifting, and to provide constraints for future modeling studies of rifting. Rifts are characterized on the basis of their structural, geomorphic, magmatic and geophysical features and the diverse character of these features and their evolutionary trends through time are discussed. Mechanisms of rifting are critically examined in terms of the physical characteristics and evolutionary trends of rifts, and it is concluded that while simple models can give valuable insight into specific processes of rifting, individual rifts can rarely, if ever, be characterized by well defined trends predicted by these models. More data are required to clearly define evolutionary trends, and the models require development to incorporate the effects of lithospheric heterogeneities and complex geologic histories.

Latitudinal and interhemispheric variation of stratospheric effects on mesospheric ice layer trends

NASA Astrophysics Data System (ADS)

Lübken, F.-J.; Berger, U.

2011-02-01

Latitudinal and interhemispheric differences of model results on trends in mesospheric ice layers and background conditions are analyzed. The model nudges to European Centre for Medium-Range Weather Forecasts data below ˜45 km. Greenhouse gas concentrations in the mesosphere are kept constant. Temperature trends in the mesosphere mainly come from shrinking of the stratosphere and from dynamical effects. Water vapor increases at noctilucent cloud (NLC) heights and decreases above due to increased freeze drying caused by temperature trends. There is no tendency for ice clouds in the Northern Hemisphere for extending farther southward with time. Trends of NLC albedo are similar to satellite measurements, but only if a time period longer than observations is considered. Ice cloud trends get smaller if albedo thresholds relevant to satellite instruments are applied, in particular at high polar latitudes. This implies that weak and moderate NLC is favored when background conditions improve for NLC formation, whereas strong NLC benefits less. Trends of ice cloud parameters are generally smaller in the Southern Hemisphere (SH) compared to the Northern Hemisphere (NH), consistent with observations. Trends in background conditions have counteracting effects on NLC: temperature trends would suggest stronger ice increase in the SH, and water vapor trends would suggest a weaker increase. Larger trends in NLC brightness or occurrence rates are not necessarily associated with larger (more negative) temperature trends. They can also be caused by larger trends of water vapor caused by larger freeze drying, which in turn can be caused by generally lower temperatures and/or more background water. Trends of NLC brightness and occurrence rates decrease with decreasing latitude in both hemispheres. The latitudinal variation of these trends is primarily determined by induced water vapor trends. Trends in NLC altitudes are generally small. Stratospheric temperature trends vary differently with altitude in the NH and SH but add up to similar trends at mesospheric cloud heights.

Effects of afforestation on runoff and sediment load in an upland Mediterranean catchment.

PubMed

Buendia, C; Bussi, G; Tuset, J; Vericat, D; Sabater, S; Palau, A; Batalla, R J

2016-01-01

This paper assesses annual and seasonal trends in runoff and sediment load resulting from climate variability and afforestation in an upland Mediterranean basin, the Ribera Salada (NE Iberian Peninsula). We implemented a hydrological and sediment transport distributed model (TETIS) with a daily time-step, using continuous discharge and sediment transport data collected at a monitoring station during the period 2009-2013. Once calibrated and validated, the model was used to simulate the hydrosedimentary response of the basin for the period 1971-2014 using historical climate and land use data. Simulated series were further used to (i) detect sediment transport and hydrologic trends at different temporal scales (annual, seasonal); (ii) assess changes in the contribution of extreme events (i.e. low and high flows) and (ii) assess the relative effect of forest expansion and climate variability on trends observed by applying a scenario of constant land use. The non-parametric Mann-Kendall test indicated upward trends for temperature and decreasing trends (although non-significant) for precipitation. Downward trends occurred for annual runoff, and less significantly for sediment yield. Reductions in runoff were less intense when afforestation was not considered in the model, while trends in sediment yield were reversed. Results also indicated that an increase in the river's torrential behaviour may have occurred throughout the studied period, with low and high flow events gaining importance with respect to the annual contribution, although its magnitude was reduced over time. Copyright © 2015 Elsevier B.V. All rights reserved.

Mountain plover population responses to black-tailed prairie dogs in Montana

USGS Publications Warehouse

Dinsmore, S.J.; White, Gary C.; Knopf, F.L.

2005-01-01

We studied a local population of mountain plovers (Charadrius montanus) in southern Phillips County, Montana, USA, from 1995 to 2000 to estimate annual rates of recruitment rate (f) and population change (??). We used Pradel models, and we modeled ?? as a constant across years, as a linear time trend, as year-specific, and with an additive effect of area occupied by prairie dogs (Cynomys ludovicianus). We modeled recruitment rate (f) as a function of area occupied by prairie dogs with the remaining model structure identical to the best model used to estimate ??. Our results indicated a strong negative effect of area occupied by prairie dogs on both ?? (slope coefficient on a log scale was -0.11; 95% CI was -0.17, -0.05) and f (slope coefficient on a logit scale was -0.23; 95% CI was -0.36, -0.10). We also found good evidence for a negative time trend on ??; this model had substantial weight (wi = 0.31), and the slope coefficient on the linear trend on a log scale was -0.10 (95% CI was -0.15, -0.05). Yearly estimates of ?? were >1 in all years except 1999, indicating that the population initially increased and then stabilized in the last year of the study. We found weak evidence for year-specific estimates of ??; the best model with year-specific estimates had a low weight (wi = 0.02), although the pattern of yearly estimates of ?? closely matched those estimated with a linear time trend. In southern Phillips County, the population trend of mountain plovers closely matched the trend in the area occupied by black-tailed prairie dogs. Black-tailed prairie dogs declined sharply in the mid-1990s in response to an outbreak of sylvatic plague, but their numbers have steadily increased since 1996 in concert with increases in plovers. The results of this study (1) increase our understanding of the dynamics of this population and how they relate to the area occupied by prairie dogs, and (2) will be useful for planning plover conservation in a prairie dog ecosystem.

Water-quality trends in the nation’s rivers and streams, 1972–2012—Data preparation, statistical methods, and trend results

USGS Publications Warehouse

Oelsner, Gretchen P.; Sprague, Lori A.; Murphy, Jennifer C.; Zuellig, Robert E.; Johnson, Henry M.; Ryberg, Karen R.; Falcone, James A.; Stets, Edward G.; Vecchia, Aldo V.; Riskin, Melissa L.; De Cicco, Laura A.; Mills, Taylor J.; Farmer, William H.

2017-04-04

Since passage of the Clean Water Act in 1972, Federal, State, and local governments have invested billions of dollars to reduce pollution entering rivers and streams. To understand the return on these investments and to effectively manage and protect the Nation’s water resources in the future, we need to know how and why water quality has been changing over time. As part of the National Water-Quality Assessment Project, of the U.S. Geological Survey’s National Water-Quality Program, data from the U.S. Geological Survey, along with multiple other Federal, State, Tribal, regional, and local agencies, have been used to support the most comprehensive assessment conducted to date of surface-water-quality trends in the United States. This report documents the methods used to determine trends in water quality and ecology because these methods are vital to ensuring the quality of the results. Specific objectives are to document (1) the data compilation and processing steps used to identify river and stream sites throughout the Nation suitable for water-quality, pesticide, and ecology trend analysis, (2) the statistical methods used to determine trends in target parameters, (3) considerations for water-quality, pesticide, and ecology data and streamflow data when modeling trends, (4) sensitivity analyses for selecting data and interpreting trend results with the Weighted Regressions on Time, Discharge, and Season method, and (5) the final trend results at each site. The scope of this study includes trends in water-quality concentrations and loads (nutrient, sediment, major ion, salinity, and carbon), pesticide concentrations and loads, and metrics for aquatic ecology (fish, invertebrates, and algae) for four time periods: (1) 1972–2012, (2) 1982–2012, (3) 1992–2012, and (4) 2002–12. In total, nearly 12,000 trends in concentration, load, and ecology metrics were evaluated in this study; there were 11,893 combinations of sites, parameters, and trend periods. The final trend results are presented with examples of how to interpret the results from each trend model. Interpretation of the trend results, such as causal analysis, is not included.

Climate model assessment of changes in winter-spring streamflow timing over North America

USGS Publications Warehouse

Kam, Jonghun; Knutson, Thomas R.; Milly, Paul C. D.

2018-01-01

Over regions where snow-melt runoff substantially contributes to winter-spring streamflows, warming can accelerate snow melt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, a detection/attribution of changes in mid-latitude North American winter-spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. In this study, robustness across models, start/end dates for trends, and assumptions about internal variability is evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central U.S., where winter-spring streamflows have been coming earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western U.S./southwestern Canada and in extreme northeastern U.S./Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow-free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Time-varying BRDFs.

PubMed

Sun, Bo; Sunkavalli, Kalyan; Ramamoorthi, Ravi; Belhumeur, Peter N; Nayar, Shree K

2007-01-01

The properties of virtually all real-world materials change with time, causing their bidirectional reflectance distribution functions (BRDFs) to be time varying. However, none of the existing BRDF models and databases take time variation into consideration; they represent the appearance of a material at a single time instance. In this paper, we address the acquisition, analysis, modeling, and rendering of a wide range of time-varying BRDFs (TVBRDFs). We have developed an acquisition system that is capable of sampling a material's BRDF at multiple time instances, with each time sample acquired within 36 sec. We have used this acquisition system to measure the BRDFs of a wide range of time-varying phenomena, which include the drying of various types of paints (watercolor, spray, and oil), the drying of wet rough surfaces (cement, plaster, and fabrics), the accumulation of dusts (household and joint compound) on surfaces, and the melting of materials (chocolate). Analytic BRDF functions are fit to these measurements and the model parameters' variations with time are analyzed. Each category exhibits interesting and sometimes nonintuitive parameter trends. These parameter trends are then used to develop analytic TVBRDF models. The analytic TVBRDF models enable us to apply effects such as paint drying and dust accumulation to arbitrary surfaces and novel materials.

Forecast models for suicide: Time-series analysis with data from Italy.

PubMed

Preti, Antonio; Lentini, Gianluca

2016-01-01

The prediction of suicidal behavior is a complex task. To fine-tune targeted preventative interventions, predictive analytics (i.e. forecasting future risk of suicide) is more important than exploratory data analysis (pattern recognition, e.g. detection of seasonality in suicide time series). This study sets out to investigate the accuracy of forecasting models of suicide for men and women. A total of 101 499 male suicides and of 39 681 female suicides - occurred in Italy from 1969 to 2003 - were investigated. In order to apply the forecasting model and test its accuracy, the time series were split into a training set (1969 to 1996; 336 months) and a test set (1997 to 2003; 84 months). The main outcome was the accuracy of forecasting models on the monthly number of suicides. These measures of accuracy were used: mean absolute error; root mean squared error; mean absolute percentage error; mean absolute scaled error. In both male and female suicides a change in the trend pattern was observed, with an increase from 1969 onwards to reach a maximum around 1990 and decrease thereafter. The variances attributable to the seasonal and trend components were, respectively, 24% and 64% in male suicides, and 28% and 41% in female ones. Both annual and seasonal historical trends of monthly data contributed to forecast future trends of suicide with a margin of error around 10%. The finding is clearer in male than in female time series of suicide. The main conclusion of the study is that models taking seasonality into account seem to be able to derive information on deviation from the mean when this occurs as a zenith, but they fail to reproduce it when it occurs as a nadir. Preventative efforts should concentrate on the factors that influence the occurrence of increases above the main trend in both seasonal and cyclic patterns of suicides.

[Improved euler algorithm for trend forecast model and its application to oil spectrum analysis].

PubMed

Zheng, Chang-song; Ma, Biao

2009-04-01

The oil atomic spectrometric analysis technology is one of the most important methods for fault diagnosis and state monitoring of large machine equipment. The gray method is preponderant in the trend forecast at the same time. With the use of oil atomic spectrometric analysis result and combining the gray forecast theory, the present paper established a gray forecast model of the Fe/Cu concentration trend in the power-shift steering transmission. Aiming at the shortage of the gray method used in the trend forecast, the improved Euler algorithm was put forward for the first time to resolve the problem of the gray model and avoid the non-precision that the old gray model's forecast value depends on the first test value. This new method can make the forecast value more precision as shown in the example. Combined with the threshold value of the oil atomic spectrometric analysis, the new method was applied on the Fe/Cu concentration forecast and the premonition of fault information was obtained. So we can take steps to prevent the fault and this algorithm can be popularized to the state monitoring in the industry.

Neuroendocrine aspects of primary endogenous depression. XV: Mathematical modeling of nocturnal melatonin secretion in major depressives and normal controls.

PubMed

Sekula, L K; Lucke, J F; Heist, E K; Czambel, R K; Rubin, R T

1997-03-24

We previously reported a trend toward a higher mean nocturnal serum melatonin (MEL) concentration, based on 30-min blood sampling over 24 h, in 23 female definite endogenous depressive compared to 23 matched normal female control subjects, and no significant difference in 15 male depressives compared to their controls (Rubin et al., 1992). In both groups of patients vs. their controls, there also were trends toward an earlier MEL rise time, by about 30 min, and a later MEL peak time, by about 90 min. Because the offset of MEL secretion was not estimated in that study, the total duration of MEL secretion could not be determined. To further delineate the nocturnal MEL secretion curve, we modeled the MEL data by a linear-Beta model, a four-parameter adaptation of the Beta function. One parameter accounted-for baseline (diurnal) MEL concentration, two accounted for the shapes of the ascending and descending phases of the nocturnal secretion curve, and the fourth accounted for the area under the curve. The model permitted estimation of the start, peak, and end times of nocturnal MEL secretion. There again was a trend toward a higher mean nocturnal MEL concentration in the female depressives compared to their matched controls. There were no significant patient-control differences in secretion onset or peak times in either the women or the men except for nocturnal MEL offset time: the female patients had a trend toward a later offset time, by about 40 min, than their controls; this difference was not present in the men. With women and men analyzed together, the difference in nocturnal MEL offset time between patients and controls just reached significance (P < 0.05). The linear-Beta model appears to satisfactorily fit the MEL data and provides estimators of the onset, peak, and offset times of the activation phase of MEL secretion. This model may be applicable to more severely skewed 24-h hormone secretion curves, such as ACTH and cortisol.

Meta-epidemiologic study showed frequent time trends in summary estimates from meta-analyses of diagnostic accuracy studies.

PubMed

Cohen, Jérémie F; Korevaar, Daniël A; Wang, Junfeng; Leeflang, Mariska M; Bossuyt, Patrick M

2016-09-01

To evaluate changes over time in summary estimates from meta-analyses of diagnostic accuracy studies. We included 48 meta-analyses from 35 MEDLINE-indexed systematic reviews published between September 2011 and January 2012 (743 diagnostic accuracy studies; 344,015 participants). Within each meta-analysis, we ranked studies by publication date. We applied random-effects cumulative meta-analysis to follow how summary estimates of sensitivity and specificity evolved over time. Time trends were assessed by fitting a weighted linear regression model of the summary accuracy estimate against rank of publication. The median of the 48 slopes was -0.02 (-0.08 to 0.03) for sensitivity and -0.01 (-0.03 to 0.03) for specificity. Twelve of 96 (12.5%) time trends in sensitivity or specificity were statistically significant. We found a significant time trend in at least one accuracy measure for 11 of the 48 (23%) meta-analyses. Time trends in summary estimates are relatively frequent in meta-analyses of diagnostic accuracy studies. Results from early meta-analyses of diagnostic accuracy studies should be considered with caution. Copyright © 2016 Elsevier Inc. All rights reserved.

The color of sea level: Importance of spatial variations in spectral shape for assessing the significance of trends

NASA Astrophysics Data System (ADS)

Hughes, Chris W.; Williams, Simon D. P.

2010-10-01

We investigate spatial variations in the shape of the spectrum of sea level variability based on a homogeneously sampled 12 year gridded altimeter data set. We present a method of plotting spectral information as color, focusing on periods between 2 and 24 weeks, which shows that significant spatial variations in the spectral shape exist and contain useful dynamical information. Using the Bayesian Information Criterion, we determine that, typically, a fifth-order autoregressive model is needed to capture the structure in the spectrum. Using this model, we show that statistical errors in fitted local trends range between less than 1 and more than 5 times of what would be calculated assuming "white" noise and that the time needed to detect a 1 mm/yr trend ranges between about 5 years and many decades. For global mean sea level, the statistical error reduces to 0.1 mm/yr over 12 years, with only 2 years needed to detect a 1 mm/yr trend. We find significant regional differences in trend from the global mean. The patterns of these regional differences are indicative of a sea level trend dominated by dynamical ocean processes over this period.

A Monte Carlo Uncertainty Analysis of Ozone Trend Predictions in a Two Dimensional Model. Revision

NASA Technical Reports Server (NTRS)

Considine, D. B.; Stolarski, R. S.; Hollandsworth, S. M.; Jackman, C. H.; Fleming, E. L.

1998-01-01

We use Monte Carlo analysis to estimate the uncertainty in predictions of total O3 trends between 1979 and 1995 made by the Goddard Space Flight Center (GSFC) two-dimensional (2D) model of stratospheric photochemistry and dynamics. The uncertainty is caused by gas-phase chemical reaction rates, photolysis coefficients, and heterogeneous reaction parameters which are model inputs. The uncertainty represents a lower bound to the total model uncertainty assuming the input parameter uncertainties are characterized correctly. Each of the Monte Carlo runs was initialized in 1970 and integrated for 26 model years through the end of 1995. This was repeated 419 times using input parameter sets generated by Latin Hypercube Sampling. The standard deviation (a) of the Monte Carlo ensemble of total 03 trend predictions is used to quantify the model uncertainty. The 34% difference between the model trend in globally and annually averaged total O3 using nominal inputs and atmospheric trends calculated from Nimbus 7 and Meteor 3 total ozone mapping spectrometer (TOMS) version 7 data is less than the 46% calculated 1 (sigma), model uncertainty, so there is no significant difference between the modeled and observed trends. In the northern hemisphere midlatitude spring the modeled and observed total 03 trends differ by more than 1(sigma) but less than 2(sigma), which we refer to as marginal significance. We perform a multiple linear regression analysis of the runs which suggests that only a few of the model reactions contribute significantly to the variance in the model predictions. The lack of significance in these comparisons suggests that they are of questionable use as guides for continuing model development. Large model/measurement differences which are many multiples of the input parameter uncertainty are seen in the meridional gradients of the trend and the peak-to-peak variations in the trends over an annual cycle. These discrepancies unambiguously indicate model formulation problems and provide a measure of model performance which can be used in attempts to improve such models.

Time trends in exposure of cattle to bovine spongiform encephalopathy and cohort effect in France and Italy: value of the classical Age-Period-Cohort approach.

PubMed

Sala, Carole; Ru, Giuseppe

2009-09-18

The Age-Period-Cohort (APC) analysis is routinely used for time trend analysis of cancer incidence or mortality rates, but in veterinary epidemiology, there are still only a few examples of this application. APC models were recently used to model the French epidemic assuming that the time trend for BSE was mainly due to a cohort effect in relation to the control measures that may have modified the BSE exposure of cohorts over time. We used a categorical APC analysis which did not require any functional form for the effect of the variables, and examined second differences to estimate the variation of the BSE trend. We also reanalysed the French epidemic and performed a simultaneous analysis of Italian data using more appropriate birth cohort categories for comparison. We used data from the exhaustive surveillance carried out in France and Italy between 2001 and 2007, and comparatively described the trend of the epidemic in both countries. At the end, the shape and irregularities of the trends were discussed in light of the main control measures adopted to control the disease. In Italy a decrease in the epidemic became apparent from 1996, following the application of rendering standards for the processing of specific risk material (SRM). For the French epidemic, the pattern of second differences in the birth cohorts confirmed the beginning of the decrease from 1995, just after the implementation of the meat and bone meal (MBM) ban for all ruminants (1994). The APC analysis proved to be highly suitable for the study of the trend in BSE epidemics and was helpful in understanding the effects of management and control of the disease. Additionally, such an approach may help in the implementation of changes in BSE regulations.

Spatial patterns of Antarctic surface temperature trends in the context of natural variability: Lessons from the CMIP5 Models

NASA Astrophysics Data System (ADS)

Smith, K. L.; Polvani, L. M.

2015-12-01

The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small and weakly negative trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a strong cooling of East Antarctic in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature (SAT) trends from five temperature reconstructions over two distinct time periods (1979-2005 and 1960-2005), and with those simulated by 40 coupled models participating in Phase 5 of the Coupled Model Intercomparison Project. We find that the observed East-West asymmetry differs substantially over the two time periods and, furthermore, is completely absent from the CMIP5 multi-model mean (from which all natural variability is eliminated by the averaging). We compare the CMIP5 SAT trends to those of 29 historical atmosphere-only simulations with prescribed sea surface temperatures (SSTs) and sea ice and find that these simulations are in better agreement with the observations. This suggests that natural multi-decadal variability associated with SSTs and sea ice and not external forcings is the primary driver of Antarctic SAT trends. We confirm this by showing that the observed trends lie within the distribution of multi-decadal trends from the CMIP5 pre-industrial integrations. These results, therefore, offer new evidence which points to natural climate variability as the more likely cause of the recent warming of West Antarctica and of the Peninsula.

Modeling of time trends and interactions in vital rates using restricted regression splines.

PubMed

Heuer, C

1997-03-01

For the analysis of time trends in incidence and mortality rates, the age-period-cohort (apc) model has became a widely accepted method. The considered data are arranged in a two-way table by age group and calendar period, which are mostly subdivided into 5- or 10-year intervals. The disadvantage of this approach is the loss of information by data aggregation and the problems of estimating interactions in the two-way layout without replications. In this article we show how splines can be useful when yearly data, i.e., 1-year age groups and 1-year periods, are given. The estimated spline curves are still smooth and represent yearly changes in the time trends. Further, it is straightforward to include interaction terms by the tensor product of the spline functions. If the data are given in a nonrectangular table, e.g., 5-year age groups and 1-year periods, the period and cohort variables can be parameterized by splines, while the age variable is parameterized as fixed effect levels, which leads to a semiparametric apc model. An important methodological issue in developing the nonparametric and semiparametric models is stability of the estimated spline curve at the boundaries. Here cubic regression splines will be used, which are constrained to be linear in the tails. Another point of importance is the nonidentifiability problem due to the linear dependency of the three time variables. This will be handled by decomposing the basis of each spline by orthogonal projection into constant, linear, and nonlinear terms, as suggested by Holford (1983, Biometrics 39, 311-324) for the traditional apc model. The advantage of using splines for yearly data compared to the traditional approach for aggregated data is the more accurate curve estimation for the nonlinear trend changes and the simple way of modeling interactions between the time variables. The method will be demonstrated with hypothetical data as well as with cancer mortality data.

Trends in concentrations and use of agricultural herbicides for Corn Belt rivers, 1996-2006

USGS Publications Warehouse

Vecchia, Aldo V.; Gilliom, Robert J.; Sullivan, Daniel J.; Lorenz, David L.; Martin, Jeffrey D.

2009-01-01

Trends in the concentrations and agricultural use of four herbicides (atrazine, acetochlor, metolachlor, and alachlor) were evaluated for major rivers of the Corn Belt for two partially overlapping time periods: 1996-2002 and 2000-2006. Trends were analyzed for 11 sites on the mainstems and selected tributaries in the Ohio, Upper Mississippi, and Missouri River Basins. Concentration trends were determined using a parametric regression model designed for analyzing seasonal variability, flow-related variability, and trends in pesticide concentrations(SEAWAVE-Q).TheSEAWAVE-Qmodel accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic conditions from changes caused by other factors, such as pesticide use. Most of the trends in atrazine and acetochlor concentrations for both time periods were relatively small and nonsignificant, but metolachlor and alachlor were dominated by varying magnitudes of concentration downtrends. Overall, with trends expressed as a percent change per year, trends in herbicide concentrations were consistent with trends in agricultural use; 84 of 88 comparisons for different sites, herbicides, and time periods showed no significant difference between concentration trends and agricultural use trends. Results indicate that decreasing use appears to have been the primary cause for the concentration downtrends during 1996-2006 and that, while there is some evidence that nonuse management factors may have reduced concentrations in some rivers, reliably evaluating the influence of these factors on pesticides in large streams and rivers will require improved, basin-specific information on both management practices and use over time. ?? 2009 American Chemical Society.

Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis.

PubMed

Remontet, Laurent; Uhry, Zoé; Bossard, Nadine; Iwaz, Jean; Belot, Aurélien; Danieli, Coraline; Charvat, Hadrien; Roche, Laurent

2018-01-01

Cancer survival trend analyses are essential to describe accurately the way medical practices impact patients' survival according to the year of diagnosis. To this end, survival models should be able to account simultaneously for non-linear and non-proportional effects and for complex interactions between continuous variables. However, in the statistical literature, there is no consensus yet on how to build such models that should be flexible but still provide smooth estimates of survival. In this article, we tackle this challenge by smoothing the complex hypersurface (time since diagnosis, age at diagnosis, year of diagnosis, and mortality hazard) using a multidimensional penalized spline built from the tensor product of the marginal bases of time, age, and year. Considering this penalized survival model as a Poisson model, we assess the performance of this approach in estimating the net survival with a comprehensive simulation study that reflects simple and complex realistic survival trends. The bias was generally small and the root mean squared error was good and often similar to that of the true model that generated the data. This parametric approach offers many advantages and interesting prospects (such as forecasting) that make it an attractive and efficient tool for survival trend analyses.

Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories.

PubMed

Chen, Xiaojian; Cui, Tingting; Fu, Jianhong; Peng, Jianwei; Shan, Jie

2016-12-01

Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.

Trend analysis of Arctic sea ice extent

NASA Astrophysics Data System (ADS)

Silva, M. E.; Barbosa, S. M.; Antunes, Luís; Rocha, Conceição

2009-04-01

The extent of Arctic sea ice is a fundamental parameter of Arctic climate variability. In the context of climate change, the area covered by ice in the Arctic is a particularly useful indicator of recent changes in the Arctic environment. Climate models are in near universal agreement that Arctic sea ice extent will decline through the 21st century as a consequence of global warming and many studies predict a ice free Arctic as soon as 2012. Time series of satellite passive microwave observations allow to assess the temporal changes in the extent of Arctic sea ice. Much of the analysis of the ice extent time series, as in most climate studies from observational data, have been focussed on the computation of deterministic linear trends by ordinary least squares. However, many different processes, including deterministic, unit root and long-range dependent processes can engender trend like features in a time series. Several parametric tests have been developed, mainly in econometrics, to discriminate between stationarity (no trend), deterministic trend and stochastic trends. Here, these tests are applied in the trend analysis of the sea ice extent time series available at National Snow and Ice Data Center. The parametric stationary tests, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP) and the KPSS, do not support an overall deterministic trend in the time series of Arctic sea ice extent. Therefore, alternative parametrizations such as long-range dependence should be considered for characterising long-term Arctic sea ice variability.

Habitat availability is a more plausible explanation than insecticide acute toxicity for U.S. grassland bird species declines

USGS Publications Warehouse

Hill, Jason M.; Egan, J. Franklin; Stauffer, Glenn E.; Diefenbach, Duane R.

2014-01-01

Grassland bird species have experienced substantial declines in North America. These declines have been largely attributed to habitat loss and degradation, especially from agricultural practices and intensification (the habitat-availability hypothesis). A recent analysis of North American Breeding Bird Survey (BBS) “grassland breeding” bird trends reported the surprising conclusion that insecticide acute toxicity was a better correlate of grassland bird declines in North America from 1980–2003 (the insecticide-acute-toxicity hypothesis) than was habitat loss through agricultural intensification. In this paper we reached the opposite conclusion. We used an alternative statistical approach with additional habitat covariates to analyze the same grassland bird trends over the same time frame. Grassland bird trends were positively associated with increases in area of Conservation Reserve Program (CRP) lands and cropland used as pasture, whereas the effect of insecticide acute toxicity on bird trends was uncertain. Our models suggested that acute insecticide risk potentially has a detrimental effect on grassland bird trends, but models representing the habitat-availability hypothesis were 1.3–21.0 times better supported than models representing the insecticide-acute-toxicity hypothesis. Based on point estimates of effect sizes, CRP area and agricultural intensification had approximately 3.6 and 1.6 times more effect on grassland bird trends than lethal insecticide risk, respectively. Our findings suggest that preserving remaining grasslands is crucial to conserving grassland bird populations. The amount of grassland that has been lost in North America since 1980 is well documented, continuing, and staggering whereas insecticide use greatly declined prior to the 1990s. Grassland birds will likely benefit from the de-intensification of agricultural practices and the interspersion of pastures, Conservation Reserve Program lands, rangelands and other grassland habitats into existing agricultural landscapes.

Habitat availability is a more plausible explanation than insecticide acute toxicity for U.S. grassland bird species declines.

PubMed

Hill, Jason M; Egan, J Franklin; Stauffer, Glenn E; Diefenbach, Duane R

2014-01-01

Grassland bird species have experienced substantial declines in North America. These declines have been largely attributed to habitat loss and degradation, especially from agricultural practices and intensification (the habitat-availability hypothesis). A recent analysis of North American Breeding Bird Survey (BBS) "grassland breeding" bird trends reported the surprising conclusion that insecticide acute toxicity was a better correlate of grassland bird declines in North America from 1980-2003 (the insecticide-acute-toxicity hypothesis) than was habitat loss through agricultural intensification. In this paper we reached the opposite conclusion. We used an alternative statistical approach with additional habitat covariates to analyze the same grassland bird trends over the same time frame. Grassland bird trends were positively associated with increases in area of Conservation Reserve Program (CRP) lands and cropland used as pasture, whereas the effect of insecticide acute toxicity on bird trends was uncertain. Our models suggested that acute insecticide risk potentially has a detrimental effect on grassland bird trends, but models representing the habitat-availability hypothesis were 1.3-21.0 times better supported than models representing the insecticide-acute-toxicity hypothesis. Based on point estimates of effect sizes, CRP area and agricultural intensification had approximately 3.6 and 1.6 times more effect on grassland bird trends than lethal insecticide risk, respectively. Our findings suggest that preserving remaining grasslands is crucial to conserving grassland bird populations. The amount of grassland that has been lost in North America since 1980 is well documented, continuing, and staggering whereas insecticide use greatly declined prior to the 1990s. Grassland birds will likely benefit from the de-intensification of agricultural practices and the interspersion of pastures, Conservation Reserve Program lands, rangelands and other grassland habitats into existing agricultural landscapes.

Habitat Availability Is a More Plausible Explanation than Insecticide Acute Toxicity for U.S. Grassland Bird Species Declines

PubMed Central

Hill, Jason M.; Egan, J. Franklin; Stauffer, Glenn E.; Diefenbach, Duane R.

2014-01-01

Grassland bird species have experienced substantial declines in North America. These declines have been largely attributed to habitat loss and degradation, especially from agricultural practices and intensification (the habitat-availability hypothesis). A recent analysis of North American Breeding Bird Survey (BBS) “grassland breeding” bird trends reported the surprising conclusion that insecticide acute toxicity was a better correlate of grassland bird declines in North America from 1980–2003 (the insecticide-acute-toxicity hypothesis) than was habitat loss through agricultural intensification. In this paper we reached the opposite conclusion. We used an alternative statistical approach with additional habitat covariates to analyze the same grassland bird trends over the same time frame. Grassland bird trends were positively associated with increases in area of Conservation Reserve Program (CRP) lands and cropland used as pasture, whereas the effect of insecticide acute toxicity on bird trends was uncertain. Our models suggested that acute insecticide risk potentially has a detrimental effect on grassland bird trends, but models representing the habitat-availability hypothesis were 1.3–21.0 times better supported than models representing the insecticide-acute-toxicity hypothesis. Based on point estimates of effect sizes, CRP area and agricultural intensification had approximately 3.6 and 1.6 times more effect on grassland bird trends than lethal insecticide risk, respectively. Our findings suggest that preserving remaining grasslands is crucial to conserving grassland bird populations. The amount of grassland that has been lost in North America since 1980 is well documented, continuing, and staggering whereas insecticide use greatly declined prior to the 1990s. Grassland birds will likely benefit from the de-intensification of agricultural practices and the interspersion of pastures, Conservation Reserve Program lands, rangelands and other grassland habitats into existing agricultural landscapes. PMID:24846309

Time trends in physical activity in the state of São Paulo, Brazil: 2002-2008.

PubMed

Matsudo, Victor K R; Matsudo, Sandra M; Araújo, Timóteo L; Andrade, Douglas R; Oliveira, Luis C; Hallal, Pedro C

2010-12-01

To document time trends in physical activity in the state of São Paulo, Brazil (2002-2008). In addition, we discuss the role of Agita São Paulo at explaining such trends. Cross-sectional surveys were carried out in 2002, 2003, 2006, and 2008 in the state of São Paulo, Brazil, using comparable sampling approaches and similar sample sizes. In all surveys, physical activity was measured using the short version of the International Physical Activity Questionnaire. Separate weekly scores of walking and moderate- and vigorous-intensity physical activities were generated; cutoff points of 0 and 150 min·wk were used. Also, we created a total physical activity score by summing these three types of activity. We used logistic regression models for adjusting time trends for the different sociodemographic compositions of the samples. The prevalence of no physical activity decreased from 9.6% in 2002 to 2.7% in 2008, whereas the proportion of subjects below the 150-min threshold decreased from 43.7% in 2002 to 11.6% in 2008. These trends were mainly explained by increases in walking and moderate-intensity physical activity. Increases in physical activity were slightly greater among females than among males. Logistic regression models confirmed that these trends were not due to the different compositions of the samples. Physical activity levels are increasing in the state of São Paulo, Brazil. Considering that the few data available in Brazil using the same instrument indicate exactly the opposite trend and that Agita São Paulo primarily incentives the involvement in moderate-intensity physical activity and walking, it seems that at least part of the trends described here are explained by the Agita São Paulo program.

Comparison of mean climate trends in the Northern Hemisphere between National Centers for Environmental Prediction and two atmosphere-ocean model forced runs

NASA Astrophysics Data System (ADS)

Lucarini, Valerio; Russell, Gary L.

2002-08-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies atmosphere-ocean model (AOM). The computed trends of surface pressure; surface temperature; 850, 500, and 200 mbar geopotential heights; and related temperatures of the model for the time frame 1960-2000 are compared with those obtained from the National Centers for Enviromental Prediction (NCEP) observations. The domain of interest is the Northern Hemisphere because of the higher reliability of both the model results and the observations. A spatial correlation analysis and a mean value comparison are performed, showing good agreement in terms of statistical significance for most of the variables considered in the winter and annual means. However, the 850 mbar temperature trends do not show significant positive correlation, and the surface pressure and 850 mbar geopotential height mean trends confidence intervals do not overlap. A brief general discussion about the statistics of trend detection is presented. The accuracy that this AOM has in describing the regional and NH mean climate trends inferred from NCEP through the atmosphere suggests that it may be reliable in forecasting future climate changes.

Trends in stratospheric ozone profiles using functional mixed models

NASA Astrophysics Data System (ADS)

Park, A.; Guillas, S.; Petropavlovskikh, I.

2013-11-01

This paper is devoted to the modeling of altitude-dependent patterns of ozone variations over time. Umkehr ozone profiles (quarter of Umkehr layer) from 1978 to 2011 are investigated at two locations: Boulder (USA) and Arosa (Switzerland). The study consists of two statistical stages. First we approximate ozone profiles employing an appropriate basis. To capture primary modes of ozone variations without losing essential information, a functional principal component analysis is performed. It penalizes roughness of the function and smooths excessive variations in the shape of the ozone profiles. As a result, data-driven basis functions (empirical basis functions) are obtained. The coefficients (principal component scores) corresponding to the empirical basis functions represent dominant temporal evolution in the shape of ozone profiles. We use those time series coefficients in the second statistical step to reveal the important sources of the patterns and variations in the profiles. We estimate the effects of covariates - month, year (trend), quasi-biennial oscillation, the solar cycle, the Arctic oscillation, the El Niño/Southern Oscillation cycle and the Eliassen-Palm flux - on the principal component scores of ozone profiles using additive mixed effects models. The effects are represented as smooth functions and the smooth functions are estimated by penalized regression splines. We also impose a heteroscedastic error structure that reflects the observed seasonality in the errors. The more complex error structure enables us to provide more accurate estimates of influences and trends, together with enhanced uncertainty quantification. Also, we are able to capture fine variations in the time evolution of the profiles, such as the semi-annual oscillation. We conclude by showing the trends by altitude over Boulder and Arosa, as well as for total column ozone. There are great variations in the trends across altitudes, which highlights the benefits of modeling ozone profiles.

Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid; Ongoma, Victor

2018-03-01

ET0 is an important hydro-meteorological phenomenon, which is influenced by changing climate like other climatic parameters. This study investigates the present and future trends of ET0 in Bangladesh using 39 years' historical and downscaled CMIP5 daily climatic data for the twenty-first century. Statistical Downscaling Model (SDSM) was used to downscale the climate data required to calculate ET0. Penman-Monteith formula was applied in ET0 calculation for both the historical and modelled data. To analyse ET0 trends and trend changing patterns, modified Mann-Kendall and Sequential Mann-Kendall tests were, respectively, done. Spatial variations of ET0 trends are presented by inverse distance weighting interpolation using ArcGIS 10.2.2. Results show that RCP8.5 (2061-2099) will experience the highest amount of ET0 totals in comparison to the historical and all other scenarios in the same time span of 39 years. Though significant positive trends were observed in the mid and last months of year from month-wise trend analysis of representative concentration pathways, significant negative trends were also found for some months using historical data in similar analysis. From long-term annual trend analysis, it was found that major part of the country represents decreasing trends using historical data, but increasing trends were observed for modelled data. Theil-Sen estimations of ET0 trends in the study depict a good consistency with the Mann-Kendall test results. The findings of the study would contribute in irrigation water management and planning of the country and also in furthering the climate change study using modelled data in the context of Bangladesh.

The end of trend-estimation for extreme floods under climate change?

NASA Astrophysics Data System (ADS)

Schulz, Karsten; Bernhardt, Matthias

2016-04-01

An increased risk of flood events is one of the major threats under future climate change conditions. Therefore, many recent studies have investigated trends in flood extreme occurences using historic long-term river discharge data as well as simulations from combined global/regional climate and hydrological models. Severe floods are relatively rare events and the robust estimation of their probability of occurrence requires long time series of data (6). Following a method outlined by the IPCC research community, trends in extreme floods are calculated based on the difference of discharge values exceeding e.g. a 100-year level (Q100) between two 30-year windows, which represents prevailing conditions in a reference and a future time period, respectively. Following this approach, we analysed multiple, synthetically derived 2,000-year trend-free, yearly maximum runoff data generated using three different extreme value distributions (EDV). The parameters were estimated from long term runoff data of four large European watersheds (Danube, Elbe, Rhine, Thames). Both, Q100-values estimated from 30-year moving windows, as well as the subsequently derived trends showed enormous variations with time: for example, estimating the Extreme Value (Gumbel) - distribution for the Danube data, trends of Q100 in the synthetic time-series range from -4,480 to 4,028 m³/s per 100 years (Q100 =10,071m³/s, for reference). Similar results were found when applying other extreme value distributions (Weibull, and log-Normal) to all of the watersheds considered. This variability or "background noise" of estimating trends in flood extremes makes it almost impossible to significantly distinguish any real trend in observed as well as modelled data when such an approach is applied. These uncertainties, even though known in principle are hardly addressed and discussed by the climate change impact community. Any decision making and flood risk management, including the dimensioning of flood protection measures, that is based on such studies might therefore be fundamentally flawed.

Revisiting Southern Hemisphere polar stratospheric temperature trends in WACCM: The role of dynamical forcing

NASA Astrophysics Data System (ADS)

Calvo, N.; Garcia, R. R.; Kinnison, D. E.

2017-04-01

The latest version of the Whole Atmosphere Community Climate Model (WACCM), which includes a new chemistry scheme and an updated parameterization of orographic gravity waves, produces temperature trends in the Antarctic lower stratosphere in excellent agreement with radiosonde observations for 1969-1998 as regards magnitude, location, timing, and persistence. The maximum trend, reached in November at 100 hPa, is -4.4 ± 2.8 K decade-1, which is a third smaller than the largest trend in the previous version of WACCM. Comparison with a simulation without the updated orographic gravity wave parameterization, together with analysis of the model's thermodynamic budget, reveals that the reduced trend is due to the effects of a stronger Brewer-Dobson circulation in the new simulations, which warms the polar cap. The effects are both direct (a trend in adiabatic warming in late spring) and indirect (a smaller trend in ozone, hence a smaller reduction in shortwave heating, due to the warmer environment).

Detecting temporal change in freshwater fisheries surveys: statistical power and the important linkages between management questions and monitoring objectives

USGS Publications Warehouse

Wagner, Tyler; Irwin, Brian J.; James R. Bence,; Daniel B. Hayes,

2016-01-01

Monitoring to detect temporal trends in biological and habitat indices is a critical component of fisheries management. Thus, it is important that management objectives are linked to monitoring objectives. This linkage requires a definition of what constitutes a management-relevant “temporal trend.” It is also important to develop expectations for the amount of time required to detect a trend (i.e., statistical power) and for choosing an appropriate statistical model for analysis. We provide an overview of temporal trends commonly encountered in fisheries management, review published studies that evaluated statistical power of long-term trend detection, and illustrate dynamic linear models in a Bayesian context, as an additional analytical approach focused on shorter term change. We show that monitoring programs generally have low statistical power for detecting linear temporal trends and argue that often management should be focused on different definitions of trends, some of which can be better addressed by alternative analytical approaches.

Simulated discharge trends indicate robustness of hydrological models in a changing climate

NASA Astrophysics Data System (ADS)

Addor, Nans; Nikolova, Silviya; Seibert, Jan

2016-04-01

Assessing the robustness of hydrological models under contrasted climatic conditions should be part any hydrological model evaluation. Robust models are particularly important for climate impact studies, as models performing well under current conditions are not necessarily capable of correctly simulating hydrological perturbations caused by climate change. A pressing issue is the usually assumed stationarity of parameter values over time. Modeling experiments using conceptual hydrological models revealed that assuming transposability of parameters values in changing climatic conditions can lead to significant biases in discharge simulations. This raises the question whether parameter values should to be modified over time to reflect changes in hydrological processes induced by climate change. Such a question denotes a focus on the contribution of internal processes (i.e., catchment processes) to discharge generation. Here we adopt a different perspective and explore the contribution of external forcing (i.e., changes in precipitation and temperature) to changes in discharge. We argue that in a robust hydrological model, discharge variability should be induced by changes in the boundary conditions, and not by changes in parameter values. In this study, we explore how well the conceptual hydrological model HBV captures transient changes in hydrological signatures over the period 1970-2009. Our analysis focuses on research catchments in Switzerland undisturbed by human activities. The precipitation and temperature forcing are extracted from recently released 2km gridded data sets. We use a genetic algorithm to calibrate HBV for the whole 40-year period and for the eight successive 5-year periods to assess eventual trends in parameter values. Model calibration is run multiple times to account for parameter uncertainty. We find that in alpine catchments showing a significant increase of winter discharge, this trend can be captured reasonably well with constant parameter values over the whole reference period. Further, preliminary results suggest that some trends in parameter values do not reflect changes in hydrological processes, as reported by others previously, but instead might stem from a modeling artifact related to the parameterization of evapotranspiration, which is overly sensitive to temperature increase. We adopt a trading-space-for-time approach to better understand whether robust relationships between parameter values and forcing can be established, and to critically explore the rationale behind time-dependent parameter values in conceptual hydrological models.

The role of internal climate variability for interpreting climate change scenarios

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-04-01

When communicating information on climate change, the use of multi-model ensembles has been advocated to sample uncertainties over a range as wide as possible. To meet the demand for easily accessible results, the ensemble is often summarised by its multi-model mean signal. In rare cases, additional uncertainty measures are given to avoid loosing all information on the ensemble spread, e.g., the highest and lowest projected values. Such approaches, however, disregard the fundamentally different nature of the different types of uncertainties and might cause wrong interpretations and subsequently wrong decisions for adaptation. Whereas scenario and climate model uncertainties are of epistemic nature, i.e., caused by an in principle reducible lack of knowledge, uncertainties due to internal climate variability are aleatory, i.e., inherently stochastic and irreducible. As wisely stated in the proverb "climate is what you expect, weather is what you get", a specific region will experience one stochastic realisation of the climate system, but never exactly the expected climate change signal as given by a multi model mean. Depending on the meteorological variable, region and lead time, the signal might be strong or weak compared to the stochastic component. In cases of a low signal-to-noise ratio, even if the climate change signal is a well defined trend, no trends or even opposite trends might be experienced. Here I propose to use the time of emergence (TOE) to quantify and communicate when climate change trends will exceed the internal variability. The TOE provides a useful measure for end users to assess the time horizon for implementing adaptation measures. Furthermore, internal variability is scale dependent - the more local the scale, the stronger the influence of internal climate variability. Thus investigating the TOE as a function of spatial scale could help to assess the required spatial scale for implementing adaptation measures. I exemplify this proposal with a recently published study on the TOE for mean and heavy precipitation trends in Europe. In some regions trends emerge only late in the 21st century or even later, suggesting that in these regions adaptation to internal variability rather than to climate change is required. Yet in other regions the climate change signal is strong, urging for timely adaptation. Douglas Maraun, When at what scale will trends in European mean and heavy precipitation emerge? Env. Res. Lett., in press, 2013.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

Seasonal trend analysis and ARIMA modeling of relative humidity and wind speed time series around Yamula Dam

NASA Astrophysics Data System (ADS)

Eymen, Abdurrahman; Köylü, Ümran

2018-02-01

Local climate change is determined by analysis of long-term recorded meteorological data. In the statistical analysis of the meteorological data, the Mann-Kendall rank test, which is one of the non-parametrical tests, has been used; on the other hand, for determining the power of the trend, Theil-Sen method has been used on the data obtained from 16 meteorological stations. The stations cover the provinces of Kayseri, Sivas, Yozgat, and Nevşehir in the Central Anatolia region of Turkey. Changes in land-use affect local climate. Dams are structures that cause major changes on the land. Yamula Dam is located 25 km northwest of Kayseri. The dam has huge water body which is approximately 85 km2. The mentioned tests have been used for detecting the presence of any positive or negative trend in meteorological data. The meteorological data in relation to the seasonal average, maximum, and minimum values of the relative humidity and seasonal average wind speed have been organized as time series and the tests have been conducted accordingly. As a result of these tests, the following have been identified: increase was observed in minimum relative humidity values in the spring, summer, and autumn seasons. As for the seasonal average wind speed, decrease was detected for nine stations in all seasons, whereas increase was observed in four stations. After the trend analysis, pre-dam mean relative humidity time series were modeled with Autoregressive Integrated Moving Averages (ARIMA) model which is statistical modeling tool. Post-dam relative humidity values were predicted by ARIMA models.

A varying coefficient model to measure the effectiveness of mass media anti-smoking campaigns in generating calls to a Quitline.

PubMed

Bui, Quang M; Huggins, Richard M; Hwang, Wen-Han; White, Victoria; Erbas, Bircan

2010-01-01

Anti-smoking advertisements are an effective population-based smoking reduction strategy. The Quitline telephone service provides a first point of contact for adults considering quitting. Because of data complexity, the relationship between anti-smoking advertising placement, intensity, and time trends in total call volume is poorly understood. In this study we use a recently developed semi-varying coefficient model to elucidate this relationship. Semi-varying coefficient models comprise parametric and nonparametric components. The model is fitted to the daily number of calls to Quitline in Victoria, Australia to estimate a nonparametric long-term trend and parametric terms for day-of-the-week effects and to clarify the relationship with target audience rating points (TARPs) for the Quit and nicotine replacement advertising campaigns. The number of calls to Quitline increased with the TARP value of both the Quit and other smoking cessation advertisement; the TARP values associated with the Quit program were almost twice as effective. The varying coefficient term was statistically significant for peak periods with little or no advertising. Semi-varying coefficient models are useful for modeling public health data when there is little or no information on other factors related to the at-risk population. These models are well suited to modeling call volume to Quitline, because the varying coefficient allowed the underlying time trend to depend on fixed covariates that also vary with time, thereby explaining more of the variation in the call model.

A Varying Coefficient Model to Measure the Effectiveness of Mass Media Anti-Smoking Campaigns in Generating Calls to a Quitline

PubMed Central

Bui, Quang M.; Huggins, Richard M.; Hwang, Wen-Han; White, Victoria; Erbas, Bircan

2010-01-01

Background Anti-smoking advertisements are an effective population-based smoking reduction strategy. The Quitline telephone service provides a first point of contact for adults considering quitting. Because of data complexity, the relationship between anti-smoking advertising placement, intensity, and time trends in total call volume is poorly understood. In this study we use a recently developed semi-varying coefficient model to elucidate this relationship. Methods Semi-varying coefficient models comprise parametric and nonparametric components. The model is fitted to the daily number of calls to Quitline in Victoria, Australia to estimate a nonparametric long-term trend and parametric terms for day-of-the-week effects and to clarify the relationship with target audience rating points (TARPs) for the Quit and nicotine replacement advertising campaigns. Results The number of calls to Quitline increased with the TARP value of both the Quit and other smoking cessation advertisement; the TARP values associated with the Quit program were almost twice as effective. The varying coefficient term was statistically significant for peak periods with little or no advertising. Conclusions Semi-varying coefficient models are useful for modeling public health data when there is little or no information on other factors related to the at-risk population. These models are well suited to modeling call volume to Quitline, because the varying coefficient allowed the underlying time trend to depend on fixed covariates that also vary with time, thereby explaining more of the variation in the call model. PMID:20827036

Evaluation of reported NOx emission trends between 2005 and 2013 by assimilation of OMI-NO2 data into LOTOS-EUROS

NASA Astrophysics Data System (ADS)

Schaap, Martijn; Segers, Arjo; Curier, Lyana; Timmermans, Renske

2016-04-01

Consistent and long time series of remotely sensed trace gas levels may provide a useful tool to estimate surface emissions and emission trends. We use the OMI-NO2 product in conjunction with the LOTOS-EUROS CTM to estimate European emission trends through correction of the OMI-time series for meteorological variability as well as through assimilation using an ensemble kalman filter system (EnKF). The chemistry transport model captures a large fraction of the variability in NO2 columns at a synoptic timescale, although a seasonal signal in the bias between the modeled and retrieved column data remains. Prior to the assimilation, the OMI-NO2 data have been analyzed to establish the spatially variable temporal and spatial correlation lengths, required for the settings in the EnKF system. The assimilation run for 2005-2013 was performed using constant 2005 emissions to be able to quantify the emission change. The assimilation reduces the model-observation differences considerably. Significant negative trends of 2-3 % per year (as compared to 2005) were found in highly industrialized areas across Western Europe. The assimilation system also identifies the areas with major emission reductions in e.g. northern Spain as identified in earlier studies. Comparison of the trends derived from the assimilation and the data itself shows a high level of agreement, both the trends found in this way are smaller than those reported.

Modeling Systematic Change in Stopover Duration Does Not Improve Bias in Trends Estimated from Migration Counts.

PubMed

Crewe, Tara L; Taylor, Philip D; Lepage, Denis

2015-01-01

The use of counts of unmarked migrating animals to monitor long term population trends assumes independence of daily counts and a constant rate of detection. However, migratory stopovers often last days or weeks, violating the assumption of count independence. Further, a systematic change in stopover duration will result in a change in the probability of detecting individuals once, but also in the probability of detecting individuals on more than one sampling occasion. We tested how variation in stopover duration influenced accuracy and precision of population trends by simulating migration count data with known constant rate of population change and by allowing daily probability of survival (an index of stopover duration) to remain constant, or to vary randomly, cyclically, or increase linearly over time by various levels. Using simulated datasets with a systematic increase in stopover duration, we also tested whether any resulting bias in population trend could be reduced by modeling the underlying source of variation in detection, or by subsampling data to every three or five days to reduce the incidence of recounting. Mean bias in population trend did not differ significantly from zero when stopover duration remained constant or varied randomly over time, but bias and the detection of false trends increased significantly with a systematic increase in stopover duration. Importantly, an increase in stopover duration over time resulted in a compounding effect on counts due to the increased probability of detection and of recounting on subsequent sampling occasions. Under this scenario, bias in population trend could not be modeled using a covariate for stopover duration alone. Rather, to improve inference drawn about long term population change using counts of unmarked migrants, analyses must include a covariate for stopover duration, as well as incorporate sampling modifications (e.g., subsampling) to reduce the probability that individuals will be detected on more than one occasion.

Trends in participation rates for wildlife-associated outdoor recreation activities by age and race/ethnicity: implications for cohort-component projection models

Treesearch

John F. Dwyer; Allan Marsinko

1998-01-01

Cohort-component projection models have been used to explore the implications of increased aging and growth of racial/ethnic minority groups on number of participants in outdoor recreation activities in the years ahead. Projections usually assume that participation rates by age and race/ethnicity remain constant over time. This study looks at trends in activity...

A Discussion of Upper Stratospheric Ozone Asymmetry and Ozone Trend Changes

NASA Technical Reports Server (NTRS)

Li, Jinlong; Cunnold, Derek M.; Wang, Hsiang-Jui; Yang, Eun-Su; Newchurch, Mike J.

2002-01-01

Analyses from SAGE I/II version 6.0 data exhibit upper stratospheric ozone trends which are not significantly different from those in version 5.96 data. Trend calculations show larger downward trends at mid-high latitudes in the Southern Hemisphere than in the Northern Hemisphere, particularly in 1980s. There are also indications of decreasing downward trends with time from 1979 to 1999. We have used a chemical box model and the UARS measurements of long lived gases, CH4, H2O, NO(x), and temperature to show that, with a constant Cl(sub y) trend, a hemispheric ozone trend asymmetry of 1%/decade at 45 deg. around 43 km is expected due to the hemispheric differences of temperature and CH4 during late winter/early. Also ozone trends should have been approximately 1%/decade more negative from 1979-1989 than from 1989-1999 because of the chemical feedbacks. The model results further indicate that both the reported decrease in CH4 and the increase in H2O in HALOE measurements will result in a larger downward ozone trend and a decrease in the hemispheric ozone trend asymmetry.

A statistical model and national data set for partioning fish-tissue mercury concentration variation between spatiotemporal and sample characteristic effects

USGS Publications Warehouse

Wente, Stephen P.

2004-01-01

Many Federal, Tribal, State, and local agencies monitor mercury in fish-tissue samples to identify sites with elevated fish-tissue mercury (fish-mercury) concentrations, track changes in fish-mercury concentrations over time, and produce fish-consumption advisories. Interpretation of such monitoring data commonly is impeded by difficulties in separating the effects of sample characteristics (species, tissues sampled, and sizes of fish) from the effects of spatial and temporal trends on fish-mercury concentrations. Without such a separation, variation in fish-mercury concentrations due to differences in the characteristics of samples collected over time or across space can be misattributed to temporal or spatial trends; and/or actual trends in fish-mercury concentration can be misattributed to differences in sample characteristics. This report describes a statistical model and national data set (31,813 samples) for calibrating the aforementioned statistical model that can separate spatiotemporal and sample characteristic effects in fish-mercury concentration data. This model could be useful for evaluating spatial and temporal trends in fishmercury concentrations and developing fish-consumption advisories. The observed fish-mercury concentration data and model predictions can be accessed, displayed geospatially, and downloaded via the World Wide Web (http://emmma.usgs.gov). This report and the associated web site may assist in the interpretation of large amounts of data from widespread fishmercury monitoring efforts.

Predicting clicks of PubMed articles.

PubMed

Mao, Yuqing; Lu, Zhiyong

2013-01-01

Predicting the popularity or access usage of an article has the potential to improve the quality of PubMed searches. We can model the click trend of each article as its access changes over time by mining the PubMed query logs, which contain the previous access history for all articles. In this article, we examine the access patterns produced by PubMed users in two years (July 2009 to July 2011). We explore the time series of accesses for each article in the query logs, model the trends with regression approaches, and subsequently use the models for prediction. We show that the click trends of PubMed articles are best fitted with a log-normal regression model. This model allows the number of accesses an article receives and the time since it first becomes available in PubMed to be related via quadratic and logistic functions, with the model parameters to be estimated via maximum likelihood. Our experiments predicting the number of accesses for an article based on its past usage demonstrate that the mean absolute error and mean absolute percentage error of our model are 4.0% and 8.1% lower than the power-law regression model, respectively. The log-normal distribution is also shown to perform significantly better than a previous prediction method based on a human memory theory in cognitive science. This work warrants further investigation on the utility of such a log-normal regression approach towards improving information access in PubMed.

Predicting clicks of PubMed articles

PubMed Central

Mao, Yuqing; Lu, Zhiyong

2013-01-01

Predicting the popularity or access usage of an article has the potential to improve the quality of PubMed searches. We can model the click trend of each article as its access changes over time by mining the PubMed query logs, which contain the previous access history for all articles. In this article, we examine the access patterns produced by PubMed users in two years (July 2009 to July 2011). We explore the time series of accesses for each article in the query logs, model the trends with regression approaches, and subsequently use the models for prediction. We show that the click trends of PubMed articles are best fitted with a log-normal regression model. This model allows the number of accesses an article receives and the time since it first becomes available in PubMed to be related via quadratic and logistic functions, with the model parameters to be estimated via maximum likelihood. Our experiments predicting the number of accesses for an article based on its past usage demonstrate that the mean absolute error and mean absolute percentage error of our model are 4.0% and 8.1% lower than the power-law regression model, respectively. The log-normal distribution is also shown to perform significantly better than a previous prediction method based on a human memory theory in cognitive science. This work warrants further investigation on the utility of such a log-normal regression approach towards improving information access in PubMed. PMID:24551386

Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time.

PubMed

Yin, Liangying; Huang, Zhengxing; Dong, Wei; He, Chunhua; Duan, Huilong

2017-05-05

Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time. This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps. We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented). Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.

The value of vital sign trends for detecting clinical deterioration on the wards

PubMed Central

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-01-01

Aim Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient’s current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Methods Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). Results A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC −0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Conclusion Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. PMID:26898412

The value of vital sign trends for detecting clinical deterioration on the wards.

PubMed

Churpek, Matthew M; Adhikari, Richa; Edelson, Dana P

2016-05-01

Early detection of clinical deterioration on the wards may improve outcomes, and most early warning scores only utilize a patient's current vital signs. The added value of vital sign trends over time is poorly characterized. We investigated whether adding trends improves accuracy and which methods are optimal for modelling trends. Patients admitted to five hospitals over a five-year period were included in this observational cohort study, with 60% of the data used for model derivation and 40% for validation. Vital signs were utilized to predict the combined outcome of cardiac arrest, intensive care unit transfer, and death. The accuracy of models utilizing both the current value and different trend methods were compared using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patient admissions were included, which resulted in 16,452 outcomes. Overall, trends increased accuracy compared to a model containing only current vital signs (AUC 0.78 vs. 0.74; p<0.001). The methods that resulted in the greatest average increase in accuracy were the vital sign slope (AUC improvement 0.013) and minimum value (AUC improvement 0.012), while the change from the previous value resulted in an average worsening of the AUC (change in AUC -0.002). The AUC increased most for systolic blood pressure when trends were added (AUC improvement 0.05). Vital sign trends increased the accuracy of models designed to detect critical illness on the wards. Our findings have important implications for clinicians at the bedside and for the development of early warning scores. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Variability of differences between two approaches for determining ground-water discharge and pumpage, including effects of time trends, Lower Arkansas River Basin, southeastern Colorado, 1998-2002

USGS Publications Warehouse

Troutman, Brent M.; Edelmann, Patrick; Dash, Russell G.

2005-01-01

In the mid-1990s, the Colorado Division of Water Resources (CDWR) adopted rules governing measurement of tributary ground-water pumpage for the Arkansas River Basin. The rules allowed ground-water pumpage to be determined using one of two approaches?power conversion coefficient (PCC) or totalizing flowmeters (TFM). In addition, the rules allowed a PCC to be applied to the electrical power usage up to 4 years in the future to estimate ground-water pumpage. As a result of concerns about potential errors in applying the PCC approach forward in time, a study was done by the U.S. Geological Survey, in cooperation with CDWR and Colorado Water Conservation Board, to evaluate the variability in differences in pumpage between the two approaches, including the effects of time trends. This report compared measured ground-water pumpage using TFMs to computed ground-water pumpage using PCCs by developing statistical models of relations between explanatory variables, such as site, time, and pumping water level, and dependent variables, which are based on discharge, PCC, and pumpage. When differences in pumpage (diffP) were computed using PCC measurements and power consumption for the same year (1998-2002), the median diffP, depending on the year, ranged from +0.1 to -2.9 percent; the median diffP for the entire period was -1.5 percent. However, when diffP was computed using PCC measurements applied to the next year's power consumption, the median diffP was -0.3 percent; and when PCC measurements were applied 2, 3, or 4 years into the future, median diffPs were +1.8 percent for a 2-year forward lag and +5.3 percent for a 4-year forward lag, indicating that pumpage computed with the PCC approach, as generally applied under the ground-water pumpage measurement rules by CDWR, tended to overestimate pumpage as compared to pumpage using TFMs when PCC measurement was applied to future years of measured power consumption. Analyses were done to better understand the causes of the time trend; an estimate of the overall trend with time (uncorrected for pumping water-level changes) yielded a trend of about 2.2 percent per lag year for diffP. A separate analysis that incorporated a surface-water diversion term in the statistical model rendered the time-trend term insignificant, indicating that the time trend in the models served as a surrogate for other variables, some of which reflect underlying hydrologic conditions. A more precise explanation of the potential causes of the time trend was not obtained with the available data. However, the model results with the surface-water diversion term indicate that much of the trend of 2.2 percent per lag year in diffP resulted from applying a PCC to estimate pumpage under hydrologic conditions different from those under which the PCC was measured. Although there is no evidence to conclude that the upward time trend determined in the data for this 5-year period would hold in the future, historical static ground-water levels in the study area generally have exhibited small variations over multidecadal time scales. Therefore, the approximately 2 percent per lag year trend determined in these data is expected to be a reasonable guideline for estimating potential errors in the PCC approach resulting from temporally varying hydrologic conditions between time of PCC measurement and pumpage estimation. Comparisons also were made between total, or aggregated, pumpage for a network of wells as computed by the PCC approach and the TFM approach. For 100 wells and a lag of 4 years between PCC measurement and pumpage estimation, there was a 95-percent probability that the difference between total network pumpage measured by the PCC approach and that measured using a TFM would be between 5.2 and 14.4 percent. These estimates were based on a bias of 2.2 percent per lag year estimated for the period 1998-2002 during which hydrologic conditions were known to have changed. Using the same assumptions, the estimated d

Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories

PubMed Central

Chen, Xiaojian; Cui, Tingting; Fu, Jianhong; Peng, Jianwei; Shan, Jie

2016-01-01

Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques. PMID:27916944

Positive impact of the participation in the ENCHANTED trial in reducing Door-to-Needle Time.

PubMed

Yang, Jie; Wang, Xia; Yu, Jian Ping; Hang, Jing; Lavados, Pablo; Robinson, Thompson; Arima, Hisatomi; Lindley, Richard I; Anderson, Craig S; Chalmers, John

2017-10-26

Door-to-needle time (DNT) is a key performance indicator for efficient use of intravenous thrombolysis in acute ischemic stroke (AIS). We aimed to determine whether DNT improved over time in the Enhanced Control of Hypertension and Acute Stroke Study (ENCHANTED) and the clinical predictors of DNT. Temporal trends in DNT were assessed across fourths of time since activation of study centers using generalized linear model. Predictors of long DNT (>60 min) were determined in logistic regression models. Overall mean DNT (min) was 71.8 (95% confidence interval [CI] 70.4-73.2), but decreased significantly over time (fourths): 77.9 (74.9-80.9), 69.3 (66.7-72.0), 69.1 (66.5-71.8) and 71.4 (68.7-74.2) (P for trend, 0.003). The reduction in DNT was particularly marked in China (P for trend, 0.001), but was not significant across the other participating countries (P for trend, 0.065). Independent predictors of long DNT were recruitment from China, short onset-to-door time, lower numbers of patients treated per center, higher diastolic blood pressure, off-hour admission, and absence of proximal clot occlusion. DNT in ENCHANTED declined progressively during the trial, especially in China. However, DNT in China is still longer than the key performance parameter of ≤60 minutes recommended in guidelines. Effective national programs are needed to improve DNT in China.

Reversal of Increasing Tropical Ocean Hypoxia Trends With Sustained Climate Warming

NASA Astrophysics Data System (ADS)

Fu, Weiwei; Primeau, Francois; Keith Moore, J.; Lindsay, Keith; Randerson, James T.

2018-04-01

Dissolved oxygen (O2) is essential for the survival of marine animals. Climate change impacts on future oxygen distributions could modify species biogeography, trophic interactions, biodiversity, and biogeochemistry. The Coupled Model Intercomparison Project Phase 5 models predict a decreasing trend in marine O2 over the 21st century. Here we show that this increasing hypoxia trend reverses in the tropics after 2100 in the Community Earth System Model forced by atmospheric CO2 from the Representative Concentration Pathway 8.5 and Extended Concentration Pathway 8.5. In tropical intermediate waters between 200 and 1,000 m, the model predicts a steady decline of O2 and an expansion of oxygen minimum zones (OMZs) during the 21st century. By 2150, however, the trend reverses with oxygen concentration increasing and OMZ volume shrinking through 2300. A novel five-box model approach in conjunction with output from the full Earth system model is used to separate the contributions of biological and physical processes to the trends in tropical oxygen. The tropical O2 recovery is caused mainly by reductions in tropical biological export, coupled with a modest increase in ventilation after 2200. The time-evolving oxygen distribution impacts marine nitrogen cycling, with potentially important climate feedbacks.

State-space based analysis and forecasting of macroscopic road safety trends in Greece.

PubMed

Antoniou, Constantinos; Yannis, George

2013-11-01

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Is solar correction for long-term trend studies stable?

NASA Astrophysics Data System (ADS)

Laštovička, Jan

2017-04-01

When calculating long-term trends in the ionosphere, the effect of the 11-year solar cycle (i.e. of solar activity) must be removed from data, because it is much stronger than the long-term trend. When a data series is analyzed for trend, usual approach is first to calculate from all data their dependence on solar activity and create an observational model of dependence on solar activity. Then the model data are subtracted from observations and trend is computed from residuals. This means that it is assumed that the solar activity dependence is stable over the whole data series period of time. But what happens if it is not the case? As an ionospheric parameter we consider foE from two European stations with the best long data series of parameters of the ionospheric E layer, Slough/Chilton and Juliusruh over 1975-2014 (40 years). Noon-time medians (10-14 LT) are analyzed. The trend pattern after removing solar influence with one correction for the whole period is complex. For yearly average values for both stations first foE is slightly decreasing in 1975-1990, then the trend levels off or a very little increase occurs in 1990-2005, and finally in 2006-2014 a remarkable decrease is observed. This does not seem to be physically plausible. However, when the solar correction is calculated separately for the three above periods, we obtain a smooth slightly negative trend which changes after the mid-1990 into no trend in coincidence with change of ozone trend. While solar corrections for the first two periods are similar (even though not equal), the solar activity dependence of foE in the third period (lower solar activity) is clearly different. Also foF2 trend revealed some effect of unstable solar correction. Thus the stability of solar correction should be carefully tested when calculating ionospheric trends. This could perhaps explain some of differences between the past published trend results.

Road safety forecasts in five European countries using structural time series models.

PubMed

Antoniou, Constantinos; Papadimitriou, Eleonora; Yannis, George

2014-01-01

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland). Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios. The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country. A concluding section providing perspectives and directions for future research is presented.

Gender-Specific Trends in Educational Attainment and Self-Rated Health, 1972–2002

PubMed Central

Hill, Terrence D.; Needham, Belinda L.

2006-01-01

Objectives. We tested whether self-rated health has improved over time (1972–2002) for women and men. We also considered the degree to which historical gains in educational attainment help to explain any observed trends. Methods. Using 21 years of repeated cross-sectional data from the General Social Survey, we estimated a series of ordered logistic regression models predicting self-rated health. Results. Our results show that women’s health status has steadily improved over the 30-year period under study, and these improvements are largely explained by gains in educational attainment. We also found that the health trend for men is nonlinear, suggesting significant fluctuations in health status over time. Conclusions. Based on the linear health status trend and strong mediation pattern for women, and the nonlinear health status trend for men, women have benefited more than men, in terms of self-rated health, from increased educational attainment. PMID:16735623

Simulating water-quality trends in public-supply wells in transient flow systems

USGS Publications Warehouse

Starn, J. Jeffrey; Green, Christopher T.; Hinkle, Stephen R.; Bagtzoglou, Amvrossios C.; Stolp, Bernard J.

2014-01-01

Models need not be complex to be useful. An existing groundwater-flow model of Salt Lake Valley, Utah, was adapted for use with convolution-based advective particle tracking to explain broad spatial trends in dissolved solids. This model supports the hypothesis that water produced from wells is increasingly younger with higher proportions of surface sources as pumping changes in the basin over time. At individual wells, however, predicting specific water-quality changes remains challenging. The influence of pumping-induced transient groundwater flow on changes in mean age and source areas is significant. Mean age and source areas were mapped across the model domain to extend the results from observation wells to the entire aquifer to see where changes in concentrations of dissolved solids are expected to occur. The timing of these changes depends on accurate estimates of groundwater velocity. Calibration to tritium concentrations was used to estimate effective porosity and improve correlation between source area changes, age changes, and measured dissolved solids trends. Uncertainty in the model is due in part to spatial and temporal variations in tracer inputs, estimated tracer transport parameters, and in pumping stresses at sampling points. For tracers such as tritium, the presence of two-limbed input curves can be problematic because a single concentration can be associated with multiple disparate travel times. These shortcomings can be ameliorated by adding hydrologic and geologic detail to the model and by adding additional calibration data. However, the Salt Lake Valley model is useful even without such small-scale detail.

When will trends in European mean and heavy daily precipitation emerge?

NASA Astrophysics Data System (ADS)

Maraun, Douglas

2013-03-01

A multi-model ensemble of regional climate projections for Europe is employed to investigate how the time of emergence (TOE) for seasonal sums and maxima of daily precipitation depends on spatial scale. The TOE is redefined for emergence from internal variability only; the spread of the TOE due to imperfect climate model formulation is used as a measure of uncertainty in the TOE itself. Thereby, the TOE becomes a fundamentally limiting timescale and translates into a minimum spatial scale on which robust conclusions can be drawn about precipitation trends. Thus, minimum temporal and spatial scales for adaptation planning are also given. In northern Europe, positive winter trends in mean and heavy precipitation, and in southwestern and southeastern Europe, summer trends in mean precipitation already emerge within the next few decades. However, across wide areas, especially for heavy summer precipitation, the local trend emerges only late in the 21st century or later. For precipitation averaged to larger scales, the trend, in general, emerges earlier.

Statistical power for detecting trends with applications to seabird monitoring

USGS Publications Warehouse

Hatch, Shyla A.

2003-01-01

Power analysis is helpful in defining goals for ecological monitoring and evaluating the performance of ongoing efforts. I examined detection standards proposed for population monitoring of seabirds using two programs (MONITOR and TRENDS) specially designed for power analysis of trend data. Neither program models within- and among-years components of variance explicitly and independently, thus an error term that incorporates both components is an essential input. Residual variation in seabird counts consisted of day-to-day variation within years and unexplained variation among years in approximately equal parts. The appropriate measure of error for power analysis is the standard error of estimation (S.E.est) from a regression of annual means against year. Replicate counts within years are helpful in minimizing S.E.est but should not be treated as independent samples for estimating power to detect trends. Other issues include a choice of assumptions about variance structure and selection of an exponential or linear model of population change. Seabird count data are characterized by strong correlations between S.D. and mean, thus a constant CV model is appropriate for power calculations. Time series were fit about equally well with exponential or linear models, but log transformation ensures equal variances over time, a basic assumption of regression analysis. Using sample data from seabird monitoring in Alaska, I computed the number of years required (with annual censusing) to detect trends of -1.4% per year (50% decline in 50 years) and -2.7% per year (50% decline in 25 years). At ??=0.05 and a desired power of 0.9, estimated study intervals ranged from 11 to 69 years depending on species, trend, software, and study design. Power to detect a negative trend of 6.7% per year (50% decline in 10 years) is suggested as an alternative standard for seabird monitoring that achieves a reasonable match between statistical and biological significance.

Multiscale modeling of multi-decadal trends in air pollutant concentrations and their radiative properties: the role of models in an integrated observing system

NASA Astrophysics Data System (ADS)

Mathur, R.; Xing, J.; Szykman, J.; Gan, C. M.; Hogrefe, C.; Pleim, J. E.

2015-12-01

Air Pollution simulation models must address the increasing complexity arising from new model applications that treat multi-pollutant interactions across varying space and time scales. Setting and attaining lower ambient air quality standards requires an improved understanding and quantification of source attribution amongst the multiple anthropogenic and natural sources, on time scales ranging from episodic to annual and spatial scales ranging from urban to continental. Changing emission patterns over the developing regions of the world are likely to exacerbate the impacts of long-range pollutant transport on background pollutant levels, which may then impact the attainment of local air quality standards. Thus, strategies for reduction of pollution levels of surface air over a region are complicated not only by the interplay of local emissions sources and several complex physical, chemical, dynamical processes in the atmosphere, but also hemispheric background levels of pollutants. Additionally, as short-lived climate forcers, aerosols and ozone exert regionally heterogeneous radiative forcing and influence regional climate trends. EPA's coupled WRF-CMAQ modeling system is applied over a domain encompassing the northern hemisphere for the period spanning 1990-2010. This period has witnessed significant reductions in anthropogenic emissions in North America and Europe as a result of implementation of control measures and dramatic increases across Asia associated with economic and population growth, resulting in contrasting trends in air pollutant distributions and transport patterns across the northern hemisphere. Model results (trends in pollutant concentrations, optical and radiative characteristics) across the northern hemisphere are analyzed in conjunction with surface, aloft and remote sensing measurements to contrast the differing trends in air pollution and aerosol-radiation interactions in these regions over the past two decades. Given the future LEO (TropOMI) and GEO (Sentinel-4, GEMS, and TEMPO) atmospheric chemistry satellite observing capabilities, the results from these model applications will be discussed in the context of how the new satellite observations could help constrain and reduce uncertainties in the models.

Fifty years of fat: news coverage of trends that predate obesity prevalence.

PubMed

Davis, Brennan; Wansink, Brian

2015-07-10

Obesity prevalence has risen in fifty years. While people generally expect media mentions of health risks like obesity prevalence to follow health risk trends, food consumption trends may precede obesity prevalence trends. Therefore, this research investigates whether media mentions of food predate obesity prevalence. Fifty years of non-advertising articles in the New York Times (and 17 years for the London Times) are coded for the mention of less healthy (5 salty and 5 sweet snacks) and healthy (5 fruits and 5 vegetables) food items by year and then associated with annual obesity prevalence in subsequent years. Time-series generalized linear models test whether food-related mentions predate or postdate obesity prevalence in each country. United States obesity prevalence is positively associated with New York Times mentions of sweet snacks (b = 55.2, CI = 42.4 to 68.1, p = .000) and negatively associated with mentions of fruits (b = -71.28, CI -91.5 to -51.1, p = .000) and vegetables (b = -13.6, CI = -17.5 to -9.6, p = .000). Similar results are found for the United Kingdom and The London Times. Importantly, the "obesity followed mentions" models are stronger than the "obesity preceded mentions" models. It may be possible to estimate a nation's future obesity prevalence (e.g., three years from now) based on how frequently national media mention sweet snacks (positively related) and vegetables or fruits (negatively related) today. This may provide public health officials and epidemiologists with new tools to more quickly assess the effectiveness of current obesity interventions based on what is mentioned in the media today.

Latent Growth Modeling of nursing care dependency of acute neurological inpatients.

PubMed

Piredda, M; Ghezzi, V; De Marinis, M G; Palese, A

2015-01-01

Longitudinal three-time point study, addressing how neurological adult patient care dependency varies from the admission time to the 3rd day of acute hospitalization. Nursing care dependency was measured with the Care Dependency Scale (CDS) and a Latent Growth Modeling approach was used to analyse the CDS trend in 124 neurosurgical and stroke inpatients. Care dependence followed a decreasing linear trend. Results can help nurse-managers planning an appropriate amount of nursing care for acute neurological patients during their initial stage of hospitalization. Further studies are needed aimed at investigating the determinants of nursing care dependence during the entire in-hospital stay.

Assessment of groundwater level estimation uncertainty using sequential Gaussian simulation and Bayesian bootstrapping

NASA Astrophysics Data System (ADS)

Varouchakis, Emmanouil; Hristopulos, Dionissios

2015-04-01

Space-time geostatistical approaches can improve the reliability of dynamic groundwater level models in areas with limited spatial and temporal data. Space-time residual Kriging (STRK) is a reliable method for spatiotemporal interpolation that can incorporate auxiliary information. The method usually leads to an underestimation of the prediction uncertainty. The uncertainty of spatiotemporal models is usually estimated by determining the space-time Kriging variance or by means of cross validation analysis. For de-trended data the former is not usually applied when complex spatiotemporal trend functions are assigned. A Bayesian approach based on the bootstrap idea and sequential Gaussian simulation are employed to determine the uncertainty of the spatiotemporal model (trend and covariance) parameters. These stochastic modelling approaches produce multiple realizations, rank the prediction results on the basis of specified criteria and capture the range of the uncertainty. The correlation of the spatiotemporal residuals is modeled using a non-separable space-time variogram based on the Spartan covariance family (Hristopulos and Elogne 2007, Varouchakis and Hristopulos 2013). We apply these simulation methods to investigate the uncertainty of groundwater level variations. The available dataset consists of bi-annual (dry and wet hydrological period) groundwater level measurements in 15 monitoring locations for the time period 1981 to 2010. The space-time trend function is approximated using a physical law that governs the groundwater flow in the aquifer in the presence of pumping. The main objective of this research is to compare the performance of two simulation methods for prediction uncertainty estimation. In addition, we investigate the performance of the Spartan spatiotemporal covariance function for spatiotemporal geostatistical analysis. Hristopulos, D.T. and Elogne, S.N. 2007. Analytic properties and covariance functions for a new class of generalized Gibbs random fields. IΕΕΕ Transactions on Information Theory, 53:4667-4467. Varouchakis, E.A. and Hristopulos, D.T. 2013. Improvement of groundwater level prediction in sparsely gauged basins using physical laws and local geographic features as auxiliary variables. Advances in Water Resources, 52:34-49. Research supported by the project SPARTA 1591: "Development of Space-Time Random Fields based on Local Interaction Models and Applications in the Processing of Spatiotemporal Datasets". "SPARTA" is implemented under the "ARISTEIA" Action of the operational programme Education and Lifelong Learning and is co-funded by the European Social Fund (ESF) and National Resources.

Trends in pesticide concentrations in streams of the western United States, 1993-2005

USGS Publications Warehouse

Johnson, H.M.; Domagalski, Joseph L.; Saleh, D.K.

2011-01-01

Trends in pesticide concentrations for 15 streams in California, Oregon, Washington, and Idaho were determined for the organophosphate insecticides chlorpyrifos and diazinon and the herbicides atrazine, s-ethyl diproplythiocarbamate (EPTC), metolachlor, simazine, and trifluralin. A parametric regression model was used to account for flow, seasonality, and antecedent hydrologic conditions and thereby estimate trends in pesticide concentrations in streams arising from changes in use amount and application method in their associated catchments. Decreasing trends most often were observed for diazinon, and reflect the shift to alternative pesticides by farmers, commercial applicators, and homeowners because of use restrictions and product cancelation. Consistent trends were observed for several herbicides, including upward trends in simazine at urban-influenced sites from 2000 to 2005, and downward trends in atrazine and EPTC at agricultural sites from the mid-1990s to 2005. The model provided additional information about pesticide occurrence and transport in the modeled streams. Two examples are presented and briefly discussed: (1) timing of peak concentrations for individual compounds varied greatly across this geographic gradient because of different application periods and the effects of local rain patterns, irrigation, and soil drainage and (2) reconstructions of continuous diazinon concentrations at sites in California are used to evaluate compliance with total maximum daily load targets.

Visible Infrared Imaging Radiometer Suite (VIIRS) and uncertainty in the ocean color calibration methodology

NASA Astrophysics Data System (ADS)

Turpie, Kevin R.; Eplee, Robert E.; Meister, Gerhard

2015-09-01

During the first few years of the Suomi National Polar-orbiting Partnership (NPP) mission, the NASA Ocean Color calibration team continued to improve on their approach to the on-orbit calibration of the Visible Infrared Imaging Radiometer Suite (VIIRS). As the calibration was adjusted for changes in ocean band responsitivity, the team also estimated a theoretic residual error in the calibration trends well within a few tenths of a percent, which could be translated into trend uncertainties in regional time series of surface reflectance and derived products, where biases as low as a few tenths of a percent in certain bands can lead to significant effects. This study looks at effects from spurious trends inherent to the calibration and biases that arise between reprocessing efforts because of extrapolation of the timedependent calibration table. With the addition of new models for instrument and calibration system trend artifacts, new calibration trends led to improved estimates of ocean time series uncertainty. Table extrapolation biases are presented for the first time. The results further the understanding of uncertainty in measuring regional and global biospheric trends in the ocean using VIIRS, which better define the roles of such records in climate research.

The importance of within-year repeated counts and the influence of scale on long-term monitoring of sage-grouse

USGS Publications Warehouse

Fedy, B.C.; Aldridge, Cameron L.

2011-01-01

Long-term population monitoring is the cornerstone of animal conservation and management. The accuracy and precision of models developed using monitoring data can be influenced by the protocols guiding data collection. The greater sage-grouse (Centrocercus urophasianus) is a species of concern that has been monitored over decades, primarily, by counting the number of males that attend lek (breeding) sites. These lek count data have been used to assess long-term population trends and for multiple mechanistic studies. However, some studies have questioned the efficacy of lek counts to accurately identify population trends. In response, monitoring protocols were changed to have a goal of counting lek sites multiple times within a season. We assessed the influence of this change in monitoring protocols on model accuracy and precision applying generalized additive models to describe trends over time. We found that at large spatial scales including >50 leks, the absence of repeated counts within a year did not significantly alter population trend estimates or interpretation. Increasing sample size decreased the model confidence intervals. We developed a population trend model for Wyoming greater sage-grouse from 1965 to 2008, identifying significant changes in the population indices and capturing the cyclic nature of this species. Most sage-grouse declines in Wyoming occurred between 1965 and the 1990s and lek count numbers generally increased from the mid-1990s to 2008. Our results validate the combination of monitoring data collected under different protocols in past and future studies-provided those studies are addressing large-scale questions. We suggest that a larger sample of individual leks is preferable to multiple counts of a smaller sample of leks. ?? 2011 The Wildlife Society.

Modelled interglacial carbon cycle dynamics during the Holocene, the Eemian and Marine Isotope Stage (MIS) 11

NASA Astrophysics Data System (ADS)

Kleinen, Thomas; Brovkin, Victor; Munhoven, Guy

2016-11-01

Trends in the atmospheric concentration of CO2 during three recent interglacials - the Holocene, the Eemian and Marine Isotope Stage (MIS) 11 - are investigated using an earth system model of intermediate complexity, which we extended with process-based modules to consider two slow carbon cycle processes - peat accumulation and shallow-water CaCO3 sedimentation (coral reef formation). For all three interglacials, model simulations considering peat accumulation and shallow-water CaCO3 sedimentation substantially improve the agreement between model results and ice core CO2 reconstructions in comparison to a carbon cycle set-up neglecting these processes. This enables us to model the trends in atmospheric CO2, with modelled trends similar to the ice core data, forcing the model only with orbital and sea level changes. During the Holocene, anthropogenic CO2 emissions are required to match the observed rise in atmospheric CO2 after 3 ka BP but are not relevant before this time. Our model experiments show a considerable improvement in the modelled CO2 trends by the inclusion of the slow carbon cycle processes, allowing us to explain the CO2 evolution during the Holocene and two recent interglacials consistently using an identical model set-up.

Longitudinal trends in serum ferritin levels and associated factors in a national incident hemodialysis cohort.

PubMed

Kim, Taehee; Rhee, Connie M; Streja, Elani; Obi, Yoshitsugu; Brunelli, Steven M; Kovesdy, Csaba P; Kalantar-Zadeh, Kamyar

2017-02-01

The rise in serum ferritin levels among US maintenance hemodialysis patients has been attributed to higher intravenous iron administration and other changes in practice. We examined ferritin trends over time in hemodialysis patients and whether iron utilization patterns and other factors [erythropoietin-stimulating agent (ESA) prescribing patterns, inflammatory markers] were associated with ferritin trajectory. In a 5-year (January 2007–December 2011) cohort of 81 864 incident US hemodialysis patients, we examined changes in ferritin averaged over 3-month intervals using linear mixed effects models adjusted for intravenous iron dose, malnutrition and inflammatory markers. We then examined ferritin trends across strata of baseline ferritin level, dialysis initiation year, cumulative iron and ESA use in the first dialysis year and baseline hemoglobin level. In models adjusted for iron dose, malnutrition and inflammation, mean ferritin levels increased over time in the overall cohort and across the three lower baseline ferritin strata. Among patients initiating dialysis in 2007, mean ferritin levels increased sharply in the first versus second year of dialysis and again abruptly increased in the fifth year independent of iron dose, malnutrition and inflammatory markers; similar trends were observed among patients who initiated dialysis in 2008 and 2009. In analyses stratified by cumulative iron use, mean ferritin increased among groups receiving iron, but decreased in the no iron group. In analyses stratified by cumulative ESA dose and baseline hemoglobin, mean ferritin increased over time. While ferritin trends correlated with patterns of iron use, increases in ferritin over time persisted independent of intravenous iron and ESA exposure, malnutrition and inflammation.

Fifteen-year trends in the prevalence of barriers to healthy eating in a high-income country.

PubMed

de Mestral, Carlos; Khalatbari-Soltani, Saman; Stringhini, Silvia; Marques-Vidal, Pedro

2017-03-01

Background: Despite increasing levels of education and income in the Swiss population over time and greater food diversity due to globalization, adherence to dietary guidelines has remained persistently low. This may be because of barriers to healthy eating hampering adherence, but whether these barriers have evolved in prevalence over time has never been assessed, to our knowledge. Objective: We assessed 15-y trends in the prevalence of self-reported barriers to healthy eating in Switzerland overall and according to sex, age, education, and income. Design: We used data from 4 national Swiss Health Surveys conducted between 1997 and 2012 (52,238 participants aged ≥18 y, 55% women), applying multivariable-adjusted logistic regression models to assess trends in prevalence of 6 barriers to healthy eating (taste, price, daily habits, time, lack of willpower, and limited options). Results: The prevalence of 3 barriers exhibited an increasing trend until 2007, followed by a decrease in 2012 (from 44% in 1997 to 50% in 2007 and then to 44% in 2012 for taste, from 40% to 52% and then to 39% for price, and from 29% to 34% and then to 32% for time; quadratic P -trend < 0.0001). Limited options decreased slightly until 2007 (35-33%) and then sharply by 2012 (18%) (linear P -trend < 0.0001). Daily habits remained relatively stable across time from 42% in 1997 to 38% in 2012 (linear P -trend < 0.0001). Conversely, lack of willpower decreased steadily over time from 26% in 1997 to 21% in 2012 (linear P -trend < 0.0001). Trends were similar for all barriers irrespective of sex, age, education, and income. Conclusion: Between 1997 and 2012, barriers to healthy eating remained highly prevalent (≥20%) in the Swiss population and evolved similarly irrespective of age, sex, education, and income. © 2017 American Society for Nutrition.

Detection time for global and regional sea level trends and accelerations

NASA Astrophysics Data System (ADS)

Jordà, G.

2014-10-01

Many studies analyze trends on sea level data with the underlying purpose of finding indications of a long-term change that could be interpreted as the signature of anthropogenic climate change. The identification of a long-term trend is a signal-to-noise problem where the natural variability (the "noise") can mask the long-term trend (the "signal"). The signal-to-noise ratio depends on the magnitude of the long-term trend, on the magnitude of the natural variability, and on the length of the record, as the climate noise is larger when averaged over short time scales and becomes smaller over longer averaging periods. In this paper, we evaluate the time required to detect centennial sea level linear trends and accelerations at global and regional scales. Using model results and tide gauge observations, we find that the averaged detection time for a centennial linear trend is 87.9, 76.0, 59.3, 40.3, and 25.2 years for trends of 0.5, 1.0, 2.0, 5.0, and 10.0 mm/yr, respectively. However, in regions with large decadal variations like the Gulf Stream or the Circumpolar current, these values can increase up to a 50%. The spatial pattern of the detection time for sea level accelerations is almost identical. The main difference is that the length of the records has to be about 40-60 years longer to detect an acceleration than to detect a linear trend leading to an equivalent change after 100 years. Finally, we have used a new sea level reconstruction, which provides a more accurate representation of interannual variability for the last century in order to estimate the detection time for global mean sea level trends and accelerations. Our results suggest that the signature of natural variability in a 30 year global mean sea level record would be less than 1 mm/yr. Therefore, at least 2.2 mm/yr of the recent sea level trend estimated by altimetry cannot be attributed to natural multidecadal variability. This article was corrected on 19 NOV 2014. See the end of the full text for details.

Short-Term Enrollment Forecasting for Accurate Budget Planning.

ERIC Educational Resources Information Center

Salley, Charles D.

1979-01-01

Reliance on enrollment trend models for revenue projections has led to a scenario of alternating overbudgeted and underbudgeted years. A study of a large, public university indicates that time series analysis should be used instead to anticipate the orderly seasonal and cyclical patterns that are visible in a period of moderate trend growth.…

Controls on Arctic sea ice from first-year and multi-year ice survival rates

NASA Astrophysics Data System (ADS)

Armour, K.; Bitz, C. M.; Hunke, E. C.; Thompson, L.

2009-12-01

The recent decrease in Arctic sea ice cover has transpired with a significant loss of multi-year (MY) ice. The transition to an Arctic that is populated by thinner first-year (FY) sea ice has important implications for future trends in area and volume. We develop a reduced model for Arctic sea ice with which we investigate how the survivability of FY and MY ice control various aspects of the sea-ice system. We demonstrate that Arctic sea-ice area and volume behave approximately as first-order autoregressive processes, which allows for a simple interpretation of September sea-ice in which its mean state, variability, and sensitivity to climate forcing can be described naturally in terms of the average survival rates of FY and MY ice. This model, used in concert with a sea-ice simulation that traces FY and MY ice areas to estimate the survival rates, reveals that small trends in the ice survival rates explain the decline in total Arctic ice area, and the relatively larger loss of MY ice area, over the period 1979-2006. Additionally, our model allows for a calculation of the persistence time scales of September area and volume anomalies. A relatively short memory time scale for ice area (~ 1 year) implies that Arctic ice area is nearly in equilibrium with long-term climate forcing at all times, and therefore observed trends in area are a clear indication of a changing climate. A longer memory time scale for ice volume (~ 5 years) suggests that volume can be out of equilibrium with climate forcing for long periods of time, and therefore trends in ice volume are difficult to distinguish from its natural variability. With our reduced model, we demonstrate the connection between memory time scale and sensitivity to climate forcing, and discuss the implications that a changing memory time scale has on the trajectory of ice area and volume in a warming climate. Our findings indicate that it is unlikely that a “tipping point” in September ice area and volume will be reached as the climate is further warmed. Finally, we suggest novel model validation techniques based upon comparing the characteristics of FY and MY ice within models to observations. We propose that keeping an account of FY and MY ice area within sea ice models offers a powerful new way to evaluate model projections of sea ice in a greenhouse warming climate.

Quantifying alluvial fan sensitivity to climate in Death Valley, California, from field observations and numerical models

NASA Astrophysics Data System (ADS)

Brooke, Sam; Whittaker, Alexander; Armitage, John; D'Arcy, Mitch; Watkins, Stephen

2017-04-01

A quantitative understanding of landscape sensitivity to climate change remains a key challenge in the Earth Sciences. The stream-flow deposits of coupled catchment-fan systems offer one way to decode past changes in external boundary conditions as they comprise simple, closed systems that can be represented effectively by numerical models. Here we combine the collection and analysis of grain size data on well-dated alluvial fan surfaces in Death Valley, USA, with numerical modelling to address the extent to which sediment routing systems record high-frequency, high-magnitude climate change. We compile a new database of Holocene and Late-Pleistocene grain size trends from 11 alluvial fans in Death Valley, capturing high-resolution grain size data ranging from the Recent to 100 kyr in age. We hypothesise the observed changes in average surface grain size and fining rate over time are a record of landscape response to glacial-interglacial climatic forcing. With this data we are in a unique position to test the predictions of landscape evolution models and evaluate the extent to which climate change has influenced the volume and calibre of sediment deposited on alluvial fans. To gain insight into our field data and study area, we employ an appropriately-scaled catchment-fan model that calculates an eroded volumetric sediment budget to be deposited in a subsiding basin according to mass balance where grain size trends are predicted by a self-similarity fining model. We use the model to compare predicted trends in alluvial fan stratigraphy as a function of boundary condition change for a range of model parameters and input grain size distributions. Subsequently, we perturb our model with a plausible glacial-interglacial magnitude precipitation change to estimate the requisite sediment flux needed to generate observed field grain size trends in Death Valley. Modelled fluxes are then compared with independent measurements of sediment supply over time. Our results constitute one of the first attempts to combine the detailed collection of alluvial fan grain size data in time and space with coupled catchment-fan models, affording us the means to evaluate how well field and model data can be reconciled for simple sediment routing systems.

Urinary and dietary analysis of 18,470 bangladeshis reveal a correlation of rice consumption with arsenic exposure and toxicity.

PubMed

Melkonian, Stephanie; Argos, Maria; Hall, Megan N; Chen, Yu; Parvez, Faruque; Pierce, Brandon; Cao, Hongyuan; Aschebrook-Kilfoy, Briseis; Ahmed, Alauddin; Islam, Tariqul; Slavcovich, Vesna; Gamble, Mary; Haris, Parvez I; Graziano, Joseph H; Ahsan, Habibul

2013-01-01

We utilized data from the Health Effects of Arsenic Longitudinal Study (HEALS) in Araihazar, Bangladesh, to evaluate the association of steamed rice consumption with urinary total arsenic concentration and arsenical skin lesions in the overall study cohort (N=18,470) and in a subset with available urinary arsenic metabolite data (N=4,517). General linear models with standardized beta coefficients were used to estimate associations between steamed rice consumption and urinary total arsenic concentration and urinary arsenic metabolites. Logistic regression models were used to estimate prevalence odds ratios (ORs) and their 95% confidence intervals (CIs) for the associations between rice intake and prevalent skin lesions at baseline. Discrete time hazard models were used to estimate discrete time (HRs) ratios and their 95% CIs for the associations between rice intake and incident skin lesions. Steamed rice consumption was positively associated with creatinine-adjusted urinary total arsenic (β=0.041, 95% CI: 0.032-0.051) and urinary total arsenic with statistical adjustment for creatinine in the model (β=0.043, 95% CI: 0.032-0.053). Additionally, we observed a significant trend in skin lesion prevalence (P-trend=0.007) and a moderate trend in skin lesion incidence (P-trend=0.07) associated with increased intake of steamed rice. This study suggests that rice intake may be a source of arsenic exposure beyond drinking water.

Hydrologic Response to Climate Change: Missing Precipitation Data Matters for Computed Timing Trends

NASA Astrophysics Data System (ADS)

Daniels, B.

2016-12-01

This work demonstrates the derivation of climate timing statistics and applying them to determine resulting hydroclimate impacts. Long-term daily precipitation observations from 50 California stations were used to compute climate trends of precipitation event Intensity, event Duration and Pause between events. Each precipitation event trend was then applied as input to a PRMS hydrology model which showed hydrology changes to recharge, baseflow, streamflow, etc. An important concern was precipitation uncertainty induced by missing observation values and causing errors in quantification of precipitation trends. Many standard statistical techniques such as ARIMA and simple endogenous or even exogenous imputation were applied but failed to help resolve these uncertainties. What helped resolve these uncertainties was use of multiple imputation techniques. This involved fitting of Weibull probability distributions to multiple imputed values for the three precipitation trends.Permutation resampling techniques using Monte Carlo processing were then applied to the multiple imputation values to derive significance p-values for each trend. Significance at the 95% level for Intensity was found for 11 of the 50 stations, Duration from 16 of the 50, and Pause from 19, of which 12 were 99% significant. The significance weighted trends for California are Intensity -4.61% per decade, Duration +3.49% per decade, and Pause +3.58% per decade. Two California basins with PRMS hydrologic models were studied: Feather River in the northern Sierra Nevada mountains and the central coast Soquel-Aptos. Each local trend was changed without changing the other trends or the total precipitation. Feather River Basin's critical supply to Lake Oroville and the State Water Project benefited from a total streamflow increase of 1.5%. The Soquel-Aptos Basin water supply was impacted by a total groundwater recharge decrease of -7.5% and streamflow decrease of -3.2%.

Modeling the Trajectory of Analgesic Demand Over Time After Total Knee Arthroplasty Using the Latent Curve Analysis.

PubMed

Lo, Po-Han; Tsou, Mei-Yung; Chang, Kuang-Yi

2015-09-01

Patient-controlled epidural analgesia (PCEA) is commonly used for pain relief after total knee arthroplasty (TKA). This study aimed to model the trajectory of analgesic demand over time after TKA and explore its influential factors using latent curve analysis. Data were retrospectively collected from 916 patients receiving unilateral or bilateral TKA and postoperative PCEA. PCEA demands during 12-hour intervals for 48 hours were directly retrieved from infusion pumps. Potentially influential factors of PCEA demand, including age, height, weight, body mass index, sex, and infusion pump settings, were also collected. A latent curve analysis with 2 latent variables, the intercept (baseline) and slope (trend), was applied to model the changes in PCEA demand over time. The effects of influential factors on these 2 latent variables were estimated to examine how these factors interacted with time to alter the trajectory of PCEA demand over time. On average, the difference in analgesic demand between the first and second 12-hour intervals was only 15% of that between the first and third 12-hour intervals. No significant difference in PCEA demand was noted between the third and fourth 12-hour intervals. Aging tended to decrease the baseline PCEA demand but body mass index and infusion rate were positively correlated with the baseline. Only sex significantly affected the trend parameter and male individuals tended to have a smoother decreasing trend of analgesic demands over time. Patients receiving bilateral procedures did not consume more analgesics than their unilateral counterparts. Goodness of fit analysis indicated acceptable model fit to the observed data. Latent curve analysis provided valuable information about how analgesic demand after TKA changed over time and how patient characteristics affected its trajectory.

Accurate estimation of influenza epidemics using Google search data via ARGO.

PubMed

Yang, Shihao; Santillana, Mauricio; Kou, S C

2015-11-24

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search-based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people's online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions.

Temporal and long-term trend analysis of class C notifiable diseases in China from 2009 to 2014

PubMed Central

Zhang, Xingyu; Hou, Fengsu; Qiao, Zhijiao; Li, Xiaosong; Zhou, Lijun; Liu, Yuanyuan; Zhang, Tao

2016-01-01

Objectives Time series models are effective tools for disease forecasting. This study aims to explore the time series behaviour of 11 notifiable diseases in China and to predict their incidence through effective models. Settings and participants The Chinese Ministry of Health started to publish class C notifiable diseases in 2009. The monthly reported case time series of 11 infectious diseases from the surveillance system between 2009 and 2014 was collected. Methods We performed a descriptive and a time series study using the surveillance data. Decomposition methods were used to explore (1) their seasonality expressed in the form of seasonal indices and (2) their long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models have been established for each disease. Results The number of cases and deaths caused by hand, foot and mouth disease ranks number 1 among the detected diseases. It occurred most often in May and July and increased, on average, by 0.14126/100 000 per month. The remaining incidence models show good fit except the influenza and hydatid disease models. Both the hydatid disease and influenza series become white noise after differencing, so no available ARIMA model can be fitted for these two diseases. Conclusion Time series analysis of effective surveillance time series is useful for better understanding the occurrence of the 11 types of infectious disease. PMID:27797981

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area.

PubMed

Aguirre-Salado, Alejandro Ivan; Vaquera-Huerta, Humberto; Aguirre-Salado, Carlos Arturo; Reyes-Mora, Silvia; Olvera-Cervantes, Ana Delia; Lancho-Romero, Guillermo Arturo; Soubervielle-Montalvo, Carlos

2017-07-06

We implemented a spatial model for analysing PM 10 maxima across the Mexico City metropolitan area during the period 1995-2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM 10 maxima in space and time. We evaluated the statistical model's performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM 10 maxima and the longitude and latitude. The relationship between time and the PM 10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM 10 maxima presenting levels above 1000 μ g/m 3 (return period: 25 yr) was observed in the northwestern region of the study area.

Trends in Ocean Irradiance using a Radiative Model Forced with Terra Aerosols and Clouds

NASA Technical Reports Server (NTRS)

Gregg, Watson; Casey, Nancy; Romanou, Anastasia

2010-01-01

Aerosol and cloud information from MODIS on Terra provide enhanced capability to understand surface irradiance over the oceans and its variability. These relationships can be important for ocean biology and carbon cycles. An established radiative transfer model, the Ocean-Atmosphere Spectral Irradiance Model (OASIM) is used to describe ocean irradiance variability on seasonal to decadal time scales. The model is forced with information on aerosols and clouds from the MODIS sensor on Terra and Aqua. A 7-year record (2000-2006) showed no trends in global ocean surface irradiance or photosynthetic available irradiance (PAR). There were significant (P<0.05) negative trends in the Mediterranean Sea, tropical Pacific) and tropical Indian Oceans, of -7.0, -5.0 and -2.7 W/sq m respectively. Global interannual variability was also modest. Regional interannual variability was quite large in some ocean basins, where monthly excursions from climatology were often >20 W/sq m. The trends using MODIS data contrast with results from OASIM using liquid water path estimates from the International Satellite Cloud Climatology Project (ISCCP). Here, a global trend of -2 W/sq m was observed, largely dues to a large negative trend in the Antarctic -12 W/sq m. These results suggest the importance of the choice of liquid water path data sets in assessments of medium-length trends in ocean surface irradiance. The choices also impact the evaluation of changes in ocean biogeochemistry.

Age at stroke: temporal trends in stroke incidence in a large, biracial population.

PubMed

Kissela, Brett M; Khoury, Jane C; Alwell, Kathleen; Moomaw, Charles J; Woo, Daniel; Adeoye, Opeolu; Flaherty, Matthew L; Khatri, Pooja; Ferioli, Simona; De Los Rios La Rosa, Felipe; Broderick, Joseph P; Kleindorfer, Dawn O

2012-10-23

We describe temporal trends in stroke incidence stratified by age from our population-based stroke epidemiology study. We hypothesized that stroke incidence in younger adults (age 20-54) increased over time, most notably between 1999 and 2005. The Greater Cincinnati/Northern Kentucky region includes an estimated population of 1.3 million. Strokes were ascertained in the population between July 1, 1993, and June 30, 1994, and in calendar years 1999 and 2005. Age-, race-, and gender-specific incidence rates with 95 confidence intervals were calculated assuming a Poisson distribution. We tested for differences in age trends over time using a mixed-model approach, with appropriate link functions. The mean age at stroke significantly decreased from 71.2 years in 1993/1994 to 69.2 years in 2005 (p < 0.0001). The proportion of all strokes under age 55 increased from 12.9% in 1993/1994 to 18.6% in 2005. Regression modeling showed a significant change over time (p = 0.002), characterized as a shift to younger strokes in 2005 compared with earlier study periods. Stroke incidence rates in those 20-54 years of age were significantly increased in both black and white patients in 2005 compared to earlier periods. We found trends toward increasing stroke incidence at younger ages. This is of great public health significance because strokes in younger patients carry the potential for greater lifetime burden of disability and because some potential contributors identified for this trend are modifiable.

State-space modeling of population sizes and trends in Nihoa Finch and Millerbird

USGS Publications Warehouse

Gorresen, P. Marcos; Brinck, Kevin W.; Camp, Richard J.; Farmer, Chris; Plentovich, Sheldon M.; Banko, Paul C.

2016-01-01

Both of the 2 passerines endemic to Nihoa Island, Hawai‘i, USA—the Nihoa Millerbird (Acrocephalus familiaris kingi) and Nihoa Finch (Telespiza ultima)—are listed as endangered by federal and state agencies. Their abundances have been estimated by irregularly implemented fixed-width strip-transect sampling from 1967 to 2012, from which area-based extrapolation of the raw counts produced highly variable abundance estimates for both species. To evaluate an alternative survey method and improve abundance estimates, we conducted variable-distance point-transect sampling between 2010 and 2014. We compared our results to those obtained from strip-transect samples. In addition, we applied state-space models to derive improved estimates of population size and trends from the legacy time series of strip-transect counts. Both species were fairly evenly distributed across Nihoa and occurred in all or nearly all available habitat. Population trends for Nihoa Millerbird were inconclusive because of high within-year variance. Trends for Nihoa Finch were positive, particularly since the early 1990s. Distance-based analysis of point-transect counts produced mean estimates of abundance similar to those from strip-transects but was generally more precise. However, both survey methods produced biologically unrealistic variability between years. State-space modeling of the long-term time series of abundances obtained from strip-transect counts effectively reduced uncertainty in both within- and between-year estimates of population size, and allowed short-term changes in abundance trajectories to be smoothed into a long-term trend.

Adjustment of pesticide concentrations for temporal changes in analytical recovery, 1992–2010

USGS Publications Warehouse

Martin, Jeffrey D.; Eberle, Michael

2011-01-01

Recovery is the proportion of a target analyte that is quantified by an analytical method and is a primary indicator of the analytical bias of a measurement. Recovery is measured by analysis of quality-control (QC) water samples that have known amounts of target analytes added ("spiked" QC samples). For pesticides, recovery is the measured amount of pesticide in the spiked QC sample expressed as a percentage of the amount spiked, ideally 100 percent. Temporal changes in recovery have the potential to adversely affect time-trend analysis of pesticide concentrations by introducing trends in apparent environmental concentrations that are caused by trends in performance of the analytical method rather than by trends in pesticide use or other environmental conditions. This report presents data and models related to the recovery of 44 pesticides and 8 pesticide degradates (hereafter referred to as "pesticides") that were selected for a national analysis of time trends in pesticide concentrations in streams. Water samples were analyzed for these pesticides from 1992 through 2010 by gas chromatography/mass spectrometry. Recovery was measured by analysis of pesticide-spiked QC water samples. Models of recovery, based on robust, locally weighted scatterplot smooths (lowess smooths) of matrix spikes, were developed separately for groundwater and stream-water samples. The models of recovery can be used to adjust concentrations of pesticides measured in groundwater or stream-water samples to 100 percent recovery to compensate for temporal changes in the performance (bias) of the analytical method.

Worldwide patterns of ischemic heart disease mortality from 1980 to 2010.

PubMed

Gouvinhas, Cláudia; Severo, Milton; Azevedo, Ana; Lunet, Nuno

2014-01-01

The trends in the IHD mortality rates vary widely across countries, reflecting the heterogeneity in the variation of the exposure to the main risk factors and in the access to different management strategies among settings. We aimed to identify model-based patterns in the time trends in IHD mortality in 50 countries from the five continents, between 1980 and 2010. Mixed models were used to identify time trends in age-standardized mortality rates (ASMR) (age group 35+years; world standard population), all including random terms for intercept, slope, quadratic and cubic. Model-based clustering was used to identify the patterns. We identified five main patterns of IHD mortality trends in the last three decades, similar for men and women. Pattern 1 had the highest ASMR and pattern 2 exhibited the most pronounced decrease in ASMR during the entire study period. Pattern 3 was characterized by an initial increase in ASMR, followed by a sharp decline. Countries in pattern 4 had the lowest ASMR throughout the study period. It was further divided into patterns 4a (consistent decrease in ASMR throughout the period of analysis) and 4b (less pronounced declines and highest rates observed mostly between 1996 and 2004). There was no correspondence between the geographic or economical grouping of the analyzed countries and the patterns found in this study. Our study yielded a new framework for the description, interpretation and prediction of IHD mortality trends worldwide. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Hierarchical model analysis of the Atlantic Flyway Breeding Waterfowl Survey

USGS Publications Warehouse

Sauer, John R.; Zimmerman, Guthrie S.; Klimstra, Jon D.; Link, William A.

2014-01-01

We used log-linear hierarchical models to analyze data from the Atlantic Flyway Breeding Waterfowl Survey. The survey has been conducted by state biologists each year since 1989 in the northeastern United States from Virginia north to New Hampshire and Vermont. Although yearly population estimates from the survey are used by the United States Fish and Wildlife Service for estimating regional waterfowl population status for mallards (Anas platyrhynchos), black ducks (Anas rubripes), wood ducks (Aix sponsa), and Canada geese (Branta canadensis), they are not routinely adjusted to control for time of day effects and other survey design issues. The hierarchical model analysis permits estimation of year effects and population change while accommodating the repeated sampling of plots and controlling for time of day effects in counting. We compared population estimates from the current stratified random sample analysis to population estimates from hierarchical models with alternative model structures that describe year to year changes as random year effects, a trend with random year effects, or year effects modeled as 1-year differences. Patterns of population change from the hierarchical model results generally were similar to the patterns described by stratified random sample estimates, but significant visibility differences occurred between twilight to midday counts in all species. Controlling for the effects of time of day resulted in larger population estimates for all species in the hierarchical model analysis relative to the stratified random sample analysis. The hierarchical models also provided a convenient means of estimating population trend as derived statistics from the analysis. We detected significant declines in mallard and American black ducks and significant increases in wood ducks and Canada geese, a trend that had not been significant for 3 of these 4 species in the prior analysis. We recommend using hierarchical models for analysis of the Atlantic Flyway Breeding Waterfowl Survey.

Hot spots, hot moments and time-span of changes in drivers and their responses on carbon cycling in Europe

NASA Astrophysics Data System (ADS)

Tomelleri, E.; Forkel, M.; Fuchs, R.; Jung, M.; Mahecha, M. D.; Reichstein, M.; Weber, U.

2012-12-01

The objective of this study is to provide a complete quantitative assessment of the annual to decadal variability, hotspots of changes and the temporal magnitude of regional trends and variability for the main drivers of carbon cycle like climate and land use and their responses for Europe. For this purpose we used an harmonized climatic data set (ERA Interim and WATCH) and an historical land-use change reconstruction (HILDAv1, Fuchs in prep.). Both the data sets cover the period 1900-2010 and have a 0.25 deg spatial resolution. As driver response we used two different empirically up-scaled GPP fields: the first (MTE) obtained by the application of model trees (Jung et al. 2009) and a second (LUE) based on a light use efficiency model (Tomelleri in prep.). Both the approaches are based on the up-scaling of Fluxnet observations. The response fields have monthly temporal resolution and are limited to the period 1982-2011. We estimated break-points in time series of driver and response variables based on the method of Bai and Perron (2003) to identify changes in trends. This method was implemented in Verbesselt et al. 2010 and applied by deJong et al. 2011 to detect phenological and abrupt changes and trends in vegetation activity based on satellite-derived vegetation index time series. The analysis of drivers and responses allowed to identify the dominant factors driving the biosphere-atmosphere carbon exchange. The synchronous analysis of climatic drivers and land use change allowed us to explain most of the temporal and spatial variability showing that in the regions and time period where the most land use change occurred the climatic drivers are not sufficient to explain trends and oscillation in carbon cycling. The comparison of our analysis for the up-scaling methods shows some agreement: we found inconsistency in the spatial and temporal patterns in regions where the Fluxnet network is less dense. This can be explained by the conceptual difference in the up-scaling methods: while one is on pixel basis (MTE) the other (LUE) is up-scaling model parameters by bioclimatic regions. Our study shows the value of up-scaling methods for understanding the spatial-temporal variability of carbon cycling and how these are a valuable tool for spatial and temporal analysis. Furthermore, the use of climatic drivers and land-use change demonstrated the need of taking natural and anthropogenic drivers into consideration for explaining trends and oscillations. Possibly a further analysis including detailed management practices for forestry and agriculture would help in explaining the remaining variance. References: Bai, J., Perron, P.: Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 2003. Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6, 2009. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D.: Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment,114(1), 2010. de Jong, R., Verbesselt, J., Schaepman, M.E., Bruin, S.: Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Global Change Biology, 18, 2011.

Trends in the quality of water in New Jersey streams, water years 1971–2011

USGS Publications Warehouse

Hickman, R. Edward; Hirsch, Robert M.

2017-02-27

In a study conducted by the U.S. Geological Survey in cooperation with the New Jersey Department of Environmental Protection and the Delaware River Basin Commission, trend tests were conducted on selected water-quality characteristics measured at stations on streams in New Jersey during selected periods over water years 1971‒2011. Tests were conducted on 3 nutrients (total nitrogen, filtered nitrate plus nitrite, and total phosphorus) at 28 water-quality stations. At 4 of these stations, tests were also conducted on 3 measures of major ions (specific conductance, filtered chloride, and total dissolved solids).Two methods were used to identify trends—Weighted Regressions on Time, Discharge, and Season (WRTDS) models and seasonal rank-sum tests. For this report, the use of WRTDS models included the use of the WRTDS Bootstrap Test (WBT). WRTDS models identified trends in flow-normalized annual concentrations and flow-normalized annual fluxes over water years 1980‒2011 and 2000‒11 for each nutrient, filtered chloride, and total dissolved solids. WRTDS models were developed for each nutrient at the 20 or 21 stations at which streamflow was measured or estimated. Trends in nutrient concentration were reported for these stations; trends in nutrient fluxes were reported only for 15–17 of these stations.The results of WRTDS models for water years 1980‒2011 identified more stations with downward trends in concentrations of either total nitrogen or total phosphorus than upward trends. For total nitrogen, there were downward trends at 9 stations and an upward trend at 1 station. For total phosphorus, there were downward trends at 8 stations and an upward trend at 1 station. For filtered nitrate plus nitrite, there were downward trends at 6 stations and upward trends at 6 stations. The result of the trend test in flux for a selected nutrient at a selected station (downward trend, no trend, or upward trend) usually matched the trend result in concentration.Seasonal rank-sum tests, the second method used, identified step trends in water-quality measured in different decades—1970s, 1980s, 1990s, and 2000s. Tests were conducted on all nutrients at 28 stations and on all measures of major ions at the 4 selected stations. Results of seasonal rank-sum tests between the 1980s and the 2000s identified more stations with downward trends in concentrations of total nitrogen (14) than stations with upward trends (2) and more stations with downward trends in concentrations of total phosphorus (18) than stations with upward trends (1).A combined dataset of trend results for concentrations over water years 1980‒2011 was created from the results of the two tests for the period. Results of WRTDS models were included in this combined dataset, if available. Otherwise, the results of the seasonal rank-sum tests between water-quality characteristics measured in the 1980s and 2000s were included.Trend results over water years 1980‒2011 in the combined dataset show that few of the 28 stations had upward trends in concentrations of either total nitrogen or total phosphorus. There were only 2 stations with upward trends in total nitrogen concentration and 1 station with an upward trend in total phosphorus concentration. Results for filtered nitrate plus nitrite show about the same number of stations with upward trends (9) as stations with downward trends (7). Results for all measures of major ions show upward trends at the four stations tested.

Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century.

PubMed

Dessler, A E; Ye, H; Wang, T; Schoeberl, M R; Oman, L D; Douglass, A R; Butler, A H; Rosenlof, K H; Davis, S M; Portmann, R W

2016-03-16

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by ~1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50-80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales - on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community.

Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century

PubMed Central

Dessler, A.E.; Ye, H.; Wang, T.; Schoeberl, M.R.; Oman, L.D.; Douglass, A.R.; Butler, A.H.; Rosenlof, K.H.; Davis, S.M.; Portmann, R.W.

2018-01-01

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by ~1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50–80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales — on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community. PMID:29551841

Transport of Ice into the Stratosphere and the Humidification of the Stratosphere over the 21st Century

NASA Technical Reports Server (NTRS)

Dessler, A. E.; Ye, H.; Wang, T.; Schoeberl, M. R.; Oman, L. D.; Douglass, A. R.; Butler, A. H.; Rosenlof, K. H.; Davis, S. M.; Portmann, R. W.

2016-01-01

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by approx. 1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50-80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales - on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community.

Estimation of trends

NASA Technical Reports Server (NTRS)

1981-01-01

The application of statistical methods to recorded ozone measurements. The effects of a long term depletion of ozone at magnitudes predicted by the NAS is harmful to most forms of life. Empirical prewhitening filters the derivation of which is independent of the underlying physical mechanisms were analyzed. Statistical analysis performs a checks and balances effort. Time series filters variations into systematic and random parts, errors are uncorrelated, and significant phase lag dependencies are identified. The use of time series modeling to enhance the capability of detecting trends is discussed.

Analysis of the eight-year trend in ozone depletion from empirical models of solar backscattered ultraviolet instrument degradation

NASA Technical Reports Server (NTRS)

Herman, J. R.; Hudson, R. D.; Serafino, G.

1990-01-01

Arguments are presented showing that the basic empirical model of the solar backscatter UV (SBUV) instrument degradation used by Cebula et al. (1988) in their analysis of the SBUV data is likely to lead to an incorrect estimate of the ozone trend. A correction factor is given as a function of time and altitude that brings the SBUV data into approximate agreement with the SAGE, SME, and Dobson network ozone trends. It is suggested that the currently archived SBUV ozone data should be used with caution for periods of analysis exceeding 1 yr, since it is likely that the yearly decreases contained in the archived data are too large.

Wave climate and trends along the eastern Chukchi Arctic Alaska coast

USGS Publications Warehouse

Erikson, L.H.; Storlazzi, C.D.; Jensen, R.E.

2011-01-01

Due in large part to the difficulty of obtaining measurements in the Arctic, little is known about the wave climate along the coast of Arctic Alaska. In this study, numerical model simulations encompassing 40 years of wave hind-casts were used to assess mean and extreme wave conditions. Results indicate that the wave climate was strongly modulated by large-scale atmospheric circulation patterns and that mean and extreme wave heights and periods exhibited increasing trends in both the sea and swell frequency bands over the time-period studied (1954-2004). Model simulations also indicate that the upward trend was not due to a decrease in the minimum icepack extent. ?? 2011 ASCE.

Dynamic compositional modeling of pedestrian crash counts on urban roads in Connecticut.

PubMed

Serhiyenko, Volodymyr; Ivan, John N; Ravishanker, Nalini; Islam, Md Saidul

2014-03-01

Uncovering the temporal trend in crash counts provides a good understanding of the context for pedestrian safety. With a rareness of pedestrian crashes it is impossible to investigate monthly temporal effects with an individual segment/intersection level data, thus the time dependence should be derived from the aggregated level data. Most previous studies have used annual data to investigate the differences in pedestrian crashes between different regions or countries in a given year, and/or to look at time trends of fatal pedestrian injuries annually. Use of annual data unfortunately does not provide sufficient information on patterns in time trends or seasonal effects. This paper describes statistical methods uncovering patterns in monthly pedestrian crashes aggregated on urban roads in Connecticut from January 1995 to December 2009. We investigate the temporal behavior of injury severity levels, including fatal (K), severe injury (A), evident minor injury (B), and non-evident possible injury and property damage only (C and O), as proportions of all pedestrian crashes in each month, taking into consideration effects of time trend, seasonal variations and VMT (vehicle miles traveled). This type of dependent multivariate data is characterized by positive components which sum to one, and occurs in several applications in science and engineering. We describe a dynamic framework with vector autoregressions (VAR) for modeling and predicting compositional time series. Combining these predictions with predictions from a univariate statistical model for total crash counts will then enable us to predict pedestrian crash counts with the different injury severity levels. We compare these predictions with those obtained from fitting separate univariate models to time series of crash counts at each injury severity level. We also show that the dynamic models perform better than the corresponding static models. We implement the Integrated Nested Laplace Approximation (INLA) approach to enable fast Bayesian posterior computation. Taking CO injury severity level as a baseline for the compositional analysis, we conclude that there was a noticeable shift in the proportion of pedestrian crashes from injury severity A to B, while the increase for injury severity K was extremely small over time. This shift to the less severe injury level (from A to B) suggests that the overall safety on urban roads in Connecticut is improving. In January and February, there was some increase in the proportions for levels A and B over the baseline, indicating a seasonal effect. We found evidence that an increase in VMT would result in a decrease of proportions over the baseline for all injury severity levels. Our dynamic model uncovered a decreasing trend in all pedestrian crash counts before April 2005, followed by a noticeable increase and a flattening out until the end of the fitting period. This appears to be largely due to the behavior of injury severity level A pedestrian crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

A multi-scale methodology for comparing GCM and RCM results over the Eastern Mediterranean

NASA Astrophysics Data System (ADS)

Samuels, Rana; Krichak, Simon; Breitgand, Joseph; Alpert, Pinhas

2010-05-01

The importance of skillful climate modeling is increasingly being realized as results are being incorporated into environmental, economic, and even business planning. Global circulation models (GCMs) employed by the IPCC provide results at spatial scales of hundreds of kilometers, which is useful for understanding global trends but not appropriate for use as input into regional and local impacts models used to inform policy and development. To address this shortcoming, regional climate models (RCMs) which dynamically downscale the results of the GCMs are used. In this study we present first results of a dynamically downscaled RCM focusing on the Eastern Mediterranean region. For the historical 1960-2000 time period, results at a spatial scale of both 25 km and 50 km are compared with historical station data from 5 locations across Israel as well as with the results of 3 GCM models (ECHAM5, NOAA GFDL, and CCCMA) at annual, monthly and daily time scales. Results from a recently completed Japanese GCM at a spatial scale of 20 km are also included. For the historical validation period, we show that as spatial scale increases the skill in capturing annual and inter-annual temperature and rainfall also increases. However, for intra-seasonal rainfall characteristics important for hydrological and agricultural planning (eg. dry and wet spells, number of rain days) the GCM results (including the 20 km Japanese model) capture the historical trends better than the dynamically downscaled RegCM. For future scenarios of temperature and precipitation changes, we compare results across the models for the available time periods, generating a range of future trends.

Stochastic Modeling and Global Warming Trend Extraction For Ocean Acoustic Travel Times.

DTIC Science & Technology

1995-01-06

consideration and that these models can not currently be relied upon by themselves to predict global warming . Experimental data is most certainly needed, not...only to measure global warming itself, but to help improve the ocean model themselves. (AN)

Long-term changes (1980-2003) in total ozone time series over Northern Hemisphere midlatitudes

NASA Astrophysics Data System (ADS)

Białek, Małgorzata

2006-03-01

Long-term changes in total ozone time series for Arosa, Belsk, Boulder and Sapporo stations are examined. For each station we analyze time series of the following statistical characteristics of the distribution of daily ozone data: seasonal mean, standard deviation, maximum and minimum of total daily ozone values for all seasons. The iterative statistical model is proposed to estimate trends and long-term changes in the statistical distribution of the daily total ozone data. The trends are calculated for the period 1980-2003. We observe lessening of negative trends in the seasonal means as compared to those calculated by WMO for 1980-2000. We discuss a possibility of a change of the distribution shape of ozone daily data using the Kolmogorov-Smirnov test and comparing trend values in the seasonal mean, standard deviation, maximum and minimum time series for the selected stations and seasons. The distribution shift toward lower values without a change in the distribution shape is suggested with the following exceptions: the spreading of the distribution toward lower values for Belsk during winter and no decisive result for Sapporo and Boulder in summer.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios

PubMed Central

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-01-01

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN. PMID:26593919

A Model Assessment of Satellite Observed Trends in Polar Sea Ice Extents

NASA Technical Reports Server (NTRS)

Vinnikov, Konstantin Y.; Cavalieri, Donald J.; Parkinson, Claire L.

2005-01-01

For more than three decades now, satellite passive microwave observations have been used to monitor polar sea ice. Here we utilize sea ice extent trends determined from primarily satellite data for both the Northern and Southern Hemispheres for the period 1972(73)-2004 and compare them with results from simulations by eleven climate models. In the Northern Hemisphere, observations show a statistically significant decrease of sea ice extent and an acceleration of sea ice retreat during the past three decades. However, from the modeled natural variability of sea ice extents in control simulations, we conclude that the acceleration is not statistically significant and should not be extrapolated into the future. Observations and model simulations show that the time scale of climate variability in sea ice extent in the Southern Hemisphere is much larger than in the Northern Hemisphere and that the Southern Hemisphere sea ice extent trends are not statistically significant.

Capacity Model and Constraints Analysis for Integrated Remote Wireless Sensor and Satellite Network in Emergency Scenarios.

PubMed

Zhang, Wei; Zhang, Gengxin; Dong, Feihong; Xie, Zhidong; Bian, Dongming

2015-11-17

This article investigates the capacity problem of an integrated remote wireless sensor and satellite network (IWSSN) in emergency scenarios. We formulate a general model to evaluate the remote sensor and satellite network capacity. Compared to most existing works for ground networks, the proposed model is time varying and space oriented. To capture the characteristics of a practical network, we sift through major capacity-impacting constraints and analyze the influence of these constraints. Specifically, we combine the geometric satellite orbit model and satellite tool kit (STK) engineering software to quantify the trends of the capacity constraints. Our objective in analyzing these trends is to provide insights and design guidelines for optimizing the integrated remote wireless sensor and satellite network schedules. Simulation results validate the theoretical analysis of capacity trends and show the optimization opportunities of the IWSSN.

Patterns of breast cancer mortality trends in Europe.

PubMed

Amaro, Joana; Severo, Milton; Vilela, Sofia; Fonseca, Sérgio; Fontes, Filipa; La Vecchia, Carlo; Lunet, Nuno

2013-06-01

To identify patterns of variation in breast cancer mortality in Europe (1980-2010), using a model-based approach. Mortality data were obtained from the World Health Organization database and mixed models were used to describe the time trends in the age-standardized mortality rates (ASMR). Model-based clustering was used to identify clusters of countries with homogeneous variation in ASMR. Three patterns were identified. Patterns 1 and 2 are characterized by stable or slightly increasing trends in ASMR in the first half of the period analysed, and a clear decline is observed thereafter; in pattern 1 the median of the ASMR is higher, and the highest rates were achieved sooner. Pattern 3 is characterised by a rapid increase in mortality until 1999, declining slowly thereafter. This study provides a general model for the description and interpretation of the variation in breast cancer mortality in Europe, based in three main patterns. Copyright © 2013 Elsevier Ltd. All rights reserved.

Regional transport modelling for nitrate trend assessment and forecasting in a chalk aquifer.

PubMed

Orban, Philippe; Brouyère, Serge; Batlle-Aguilar, Jordi; Couturier, Julie; Goderniaux, Pascal; Leroy, Mathieu; Maloszewski, Piotr; Dassargues, Alain

2010-10-21

Regional degradation of groundwater resources by nitrate has become one of the main challenges for water managers worldwide. Regulations have been defined to reverse observed nitrate trends in groundwater bodies, such as the Water Framework Directive and the Groundwater Daughter Directive in the European Union. In such a context, one of the main challenges remains to develop efficient approaches for groundwater quality assessment at regional scale, including quantitative numerical modelling, as a decision support for groundwater management. A new approach combining the use of environmental tracers and the innovative 'Hybrid Finite Element Mixing Cell' (HFEMC) modelling technique is developed to study and forecast the groundwater quality at the regional scale, with an application to a regional chalk aquifer in the Geer basin in Belgium. Tritium data and nitrate time series are used to produce a conceptual model for regional groundwater flow and contaminant transport in the combined unsaturated and saturated zones of the chalk aquifer. This shows that the spatial distribution of the contamination in the Geer basin is essentially linked to the hydrodynamic conditions prevailing in the basin, more precisely to groundwater age and mixing and not to the spatial patterns of land use or local hydrodispersive processes. A three-dimensional regional scale groundwater flow and solute transport model is developed. It is able to reproduce the spatial patterns of tritium and nitrate and the observed nitrate trends in the chalk aquifer and it is used to predict the evolution of nitrate concentrations in the basin. The modelling application shows that the global inertia of groundwater quality is strong in the basin and trend reversal is not expected to occur before the 2015 deadline fixed by the European Water Framework Directive. The expected time required for trend reversal ranges between 5 and more than 50 years, depending on the location in the basin and the expected reduction in nitrate application. To reach a good chemical status, nitrate concentrations in the infiltrating water should be reduced as soon as possible below 50mg/l; however, even in that case, more than 50 years is needed to fully reverse upward trends. Copyright © 2010 Elsevier B.V. All rights reserved.

Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors Over Time*

PubMed Central

Yin, Liangying; Dong, Wei; He, Chunhua; Duan, Huilong

2017-01-01

Summary Objectives Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time. Methods This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps. Results We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented). Conclusions Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement. PMID:28474729

Assessment of the effects of horizontal grid resolution on long ...

EPA Pesticide Factsheets

The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission to protect human health and the environment.

Dependence of long-term persistence properties of precipitation on spatial and regional characteristics

NASA Astrophysics Data System (ADS)

Tyralis, Hristos; Dimitriadis, Panayiotis; Iliopoulou, Theano; Tzouka, Katerina; Koutsoyiannis, Demetris

2017-04-01

The long-term persistence (LTP), else known in hydrological science as the Hurst phenomenon, is a behaviour observed in geophysical processes in which wet years or dry years are clustered to respective long time periods. A common practice for evaluating the presence of the LTP is to model the geophysical time series with the Hurst-Kolmogorov process (HKp) and estimate its Hurst parameter H where high values of H indicate strong LTP. We estimate H of the mean annual precipitation using instrumental data from approximately 1 500 stations which cover a big area of the earth's surface and span from 1916 to 2015. We regress the H estimates of all stations on their spatial and regional characteristics (i.e. their location, elevation and Köppen-Geiger climate class) using a random forest algorithm. Furthermore, we apply the Mann-Kendall test under the LTP assumption (MKt-LTP) to all time series to assess the significance of observed trends of the mean annual precipitation. To summarize the results, the LTP seems to depend mostly on the location of the stations, while the predictive value of the fitted regression model is good. Thus when investigating for LTP properties we recommend that the local characteristics should be considered. Additionally, the application of the MKt-LTP suggests that no significant monotonic trend can characterize the global precipitation. Dominant positive significant trends are observed mostly in main climate type D (snow), while in the other climate types the percentage of stations with positive significant trends was approximately equal to that of negative significant trends. Furthermore, 50% of all stations do not exhibit significant trends at all.

Morphological change to birds over 120 years is not explained by thermal adaptation to climate change.

PubMed

Salewski, Volker; Siebenrock, Karl-Heinz; Hochachka, Wesley M; Woog, Friederike; Fiedler, Wolfgang

2014-01-01

Changes in morphology have been postulated as one of the responses of animals to global warming, with increasing ambient temperatures leading to decreasing body size. However, the results of previous studies are inconsistent. Problems related to the analyses of trends in body size may be related to the short-term nature of data sets, to the selection of surrogates for body size, to the appropriate models for data analyses, and to the interpretation as morphology may change in response to ecological drivers other than climate and irrespective of size. Using generalized additive models, we analysed trends in three morphological traits of 4529 specimens of eleven bird species collected between 1889 and 2010 in southern Germany and adjacent areas. Changes and trends in morphology over time were not consistent when all species and traits were considered. Six of the eleven species displayed a significant association of tarsus length with time but the direction of the association varied. Wing length decreased in the majority of species but there were few significant trends in wing pointedness. Few of the traits were significantly associated with mean ambient temperatures. We argue that although there are significant changes in morphology over time there is no consistent trend for decreasing body size and therefore no support for the hypothesis of decreasing body size because of climate change. Non-consistent trends of change in surrogates for size within species indicate that fluctuations are influenced by factors other than temperature, and that not all surrogates may represent size appropriately. Future analyses should carefully select measures of body size and consider alternative hypotheses for change.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

PubMed

Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

Spatial patterns of March and September streamflow trends in Pacific Northwest Streams, 1958-2008

USGS Publications Warehouse

Chang, Heejun; Jung, Il-Won; Steele, Madeline; Gannett, Marshall

2012-01-01

Summer streamflow is a vital water resource for municipal and domestic water supplies, irrigation, salmonid habitat, recreation, and water-related ecosystem services in the Pacific Northwest (PNW) in the United States. This study detects significant negative trends in September absolute streamflow in a majority of 68 stream-gauging stations located on unregulated streams in the PNW from 1958 to 2008. The proportion of March streamflow to annual streamflow increases in most stations over 1,000 m elevation, with a baseflow index of less than 50, while absolute March streamflow does not increase in most stations. The declining trends of September absolute streamflow are strongly associated with seven-day low flow, January–March maximum temperature trends, and the size of the basin (19–7,260 km2), while the increasing trends of the fraction of March streamflow are associated with elevation, April 1 snow water equivalent, March precipitation, center timing of streamflow, and October–December minimum temperature trends. Compared with ordinary least squares (OLS) estimated regression models, spatial error regression and geographically weighted regression (GWR) models effectively remove spatial autocorrelation in residuals. The GWR model results show spatial gradients of local R 2 values with consistently higher local R 2 values in the northern Cascades. This finding illustrates that different hydrologic landscape factors, such as geology and seasonal distribution of precipitation, also influence streamflow trends in the PNW. In addition, our spatial analysis model results show that considering various geographic factors help clarify the dynamics of streamflow trends over a large geographical area, supporting a spatial analysis approach over aspatial OLS-estimated regression models for predicting streamflow trends. Results indicate that transitional rain–snow surface water-dominated basins are likely to have reduced summer streamflow under warming scenarios. Consequently, a better understanding of the relationships among summer streamflow, precipitation, snowmelt, elevation, and geology can help water managers predict the response of regional summer streamflow to global warming.

Modeling long-term trends of chlorinated ethene contamination at a public supply well

USGS Publications Warehouse

Chapelle, Francis H.; Kauffman, Leon J.; Widdowson, Mark A.

2015-01-01

A mass-balance solute-transport modeling approach was used to investigate the effects of dense nonaqueous phase liquid (DNAPL) volume, composition, and generation of daughter products on simulated and measured long-term trends of chlorinated ethene (CE) concentrations at a public supply well. The model was built by telescoping a calibrated regional three-dimensional MODFLOW model to the capture zone of a public supply well that has a history of CE contamination. The local model was then used to simulate the interactions between naturally occurring organic carbon that acts as an electron donor, and dissolved oxygen (DO), CEs, ferric iron, and sulfate that act as electron acceptors using the Sequential Electron Acceptor Model in three dimensions (SEAM3D) code. The modeling results indicate that asymmetry between rapidly rising and more gradual falling concentration trends over time suggests a DNAPL rather than a dissolved source of CEs. Peak concentrations of CEs are proportional to the volume and composition of the DNAPL source. The persistence of contamination, which can vary from a few years to centuries, is proportional to DNAPL volume, but is unaffected by DNAPL composition. These results show that monitoring CE concentrations in raw water produced by impacted public supply wells over time can provide useful information concerning the nature of contaminant sources and the likely future persistence of contamination.

Different time trends of caloric and fat intake between statin users and nonusers among US adults: gluttony in the time of statins?

PubMed

Sugiyama, Takehiro; Tsugawa, Yusuke; Tseng, Chi-Hong; Kobayashi, Yasuki; Shapiro, Martin F

2014-07-01

Both dietary modification and use of statins can lower blood cholesterol. The increase in caloric intake among the general population is reported to have plateaued in the last decade, but no study has examined the relationship between the time trends of caloric intake and statin use. To examine the difference in the temporal trends of caloric and fat intake between statin users and nonusers among US adults. A repeated cross-sectional study in a nationally representative sample of 27,886 US adults, 20 years or older, from the National Health and Nutrition Examination Survey, 1999 through 2010. Statin use. Caloric and fat intake measured through 24-hour dietary recall. Generalized linear models with interaction term between survey cycle and statin use were constructed to investigate the time trends of dietary intake for statin users and nonusers after adjustment for possible confounders. We calculated model-adjusted caloric and fat intake using these models and examined if the time trends differed by statin use. Body mass index (BMI) changes were also compared between statin users and nonusers. In the 1999-2000 period, the caloric intake was significantly less for statin users compared with nonusers (2000 vs 2179 kcal/d; P = .007). The difference between the groups became smaller as time went by, and there was no statistical difference after the 2005-2006 period. Among statin users, caloric intake in the 2009-2010 period was 9.6% higher (95% CI, 1.8-18.1; P = .02) than that in the 1999-2000 period. In contrast, no significant change was observed among nonusers during the same study period. Statin users also consumed significantly less fat in the 1999-2000 period (71.7 vs 81.2 g/d; P = .003). Fat intake increased 14.4% among statin users (95% CI, 3.8-26.1; P = .007) while not changing significantly among nonusers. Also, BMI increased more among statin users (+1.3) than among nonusers (+0.4) in the adjusted model (P = .02). Caloric and fat intake have increased among statin users over time, which was not true for nonusers. The increase in BMI was faster for statin users than for nonusers. Efforts aimed at dietary control among statin users may be becoming less intensive. The importance of dietary composition may need to be reemphasized for statin users.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Divergent responses to spring and winter warming drive community level flowering trends

PubMed Central

Cook, Benjamin I.; Wolkovich, Elizabeth M.; Parmesan, Camille

2012-01-01

Analyses of datasets throughout the temperate midlatitude regions show a widespread tendency for species to advance their springtime phenology, consistent with warming trends over the past 20–50 y. Within these general trends toward earlier spring, however, are species that either have insignificant trends or have delayed their timing. Various explanations have been offered to explain this apparent nonresponsiveness to warming, including the influence of other abiotic cues (e.g., photoperiod) or reductions in fall/winter chilling (vernalization). Few studies, however, have explicitly attributed the historical trends of nonresponding species to any specific factor. Here, we analyzed long-term data on phenology and seasonal temperatures from 490 species on two continents and demonstrate that (i) apparent nonresponders are indeed responding to warming, but their responses to fall/winter and spring warming are opposite in sign and of similar magnitude; (ii) observed trends in first flowering date depend strongly on the magnitude of a given species’ response to fall/winter vs. spring warming; and (iii) inclusion of fall/winter temperature cues strongly improves hindcast model predictions of long-term flowering trends compared with models with spring warming only. With a few notable exceptions, climate change research has focused on the overall mean trend toward phenological advance, minimizing discussion of apparently nonresponding species. Our results illuminate an understudied source of complexity in wild species responses and support the need for models incorporating diverse environmental cues to improve predictability of community level responses to anthropogenic climate change. PMID:22615406

Detection, attribution, and sensitivity of trends toward earlier streamflow in the Sierra Nevada

USGS Publications Warehouse

Maurer, E.P.; Stewart, I.T.; Bonfils, Celine; Duffy, P.B.; Cayan, D.

2007-01-01

Observed changes in the timing of snowmelt dominated streamflow in the western United States are often linked to anthropogenic or other external causes. We assess whether observed streamflow timing changes can be statistically attributed to external forcing, or whether they still lie within the bounds of natural (internal) variability for four large Sierra Nevada (CA) basins, at inflow points to major reservoirs. Streamflow timing is measured by "center timing" (CT), the day when half the annual flow has passed a given point. We use a physically based hydrology model driven by meteorological input from a global climate model to quantify the natural variability in CT trends. Estimated 50-year trends in CT due to natural climate variability often exceed estimated actual CT trends from 1950 to 1999. Thus, although observed trends in CT to date may be statistically significant, they cannot yet be statistically attributed to external influences on climate. We estimate that projected CT changes at the four major reservoir inflows will, with 90% confidence, exceed those from natural variability within 1-4 decades or 4-8 decades, depending on rates of future greenhouse gas emissions. To identify areas most likely to exhibit CT changes in response to rising temperatures, we calculate changes in CT under temperature increases from 1 to 5??. We find that areas with average winter temperatures between -2??C and -4??C are most likely to respond with significant CT shifts. Correspondingly, elevations from 2000 to 2800 in are most sensitive to temperature increases, with CT changes exceeding 45 days (earlier) relative to 1961-1990. Copyright 2007 by the American Geophysical Union.

Accurate estimation of influenza epidemics using Google search data via ARGO

PubMed Central

Yang, Shihao; Santillana, Mauricio; Kou, S. C.

2015-01-01

Accurate real-time tracking of influenza outbreaks helps public health officials make timely and meaningful decisions that could save lives. We propose an influenza tracking model, ARGO (AutoRegression with GOogle search data), that uses publicly available online search data. In addition to having a rigorous statistical foundation, ARGO outperforms all previously available Google-search–based tracking models, including the latest version of Google Flu Trends, even though it uses only low-quality search data as input from publicly available Google Trends and Google Correlate websites. ARGO not only incorporates the seasonality in influenza epidemics but also captures changes in people’s online search behavior over time. ARGO is also flexible, self-correcting, robust, and scalable, making it a potentially powerful tool that can be used for real-time tracking of other social events at multiple temporal and spatial resolutions. PMID:26553980

Efficient hemodynamic event detection utilizing relational databases and wavelet analysis

NASA Technical Reports Server (NTRS)

Saeed, M.; Mark, R. G.

2001-01-01

Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.

Observed changes in the Earth's dynamic oblateness from GRACE data and geophysical models.

PubMed

Sun, Y; Ditmar, P; Riva, R

A new methodology is proposed to estimate changes in the Earth's dynamic oblateness ([Formula: see text] or equivalently, [Formula: see text]) on a monthly basis. The algorithm uses monthly Gravity Recovery and Climate Experiment (GRACE) gravity solutions, an ocean bottom pressure model and a glacial isostatic adjustment (GIA) model. The resulting time series agree remarkably well with a solution based on satellite laser ranging (SLR) data. Seasonal variations of the obtained time series show little sensitivity to the choice of GRACE solutions. Reducing signal leakage in coastal areas when dealing with GRACE data and accounting for self-attraction and loading effects when dealing with water redistribution in the ocean is crucial in achieving close agreement with the SLR-based solution in terms of de-trended solutions. The obtained trend estimates, on the other hand, may be less accurate due to their dependence on the GIA models, which still carry large uncertainties.

Global long-term ozone trends derived from different observed and modelled data sets

NASA Astrophysics Data System (ADS)

Coldewey-Egbers, M.; Loyola, D.; Zimmer, W.; van Roozendael, M.; Lerot, C.; Dameris, M.; Garny, H.; Braesicke, P.; Koukouli, M.; Balis, D.

2012-04-01

The long-term behaviour of stratospheric ozone amounts during the past three decades is investigated on a global scale using different observed and modelled data sets. Three European satellite sensors GOME/ERS-2, SCIAMACHY/ENVISAT, and GOME-2/METOP are combined and a merged global monthly mean total ozone product has been prepared using an inter-satellite calibration approach. The data set covers the 16-years period from June 1995 to June 2011 and it exhibits an excellent long-term stability, which is required for such trend studies. A multiple linear least-squares regression algorithm using different explanatory variables is applied to the time series and statistically significant positive trends are detected in the northern mid latitudes and subtropics. Global trends are also estimated using a second satellite-based Merged Ozone Data set (MOD) provided by NASA. For few selected geographical regions ozone trends are additionally calculated using well-maintained measurements of individual Dobson/Brewer ground-based instruments. A reasonable agreement in the spatial patterns of the trends is found amongst the European satellite, the NASA satellite, and the ground-based observations. Furthermore, two long-term simulations obtained with the Chemistry-Climate Models E39C-A provided by German Aerospace Center and UMUKCA-UCAM provided by University of Cambridge are analysed.

The imperative for emergency medicine to create its own alternative payment model.

PubMed

Medford-Davis, Laura N

2017-06-01

Seven years after the Affordable Care Act legislated Alternative Payment Models, it is time for Emergency Medicine to find its place within this value-based trend by developing its own Alternative Payment Model. Copyright © 2017 Elsevier Inc. All rights reserved.

Decadal trends in tropospheric ozone over East Asian Pacific rim during 1998-2007: Implications for emerging Asian emissions impacts and comparison to European and North American records (Invited)

NASA Astrophysics Data System (ADS)

Tanimoto, H.; Ohara, T.; Uno, I.

2010-12-01

We examine springtime ozone trends at nine remote locations in East Asian Pacific rim during the last decade (1998-2007). The observed decadal ozone trends are relatively small at surface sites but are substantially larger at a mountainous site. The level and increasing rate of ozone at the mountainous site are both higher than those observed at background sites in Europe and North America. We use a regional chemistry-transport model to explore the observed changes and how changes in Asian anthropogenic emissions have contributed to the observed increasing trends. The model with yearly-dependent regional emissions successfully reproduces the levels, variability, and interannual variations of ozone at all the surface sites. It predicts increasing trends at the mountainous site, suggesting that increasing Asian anthropogenic emissions account for about half the observed increase. However, the discrepancy between the observation and model results after 2003 (the time of largest emission increase) suggests significant underestimation of the actual growth of the Asian anthropogenic emissions and/or incompleteness in the modeling of pollution export from continental Asia. These findings imply that improving emissions inventory and transport scheme is needed to better understand rapidly evolving tropospheric ozone in East Asia and its potential climatic and environmental impacts.

Detecting temporal trends in species assemblages with bootstrapping procedures and hierarchical models

USGS Publications Warehouse

Gotelli, Nicholas J.; Dorazio, Robert M.; Ellison, Aaron M.; Grossman, Gary D.

2010-01-01

Quantifying patterns of temporal trends in species assemblages is an important analytical challenge in community ecology. We describe methods of analysis that can be applied to a matrix of counts of individuals that is organized by species (rows) and time-ordered sampling periods (columns). We first developed a bootstrapping procedure to test the null hypothesis of random sampling from a stationary species abundance distribution with temporally varying sampling probabilities. This procedure can be modified to account for undetected species. We next developed a hierarchical model to estimate species-specific trends in abundance while accounting for species-specific probabilities of detection. We analysed two long-term datasets on stream fishes and grassland insects to demonstrate these methods. For both assemblages, the bootstrap test indicated that temporal trends in abundance were more heterogeneous than expected under the null model. We used the hierarchical model to estimate trends in abundance and identified sets of species in each assemblage that were steadily increasing, decreasing or remaining constant in abundance over more than a decade of standardized annual surveys. Our methods of analysis are broadly applicable to other ecological datasets, and they represent an advance over most existing procedures, which do not incorporate effects of incomplete sampling and imperfect detection.

Understanding observed and simulated historical temperature trends in California

NASA Astrophysics Data System (ADS)

Bonfils, C. J.; Duffy, P. B.; Santer, B. D.; Lobell, D. B.; Wigley, T. M.

2006-12-01

In our study, we attempt 1) to improve our understanding of observed historical temperature trends and their underlying causes in the context of regional detection of climate change and 2) to identify possible neglected forcings and errors in the model response to imposed forcings at the origin of inconsistencies between models and observations. From eight different observational datasets, we estimate California-average temperature trends over 1950- 1999 and compare them to trends from a suite of IPCC control simulations of natural internal climate variability. We find that the substantial night-time warming occurring from January to September is inconsistent with model-based estimates of natural internal climate variability, and thus requires one or more external forcing agents to be explained. In contrast, we find that a significant day-time warming occurs only from January to March. Our confidence in these findings is increased because there is no evidence that the models systematically underestimate noise on interannual and decadal timescales. However, we also find that IPCC simulations of the 20th century that include combined anthropogenic and natural forcings are not able to reproduce such a pronounced seasonality of the trends. Our first hypothesis is that the warming of Californian winters over the second half of the twentieth century is associated with changes in large-scale atmospheric circulation that are likely to be human-induced. This circulation change is underestimated in the historical simulations, which may explain why the simulated warming of Californian winters is too weak. We also hypothesize that the lack of a detectable observed increase in summertime maximum temperature arises from a cooling associated with large-scale irrigation. This cooling may have, until now, counteracted the warming induced by increasing greenhouse gases and urbanization effects. Omitting to include this forcing in the simulations can result in overestimating the summertime maximum temperature trends. We conduct an empirical study based on observed climate and irrigation changes to evaluate this assumption.

Water-quality trend analysis and sampling design for the Souris River, Saskatchewan, North Dakota, and Manitoba

USGS Publications Warehouse

Vecchia, Aldo V.

2000-01-01

The Souris River Basin is a 24,600-square-mile basin located in southeast Saskatchewan, north-central North Dakota, and southwest Manitoba.  The Souris River Bilateral Water Quality Monitoring Group, formed in 1989 by the governments of Canada and the United States, is responsible for documenting trends in water quality in the Souris River and making recommendations for monitoring future water-quality conditions.  This report presents results of a study conducted for the Bilateral Water Quality Monitoring Group by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, to analyze historic trends in water quality in the Souris River and to determine efficient sampling designs for monitoring future trends.  U.S. Geological Survey and Environment Canada water-quality data collected during 1977-96 from four sites near the boundary crossings between Canada and the United States were included in the trend analysis. A parametric time-series model was developed for detecting trends in historic constituent concentration data.  The model can be applied to constituents that have at least 90 percent of observations above detection limits of the analyses, which, for the Souris River, includes most major ions and nutrients and many trace elements.  The model can detect complex nonmonotonic trends in concentration in the presence of complex interannual and seasonal variability in daily discharge.  A key feature of the model is its ability to handle highly irregular sampling intervals.  For example, the intervals between concentration measurements may be be as short as 10 days to as long as several months, and the number of samples in any given year can range from zero to 36. Results from the trend analysis for the Souris River indicated numerous trends in constituent concentration.  The most significant trends at the two sites located near the upstream boundary crossing between Saskatchewan and North Dakota consisted of increases in concentrations of most major ions, dissolved boron, and dissolved arsenic during 1987-91 and decreases in concentrations of the same constituents during 1992-96.  Significant trends at the two sites located near the downstream boundary crossing between North Dakota and Manitoba included increases in dissolved sodium, dissolved chloride, and total phosphorus during 1977-86, decreases in dissolved oxygen and dissolved boron and increases in total phosphorus and dissolved iron during 1987-91, and a decrease in total phosphorus during 1992-96. The time-series model also was used to determine the sensitivity of various sampling designs for monitoring future water-quality trends in the Souris River.  It was determined that at least two samples per year are required in each of three seasons--March through June, July through October, and November through February--to obtain reasonable sensitivity for detecting trends in each season.  In addition, substantial improvements occurred in sensitivity for detecting trends by adding a third sample for major ions and trace elements in March through June, adding a third sample for nutrients in July through October, and adding a third sample for nutrients, trace elements, and dissolved oxygen in November through February.

Modelling road accidents: An approach using structural time series

NASA Astrophysics Data System (ADS)

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-09-01

In this paper, the trend of road accidents in Malaysia for the years 2001 until 2012 was modelled using a structural time series approach. The structural time series model was identified using a stepwise method, and the residuals for each model were tested. The best-fitted model was chosen based on the smallest Akaike Information Criterion (AIC) and prediction error variance. In order to check the quality of the model, a data validation procedure was performed by predicting the monthly number of road accidents for the year 2012. Results indicate that the best specification of the structural time series model to represent road accidents is the local level with a seasonal model.

Urinary and Dietary Analysis of 18,470 Bangladeshis Reveal a Correlation of Rice Consumption with Arsenic Exposure and Toxicity

PubMed Central

Melkonian, Stephanie; Argos, Maria; Hall, Megan N.; Chen, Yu; Parvez, Faruque; Pierce, Brandon; Cao, Hongyuan; Aschebrook-Kilfoy, Briseis; Ahmed, Alauddin; Islam, Tariqul; Slavcovich, Vesna; Gamble, Mary; Haris, Parvez I.; Graziano, Joseph H.; Ahsan, Habibul

2013-01-01

Background We utilized data from the Health Effects of Arsenic Longitudinal Study (HEALS) in Araihazar, Bangladesh, to evaluate the association of steamed rice consumption with urinary total arsenic concentration and arsenical skin lesions in the overall study cohort (N=18,470) and in a subset with available urinary arsenic metabolite data (N=4,517). Methods General linear models with standardized beta coefficients were used to estimate associations between steamed rice consumption and urinary total arsenic concentration and urinary arsenic metabolites. Logistic regression models were used to estimate prevalence odds ratios (ORs) and their 95% confidence intervals (CIs) for the associations between rice intake and prevalent skin lesions at baseline. Discrete time hazard models were used to estimate discrete time (HRs) ratios and their 95% CIs for the associations between rice intake and incident skin lesions. Results Steamed rice consumption was positively associated with creatinine-adjusted urinary total arsenic (β=0.041, 95% CI: 0.032-0.051) and urinary total arsenic with statistical adjustment for creatinine in the model (β=0.043, 95% CI: 0.032-0.053). Additionally, we observed a significant trend in skin lesion prevalence (P-trend=0.007) and a moderate trend in skin lesion incidence (P-trend=0.07) associated with increased intake of steamed rice. Conclusions This study suggests that rice intake may be a source of arsenic exposure beyond drinking water. PMID:24260455

Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.

Reservoir monitoring and characterization using satellite geodetic data: Interferometric Synthetic Aperture Radar observations from the Krechba field, Algeria

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vasco, D.W.; Ferretti, Alessandro; Novali, Fabrizio

2008-05-01

Deformation in the material overlying an active reservoir is used to monitor pressure change at depth. A sequence of pressure field estimates, eleven in all, allow us to construct a measure of diffusive travel time throughout the reservoir. The dense distribution of travel time values means that we can construct an exactly linear inverse problem for reservoir flow properties. Application to Interferometric Synthetic Aperture Radar (InSAR) data gathered over a CO{sub 2} injection in Algeria reveals pressure propagation along two northwest trending corridors. An inversion of the travel times indicates the existence of two northwest-trending high permeability zones. The highmore » permeability features trend in the same direction as the regional fault and fracture zones. Model parameter resolution estimates indicate that the features are well resolved.« less

Indicator saturation: a novel approach to detect multiple breaks in geodetic time series.

NASA Astrophysics Data System (ADS)

Jackson, L. P.; Pretis, F.; Williams, S. D. P.

2016-12-01

Geodetic time series can record long term trends, quasi-periodic signals at a variety of time scales from days to decades, and sudden breaks due to natural or anthropogenic causes. The causes of breaks range from instrument replacement to earthquakes to unknown (i.e. no attributable cause). Furthermore, breaks can be permanent or short-lived and range at least two orders of magnitude in size (mm to 100's mm). To account for this range of possible signal-characteristics requires a flexible time series method that can distinguish between true and false breaks, outliers and time-varying trends. One such method, Indicator Saturation (IS) comes from the field of econometrics where analysing stochastic signals in these terms is a common problem. The IS approach differs from alternative break detection methods by considering every point in the time series as a break until it is demonstrated statistically that it is not. A linear model is constructed with a break function at every point in time, and all but statistically significant breaks are removed through a general-to-specific model selection algorithm for more variables than observations. The IS method is flexible because it allows multiple breaks of different forms (e.g. impulses, shifts in the mean, and changing trends) to be detected, while simultaneously modelling any underlying variation driven by additional covariates. We apply the IS method to identify breaks in a suite of synthetic GPS time series used for the Detection of Offsets in GPS Experiments (DOGEX). We optimise the method to maximise the ratio of true-positive to false-positive detections, which improves estimates of errors in the long term rates of land motion currently required by the GPS community.

Spatial patterns of recent Antarctic surface temperature trends and the importance of natural variability: lessons from multiple reconstructions and the CMIP5 models

NASA Astrophysics Data System (ADS)

Smith, Karen L.; Polvani, Lorenzo M.

2017-04-01

The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a cooling of East Antarctica in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature trends over two distinct time periods (1960-2005 and 1979-2005), and with those simulated by 40 models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We find that the observed East-West asymmetry differs substantially between the two periods and, furthermore, that it is completely absent from the forced response seen in the CMIP5 multi-model mean, from which all natural variability is eliminated by the averaging. We also examine the relationship between the Southern Annular mode (SAM) and Antarctic temperature trends, in both models and reanalyses, and again conclude that there is little evidence of anthropogenic SAM-induced driving of the recent temperature trends. These results offer new, compelling evidence pointing to natural climate variability as a key contributor to the recent warming of West Antarctica and of the Peninsula.

The Dependence of Cloud Property Trend Detection on Absolute Calibration Accuracy of Passive Satellite Sensors

NASA Astrophysics Data System (ADS)

Shea, Y.; Wielicki, B. A.; Sun-Mack, S.; Minnis, P.; Zelinka, M. D.

2016-12-01

Detecting trends in climate variables on global, decadal scales requires highly accurate, stable measurements and retrieval algorithms. Trend uncertainty depends on its magnitude, natural variability, and instrument and retrieval algorithm accuracy and stability. We applied a climate accuracy framework to quantify the impact of absolute calibration on cloud property trend uncertainty. The cloud properties studied were cloud fraction, effective temperature, optical thickness, and effective radius retrieved using the Clouds and the Earth's Radiant Energy System (CERES) Cloud Property Retrieval System, which uses Moderate-resolution Imaging Spectroradiometer measurements (MODIS). Modeling experiments from the fifth phase of the Climate Model Intercomparison Project (CMIP5) agree that net cloud feedback is likely positive but disagree regarding its magnitude, mainly due to uncertainty in shortwave cloud feedback. With the climate accuracy framework we determined the time to detect trends for instruments with various calibration accuracies. We estimated a relationship between cloud property trend uncertainty, cloud feedback, and Equilibrium Climate Sensitivity and also between effective radius trend uncertainty and aerosol indirect effect trends. The direct relationship between instrument accuracy requirements and climate model output provides the level of instrument absolute accuracy needed to reduce climate model projection uncertainty. Different cloud types have varied radiative impacts on the climate system depending on several attributes, such as their thermodynamic phase, altitude, and optical thickness. Therefore, we also conducted these studies by cloud types for a clearer understanding of instrument accuracy requirements needed to detect changes in their cloud properties. Combining this information with the radiative impact of different cloud types helps to prioritize among requirements for future satellite sensors and understanding the climate detection capabilities of existing sensors.

Detecting Land Cover Change by Trend and Seasonality of Remote Sensing Time Series

NASA Astrophysics Data System (ADS)

Oliveira, J. C.; Epiphanio, J. N.; Mello, M. P.

2013-05-01

Natural resource managers demand knowledge of information on the spatiotemporal dynamics of land use and land cover change, and detection and characteristics change over time is an initial step for the understanding of the mechanism of change. The propose of this research is the use the approach BFAST (Breaks For Additive Seasonal and Trend) for detects trend and seasonal changes within Normalized Difference Vegetation Index (NDVI) time series. BFAST integrates the decomposition of time series into trend, seasonal, and noise components with methods for detecting change within time series without the need to select a reference period, set a threshold, or define a change trajectory. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. The general model is of the form Yt = Tt + St + et (t= 1,2,3,…, n) where Yt is the observed data at time t, Tt is the trend component, St is the seasonal component, and et is the remainder component. In this study was used MODIS NDVI time series datasets (MOD13Q1) over 11 years (2000 - 2010) on an intensive agricultural area in Mato Grosso - Brazil. At first it was applied a filter for noise reduction (4253H twice) over spectral curve of each MODIS pixel, and subsequently each time series was decomposed into seasonal, trend, and remainder components by BFAST. Were detected one abrupt change from a single pixel of forest and two abrupt changes on trend component to a pixel of the agricultural area. Figure 1 shows the number of phonological change with base in seasonal component for study area. This paper demonstrated the ability of the BFAST to detect long-term phenological change by analyzing time series while accounting for abrupt and gradual changes. The algorithm iteratively estimates the dates and number of changes occurring within seasonal and trend components, and characterizes changes by extracting the magnitude and direction of change. Changes occurring in the seasonal component indicate phenological changes, while changes occurring in the trend component indicate gradual and abrupt change. BFAST can be used to analyze different types of remotely sensed time series and can be applied to other time series such as econometrics, climatology, and hydrology. The algorithm used in this study is available in BFAT package for R from CRAN (http://cran.r-project.org/package=bfast).; Figure 1 - Number of the phonological change with base in seasonal component.

Research on trend of warm-humid climate in Central Asia

NASA Astrophysics Data System (ADS)

Gong, Zhi; Peng, Dailiang; Wen, Jingyi; Cai, Zhanqing; Wang, Tiantian; Hu, Yuekai; Ma, Yaxin; Xu, Junfeng

2017-07-01

Central Asia is a typical arid area, which is sensitive and vulnerable part of climate changes, at the same time, Central Asia is the Silk Road Economic Belt of the core district, the warm-humid climate change will affect the production and economic development of neighboring countries. The average annual precipitation, average anneal temperature and evapotranspiration are the important indexes to weigh the climate change. In this paper, the annual precipitation, annual average temperature and evapotranspiration data of every pixel point in Central Asia are analyzed by using long-time series remote sensing data to analyze the trend of warm and humid conditions. Finally, using the model to analyzed the distribution of warm-dry trend, the warm-wet trend, the cold-dry trend and the cold-wet trend in Central Asia and Xinjiang area. The results showed that most of the regions of Central Asia were warm-humid and warm-dry trends, but only a small number of regions showed warm-dry and cold-dry trends. It is of great significance to study the climatic change discipline and guarantee the ecological safety and improve the ability to cope with climate change in the region. It also provide scientific basis for the formulation of regional climate change program. The first section in your paper

Are GRACE-era terrestrial water trends driven by anthropogenic climate change?

DOE PAGES

Fasullo, J. T.; Lawrence, D. M.; Swenson, S. C.

2016-01-01

To provide context for observed trends in terrestrial water storage (TWS) during GRACE (2003–2014), trends and variability in the CESM1-CAM5 Large Ensemble (LE) are examined. Motivated in part by the anomalous nature of climate variability during GRACE, the characteristics of both forced change and internal modes are quantified and their influences on observations are estimated. Trends during the GRACE era in the LE are dominated by internal variability rather than by the forced response, with TWS anomalies in much of the Americas, eastern Australia, Africa, and southwestern Eurasia largely attributable to the negative phases of the Pacific Decadal Oscillation (PDO)more » and Atlantic Multidecadal Oscillation (AMO). While similarities between observed trends and the model-inferred forced response also exist, it is inappropriate to attribute such trends mainly to anthropogenic forcing. For several key river basins, trends in the mean state and interannual variability and the time at which the forced response exceeds background variability are also estimated while aspects of global mean TWS, including changes in its annual amplitude and decadal trends, are quantified. Lastly, the findings highlight the challenge of detecting anthropogenic climate change in temporally finite satellite datasets and underscore the benefit of utilizing models in the interpretation of the observed record.« less

Are GRACE-era terrestrial water trends driven by anthropogenic climate change?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fasullo, J. T.; Lawrence, D. M.; Swenson, S. C.

To provide context for observed trends in terrestrial water storage (TWS) during GRACE (2003–2014), trends and variability in the CESM1-CAM5 Large Ensemble (LE) are examined. Motivated in part by the anomalous nature of climate variability during GRACE, the characteristics of both forced change and internal modes are quantified and their influences on observations are estimated. Trends during the GRACE era in the LE are dominated by internal variability rather than by the forced response, with TWS anomalies in much of the Americas, eastern Australia, Africa, and southwestern Eurasia largely attributable to the negative phases of the Pacific Decadal Oscillation (PDO)more » and Atlantic Multidecadal Oscillation (AMO). While similarities between observed trends and the model-inferred forced response also exist, it is inappropriate to attribute such trends mainly to anthropogenic forcing. For several key river basins, trends in the mean state and interannual variability and the time at which the forced response exceeds background variability are also estimated while aspects of global mean TWS, including changes in its annual amplitude and decadal trends, are quantified. Lastly, the findings highlight the challenge of detecting anthropogenic climate change in temporally finite satellite datasets and underscore the benefit of utilizing models in the interpretation of the observed record.« less

Time series forecasting of future claims amount of SOCSO's employment injury scheme (EIS)

NASA Astrophysics Data System (ADS)

Zulkifli, Faiz; Ismail, Isma Liana; Chek, Mohd Zaki Awang; Jamal, Nur Faezah; Ridzwan, Ahmad Nur Azam Ahmad; Jelas, Imran Md; Noor, Syamsul Ikram Mohd; Ahmad, Abu Bakar

2012-09-01

The Employment Injury Scheme (EIS) provides protection to employees who are injured due to accidents whilst working, commuting from home to the work place or during employee takes a break during an authorized recess time or while travelling that is related with his work. The main purpose of this study is to forecast value on claims amount of EIS for the year 2011 until 2015 by using appropriate models. These models were tested on the actual EIS data from year 1972 until year 2010. Three different forecasting models are chosen for comparisons. These are the Naïve with Trend Model, Average Percent Change Model and Double Exponential Smoothing Model. The best model is selected based on the smallest value of error measures using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). From the result, the best model that best fit the forecast for the EIS is the Average Percent Change Model. Furthermore, the result also shows the claims amount of EIS for the year 2011 to year 2015 continue to trend upwards from year 2010.

Establishing an index arbitrage model by applying neural networks method--a case study of Nikkei 225 index.

PubMed

Chen, A P; Chianglin, C Y; Chung, H P

2001-10-01

This paper applies the neural network method to establish an index arbitrage model and compares the arbitrage performances to that from traditional cost of carry arbitrage model. From the empirical results of the Nikkei 225 stock index market, following conclusions can be stated: (1) The basis will get enlarged for a time period, more profitability may be obtained from the trend. (2) If the neural network is applied within the index arbitrage model, twofold of return would be obtained than traditional arbitrage model can do. (3) If the T_basis has volatile trend, the neural network arbitrage model will ignore the peak. Although arbitrageur would lose the chance to get profit, they may reduce the market impact risk.

First Measurements of the HCFC-142b Trend from Atmospheric Chemistry Experiment (ACE) Solar Occultation Spectra

NASA Technical Reports Server (NTRS)

Rinsland, Curtis P.; Chiou, Linda; Boone,Chris; Bernath, Peter; Mahieu, Emmanuel

2009-01-01

The first measurement of the HCFC-142b (CH3CClF2) trend near the tropopause has been derived from volume mixing ratio (VMR) measurements at northern and southern hemisphere mid-latitudes for the 2004-2008 time period from spaceborne solar occultation observations recorded at 0.02/cm resolution with the ACE (atmospheric chemistry experiment) Fourier transform spectrometer. The HCFC-142b molecule is currently the third most abundant HCFC (hydrochlorofluorocarbon) in the atmosphere and ACE measurements over this time span show a continuous rise in its volume mixing ratio. Monthly average measurements at northern and southern hemisphere midlatitudes have similar increase rates that are consistent with surface trend measurements for a similar time span. A mean northern hemisphere profile for the time span shows a near constant VMR at 8-20km altitude range, consistent on average for the same time span with in situ results. The nearly constant vertical VMR profile also agrees with model predictions of a long lifetime in the lower atmosphere.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks.

PubMed

Ghosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O; Cohn, Emily; Mekaru, Sumiko R; Brownstein, John S; Ramakrishnan, Naren

2017-01-19

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

NASA Astrophysics Data System (ADS)

Ghosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren

2017-01-01

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations.

Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

PubMed Central

Ghosh, Saurav; Chakraborty, Prithwish; Nsoesie, Elaine O.; Cohn, Emily; Mekaru, Sumiko R.; Brownstein, John S.; Ramakrishnan, Naren

2017-01-01

In retrospective assessments, internet news reports have been shown to capture early reports of unknown infectious disease transmission prior to official laboratory confirmation. In general, media interest and reporting peaks and wanes during the course of an outbreak. In this study, we quantify the extent to which media interest during infectious disease outbreaks is indicative of trends of reported incidence. We introduce an approach that uses supervised temporal topic models to transform large corpora of news articles into temporal topic trends. The key advantages of this approach include: applicability to a wide range of diseases and ability to capture disease dynamics, including seasonality, abrupt peaks and troughs. We evaluated the method using data from multiple infectious disease outbreaks reported in the United States of America (U.S.), China, and India. We demonstrate that temporal topic trends extracted from disease-related news reports successfully capture the dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue outbreaks in India (2013) and China (2014). Our observations also suggest that, when news coverage is uniform, efficient modeling of temporal topic trends using time-series regression techniques can estimate disease case counts with increased precision before official reports by health organizations. PMID:28102319

Data for Figures and Tables in Journal Article Assessment of the Effects of Horizontal Grid Resolution on Long-Term Air Quality Trends using Coupled WRF-CMAQ Simulations, doi:10.1016/j.atmosenv.2016.02.036

EPA Pesticide Factsheets

The dataset represents the data depicted in the Figures and Tables of a Journal Manuscript with the following abstract: The objective of this study is to determine the adequacy of using a relatively coarse horizontal resolution (i.e. 36 km) to simulate long-term trends of pollutant concentrations and radiation variables with the coupled WRF-CMAQ model. WRF-CMAQ simulations over the continental United State are performed over the 2001 to 2010 time period at two different horizontal resolutions of 12 and 36 km. Both simulations used the same emission inventory and model configurations. Model results are compared both in space and time to assess the potential weaknesses and strengths of using coarse resolution in long-term air quality applications. The results show that the 36 km and 12 km simulations are comparable in terms of trends analysis for both pollutant concentrations and radiation variables. The advantage of using the coarser 36 km resolution is a significant reduction of computational cost, time and storage requirement which are key considerations when performing multiple years of simulations for trend analysis. However, if such simulations are to be used for local air quality analysis, finer horizontal resolution may be beneficial since it can provide information on local gradients. In particular, divergences between the two simulations are noticeable in urban, complex terrain and coastal regions.This dataset is associated with the following publication

Trends in radiopharmaceutical dispensing in a regional nuclear pharmacy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basmadjian, G.P.; Johnston, J.; Barker, K.

1982-11-01

Dispensing trends for radiopharmaceuticals at a regional nuclear pharmacy over a 51-month period were studied. dispensing records of a regional nuclear pharmacy were analyzed with a forecasting procedure that uses univariate time data to produce time trends and autoregressive models. The overall number of prescriptions increased from 3500 to 5500 per quarter. Radiopharmaceuticals used in nuclear cardiology studies increased from less than 0.1% to 17.5% of total prescriptions dispensed, while radiopharmaceuticals used for brain imaging showed a steady decline from 29% to 11% of total prescriptions dispensed. The demand for other radiopharmaceuticals increased in areas such as renal studies, bonemore » studies, lung studies, liver-function studies, and /sup 67/Ga tumor-uptake studies, and declined slightly for static liver studies. Changes in dispensing trends for radiopharmaceuticals will continue as the practice of nuclear medicine concentrates more on functional studies and as newer imaging techniques become used for other purposes.« less

Time-series analyses of air pollution and mortality in the United States: a subsampling approach.

PubMed

Moolgavkar, Suresh H; McClellan, Roger O; Dewanji, Anup; Turim, Jay; Luebeck, E Georg; Edwards, Melanie

2013-01-01

Hierarchical Bayesian methods have been used in previous papers to estimate national mean effects of air pollutants on daily deaths in time-series analyses. We obtained maximum likelihood estimates of the common national effects of the criteria pollutants on mortality based on time-series data from ≤ 108 metropolitan areas in the United States. We used a subsampling bootstrap procedure to obtain the maximum likelihood estimates and confidence bounds for common national effects of the criteria pollutants, as measured by the percentage increase in daily mortality associated with a unit increase in daily 24-hr mean pollutant concentration on the previous day, while controlling for weather and temporal trends. We considered five pollutants [PM10, ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2)] in single- and multipollutant analyses. Flexible ambient concentration-response models for the pollutant effects were considered as well. We performed limited sensitivity analyses with different degrees of freedom for time trends. In single-pollutant models, we observed significant associations of daily deaths with all pollutants. The O3 coefficient was highly sensitive to the degree of smoothing of time trends. Among the gases, SO2 and NO2 were most strongly associated with mortality. The flexible ambient concentration-response curve for O3 showed evidence of nonlinearity and a threshold at about 30 ppb. Differences between the results of our analyses and those reported from using the Bayesian approach suggest that estimates of the quantitative impact of pollutants depend on the choice of statistical approach, although results are not directly comparable because they are based on different data. In addition, the estimate of the O3-mortality coefficient depends on the amount of smoothing of time trends.

Numerical and Qualitative Contrasts of Two Statistical Models for Water Quality Change in Tidal Waters

EPA Science Inventory

Two statistical approaches, weighted regression on time, discharge, and season and generalized additive models, have recently been used to evaluate water quality trends in estuaries. Both models have been used in similar contexts despite differences in statistical foundations and...

Topics in the Journal of Counseling Psychology, 1963-2015.

PubMed

Oh, JungSu; Stewart, Alan E; Phelps, Rosemary E

2017-11-01

Historical trends in a scientific field should be apparent in the changing content of journal articles over time. Using a topic modeling approach, a statistical method for quantifying the thematic content of text, 70 topics were extracted from the abstracts of 3,603 articles published in the Journal of Counseling Psychology from 1963 to 2015. After examining interpretability of 70 topics derived from the model, 64 meaningful topics and their trends were named. In addition, the authors also classified some of the related topics into 4 categories-counseling process and outcome, multiculturalism, research methodology, and vocational psychology. Counseling process and outcome related topics have decreased recently, while topics relating to multiculturalism and diversity have shown increasing trends. The authors also discussed trends that were observed and tried to account for the changing frequencies of some important research topics within these categories. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A model for inferring transport rates from observed confinement times in field-reversed configurations

NASA Astrophysics Data System (ADS)

Steinhauer, Loren C.; Milroy, Richard D.; Slough, John T.

1985-03-01

A one-dimensional transport model is developed to simulate the confinement of plasma and magnetic flux in a field-reversed configuration. Given the resistivity, the confinement times can be calculated. Approximate expressions are found which yield the magnitude and gross profile of the resistivity if the confinement times are known. These results are applied to experimental data from experiments, primarily TRX-1, to uncover trends in the transport properties. Several important conclusions emerge. The transport depends profoundly, and inexplicably, on the plasma formation mode. The inferred transport differs in several ways from the predictions of local lower-hybrid-drift turbulence theory. Finally, the gross resistivity exhibits an unusual trend with xs (separatrix radius rs divided by the conducting wall radius rc ), and is peaked near the magnetic axis for certain predictable conditions.

Ten-year trends in adolescents' self-reported emotional and behavioral problems in the Netherlands.

PubMed

Duinhof, Elisa L; Stevens, Gonneke W J M; van Dorsselaer, Saskia; Monshouwer, Karin; Vollebergh, Wilma A M

2015-09-01

Changes in social, cultural, economic, and governmental systems over time may affect adolescents' development. The present study examined 10-year trends in self-reported emotional and behavioral problems among 11- to 16-year-old adolescents in the Netherlands. In addition, gender (girls versus boys), ethnic (Dutch versus non western) and educational (vocational versus academic) differences in these trends were examined. By means of the Strengths and Difficulties Questionnaire, trends in emotional and behavioral problems were studied in adolescents belonging to one of five independent population representative samples (2003: n = 6,904; 2005: n = 5,183; 2007: n = 6,228; 2009: n = 5,559; 2013: n = 5,478). Structural equation models indicated rather stable levels of emotional and behavioral problems over time. Whereas some small changes were found between different time points, these changes did not represent consistent changes in problem levels. Similarly, gender, ethnic and educational differences in self-reported problems on each time point were highly comparable, indicating stable mental health inequalities between groups of adolescents over time. Future internationally comparative studies using multiple measurement moments are needed to monitor whether these persistent mental health inequalities hold over extended periods of time and in different countries.

Nature's style: Naturally trendy

USGS Publications Warehouse

Cohn, T.A.; Lins, H.F.

2005-01-01

Hydroclimatological time series often exhibit trends. While trend magnitude can be determined with little ambiguity, the corresponding statistical significance, sometimes cited to bolster scientific and political argument, is less certain because significance depends critically on the null hypothesis which in turn reflects subjective notions about what one expects to see. We consider statistical trend tests of hydroclimatological data in the presence of long-term persistence (LTP). Monte Carlo experiments employing FARIMA models indicate that trend tests which fail to consider LTP greatly overstate the statistical significance of observed trends when LTP is present. A new test is presented that avoids this problem. From a practical standpoint, however, it may be preferable to acknowledge that the concept of statistical significance is meaningless when discussing poorly understood systems.

Nature's style: Naturally trendy

NASA Astrophysics Data System (ADS)

Cohn, Timothy A.; Lins, Harry F.

2005-12-01

Hydroclimatological time series often exhibit trends. While trend magnitude can be determined with little ambiguity, the corresponding statistical significance, sometimes cited to bolster scientific and political argument, is less certain because significance depends critically on the null hypothesis which in turn reflects subjective notions about what one expects to see. We consider statistical trend tests of hydroclimatological data in the presence of long-term persistence (LTP). Monte Carlo experiments employing FARIMA models indicate that trend tests which fail to consider LTP greatly overstate the statistical significance of observed trends when LTP is present. A new test is presented that avoids this problem. From a practical standpoint, however, it may be preferable to acknowledge that the concept of statistical significance is meaningless when discussing poorly understood systems.

An assessment of historical Antarctic precipitation and temperature trend using CMIP5 models and reanalysis datasets

NASA Astrophysics Data System (ADS)

Tang, Malcolm S. Y.; Chenoli, Sheeba Nettukandy; Samah, Azizan Abu; Hai, Ooi See

2018-03-01

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts "Interim" reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979-2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year-1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.

Hybrid Modeling Improves Health and Performance Monitoring

NASA Technical Reports Server (NTRS)

2007-01-01

Scientific Monitoring Inc. was awarded a Phase I Small Business Innovation Research (SBIR) project by NASA's Dryden Flight Research Center to create a new, simplified health-monitoring approach for flight vehicles and flight equipment. The project developed a hybrid physical model concept that provided a structured approach to simplifying complex design models for use in health monitoring, allowing the output or performance of the equipment to be compared to what the design models predicted, so that deterioration or impending failure could be detected before there would be an impact on the equipment's operational capability. Based on the original modeling technology, Scientific Monitoring released I-Trend, a commercial health- and performance-monitoring software product named for its intelligent trending, diagnostics, and prognostics capabilities, as part of the company's complete ICEMS (Intelligent Condition-based Equipment Management System) suite of monitoring and advanced alerting software. I-Trend uses the hybrid physical model to better characterize the nature of health or performance alarms that result in "no fault found" false alarms. Additionally, the use of physical principles helps I-Trend identify problems sooner. I-Trend technology is currently in use in several commercial aviation programs, and the U.S. Air Force recently tapped Scientific Monitoring to develop next-generation engine health-management software for monitoring its fleet of jet engines. Scientific Monitoring has continued the original NASA work, this time under a Phase III SBIR contract with a joint NASA-Pratt & Whitney aviation security program on propulsion-controlled aircraft under missile-damaged aircraft conditions.

A high resolution model of linear trend in mass variations from DMT-2: Added value of accounting for coloured noise in GRACE data

NASA Astrophysics Data System (ADS)

Farahani, Hassan H.; Ditmar, Pavel; Inácio, Pedro; Didova, Olga; Gunter, Brian; Klees, Roland; Guo, Xiang; Guo, Jing; Sun, Yu; Liu, Xianglin; Zhao, Qile; Riva, Riccardo

2017-01-01

We present a high resolution model of the linear trend in the Earth's mass variations based on DMT-2 (Delft Mass Transport model, release 2). DMT-2 was produced primarily from K-Band Ranging (KBR) data of the Gravity Recovery And Climate Experiment (GRACE). It comprises a time series of monthly solutions complete to spherical harmonic degree 120. A novel feature in its production was the accurate computation and incorporation of stochastic properties of coloured noise when processing KBR data. The unconstrained DMT-2 monthly solutions are used to estimate the linear trend together with a bias, as well as annual and semi-annual sinusoidal terms. The linear term is further processed with an anisotropic Wiener filter, which uses full noise and signal covariance matrices. Given the fact that noise in an unconstrained model of the trend is reduced substantially as compared to monthly solutions, the Wiener filter associated with the trend is much less aggressive compared to a Wiener filter applied to monthly solutions. Consequently, the trend estimate shows an enhanced spatial resolution. It allows signals in relatively small water bodies, such as Aral sea and Ladoga lake, to be detected. Over the ice sheets, it allows for a clear identification of signals associated with some outlet glaciers or their groups. We compare the obtained trend estimate with the ones from the CSR-RL05 model using (i) the same approach based on monthly noise covariance matrices and (ii) a commonly-used approach based on the DDK-filtered monthly solutions. We use satellite altimetry data as independent control data. The comparison demonstrates a high spatial resolution of the DMT-2 linear trend. We link this to the usage of high-accuracy monthly noise covariance matrices, which is due to an accurate computation and incorporation of coloured noise when processing KBR data. A preliminary comparison of the linear trend based on DMT-2 with that computed from GSFC_global_mascons_v01 reveals, among other, a high concentration of the signal along the coast for both models in areas like the ice sheets, Gulf of Alaska, and Iceland.

Red blood cell use in Switzerland: trends and demographic challenges

PubMed Central

Volken, Thomas; Buser, Andreas; Castelli, Damiano; Fontana, Stefano; Frey, Beat M.; Rüsges-Wolter, Ilka; Sarraj, Amira; Sigle, Jörg; Thierbach, Jutta; Weingand, Tina; Taleghani, Behrouz Mansouri

2018-01-01

Background Several studies have raised concerns that future demand for blood products may not be met. The ageing of the general population and the fact that a large proportion of blood products is transfused to elderly patients has been identified as an important driver of blood shortages. The aim of this study was to collect, for the first time, nationally representative data regarding blood donors and transfusion recipients in order to predict the future evolution of blood donations and red blood cell (RBC) use in Switzerland between 2014 and 2035. Materials and methods Blood donor and transfusion recipient data, subdivided by the subjects’ age and gender were obtained from Regional Blood Services and nine large, acute-care hospitals in various regions of Switzerland. Generalised additive regression models and time-series models with exponential smoothing were employed to estimate trends of whole blood donations and RBC transfusions. Results The trend models employed suggested that RBC demand could equal supply by 2018 and could eventually cause an increasing shortfall of up to 77,000 RBC units by 2035. Discussion Our study highlights the need for continuous monitoring of trends of blood donations and blood transfusions in order to take proactive measures aimed at preventing blood shortages in Switzerland. Measures should be taken to improve donor retention in order to prevent a further erosion of the blood donor base. PMID:27723455

Trends and fluctuations in the severity of interstate wars

PubMed Central

Clauset, Aaron

2018-01-01

Since 1945, there have been relatively few large interstate wars, especially compared to the preceding 30 years, which included both World Wars. This pattern, sometimes called the long peace, is highly controversial. Does it represent an enduring trend caused by a genuine change in the underlying conflict-generating processes? Or is it consistent with a highly variable but otherwise stable system of conflict? Using the empirical distributions of interstate war sizes and onset times from 1823 to 2003, we parameterize stationary models of conflict generation that can distinguish trends from statistical fluctuations in the statistics of war. These models indicate that both the long peace and the period of great violence that preceded it are not statistically uncommon patterns in realistic but stationary conflict time series. This fact does not detract from the importance of the long peace or the proposed mechanisms that explain it. However, the models indicate that the postwar pattern of peace would need to endure at least another 100 to 140 years to become a statistically significant trend. This fact places an implicit upper bound on the magnitude of any change in the true likelihood of a large war after the end of the Second World War. The historical patterns of war thus seem to imply that the long peace may be substantially more fragile than proponents believe, despite recent efforts to identify mechanisms that reduce the likelihood of interstate wars. PMID:29507877

Observing climate change trends in ocean biogeochemistry: when and where.

PubMed

Henson, Stephanie A; Beaulieu, Claudie; Lampitt, Richard

2016-04-01

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change-driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long-term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long-term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the 'footprint' of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9-15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change-driven responses in the marine ecosystem. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Time Series Analysis of Onchocerciasis Data from Mexico: A Trend towards Elimination

PubMed Central

Pérez-Rodríguez, Miguel A.; Adeleke, Monsuru A.; Orozco-Algarra, María E.; Arrendondo-Jiménez, Juan I.; Guo, Xianwu

2013-01-01

Background In Latin America, there are 13 geographically isolated endemic foci distributed among Mexico, Guatemala, Colombia, Venezuela, Brazil and Ecuador. The communities of the three endemic foci found within Mexico have been receiving ivermectin treatment since 1989. In this study, we predicted the trend of occurrence of cases in Mexico by applying time series analysis to monthly onchocerciasis data reported by the Mexican Secretariat of Health between 1988 and 2011 using the software R. Results A total of 15,584 cases were reported in Mexico from 1988 to 2011. The data of onchocerciasis cases are mainly from the main endemic foci of Chiapas and Oaxaca. The last case in Oaxaca was reported in 1998, but new cases were reported in the Chiapas foci up to 2011. Time series analysis performed for the foci in Mexico showed a decreasing trend of the disease over time. The best-fitted models with the smallest Akaike Information Criterion (AIC) were Auto-Regressive Integrated Moving Average (ARIMA) models, which were used to predict the tendency of onchocerciasis cases for two years ahead. According to the ARIMA models predictions, the cases in very low number (below 1) are expected for the disease between 2012 and 2013 in Chiapas, the last endemic region in Mexico. Conclusion The endemic regions of Mexico evolved from high onchocerciasis-endemic states to the interruption of transmission due to the strategies followed by the MSH, based on treatment with ivermectin. The extremely low level of expected cases as predicted by ARIMA models for the next two years suggest that the onchocerciasis is being eliminated in Mexico. To our knowledge, it is the first study utilizing time series for predicting case dynamics of onchocerciasis, which could be used as a benchmark during monitoring and post-treatment surveillance. PMID:23459370

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China.

PubMed

Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng

2017-07-01

Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.

Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska.

PubMed

Araz, Ozgur M; Bentley, Dan; Muelleman, Robert L

2014-09-01

Emergency department (ED) visits increase during the influenza seasons. It is essential to identify statistically significant correlates in order to develop an accurate forecasting model for ED visits. Forecasting influenza-like-illness (ILI)-related ED visits can significantly help in developing robust resource management strategies at the EDs. We first performed correlation analyses to understand temporal correlations between several predictors of ILI-related ED visits. We used the data available for Douglas County, the biggest county in Nebraska, for Omaha, the biggest city in the state, and for a major hospital in Omaha. The data set included total and positive influenza test results from the hospital (ie, Antigen rapid (Ag) and Respiratory Syncytial Virus Infection (RSV) tests); an Internet-based influenza surveillance system data, that is, Google Flu Trends, for both Nebraska and Omaha; total ED visits in Douglas County attributable to ILI; and ILI surveillance network data for Douglas County and Nebraska as the predictors and data for the hospital's ILI-related ED visits as the dependent variable. We used Seasonal Autoregressive Integrated Moving Average and Holt Winters methods with3 linear regression models to forecast ILI-related ED visits at the hospital and evaluated model performances by comparing the root means square errors (RMSEs). Because of strong positive correlations with ILI-related ED visits between 2008 and 2012, we validated the use of Google Flu Trends data as a predictor in an ED influenza surveillance tool. Of the 5 forecasting models we have tested, linear regression models performed significantly better when Google Flu Trends data were included as a predictor. Regression models including Google Flu Trends data as a predictor variable have lower RMSE, and the lowest is achieved when all other variables are also included in the model in our forecasting experiments for the first 5 weeks of 2013 (with RMSE = 57.61). Google Flu Trends data statistically improve the performance of predicting ILI-related ED visits in Douglas County, and this result can be generalized to other communities. Timely and accurate estimates of ED volume during the influenza season, as well as during pandemic outbreaks, can help hospitals plan their ED resources accordingly and lower their costs by optimizing supplies and staffing and can improve service quality by decreasing ED wait times and overcrowding. Copyright © 2014 Elsevier Inc. All rights reserved.

U.S. NO₂ trends (2005–2013): EPA air quality system (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI)

DOE PAGES

Lamsal, Lok N.; Duncan, Bryan N.; Yoshida, Yasuko; ...

2015-06-01

Emissions of nitrogen oxides (NO x) and, subsequently, atmospheric levels of nitrogen dioxide (NO₂) have decreased over the U.S. due to a combination of environmental policies and technological change. Consequently, NO₂ levels have decreased by 30–40% in the last decade. We quantify NO₂ trends (2005–2013) over the U.S. using surface measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and an improved tropospheric NO₂ vertical column density (VCD) data product from the Ozone Monitoring Instrument (OMI) on the Aura satellite.We demonstrate that the current OMI NO₂ algorithm is of sufficient maturity to allow a favorable correspondence ofmore » trends and variations in OMI and AQS data. Our trend model accounts for the non-linear dependence of NO₂ concentration on emissions associated with the seasonal variation of the chemical lifetime, including the change in the amplitude of the seasonal cycle associated with the significant change in NO x emissions that occurred over the last decade. The direct relationship between observations and emissions becomes more robust when one accounts for these non-linear dependencies. We improve the OMI NO₂ standard retrieval algorithm and, subsequently, the data product by using monthly vertical concentration profiles, a required algorithm input, from a high-resolution chemistry and transport model (CTM) simulation with varying emissions (2005-2013). The impact of neglecting the time-dependence of the profiles leads to errors in trend estimation, particularly in regions where emissions have changed substantially. For example, trends calculated from retrievals based on time-dependent profiles offer 18% more instances of significant trends and up to 15% larger total NO₂ reduction versus the results based on profiles for 2005. Using a CTM, we explore the theoretical relation of the trends estimated from NO₂ VCDs to those estimated from ground-level concentrations. The model-simulated trends in VCDs strongly correlate with those estimated from surface concentrations (r = 0.83, N = 355). We then explore the observed correspondence of trends estimated from OMI and AQS data. We find a significant, but slightly weaker, correspondence (i.e., r = 0.68, N = 208) than predicted by the model and discuss some of the important factors affecting the relationship, including known problems (e.g., NO z interferents) associated with the AQS data. This significant correspondence gives confidence in trend and surface concentration estimates from OMI VCDs for locations, such as the majority of the U.S. and globe, that are not covered by surface monitoring networks. Using our improved trend model and our enhanced OMI data product, we find that both OMI and AQS data show substantial downward trends from 2005 to 2013, with an average reduction of 38% for each over the U.S. The annual reduction rates inferred from OMI and AQS measurements are larger (–4.8 ± 1.9%/yr, –3.7 ± 1.5%/yr) from 2005 to 2008 than 2010 to 2013 (–1.2 ± 1.2%/yr, –2.1 ± 1.4%/yr). We quantify NO₂ trends for major U.S. cities and power plants; the latter suggest larger negative trend (–4.0 ± 1.5%/yr) between 2005 and 2008 and smaller or insignificant changes (–0.5 ± 1.2%/yr) during 2010-2013.« less

U.S. NO₂ trends (2005–2013): EPA air quality system (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamsal, Lok N.; Duncan, Bryan N.; Yoshida, Yasuko

Emissions of nitrogen oxides (NO x) and, subsequently, atmospheric levels of nitrogen dioxide (NO₂) have decreased over the U.S. due to a combination of environmental policies and technological change. Consequently, NO₂ levels have decreased by 30–40% in the last decade. We quantify NO₂ trends (2005–2013) over the U.S. using surface measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and an improved tropospheric NO₂ vertical column density (VCD) data product from the Ozone Monitoring Instrument (OMI) on the Aura satellite.We demonstrate that the current OMI NO₂ algorithm is of sufficient maturity to allow a favorable correspondence ofmore » trends and variations in OMI and AQS data. Our trend model accounts for the non-linear dependence of NO₂ concentration on emissions associated with the seasonal variation of the chemical lifetime, including the change in the amplitude of the seasonal cycle associated with the significant change in NO x emissions that occurred over the last decade. The direct relationship between observations and emissions becomes more robust when one accounts for these non-linear dependencies. We improve the OMI NO₂ standard retrieval algorithm and, subsequently, the data product by using monthly vertical concentration profiles, a required algorithm input, from a high-resolution chemistry and transport model (CTM) simulation with varying emissions (2005-2013). The impact of neglecting the time-dependence of the profiles leads to errors in trend estimation, particularly in regions where emissions have changed substantially. For example, trends calculated from retrievals based on time-dependent profiles offer 18% more instances of significant trends and up to 15% larger total NO₂ reduction versus the results based on profiles for 2005. Using a CTM, we explore the theoretical relation of the trends estimated from NO₂ VCDs to those estimated from ground-level concentrations. The model-simulated trends in VCDs strongly correlate with those estimated from surface concentrations (r = 0.83, N = 355). We then explore the observed correspondence of trends estimated from OMI and AQS data. We find a significant, but slightly weaker, correspondence (i.e., r = 0.68, N = 208) than predicted by the model and discuss some of the important factors affecting the relationship, including known problems (e.g., NO z interferents) associated with the AQS data. This significant correspondence gives confidence in trend and surface concentration estimates from OMI VCDs for locations, such as the majority of the U.S. and globe, that are not covered by surface monitoring networks. Using our improved trend model and our enhanced OMI data product, we find that both OMI and AQS data show substantial downward trends from 2005 to 2013, with an average reduction of 38% for each over the U.S. The annual reduction rates inferred from OMI and AQS measurements are larger (–4.8 ± 1.9%/yr, –3.7 ± 1.5%/yr) from 2005 to 2008 than 2010 to 2013 (–1.2 ± 1.2%/yr, –2.1 ± 1.4%/yr). We quantify NO₂ trends for major U.S. cities and power plants; the latter suggest larger negative trend (–4.0 ± 1.5%/yr) between 2005 and 2008 and smaller or insignificant changes (–0.5 ± 1.2%/yr) during 2010-2013.« less

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

PubMed Central

Aguirre-Salado, Alejandro Ivan; Vaquera-Huerta, Humberto; Aguirre-Salado, Carlos Arturo; Reyes-Mora, Silvia; Olvera-Cervantes, Ana Delia; Lancho-Romero, Guillermo Arturo; Soubervielle-Montalvo, Carlos

2017-01-01

We implemented a spatial model for analysing PM10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM10 maxima and the longitude and latitude. The relationship between time and the PM10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM10 maxima presenting levels above 1000 μg/m3 (return period: 25 yr) was observed in the northwestern region of the study area. PMID:28684720

Black Sea thermohaline properties: Long‐term trends and variations

PubMed Central

Stips, A.; Garcia‐Gorriz, E.; Macias Moy, D.

2017-01-01

Abstract The current knowledge about spatial and temporal dynamics of the Black Sea's thermohaline structure is incomplete because of missing data and sparse distribution of existing measurements in space and time. This study presents 56 year continuous simulations of the Black Sea's hydrodynamics using the 3D General Estuarine Transport Model (GETM), without incorporating any relaxation toward climatological or observational data fields. This property of the model allows us to estimate independent temporal trends, in addition to resolving the spatial structure. The simulations suggest that the intermediate layer temperature is characterized by a weak positive trend (warming), whereas the surface temperature does not show a clear linear trend. Different salinity trends have been established at the surface (negative), upper (weaker negative) and main halocline (positive). Three distinct dynamic periods are identified (1960–1970, 1970–1995, 1995–2015), which exhibit pronounced changes in the Black Sea's thermohaline properties and basin circulation. Strengthening of the main cyclonic circulation, accompanied by intensification of the mesoscale anticyclonic eddy formation is found. Both events strongly affect the sea surface salinity but contribute in opposing directions. Specifically, strong composite large‐scale circulation leads to an increase in sea surface salinity, while enhanced formation of mesoscale anticyclones decreases it. Salinity evolution with time is thus the result of the competition of these two opposing yet interdependent processes. PMID:28989833

Trends in pesticide concentrations in corn-belt streams, 1996-2006

USGS Publications Warehouse

Sullivan, Daniel J.; Vecchia, Aldo V.; Lorenz, David L.; Gilliom, Robert J.; Martin, Jeffrey D.

2009-01-01

Trends in the concentrations of commonly occurring pesticides in the Corn Belt of the United States were assessed, and the performance and application of several statistical methods for trend analysis were evaluated. Trends in the concentrations of 11 pesticides with sufficient data for trend assessment were assessed at up to 31 stream sites for two time periods: 1996–2002 and 2000–2006. Pesticides included in the trend analyses were atrazine, acetochlor, metolachlor, alachlor, cyanazine, EPTC, simazine, metribuzin, prometon, chlorpyrifos, and diazinon.The statistical methods applied and compared were (1) a modified version of the nonparametric seasonal Kendall test (SEAKEN), (2) a modified version of the Regional Kendall test, (3) a parametric regression model with seasonal wave (SEAWAVE), and (4) a version of SEAWAVE with adjustment for streamflow (SEAWAVE-Q). The SEAKEN test is a statistical hypothesis test for detecting monotonic trends in seasonal time-series data such as pesticide concentrations at a particular site. Trends across a region, represented by multiple sites, were evaluated using the regional seasonal Kendall test, which computes a test for an overall trend within a region by computing a score for each season at each site and adding the scores to compute the total for the region. The SEAWAVE model is a parametric regression model specifically designed for analyzing seasonal variability and trends in pesticide concentrations. The SEAWAVE-Q model accounts for the effect of changing flow conditions in order to separate changes caused by hydrologic trends from changes caused by other factors, such as pesticide use.There was broad, general agreement between unadjusted trends (no adjustment for streamflow effects) identified by the SEAKEN and SEAWAVE methods, including the regional seasonal Kendall test. Only about 10 percent of the paired comparisons between SEAKEN and SEAWAVE indicated a difference in the direction of trend, and none of these had differences significant at the 10-percent significance level. This consistency of results supports the validity and robustness of all three approaches as trend analysis tools. The SEAWAVE method is favored, however, because it has less restrictive data requirements, enabling analysis for more site/pesticide combinations, and can incorporate adjustment for streamflow (SEAWAVE-Q) with substantially fewer measurements than the flow-adjustment procedure used with SEAKEN.Analysis of flow-adjusted trends is preferable to analysis of non-adjusted trends for evaluating potential effects of changes in pesticide use or management practices because flow-adjusted trends account for the influence of flow-related variability.Analysis of flow-adjusted trends by SEAWAVE-Q showed that all of the pesticides assessed, except simazine and acetochlor, were dominated by varying degrees of concentration downtrends in one or both analysis periods. Atrazine, metolachlor, alachlor, cyanazine, EPTC, and metribuzin—all major corn herbicides, as well as prometon and chlorpyrifos, showed more prevalent concentration downtrends during 1996–2002 compared to 2000–2006. Diazinon had no clear trends during 1996–2002, but had predominantly downward trends during 2000–2006. Acetochlor trends were mixed during 1996–2002 and slightly upward during 2000–2006, but most of the trends were not statistically significant. Simazine concentrations trended upward at most sites during both 1996–2002 and 2000–2006.Comparison of concentration trends to agricultural-use trends indicated similarity in direction and magnitude for acetochlor, metolachlor, alachlor, cyanazine, EPTC, and metribuzin. Concentration downtrends for atrazine, chlorpyrifos, and diazinon were steeper than agricultural-use downtrends at some sites, indicating the possibility that agricultural management practices may have increasingly reduced transport to streams (particularly atrazine) or, for chlorpyrifos and diazinon, that nonagricultural uses declined substantially. Concentration uptrends for simazine generally were steeper than agricultural-use uptrends, indicating the possibility that nonagricultural uses of this herbicide increased during the study period.

Trends in physical activity, health-related fitness, and gross motor skills in children during a two-year comprehensive school physical activity program.

PubMed

Brusseau, Timothy A; Hannon, James C; Fu, You; Fang, Yi; Nam, Kahyun; Goodrum, Sara; Burns, Ryan D

2018-01-06

The purpose of this study was to examine the trends in school-day step counts, health-related fitness, and gross motor skills during a two-year Comprehensive School Physical Activity Program (CSPAP) in children. Longitudinal trend analysis. Participants were a sample of children (N=240; mean age=7.9±1.2 years; 125 girls, 115 boys) enrolled in five low-income schools. Outcome variables consisted of school day step counts, Body Mass Index (BMI), estimated VO 2 Peak , and gross motor skill scores assessed using the Test of Gross Motor Development-3rd Edition (TGMD-3). Measures were collected over a two-year CSPAP including a baseline and several follow-up time-points. Multi-level mixed effects models were employed to examine time trends on each continuous outcome variable. Markov-chain transition models were employed to examine time trends for derived binary variables for school day steps, BMI, and estimated VO 2 Peak . There were statistically significant time coefficients for estimated VO 2 Peak (b=1.10mL/kg/min, 95% C.I. [0.35mL/kg/min-2.53mL/kg/min], p=0.009) and TGMD-3 scores (b=7.8, 95% C.I. [6.2-9.3], p<0.001). There were no significant changes over time for school-day step counts or BMI. Boys had greater change in odds of achieving a step count associating with 30min of school day MVPA (OR=1.25, 95% C.I. [1.02-1.48], p=0.044). A two-year CSPAP related to increases in cardio-respiratory endurance and TGMD-3 scores. School day steps and BMI were primarily stable across the two-year intervention. Copyright © 2018 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Global Changes of the Water Cycle Intensity

NASA Technical Reports Server (NTRS)

Bosilovich, Michael G.; Schubert, Siegfried D.; Walker, Gregory K.

2003-01-01

In this study, we evaluate numerical simulations of the twentieth century climate, focusing on the changes in the intensity of the global water cycle. A new diagnostic of atmospheric water vapor cycling rate is developed and employed, that relies on constituent tracers predicted at the model time step. This diagnostic is compared to a simplified traditional calculation of cycling rate, based on monthly averages of precipitation and total water content. The mean sensitivity of both diagnostics to variations in climate forcing is comparable. However, the new diagnostic produces systematically larger values and more variability than the traditional average approach. Climate simulations were performed using SSTs of the early (1902-1921) and late (1979- 1998) twentieth century along with the appropriate C02 forcing. In general, the increase of global precipitation with the increases in SST that occurred between the early and late twentieth century is small. However, an increase of atmospheric temperature leads to a systematic increase in total precipitable water. As a result, the residence time of water in the atmosphere increased, indicating a reduction of the global cycling rate. This result was explored further using a number of 50-year climate simulations from different models forced with observed SST. The anomalies and trends in the cycling rate and hydrologic variables of different GCMs are remarkably similar. The global annual anomalies of precipitation show a significant upward trend related to the upward trend of surface temperature, during the latter half of the twentieth century. While this implies an increase in the hydrologic cycle intensity, a concomitant increase of total precipitable water again leads to a decrease in the calculated global cycling rate. An analysis of the land/sea differences shows that the simulated precipitation over land has a decreasing trend while the oceanic precipitation has an upward trend consistent with previous studies and the available observations. The decreasing continental trend in precipitation is located primarily over tropical land regions, with some other regions, such as North America experiencing an increasing trend. Precipitation trends are diagnosed further using the water tracers to delineate the precipitation that occurs because of continental evaporation, as opposed to oceanic evaporation. These diagnostics show that over global land areas, the recycling of continental moisture is decreasing in time. However, the recycling changes are not spatially uniform so that some regions, most notably over the United States, experience continental recycling of water that increases in time.

Heterogeneity in geographical trends of HIV epidemics among key populations in Pakistan: a mathematical modeling study of survey data.

PubMed

Melesse, Dessalegn Y; Shafer, Leigh Anne; Emmanuel, Faran; Reza, Tahira; Achakzai, Baseer K; Furqan, Sofia; Blanchard, James F

2018-06-01

Assessing patterns and trends in new infections is key to better understanding of HIV epidemics, and is best done through monitoring changes in incidence over time. In this study, we examined disparities in geographical trends of HIV epidemics among people who inject drugs (PWIDs), female sex workers (FSWs) and hijra /transgender/male sex workers (H/MSWs), in Pakistan. The UNAIDS Estimation and Projection Package (EPP) mathematical model was used to explore geographical trends in HIV epidemics. Four rounds of mapping and surveillance data collected among key populations (KPs) across 20 cities in Pakistan between 2005-2011 was used for modeling. Empirical estimates of HIV prevalence of each KP in each city were used to fit the model to estimate prevalence and incidence over time. HIV incidence among PWIDs in Pakistan reached its peak in 2011, estimated at 45.3 per 1000 person-years. Incidence was projected to continue to rise from 18.9 in 2015 to 24.3 in 2020 among H/MSWs and from 3.2 in 2015 to 6.3 in 2020 among FSWs. The number of people living with HIV in Pakistan was estimated to steadily increase through at least 2020. HIV incidence peak among PWIDs ranged from 16.2 in 1997 in Quetta to 71.0 in 2010 in Faisalabad (per 1000 person-years). Incidence among H/MSWs may continue to rise through 2020 in all the cities, except in Larkana where it peaked in the early 2000s. In 2015, model estimated incidence among FSWs was 8.1 in Karachi, 6.6 in Larkana, 2.0 in Sukkur and 1.2 in Lahore (per 1000 person-years). There exists significant geographical heterogeneity in patterns and trends of HIV sub-epidemics in Pakistan. Focused interventions and service delivery approaches, different by KP and city, are recommended.

Heterogeneity in geographical trends of HIV epidemics among key populations in Pakistan: a mathematical modeling study of survey data

PubMed Central

Melesse, Dessalegn Y; Shafer, Leigh Anne; Emmanuel, Faran; Reza, Tahira; Achakzai, Baseer K; Furqan, Sofia; Blanchard, James F

2018-01-01

Background Assessing patterns and trends in new infections is key to better understanding of HIV epidemics, and is best done through monitoring changes in incidence over time. In this study, we examined disparities in geographical trends of HIV epidemics among people who inject drugs (PWIDs), female sex workers (FSWs) and hijra/transgender/male sex workers (H/MSWs), in Pakistan. Methods The UNAIDS Estimation and Projection Package (EPP) mathematical model was used to explore geographical trends in HIV epidemics. Four rounds of mapping and surveillance data collected among key populations (KPs) across 20 cities in Pakistan between 2005-2011 was used for modeling. Empirical estimates of HIV prevalence of each KP in each city were used to fit the model to estimate prevalence and incidence over time. Results HIV incidence among PWIDs in Pakistan reached its peak in 2011, estimated at 45.3 per 1000 person-years. Incidence was projected to continue to rise from 18.9 in 2015 to 24.3 in 2020 among H/MSWs and from 3.2 in 2015 to 6.3 in 2020 among FSWs. The number of people living with HIV in Pakistan was estimated to steadily increase through at least 2020. HIV incidence peak among PWIDs ranged from 16.2 in 1997 in Quetta to 71.0 in 2010 in Faisalabad (per 1000 person-years). Incidence among H/MSWs may continue to rise through 2020 in all the cities, except in Larkana where it peaked in the early 2000s. In 2015, model estimated incidence among FSWs was 8.1 in Karachi, 6.6 in Larkana, 2.0 in Sukkur and 1.2 in Lahore (per 1000 person-years). Conclusions There exists significant geographical heterogeneity in patterns and trends of HIV sub-epidemics in Pakistan. Focused interventions and service delivery approaches, different by KP and city, are recommended. PMID:29770215

The National Water-Quality Assessment Program of the United States: Strategies for Monitoring Trends and Results from the First Two Decades of Study: 1991-2011

NASA Astrophysics Data System (ADS)

Lindsey, B.; McMahon, P.; Rupert, M.; Tesoriero, J.; Starn, J.; Anning, D.; Green, C.

2012-04-01

The U.S. Geological Survey National Water-Quality Assessment (NAWQA) Program was implemented in 1991 to provide long-term, consistent, and comparable information on the quality of surface and groundwater resources of the United States. Findings are used to support national, regional, state, and local information needs with respect to water quality. The three main goals of the program are to 1) assess the condition of the nation's streams, rivers, groundwater, and aquatic systems; 2) assess how conditions are changing over time; and 3) determine how natural features and human activities affect these conditions, and where those effects are most pronounced. As data collection progressed into the second decade, the emphasis of the interpretation of the data has shifted from primarily understanding status, to evaluation of trends. The program has conducted national and regional evaluations of change in the quality of water in streams, rivers, groundwater, and health of aquatic systems. Evaluating trends in environmental systems requires complex analytical and statistical methods, and a periodic re-evaluation of the monitoring methods used to collect these data. Examples given herein summarize the lessons learned from the evaluation of changes in water quality during the past two decades with an emphasis on the finding with respect to groundwater. The analysis of trends in groundwater is based on 56 well networks located in 22 principal aquifers of the United States. Analysis has focused on 3 approaches: 1) a statistical analysis of results of sampling over various time scales, 2) studies of factors affecting trends in groundwater quality, and 3) use of models to simulate groundwater trends and forecast future trends. Data collection for analysis of changes in groundwater-quality has focused on decadal resampling of wells. Understanding the trends in groundwater quality and the factors affecting those trends has been conducted using quarterly sampling, biennial sampling, and more recently continuous monitoring of selected parameters in a small number of wells. Models such as MODFLOW have been used for simulation and forecasting of future trends. Important outcomes from the groundwater-trends studies include issues involving statistics, sampling frequency, changes in laboratory analytical methods over time, the need for groundwater age-dating information, the value of understanding geochemical conditions and contaminant degradation, the need to understand groundwater-surface water interaction, and the value of modeling in understanding trends and forecasting potential future conditions. Statistically significant increases in chloride, dissolved solids, and nitrate concentrations were found in a large number of well networks over the first decadal sampling period. Statistically significant decreases of chloride, dissolved solids, and nitrate concentrations were found in a very small number of networks. Trends in surface-water are analyzed within 8 large major river basins within the United States with a focus on issues of regional importance. Examples of regional surface-water issues include an analysis of trends in dissolved solids in the Southeastern United States, trends in pesticides in the north-central United States, and trends in nitrate in the Mississippi River Basin. Evaluations of ecological indicators of water quality include temporal changes in stream habitat, and aquatic-invertebrate and fish assemblages.

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables

NASA Astrophysics Data System (ADS)

Madonna, Erica; Ginsbourger, David; Martius, Olivia

2018-05-01

In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

Statistical estimation via convex optimization for trending and performance monitoring

NASA Astrophysics Data System (ADS)

Samar, Sikandar

This thesis presents an optimization-based statistical estimation approach to find unknown trends in noisy data. A Bayesian framework is used to explicitly take into account prior information about the trends via trend models and constraints. The main focus is on convex formulation of the Bayesian estimation problem, which allows efficient computation of (globally) optimal estimates. There are two main parts of this thesis. The first part formulates trend estimation in systems described by known detailed models as a convex optimization problem. Statistically optimal estimates are then obtained by maximizing a concave log-likelihood function subject to convex constraints. We consider the problem of increasing problem dimension as more measurements become available, and introduce a moving horizon framework to enable recursive estimation of the unknown trend by solving a fixed size convex optimization problem at each horizon. We also present a distributed estimation framework, based on the dual decomposition method, for a system formed by a network of complex sensors with local (convex) estimation. Two specific applications of the convex optimization-based Bayesian estimation approach are described in the second part of the thesis. Batch estimation for parametric diagnostics in a flight control simulation of a space launch vehicle is shown to detect incipient fault trends despite the natural masking properties of feedback in the guidance and control loops. Moving horizon approach is used to estimate time varying fault parameters in a detailed nonlinear simulation model of an unmanned aerial vehicle. An excellent performance is demonstrated in the presence of winds and turbulence.

Using trend templates in a neonatal seizure algorithm improves detection of short seizures in a foetal ovine model.

PubMed

Zwanenburg, Alex; Andriessen, Peter; Jellema, Reint K; Niemarkt, Hendrik J; Wolfs, Tim G A M; Kramer, Boris W; Delhaas, Tammo

2015-03-01

Seizures below one minute in duration are difficult to assess correctly using seizure detection algorithms. We aimed to improve neonatal detection algorithm performance for short seizures through the use of trend templates for seizure onset and end. Bipolar EEG were recorded within a transiently asphyxiated ovine model at 0.7 gestational age, a common experimental model for studying brain development in humans of 30-34 weeks of gestation. Transient asphyxia led to electrographic seizures within 6-8 h. A total of 3159 seizures, 2386 shorter than one minute, were annotated in 1976 h-long EEG recordings from 17 foetal lambs. To capture EEG characteristics, five features, sensitive to seizures, were calculated and used to derive trend information. Feature values and trend information were used as input for support vector machine classification and subsequently post-processed. Performance metrics, calculated after post-processing, were compared between analyses with and without employing trend information. Detector performance was assessed after five-fold cross-validation conducted ten times with random splits. The use of trend templates for seizure onset and end in a neonatal seizure detection algorithm significantly improves the correct detection of short seizures using two-channel EEG recordings from 54.3% (52.6-56.1) to 59.5% (58.5-59.9) at FDR 2.0 (median (range); p < 0.001, Wilcoxon signed rank test). Using trend templates might therefore aid in detection of short seizures by EEG monitoring at the NICU.

Century Scale Evaporation Trend: An Observational Study

NASA Technical Reports Server (NTRS)

Bounoui, Lahouari

2012-01-01

Several climate models with different complexity indicate that under increased CO2 forcing, runoff would increase faster than precipitation overland. However, observations over large U.S watersheds indicate otherwise. This inconsistency between models and observations suggests that there may be important feedbacks between climate and land surface unaccounted for in the present generation of models. We have analyzed century-scale observed annual runoff and precipitation time-series over several United States Geological Survey hydrological units covering large forested regions of the Eastern United States not affected by irrigation. Both time-series exhibit a positive long-term trend; however, in contrast to model results, these historic data records show that the rate of precipitation increases at roughly double the rate of runoff increase. We considered several hydrological processes to close the water budget and found that none of these processes acting alone could account for the total water excess generated by the observed difference between precipitation and runoff. We conclude that evaporation has increased over the period of observations and show that the increasing trend in precipitation minus runoff is correlated to observed increase in vegetation density based on the longest available global satellite record. The increase in vegetation density has important implications for climate; it slows but does not alleviate the projected warming associated with greenhouse gases emission.

Beyond linear methods of data analysis: time series analysis and its applications in renal research.

PubMed

Gupta, Ashwani K; Udrea, Andreea

2013-01-01

Analysis of temporal trends in medicine is needed to understand normal physiology and to study the evolution of disease processes. It is also useful for monitoring response to drugs and interventions, and for accountability and tracking of health care resources. In this review, we discuss what makes time series analysis unique for the purposes of renal research and its limitations. We also introduce nonlinear time series analysis methods and provide examples where these have advantages over linear methods. We review areas where these computational methods have found applications in nephrology ranging from basic physiology to health services research. Some examples include noninvasive assessment of autonomic function in patients with chronic kidney disease, dialysis-dependent renal failure and renal transplantation. Time series models and analysis methods have been utilized in the characterization of mechanisms of renal autoregulation and to identify the interaction between different rhythms of nephron pressure flow regulation. They have also been used in the study of trends in health care delivery. Time series are everywhere in nephrology and analyzing them can lead to valuable knowledge discovery. The study of time trends of vital signs, laboratory parameters and the health status of patients is inherent to our everyday clinical practice, yet formal models and methods for time series analysis are not fully utilized. With this review, we hope to familiarize the reader with these techniques in order to assist in their proper use where appropriate.

Global Crop Yields, Climatic Trends and Technology Enhancement

NASA Astrophysics Data System (ADS)

Najafi, E.; Devineni, N.; Khanbilvardi, R.; Kogan, F.

2016-12-01

During the last decades the global agricultural production has soared up and technology enhancement is still making positive contribution to yield growth. However, continuing population, water crisis, deforestation and climate change threaten the global food security. Attempts to predict food availability in the future around the world can be partly understood from the impact of changes to date. A new multilevel model for yield prediction at the country scale using climate covariates and technology trend is presented in this paper. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling and/or clustering to automatically group and reduce estimation uncertainties. El Niño Southern Oscillation (ENSO), Palmer Drought Severity Index (PDSI), Geopotential height (GPH), historical CO2 level and time-trend as a relatively reliable approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2007. Results show that these indicators can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications.

Time series models of decadal trends in the harmful algal species Karlodinium veneficum in Chesapeake Bay.

PubMed

Lin, Chih-Hsien Michelle; Lyubchich, Vyacheslav; Glibert, Patricia M

2018-03-01

The harmful dinoflagellate, Karlodnium veneficum, has been implicated in fish-kill and other toxic, harmful algal bloom (HAB) events in waters worldwide. Blooms of K. veneficum are known to be related to coastal nutrient enrichment but the relationship is complex because this HAB taxon relies not only on dissolved nutrients but also particulate prey, both of which have also changed over time. Here, applying cross-correlations of climate-related physical factors, nutrients and prey, with abundance of K. veneficum over a 10-year (2002-2011) period, a synthesis of the interactive effects of multiple factors on this species was developed for Chesapeake Bay, where blooms of the HAB have been increasing. Significant upward trends in the time series of K. veneficum were observed in the mesohaline stations of the Bay, but not in oligohaline tributary stations. For the mesohaline regions, riverine sources of nutrients with seasonal lags, together with particulate prey with zero lag, explained 15%-46% of the variation in the K. veneficum time series. For the oligohaline regions, nutrients and particulate prey generally showed significant decreasing trends with time, likely a reflection of nutrient reduction efforts. A conceptual model of mid-Bay blooms is presented, in which K. veneficum, derived from the oceanic end member of the Bay, may experience enhanced growth if it encounters prey originating from the tributaries with different patterns of nutrient loading and which are enriched in nitrogen. For all correlation models developed herein, prey abundance was a primary factor in predicting K. veneficum abundance. Copyright © 2018 Elsevier B.V. All rights reserved.

Increased trend in extracorporeal membrane oxygenation use by adults in the United States since 2007.

PubMed

Gerke, Alicia K; Tang, Fan; Cavanaugh, Joseph E; Doerschug, Kevin C; Polgreen, Philip M

2015-11-18

Extracorporeal membrane oxygenation (ECMO) has been increasingly studied as a life support modality, but it is unclear if its use has changed over time. Recent publication shows no significant trend in use of ECMO over time; however, this report does not include more recent data. We performed trend analysis to determine if and when the use of ECMO changed in the past decade. We identified hospitalizations (2000-2011) in the Nationwide Inpatient Sample during which ECMO was recorded. We used a segmented linear regression model to determine trend and to identify a temporal change point when rate of ECMO use increased. ECMO use gradually grew until 2007, at which time there was a dramatic increase in the rate (p = 0.0003). There was no difference in mortality after 2007 (p = 0.3374), but there was longer length of stay (p = 0.0001) and smaller percentage of women (p = 0.005). There has been a marked increase in ECMO use since 2007. As ECMO use becomes more common, further study regarding indications, cost-effectiveness, and outcomes is warranted to guide optimal use.

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China

PubMed Central

Liu, Dong-jun; Li, Li

2015-01-01

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field. PMID:26110332

Application Study of Comprehensive Forecasting Model Based on Entropy Weighting Method on Trend of PM2.5 Concentration in Guangzhou, China.

PubMed

Liu, Dong-jun; Li, Li

2015-06-23

For the issue of haze-fog, PM2.5 is the main influence factor of haze-fog pollution in China. The trend of PM2.5 concentration was analyzed from a qualitative point of view based on mathematical models and simulation in this study. The comprehensive forecasting model (CFM) was developed based on the combination forecasting ideas. Autoregressive Integrated Moving Average Model (ARIMA), Artificial Neural Networks (ANNs) model and Exponential Smoothing Method (ESM) were used to predict the time series data of PM2.5 concentration. The results of the comprehensive forecasting model were obtained by combining the results of three methods based on the weights from the Entropy Weighting Method. The trend of PM2.5 concentration in Guangzhou China was quantitatively forecasted based on the comprehensive forecasting model. The results were compared with those of three single models, and PM2.5 concentration values in the next ten days were predicted. The comprehensive forecasting model balanced the deviation of each single prediction method, and had better applicability. It broadens a new prediction method for the air quality forecasting field.

Trends in antibiotic resistance over time among pathogens from Canadian hospitals: results of the CANWARD study 2007-11.

PubMed

Lagacé-Wiens, Philippe R S; Adam, Heather J; Low, Donald E; Blondeau, Joseph M; Baxter, Melanie R; Denisuik, Andrew J; Nichol, Kimberly A; Walkty, Andrew; Karlowsky, James A; Mulvey, Michael R; Hoban, Daryl J; Zhanel, George G

2013-05-01

Antimicrobial resistance patterns change over time and longitudinal surveillance studies provide insight into these trends. We sought to describe the important trends in antimicrobial resistance in key pathogens across Canada to provide useful information to clinicians, policy makers and industry, to assist in optimizing antimicrobial therapy, formulary choices and drug development. We analysed longitudinal data from the CANWARD study using a multivariate regression model to control for possible effects of patient demographics on resistance, in order to assess the impact of time on antimicrobial resistance independent of other measured variables. We identified several key trends in common pathogens. In particular, we observed a statistically significant increase in the proportion of Escherichia coli isolates that were resistant to extended-spectrum cephalosporins and fluoroquinolones, an increase in the proportion of Klebsiella pneumoniae isolates that were resistant to extended-spectrum cephalosporins, a reduction in the proportion of Staphylococcus aureus that were methicillin, clindamycin and trimethoprim/sulfamethoxazole resistant, and a reduction in the proportion of Pseudomonas aeruginosa that were fluoroquinolone and gentamicin resistant. Although some of these trends, such as the dramatic increase in fluoroquinolone and cephalosporin resistance in E. coli, can be attributed to the emergence and global spread of resistant clones (e.g. ST131 E. coli), others remain unexplained. However, recognizing these trends remains important to guide changes in empirical antimicrobial therapy and drug development.

Spatial heterogeneity in the timing of birch budburst in response to future climate warming in Ireland

NASA Astrophysics Data System (ADS)

Caffarra, Amelia; Zottele, Fabio; Gleeson, Emily; Donnelly, Alison

2014-05-01

In order to predict the impact of future climate warming on trees it is important to quantify the effect climate has on their development. Our understanding of the phenological response to environmental drivers has given rise to various mathematical models of the annual growth cycle of plants. These models simulate the timing of phenophases by quantifying the relationship between development and its triggers, typically temperature. In addition, other environmental variables have an important role in determining the timing of budburst. For example, photoperiod has been shown to have a strong influence on phenological events of a number of tree species, including Betula pubescens (birch). A recently developed model for birch (DORMPHOT), which integrates the effects of temperature and photoperiod on budburst, was applied to future temperature projections from a 19-member ensemble of regional climate simulations (on a 25 km grid) generated as part of the ENSEMBLES project, to simulate the timing of birch budburst in Ireland each year up to the end of the present century. Gridded temperature time series data from the climate simulations were used as input to the DORMPHOT model to simulate future budburst timing. The results showed an advancing trend in the timing of birch budburst over most regions in Ireland up to 2100. Interestingly, this trend appeared greater in the northeast of the country than in the southwest, where budburst is currently relatively early. These results could have implications for future forest planning, species distribution modeling, and the birch allergy season.

Water quality trend analysis for the Karoon River in Iran.

PubMed

Naddafi, K; Honari, H; Ahmadi, M

2007-11-01

The Karoon River basin, with a basin area of 67,000 km(2), is located in the southern part of Iran. Monthly measurements of the discharge and the water quality variables have been monitored at the Gatvand and Khorramshahr stations of the Karoon River on a monthly basis for the period 1967-2005 and 1969-2005 for Gatvand and Khorramshahr stations, respectively. In this paper the time series of monthly values of water quality parameters and the discharge were analyzed using statistical methods and the existence of trends and the evaluation of the best fitted models were performed. The Kolmogorov-Smirnov test was used to select the theoretical distribution which best fitted the data. Simple regression was used to examine the concentration-time relationships. The concentration-time relationships showed better correlation in Khorramshahr station than that of Gatvand station. The exponential model expresses better concentration - time relationships in Khorramshahr station, but in Gatvand station the logarithmic model is more fitted. The correlation coefficients are positive for all of the variables in Khorramshahr station also in Gatvand station all of the variables are positive except magnesium (Mg2+), bicarbonates (HCO3-) and temporary hardness which shows a decreasing relationship. The logarithmic and the exponential models describe better the concentration-time relationships for two stations.

Attribution of Trends and Variability in Surface Ozone over the United States

NASA Technical Reports Server (NTRS)

Strode, Sarah; Cooper, Owen; Damo, Megan; Logan, Jennifer; Rodriquez, Jose; Strahan, Susan; Witte, Jacquie

2013-01-01

Concentrations of tropospheric ozone, a greenhouse gas and air pollutant, are impacted by changes in precursor emissions as well meteorology and influx from the stratosphere. Observations show a decreasing trend in summertime surface ozone at rural stations in the eastern United States, while some western stations show increasing trends, particularly in springtime. We use the Global Modeling Initiative (GMI) global chemical transport model to investigate the roles of precursor emission changes, meteorological variability, and stratosphere-troposphere exchange (STE) in explaining observed trends in surface ozone from rural sites in the United States from 1991-2010. The model's interannual variability shows significant correlations with observations from many of the surface sites. We also compare the simulated ozone to ozonesonde data for several locations with sufficiently long records. We compare a simulation with time-dependent precursor emissions, including emission reductions over the United States and Europe and increases over Asia, to a simulation with fixed emissions to quantify the impact of changing emissions on the surface trends. The simulation with varying emissions reproduces much of the east-west difference in summertime ozone over the U.S., although it generally underestimates the negative trend in the East. In contrast, the fixed-emission simulation shows increasing ozone at both eastern and western sites. We will discuss possible causes of this behavior, including long-range transport and STE.

Status, trends, and changes in freshwater inflows to bay systems in the Corpus Christi Bay National Estuary Program study area

USGS Publications Warehouse

Asquith, W.H.; Mosier, J. G.; Bush, P.W.

1997-01-01

The watershed simulation model Hydrologic Simulation Program—Fortran (HSPF) was used to generate simulated flow (runoff) from the 13 watersheds to the six bay systems because adequate gaged streamflow data from which to estimate freshwater inflows are not available; only about 23 percent of the adjacent contributing watershed area is gaged. The model was calibrated for the gaged parts of three watersheds—that is, selected input parameters (meteorologic and hydrologic properties and conditions) that control runoff were adjusted in a series of simulations until an adequate match between model-generated flows and a set (time series) of gaged flows was achieved. The primary model input is rainfall and evaporation data and the model output is a time series of runoff volumes. After calibration, simulations driven by daily rainfall for a 26-year period (1968–93) were done for the 13 watersheds to obtain runoff under current (1983–93), predevelopment (pre-1940 streamflow and pre-urbanization), and future (2010) land-use conditions for estimating freshwater inflows and for comparing runoff under the three land-use conditions; and to obtain time series of runoff from which to estimate time series of freshwater inflows for trend analysis.

Trends in socioeconomic inequalities in mortality in small areas of 33 Spanish cities.

PubMed

Marí-Dell'Olmo, Marc; Gotsens, Mercè; Palència, Laia; Rodríguez-Sanz, Maica; Martinez-Beneito, Miguel A; Ballesta, Mónica; Calvo, Montse; Cirera, Lluís; Daponte, Antonio; Domínguez-Berjón, Felicitas; Gandarillas, Ana; Goñi, Natividad Izco; Martos, Carmen; Moreno-Iribas, Conchi; Nolasco, Andreu; Salmerón, Diego; Taracido, Margarita; Borrell, Carme

2016-07-29

In Spain, several ecological studies have analyzed trends in socioeconomic inequalities in mortality from all causes in urban areas over time. However, the results of these studies are quite heterogeneous finding, in general, that inequalities decreased, or remained stable. Therefore, the objectives of this study are: (1) to identify trends in geographical inequalities in all-cause mortality in the census tracts of 33 Spanish cities between the two periods 1996-1998 and 2005-2007; (2) to analyse trends in the relationship between these geographical inequalities and socioeconomic deprivation; and (3) to obtain an overall measure which summarises the relationship found in each one of the cities and to analyse its variation over time. Ecological study of trends with 2 cross-sectional cuts, corresponding to two periods of analysis: 1996-1998 and 2005-2007. Units of analysis were census tracts of the 33 Spanish cities. A deprivation index calculated for each census tracts in all cities was included as a covariate. A Bayesian hierarchical model was used to estimate smoothed Standardized Mortality Ratios (sSMR) by each census tract and period. The geographical distribution of these sSMR was represented using maps of septiles. In addition, two different Bayesian hierarchical models were used to measure the association between all-cause mortality and the deprivation index in each city and period, and by sex: (1) including the association as a fixed effect for each city; (2) including the association as random effects. In both models the data spatial structure can be controlled within each city. The association in each city was measured using relative risks (RR) and their 95 % credible intervals (95 % CI). For most cities and in both sexes, mortality rates decline over time. For women, the mortality and deprivation patterns are similar in the first period, while in the second they are different for most cities. For men, RRs remain stable over time in 29 cities, in 3 diminish and in 1 increase. For women, in 30 cities, a non-significant change over time in RR is observed. However, in 4 cities RR diminishes. In overall terms, inequalities decrease (with a probability of 0.9) in both men (RR = 1.13, 95 % CI = 1.12-1.15 in the 1st period; RR = 1.11, 95 % CI = 1.09-1.13 in the 2nd period) and women (RR = 1.07, 95 % CI = 1.05-1.08 in the 1st period; RR = 1.04, 95 % CI = 1.02-1.06 in the 2nd period). In the future, it is important to conduct further trend studies, allowing to monitoring trends in socioeconomic inequalities in mortality and to identify (among other things) temporal factors that may influence these inequalities.

Application of Seasonal Trend Loess to GPS data in Cascadia

NASA Astrophysics Data System (ADS)

Bal, A.; Bartlow, N. M.

2016-12-01

Plate Boundary Observatory GPS stations provide crucial data for the study of slow slip events and volcanic hazards in the Cascadia region. However, these GPS stations also record seasonal changes in deformation caused by hydrologic, atmospheric, and other seasonal loading. Removing these signals is necessary for accurately modeling the tectonic sources of deformation. Traditionally, seasonal trends in data been accounted for by fitting and removing sine curves from the data. However, not all seasonal trends follow a sinusoidal shape. Seasonal Trend Loess, or STL, is a filtering procedure for a decomposing a time series into trend, seasonal, and remainder components (Cleveland et. al, Journal of Official Statistics, 1990). STL has a simple design that consists of a sequence of applications of the loess smoother which allows for fast computation of large amounts of trend and seasonal smoothing. STL allows for non-sinusoidal shapes in seasonal deformation signals, and allows for evolution of seasonal signals over time. We applied Seasonal Trend Loess to GPS data from the Cascadia region. We compared our results to a traditional sine wave fit for seasonal removal at selected stations, including stations with slow slip event and volcanic signals. We hope that the STL method may be able to more accurately differentiate seasonal and tectonic deformation signals.

Decadal trends in global pelagic ocean chlorophyll: A new assessment integrating multiple satellites, in situ data, and models.

PubMed

Gregg, Watson W; Rousseaux, Cécile S

2014-09-01

Quantifying change in ocean biology using satellites is a major scientific objective. We document trends globally for the period 1998-2012 by integrating three diverse methodologies: ocean color data from multiple satellites, bias correction methods based on in situ data, and data assimilation to provide a consistent and complete global representation free of sampling biases. The results indicated no significant trend in global pelagic ocean chlorophyll over the 15 year data record. These results were consistent with previous findings that were based on the first 6 years and first 10 years of the SeaWiFS mission. However, all of the Northern Hemisphere basins (north of 10° latitude), as well as the Equatorial Indian basin, exhibited significant declines in chlorophyll. Trend maps showed the local trends and their change in percent per year. These trend maps were compared with several other previous efforts using only a single sensor (SeaWiFS) and more limited time series, showing remarkable consistency. These results suggested the present effort provides a path forward to quantifying global ocean trends using multiple satellite missions, which is essential if we are to understand the state, variability, and possible changes in the global oceans over longer time scales.

Modeled distribution and abundance of a pelagic seabird reveal trends in relation to fisheries

USGS Publications Warehouse

Renner, Martin; Parrish, Julia K.; Piatt, John F.; Kuletz, Kathy J.; Edwards, Ann E.; Hunt, George L.

2013-01-01

The northern fulmar Fulmarus glacialis is one of the most visible and widespread seabirds in the eastern Bering Sea and Aleutian Islands. However, relatively little is known about its abundance, trends, or the factors that shape its distribution. We used a long-term pelagic dataset to model changes in fulmar at-sea distribution and abundance since the mid-1970s. We used an ensemble model, based on a weighted average of generalized additive model (GAM), multivariate adaptive regression splines (MARS), and random forest models to estimate the pelagic distribution and density of fulmars in the waters of the Aleutian Archipelago and Bering Sea. The most important predictor variables were colony effect, sea surface temperature, distribution of fisheries, location, and primary productivity. We calculated a time series from the ratio of observed to predicted values and found that fulmar at-sea abundance declined from the 1970s to the 2000s at a rate of 0.83% (± 0.39% SE) per annum. Interpolating fulmar densities on a spatial grid through time, we found that the center of fulmar distribution in the Bering Sea has shifted north, coinciding with a northward shift in fish catches and a warming ocean. Our study shows that fisheries are an important, but not the only factor, shaping fulmar distribution and abundance trends in the eastern Bering Sea and Aleutian Islands.

Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

NASA Technical Reports Server (NTRS)

French, V. (Principal Investigator)

1982-01-01

An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

A Global Model Simulation of Aerosol Effects of Surface Radiation Budget- Toward Understanding of the "Dimming to Brightening" Transition

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, Thomas; Bian, Huisheng; Yu, Hongbin

2008-01-01

We present a global model study on the role aerosols play in the change of solar radiation at Earth's surface that transitioned from a decreasing (dimming) trend to an increasing (brightening) trend. Our primary objective is to understand the relationship between the long-term trends of aerosol emission, atmospheric burden, and surface solar radiation. More specifically, we use the recently compiled comprehensive global emission datasets of aerosols and precursors from fuel combustion, biomass burning, volcanic eruptions and other sources from 1980 to 2006 to simulate long-term variations of aerosol distributions and optical properties, and then calculate the multi-decadal changes of short-wave radiative fluxes at the surface and at the top of the atmosphere by coupling the GOCART model simulated aerosols with the Goddard radiative transfer model. The model results are compared with long-term observational records from ground-based networks and satellite data. We will address the following critical questions: To what extent can the observed surface solar radiation trends, known as the transition from dimming to brightening, be explained by the changes of anthropogenic and natural aerosol loading on global and regional scales? What are the relative contributions of local emission and long-range transport to the surface radiation budget and how do these contributions change with time?

Greater sage-grouse population trends across Wyoming

USGS Publications Warehouse

Edmunds, David; Aldridge, Cameron L.; O'Donnell, Michael; Monroe, Adrian

2018-01-01

The scale at which analyses are performed can have an effect on model results and often one scale does not accurately describe the ecological phenomena of interest (e.g., population trends) for wide-ranging species: yet, most ecological studies are performed at a single, arbitrary scale. To best determine local and regional trends for greater sage-grouse (Centrocercus urophasianus) in Wyoming, USA, we modeled density-independent and -dependent population growth across multiple spatial scales relevant to management and conservation (Core Areas [habitat encompassing approximately 83% of the sage-grouse population on ∼24% of surface area in Wyoming], local Working Groups [7 regional areas for which groups of local experts are tasked with implementing Wyoming's statewide sage-grouse conservation plan at the local level], Core Area status (Core Area vs. Non-Core Area) by Working Groups, and Core Areas by Working Groups). Our goal was to determine the influence of fine-scale population trends (Core Areas) on larger-scale populations (Working Group Areas). We modeled the natural log of change in population size ( peak M lek counts) by time to calculate the finite rate of population growth (λ) for each population of interest from 1993 to 2015. We found that in general when Core Area status (Core Area vs. Non-Core Area) was investigated by Working Group Area, the 2 populations trended similarly and agreed with the overall trend of the Working Group Area. However, at the finer scale where Core Areas were analyzed separately, Core Areas within the same Working Group Area often trended differently and a few large Core Areas could influence the overall Working Group Area trend and mask trends occurring in smaller Core Areas. Relatively close fine-scale populations of sage-grouse can trend differently, indicating that large-scale trends may not accurately depict what is occurring across the landscape (e.g., local effects of gas and oil fields may be masked by increasing larger populations). 

Effect of climatic variability on malaria trends in Baringo County, Kenya.

PubMed

Kipruto, Edwin K; Ochieng, Alfred O; Anyona, Douglas N; Mbalanya, Macrae; Mutua, Edna N; Onguru, Daniel; Nyamongo, Isaac K; Estambale, Benson B A

2017-05-25

Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

Changes in intense tropical cyclone activity for the western North Pacific during the last decades derived from a regional climate model simulation

NASA Astrophysics Data System (ADS)

Barcikowska, Monika; Feser, Frauke; Zhang, Wei; Mei, Wei

2017-11-01

An atmospheric regional climate model (CCLM) was employed to dynamically downscale atmospheric reanalyses (NCEP/NCAR 1, ERA 40) over the western North Pacific and South East Asia. This approach is used for the first time to reconstruct a tropical cyclone climatology, which extends beyond the satellite era and serves as an alternative data set for inhomogeneous observation-derived records (Best Track Data sets). The simulated TC climatology skillfully reproduces observations of the recent decades (1978-2010), including spatial patterns, frequency, lifetime, trends, variability on interannual and decadal time scales and their association with the large-scale circulation patterns. These skills, facilitated here with the spectral nudging method, seem to be a prerequisite to understand the factors determining spatio-temporal variability of TC activity over the western North Pacific. Long-term trends (1948-2011 and 1959-2001) in both simulations show a strong increase of intense tropical cyclone activity. This contrasts with pronounced multidecadal variations found in observations. The discrepancy may partly originate from temporal inhomogeneities in atmospheric reanalyses and Best Track Data, which affect both the model-based and observational-based trends. An adjustment, which removes the simulated upward trend, reduces the apparent discrepancy. Ultimately, our observational and modeling analysis suggests an important contribution of multi-decadal fluctuations in the TC activity during the last six decades. Nevertheless, due to the uncertainties associated with the inconsistencies and quality changes of those data sets, we call for special caution when reconstructing long-term TC statistics either from atmospheric reanalyses or Best Track Data.

Damage evaluation by a guided wave-hidden Markov model based method

NASA Astrophysics Data System (ADS)

Mei, Hanfei; Yuan, Shenfang; Qiu, Lei; Zhang, Jinjin

2016-02-01

Guided wave based structural health monitoring has shown great potential in aerospace applications. However, one of the key challenges of practical engineering applications is the accurate interpretation of the guided wave signals under time-varying environmental and operational conditions. This paper presents a guided wave-hidden Markov model based method to improve the damage evaluation reliability of real aircraft structures under time-varying conditions. In the proposed approach, an HMM based unweighted moving average trend estimation method, which can capture the trend of damage propagation from the posterior probability obtained by HMM modeling is used to achieve a probabilistic evaluation of the structural damage. To validate the developed method, experiments are performed on a hole-edge crack specimen under fatigue loading condition and a real aircraft wing spar under changing structural boundary conditions. Experimental results show the advantage of the proposed method.

Estimating front-wave velocity of infectious diseases: a simple, efficient method applied to bluetongue.

PubMed

Pioz, Maryline; Guis, Hélène; Calavas, Didier; Durand, Benoît; Abrial, David; Ducrot, Christian

2011-04-20

Understanding the spatial dynamics of an infectious disease is critical when attempting to predict where and how fast the disease will spread. We illustrate an approach using a trend-surface analysis (TSA) model combined with a spatial error simultaneous autoregressive model (SAR(err) model) to estimate the speed of diffusion of bluetongue (BT), an infectious disease of ruminants caused by bluetongue virus (BTV) and transmitted by Culicoides. In a first step to gain further insight into the spatial transmission characteristics of BTV serotype 8, we used 2007-2008 clinical case reports in France and TSA modelling to identify the major directions and speed of disease diffusion. We accounted for spatial autocorrelation by combining TSA with a SAR(err) model, which led to a trend SAR(err) model. Overall, BT spread from north-eastern to south-western France. The average trend SAR(err)-estimated velocity across the country was 5.6 km/day. However, velocities differed between areas and time periods, varying between 2.1 and 9.3 km/day. For more than 83% of the contaminated municipalities, the trend SAR(err)-estimated velocity was less than 7 km/day. Our study was a first step in describing the diffusion process for BT in France. To our knowledge, it is the first to show that BT spread in France was primarily local and consistent with the active flight of Culicoides and local movements of farm animals. Models such as the trend SAR(err) models are powerful tools to provide information on direction and speed of disease diffusion when the only data available are date and location of cases.

A joint modelling exercise designed to assess the respective impact of emission changes and meteorological variability on the observed air quality trends in major urban hotspots.

NASA Astrophysics Data System (ADS)

Colette, Augustin; Bessagnet, Bertrand; Dangiola, Ariela; D'Isidoro, Massimo; Gauss, Michael; Granier, Claire; Hodnebrog, Øivind; Jakobs, Hermann; Kanakidou, Maria; Khokhar, Fahim; Law, Kathy; Maurizi, Alberto; Meleux, Frederik; Memmesheimer, Michael; Nyiri, Agnes; Rouil, Laurence; Stordal, Frode; Tampieri, Francesco

2010-05-01

With the growth of urban agglomerations, assessing the drivers of variability of air quality in and around the main anthropogenic emission hotspots has become a major societal concern as well as a scientific challenge. These drivers include emission changes and meteorological variability; both of them can be investigated by means of numerical modelling of trends over the past few years. A collaborative effort has been developed in the framework of the CityZen European project to address this question. Several chemistry and transport models (CTMs) are deployed in this activity: four regional models (BOLCHEM, CHIMERE, EMEP and EURAD) and three global models (CTM2, MOZART, and TM4). The period from 1998 to 2007 has been selected for the historic reconstruction. The focus for the present preliminary presentation is Europe. A consistent set of emissions is used by all partners (EMEP for the European domain and IPCC-AR5 beyond) while a variety of meteorological forcing is used to gain robustness in the ensemble spread amongst models. The results of this experiment will be investigated to address the following questions: - Is the envelope of models able to reproduce the observed trends of the key chemical constituents? - How the variability amongst models changes in time and space and what does it tell us about the processes driving the observed trends? - Did chemical regimes and aerosol formation processes changed in selected hotspots? Answering the above questions will contribute to fulfil the ultimate goal of the present study: distinguishing the respective contribution of meteorological variability and emissions changes on air quality trends in major anthropogenic emissions hotspots.

Long-Term Trends and Variability in Spring Development of Calanus finmarchicus in the Southeastern Norwegian Sea during 1996-2012

NASA Astrophysics Data System (ADS)

Dupont, N.; Bagøien, E.; Melle, W.

2016-02-01

Calanus finmarchicus is the dominant copepod species in the Norwegian Sea in terms of biomass, playing a key role in the ecosystem by transferring energy from primary producers to higher trophic levels. This study analyses the long-term trend of a 17-year time series (1996-2012) on abundance of adult Calanus finmarchicus in the Atlantic water-mass of the southern Norwegian Sea during spring. The long-term trend in spring abundance was assessed by using Generalised Additive Models, while simultaneously accounting for both general population development and inter-annual variation in population development throughout the study period. In one model, we focus on inter-annual changes in timing of the Calanus spring seasonal development by including Mean Stage Composition as a measure for state of population development. Following a short increase during the years 1996 to 2000, the abundance of Calanus finmarchicus decreased strongly until about the year 2010. For the two last years of the studied period, 2011-2012, increasing population abundances are suggested but with less certainty. The model results suggest that the analysis is capturing the G0 generation, displaying a peak for the adults in about mid-April. Inter-annual differences in spring seasonal development, with the peak of adults shifting towards earlier in the season as well as a shorter generation time are suggested. Considering the importance of Calanus finmarchicus as food for planktivorous predators in the Norwegian Sea, our time series analysis suggests relevant changes both with respect to the spring abundance and timing of this food source. The next step is to relate variation in the Calanus time series to environmental factors with special emphasis on climatic drivers.

Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

NASA Astrophysics Data System (ADS)

Armal, S.; Devineni, N.; Khanbilvardi, R.

2017-12-01

This study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1,244 stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: Trend can be attributed to changes in global surface temperature anomalies, or to a combination of cyclical climate modes with varying quasi-periodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypotheses is made based on Watanabe-Akaike Information Criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1,244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to the El Nino Southern Oscillations, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic Multi-Decadal Oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the Northwest, West and Southwest climate regions.

Global Distribution and Trends of Tropospheric Ozone: An Observation-Based Review

NASA Technical Reports Server (NTRS)

Cooper, O. R.; Parrish, D. D.; Ziemke, J.; Cupeiro, M.; Galbally, I. E.; Gilge, S.; Horowitz, L.; Jensen, N. R.; Lamarque, J.-F.; Naik, V.;

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

On baseline corrections and uncertainty in response spectrafor baseline variations commonly encountered in digital accelerograph records

USGS Publications Warehouse

Akkar, Sinan; Boore, David M.

2009-01-01

Most digital accelerograph recordings are plagued by long-period drifts, best seen in the velocity and displacement time series obtained from integration of the acceleration time series. These drifts often result in velocity values that are nonzero near the end of the record. This is clearly unphysical and can lead to inaccurate estimates of peak ground displacement and long-period spectral response. The source of the long-period noise seems to be variations in the acceleration baseline in many cases. These variations could be due to true ground motion (tilting and rotation, as well as local permanent ground deformation), instrumental effects, or analog-to-digital conversion. Very often the trends in velocity are well approximated by a linear trend after the strong shaking subsides. The linearity of the trend in velocity implies that no variations in the baseline could have occurred after the onset of linearity in the velocity time series. This observation, combined with the lack of any trends in the pre-event motion, allows us to compute the time interval in which any baseline variations could occur. We then use several models of the variations in a Monte Carlo procedure to derive a suite of baseline-corrected accelerations for each noise model using records from the 1999 Chi-Chi earthquake and several earthquakes in Turkey. Comparisons of the mean values of the peak ground displacements, spectral displacements, and residual displacements computed from these corrected accelerations for the different noise models can be used as a guide to the accuracy of the baseline corrections. For many of the records considered here the mean values are similar for each noise model, giving confidence in the estimation of the mean values. The dispersion of the ground-motion measures increases with period and is noise-model dependent. The dispersion of inelastic spectra is greater than the elastic spectra at short periods but approaches that of the elastic spectra at longer periods. The elastic spectra from the most basic processing, in which only the pre-event mean is removed from the acceleration time series, do not diverge from the baseline-corrected spectra until periods of 10-20 sec or more for the records studied here, implying that for many engineering purposes elastic spectra can be used from records with no baseline correction or filtering.

Hierarchical Bayes Models for Response Time Data

ERIC Educational Resources Information Center

Craigmile, Peter F.; Peruggia, Mario; Van Zandt, Trisha

2010-01-01

Human response time (RT) data are widely used in experimental psychology to evaluate theories of mental processing. Typically, the data constitute the times taken by a subject to react to a succession of stimuli under varying experimental conditions. Because of the sequential nature of the experiments there are trends (due to learning, fatigue,…

Battery Lifespan | Transportation Research | NREL

Science.gov Websites

over time (ranging from 0 to 15 years) for three different climates (represented by Minneapolis . Trend lines from upper left to lower right reflect diminished capacity over time and shorter lifespan in Battery Life Model. Graph of relative capacity (ranging from .75 to 1) of battery in percent over time

Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends

NASA Astrophysics Data System (ADS)

Gettelman, A.; Hegglin, M. I.; Son, S.-W.; Kim, J.; Fujiwara, M.; Birner, T.; Kremser, S.; Rex, M.; AñEl, J. A.; Akiyoshi, H.; Austin, J.; Bekki, S.; Braesike, P.; Brühl, C.; Butchart, N.; Chipperfield, M.; Dameris, M.; Dhomse, S.; Garny, H.; Hardiman, S. C.; JöCkel, P.; Kinnison, D. E.; Lamarque, J. F.; Mancini, E.; Marchand, M.; Michou, M.; Morgenstern, O.; Pawson, S.; Pitari, G.; Plummer, D.; Pyle, J. A.; Rozanov, E.; Scinocca, J.; Shepherd, T. G.; Shibata, K.; Smale, D.; TeyssèDre, H.; Tian, W.

2010-01-01

The performance of 18 coupled Chemistry Climate Models (CCMs) in the Tropical Tropopause Layer (TTL) is evaluated using qualitative and quantitative diagnostics. Trends in tropopause quantities in the tropics and the extratropical Upper Troposphere and Lower Stratosphere (UTLS) are analyzed. A quantitative grading methodology for evaluating CCMs is extended to include variability and used to develop four different grades for tropical tropopause temperature and pressure, water vapor and ozone. Four of the 18 models and the multi-model mean meet quantitative and qualitative standards for reproducing key processes in the TTL. Several diagnostics are performed on a subset of the models analyzing the Tropopause Inversion Layer (TIL), Lagrangian cold point and TTL transit time. Historical decreases in tropical tropopause pressure and decreases in water vapor are simulated, lending confidence to future projections. The models simulate continued decreases in tropopause pressure in the 21st century, along with ˜1K increases per century in cold point tropopause temperature and 0.5-1 ppmv per century increases in water vapor above the tropical tropopause. TTL water vapor increases below the cold point. In two models, these trends are associated with 35% increases in TTL cloud fraction. These changes indicate significant perturbations to TTL processes, specifically to deep convective heating and humidity transport. Ozone in the extratropical lowermost stratosphere has significant and hemispheric asymmetric trends. O3 is projected to increase by nearly 30% due to ozone recovery in the Southern Hemisphere (SH) and due to enhancements in the stratospheric circulation. These UTLS ozone trends may have significant effects in the TTL and the troposphere.

Space-time latent component modeling of geo-referenced health data.

PubMed

Lawson, Andrew B; Song, Hae-Ryoung; Cai, Bo; Hossain, Md Monir; Huang, Kun

2010-08-30

Latent structure models have been proposed in many applications. For space-time health data it is often important to be able to find the underlying trends in time, which are supported by subsets of small areas. Latent structure modeling is one such approach to this analysis. This paper presents a mixture-based approach that can be applied to component selection. The analysis of a Georgia ambulatory asthma county-level data set is presented and a simulation-based evaluation is made. Copyright (c) 2010 John Wiley & Sons, Ltd.

Dynamic modeling of Tampa Bay urban development using parallel computing

USGS Publications Warehouse

Xian, G.; Crane, M.; Steinwand, D.

2005-01-01

Urban land use and land cover has changed significantly in the environs of Tampa Bay, Florida, over the past 50 years. Extensive urbanization has created substantial change to the region's landscape and ecosystems. This paper uses a dynamic urban-growth model, SLEUTH, which applies six geospatial data themes (slope, land use, exclusion, urban extent, transportation, hillside), to study the process of urbanization and associated land use and land cover change in the Tampa Bay area. To reduce processing time and complete the modeling process within an acceptable period, the model is recoded and ported to a Beowulf cluster. The parallel-processing computer system accomplishes the massive amount of computation the modeling simulation requires. SLEUTH calibration process for the Tampa Bay urban growth simulation spends only 10 h CPU time. The model predicts future land use/cover change trends for Tampa Bay from 1992 to 2025. Urban extent is predicted to double in the Tampa Bay watershed between 1992 and 2025. Results show an upward trend of urbanization at the expense of a decline of 58% and 80% in agriculture and forested lands, respectively.

Concentration, flux, and trend estimates with uncertainty for nutrients, chloride, and total suspended solids in tributaries of Lake Champlain, 1990–2014

USGS Publications Warehouse

Medalie, Laura

2016-12-20

The U.S. Geological Survey, in cooperation with the New England Interstate Water Pollution Control Commission and the Vermont Department of Environmental Conservation, estimated daily and 9-month concentrations and fluxes of total and dissolved phosphorus, total nitrogen, chloride, and total suspended solids from 1990 (or first available date) through 2014 for 18 tributaries of Lake Champlain. Estimates of concentration and flux, provided separately in Medalie (2016), were made by using the Weighted Regressions on Time, Discharge, and Season (WRTDS) regression model and update previously published WRTDS model results with recent data. Assessment of progress towards meeting phosphorus-reduction goals outlined in the Lake Champlain management plan relies on annual estimates of phosphorus flux. The percent change in annual concentration and flux is provided for two time periods. The R package EGRETci was used to estimate the uncertainty of the trend estimate. Differences in model specification and function between this study and previous studies that used WRTDS to estimate concentration and flux using data from Lake Champlain tributaries are described. Winter data were too sparse and nonrepresentative to use for estimates of concentration and flux but were sufficient for estimating the percentage of total annual flux over the period of record. Median winter-to-annual fractions ranged between 21 percent for total suspended solids and 27 percent for dissolved phosphorus. The winter contribution was largest for all constituents from the Mettawee River and smallest from the Ausable River. For the full record (1991 through 2014 for total and dissolved phosphorus and chloride and 1993 through 2014 for nitrogen and total suspended solids), 6 tributaries had decreasing trends in concentrations of total phosphorus, and 12 had increasing trends; concentrations of dissolved phosphorus decreased in 6 and increased in 8 tributaries; fluxes of total phosphorus decreased in 5 and increased in 10 tributaries; and fluxes of dissolved phosphorus decreased in 4 and increased in 10 tributaries (where the number of increasing and decreasing trends does not add up to 18, the remainder of tributaries had no trends). Concentrations and fluxes of nitrogen decreased in 10 and increased in 4 tributaries and of chloride decreased in 2 and increased in 15 tributaries. Concentrations of total suspended solids decreased in 4 and increased in 8 tributaries, and fluxes of total suspended solids decreased in 3 and increased in 11 tributaries. Although time intervals for the percent changes from this report are not completely synchronous with those from previous studies, the numbers of and specific tributaries with overall negative percent changes in concentration and flux are similar. Concentration estimates of total phosphorus in the Winooski River were used to trace whether changes in trends between a previous study and the current study were due generally to differences in model specifications or differences from 4 years of additional data. The Winooski River analysis illustrates several things: that keeping all model specifications equal, concentration estimates increased from 2010 to 2014; the effects of a smoothing algorithm used in the current study that was not available previously; that narrowing model half-window widths increased year-to-year variations; and that the change from an annual to a 9-month basis by omitting winter estimates changed a few individual points but not the overall shape of the flow-normalized curve. Similar tests for other tributaries showed that the primary effect of differences in model specifications between the previous and current studies was perhaps to increase scatter over time but that changes in trends generally were the result of 4 years of additional data rather than artifacts of model differences.

Age-period-cohort analysis of suicides among Japanese 1950-2003: a Bayesian cohort model analysis.

PubMed

Ooe, Yosuke; Ohno, Yuko; Nakamura, Takashi

2009-07-01

The suicide rate in Japan is one of the highest in the world and presents us with a considerable challenge. Demographic statistics show that the number of suicides is on the rise, and at roughly 30,000 people per year have committed suicide since 1998. Suicide trends are not only related to economic boom and bust but also to certain generations and age groups. During the 1950s, there was a remarkably high suicide rate among people in their 20s, and this cohort was identical to that of the middle-age generation in the 1980s. It is important to separately understand both the trend of suicide rates and the numbers analyzed to determine the different factors that influence suicide. These include age, time period, cohort, interaction between age and time period, and changes in population composition. We performed an age-period-cohort analysis of annual trends of suicide rates by age group in Japan using a Bayesian cohort model. With the help of the Nakamura method, we have been able to break down the effects of age, time period, cohort, and the age-by-period interaction. The cohort comprised of people born in the 1930s demonstrated a relatively high suicide rate. Men currently in their 50s also belong to a high suicide rate cohort. Regarding the period effect, business cycles and by-period interaction effect, it became apparent that the high suicide rate among young adults in their early 20s around 1960 was slowing, especially among men. Instead, there was an obvious recent trend for men in their late 50s to have the highest suicide rate. This study confirmed that age-period-cohort analysis can describe these trends of suicide mortality of the Japanese.

Assessing temporal trends and source regions of per- and polyfluoroalkyl substances (PFASs) in air under the Arctic Monitoring and Assessment Programme (AMAP)

NASA Astrophysics Data System (ADS)

Wong, Fiona; Shoeib, Mahiba; Katsoyiannis, Athanasios; Eckhardt, Sabine; Stohl, Andreas; Bohlin-Nizzetto, Pernilla; Li, Henrik; Fellin, Phil; Su, Yushan; Hung, Hayley

2018-01-01

Long-term Arctic air monitoring of per- and polyfluoroalkyl substances (PFASs) is essential in assessing their long-range transport and for evaluating the effectiveness of chemical control initiatives. We report for the first time temporal trends of neutral and ionic PFASs in air from three arctic stations: Alert (Canada, 2006-2014); Zeppelin (Svalbard, Norway, 2006-2014) and Andøya (Norway, 2010-2014). The most abundant PFASs were the perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorobutanoic acid (PFBA), and fluorotelomer alcohols (FTOHs). All of these chemicals exhibited increasing trends at Alert with doubling times (t2) of 3.7 years (y) for PFOA, 2.9 y for PFOS, 2.5 y for PFBA, 5.0 y for 8:2 FTOH and 7.0 y for 10:2 FTOH. In contrast, declining or non-changing trends, were observed for PFOA and PFOS at Zeppelin (PFOA, half-life, t1/2 = 7.2 y; PFOS t1/2 = 67 y), and Andøya (PFOA t1/2 = 1.9 y; PFOS t1/2 = 11 y). The differences in air concentrations and in time trends between the three sites may reflect the differences in regional regulations and source regions. We investigate the source region for particle associated compounds using the Lagrangian particle dispersion model FLEXPART. Model results showed that PFOA and PFOS are impacted by air masses originating from the ocean or land. For instance, PFOA at Alert and PFOS at Zeppelin were dominated by oceanic air masses whereas, PFOS at Alert and PFOA at Zeppelin were influenced by air masses transported from land.

A parsimonious characterization of change in global age-specific and total fertility rates

PubMed Central

2018-01-01

This study aims to understand trends in global fertility from 1950-2010 though the analysis of age-specific fertility rates. This approach incorporates both the overall level, as when the total fertility rate is modeled, and different patterns of age-specific fertility to examine the relationship between changes in age-specific fertility and fertility decline. Singular value decomposition is used to capture the variation in age-specific fertility curves while reducing the number of dimensions, allowing curves to be described nearly fully with three parameters. Regional patterns and trends over time are evident in parameter values, suggesting this method provides a useful tool for considering fertility decline globally. The second and third parameters were analyzed using model-based clustering to examine patterns of age-specific fertility over time and place; four clusters were obtained. A country’s demographic transition can be traced through time by membership in the different clusters, and regional patterns in the trajectories through time and with fertility decline are identified. PMID:29377899

Linking plant functional trait plasticity and the large increase in forest water use efficiency

NASA Astrophysics Data System (ADS)

Mastrotheodoros, Theodoros; Pappas, Christoforos; Molnar, Peter; Burlando, Paolo; Keenan, Trevor F.; Gentine, Pierre; Gough, Christopher M.; Fatichi, Simone

2017-09-01

Elevated atmospheric CO2 concentrations are expected to enhance photosynthesis and reduce stomatal conductance, thus increasing plant water use efficiency. A recent study based on eddy covariance flux observations from Northern Hemisphere forests showed a large increase in inherent water use efficiency (IWUE). Here we used an updated version of the same data set and robust uncertainty quantification to revisit these contemporary IWUE trends. We tested the hypothesis that the observed IWUE increase could be attributed to interannual trends in plant functional traits, potentially triggered by environmental change. We found that IWUE increased by 1.3% yr-1, which is less than previously reported but still larger than theoretical expectations. Numerical simulations with the Tethys-Chloris ecosystem model using temporally static plant functional traits cannot explain this increase. Simulations with plant functional trait plasticity, i.e., temporal changes in model parameters such as specific leaf area and maximum Rubisco capacity, match the observed trends in IWUE. Our results show that trends in plant functional traits, equal to 1.0% yr-1, can explain the observed IWUE trends. Thus, at decadal or longer time scales, trait plasticity could potentially influence forest water, carbon, and energy fluxes with profound implications for both the monitoring of temporal changes in plant functional traits and their representation in Earth system models.

Trend in frequency of extreme precipitation events over Ontario from ensembles of multiple GCMs

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Qiu, Xin; Liu, Jinliang; Madras, Neal; Wang, Xiaogang; Zhu, Huaiping

2016-05-01

As one of the most important extreme weather event types, extreme precipitation events have significant impacts on human and natural environment. This study assesses the projected long term trends in frequency of occurrence of extreme precipitation events represented by heavy precipitation days, very heavy precipitation days, very wet days and extreme wet days over Ontario, based on results of 21 CMIP3 GCM runs. To achieve this goal, first, all model data are linearly interpolated onto 682 grid points (0.45° × 0.45°) in Ontario; Next, biases in model daily precipitation amount are corrected with a local intensity scaling method to make the total wet days and total wet day precipitation from each of the GCMs are consistent with that from the climate forecast system reanalysis data, and then the four indices are estimated for each of the 21 GCM runs for 1968-2000, 2046-2065 and 2081-2100. After that, with the assumption that the rate parameter of the Poisson process for the occurrence of extreme precipitation events may vary with time as climate changes, the Poisson regression model which expresses the log rate as a linear function of time is used to detect the trend in frequency of extreme events in the GCMs simulations; Finally, the trends and their uncertainty are estimated. The result shows that in the twenty-first century annual heavy precipitation days, very heavy precipitation days and very wet days and extreme wet days are likely to significantly increase over major parts of Ontario and particularly heavy precipitation days, very wet days are very likely to significantly increase in some sub-regions in eastern Ontario. However, trends of seasonal indices are not significant.

Bladder cancer mortality trends and patterns in Córdoba, Argentina (1986-2006).

PubMed

Pou, Sonia Alejandra; Osella, Alberto Ruben; Diaz, Maria Del Pilar

2011-03-01

Bladder cancer is common worldwide and the fourth most commonly diagnosed malignancy in men in Argentina. To describe bladder cancer mortality trends in Córdoba (1986-2006), considering the effect of age, period, and cohort, and to estimate the effect of arsenic exposure on bladder cancer, and its interaction with sex, while controlling by smoking habits and space and time variation of the rates. A joinpoint regression was performed to compute the estimated annual percentage changes (EAPC) of the age-standardized mortality rates (ASMR) in an adult population from Córdoba, Argentina. A Poisson model was fitted to estimate the effect of age, period, and cohort. The influence of gender, tobacco smoking (using lung cancer ASMR as surrogate), and arsenic in drinking water was examined using a hierarchical model. A favorable trend (1986-2006) in bladder cancer ASMR in both sexes was found: EAPC of -2.54 in men and -1.69 in women. There was a decreasing trend in relative risk (RR) for cohorts born in 1931 or after. The multilevel model showed an increasing risk for each increase in lung cancer ASMR unit (RR = 1.001) and a biological interaction between sex and arsenic exposure. RR was higher among men exposed to increasing As-exposure categories (RR male low exposure 3.14, RR male intermediate exposure 4.03, RR male high exposure 4.71 versus female low exposure). A non-random space-time distribution of the rates was observed. There has been a decreasing trend in ASMR for bladder cancer in Córdoba. This study confirms that bladder cancer is associated with age, gender, smoking habit, and exposure to arsenic. Moreover, an effect measure modification between exposure to arsenic and sex was found.

O3 variability/trends in the troposphere from IASI observations in 2008-2017

NASA Astrophysics Data System (ADS)

Wespes, C.; Hurtmans, D.; Clerbaux, C.; Pierre-Francois, C.

2017-12-01

In this study, we describe the recent changes in the tropospheric ozone (O3) columns (TOCs) measured by the Infrared Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites during the first ten years of the IASI operation (2008-2017). The instrument provides a unique dataset of vertically-resolved O3 profiles with a twice daily global coverage and a fairly good vertical resolution allowing us to monitor the year-to-year variability in the troposphere. The retrievals are performed using the FORLI software, a fast radiative transfer model based on the optimal estimation method, set up for near real time and large scale processing of IASI data. We differentiate trend characteristics from the seasonal and non-seasonal O3 variations captured by IASI in the troposphere by applying appropriate annual and seasonal multivariate regression models, which include important geophysical drivers of O3 variation (e.g. quasi biennial oscillations - QBO, El Niño/Southern Oscillation - ENSO, North Atlantic Oscillation-NAO) and a linear trend term, on time series of spatially gridded averaged O3. The performances of the regression models (annual vs seasonal) are first investigated. Given the large contribution of the interannual variability, we will then describe the effects of the main contributing O3 proxies (e.g. positive - or negatives - ENSO indexes measured during moderate to intense El Niño - or La Niña - episodes in the tropics) in addition to the adjusted O3 trend patterns. A special focus will be given over the Northern Hemisphere which is characterized by decreasing O3 precursor emissions (mainly over Europe and the US). FORLI O3-CO correlations patterns will also be discussed to evaluate the continental influence on the tropospheric O3 trends.

Future climate data from RCP 4.5 and occurrence of malaria in Korea.

PubMed

Kwak, Jaewon; Noh, Huiseong; Kim, Soojun; Singh, Vijay P; Hong, Seung Jin; Kim, Duckgil; Lee, Keonhaeng; Kang, Narae; Kim, Hung Soo

2014-10-15

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001-2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future.

Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea

PubMed Central

Kwak, Jaewon; Noh, Huiseong; Kim, Soojun; Singh, Vijay P.; Hong, Seung Jin; Kim, Duckgil; Lee, Keonhaeng; Kang, Narae; Kim, Hung Soo

2014-01-01

Since its reappearance at the Military Demarcation Line in 1993, malaria has been occurring annually in Korea. Malaria is regarded as a third grade nationally notifiable disease susceptible to climate change. The objective of this study is to quantify the effect of climatic factors on the occurrence of malaria in Korea and construct a malaria occurrence model for predicting the future trend of malaria under the influence of climate change. Using data from 2001–2011, the effect of time lag between malaria occurrence and mean temperature, relative humidity and total precipitation was investigated using spectral analysis. Also, a principal component regression model was constructed, considering multicollinearity. Future climate data, generated from RCP 4.5 climate change scenario and CNCM3 climate model, was applied to the constructed regression model to simulate future malaria occurrence and analyze the trend of occurrence. Results show an increase in the occurrence of malaria and the shortening of annual time of occurrence in the future. PMID:25321875

Of mental models, assumptions and heuristics: The case of acids and acid strength

NASA Astrophysics Data System (ADS)

McClary, Lakeisha Michelle

This study explored what cognitive resources (i.e., units of knowledge necessary to learn) first-semester organic chemistry students used to make decisions about acid strength and how those resources guided the prediction, explanation and justification of trends in acid strength. We were specifically interested in the identifying and characterizing the mental models, assumptions and heuristics that students relied upon to make their decisions, in most cases under time constraints. The views about acids and acid strength were investigated for twenty undergraduate students. Data sources for this study included written responses and individual interviews. The data was analyzed using a qualitative methodology to answer five research questions. Data analysis regarding these research questions was based on existing theoretical frameworks: problem representation (Chi, Feltovich & Glaser, 1981), mental models (Johnson-Laird, 1983); intuitive assumptions (Talanquer, 2006), and heuristics (Evans, 2008). These frameworks were combined to develop the framework from which our data were analyzed. Results indicated that first-semester organic chemistry students' use of cognitive resources was complex and dependent on their understanding of the behavior of acids. Expressed mental models were generated using prior knowledge and assumptions about acids and acid strength; these models were then employed to make decisions. Explicit and implicit features of the compounds in each task mediated participants' attention, which triggered the use of a very limited number of heuristics, or shortcut reasoning strategies. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength. Many students, however, were able to apply more effortful analytic reasoning, though correct trends were predicted infrequently. Most students continued to use their mental models, assumptions and heuristics to explain a given trend in acid strength and to justify their predicted trends, but the tasks influenced a few students to shift from one model to another model. An emergent finding from this project was that the problem representation greatly influenced students' ability to make correct predictions in acid strength.

An Integrated Architecture for On-Board Aircraft Engine Performance Trend Monitoring and Gas Path Fault Diagnostics

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2010-01-01

Aircraft engine performance trend monitoring and gas path fault diagnostics are closely related technologies that assist operators in managing the health of their gas turbine engine assets. Trend monitoring is the process of monitoring the gradual performance change that an aircraft engine will naturally incur over time due to turbomachinery deterioration, while gas path diagnostics is the process of detecting and isolating the occurrence of any faults impacting engine flow-path performance. Today, performance trend monitoring and gas path fault diagnostic functions are performed by a combination of on-board and off-board strategies. On-board engine control computers contain logic that monitors for anomalous engine operation in real-time. Off-board ground stations are used to conduct fleet-wide engine trend monitoring and fault diagnostics based on data collected from each engine each flight. Continuing advances in avionics are enabling the migration of portions of the ground-based functionality on-board, giving rise to more sophisticated on-board engine health management capabilities. This paper reviews the conventional engine performance trend monitoring and gas path fault diagnostic architecture commonly applied today, and presents a proposed enhanced on-board architecture for future applications. The enhanced architecture gains real-time access to an expanded quantity of engine parameters, and provides advanced on-board model-based estimation capabilities. The benefits of the enhanced architecture include the real-time continuous monitoring of engine health, the early diagnosis of fault conditions, and the estimation of unmeasured engine performance parameters. A future vision to advance the enhanced architecture is also presented and discussed

The tropical Pacific as a key pacemaker of the variable rates of global warming

NASA Astrophysics Data System (ADS)

Kosaka, Yu; Xie, Shang-Ping

2016-09-01

Global mean surface temperature change over the past 120 years resembles a rising staircase: the overall warming trend was interrupted by the mid-twentieth-century big hiatus and the warming slowdown since about 1998. The Interdecadal Pacific Oscillation has been implicated in modulations of global mean surface temperatures, but which part of the mode drives the variability in warming rates is unclear. Here we present a successful simulation of the global warming staircase since 1900 with a global ocean-atmosphere coupled model where tropical Pacific sea surface temperatures are forced to follow the observed evolution. Without prescribed tropical Pacific variability, the same model, on average, produces a continual warming trend that accelerates after the 1960s. We identify four events where the tropical Pacific decadal cooling markedly slowed down the warming trend. Matching the observed spatial and seasonal fingerprints we identify the tropical Pacific as a key pacemaker of the warming staircase, with radiative forcing driving the overall warming trend. Specifically, tropical Pacific variability amplifies the first warming epoch of the 1910s-1940s and determines the timing when the big hiatus starts and ends. Our method of removing internal variability from the observed record can be used for real-time monitoring of anthropogenic warming.

Trends in risk factors for coronary heart disease in the Netherlands.

PubMed

Koopman, C; Vaartjes, I; Blokstra, A; Verschuren, W M M; Visser, M; Deeg, D J H; Bots, M L; van Dis, I

2016-08-19

Favourable trends in risk factor levels in the general population may partly explain the decline in coronary heart disease (CHD) morbidity and mortality. Our aim was to present long-term national trends in established risk factors for CHD. Data were obtained from five data sources including several large scale population based surveys, cohort studies and general practitioner registers between 1988 and 2012. We applied linear regression models to age-standardized time trends to test for statistical significant trends. Analyses were stratified by sex and age (younger <65 and older ≥65 years adults). The results demonstrated favourable trends in smoking (except in older women) and physical activity (except in older men). Unfavourable trends were found for body mass index (BMI) and diabetes mellitus prevalence. Although systolic blood pressure (SBP) and total cholesterol trends were favourable for older persons, SBP and total cholesterol remained stable in younger persons. Four out of six risk factors for CHD showed a favourable or stable trend. The rise in diabetes mellitus and BMI is worrying with respect to CHD morbidity and mortality.

Real Time Updating Genetic Network Programming for Adapting to the Change of Stock Prices

NASA Astrophysics Data System (ADS)

Chen, Yan; Mabu, Shingo; Shimada, Kaoru; Hirasawa, Kotaro

The key in stock trading model is to take the right actions for trading at the right time, primarily based on the accurate forecast of future stock trends. Since an effective trading with given information of stock prices needs an intelligent strategy for the decision making, we applied Genetic Network Programming (GNP) to creating a stock trading model. In this paper, we propose a new method called Real Time Updating Genetic Network Programming (RTU-GNP) for adapting to the change of stock prices. There are three important points in this paper: First, the RTU-GNP method makes a stock trading decision considering both the recommendable information of technical indices and the candlestick charts according to the real time stock prices. Second, we combine RTU-GNP with a Sarsa learning algorithm to create the programs efficiently. Also, sub-nodes are introduced in each judgment and processing node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. Third, a Real Time Updating system has been firstly introduced in our paper considering the change of the trend of stock prices. The experimental results on the Japanese stock market show that the trading model with the proposed RTU-GNP method outperforms other models without real time updating. We also compared the experimental results using the proposed method with Buy&Hold method to confirm its effectiveness, and it is clarified that the proposed trading model can obtain much higher profits than Buy&Hold method.

Comparison of statistical models for analyzing wheat yield time series.

PubMed

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha⁻¹ year⁻¹ in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.

Detection of conveyance changes in St. Clair River using historical water-level and flow data with inverse one-dimensional hydrodynamic modeling

USGS Publications Warehouse

Holtschlag, David J.; Hoard, C.J.

2009-01-01

St. Clair River is a connecting channel that transports water from Lake Huron to the St. Clair River Delta and Lake St. Clair. A negative trend has been detected in differences between water levels on Lake Huron and Lake St. Clair. This trend may indicate a combination of flow and conveyance changes within St. Clair River. To identify where conveyance change may be taking place, eight water-level gaging stations along St. Clair River were selected to delimit seven reaches. Positive trends in water-level fall were detected in two reaches, and negative trends were detected in two other reaches. The presence of both positive and negative trends in water-level fall indicates that changes in conveyance are likely occurring among some reaches because all reaches transmit essentially the same flow. Annual water-level fall in reaches and reach lengths was used to compute conveyance ratios for all pairs of reaches by use of water-level data from 1962 to 2007. Positive and negative trends in conveyance ratios indicate that relative conveyance is changing among some reaches. Inverse one-dimensional (1-D) hydrodynamic modeling was used to estimate a partial annual series of effective channel-roughness parameters in reaches forming the St. Clair River for 21 years when flow measurements were sufficient to support parameter estimation. Monotonic, persistent but non-monotonic, and irregular changes in estimated effective channel roughness with time were interpreted as systematic changes in conveyances in five reaches. Time-varying parameter estimates were used to simulate flow throughout the St. Clair River and compute changes in conveyance with time. Based on the partial annual series of parameters, conveyance in the St. Clair River increased about 10 percent from 1962 to 2002. Conveyance decreased, however, about 4.1 percent from 2003 to 2007, so that conveyance was about 5.9 percent higher in 2007 than in 1962.

Trends in tissue engineering research.

PubMed

Hacker, Michael C; Mikos, Antonios G

2006-08-01

For more than a decade, Tissue Engineering has been devoted to the reporting and discussion of scientific advances in the interdisciplinary field of tissue engineering. In this study, 779 original articles published in the journal since its inception were analyzed and classified according to different attributes, such as focus of research and tissue of interest, to reveal trends in tissue engineering research. In addition, the use of different biomaterials, scaffold architectures, surface and bulk modification agents, cells, differentiation factors, gene delivery vectors, and animal models was examined. The results of this survey show interesting trends over time and by continental origin.

Applications in Robotics and Controls

NASA Astrophysics Data System (ADS)

Youcef-Toumi, Kamal

2008-06-01

Recent industry trends have set new standards in business dealings and trades. Issues such as time to market, shoter market wondows, product performance, rapid increase of product complexity, costly mistakes, costly late introductions, and customer expectations have changed significantly. These trends have also influenced to a great extend the academic world. Some of these trends will be illustrated through examples which include automated systems, robotics, biotechnollogy, and nanotechnology. The examples will include concepts and prototypes of engineering systems in the macro, micro and nanodomains. The presentation also amphasizes the merging of the traditionally segregated disciplines into one multidisciplinary modeling, design, optimization and control approach.

Trends analysis of PM source contributions and chemical tracers in NE Spain during 2004-2014: a multi-exponential approach

NASA Astrophysics Data System (ADS)

Pandolfi, Marco; Alastuey, Andrés; Pérez, Noemi; Reche, Cristina; Castro, Iria; Shatalov, Victor; Querol, Xavier

2016-09-01

In this work for the first time data from two twin stations (Barcelona, urban background, and Montseny, regional background), located in the northeast (NE) of Spain, were used to study the trends of the concentrations of different chemical species in PM10 and PM2.5 along with the trends of the PM10 source contributions from the positive matrix factorization (PMF) model. Eleven years of chemical data (2004-2014) were used for this study. Trends of both species concentrations and source contributions were studied using the Mann-Kendall test for linear trends and a new approach based on multi-exponential fit of the data. Despite the fact that different PM fractions (PM2.5, PM10) showed linear decreasing trends at both stations, the contributions of specific sources of pollutants and of their chemical tracers showed exponential decreasing trends. The different types of trends observed reflected the different effectiveness and/or time of implementation of the measures taken to reduce the concentrations of atmospheric pollutants. Moreover, the trends of the contributions of specific sources such as those related with industrial activities and with primary energy consumption mirrored the effect of the financial crisis in Spain from 2008. The sources that showed statistically significant downward trends at both Barcelona (BCN) and Montseny (MSY) during 2004-2014 were secondary sulfate, secondary nitrate, and V-Ni-bearing source. The contributions from these sources decreased exponentially during the considered period, indicating that the observed reductions were not gradual and consistent over time. Conversely, the trends were less steep at the end of the period compared to the beginning, thus likely indicating the attainment of a lower limit. Moreover, statistically significant decreasing trends were observed for the contributions to PM from the industrial/traffic source at MSY (mixed metallurgy and road traffic) and from the industrial (metallurgy mainly) source at BCN. These sources were clearly linked with anthropogenic activities, and the observed decreasing trends confirmed the effectiveness of pollution control measures implemented at European or regional/local levels. Conversely, at regional level, the contributions from sources mostly linked with natural processes, such as aged marine and aged organics, did not show statistically significant trends. The trends observed for the PM10 source contributions reflected the trends observed for the chemical tracers of these pollutant sources well.

OAST planning model for space systems technology

NASA Technical Reports Server (NTRS)

Sadin, S. R.

1978-01-01

The NASA Office of Aeronautics and Space Technology (OAST) planning model for space systems technology is described, and some space technology forecasts of a general nature are reported. Technology forecasts are presented as a span of technology levels; uncertainties in level of commitment to project and in required time are taken into account, with emphasis on differences resulting from high or low commitment. Forecasts are created by combining several types of data, including information on past technology trends, the trends of past predictions, the rate of advancement predicted by experts in the field, and technology forecasts already published.

Qualitative comparison of air temperature trends based on ncar/ncep reanalysis, model simulations and aerological observations data

NASA Astrophysics Data System (ADS)

Rubinstein, K. G.; Khan, V. M.; Sterin, A. M.

In the present study we discuss two points. The first one is related with applicability of reanalysis data to investigating long-term climate variability. We present results of comparison of long term air temperature trends for the troposphere and the low stratosphere calculated using monthly averaged NCAR/NCEP reanalysis data on one hand and direct rawinsond observations from 443 stations on the other. The trends and other statistical characteristics are calculated for two overlapping time periods, namely 1964 through 1998, and 1979 through 1998. These two intervals were chosen in order to examine the influence of satellite observations on the reanalysis data, given that most satellite data have appeared after 1979. Vertical profiles of air temperature trends are also analyzed using the two types of data for different seasons. A special criterion is applied to evaluate the degree of coincidence by sign between the air temperatures trends derived from the two types of data. Vertical sections of the linear trend averaged over the 10-degrees zones for the both hemispheres are analyzed. It is shown that the two types of data exhibit good coincidence in the terms of the trend sign for the low and middle troposphere and low stratosphere over the areas well covered by the rawinsond observation net. Significant differences of the air temperature trend values are observed near the land surface and in the tropopause layer. The absolute value of the cooling rate of the tropical low stratosphere based on the rawinsond data is larger then that based on the reanalysis data. The presence of a positive trend in the low troposphere in the belt from ˜ 40N to ˜ 70N is evident in the two data sets. A comparative analysis of the trends for the both periods of observation shows that introducing satellite information in the reanalysis data resulted in an increase of the number of stations where the signs of the trend derived from the two sets of data coincide, especially in the southeastern part of Eurasia. The second part of the present study is related with another question. How do well climate model simulations match temperature observations throughout the atmosphere? Estimates of monthly-mean troposphere and stratospheric temperature trends over the past twenty years, from different hydrodynamical models (INM - model of Institute of Numerical Mathematics, RHMC - model of Hydrometeorological Center of Russia) are compared both with each other and with the observed trend analyses using aerological observations. We verified if the agreement is good between models and observations in term of cooling in the lower stratosphere and the tropospheric warming, which are strong indicators of climate change. Spatial inconsistencies between the observed and modelled vertical patterns of temperature change are identified. This work was partially supported by RFFI foundation N 03-05-64312, NATO grant EST.CLG.978911 and INTAS grant 03515296.

Quantifying the effect of aerosol on vertical velocity and effective terminal velocity in warm convective clouds

NASA Astrophysics Data System (ADS)

Dagan, Guy; Koren, Ilan; Altaratz, Orit

2018-05-01

Better representation of cloud-aerosol interactions is crucial for an improved understanding of natural and anthropogenic effects on climate. Recent studies have shown that the overall aerosol effect on warm convective clouds is non-monotonic. Here, we reduce the system's dimensions to its center of gravity (COG), enabling distillation and simplification of the overall trend and its temporal evolution. Within the COG framework, we show that the aerosol effects are nicely reflected by the interplay of the system's characteristic vertical velocities, namely the updraft (w) and the effective terminal velocity (η). The system's vertical velocities can be regarded as a sensitive measure for the evolution of the overall trends with time. Using a bin-microphysics cloud-scale model, we analyze and follow the trends of the aerosol effect on the magnitude and timing of w and η, and therefore the overall vertical COG velocity. Large eddy simulation (LES) model runs are used to upscale the analyzed trends to the cloud-field scale and study how the aerosol effects on the temporal evolution of the field's thermodynamic properties are reflected by the interplay between the two velocities. Our results suggest that aerosol effects on air vertical motion and droplet mobility imply an effect on the way in which water is distributed along the atmospheric column. Moreover, the interplay between w and η predicts the overall trend of the field's thermodynamic instability. These factors have an important effect on the local energy balance.

On using surface-source downhole-receiver logging to determine seismic slownesses

USGS Publications Warehouse

Boore, D.M.; Thompson, E.M.

2007-01-01

We present a method to solve for slowness models from surface-source downhole-receiver seismic travel-times. The method estimates the slownesses in a single inversion of the travel-times from all receiver depths and accounts for refractions at layer boundaries. The number and location of layer interfaces in the model can be selected based on lithologic changes or linear trends in the travel-time data. The interfaces based on linear trends in the data can be picked manually or by an automated algorithm. We illustrate the method with example sites for which geologic descriptions of the subsurface materials and independent slowness measurements are available. At each site we present slowness models that result from different interpretations of the data. The examples were carefully selected to address the reliability of interface-selection and the ability of the inversion to identify thin layers, large slowness contrasts, and slowness gradients. Additionally, we compare the models in terms of ground-motion amplification. These plots illustrate the sensitivity of site amplifications to the uncertainties in the slowness model. We show that one-dimensional site amplifications are insensitive to thin layers in the slowness models; although slowness is variable over short ranges of depth, this variability has little affect on ground-motion amplification at frequencies up to 5 Hz.

Trends in exposure to respirable crystalline silica (1986-2014) in Australian mining.

PubMed

Peters, Susan; Vermeulen, Roel; Fritschi, Lin; Musk, Aw Bill; Reid, Alison; de Klerk, Nicholas

2017-08-01

Respirable crystalline silica (RCS) has been associated with severe health risks. Exposures in Western Australia (WA) have been typically high in hard-rock mining and have reduced substantially since the mid-1900s. We described trends in RCS exposure in WA miners over the past 30 years. A total of 79 445 reported personal RCS exposure measurements, covering the years 1986-2014, were examined. Mixed-effects models were applied to estimate RCS exposure levels, including spline terms to estimate a time trend. An overall downward trend of about -8% per year was observed for RCS exposures in WA mining. Highest RCS exposure levels were modeled for base metal mining and exploration settings. Drilling occupations were among the highest exposed jobs. RCS exposure levels have fallen considerably in the last three decades. However, there are still mining occupations that may need further attention to avoid adverse health effects in these workers. © 2017 Wiley Periodicals, Inc.

Dynamic analysis of pan evaporation variations in the Huai River Basin, a climate transition zone in eastern China.

PubMed

Li, Meng; Chu, Ronghao; Shen, Shuanghe; Islam, Abu Reza Md Towfiqul

2018-06-01

Pan evaporation (E pan ), which we examine in this study to better understand atmospheric evaporation demand, represents a pivotal indicator of the terrestrial ecosystem and hydrological cycle, particularly in the Huai River Basin (HRB) in eastern China, where high potential risks of drought and flooding are commonly observed. In this study, we examine the spatiotemporal trend patterns of climatic factors and E pan by using the Mann-Kendall test and the Theil-Sen estimator based on a daily meteorological dataset from 89 weather stations during 1965-2013 in the HRB. Furthermore, the PenPan model is employed to estimate E pan at a monthly time scale, and a differential equation method is applied to quantify contributions from four meteorological variables to E pan trends. The results show that E pan significantly decreased (P<0.001) at an average rate of -8.119mm·a -2 at annual time scale in the whole HRB, with approximately 90% of stations occupied. Meanwhile, the generally higher E pan values were detected in the northern HRB. The values of the aerodynamic components in the PenPan model were much greater than those of the radiative components, which were responsible for the variations in the E pan trend. The significantly decreasing wind speed (u 2 ) was the most dominant factor that controlled the decreasing E pan trend at each time scale, followed by the notable decreasing net radiation (R n ) at the annual time scale also in growing season and summer. However, the second dominant factor shifted to the mean temperature (T a ) during the spring and winter and the vapor pressure deficit (vpd) during the autumn. These phenomena demonstrated a positive link between the significance of climate variables and their control over the E pan trend. Copyright © 2017 Elsevier B.V. All rights reserved.

Using a time-series statistical framework to quantify trends and abrupt change in US corn, soybean, and wheat yields from 1970-2016

NASA Astrophysics Data System (ADS)

Zhang, J.; Ives, A. R.; Turner, M. G.; Kucharik, C. J.

2017-12-01

Previous studies have identified global agricultural regions where "stagnation" of long-term crop yield increases has occurred. These studies have used a variety of simple statistical methods that often ignore important aspects of time series regression modeling. These methods can lead to differing and contradictory results, which creates uncertainty regarding food security given rapid global population growth. Here, we present a new statistical framework incorporating time series-based algorithms into standard regression models to quantify spatiotemporal yield trends of US maize, soybean, and winter wheat from 1970-2016. Our primary goal was to quantify spatial differences in yield trends for these three crops using USDA county level data. This information was used to identify regions experiencing the largest changes in the rate of yield increases over time, and to determine whether abrupt shifts in the rate of yield increases have occurred. Although crop yields continue to increase in most maize-, soybean-, and winter wheat-growing areas, yield increases have stagnated in some key agricultural regions during the most recent 15 to 16 years: some maize-growing areas, except for the northern Great Plains, have shown a significant trend towards smaller annual yield increases for maize; soybean has maintained an consistent long-term yield gains in the Northern Great Plains, the Midwest, and southeast US, but has experienced a shift to smaller annual increases in other regions; winter wheat maintained a moderate annual increase in eastern South Dakota and eastern US locations, but showed a decline in the magnitude of annual increases across the central Great Plains and western US regions. Our results suggest that there were abrupt shifts in the rate of annual yield increases in a variety of US regions among the three crops. The framework presented here can be broadly applied to additional yield trend analyses for different crops and regions of the Earth.

Long terms trends in CD4+ cell counts, CD8+ cell counts, and the CD4+ : CD8+ ratio

PubMed Central

Hughes, Rachael A.; May, Margaret T.; Tilling, Kate; Taylor, Ninon; Wittkop, Linda; Reiss, Peter; Gill, John; Schommers, Philipp; Costagliola, Dominique; Guest, Jodie L.; Lima, Viviane D.; d’Arminio Monforte, Antonella; Smith, Colette; Cavassini, Matthias; Saag, Michael; Castilho, Jessica L.; Sterne, Jonathan A.C.

2018-01-01

Objective: Model trajectories of CD4+ and CD8+ cell counts after starting combination antiretroviral therapy (ART) and use the model to predict trends in these counts and the CD4+ : CD8+ ratio. Design: Cohort study of antiretroviral-naïve HIV-positive adults who started ART after 1997 (ART Cohort Collaboration) with more than 6 months of follow-up data. Methods: We jointly estimated CD4+ and CD8+ cell count trends and their correlation using a bivariate random effects model, with linear splines describing their population trends, and predicted the CD4+ : CD8+ ratio trend from this model. We assessed whether CD4+ and CD8+ cell count trends and the CD4+ : CD8+ ratio trend varied according to CD4+ cell count at start of ART (baseline), and, whether these trends differed in patients with and without virological failure more than 6 months after starting ART. Results: A total of 39 979 patients were included (median follow-up was 53 months). Among patients with baseline CD4+ cell count at least 50 cells/μl, predicted mean CD8+ cell counts continued to decrease between 3 and 15 years post-ART, partly driving increases in the predicted mean CD4+ : CD8+ ratio. During 15 years of follow-up, normalization of the predicted mean CD4+ : CD8+ ratio (to >1) was only observed among patients with baseline CD4+ cell count at least 200 cells/μl. A higher baseline CD4+ cell count predicted a shorter time to normalization. Conclusion: Declines in CD8+ cell count and increases in CD4+ : CD8+ ratio occurred up to 15 years after starting ART. The likelihood of normalization of the CD4+ : CD8+ ratio is strongly related to baseline CD4+ cell count. PMID:29851663

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

Landbird trends in national parks of the North Coast and Cascades Network, 2005-12

USGS Publications Warehouse

Saracco, James F.; Holmgren, Amanda L.; Wilkerson, Robert L.; Siegel, Rodney B.; Kuntz, Robert C.; Jenkins, Kurt J.; Happe, Patricia J.; Boetsch, John R.; Huff, Mark H.

2014-01-01

National parks in the North Coast and Cascades Network (NCCN) can fulfill vital roles as refuges for bird species dependent on late-successional forest conditions and as reference sites for assessing the effects of land-use and land-cover changes on bird populations throughout the larger Pacific Northwest region. Additionally, long-term monitoring of landbirds throughout the NCCN provides information that can inform decisions about important management issues in the parks, including visitor impacts, fire management, and the effects of introduced species. In 2005, the NCCN began implementing a network-wide Landbird Monitoring Project as part of the NPS Inventory and Monitoring Program. In this report, we discuss 8-year trends (2005–12) of bird populations in the NCCN, based on a sampling framework of point counts established in three large wilderness parks (Mount Rainier, North Cascades, and Olympic National Parks), 7-year trends at Lewis and Clark National Historical Park (sampled in 2006, 2008, 2010, and 2012), and 5-year trends at San Juan Islands National Historical Park (sampled in 2007, 2009, and 2011). Our analysis encompasses a fairly short time span for this long-term monitoring program. The first 2 years of the time series (2005 and 2006) were implemented as part of a limited pilot study that included only a small subset of the transects. The subsequent 6 years (2007–12) represent just a single cycle through 5 years of alternating panels of transects in the large parks, with the first of five alternating panels revisited for the first time in 2012. Of 204 transects that comprise the six sampling panels in the large parks, only 68 (one-third) have thus been eligible for revisit surveys (34 during every year after 2005, and an additional 34 only in 2012) and can contribute to our current trend estimates. We therefore initiated the current analysis with a primary goal of testing our analytical procedures rather than detecting trends that might be strong enough to drive conservation or management decisions in the parks or elsewhere. We expect that aggregated trend detection results may change substantially over the next several years, as the number of transects with revisit histories triples and the spatial dispersion of transects contributing to trend estimates also improves greatly. In the meantime, caution should be exercised in interpreting the importance of trends, as individual years can have very large influences on the direction and magnitude of trends in a time series of such limited duration (and limited numbers of repeat visits at the small parks). Nevertheless, we estimated trends for 43 species at Mount Rainier National Park, 53 species at North Cascades National Park Complex, and 41 species at Olympic National Park. Of 137 park-species combinations (including combined-park analyses), we found 16 significant decreases (12 percent) and five significant increases (4 percent). We identify several limitations of the current analytical framework for trend assessment but suggest that the overall sampling design is strong and amenable to analysis by more recently developed model-based methods. These could provide a more flexible framework for examining trends and other population parameters of interest, as well as testing hypotheses that relate the distribution and abundance of species to environmental covariates. A model-based approach would allow for modeling various components of the detection process and analyzing observations (detection process), population state (occupancy, population size, density), and change (trend, local extinction and colonization rates turnover) simultaneously. Finally, we also evaluate operational aspects of NCCN Landbird Monitoring Project, and conclude that our robust, multi-party partnership is successfully implementing the project as it was envisioned.

Trends in Average Living Children at the Time of Terminal Contraception: A Time Series Analysis Over 27 Years Using ARIMA (p, d, q) Nonseasonal Model.

PubMed

Mumbare, Sachin S; Gosavi, Shriram; Almale, Balaji; Patil, Aruna; Dhakane, Supriya; Kadu, Aniruddha

2014-10-01

India's National Family Welfare Programme is dominated by sterilization, particularly tubectomy. Sterilization, being a terminal method of contraception, decides the final number of children for that couple. Many studies have shown the declining trend in the average number of living children at the time of sterilization over a short period of time. So this study was planned to do time series analysis of the average children at the time of terminal contraception, to do forecasting till 2020 for the same and to compare the rates of change in various subgroups of the population. Data was preprocessed in MS Access 2007 by creating and running SQL queries. After testing stationarity of every series with augmented Dickey-Fuller test, time series analysis and forecasting was done using best-fit Box-Jenkins ARIMA (p, d, q) nonseasonal model. To compare the rates of change of average children in various subgroups, at sterilization, analysis of covariance (ANCOVA) was applied. Forecasting showed that the replacement level of 2.1 total fertility rate (TFR) will be achieved in 2018 for couples opting for sterilization. The same will be achieved in 2020, 2016, 2018, and 2019 for rural area, urban area, Hindu couples, and Buddhist couples, respectively. It will not be achieved till 2020 in Muslim couples. Every stratum of population showed the declining trend. The decline for male children and in rural area was significantly faster than the decline for female children and in urban area, respectively. The decline was not significantly different in Hindu, Muslim, and Buddhist couples.

Multi-decadal trend and space-time variability of sea level over the Indian Ocean since the 1950s: impact of decadal climate modes

NASA Astrophysics Data System (ADS)

Han, W.; Stammer, D.; Meehl, G. A.; Hu, A.; Sienz, F.

2016-12-01

Sea level varies on decadal and multi-decadal timescales over the Indian Ocean. The variations are not spatially uniform, and can deviate considerably from the global mean sea level rise (SLR) due to various geophysical processes. One of these processes is the change of ocean circulation, which can be partly attributed to natural internal modes of climate variability. Over the Indian Ocean, the most influential climate modes on decadal and multi-decadal timescales are the Interdecadal Pacific Oscillation (IPO) and decadal variability of the Indian Ocean dipole (IOD). Here, we first analyze observational datasets to investigate the impacts of IPO and IOD on spatial patterns of decadal and interdecadal (hereafter decal) sea level variability & multi-decadal trend over the Indian Ocean since the 1950s, using a new statistical approach of Bayesian Dynamical Linear regression Model (DLM). The Bayesian DLM overcomes the limitation of "time-constant (static)" regression coefficients in conventional multiple linear regression model, by allowing the coefficients to vary with time and therefore measuring "time-evolving (dynamical)" relationship between climate modes and sea level. For the multi-decadal sea level trend since the 1950s, our results show that climate modes and non-climate modes (the part that cannot be explained by climate modes) have comparable contributions in magnitudes but with different spatial patterns, with each dominating different regions of the Indian Ocean. For decadal variability, climate modes are the major contributors for sea level variations over most region of the tropical Indian Ocean. The relative importance of IPO and decadal variability of IOD, however, varies spatially. For example, while IOD decadal variability dominates IPO in the eastern equatorial basin (85E-100E, 5S-5N), IPO dominates IOD in causing sea level variations in the tropical southwest Indian Ocean (45E-65E, 12S-2S). To help decipher the possible contribution of external forcing to the multi-decadal sea level trend and decadal variability, we also analyze the model outputs from NCAR's Community Earth System Model (CESM) Large Ensemble Experiments, and compare the results with our observational analyses.

Constraints on High Northern Photosynthesis Increase Using Earth System Models and a Set of Independent Observations

NASA Astrophysics Data System (ADS)

Winkler, A. J.; Brovkin, V.; Myneni, R.; Alexandrov, G.

2017-12-01

Plant growth in the northern high latitudes benefits from increasing temperature (radiative effect) and CO2 fertilization as a consequence of rising atmospheric CO2 concentration. This enhanced gross primary production (GPP) is evident in large scale increase in summer time greening over the 36-year record of satellite observations. In this time period also various global ecosystem models simulate a greening trend in terms of increasing leaf area index (LAI). We also found a persistent greening trend analyzing historical simulations of Earth system models (ESM) participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). However, these models span a large range in strength of the LAI trend, expressed as sensitivity to both key environmental factors, temperature and CO2 concentration. There is also a wide spread in magnitude of the associated increase of terrestrial GPP among the ESMs, which contributes to pronounced uncertainties in projections of future climate change. Here we demonstrate that there is a linear relationship across the CMIP5 model ensemble between projected GPP changes and historical LAI sensitivity, which allows using the observed LAI sensitivity as an "emerging constraint" on GPP estimation at future CO2 concentration. This constrained estimate of future GPP is substantially higher than the traditional multi-model mean suggesting that the majority of current ESMs may be significantly underestimating carbon fixation by vegetation in NHL. We provide three independent lines of evidence in analyzing observed and simulated CO2 amplitude as well as atmospheric CO2 inversion products to arrive at the same conclusion.

Are groundwater nitrate concentrations reaching a turning point in some chalk aquifers?

PubMed

Smith, J T; Clarke, R T; Bowes, M J

2010-09-15

In past decades, there has been much scientific effort dedicated to the development of models for simulation and prediction of nitrate concentrations in groundwaters, but producing truly predictive models remains a major challenge. A time-series model, based on long-term variations in nitrate fertiliser applications and average rainfall, was calibrated against measured concentrations from five boreholes in the River Frome catchment of Southern England for the period spanning from the mid-1970s to 2003. The model was then used to "blind" predict nitrate concentrations for the period 2003-2008. To our knowledge, this represents the first "blind" test of a model for predicting nitrate concentrations in aquifers. It was found that relatively simple time-series models could explain and predict a significant proportion of the variation in nitrate concentrations in these groundwater abstraction points (R(2)=0.6-0.9 and mean absolute prediction errors 4.2-8.0%). The study highlighted some important limitations and uncertainties in this, and other modelling approaches, in particular regarding long-term nitrate fertiliser application data. In three of the five groundwater abstraction points (Hooke, Empool and Eagle Lodge), once seasonal variations were accounted for, there was a recent change in the generally upward historical trend in nitrate concentrations. This may be an early indication of a response to levelling-off (and declining) fertiliser application rates since the 1980s. There was no clear indication of trend change at the Forston and Winterbourne Abbas sites nor in the trend of nitrate concentration in the River Frome itself from 1965 to 2008. Copyright 2010 Elsevier B.V. All rights reserved.

The changing trend in nitrate concentrations in major aquifers due to historical nitrate loading from agricultural land across England and Wales from 1925 to 2150.

PubMed

Wang, L; Stuart, M E; Lewis, M A; Ward, R S; Skirvin, D; Naden, P S; Collins, A L; Ascott, M J

2016-01-15

Nitrate is necessary for agricultural productivity, but can cause considerable problems if released into aquatic systems. Agricultural land is the major source of nitrates in UK groundwater. Due to the long time-lag in the groundwater system, it could take decades for leached nitrate from the soil to discharge into freshwaters. However, this nitrate time-lag has rarely been considered in environmental water management. Against this background, this paper presents an approach to modelling groundwater nitrate at the national scale, to simulate the impacts of historical nitrate loading from agricultural land on the evolution of groundwater nitrate concentrations. An additional process-based component was constructed for the saturated zone of significant aquifers in England and Wales. This uses a simple flow model which requires modelled recharge values, together with published aquifer properties and thickness data. A spatially distributed and temporally variable nitrate input function was also introduced. The sensitivity of parameters was analysed using Monte Carlo simulations. The model was calibrated using national nitrate monitoring data. Time series of annual average nitrate concentrations along with annual spatially distributed nitrate concentration maps from 1925 to 2150 were generated for 28 selected aquifer zones. The results show that 16 aquifer zones have an increasing trend in nitrate concentration, while average nitrate concentrations in the remaining 12 are declining. The results are also indicative of the trend in the flux of groundwater nitrate entering rivers through baseflow. The model thus enables the magnitude and timescale of groundwater nitrate response to be factored into source apportionment tools and to be taken into account alongside current planning of land-management options for reducing nitrate losses. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Bayesian hierarchical modelling of North Atlantic windiness

NASA Astrophysics Data System (ADS)

Vanem, E.; Breivik, O. N.

2013-03-01

Extreme weather conditions represent serious natural hazards to ship operations and may be the direct cause or contributing factor to maritime accidents. Such severe environmental conditions can be taken into account in ship design and operational windows can be defined that limits hazardous operations to less extreme conditions. Nevertheless, possible changes in the statistics of extreme weather conditions, possibly due to anthropogenic climate change, represent an additional hazard to ship operations that is less straightforward to account for in a consistent way. Obviously, there are large uncertainties as to how future climate change will affect the extreme weather conditions at sea and there is a need for stochastic models that can describe the variability in both space and time at various scales of the environmental conditions. Previously, Bayesian hierarchical space-time models have been developed to describe the variability and complex dependence structures of significant wave height in space and time. These models were found to perform reasonably well and provided some interesting results, in particular, pertaining to long-term trends in the wave climate. In this paper, a similar framework is applied to oceanic windiness and the spatial and temporal variability of the 10-m wind speed over an area in the North Atlantic ocean is investigated. When the results from the model for North Atlantic windiness is compared to the results for significant wave height over the same area, it is interesting to observe that whereas an increasing trend in significant wave height was identified, no statistically significant long-term trend was estimated in windiness. This may indicate that the increase in significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

Groundwater salinity in a floodplain forest impacted by saltwater intrusion

NASA Astrophysics Data System (ADS)

Kaplan, David A.; Muñoz-Carpena, Rafael

2014-11-01

Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff = 0.85; 0.52 ≤ Ceff ≤ 0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff = 0.58; 0.38 ≤ Ceff ≤ 0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain.

A MODEL TO EVALUATE PAST EXPOSURE TO 2,3,7,8 ...

EPA Pesticide Factsheets

Data from several studies suggest that concentrations of dioxins rose in the environment from the 1930s to about the 1960s/70s and have been declining over the last decade or two. The most direct evidence of this trend comes from lake core sediments, which can be used to estimate past atmospheric depositions of dioxins. The primary source of human exposure to dioxins is through the food supply. The pathway relating atmospheric depositions to concentrations in food is quite complex, and accordingly, it is not known to what extent the trend in human exposure mirrors the trend in atmospheric depositions. This paper describes an attempt to statistically reconstruct the pattern of past human exposure to the most toxic dioxin congener, 2,3,7,8-TCDD (abbreviated TCDD), through use of a simple pharmacokinetic (PK) model which included a time-varying TCDD exposure dose. This PK model was fit to TCDD body burden data (i.e., TCDD concentrations in lipid) from five U.S. studies dating from 1972 to 1987 and covering a wide age range. A Bayesian statistical approach was used to fit TCDD exposure; model parameters other than exposure were all previously known or estimated from other data sources. The primary results of the analysis are as follows: 1.) use of a time-varying exposure dose provided a far better fit to the TCDD body burden data than did using a dose that was constant over time; this is strong evidence that exposure to TCDD has, in fact, varied during the

Simulated high-latitude soil thermal dynamics during the past four decades

USGS Publications Warehouse

Peng, S.; Ciais, P.; Wang, T.; Gouttevin, I.; McGuire, A.D.; Lawrence, D.; Burke, E.; Chen, X.; Delire, C.; Koven, C.; MacDougall, A.; Rinke, A.; Saito, K.; Zhang, W.; Alkama, R.; Bohn, T. J.; Decharme, B.; Hajima, T.; Ji, D.; Lettenmaier, D.P.; Miller, P.A.; Moore, J.C.; Smith, B.; Sueyoshi, T.

2015-01-01

Soil temperature (Ts ) change is a key indicator of the dynamics of permafrost. On seasonal and inter-annual time scales, the variability of Ts determines the active layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T 5 s not only drives permafrost thaw/retreat, but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of Ts 10 in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 ◦C yr−1 . Most models show smaller increase in Ts with increasing depth. Air temperature (Ta ) and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ 15 amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 ◦C yr−1 , mean ± SD) than the uncertainty of model structure (0.012 ± 0.001 ◦C yr−1 ), diagnosed from the range of response between different mod- 20 els, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active layer thickness (ALT) is less than 3 m loss rate is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area (R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T 25 s at 1 m, is estimated to be of −2.80 ± 0.67 million km2 ◦C −1 . Finally, by using two long-term LWDR datasets and relationships between trends of LWDR and Ts across models, we infer an observationconstrained total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000. This corresponds to 9– 18 % degradation of the current permafrost area.

The Full-Time Workweek in the United States, 1900-1970

ERIC Educational Resources Information Center

Kniesner, Thomas J.

1976-01-01

The average workweek of full-time workers declined by 35 percent between 1900 and 1940, but has not changed significnatly since then, and the secular rigidity of the full-time workweek remains. An expanded model which incorporates the effects of growth in education and in the female wage explains the post-1940 secular trend. (Editor/HD)

Controlling for seasonal patterns and time varying confounders in time-series epidemiological models: a simulation study.

PubMed

Perrakis, Konstantinos; Gryparis, Alexandros; Schwartz, Joel; Le Tertre, Alain; Katsouyanni, Klea; Forastiere, Francesco; Stafoggia, Massimo; Samoli, Evangelia

2014-12-10

An important topic when estimating the effect of air pollutants on human health is choosing the best method to control for seasonal patterns and time varying confounders, such as temperature and humidity. Semi-parametric Poisson time-series models include smooth functions of calendar time and weather effects to control for potential confounders. Case-crossover (CC) approaches are considered efficient alternatives that control seasonal confounding by design and allow inclusion of smooth functions of weather confounders through their equivalent Poisson representations. We evaluate both methodological designs with respect to seasonal control and compare spline-based approaches, using natural splines and penalized splines, and two time-stratified CC approaches. For the spline-based methods, we consider fixed degrees of freedom, minimization of the partial autocorrelation function, and general cross-validation as smoothing criteria. Issues of model misspecification with respect to weather confounding are investigated under simulation scenarios, which allow quantifying omitted, misspecified, and irrelevant-variable bias. The simulations are based on fully parametric mechanisms designed to replicate two datasets with different mortality and atmospheric patterns. Overall, minimum partial autocorrelation function approaches provide more stable results for high mortality counts and strong seasonal trends, whereas natural splines with fixed degrees of freedom perform better for low mortality counts and weak seasonal trends followed by the time-season-stratified CC model, which performs equally well in terms of bias but yields higher standard errors. Copyright © 2014 John Wiley & Sons, Ltd.

Time trends in absolute and relative socioeconomic inequalities in leisure time physical inactivity in northern Sweden.

PubMed

Szilcz, Máté; Mosquera, Paola A; Sebastián, Miguel San; Gustafsson, Per E

2018-02-01

The aim was to investigate the time trends in educational, occupational, and income-related inequalities in leisure time physical inactivity in 2006, 2010, and 2014 in northern Swedish women and men. This study was based on data obtained from the repeated cross-sectional Health on Equal Terms survey of 2006, 2010, and 2014. The analytical sample consisted of 20,667 (2006), 31,787 (2010), and 21,613 (2014) individuals, aged 16-84. Logistic regressions were used to model the probability of physical inactivity given a set of explanatory variables. Slope index of inequality (SII) and relative index of inequality (RII) were used as summary measures of the social gradient in physical inactivity. The linear trend in inequalities and difference between gender and years were estimated by interaction analyses. The year 2010 displayed the highest physical inactivity inequalities for all socioeconomic position indicators, but educational and occupational inequalities decreased in 2014. However, significant positive linear trends were found in absolute and relative income inequalities. Moreover, women had significantly higher RII of education in physical inactivity in 2014 and significantly higher SII and RII of income in physical inactivity in 2010, than did men in the same years. The recent reduction in educational and occupational inequalities following the high inequalities around the time of the great recession in 2010 suggests that the current policies might be fairly effective. However, to eventually alleviate inequities in physical inactivity, the focus of the researchers and policymakers should be directed toward the widening trends of income inequalities in physical inactivity.

Operating Room Efficiency before and after Entrance in a Benchmarking Program for Surgical Process Data.

PubMed

Pedron, Sara; Winter, Vera; Oppel, Eva-Maria; Bialas, Enno

2017-08-23

Operating room (OR) efficiency continues to be a high priority for hospitals. In this context the concept of benchmarking has gained increasing importance as a means to improve OR performance. The aim of this study was to investigate whether and how participation in a benchmarking and reporting program for surgical process data was associated with a change in OR efficiency, measured through raw utilization, turnover times, and first-case tardiness. The main analysis is based on panel data from 202 surgical departments in German hospitals, which were derived from the largest database for surgical process data in Germany. Panel regression modelling was applied. Results revealed no clear and univocal trend of participation in a benchmarking and reporting program for surgical process data. The largest trend was observed for first-case tardiness. In contrast to expectations, turnover times showed a generally increasing trend during participation. For raw utilization no clear and statistically significant trend could be evidenced. Subgroup analyses revealed differences in effects across different hospital types and department specialties. Participation in a benchmarking and reporting program and thus the availability of reliable, timely and detailed analysis tools to support the OR management seemed to be correlated especially with an increase in the timeliness of staff members regarding first-case starts. The increasing trend in turnover time revealed the absence of effective strategies to improve this aspect of OR efficiency in German hospitals and could have meaningful consequences for the medium- and long-run capacity planning in the OR.

Dry spell trend analysis in Kenya and the Murray Darling Basin using daily rainfall

NASA Astrophysics Data System (ADS)

Muita, R. R.; van Ogtrop, F. F.; Vervoort, R. W.

2012-04-01

Important agricultural areas in Kenya and the Murray Darling Basin (MDB) in Australia are largely semi-arid to arid. Persistent dry periods and timing of dry spells directly impact the availability of soil moisture and hence crop production in these regions. Most studies focus on the analysis of dry spell lengths at an annual scale. However, timing and length of dry spells at finer temporal scales is more beneficial for cropping when considering a trade-off between the time scale and the ability to analyse dry spell length. The aim of this study was to analyse the interannual and intra annual variations in dry spell lengths in the regions to inform crop management. This study analysed monthly dry spells based on daily rainfall for 1961-2010 on a limited dataset of 13 locations in Kenya and 17 locations in the MDB. This dataset was the most consistent across both regions and future analysis will incorporate more stations and longer time periods where available. Dry spell lengths were analysed by month and year and trends in monthly and annual dry spell lengths were analysed using Generalised Linear Models (GLM) and the Mann Kendall test (MK). Overall, monthly dryspell lengths are right skewed with higher frequency of shorter dryspells (3-25 days). In Kenya, significant increases in mean dry spell lengths (p≤0.02) are observed in inland arid-to semi humid locations but this temporal trend appears to decrease in highland and the coastal regions. Analysis of the MDB stations suggests changes in seasonality. For example, spatial trends suggest a North-South increase in dry spell length in summer (December - February), but a shortening after February. Generally, the GLM and MK results are similar in the two regions but the MK test tends to give higher values of positive slope coefficients and lower values for negative coefficients compared to GLM. This may limit the ability of finding the best estimates for model coefficients. Previous studies in Australia and Kenya have relied on continuous climatic indices based on global climate models and stochastic processes resulting in limited and mixed results. For agronomical purposes, our results show that direct assessment of dry spells lengths from daily rainfall also indicates changes in dry spells trends in Kenya and the MDB and that such an analysis is easy to use and requires limited assumptions. This initial analysis identifies significant increasing trends in the dry spell lengths in some areas and periods in Kenya and the MDB. This has major implications for crop production in these regions and it is recommended that this information be incorporated in the regions' management decisions. KEY WORDS: monthly dry spell length; Generalized Linear Models; Mann -Kendall test; month; Kenya, Murray Darling Basin (MDB).

Some Questions Concerning the Standards of External Examinations.

ERIC Educational Resources Information Center

Kahn, Michael J.

1990-01-01

Variance as a function of time is described for the Cambridge Local Examinations Syndicate's examination standards, with emphasis on the performance of candidates from Botswana and Zimbabwe. Results demonstrate the value of simple linear modeling in extracting performance trends for a range of subjects over time across six countries. (TJH)

Mobile Interventional Stroke Teams Lead to Faster Treatment Times for Thrombectomy in Large Vessel Occlusion.

PubMed

Wei, Daniel; Oxley, Thomas J; Nistal, Dominic A; Mascitelli, Justin R; Wilson, Natalie; Stein, Laura; Liang, John; Turkheimer, Lena M; Morey, Jacob R; Schwegel, Claire; Awad, Ahmed J; Shoirah, Hazem; Kellner, Christopher P; De Leacy, Reade A; Mayer, Stephan A; Tuhrim, Stanley; Paramasivam, Srinivasan; Mocco, J; Fifi, Johanna T

2017-12-01

Endovascular recanalization treatment for acute ischemic stroke is a complex, time-sensitive intervention. Trip-and-treat is an interhospital service delivery model that has not previously been evaluated in the literature and consists of a shared mobile interventional stroke team that travels to primary stroke centers to provide on-site interventional capability. We compared treatment times between the trip-and-treat model and the traditional drip-and-ship model. We performed a retrospective analysis on 86 consecutive eligible patients with acute ischemic stroke secondary to large vessel occlusion who received endovascular treatment at 4 hospitals in Manhattan. Patients were divided into 2 cohorts: trip-and-treat (n=39) and drip-and-ship (n=47). The primary outcome was initial door-to-puncture time, defined as the time between arrival at any hospital and arterial puncture. We also recorded and analyzed the times of last known well, IV-tPA (intravenous tissue-type plasminogen activator) administration, transfer, and reperfusion. Mean initial door-to-puncture time was 143 minutes for trip-and-treat and 222 minutes for drip-and-ship ( P <0.0001). Although there was a trend in longer puncture-to-recanalization times for trip-and-treat ( P =0.0887), initial door-to-recanalization was nonetheless 79 minutes faster for trip-and-treat ( P <0.0001). There was a trend in improved admission-to-discharge change in National Institutes of Health Stroke Scale for trip-and-treat compared with drip-and-ship ( P =0.0704). Compared with drip-and-ship, the trip-and-treat model demonstrated shorter treatment times for endovascular therapy in our series. The trip-and-treat model offers a valid alternative to current interhospital stroke transfers in urban environments. © 2017 American Heart Association, Inc.

Time series trends of the safety effects of pavement resurfacing.

PubMed

Park, Juneyoung; Abdel-Aty, Mohamed; Wang, Jung-Han

2017-04-01

This study evaluated the safety performance of pavement resurfacing projects on urban arterials in Florida using the observational before and after approaches. The safety effects of pavement resurfacing were quantified in the crash modification factors (CMFs) and estimated based on different ranges of heavy vehicle traffic volume and time changes for different severity levels. In order to evaluate the variation of CMFs over time, crash modification functions (CMFunctions) were developed using nonlinear regression and time series models. The results showed that pavement resurfacing projects decrease crash frequency and are found to be more safety effective to reduce severe crashes in general. Moreover, the results of the general relationship between the safety effects and time changes indicated that the CMFs increase over time after the resurfacing treatment. It was also found that pavement resurfacing projects for the urban roadways with higher heavy vehicle volume rate are more safety effective than the roadways with lower heavy vehicle volume rate. Based on the exploration and comparison of the developed CMFucntions, the seasonal autoregressive integrated moving average (SARIMA) and exponential functional form of the nonlinear regression models can be utilized to identify the trend of CMFs over time. Copyright © 2017 Elsevier Ltd. All rights reserved.

Tech Prep Model for Marketing Education.

ERIC Educational Resources Information Center

Ruhland, Sheila K.; King, Binky M.

A project was conducted to develop two tech prep models for marketing education (ME) in Missouri to provide a sequence of courses for skill-enhanced and time-shortened programs. First, labor market trends, employment growth projections, and business and industry labor needs in Missouri were researched and analyzed. The analysis results were used…

Stratospheric ozone profile and total ozone trends derived from the SAGE I and SAGE II data

NASA Technical Reports Server (NTRS)

Mccormick, M. P.; Veiga, Robert E.; Chu, William P.

1992-01-01

Global trends in both stratospheric column ozone and as a function of altitude are derived on the basis of SAGE I/II ozone data from the period 1979-1991. A statistical model containing quasi-biennial, seasonal, and semiannual oscillations, a linear component, and a first-order autoregressive noise process was fit to the time series of SAGE I/II monthly zonal mean data. The linear trend in column ozone above 17-km altitude, averaged between 65 deg S and 65 deg N, is -0.30 +/-0.19 percent/yr, or -3.6 percent over the time period February 1979 through April 1991. The data show that the column trend above 17 km is nearly zero in the tropics and increases towards the high latitudes with values of -0.6 percent/yr at 60 deg S and -0.35 percent/yr at 60 deg N. Both these results are in agreement with the recent TOMS results. The profile trend analyses show that the column ozone losses are occurring below 25 km, with most of the loss coming from the region between 17 and 20 km. Negative trend values on the order of -2 percent/yr are found at 17 km in midlatitudes.

Accounting for imperfect detection is critical for inferring marine turtle nesting population trends.

PubMed

Pfaller, Joseph B; Bjorndal, Karen A; Chaloupka, Milani; Williams, Kristina L; Frick, Michael G; Bolten, Alan B

2013-01-01

Assessments of population trends based on time-series counts of individuals are complicated by imperfect detection, which can lead to serious misinterpretations of data. Population trends of threatened marine turtles worldwide are usually based on counts of nests or nesting females. We analyze 39 years of nest-count, female-count, and capture-mark-recapture (CMR) data for nesting loggerhead turtles (Caretta caretta) on Wassaw Island, Georgia, USA. Annual counts of nests and females, not corrected for imperfect detection, yield significant, positive trends in abundance. However, multistate open robust design modeling of CMR data that accounts for changes in imperfect detection reveals that the annual abundance of nesting females has remained essentially constant over the 39-year period. The dichotomy could result from improvements in surveys or increased within-season nest-site fidelity in females, either of which would increase detection probability. For the first time in a marine turtle population, we compare results of population trend analyses that do and do not account for imperfect detection and demonstrate the potential for erroneous conclusions. Past assessments of marine turtle population trends based exclusively on count data should be interpreted with caution and re-evaluated when possible. These concerns apply equally to population assessments of all species with imperfect detection.

Changes in the timing of snowmelt and streamflow in Colorado: A response to recent warming

USGS Publications Warehouse

Clow, David W.

2010-01-01

Trends in the timing of snowmelt and associated runoff in Colorado were evaluated for the 1978-2007 water years using the regional Kendall test (RKT) on daily snow-water equivalent (SWE) data from snowpack telemetry (SNOTEL) sites and daily streamflow data from headwater streams. The RKT is a robust, nonparametric test that provides an increased power of trend detection by grouping data from multiple sites within a given geographic region. The RKT analyses indicated strong, pervasive trends in snowmelt and streamflow timing, which have shifted toward earlier in the year by a median of 2-3 weeks over the 29-yr study period. In contrast, relatively few statistically significant trends were detected using simple linear regression. RKT analyses also indicated that November-May air temperatures increased by a median of 0.9 degrees C decade-1, while 1 April SWE and maximum SWE declined by a median of 4.1 and 3.6 cm decade-1, respectively. Multiple linear regression models were created, using monthly air temperatures, snowfall, latitude, and elevation as explanatory variables to identify major controlling factors on snowmelt timing. The models accounted for 45% of the variance in snowmelt onset, and 78% of the variance in the snowmelt center of mass (when half the snowpack had melted). Variations in springtime air temperature and SWE explained most of the interannual variability in snowmelt timing. Regression coefficients for air temperature were negative, indicating that warm temperatures promote early melt. Regression coefficients for SWE, latitude, and elevation were positive, indicating that abundant snowfall tends to delay snowmelt, and snowmelt tends to occur later at northern latitudes and high elevations. Results from this study indicate that even the mountains of Colorado, with their high elevations and cold snowpacks, are experiencing substantial shifts in the timing of snowmelt and snowmelt runoff toward earlier in the year.

A hybrid prognostic model for multistep ahead prediction of machine condition

NASA Astrophysics Data System (ADS)

Roulias, D.; Loutas, T. H.; Kostopoulos, V.

2012-05-01

Prognostics are the future trend in condition based maintenance. In the current framework a data driven prognostic model is developed. The typical procedure of developing such a model comprises a) the selection of features which correlate well with the gradual degradation of the machine and b) the training of a mathematical tool. In this work the data are taken from a laboratory scale single stage gearbox under multi-sensor monitoring. Tests monitoring the condition of the gear pair from healthy state until total brake down following several days of continuous operation were conducted. After basic pre-processing of the derived data, an indicator that correlated well with the gearbox condition was obtained. Consecutively the time series is split in few distinguishable time regions via an intelligent data clustering scheme. Each operating region is modelled with a feed-forward artificial neural network (FFANN) scheme. The performance of the proposed model is tested by applying the system to predict the machine degradation level on unseen data. The results show the plausibility and effectiveness of the model in following the trend of the timeseries even in the case that a sudden change occurs. Moreover the model shows ability to generalise for application in similar mechanical assets.

Integrating Address Geocoding, Land Use Regression, and Spatiotemporal Geostatistical Estimation for Groundwater Tetrachloroethylene

PubMed Central

Messier, Kyle P.; Akita, Yasuyuki; Serre, Marc L.

2012-01-01

Geographic Information Systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for Tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend. PMID:22264162

Integrating address geocoding, land use regression, and spatiotemporal geostatistical estimation for groundwater tetrachloroethylene.

PubMed

Messier, Kyle P; Akita, Yasuyuki; Serre, Marc L

2012-03-06

Geographic information systems (GIS) based techniques are cost-effective and efficient methods used by state agencies and epidemiology researchers for estimating concentration and exposure. However, budget limitations have made statewide assessments of contamination difficult, especially in groundwater media. Many studies have implemented address geocoding, land use regression, and geostatistics independently, but this is the first to examine the benefits of integrating these GIS techniques to address the need of statewide exposure assessments. A novel framework for concentration exposure is introduced that integrates address geocoding, land use regression (LUR), below detect data modeling, and Bayesian Maximum Entropy (BME). A LUR model was developed for tetrachloroethylene that accounts for point sources and flow direction. We then integrate the LUR model into the BME method as a mean trend while also modeling below detects data as a truncated Gaussian probability distribution function. We increase available PCE data 4.7 times from previously available databases through multistage geocoding. The LUR model shows significant influence of dry cleaners at short ranges. The integration of the LUR model as mean trend in BME results in a 7.5% decrease in cross validation mean square error compared to BME with a constant mean trend.

Endurance of larch forest ecosystems in eastern Siberia under warming trends

NASA Astrophysics Data System (ADS)

Sato, H.; Iwahana, G.; Ohta, T.

2015-12-01

The larch (Larix spp.) forest in eastern Siberia is the world's largest coniferous forest. However, its existence depends on near-surface permafrost, which increases water availability for trees, and the boundary of the forest closely follows the permafrost zone. Therefore, the degradation of near-surface permafrost due to forecasted warming trends during the 21st century is expected to affect the larch forest in Siberia. However, predictions of how warming trends will affect this forest vary greatly, and many uncertainties remain about land-atmospheric interactions within the ecosystem. We developed an integrated land surface model to analyze how the Siberian larch forest will react to current warming trends. This model analyzed interactions between vegetation dynamics and thermo-hydrology and showed that, under climatic conditions predicted by the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) scenarios 2.6 and 8.5, annual larch net primary production (NPP) increased about 2 and 3 times, respectively, by the end of 21st century compared with that in the 20th century. Soil water content during larch growing season showed no obvious trend, even after decay of surface permafrost and accompanying sub-surface runoff. A sensitivity test showed that the forecasted warming and pluvial trends extended leafing days of larches and reduced water shortages during the growing season, thereby increasing productivity.

Trends in caries experience and associated contextual factors among indigenous children.

PubMed

Ha, Diep Hong; Lalloo, Ratilal; Jamieson, Lisa M; Giang Do, Loc

2016-06-01

To assess dental caries trends in indigenous children in South Australia, 2001-2010; and contribution by area-level socioeconomic status (SES), remoteness and water fluoridation status. This study is a part of the Child Dental Health Survey (CDHS) is an ongoing national surveillance survey in Australia including children enrolled in the School Dental Services (SDS). Postcode-level adjusted mean deciduous and permanent caries experience was estimated at each year. Time trend of dental caries experience was estimated using mixed effect models. Area-level socioeconomic status, remoteness, water fluoridation status were independent variables in the models. There was a significant upward trend of dental caries experience over the 10 years. Dental caries experience of indigenous children living in low SES areas had nearly one more deciduous tooth and a half permanent tooth with caries than indigenous children living in higher SES areas. The remote postcodes showed higher levels of decay in deciduous dentition (+1.25 teeth) compared with others regions. The dental caries trend increased in South Australian indigenous children over the study period, and was associated with area-level SES and remoteness. The increasing trend in dental caries in indigenous children is important evidence to inform policies to improve oral health. © 2015 American Association of Public Health Dentistry.

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data

PubMed Central

Scanlon, Bridget R.; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y.; van Beek, Ludovicus P. H.; Wiese, David N.; Reedy, Robert C.; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F. P.

2018-01-01

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002–2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤−0.5 km3/y) and increasing (≥0.5 km3/y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km3/y, whereas most models estimate decreasing trends (−71 to 11 km3/y). Land water storage trends, summed over all basins, are positive for GRACE (∼71–82 km3/y) but negative for models (−450 to −12 km3/y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. PMID:29358394

Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data.

PubMed

Scanlon, Bridget R; Zhang, Zizhan; Save, Himanshu; Sun, Alexander Y; Müller Schmied, Hannes; van Beek, Ludovicus P H; Wiese, David N; Wada, Yoshihide; Long, Di; Reedy, Robert C; Longuevergne, Laurent; Döll, Petra; Bierkens, Marc F P

2018-02-06

Assessing reliability of global models is critical because of increasing reliance on these models to address past and projected future climate and human stresses on global water resources. Here, we evaluate model reliability based on a comprehensive comparison of decadal trends (2002-2014) in land water storage from seven global models (WGHM, PCR-GLOBWB, GLDAS NOAH, MOSAIC, VIC, CLM, and CLSM) to trends from three Gravity Recovery and Climate Experiment (GRACE) satellite solutions in 186 river basins (∼60% of global land area). Medians of modeled basin water storage trends greatly underestimate GRACE-derived large decreasing (≤-0.5 km 3 /y) and increasing (≥0.5 km 3 /y) trends. Decreasing trends from GRACE are mostly related to human use (irrigation) and climate variations, whereas increasing trends reflect climate variations. For example, in the Amazon, GRACE estimates a large increasing trend of ∼43 km 3 /y, whereas most models estimate decreasing trends (-71 to 11 km 3 /y). Land water storage trends, summed over all basins, are positive for GRACE (∼71-82 km 3 /y) but negative for models (-450 to -12 km 3 /y), contributing opposing trends to global mean sea level change. Impacts of climate forcing on decadal land water storage trends exceed those of modeled human intervention by about a factor of 2. The model-GRACE comparison highlights potential areas of future model development, particularly simulated water storage. The inability of models to capture large decadal water storage trends based on GRACE indicates that model projections of climate and human-induced water storage changes may be underestimated. Copyright © 2018 the Author(s). Published by PNAS.

The trend of changes in the evaluation scores of faculty members from administrators' and students' perspectives at the medical school over 10 years.

PubMed

Yamani, Nikoo; Changiz, Tahereh; Feizi, Awat; Kamali, Farahnaz

2018-01-01

To assess the trend of changes in the evaluation scores of faculty members and discrepancy between administrators' and students' perspectives in a medical school from 2006 to 2015. This repeated cross-sectional study was conducted on the 10-year evaluation scores of all faculty members of a medical school (n=579) in an urban area of Iran. Data on evaluation scores given by students and administrators and the total of these scores were evaluated. Data were analyzed using descriptive and inferential statistics including linear mixed effect models for repeated measures via the SPSS software. There were statistically significant differences between the students' and administrators' perspectives over time ( p <0.001). The mean of the total evaluation scores also showed a statistically significant change over time ( p <0.001). Furthermore, the mean of changes over time in the total evaluation score between different departments was statistically significant ( p <0.001). The trend of changes in the student's evaluations was clear and positive, but the trend of administrators' evaluation was unclear. Since the evaluation of faculty members is affected by many other factors, there is a need for more future studies.

Prevalence of smoking in movies as perceived by teenagers longitudinal trends and predictors.

PubMed

Choi, Kelvin; Forster, Jean L; Erickson, Darin J; Lazovich, Deann; Southwell, Brian G

2011-08-01

Smoking in movies is prevalent. However, use of content analysis to describe trends in smoking in movies has provided mixed results and has not tapped what adolescents actually perceive. To assess the prospective trends in the prevalence of smoking in movies as perceived by teenagers and identify predictors associated with these trends. Using data from the Minnesota Adolescent Community Cohort Study collected during 2000-2006 when participants were aged between 12 and 18 years (N=4735), latent variable growth models were employed to describe the longitudinal trends in the perceived prevalence of smoking in movies using a four-level scale (never to most of the time) measured every 6 months, and examined associations between these trends and demographic, smoking-related attitudinal and socio-environmental predictors. Analysis was conducted in 2009. At baseline, about 50% of participants reported seeing smoking in movies some of the time, and another 36% reported most of the time. The prevalence of smoking in movies as perceived by teenagers declined over time, and the decline was steeper in those who were aged 14-16 years than those who were younger at baseline (p≤0.05). Despite the decline, teenagers still reported seeing smoking in movies some of the time. Teenagers who reported more close friends who smoked also reported a higher prevalence of smoking in movies at baseline (regression coefficients=0.04-0.18, p<0.01). Teenagers' perception of the prevalence of smoking in movies declined over time, which may be attributable to changes made by the movie industry. Despite the decline, teenagers were still exposed to a moderate amount of smoking imagery. Interventions that further reduce teenage exposure to smoking in movies may be needed to have an effect on adolescent smoking. Copyright © 2011 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Forecasting daily meteorological time series using ARIMA and regression models

NASA Astrophysics Data System (ADS)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

100 years of mortality due to chronic obstructive pulmonary disease in Australia: the role of tobacco consumption.

PubMed

Adair, T; Hoy, D; Dettrick, Z; Lopez, A D

2012-12-01

Global studies of the long-term association between tobacco consumption and chronic obstructive pulmonary disease (COPD) have relied upon descriptions of trends. To statistically analyse the relationship of tobacco consumption with data on mortality due to COPD over the past 100 years in Australia. Tobacco consumption was reconstructed back to 1887. Log-linear Poisson regression models were used to analyse cumulative cohort and lagged time-specific smoking data and its relationship with COPD mortality. Age-standardised COPD mortality, although likely misclassified with other diseases, decreased for males and females from 1907 until the start of the Second World War in contrast to steadily rising tobacco consumption. Thereafter, COPD mortality rose sharply in line with trends in smoking, peaking in the early 1970s for males and over 20 years later for females, before falling again. Regression models revealed both cumulative and time-specific tobacco consumption to be strongly predictive of COPD mortality, with a time lag of 15 years for males and 20 years for females. Sharp falls in COPD mortality before the Second World War were unrelated to tobacco consumption. Smoking was the primary driver of post-War trends, and the success of anti-smoking campaigns has sharply reduced COPD mortality levels.

Climate change or climate cycles? Snowpack trends in the Olympic and Cascade Mountains, Washington, USA.

PubMed

Barry, Dwight; McDonald, Shea

2013-01-01

Climate change could significantly influence seasonal streamflow and water availability in the snowpack-fed watersheds of Washington, USA. Descriptions of snowpack decline often use linear ordinary least squares (OLS) models to quantify this change. However, the region's precipitation is known to be related to climate cycles. If snowpack decline is more closely related to these cycles, an OLS model cannot account for this effect, and thus both descriptions of trends and estimates of decline could be inaccurate. We used intervention analysis to determine whether snow water equivalent (SWE) in 25 long-term snow courses within the Olympic and Cascade Mountains are more accurately described by OLS (to represent gradual change), stationary (to represent no change), or step-stationary (to represent climate cycling) models. We used Bayesian information-theoretic methods to determine these models' relative likelihood, and we found 90 models that could plausibly describe the statistical structure of the 25 snow courses' time series. Posterior model probabilities of the 29 "most plausible" models ranged from 0.33 to 0.91 (mean = 0.58, s = 0.15). The majority of these time series (55%) were best represented as step-stationary models with a single breakpoint at 1976/77, coinciding with a major shift in the Pacific Decadal Oscillation. However, estimates of SWE decline differed by as much as 35% between statistically plausible models of a single time series. This ambiguity is a critical problem for water management policy. Approaches such as intervention analysis should become part of the basic analytical toolkit for snowpack or other climatic time series data.

Statistical assessment of changes in extreme maximum temperatures over Saudi Arabia, 1985-2014

NASA Astrophysics Data System (ADS)

Raggad, Bechir

2018-05-01

In this study, two statistical approaches were adopted in the analysis of observed maximum temperature data collected from fifteen stations over Saudi Arabia during the period 1985-2014. In the first step, the behavior of extreme temperatures was analyzed and their changes were quantified with respect to the Expert Team on Climate Change Detection Monitoring indices. The results showed a general warming trend over most stations, in maximum temperature-related indices, during the period of analysis. In the second step, stationary and non-stationary extreme-value analyses were conducted for the temperature data. The results revealed that the non-stationary model with increasing linear trend in its location parameter outperforms the other models for two-thirds of the stations. Additionally, the 10-, 50-, and 100-year return levels were found to change with time considerably and that the maximum temperature could start to reappear in the different T-year return period for most stations. This analysis shows the importance of taking account the change over time in the estimation of return levels and therefore justifies the use of the non-stationary generalized extreme value distribution model to describe most of the data. Furthermore, these last findings are in line with the result of significant warming trends found in climate indices analyses.

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends.

PubMed

Ionides, Edward L; Wang, Zhen; Tapia Granados, José A

2013-10-03

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association.

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends

PubMed Central

Ionides, Edward L.; Wang, Zhen; Tapia Granados, José A.

2013-01-01

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association. PMID:24587843

Stochastic approaches for time series forecasting of boron: a case study of Western Turkey.

PubMed

Durdu, Omer Faruk

2010-10-01

In the present study, a seasonal and non-seasonal prediction of boron concentrations time series data for the period of 1996-2004 from Büyük Menderes river in western Turkey are addressed by means of linear stochastic models. The methodology presented here is to develop adequate linear stochastic models known as autoregressive integrated moving average (ARIMA) and multiplicative seasonal autoregressive integrated moving average (SARIMA) to predict boron content in the Büyük Menderes catchment. Initially, the Box-Whisker plots and Kendall's tau test are used to identify the trends during the study period. The measurements locations do not show significant overall trend in boron concentrations, though marginal increasing and decreasing trends are observed for certain periods at some locations. ARIMA modeling approach involves the following three steps: model identification, parameter estimation, and diagnostic checking. In the model identification step, considering the autocorrelation function (ACF) and partial autocorrelation function (PACF) results of boron data series, different ARIMA models are identified. The model gives the minimum Akaike information criterion (AIC) is selected as the best-fit model. The parameter estimation step indicates that the estimated model parameters are significantly different from zero. The diagnostic check step is applied to the residuals of the selected ARIMA models and the results indicate that the residuals are independent, normally distributed, and homoscadastic. For the model validation purposes, the predicted results using the best ARIMA models are compared to the observed data. The predicted data show reasonably good agreement with the actual data. The comparison of the mean and variance of 3-year (2002-2004) observed data vs predicted data from the selected best models show that the boron model from ARIMA modeling approaches could be used in a safe manner since the predicted values from these models preserve the basic statistics of observed data in terms of mean. The ARIMA modeling approach is recommended for predicting boron concentration series of a river.

Time trend and age-period-cohort effect on kidney cancer mortality in Europe, 1981-2000.

PubMed

Pérez-Farinós, Napoleón; López-Abente, Gonzalo; Pastor-Barriuso, Roberto

2006-05-03

The incorporation of diagnostic and therapeutic improvements, as well as the different smoking patterns, may have had an influence on the observed variability in renal cancer mortality across Europe. This study examined time trends in kidney cancer mortality in fourteen European countries during the last two decades of the 20th century. Kidney cancer deaths and population estimates for each country during the period 1981-2000 were drawn from the World Health Organization Mortality Database. Age- and period-adjusted mortality rates, as well as annual percentage changes in age-adjusted mortality rates, were calculated for each country and geographical region. Log-linear Poisson models were also fitted to study the effect of age, death period, and birth cohort on kidney cancer mortality rates within each country. For men, the overall standardized kidney cancer mortality rates in the eastern, western, and northern European countries were 20, 25, and 53% higher than those for the southern European countries, respectively. However, age-adjusted mortality rates showed a significant annual decrease of -0.7% in the north of Europe, a moderate rise of 0.7% in the west, and substantial increases of 1.4% in the south and 2.0% in the east. This trend was similar among women, but with lower mortality rates. Age-period-cohort models showed three different birth-cohort patterns for both men and women: a decrease in mortality trend for those generations born after 1920 in the Nordic countries, a similar but lagged decline for cohorts born after 1930 in western and southern European countries, and a continuous increase throughout all birth cohorts in eastern Europe. Similar but more heterogeneous regional patterns were observed for period effects. Kidney cancer mortality trends in Europe showed a clear north-south pattern, with high rates on a downward trend in the north, intermediate rates on a more marked rising trend in the east than in the west, and low rates on an upward trend in the south. The downward pattern observed for cohorts born after 1920-1930 in northern, western, and southern regions suggests more favourable trends in coming years, in contrast to the eastern countries where birth-cohort pattern remains upward.

A quarter of a century of job transitions in Germany☆

PubMed Central

Kattenbach, Ralph; Schneidhofer, Thomas M.; Lücke, Janine; Latzke, Markus; Loacker, Bernadette; Schramm, Florian; Mayrhofer, Wolfgang

2014-01-01

By examining trends in intra-organizational and inter-organizational job transition probabilities among professional and managerial employees in Germany, we test the applicability of mainstream career theory to a specific context and challenge its implied change assumption. Drawing on data from the German Socio-Economic Panel (GSOEP), we apply linear probability models to show the influence of time, economic cycle and age on the probability of job transitions between 1984 and 2010. Results indicate a slight negative trend in the frequency of job transitions during the analyzed time span, owing to a pronounced decrease in intra-organizational transitions, which is only partly offset by a comparatively weaker positive trend towards increased inter-organizational transitions. The latter is strongly influenced by fluctuations in the economic cycle. Finally, the probability of job transitions keeps declining steadily through the course of one's working life. In contrast to inter-organizational transitions, however, this age effect for intra-organizational transitions has decreased over time. PMID:24493876

Time trends in age at onset of anorexia nervosa and bulimia nervosa.

PubMed

Favaro, Angela; Caregaro, Lorenza; Tenconi, Elena; Bosello, Romina; Santonastaso, Paolo

2009-12-01

This study aims to explore the time trends in age at onset of anorexia nervosa and bulimia nervosa. The sample was composed of 1,666 anorexia nervosa subjects and 793 bulimia nervosa subjects (according to DSM-IV criteria) without previous anorexia nervosa consecutively referred to our outpatient unit in the period between 1985 and 2008. Time trends in illness onset were analyzed according to the year of birth of subjects. In both anorexia nervosa and bulimia nervosa, age at onset showed a significant decrease according to year of birth. A regression model showed a significant independent effect of socioeconomic status, age at menarche, and number of siblings in predicting age at onset lower than 16 years. Age at onset of anorexia nervosa and bulimia nervosa is decreasing in younger generations. The implications of our findings in terms of long-term outcome remain to be understood. Biologic and sociocultural factors explaining this phenomenon need to be explored in future studies. Copyright 2009 Physicians Postgraduate Press, Inc.

A quarter of a century of job transitions in Germany.

PubMed

Kattenbach, Ralph; Schneidhofer, Thomas M; Lücke, Janine; Latzke, Markus; Loacker, Bernadette; Schramm, Florian; Mayrhofer, Wolfgang

2014-02-01

By examining trends in intra-organizational and inter-organizational job transition probabilities among professional and managerial employees in Germany, we test the applicability of mainstream career theory to a specific context and challenge its implied change assumption. Drawing on data from the German Socio-Economic Panel (GSOEP), we apply linear probability models to show the influence of time, economic cycle and age on the probability of job transitions between 1984 and 2010. Results indicate a slight negative trend in the frequency of job transitions during the analyzed time span, owing to a pronounced decrease in intra-organizational transitions, which is only partly offset by a comparatively weaker positive trend towards increased inter-organizational transitions. The latter is strongly influenced by fluctuations in the economic cycle. Finally, the probability of job transitions keeps declining steadily through the course of one's working life. In contrast to inter-organizational transitions, however, this age effect for intra-organizational transitions has decreased over time.

Spatio-Temporal Trends and Risk Factors for Shigella from 2001 to 2011 in Jiangsu Province, People's Republic of China

PubMed Central

Bao, Changjun; Hu, Jianli; Liu, Wendong; Liang, Qi; Wu, Ying; Norris, Jessie; Peng, Zhihang; Yu, Rongbin; Shen, Hongbing; Chen, Feng

2014-01-01

Objective This study aimed to describe the spatial and temporal trends of Shigella incidence rates in Jiangsu Province, People's Republic of China. It also intended to explore complex risk modes facilitating Shigella transmission. Methods County-level incidence rates were obtained for analysis using geographic information system (GIS) tools. Trend surface and incidence maps were established to describe geographic distributions. Spatio-temporal cluster analysis and autocorrelation analysis were used for detecting clusters. Based on the number of monthly Shigella cases, an autoregressive integrated moving average (ARIMA) model successfully established a time series model. A spatial correlation analysis and a case-control study were conducted to identify risk factors contributing to Shigella transmissions. Results The far southwestern and northwestern areas of Jiangsu were the most infected. A cluster was detected in southwestern Jiangsu (LLR = 11674.74, P<0.001). The time series model was established as ARIMA (1, 12, 0), which predicted well for cases from August to December, 2011. Highways and water sources potentially caused spatial variation in Shigella development in Jiangsu. The case-control study confirmed not washing hands before dinner (OR = 3.64) and not having access to a safe water source (OR = 2.04) as the main causes of Shigella in Jiangsu Province. Conclusion Improvement of sanitation and hygiene should be strengthened in economically developed counties, while access to a safe water supply in impoverished areas should be increased at the same time. PMID:24416167

Acoustic characteristics of 1/20-scale model helicopter rotors

NASA Technical Reports Server (NTRS)

Shenoy, Rajarama K.; Kohlhepp, Fred W.; Leighton, Kenneth P.

1986-01-01

A wind tunnel test to study the effects of geometric scale on acoustics and to investigate the applicability of very small scale models for the study of acoustic characteristics of helicopter rotors was conducted in the United Technologies Research Center Acoustic Research Tunnel. The results show that the Reynolds number effects significantly alter the Blade-Vortex-Interaction (BVI) Noise characteristics by enhancing the lower frequency content and suppressing the higher frequency content. In the time domain this is observed as an inverted thickness noise impulse rather than the typical positive-negative impulse of BVI noise. At higher advance ratio conditions, in the absence of BVI, the 1/20 scale model acoustic trends with Mach number follow those of larger scale models. However, the 1/20 scale model acoustic trends appear to indicate stall at higher thrust and advance ratio conditions.

Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993-2009.

PubMed

Assaf, Shireen; Campostrini, Stefano; Di Novi, Cinzia; Xu, Fang; Gotway Crawford, Carol

2017-04-01

To explore the changing disparities in access to health care insurance in the United States using time-varying coefficient models. Secondary data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1993 to 2009 was used. A time-varying coefficient model was constructed using a binary outcome of no enrollment in health insurance plan versus enrolled. The independent variables included age, sex, education, income, work status, race, and number of health conditions. Smooth functions of odds ratios and time were used to produce odds ratio plots. Significant time-varying coefficients were found for all the independent variables with the odds ratio plots showing changing trends except for a constant line for the categories of male, student, and having three health conditions. Some categories showed decreasing disparities, such as the income categories. However, some categories had increasing disparities in health insurance enrollment such as the education and race categories. As the Affordable Care Act is being gradually implemented, studies are needed to provide baseline information about disparities in access to health insurance, in order to gauge any changes in health insurance access. The use of time-varying coefficient models with BRFSS data can be useful in accomplishing this task.

A Systematic Review of Studies on Leadership Models in Educational Research from 1980 to 2014

ERIC Educational Resources Information Center

Gumus, Sedat; Bellibas, Mehmet Sukru; Esen, Murat; Gumus, Emine

2018-01-01

The purpose of this study is to reveal the extent to which different leadership models in education are studied, including the change in the trends of research on each model over time, the most prominent scholars working on each model, and the countries in which the articles are based. The analysis of the related literature was conducted by first…

Creep fatigue life prediction for engine hot section materials (isotropic)

NASA Technical Reports Server (NTRS)

Moreno, Vito; Nissley, David; Lin, Li-Sen Jim

1985-01-01

The first two years of a two-phase program aimed at improving the high temperature crack initiation life prediction technology for gas turbine hot section components are discussed. In Phase 1 (baseline) effort, low cycle fatigue (LCF) models, using a data base generated for a cast nickel base gas turbine hot section alloy (B1900+Hf), were evaluated for their ability to predict the crack initiation life for relevant creep-fatigue loading conditions and to define data required for determination of model constants. The variables included strain range and rate, mean strain, strain hold times and temperature. None of the models predicted all of the life trends within reasonable data requirements. A Cycle Damage Accumulation (CDA) was therefore developed which follows an exhaustion of material ductility approach. Material ductility is estimated based on observed similarities of deformation structure between fatigue, tensile and creep tests. The cycle damage function is based on total strain range, maximum stress and stress amplitude and includes both time independent and time dependent components. The CDA model accurately predicts all of the trends in creep-fatigue life with loading conditions. In addition, all of the CDA model constants are determinable from rapid cycle, fully reversed fatigue tests and monotonic tensile and/or creep data.

Dynamic model evaluation for secondary inorganic aerosol and its precursors over Europe between 1990 and 2009

NASA Astrophysics Data System (ADS)

Banzhaf, S.; Schaap, M.; Kranenburg, R.; Manders, A. M. M.; Segers, A. J.; Visschedijk, A. J. H.; Denier van der Gon, H. A. C.; Kuenen, J. J. P.; van Meijgaard, E.; van Ulft, L. H.; Cofala, J.; Builtjes, P. J. H.

2015-04-01

In this study we present a dynamic model evaluation of chemistry transport model LOTOS-EUROS (LOng Term Ozone Simulation - EURopean Operational Smog) to analyse the ability of the model to reproduce observed non-linear responses to emission changes and interannual variability of secondary inorganic aerosol (SIA) and its precursors over Europe from 1990 to 2009. The 20 year simulation was performed using a consistent set of meteorological data provided by RACMO2 (Regional Atmospheric Climate MOdel). Observations at European rural background sites have been used as a reference for the model evaluation. To ensure the consistency of the used observational data, stringent selection criteria were applied, including a comprehensive visual screening to remove suspicious data from the analysis. The LOTOS-EUROS model was able to capture a large part of the seasonal and interannual variability of SIA and its precursors' concentrations. The dynamic evaluation has shown that the model is able to simulate the declining trends observed for all considered sulfur and nitrogen components following the implementation of emission abatement strategies for SIA precursors over Europe. Both the observations and the model show the largest part of the decline in the 1990s, while smaller concentration changes and an increasing number of non-significant trends are observed and modelled between 2000 and 2009. Furthermore, the results confirm former studies showing that the observed trends in sulfate and total nitrate concentrations from 1990 to 2009 are lower than the trends in precursor emissions and precursor concentrations. The model captured well these non-linear responses to the emission changes. Using the LOTOS-EUROS source apportionment module, trends in the formation efficiency of SIA have been quantified for four European regions. The exercise has revealed a 20-50% more efficient sulfate formation in 2009 compared to 1990 and an up to 20% more efficient nitrate formation per unit nitrogen oxide emission, which added to the explanation of the non-linear responses. However, we have also identified some weaknesses in the model and the input data. LOTOS-EUROS underestimates the observed nitrogen dioxide concentrations throughout the whole time period, while it overestimates the observed nitrogen dioxide concentration trends. Moreover, model results suggest that the emission information of the early 1990s used in this study needs to be improved concerning magnitude and spatial distribution.

Dynamic model evaluation for secondary inorganic aerosol and its precursors over Europe between 1990 and 2009

NASA Astrophysics Data System (ADS)

Banzhaf, S.; Schaap, M.; Kranenburg, R.; Manders, A. M. M.; Segers, A. J.; Visschedijk, A. H. J.; Denier van der Gon, H. A. C.; Kuenen, J. J. P.; van Meijgaard, E.; van Ulft, L. H.; Cofala, J.; Builtjes, P. J. H.

2014-07-01

In this study we present a dynamic model evaluation of the chemistry transport model LOTOS-EUROS to analyse the ability of the model to reproduce observed non-linear responses to emission changes and interannual variability of secondary inorganic aerosol (SIA) and its precursors over Europe from 1990 to 2009. The 20 year simulation was performed using a consistent set of meteorological data provided by the regional climate model RACMO2. Observations at European rural background sites have been used as reference for the model evaluation. To ensure the consistency of the used observational data stringent selection criteria were applied including a comprehensive visual screening to remove suspicious data from the analysis. The LOTOS-EUROS model was able to capture a large part of the day-to-day, seasonal and interannual variability of SIA and its precursors' concentrations. The dynamic evaluation has shown that the model is able to simulate the declining trends observed for all considered sulphur and nitrogen components following the implementation of emission abatement strategies for SIA precursors over Europe. Both, the observations and the model show the largest part of the decline in the 1990's while smaller concentration changes and an increasing number of non-significant trends are observed and modelled between 2000-2009. Furthermore, the results confirm former studies showing that the observed trends in sulphate and total nitrate concentrations from 1990 to 2009 are significantly lower than the trends in precursor emissions and precursor concentrations. The model captured these non-linear responses to the emission changes well. Using the LOTOS-EUROS source apportionment module trends in formation efficiency of SIA have been quantified for four European regions. The exercise has revealed a 20-50% more efficient sulphate formation in 2009 compared to 1990 and an up to 20% more efficient nitrate formation per unit nitrogen oxide emission, which added to the explanation of the non-linear responses. However, we have also identified some weaknesses to the model and the input data. LOTOS-EUROS underestimates the observed nitrogen dioxide concentrations throughout the whole time period, while it overestimates the observed nitrogen dioxide concentration trends. Moreover, model results suggest that the emission information of the early 1990's used in this study needs to be improved concerning magnitude and spatial distribution.

Skin Cancer, Irradiation, and Sunspots: The Solar Cycle Effect

PubMed Central

Zurbenko, Igor

2014-01-01

Skin cancer is diagnosed in more than 2 million individuals annually in the United States. It is strongly associated with ultraviolet exposure, with melanoma risk doubling after five or more sunburns. Solar activity, characterized by features such as irradiance and sunspots, undergoes an 11-year solar cycle. This fingerprint frequency accounts for relatively small variation on Earth when compared to other uncorrelated time scales such as daily and seasonal cycles. Kolmogorov-Zurbenko filters, applied to the solar cycle and skin cancer data, separate the components of different time scales to detect weaker long term signals and investigate the relationships between long term trends. Analyses of crosscorrelations reveal epidemiologically consistent latencies between variables which can then be used for regression analysis to calculate a coefficient of influence. This method reveals that strong numerical associations, with correlations >0.5, exist between these small but distinct long term trends in the solar cycle and skin cancer. This improves modeling skin cancer trends on long time scales despite the stronger variation in other time scales and the destructive presence of noise. PMID:25126567

Psychosocial working conditions in a representative sample of working Australians 2001-2008: an analysis of changes in inequalities over time.

PubMed

LaMontagne, A D; Krnjacki, L; Kavanagh, A M; Bentley, R

2013-09-01

A number of widely prevalent job stressors have been identified as modifiable risk factors for common mental and physical illnesses such as depression and cardiovascular disease, yet there has been relatively little study of population trends in exposure to job stressors over time. The aims of this paper were to assess: (1) overall time trends in job control and security and (2) whether disparities by sex, age, skill level and employment arrangement were changing over time in the Australian working population. Job control and security were measured in eight annual waves (2000-2008) from the Australian nationally-representative Household Income and Labour Dynamics of Australia panel survey (n=13 188 unique individuals for control and n=13 182 for security). Observed and model-predicted time trends were generated. Models were generated using population-averaged longitudinal linear regression, with year fitted categorically. Changes in disparities over time by sex, age group, skill level and employment arrangement were tested as interactions between each of these stratifying variables and time. While significant disparities persisted for disadvantaged compared with advantaged groups, results suggested that inequalities in job control narrowed among young workers compared with older groups and for casual, fixed-term and self-employed compared with permanent workers. A slight narrowing of disparities over time in job security was noted for gender, age, employment arrangement and occupational skill level. Despite the favourable findings of small reductions in disparities in job control and security, significant cross-sectional disparities persist. Policy and practice intervention to improve psychosocial working conditions for disadvantaged groups could reduce these persisting disparities and associated illness burdens.

Elkhorn Slough: Detecting Eutrophication through Geospatial Modeling Applications

NASA Astrophysics Data System (ADS)

Caraballo Álvarez, I. O.; Childs, A.; Jurich, K.

2016-12-01

Elkhorn Slough in Monterey, California, has experienced substantial nutrient loading and eutrophication over the past 21 years as a result of fertilizer-rich runoff from nearby agricultural fields. This study seeks to identify and track spatial patterns of eutrophication hotspots and the correlation to land use changes, possible nutrient sources, and general climatic trends using remotely sensed and in situ data. Threats of rising sea level, subsiding marshes, and increased eutrophication hotspots demonstrate the necessity to analyze the effects of increasing nutrient loads, relative sea level changes, and sedimentation within Elkhorn Slough. The Soil & Water Assessment Tool (SWAT) model integrates specified inputs to assess nutrient and sediment loading and their sources. TerrSet's Land Change Modeler forecasts the future potential of land change transitions for various land cover classes around the slough as a result of nutrient loading, eutrophication, and increased sedimentation. TerrSet's Earth Trends Modeler provides a comprehensive analysis of image time series to rapidly assess long term eutrophication trends and detect spatial patterns of known hotspots. Results from this study will inform future coastal management practices and provide greater spatial and temporal insight into Elkhorn Slough eutrophication dynamics.

Malaria resurgence in the East African highlands: Temperature trends revisited

PubMed Central

Pascual, M.; Ahumada, J. A.; Chaves, L. F.; Rodó, X.; Bouma, M.

2006-01-01

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models. PMID:16571662

Evaluation of the GEM-AQ air quality model during the Québec smoke event of 2002: Analysis of extensive and intensive optical disparities

NASA Astrophysics Data System (ADS)

O'Neill, N. T.; Campanelli, M.; Lupu, A.; Thulasiraman, S.; Reid, J. S.; Aubé, M.; Neary, L.; Kaminski, J. W.; McConnell, J. C.

The root-mean-square (rms) differences between the Canadian air quality model GEM-AQ and measurements for intensive and extensive optical variables (aerosol optical depth or AOD and Ångström exponent or α) were investigated using data from the July 2002 Québec smoke event. In order to quantify regional differences between model and measurements we employed a three component analysis of rms differences. The behaviour of the two absolute amplitude rms components of AOD (difference of the means and the difference of the standard deviations) enabled us to infer emission properties which would otherwise have been masked by the larger 'anti-correlation' component. We found the inferred emission fluxes to be significantly higher than the original geostationary, satellite-derived FLAMBÉ (fire locating and modelling of burning emissions) emissions flux estimates employed as inputs to the simulations. The model captured the regional decrease of the intensive α exponent (increase of particle size with trajectory time), while the agreement with the extensive AOD parameter was marginal but clearly dependent on the nature of the spatio-temporal statistical tools employed to characterize model performance. In establishing the α versus trajectory time trend, the modelled AOD data was filtered in the same way as the measured data (very large AODs are eliminated). This processing of modelled results was deemed necessary in order to render the α results comparable with the measurements; in the latter case it was difficult, if not impossible, to discriminate between measured α trends due to instrumental artifacts (non-linearities at low signal strength) versus trends due to coagulative effects.

Secular Trends in Anthropometrics and Physical Fitness of Young Portuguese School-Aged Children.

PubMed

Costa, Aldo Matos; Costa, Mário Jorge; Reis, António Antunes; Ferreira, Sandra; Martins, Júlio; Pereira, Ana

2017-02-27

The purpose of this study was to analyze secular trends in anthropometrics and physical fitness of Portuguese children. A group of 1819 students (881 boys and 938 girls) between 10 and 11 years old was assessed in their 5th and 6th scholar grade throughout a 20 years' time-frame. ANCOVA models were used to analyze variations in anthropometrics (height, weight and body mass index) and physical fitness (sit and reach, curl-up, horizontal jump and sprint time) across four quinquennials (1993 - 1998; 1998 - 2003; 2003 - 2008; 2008 - 2013). Secular trends showed the presence of heavier boys and girls with higher body mass index in the 5th and 6th grade throughout the last 20 years. There was also a presence of taller girls but just until the 3rd quinquennial. Both boys and girls were able to perform better on the core strength test and sprint time but become less flexible over the years. Mean jumping performance remained unchanged for both genders. The present study provides novel data on anthropometrics and physical fitness trends over the last two decades in young Portuguese children, consistent with the results reported in other developed countries. Evidence for the start of a positive secular trend in body mass index and in some physical fitness components over the last two decades among the Portuguese youth.

Trends in HFE Methods and Tools and Their Applicability to Safety Reviews

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Hara, J.M.; Plott, C.; Milanski, J.

2009-09-30

The U.S. Nuclear Regulatory Commission's (NRC) conducts human factors engineering (HFE) safety reviews of applicant submittals for new plants and for changes to existing plants. The reviews include the evaluation of the methods and tools (M&T) used by applicants as part of their HFE program. The technology used to perform HFE activities has been rapidly evolving, resulting in a whole new generation of HFE M&Ts. The objectives of this research were to identify the current trends in HFE methods and tools, determine their applicability to NRC safety reviews, and identify topics for which the NRC may need additional guidance tomore » support the NRC's safety reviews. We conducted a survey that identified over 100 new HFE M&Ts. The M&Ts were assessed to identify general trends. Seven trends were identified: Computer Applications for Performing Traditional Analyses, Computer-Aided Design, Integration of HFE Methods and Tools, Rapid Development Engineering, Analysis of Cognitive Tasks, Use of Virtual Environments and Visualizations, and Application of Human Performance Models. We assessed each trend to determine its applicability to the NRC's review by considering (1) whether the nuclear industry is making use of M&Ts for each trend, and (2) whether M&Ts reflecting the trend can be reviewed using the current design review guidance. We concluded that M&T trends that are applicable to the commercial nuclear industry and are expected to impact safety reviews may be considered for review guidance development. Three trends fell into this category: Analysis of Cognitive Tasks, Use of Virtual Environments and Visualizations, and Application of Human Performance Models. The other trends do not need to be addressed at this time.« less

Comparison of Recent Modeled and Observed Trends in Total Column Ozone

NASA Technical Reports Server (NTRS)

Andersen, S. B.; Weatherhead, E. C.; Stevermer, A.; Austin, J.; Bruehl, C.; Fleming, E. L.; deGrandpre, J.; Grewe, V.; Isaksen, I.; Pitari, G.;

Comparison of recent modeled and observed trends in total column ozone

NASA Astrophysics Data System (ADS)

Andersen, S. B.; Weatherhead, E. C.; Stevermer, A.; Austin, J.; Brühl, C.; Fleming, E. L.; de Grandpré, J.; Grewe, V.; Isaksen, I.; Pitari, G.; Portmann, R. W.; Rognerud, B.; Rosenfield, J. E.; Smyshlyaev, S.; Nagashima, T.; Velders, G. J. M.; Weisenstein, D. K.; Xia, J.

2006-01-01

We present a comparison of trends in total column ozone from 10 two-dimensional and 4 three-dimensional models and solar backscatter ultraviolet-2 (SBUV/2) satellite observations from the period 1979-2003. Trends for the past (1979-2000), the recent 7 years (1996-2003), and the future (2000-2050) are compared. We have analyzed the data using both simple linear trends and linear trends derived with a hockey stick method including a turnaround point in 1996. If the last 7 years, 1996-2003, are analyzed in isolation, the SBUV/2 observations show no increase in ozone, and most of the models predict continued depletion, although at a lesser rate. In sharp contrast to this, the recent data show positive trends for the Northern and the Southern Hemispheres if the hockey stick method with a turnaround point in 1996 is employed for the models and observations. The analysis shows that the observed positive trends in both hemispheres in the recent 7-year period are much larger than what is predicted by the models. The trends derived with the hockey stick method are very dependent on the values just before the turnaround point. The analysis of the recent data therefore depends greatly on these years being representative of the overall trend. Most models underestimate the past trends at middle and high latitudes. This is particularly pronounced in the Northern Hemisphere. Quantitatively, there is much disagreement among the models concerning future trends. However, the models agree that future trends are expected to be positive and less than half the magnitude of the past downward trends. Examination of the model projections shows that there is virtually no correlation between the past and future trends from the individual models.

What Models and Satellites Tell Us (and Don't Tell Us) About Arctic Sea Ice Melt Season Length

NASA Astrophysics Data System (ADS)

Ahlert, A.; Jahn, A.

2017-12-01

Melt season length—the difference between the sea ice melt onset date and the sea ice freeze onset date—plays an important role in the radiation balance of the Arctic and the predictability of the sea ice cover. However, there are multiple possible definitions for sea ice melt and freeze onset in climate models, and none of them exactly correspond to the remote sensing definition. Using the CESM Large Ensemble model simulations, we show how this mismatch between model and remote sensing definitions of melt and freeze onset limits the utility of melt season remote sensing data for bias detection in models. It also opens up new questions about the precise physical meaning of the melt season remote sensing data. Despite these challenges, we find that the increase in melt season length in the CESM is not as large as that derived from remote sensing data, even when we account for internal variability and different definitions. At the same time, we find that the CESM ensemble members that have the largest trend in sea ice extent over the period 1979-2014 also have the largest melt season trend, driven primarily by the trend towards later freeze onsets. This might be an indication that an underestimation of the melt season length trend is one factor contributing to the generally underestimated sea ice loss within the CESM, and potentially climate models in general.

Joint space-time geostatistical model for air quality surveillance

NASA Astrophysics Data System (ADS)

Russo, A.; Soares, A.; Pereira, M. J.

2009-04-01

Air pollution and peoples' generalized concern about air quality are, nowadays, considered to be a global problem. Although the introduction of rigid air pollution regulations has reduced pollution from industry and power stations, the growing number of cars on the road poses a new pollution problem. Considering the characteristics of the atmospheric circulation and also the residence times of certain pollutants in the atmosphere, a generalized and growing interest on air quality issues led to research intensification and publication of several articles with quite different levels of scientific depth. As most natural phenomena, air quality can be seen as a space-time process, where space-time relationships have usually quite different characteristics and levels of uncertainty. As a result, the simultaneous integration of space and time is not an easy task to perform. This problem is overcome by a variety of methodologies. The use of stochastic models and neural networks to characterize space-time dispersion of air quality is becoming a common practice. The main objective of this work is to produce an air quality model which allows forecasting critical concentration episodes of a certain pollutant by means of a hybrid approach, based on the combined use of neural network models and stochastic simulations. A stochastic simulation of the spatial component with a space-time trend model is proposed to characterize critical situations, taking into account data from the past and a space-time trend from the recent past. To identify near future critical episodes, predicted values from neural networks are used at each monitoring station. In this paper, we describe the design of a hybrid forecasting tool for ambient NO2 concentrations in Lisbon, Portugal.

The conditional resampling model STARS: weaknesses of the modeling concept and development

NASA Astrophysics Data System (ADS)

Menz, Christoph

2016-04-01

The Statistical Analogue Resampling Scheme (STARS) is based on a modeling concept of Werner and Gerstengarbe (1997). The model uses a conditional resampling technique to create a simulation time series from daily observations. Unlike other time series generators (such as stochastic weather generators) STARS only needs a linear regression specification of a single variable as the target condition for the resampling. Since its first implementation the algorithm was further extended in order to allow for a spatially distributed trend signal, to preserve the seasonal cycle and the autocorrelation of the observation time series (Orlovsky, 2007; Orlovsky et al., 2008). This evolved version was successfully used in several climate impact studies. However a detaild evaluation of the simulations revealed two fundamental weaknesses of the utilized resampling technique. 1. The restriction of the resampling condition on a single individual variable can lead to a misinterpretation of the change signal of other variables when the model is applied to a mulvariate time series. (F. Wechsung and M. Wechsung, 2014). As one example, the short-term correlations between precipitation and temperature (cooling of the near-surface air layer after a rainfall event) can be misinterpreted as a climatic change signal in the simulation series. 2. The model restricts the linear regression specification to the annual mean time series, refusing the specification of seasonal varying trends. To overcome these fundamental weaknesses a redevelopment of the whole algorithm was done. The poster discusses the main weaknesses of the earlier model implementation and the methods applied to overcome these in the new version. Based on the new model idealized simulations were conducted to illustrate the enhancement.

Impacts of internal variability on temperature and precipitation trends in large ensemble simulations by two climate models

NASA Astrophysics Data System (ADS)

Dai, Aiguo; Bloecker, Christine E.

2018-02-01

It is known that internal climate variability (ICV) can influence trends seen in observations and individual model simulations over a period of decades. This makes it difficult to quantify the forced response to external forcing. Here we analyze two large ensembles of simulations from 1950 to 2100 by two fully-coupled climate models, namely the CESM1 and CanESM2, to quantify ICV's influences on estimated trends in annual surface air temperature (Tas) and precipitation (P) over different time periods. Results show that the observed trends since 1979 in global-mean Tas and P are within the spread of the CESM1-simulated trends while the CanESM2 overestimates the historical changes, likely due to its deficiencies in simulating historical non-CO2 forcing. Both models show considerable spreads in the Tas and P trends among the individual simulations, and the spreads decrease rapidly as the record length increases to about 40 (50) years for global-mean Tas (P). Because of ICV, local and regional P trends may remain statistically insignificant and differ greatly among individual model simulations over most of the globe until the later part of the twenty-first century even under a high emissions scenario, while local Tas trends since 1979 are already statistically significant over many low-latitude regions and are projected to become significant over most of the globe by the 2030s. The largest influences of ICV come from the Inter-decadal Pacific Oscillation and polar sea ice. In contrast to the realization-dependent ICV, the forced Tas response to external forcing has a temporal evolution that is similar over most of the globe (except its amplitude). For annual precipitation, however, the temporal evolution of the forced response is similar (opposite) to that of Tas over many mid-high latitude areas and the ITCZ (subtropical regions), but close to zero over the transition zones between the regions with positive and negative trends. The ICV in the transient climate change simulations is slightly larger than that in the control run for P (and other related variables such as water vapor), but similar for Tas. Thus, the ICV for P from a control run may need to be scaled up in detection and attribution analyses.

Comparison of Statistical Models for Analyzing Wheat Yield Time Series

PubMed Central

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha−1 year−1 in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale. PMID:24205280

Stochastic Models in the DORIS Position Time Series: Estimates from the IDS Contribution to the ITRF2014

NASA Astrophysics Data System (ADS)

Klos, A.; Bogusz, J.; Moreaux, G.

2017-12-01

This research focuses on the investigation of the deterministic and stochastic parts of the DORIS (Doppler Orbitography and Radiopositioning Integrated by Satellite) weekly coordinate time series from the IDS contribution to the ITRF2014A set of 90 stations was divided into three groups depending on when the data was collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations (these three sum up to produce the Polynomial Trend Model) and a stochastic part, all being resolved with Maximum Likelihood Estimation (MLE). We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations, meaning that the most recent data are the most reliable ones. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. We examined five different noise models to be applied to the stochastic part of the DORIS time series: a pure white noise (WN), a pure power-law noise (PL), a combination of white and power-law noise (WNPL), an autoregressive process of first order (AR(1)) and a Generalized Gauss Markov model (GGM). From our study it arises that the PL process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from AR(1) to pure PL with few stations characterized by a positive spectral index.

Striking trends in the incidence of health problems in The Netherlands (2002-05). Findings from a new strategy for surveillance in general practice.

PubMed

Biermans, Marion C J; Spreeuwenberg, Peter; Verheij, Robert A; de Bakker, Dinny H; de Vries Robbé, Pieter F; Zielhuis, Gerhard A

2009-06-01

This study aimed to detect striking trends based on a new strategy for monitoring public health. We used data over 4 years from electronic medical records of a large, nationally representative network of general practices. Episodes were either directly recorded by general practitioners (GPs) or were constructed using a new record linkage method (EPICON). The episodes were used to estimate raw morbidity rates for all codes of the International Classification of Primary Care (ICPC). Multilevel Poisson regression models were used to analyse the trend over time for 15 health problems that showed an obvious change over time. Based on these models, we calculated adjusted incidence rates corrected for clustering, sex and age. During 2002-05, both men and women increasingly consulted the GP because of concern about a drug reaction, a change in faeces/bowel movements and urination problems. Men showed an increase in consultations for prostate problems and venereal diseases. The incidence of chronic internal knee derangement decreased for both sexes. Women consulted their GP less frequently about sterilization and fear of being pregnant. The strategy developed proved to be useful to detect trends across a short period of time. Changes in the health care market, such as the increasing availability of over-the-counter drugs and various large advertising campaigns for medications may explain some of the findings. The increasing incidence of health problems in the urogenital area deserves attention as it could reflect increases in the incidence of sexually transmitted diseases (STDs) and urinary tract infections.

Shifts of Start and End of Season in Response to Air Temperature Variation Based on Gimms Dataset in Hyrcanian Forests

NASA Astrophysics Data System (ADS)

Kiapasha, K. H.; Darvishsefat, A. A.; Zargham, N.; Julien, Y.; Sobrino, J. A.; Nadi, M.

2017-09-01

Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period.

Trends in suspended-sediment concentration at selected stream sites in Kansas, 1970-2002

USGS Publications Warehouse

Putnam, James E.; Pope, Larry M.

2003-01-01

Knowledge of erosion, transport, and deposition of sediment relative to streams and impoundments is important to those involved directly or indirectly in the development and management of water resources. Monitoring the quantity of sediment in streams and impoundments is important because: (1) sediment may degrade the water quality of streams for such uses as municipal water supply, (2) sediment is detrimental to the health of some species of aquatic animals and plants, and (3) accumulation of sediment in water-supply impoundments decreases the amount of storage and, therefore, water available for users. One of the objectives of the Kansas Water Plan is to reduce the amount of sediment in Kansas streams by 2010. During the last 30 years, millions of dollars have been spent in Kansas watersheds to reduce sediment transport to streams. Because the last evaluation of trends in suspended-sediment concentrations in Kansas was completed in 1985, 14 sediment sampling sites that represent 10 of the 12 major river basins in Kansas were reestablished in 2000. The purpose of this report is to present the results of time-trend analyses at the reestablished sediment data-collection sites for the period of about 1970?2002 and to evaluate changes in the watersheds that may explain the trends. Time-trend tests for 13 of 14 sediment sampling sites in Kansas for the period from about 1970 to 2002 indicated that 3 of the 13 sites tested had statistically significant decreasing suspended-sediment concentrations; however, only 2 sites, Walnut River at Winfield and Elk River at Elk Falls, had trends that were statistically significant at the 0.05 probability level. Increasing suspended-sediment concentrations were indicated at three sites although none were statistically significant at the 0.05 probability level. Samples from five of the six sampling sites located upstream from reservoirs indicated decreasing suspended-sediment concentrations. Watershed impoundments located in the respective river basins may contribute to the decreasing suspended-sediment trends exhibited at most of the sampling sites because the impoundments are designed to trap sediment. Both sites that exhibited statistically significant decreasing suspended-sediment concentrations have a large number of watershed impoundments located in their respective drainage basins. The relation between percentage of the watershed affected by impoundments and trend in suspended-sediment concentration for 11 sites indicated that, as the number of impoundments in the watershed increases, suspended-sediment concentration decreases. Other conser-vation practices, such as terracing of farm fields and contour farming, also may contribute to the reduced suspended-sediment concentrations if their use has increased during the period of analysis. Regression models were developed for 13 of 14 sediment sampling sites in Kansas and can be used to estimate suspended-sediment concentration if the range in stream discharge for which they were developed is not exceeded and if time trends in suspended-sediment concentrations are not significant. For those sites that had a statistically significant trend in suspended-sediment concentration, a second regression model was developed using samples collected during 2000?02. Past and current studies by the U.S. Geological Survey have shown that regression models can be developed between in-stream measurements of turbidity and laboratory-analyzed sediment samples. Regression models were developed for the relations between discharge and suspended-sediment concentration and turbidity and suspended-sediment concentration for 10 sediment sampling sites using samples collected during 2000?02.

Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology

NASA Astrophysics Data System (ADS)

Serinaldi, Francesco; Kilsby, Chris G.; Lombardo, Federico

2018-01-01

The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as 'deterministic components' or 'trends' even though the complexity of hydrological systems does not allow easy deterministic explanations and attributions. Consequently, trend estimation techniques have been developed to make and justify statements about tendencies in the historical data, which are often used to predict future events. Testing trend hypothesis on observed time series is widespread in the hydro-meteorological literature mainly due to the interest in detecting consequences of human activities on the hydrological cycle. This analysis usually relies on the application of some null hypothesis significance tests (NHSTs) for slowly-varying and/or abrupt changes, such as Mann-Kendall, Pettitt, or similar, to summary statistics of hydrological time series (e.g., annual averages, maxima, minima, etc.). However, the reliability of this application has seldom been explored in detail. This paper discusses misuse, misinterpretation, and logical flaws of NHST for trends in the analysis of hydrological data from three different points of view: historic-logical, semantic-epistemological, and practical. Based on a review of NHST rationale, and basic statistical definitions of stationarity, nonstationarity, and ergodicity, we show that even if the empirical estimation of trends in hydrological time series is always feasible from a numerical point of view, it is uninformative and does not allow the inference of nonstationarity without assuming a priori additional information on the underlying stochastic process, according to deductive reasoning. This prevents the use of trend NHST outcomes to support nonstationary frequency analysis and modeling. We also show that the correlation structures characterizing hydrological time series might easily be underestimated, further compromising the attempt to draw conclusions about trends spanning the period of records. Moreover, even though adjusting procedures accounting for correlation have been developed, some of them are insufficient or are applied only to some tests, while some others are theoretically flawed but still widely applied. In particular, using 250 unimpacted stream flow time series across the conterminous United States (CONUS), we show that the test results can dramatically change if the sequences of annual values are reproduced starting from daily stream flow records, whose larger sizes enable a more reliable assessment of the correlation structures.

Monitoring Springs in the Mojave Desert Using Landsat Time Series Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2018-01-01

The purpose of this study, based on Landsat satellite data was to characterize variations and trends over 30 consecutive years (1985-2016) in perennial vegetation green cover at over 400 confirmed Mojave Desert spring locations. These springs were surveyed between in 2015 and 2016 on lands managed in California by the U.S. Bureau of Land Management (BLM) and on several land trusts within the Barstow, Needles, and Ridgecrest BLM Field Offices. The normalized difference vegetation index (NDVI) from July Landsat images was computed at each spring location and a trend model was first fit to the multi-year NDVI time series using least squares linear regression.Â

Early estimates of SEER cancer incidence, 2014.

PubMed

Lewis, Denise Riedel; Chen, Huann-Sheng; Cockburn, Myles G; Wu, Xiao-Cheng; Stroup, Antoinette M; Midthune, Douglas N; Zou, Zhaohui; Krapcho, Martin F; Miller, Daniel G; Feuer, Eric J

2017-07-01

Cancer incidence rates and trends for cases diagnosed through 2014 using data reported to the Surveillance, Epidemiology, and End Results (SEER) program in February 2016 and a validation of rates and trends for cases diagnosed through 2013 and submitted in February 2015 using the November 2015 submission are reported. New cancer sites include the pancreas, kidney and renal pelvis, corpus and uterus, and childhood cancer sites for ages birth to 19 years inclusive. A new reporting delay model is presented for these estimates for more consistent results with the model used for the usual November SEER submissions, adjusting for the large case undercount in the February submission. Joinpoint regression methodology was used to assess trends. Delay-adjusted rates and trends were checked for validity between the February 2016 and November 2016 submissions. Validation revealed that the delay model provides similar estimates of eventual counts using either February or November submission data. Trends declined through 2014 for prostate and colon and rectum cancer for males and females, male and female lung cancer, and cervical cancer. Thyroid cancer and liver and intrahepatic bile duct cancer increased. Pancreas (male and female) and corpus and uterus cancer demonstrated a modest increase. Slight increases occurred for male kidney and renal pelvis, and for all childhood cancer sites for ages birth to 19 years. Evaluating early cancer data submissions, adjusted for reporting delay, produces timely and valid incidence rates and trends. The results of the current study support using delay-adjusted February submission data for valid incidence rate and trend estimates over several data cycles. Cancer 2017;123:2524-34. © 2017 American Cancer Society. © 2017 American Cancer Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

An assessment of precipitation and surface air temperature over China by regional climate models

NASA Astrophysics Data System (ADS)

Wang, Xueyuan; Tang, Jianping; Niu, Xiaorui; Wang, Shuyu

2016-12-01

An analysis of a 20-year summer time simulation of present-day climate (1989-2008) over China using four regional climate models coupled with different land surface models is carried out. The climatic means, interannual variability, linear trends, and extremes are examined, with focus on precipitation and near surface air temperature. The models are able to reproduce the basic features of the observed summer mean precipitation and temperature over China and the regional detail due to topographic forcing. Overall, the model performance is better for temperature than that of precipitation. The models reasonably grasp the major anomalies and standard deviations over China and the five subregions studied. The models generally reproduce the spatial pattern of high interannual variability over wet regions, and low variability over the dry regions. The models also capture well the variable temperature gradient increase to the north by latitude. Both the observed and simulated linear trend of precipitation shows a drying tendency over the Yangtze River Basin and wetting over South China. The models capture well the relatively small temperature trends in large areas of China. The models reasonably simulate the characteristics of extreme precipitation indices of heavy rain days and heavy precipitation fraction. Most of the models also performed well in capturing both the sign and magnitude of the daily maximum and minimum temperatures over China.

Evaluation and adjustment of altimeter measurement and numerical hindcast in wave height trend estimation in China's coastal seas

NASA Astrophysics Data System (ADS)

Li, Shuiqing; Guan, Shoude; Hou, Yijun; Liu, Yahao; Bi, Fan

2018-05-01

A long-term trend of significant wave height (SWH) in China's coastal seas was examined based on three datasets derived from satellite measurements and numerical hindcasts. One set of altimeter data were obtained from the GlobWave, while the other two datasets of numerical hindcasts were obtained from the third-generation wind wave model, WAVEWATCH III, forced by wind fields from the Cross-Calibrated Multi-Platform (CCMP) and NCEP's Climate Forecast System Reanalysis (CFSR). The mean and extreme wave trends were estimated for the period 1992-2010 with respect to the annual mean and the 99th-percentile values of SWH, respectively. The altimeter wave trend estimates feature considerable uncertainties owing to the sparse sampling rate. Furthermore, the extreme wave trend tends to be overestimated because of the increasing sampling rate over time. Numerical wave trends strongly depend on the quality of the wind fields, as the CCMP waves significantly overestimate the wave trend, whereas the CFSR waves tend to underestimate the trend. Corresponding adjustments were applied which effectively improved the trend estimates from the altimeter and numerical data. The adjusted results show generally increasing mean wave trends, while the extreme wave trends are more spatially-varied, from decreasing trends prevailing in the South China Sea to significant increasing trends mainly in the East China Sea.

Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble

NASA Astrophysics Data System (ADS)

Stahl, K.; Tallaksen, L. M.; Hannaford, J.; van Lanen, H. A. J.

2012-02-01

An overall appraisal of runoff changes at the European scale has been hindered by "white space" on maps of observed trends due to a paucity of readily-available streamflow data. This study tested whether this white space can be filled using estimates of trends derived from model simulations of European runoff. The simulations stem from an ensemble of eight global hydrological models that were forced with the same climate input for the period 1963-2000. A validation of the derived trends for 293 grid cells across the European domain with observation-based trend estimates, allowed an assessment of the uncertainty of the modelled trends. The models agreed on the predominant continental scale patterns of trends, but disagreed on magnitudes and even on trend directions at the transition between regions with increasing and decreasing runoff trends, in complex terrain with a high spatial variability, and in snow-dominated regimes. Model estimates appeared most reliable in reproducing trends in annual runoff, winter runoff, and 7-day high flow. Modelled trends in runoff during the summer months, spring (for snow influenced regions) and autumn, and trends in summer low flow, were more variable and should be viewed with caution due to higher uncertainty. The ensemble mean overall provided the best representation of the trends in the observations. Maps of trends in annual runoff based on the ensemble mean demonstrated a pronounced continental dipole pattern of positive trends in western and northern Europe and negative trends in southern and parts of Eastern Europe, which has not previously been demonstrated and discussed in comparable detail.

Validation of test-day models for genetic evaluation of dairy goats in Norway.

PubMed

Andonov, S; Ødegård, J; Boman, I A; Svendsen, M; Holme, I J; Adnøy, T; Vukovic, V; Klemetsdal, G

2007-10-01

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.

Evaluation of trends in wheat yield models

NASA Technical Reports Server (NTRS)

Ferguson, M. C.

1982-01-01

Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.

Characterising groundwater dynamics in Western Victoria, Australia using Menyanthes software

NASA Astrophysics Data System (ADS)

Woldeyohannes, Yohannes; Webb, John

2010-05-01

Water table across much of the western Victoria, Australia have been declining for at least the last 10-15 years, and this is attributed to the consistently low rainfall for these years, but over the same period of time there has been substantial change in land use, with grazing land replaced by cropping and tree plantations appearing in some areas. Hence, it is important to determine the relative effect the climate and land use factors on the water table changes. Monitoring changes in groundwater levels to climate variables and/or land use change is helpful in indicating the degree of threat faced to agricultural and public assets. The dynamics of the groundwater system in the western Victoria, mainly on the basalt plain, have been modelled to determine the climatic influence in water table fluctuations. In this study, a standardized computer package Menyanthes was used for quantifying the influence of climatic variables on the groundwater level, statistically estimating trends in groundwater levels and identify the properties that determine the dynamics of groundwater system. This method is optimized for use on hydrological problems and is based on the use of continuous time transfer function noise model, which estimates the Impulse response function of the system from the temporal correlation between time series of groundwater level and precipitation surplus. In this approach, the spatial differences in the groundwater system are determined by the system properties, while temporal variation is driven by the dynamics of the input into the system. 80 time series models are analysed and the model output parameter values characterized by their moments. The zero-order moment Mo of a distribution function is its area and M1 is related to the mean of the impulse response function. The relation is M1/Mo. It is a measure of the system's memory. It takes approximately 3 times the mean time (M1/Mo) for the effect of a shower to disappear completely from the system. Overall, the model fitted the data well, explaining 89% (median value of R2) of variation in groundwater level using the climatic variables (rainfall and evaporation) left without significant trend (-0.046 m/yr, on average), which is within the range of variable input standard error. The average estimated system response (memory to disappear) is 5.2 years which is less than by 1/10th of the previously estimated time using Ground Water Flow System approach. The average Mo is 1.45 m, which means that a precipitation of 365 mm/yr will eventually lead to a ground water level rise of 1.45 m on the location. The Menyanthes result is compared with HARTT (Hydrograph Analysis and Time Trends) method. The trend and Mo estimate using Menyanthes and HARTT show comparable result. From a time series analysis there is no indication that the groundwater table was rising/falling due to changes in landuse, at least not during the observation period.

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models

PubMed Central

Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.

2017-01-01

Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50–65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases. PMID:28071683

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models.

PubMed

Stratton, Margaret D; Ehrlich, Hanna Y; Mor, Siobhan M; Naumova, Elena N

2017-01-10

Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.

A comparative analysis of three vector-borne diseases across Australia using seasonal and meteorological models

NASA Astrophysics Data System (ADS)

Stratton, Margaret D.; Ehrlich, Hanna Y.; Mor, Siobhan M.; Naumova, Elena N.

2017-01-01

Ross River virus (RRV), Barmah Forest virus (BFV), and dengue are three common mosquito-borne diseases in Australia that display notable seasonal patterns. Although all three diseases have been modeled on localized scales, no previous study has used harmonic models to compare seasonality of mosquito-borne diseases on a continent-wide scale. We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 1995 and 1991, respectively, through 2015) incorporating seasonal, trend, and climate (temperature and rainfall) parameters. The models captured an average of 50-65% variability of the data. Disease incidence for all three diseases generally peaked in January or February, but peak timing was most variable for dengue. The most significant predictor parameters were trend and inter-annual periodicity for BFV, intra-annual periodicity for RRV, and trend for dengue. We found that a Temperature Suitability Index (TSI), designed to reclassify climate data relative to optimal conditions for vector establishment, could be applied to this context. Finally, we extrapolated our models to estimate the impact of a false-positive BFV epidemic in 2013. Creating these models and comparing variations in periodicities may provide insight into historical outbreaks as well as future patterns of mosquito-borne diseases.

Erosion over time on severely disturbed granitic soils: a model

Treesearch

W. F. Megahan

1974-01-01

A negative exponential equation containing three parameters was derived to describe time trends in surface erosion on severely disturbed soils. Data from four different studies of surface erosion on roads constructed from the granitic materials found in the Idaho Batholith were used to develop equation parameters. The evidence suggests that surface "armoring...

The long-term changes in total ozone, as derived from Dobson measurements at Arosa (1948-2001)

NASA Astrophysics Data System (ADS)

Krzyscin, J. W.

2003-04-01

The longest possible total ozone time series (Arosa, Switzerland) is examined for a detection of trends. Two-step procedure is proposed to estimate the long-term (decadal) variations in the ozone time series. The first step consists of a standard least-squares multiple regression applied to the total ozone monthly means to parameterize "natural" (related to the oscillations in the atmospheric dynamics) variations in the analyzed time series. The standard proxies for the dynamical ozone variations are used including; the 11-year solar activity cycle, and indices of QBO, ENSO and NAO. We use the detrended time series of temperature at 100 hPa and 500 hPa over Arosa to parameterize short-term variations (with time periods<1 year) in total ozone related to local changes in the meteorological conditions over the station. The second step consists of a smooth-curve fitting to the total ozone residuals (original minus modeled "natural" time series), the time derivation applied to this curve to obtain local trends, and bootstrapping of the residual time series to estimate the standard error of local trends. Locally weighted regression and the wavelet analysis methodology are used to extract the smooth component out of the residual time series. The time integral over the local trend values provides the cumulative long-term change since the data beginning. Examining the pattern of the cumulative change we see the periods with total ozone loss (the end of 50s up to early 60s - probably the effect of the nuclear bomb tests), recovery (mid 60s up to beginning of 70s), apparent decrease (beginning of 70s lasting to mid 90s - probably the effect of the atmosphere contamination by anthropogenic substances containing chlorine), and with a kind of stabilization or recovery (starting in the mid of 90s - probably the effect of the Montreal protocol to eliminate substances reducing the ozone layer). We can also estimate that a full ozone recovery (return to the undisturbed total ozone level from the beginning of 70s) is expected around 2050. We propose to calculate both time series of local trends and the cumulative long-term change instead single trend value derived as a slope of straight line fit to the data.

Mapping monkeypox transmission risk through time and space in the Congo Basin

USGS Publications Warehouse

Nakazawa, Yoshinori J.; Lash, R. Ryan; Carroll, Darin S.; Damon, Inger K.; Karem, Kevin L.; Reynolds, Mary G.; Osorio, Jorge E.; Rocke, Tonie E.; Malekani, Jean; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A. Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

Mapping monkeypox transmission risk through time and space in the Congo Basin.

PubMed

Nakazawa, Yoshinori; Lash, R Ryan; Carroll, Darin S; Damon, Inger K; Karem, Kevin L; Reynolds, Mary G; Osorio, Jorge E; Rocke, Tonie E; Malekani, Jean M; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox.

Mapping Monkeypox Transmission Risk through Time and Space in the Congo Basin

PubMed Central

Nakazawa, Yoshinori; Lash, R. Ryan; Carroll, Darin S.; Damon, Inger K.; Karem, Kevin L.; Reynolds, Mary G.; Osorio, Jorge E.; Rocke, Tonie E.; Malekani, Jean M.; Muyembe, Jean-Jacques; Formenty, Pierre; Peterson, A. Townsend

2013-01-01

Monkeypox is a major public health concern in the Congo Basin area, with changing patterns of human case occurrences reported in recent years. Whether this trend results from better surveillance and detection methods, reduced proportions of vaccinated vs. non-vaccinated human populations, or changing environmental conditions remains unclear. Our objective is to examine potential correlations between environment and transmission of monkeypox events in the Congo Basin. We created ecological niche models based on human cases reported in the Congo Basin by the World Health Organization at the end of the smallpox eradication campaign, in relation to remotely-sensed Normalized Difference Vegetation Index datasets from the same time period. These models predicted independent spatial subsets of monkeypox occurrences with high confidence; models were then projected onto parallel environmental datasets for the 2000s to create present-day monkeypox suitability maps. Recent trends in human monkeypox infection are associated with broad environmental changes across the Congo Basin. Our results demonstrate that ecological niche models provide useful tools for identification of areas suitable for transmission, even for poorly-known diseases like monkeypox. PMID:24040344

Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.

A new time-independent formulation of fractional release

NASA Astrophysics Data System (ADS)

Ostermöller, Jennifer; Bönisch, Harald; Jöckel, Patrick; Engel, Andreas

2017-03-01

The fractional release factor (FRF) gives information on the amount of a halocarbon that is released at some point into the stratosphere from its source form to the inorganic form, which can harm the ozone layer through catalytic reactions. The quantity is of major importance because it directly affects the calculation of the ozone depletion potential (ODP). In this context time-independent values are needed which, in particular, should be independent of the trends in the tropospheric mixing ratios (tropospheric trends) of the respective halogenated trace gases. For a given atmospheric situation, such FRF values would represent a molecular property.We analysed the temporal evolution of FRF from ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulations for several halocarbons and nitrous oxide between 1965 and 2011 on different mean age levels and found that the widely used formulation of FRF yields highly time-dependent values. We show that this is caused by the way that the tropospheric trend is handled in the widely used calculation method of FRF.Taking into account chemical loss in the calculation of stratospheric mixing ratios reduces the time dependence in FRFs. Therefore we implemented a loss term in the formulation of the FRF and applied the parameterization of a mean arrival time to our data set.We find that the time dependence in the FRF can almost be compensated for by applying a new trend correction in the calculation of the FRF. We suggest that this new method should be used to calculate time-independent FRFs, which can then be used e.g. for the calculation of ODP.

Quantifying stratospheric ozone trends: Complications due to stratospheric cooling

NASA Astrophysics Data System (ADS)

McLinden, C. A.; Fioletov, V.

2011-02-01

Recent studies suggest that ozone turnaround (the second stage of ozone recovery) is near. Determining precisely when this occurs, however, will be complicated by greenhouse gas-induced stratospheric cooling as ozone trends derived from profile data in different units and/or vertical co-ordinates will not agree. Stratospheric cooling leads to simultaneous trends in air density and layer thicknesses, confounding the interpretation of ozone trends. A simple model suggests that instruments measuring ozone in different units may differ as to the onset of turnaround by a decade, with some indicting a continued decline while others an increase. This concept was illustrated by examining the long-term (1979-2005) ozone trends in the SAGE (Stratospheric Aerosol and Gas Experiment) and SBUV (Solar Backscatter Ultraviolet) time series. Trends from SAGE, which measures number density as a function of altitude, and SBUV, which measures partial column as a function of pressure, are known to differ by 4-6%/decade in the upper stratosphere. It is shown that this long-standing difference can be reconciled to within 2%/decade when the trend in temperature is properly accounted for.

Tectonic stress pattern in the Chinese Mainland from the inversion of focal mechanism data

NASA Astrophysics Data System (ADS)

Wei, Ju; Weifeng, Sun; Xiaojing, Ma

2017-04-01

The tectonic stress pattern in the Chinese Mainland and kinematic models have been subjected to much debate. In the past several decades, several tectonic stress maps have been figured out; however, they generally suffer a poor time control. In the present study, 421 focal mechanism data up to January 2010 were compiled from the Global/Harvard CMT catalogue, and 396 of them were grouped into 23 distinct regions in function of geographic proximity. Reduced stress tensors were obtained from formal stress inversion for each region. The results indicated that, in the Chinese Mainland, the directions of maximum principal stress were ˜NE-SW-trending in the northeastern region, ˜NEE-SWW-trending in the North China region, ˜N-S-trending in western Xinjiang, southern Tibet and the southern Yunnan region, ˜NNE-SSW-trending in the northern Tibet and Qinghai region, ˜NW-SE-trending in Gansu region, and ˜E-W-trending in the western Sichuan region. The average tectonic stress regime was strike-slip faulting (SS) in the eastern Chinese Mainland and northern Tibet region, normal faulting (NF) in the southern Tibet, western Xinjiang and Yunnan region, and thrust faulting (TF) in most regions of Xinjiang, Qinghai and Gansu. The results of the present study combined with GPS velocities in the Chinese Mainland supported and could provide new insights into previous tectonic models (e.g., the extrusion model). From the perspective of tectonics, the mutual actions among the Eurasian plate, Pacific plate and Indian plate caused the present-day tectonic stress field in the Chinese Mainland.

Effect of Pediatric Behavioral Health Screening and Colocated Services on Ambulatory and Inpatient Utilization.

PubMed

Hacker, Karen A; Penfold, Robert B; Arsenault, Lisa N; Zhang, Fang; Soumerai, Stephen B; Wissow, Lawrence S

2015-11-01

The study sought to determine the impact of a pediatric behavioral health screening and colocation model on utilization of behavioral health care. In 2003, Cambridge Health Alliance, a Massachusetts public health system, introduced behavioral health screening and colocation of social workers sequentially within its pediatric practices. An interrupted time-series study was conducted to determine the impact on behavioral health care utilization in the 30 months after model implementation compared with the 18 months prior. Specifically, the change in trends of ambulatory, emergency, and inpatient behavioral health utilization was examined. Utilization data for 11,223 children ages ≥4 years 9 months to <18 years 3 months seen from 2003 to 2008 contributed to the study. In the 30 months after implementation of pediatric behavioral health screening and colocation, there was a 20.4% cumulative increase in specialty behavioral health visit rates (trend of .013% per month, p=.049) and a 67.7% cumulative increase in behavioral health primary care visit rates (trend of .019% per month, p<.001) compared with the expected rates predicted by the 18-month preintervention trend. In addition, behavioral health emergency department visit rates increased 245% compared with the expected rate (trend .01% per month, p=.002). After the implementation of a behavioral health screening and colocation model, more children received behavioral health treatment. Contrary to expectations, behavioral health emergency department visits also increased. Further study is needed to determine whether this is an effect of how care was organized for children newly engaged in behavioral health care or a reflection of secular trends in behavioral health utilization or both.

Spatiotemporal mathematical modelling of mutations of the dhps gene in African Plasmodium falciparum.

PubMed

Flegg, Jennifer A; Patil, Anand P; Venkatesan, Meera; Roper, Cally; Naidoo, Inbarani; Hay, Simon I; Sibley, Carol Hopkins; Guerin, Philippe J

2013-07-17

Plasmodium falciparum has repeatedly evolved resistance to first-line anti-malarial drugs, thwarting efforts to control and eliminate the disease and in some period of time this contributed largely to an increase in mortality. Here a mathematical model was developed to map the spatiotemporal trends in the distribution of mutations in the P. falciparum dihydropteroate synthetase (dhps) gene that confer resistance to the anti-malarial sulphadoxine, and are a useful marker for the combination of alleles in dhfr and dhps that is highly correlated with resistance to sulphadoxine-pyrimethamine (SP). The aim of this study was to present a proof of concept for spatiotemporal modelling of trends in anti-malarial drug resistance that can be applied to monitor trends in resistance to components of artemisinin combination therapy (ACT) or other anti-malarials, as they emerge or spread. Prevalence measurements of single nucleotide polymorphisms in three codon positions of the dihydropteroate synthetase (dhps) gene from published studies of dhps mutations across Africa were used. A model-based geostatistics approach was adopted to create predictive surfaces of the dhps540E mutation over the spatial domain of sub-Saharan Africa from 1990-2010. The statistical model was implemented within a Bayesian framework and hence quantified the associated uncertainty of the prediction of the prevalence of the dhps540E mutation in sub-Saharan Africa. The maps presented visualize the changing prevalence of the dhps540E mutation in sub-Saharan Africa. These allow prediction of space-time trends in the parasite resistance to SP, and provide probability distributions of resistance prevalence in places where no data are available as well as insight on the spread of resistance in a way that the data alone do not allow. The results of this work will be extended to design optimal sampling strategies for the future molecular surveillance of resistance, providing a proof of concept for similar techniques to design optimal strategies to monitor resistance to ACT.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Longitudinal Trends in Sexual Behaviors Without a Condom Among Sexual Minority Youth: The P18 Cohort Study.

PubMed

Kapadia, Farzana; Bub, Kristen; Barton, Staci; Stults, Christopher B; Halkitis, Perry N

2015-12-01

Given the heightened risk for HIV and other STIs among young men who have sex with men (YMSM) as well as the racial/ethnic disparities in HIV/STI risk, an understanding of longitudinal trends in sexual behaviors is warranted as YMSM emerge into adulthood. Drawing from an ongoing prospective cohort study, the present analysis employed latent growth curve modeling to examine trends in distinct types of sexual activity without condoms over time in sample of YMSM and examine differences by race/ethnicity and perceived familial socioeconomic status (SES). Overall, White and Mixed race YMSM reported more instances of oral sex without condoms as compared to other racial/ethnic groups with rates of decline over time noted in Black YMSM. White YMSM also reported more receptive and insertive anal sex acts without a condom than Black YMSM. Declines over time in both types of anal sex acts without condoms among Black men were noted when compared to White men, while increases over time were noted for mixed race YMSM for condomless insertive anal sex. The effects for race/ethnicity were attenuated with the inclusion of perceived familial SES in these models. These findings build on previous cross sectional studies showing less frequent sex without condoms among Black YMSM despite higher rates of HIV incidence in emerging adulthood, as well as the importance of considering economic conditions in such models. Efforts to understand racial/ethnic disparities in HIV/STIs among YMSM must move beyond examination of individual-level sexual behaviors and consider both race/ethnicity and socioeconomic conditions in order to evaluate how these factors shape the sexual behaviors of YMSM.

Groundwater salinity in a floodplain forest impacted by saltwater intrusion.

PubMed

Kaplan, David A; Muñoz-Carpena, Rafael

2014-11-15

Coastal wetlands occupy a delicate position at the intersection of fresh and saline waters. Changing climate and watershed hydrology can lead to saltwater intrusion into historically freshwater systems, causing plant mortality and loss of freshwater habitat. Understanding the hydrological functioning of tidally influenced floodplain forests is essential for advancing ecosystem protection and restoration goals, however finding direct relationships between hydrological inputs and floodplain hydrology is complicated by interactions between surface water, groundwater, and atmospheric fluxes in variably saturated soils with heterogeneous vegetation and topography. Thus, an alternative method for identifying common trends and causal factors is required. Dynamic factor analysis (DFA), a time series dimension reduction technique, models temporal variation in observed data as linear combinations of common trends, which represent unexplained common variability, and explanatory variables. DFA was applied to model shallow groundwater salinity in the forested floodplain wetlands of the Loxahatchee River (Florida, USA), where altered watershed hydrology has led to changing hydroperiod and salinity regimes and undesired vegetative changes. Long-term, high-resolution groundwater salinity datasets revealed dynamics over seasonal and yearly time periods as well as over tidal cycles and storm events. DFA identified shared trends among salinity time series and a full dynamic factor model simulated observed series well (overall coefficient of efficiency, Ceff=0.85; 0.52≤Ceff≤0.99). A reduced multilinear model based solely on explanatory variables identified in the DFA had fair to good results (Ceff=0.58; 0.38≤Ceff≤0.75) and may be used to assess the effects of restoration and management scenarios on shallow groundwater salinity in the Loxahatchee River floodplain. Copyright © 2014 Elsevier B.V. All rights reserved.

Measuring trends of outpatient antibiotic use in Europe: jointly modelling longitudinal data in defined daily doses and packages.

PubMed

Bruyndonckx, Robin; Hens, Niel; Aerts, Marc; Goossens, Herman; Molenberghs, Geert; Coenen, Samuel

2014-07-01

To complement analyses of the linear trend and seasonal fluctuation of European outpatient antibiotic use expressed in defined daily doses (DDD) by analyses of data in packages, to assess the agreement between both measures and to study changes in the number of DDD per package over time. Data on outpatient antibiotic use, aggregated at the level of the active substance (WHO version 2011) were collected from 2000 to 2007 for 31 countries and expressed in DDD and packages per 1000 inhabitants per day (DID and PID, respectively). Data expressed in DID and PID were analysed separately using non-linear mixed models while the agreement between these measurements was analysed through a joint non-linear mixed model. The change in DDD per package over time was studied with a linear mixed model. Total outpatient antibiotic and penicillin use in Europe and their seasonal fluctuation significantly increased in DID, but not in PID. The use of combinations of penicillins significantly increased in DID and in PID. Broad-spectrum penicillin use did not increase significantly in DID and decreased significantly in PID. For all but one subgroup, country-specific deviations moved in the same direction whether measured in DID or PID. The correlations are not perfect. The DDD per package increased significantly over time for all but one subgroup. Outpatient antibiotic use in Europe shows contrasting trends, depending on whether DID or PID is used as the measure. The increase of the DDD per package corroborates the recommendation to adopt PID to monitor outpatient antibiotic use in Europe. © The Author 2014. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Filling the white space on maps of European runoff trends: estimates from a multi-model ensemble

NASA Astrophysics Data System (ADS)

Stahl, K.; Tallaksen, L. M.; Hannaford, J.; van Lanen, H. A. J.

2012-07-01

An overall appraisal of runoff changes at the European scale has been hindered by "white space" on maps of observed trends due to a paucity of readily-available streamflow data. This study tested whether this white space can be filled using estimates of trends derived from model simulations of European runoff. The simulations stem from an ensemble of eight global hydrological models that were forced with the same climate input for the period 1963-2000. The derived trends were validated for 293 grid cells across the European domain with observation-based trend estimates. The ensemble mean overall provided the best representation of trends in the observations. Maps of trends in annual runoff based on the ensemble mean demonstrated a pronounced continental dipole pattern of positive trends in western and northern Europe and negative trends in southern and parts of eastern Europe, which has not previously been demonstrated and discussed in comparable detail. Overall, positive trends in annual streamflow appear to reflect the marked wetting trends of the winter months, whereas negative annual trends result primarily from a widespread decrease in streamflow in spring and summer months, consistent with a decrease in summer low flow in large parts of Europe. High flow appears to have increased in rain-dominated hydrological regimes, whereas an inconsistent or decreasing signal was found in snow-dominated regimes. The different models agreed on the predominant continental-scale pattern of trends, but in some areas disagreed on the magnitude and even the direction of trends, particularly in transition zones between regions with increasing and decreasing runoff trends, in complex terrain with a high spatial variability, and in snow-dominated regimes. Model estimates appeared most reliable in reproducing observed trends in annual runoff, winter runoff, and 7-day high flow. Modelled trends in runoff during the summer months, spring (for snow influenced regions) and autumn, and trends in summer low flow were more variable - both among models and in the spatial patterns of agreement between models and the observations. The use of models to display changes in these hydrological characteristics should therefore be viewed with caution due to higher uncertainty.

Survival and recovery rates of American woodcock banded in Michigan

USGS Publications Warehouse

Krementz, David G.; Hines, James E.; Luukkonen, David R.

2003-01-01

American woodcock (Scolopax minor) population indices have declined since U.S. Fish and Wildlife Service (USFWS) monitoring began in 1968. Management to stop and/or reverse this population trend has been hampered by the lack of recent information on woodcock population parameters. Without recent information on survival rate trends, managers have had to assume that the recent declines in recruitment indices are the only parameter driving woodcock declines. Using program MARK, we estimated annual survival and recovery rates of adult and juvenile American woodcock, and estimated summer survival of local (young incapable of sustained flight) woodcock banded in Michigan between 1978 and 1998. We constructed a set of candidate models from a global model with age (local, juvenile, adult) and time (year)-dependent survival and recovery rates to no age or time-dependent survival and recovery rates. Five models were supported by the data, with all models suggesting that survival rates differed among age classes, and 4 models had survival rates that were constant over time. The fifth model suggested that juvenile and adult survival rates were linear on a logit scale over time. Survival rates averaged over likelihood-weighted model results were 0.8784 +/- 0.1048 (SE) for locals, 0.2646 +/- 0.0423 (SE) for juveniles, and 0.4898 +/- 0.0329 (SE) for adults. Weighted average recovery rates were 0.0326 +/- 0.0053 (SE) for juveniles and 0.0313 +/- 0.0047 (SE) for adults. Estimated differences between our survival estimates and those from prior years were small, and our confidence around those differences was variable and uncertain. juvenile survival rates were low.

Paleobotanical Evidence for Coupling of Temperature and pCO2 during the Early Eocene Climatic Optimum

NASA Astrophysics Data System (ADS)

Smith, R. Y.; Greenwood, D. R.; Basinger, J. F.

2009-12-01

The Early Eocene Climatic Optimum (EECO) was the warmest period of the Cenozoic, indicated by multiple proxy mean annual temperature estimates for sea and land surface. However, estimates of pCO2 from geochemical, modeling, and paleontological proxies show a wide range of values, from near modern day levels to an order of magnitude greater. Resolving the pCO2 record for this time period, and correlating it with trends in temperature, is a key task in understanding the interaction of climate and pCO2 in globally warm periods. Here we present a fine scale study of trends in temperature and pCO2 based on paleobotanical data from an early Eocene site from the Okanagan Highlands of British Columbia, Canada. Plant macrofossils were collected using an unbiased census approach from three informal units, allowing for quantitative comparison of trends within the site. Temperature estimates derived from multiple paleobotanical techniques (physiognomic and floristic approaches) suggest microthermal (MAT <13°C) but equable (CMMT >0°C) conditions for this upland site, and show a trend in declining MAT over time reflected in the three units. At the same time, stomatal frequency of Ginkgo suggests that pCO2 was high (>2x modern values), but also declining over time. These results suggest that temperature and pCO2 were coupled during this globally warm period, and that fine scale trends on the order of 103 - 104 years can be tracked within fossil sites to provide a window on climate/pCO2 interactions.

Reduction in outpatient antibiotic sales for pre-school children: interrupted time series analysis of weekly antibiotic sales data in Sweden 1992-2002.

PubMed

Högberg, Liselotte; Oke, Thimothy; Geli, Patricia; Lundborg, Cecilia Stålsby; Cars, Otto; Ekdahl, Karl

2005-07-01

The aim of this study was to use detailed weekly data on outpatient antibiotic sales for pre-school children in Sweden to test for the significance of trends during 1992-2002. We also report on the special features found in weekly antibiotic data, and how the interrupted time series (ITS) design can adjust for this. Weekly data on the total number of dispensed outpatient antibiotic prescriptions to pre-school children were studied, as well as the individual subgroups commonly used to treat respiratory tract infections in children: narrow-spectrum penicillins, broad-spectrum penicillins and macrolides. In parallel, monthly data of paracetamol sales of paediatric dosages were analysed to reflect trends in symptomatic treatment. An ITS model controlling for seasonality and autocorrelation was used to examine the datasets for significant level and trend shifts. A significant increase in mean and change in level could be found in the total antibiotic data in 1997, also reflected in broad-spectrum penicillin data where a similar trend break occurred in 1996. For macrolides, a trend break with a decrease in mean was noted in 1996, but no trend breaks were found in narrow-spectrum penicillin data. In contrast to the general decreasing trends in antibiotic sales, the yearly over-the-counter sales of paracetamol in paediatric preparations increased during the same period, with no identified trend breaks. The overall decrease in antibiotic sales and increase in paediatric paracetamol sales might suggest that symptomatic treatment in the home has increased, as antibiotics are less commonly prescribed.

Modeling method of time sequence model based grey system theory and application proceedings

NASA Astrophysics Data System (ADS)

Wei, Xuexia; Luo, Yaling; Zhang, Shiqiang

2015-12-01

This article gives a modeling method of grey system GM(1,1) model based on reusing information and the grey system theory. This method not only extremely enhances the fitting and predicting accuracy of GM(1,1) model, but also maintains the conventional routes' merit of simple computation. By this way, we have given one syphilis trend forecast method based on reusing information and the grey system GM(1,1) model.

Multi-Decadal Aerosol Variations from 1980 to 2009: A Perspective from Observations and a Global Model

NASA Technical Reports Server (NTRS)

Chin, Mian; Diehl, T.; Tan, Q.; Prospero, J. M.; Kahn, R. A.; Remer, L. A.; Yu, H.; Sayer, A. M.; Bian, H.; Geogdzhayev, I. V.;

Night-time lights: A global, long term look at links to socio-economic trends

PubMed Central

Zavala-Araiza, Daniel; Wagner, Gernot

2017-01-01

We use a parallelized spatial analytics platform to process the twenty-one year totality of the longest-running time series of night-time lights data—the Defense Meteorological Satellite Program (DMSP) dataset—surpassing the narrower scope of prior studies to assess changes in area lit of countries globally. Doing so allows a retrospective look at the global, long-term relationships between night-time lights and a series of socio-economic indicators. We find the strongest correlations with electricity consumption, CO2 emissions, and GDP, followed by population, CH4 emissions, N2O emissions, poverty (inverse) and F-gas emissions. Relating area lit to electricity consumption shows that while a basic linear model provides a good statistical fit, regional and temporal trends are found to have a significant impact. PMID:28346500

Long-term hydrometeorological trends in the Midwest region based on a century long gridded hydrometeorological dataset and simulations from a macro-scale hydrology model

NASA Astrophysics Data System (ADS)

Chiu, C. M.; Hamlet, A. F.

2014-12-01

Climate change is likely to impact the Great Lakes region and Midwest region via changes in Great Lakes water levels, agricultural impacts, river flooding, urban stormwater impacts, drought, water temperature, and impacts to terrestrial and aquatic ecosystems. Self-consistent and temporally homogeneous long-term data sets of precipitation and temperature over the entire Great Lakes region and Midwest regions are needed to provide inputs to hydrologic models, assess historical trends in hydroclimatic variables, and downscale global and regional-scale climate models. To support these needs a new hybrid gridded meteorological forcing dataset at 1/16 degree resolution based on data from co-op station records, the U. S Historical Climatology Network (HCN) , the Historical Canadian Climate Database (HCCD), and Precipitation Regression on Independent Slopes Method (PRISM) has been assembled over the Great Lakes and Midwest region from 1915-2012 at daily time step. These data were then used as inputs to the macro-scale Variable Infiltration Capacity (VIC) hydrology model, implemented over the Midwest and Great Lakes region at 1/16 degree resolution, to produce simulated hydrologic variables that are amenable to long-term trend analysis. Trends in precipitation and temperature from the new meteorological driving data sets, as well as simulated hydrometeorological variables such as snowpack, soil moisture, runoff, and evaporation over the 20th century are presented and discussed.

Satellite-based Monitoring of global Precipitation using the PERSIANN system: from Weather- to Climate-scales with some application examples

NASA Astrophysics Data System (ADS)

Switzer, A.; Yap, W.; Lauro, F.; Gouramanis, C.; Dominey-Howes, D.; Labbate, M.

2016-12-01

This presentation provides an overview of the PERSIANN precipitation products from the near real time high-resolution (4km, 30 min) PERSIANN-CCS to the most recent 34+-year PERSIANN-CDR (25km, daily). It is widely believed that the hydrologic cycle has been intensifying due to global warming and the frequency and the intensity of hydrologic extremes has also been increasing. Using the long-term historical global high resolution (daily, 0.25 degree) PERSIANN-CDR dataset covering over three decades from 1983 to the present day, we assess changes in global precipitation across different spatial scales. Our results show differences in trends, depending on which spatial scale is used, highlighting the importance of spatial scale in trend analysis. In addition, while there is an easily observable increasing global temperature trend, the global precipitation trend results created by the PERSIANN-CDR dataset used in this study are inconclusive. In addition, we use PERSIANN-CDR to assess the performance of the 32 CMIP5 models in terms of extreme precipitation indices in various continent-climate zones. The assessment can provide a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

Satellite-based Monitoring of global Precipitation using the PERSIANN system: from Weather- to Climate-scales with some application examples

NASA Astrophysics Data System (ADS)

Sorooshian, S.; Nguyen, P.; Hsu, K. L.

2017-12-01

This presentation provides an overview of the PERSIANN precipitation products from the near real time high-resolution (4km, 30 min) PERSIANN-CCS to the most recent 34+-year PERSIANN-CDR (25km, daily). It is widely believed that the hydrologic cycle has been intensifying due to global warming and the frequency and the intensity of hydrologic extremes has also been increasing. Using the long-term historical global high resolution (daily, 0.25 degree) PERSIANN-CDR dataset covering over three decades from 1983 to the present day, we assess changes in global precipitation across different spatial scales. Our results show differences in trends, depending on which spatial scale is used, highlighting the importance of spatial scale in trend analysis. In addition, while there is an easily observable increasing global temperature trend, the global precipitation trend results created by the PERSIANN-CDR dataset used in this study are inconclusive. In addition, we use PERSIANN-CDR to assess the performance of the 32 CMIP5 models in terms of extreme precipitation indices in various continent-climate zones. The assessment can provide a guide for both model developers to target regions and processes that are not yet fully captured in certain climate types, and for climate model output users to be able to select the models and/or the study areas that may best fit their applications of interest.

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

NASA Technical Reports Server (NTRS)

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; Bosilovich, Michael G.; Lee, Jaechoul; Wehner, Michael F.; Collow, Allison

2016-01-01

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

DOE PAGES

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; ...

2016-02-03

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scalemore » patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.« less

Major Crustal Fault Zone Trends and Their Relation to Mineral Belts in the North-Central Great Basin, Nevada

USGS Publications Warehouse

Rodriguez, Brian D.; Sampson, Jay A.; Williams, Jackie M.

2007-01-01

The Great Basin physiographic province covers a large part of the western United States and contains one of the world's leading gold-producing areas, the Carlin Trend. In the Great Basin, many sedimentary-rock-hosted disseminated gold deposits occur along such linear mineral-occurrence trends. The distribution and genesis of these deposits is not fully understood, but most models indicate that regional tectonic structures play an important role in their spatial distribution. Over 100 magnetotelluric (MT) soundings were acquired between 1994 and 2001 by the U.S. Geological Survey to investigate crustal structures that may underlie the linear trends in north-central Nevada. MT sounding data were used to map changes in electrical resistivity as a function of depth that are related to subsurface lithologic and structural variations. Two-dimensional (2-D) resistivity modeling of the MT data reveals primarily northerly and northeasterly trending narrow 2-D conductors (1 to 30 ohm-m) extending to mid-crustal depths (5-20 km) that are interpreted to be major crustal fault zones. There are also a few westerly and northwesterly trending 2-D conductors. However, the great majority of the inferred crustal fault zones mapped using MT are perpendicular or oblique to the generally accepted trends. The correlation of strike of three crustal fault zones with the strike of the Carlin and Getchell trends and the Alligator Ridge district suggests they may have been the root fluid flow pathways that fed faults and fracture networks at shallower levels where gold precipitated in favorable host rocks. The abundant northeasterly crustal structures that do not correlate with the major trends may be structures that are open to fluid flow at the present time.

Detecting potential anomalies in projections of rainfall trends and patterns using human observations

NASA Astrophysics Data System (ADS)

Kohfeld, K. E.; Savo, V.; Sillmann, J.; Morton, C.; Lepofsky, D.

2016-12-01

Shifting precipitation patterns are a well-documented consequence of climate change, but their spatial variability is particularly difficult to assess. While the accuracy of global models has increased, specific regional changes in precipitation regimes are not well captured by these models. Typically, researchers who wish to detect trends and patterns in climatic variables, such as precipitation, use instrumental observations. In our study, we combined observations of rainfall by subsistence-oriented communities with several metrics of rainfall estimated from global instrumental records for comparable time periods (1955 - 2005). This comparison was aimed at identifying: 1) which rainfall metrics best match human observations of changes in precipitation; 2) areas where local communities observe changes not detected by global models. The collated observations ( 3800) made by subsistence-oriented communities covered 129 countries ( 1830 localities). For comparable time periods, we saw a substantial correspondence between instrumental records and human observations (66-77%) at the same locations, regardless of whether we considered trends in general rainfall, drought, or extreme rainfall. We observed a clustering of mismatches in two specific regions, possibly indicating some climatic phenomena not completely captured by the currently available global models. Many human observations also indicated an increased unpredictability in the start, end, duration, and continuity of the rainy seasons, all of which may hamper the performance of subsistence activities. We suggest that future instrumental metrics should capture this unpredictability of rainfall. This information would be important for thousands of subsistence-oriented communities in planning, coping, and adapting to climate change.

Dynamic evaluation of two decades of WRF-CMAQ ozone ...

EPA Pesticide Factsheets

Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)– Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and/or emissions are also evaluated during the same timeframe using spectral decomposition of observed and modeled ozone time series with the aim of identifying the underlying forcing mechanisms that control ozone exceedances and making informed recommendations for the optimal use of regional-scale air quality models. The evaluation is focused on the warm season's (i.e., May–September) daily maximum 8-hr (DM8HR) ozone concentrations, the 4th highest (4th) and average of top 10 DM8HR ozone values (top10), as well as the spectrally-decomposed components of the DM8HR ozone time series using the Kolmogorov-Zurbenko (KZ) filter. Results of the dynamic evaluation are presented for six regions in the U.S., consistent with the National Oceanic and Atmospheric Administration (NOAA) climatic regions. During the earlier 11-yr period (1990–2000), the simulated and observed trends are not statistically significant. During the more recent 2000–2010 period, all trends are statistically significant and WRF-CMAQ captures the observed trend in most regions. Given large n

Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

Predicting dynamic metabolic demands in the photosynthetic eukaryote Chlorella vulgaris

DOE PAGES

Zuniga, Cristal; Levering, Jennifer; Antoniewicz, Maciek R.; ...

2017-09-26

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. We used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealedmore » an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted fluxes trends and gene expression trends was found for 65% of multi-subunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acids metabolism. Moreover, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris, iCZ946, thus increasing our knowledgebase by 10% for this model green alga.« less

Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987-2016

NASA Astrophysics Data System (ADS)

Smith, Taylor; Bookhagen, Bodo; Rheinwalt, Aljoscha

2017-10-01

High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade-1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

Future changes over the Himalayas: Maximum and minimum temperature

NASA Astrophysics Data System (ADS)

Dimri, A. P.; Kumar, D.; Choudhary, A.; Maharana, P.

2018-03-01

An assessment of the projection of minimum and maximum air temperature over the Indian Himalayan region (IHR) from the COordinated Regional Climate Downscaling EXperiment- South Asia (hereafter, CORDEX-SA) regional climate model (RCM) experiments have been carried out under two different Representative Concentration Pathway (RCP) scenarios. The major aim of this study is to assess the probable future changes in the minimum and maximum climatology and its long-term trend under different RCPs along with the elevation dependent warming over the IHR. A number of statistical analysis such as changes in mean climatology, long-term spatial trend and probability distribution function are carried out to detect the signals of changes in climate. The study also tries to quantify the uncertainties associated with different model experiments and their ensemble in space, time and for different seasons. The model experiments and their ensemble show prominent cold bias over Himalayas for present climate. However, statistically significant higher warming rate (0.23-0.52 °C/decade) for both minimum and maximum air temperature (Tmin and Tmax) is observed for all the seasons under both RCPs. The rate of warming intensifies with the increase in the radiative forcing under a range of greenhouse gas scenarios starting from RCP4.5 to RCP8.5. In addition to this, a wide range of spatial variability and disagreements in the magnitude of trend between different models describes the uncertainty associated with the model projections and scenarios. The projected rate of increase of Tmin may destabilize the snow formation at the higher altitudes in the northern and western parts of Himalayan region, while rising trend of Tmax over southern flank may effectively melt more snow cover. Such combined effect of rising trend of Tmin and Tmax may pose a potential threat to the glacial deposits. The overall trend of Diurnal temperature range (DTR) portrays increasing trend across entire area with highest magnitude under RCP8.5. This higher rate of increase is imparted from the predominant rise of Tmax as compared to Tmin.

Explaining trends in alcohol-related harms in Scotland, 1991-2011 (I): the role of incomes, effects of socio-economic and political adversity and demographic change.

PubMed

McCartney, G; Bouttell, J; Craig, N; Craig, P; Graham, L; Lakha, F; Lewsey, J; McAdams, R; MacPherson, M; Minton, J; Parkinson, J; Robinson, M; Shipton, D; Taulbut, M; Walsh, D; Beeston, C

2016-03-01

This paper tests the extent to which differing trends in income, demographic change and the consequences of an earlier period of social, economic and political change might explain differences in the magnitude and trends in alcohol-related mortality between 1991 and 2011 in Scotland compared to England & Wales (E&W). Comparative time trend analyses and arithmetic modelling. Three approaches were utilised to compare Scotland with E&W: 1. We modelled the impact of changes in income on alcohol-related deaths between 1991-2001 and 2001-2011 by applying plausible assumptions of the effect size through an arithmetic model. 2. We used contour plots, graphical exploration of age-period-cohort interactions and calculation of Intrinsic Estimator coefficients to investigate the effect of earlier exposure to social, economic and political adversity on alcohol-related mortality. 3. We recalculated the trends in alcohol-related deaths using the white population only to make a crude approximation of the maximal impact of changes in ethnic diversity. Real incomes increased during the 1990s but declined from around 2004 in the poorest 30% of the population of Great Britain. The decline in incomes for the poorest decile, the proportion of the population in the most deprived decile, and the inequality in alcohol-related deaths, were all greater in Scotland than in E&W. The model predicted less of the observed rise in Scotland (18% of the rise in men and 29% of the rise in women) than that in E&W (where 60% and 68% of the rise in men and women respectively was explained). One-third of the decline observed in alcohol-related mortality in Scottish men between 2001 and 2011 was predicted by the model, and the model was broadly consistent with the observed trends in E&W and amongst women in Scotland. An age-period interaction in alcohol-related mortality was evident for men and women during the 1990s and 2000s who were aged 40-70 years and who experienced rapidly increasing alcohol-related mortality rates. Ethnicity is unlikely to be important in explaining the trends or differences between Scotland and E&W. The decline in alcohol-related mortality in Scotland since the early 2000s and the differing trend to E&W were partly described by a model predicting the impact of declining incomes. Lagged effects from historical social, economic and political change remain plausible from the available data. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Spatio-temporal mapping of Madagascar's Malaria Indicator Survey results to assess Plasmodium falciparum endemicity trends between 2011 and 2016.

PubMed

Kang, Su Yun; Battle, Katherine E; Gibson, Harry S; Ratsimbasoa, Arsène; Randrianarivelojosia, Milijaona; Ramboarina, Stéphanie; Zimmerman, Peter A; Weiss, Daniel J; Cameron, Ewan; Gething, Peter W; Howes, Rosalind E

2018-05-23

Reliable measures of disease burden over time are necessary to evaluate the impact of interventions and assess sub-national trends in the distribution of infection. Three Malaria Indicator Surveys (MISs) have been conducted in Madagascar since 2011. They provide a valuable resource to assess changes in burden that is complementary to the country's routine case reporting system. A Bayesian geostatistical spatio-temporal model was developed in an integrated nested Laplace approximation framework to map the prevalence of Plasmodium falciparum malaria infection among children from 6 to 59 months in age across Madagascar for 2011, 2013 and 2016 based on the MIS datasets. The model was informed by a suite of environmental and socio-demographic covariates known to influence infection prevalence. Spatio-temporal trends were quantified across the country. Despite a relatively small decrease between 2013 and 2016, the prevalence of malaria infection has increased substantially in all areas of Madagascar since 2011. In 2011, almost half (42.3%) of the country's population lived in areas of very low malaria risk (<1% parasite prevalence), but by 2016, this had dropped to only 26.7% of the population. Meanwhile, the population in high transmission areas (prevalence >20%) increased from only 2.2% in 2011 to 9.2% in 2016. A comparison of the model-based estimates with the raw MIS results indicates there was an underestimation of the situation in 2016, since the raw figures likely associated with survey timings were delayed until after the peak transmission season. Malaria remains an important health problem in Madagascar. The monthly and annual prevalence maps developed here provide a way to evaluate the magnitude of change over time, taking into account variability in survey input data. These methods can contribute to monitoring sub-national trends of malaria prevalence in Madagascar as the country aims for geographically progressive elimination.

On ozone trend detection: using coupled chemistry-climate simulations to investigate early signs of total column ozone recovery

NASA Astrophysics Data System (ADS)

Keeble, James; Brown, Hannah; Abraham, N. Luke; Harris, Neil R. P.; Pyle, John A.

2018-06-01

Total column ozone values from an ensemble of UM-UKCA model simulations are examined to investigate different definitions of progress on the road to ozone recovery. The impacts of modelled internal atmospheric variability are accounted for by applying a multiple linear regression model to modelled total column ozone values, and ozone trend analysis is performed on the resulting ozone residuals. Three definitions of recovery are investigated: (i) a slowed rate of decline and the date of minimum column ozone, (ii) the identification of significant positive trends and (iii) a return to historic values. A return to past thresholds is the last state to be achieved. Minimum column ozone values, averaged from 60° S to 60° N, occur between 1990 and 1995 for each ensemble member, driven in part by the solar minimum conditions during the 1990s. When natural cycles are accounted for, identification of the year of minimum ozone in the resulting ozone residuals is uncertain, with minimum values for each ensemble member occurring at different times between 1992 and 2000. As a result of this large variability, identification of the date of minimum ozone constitutes a poor measure of ozone recovery. Trends for the 2000-2017 period are positive at most latitudes and are statistically significant in the mid-latitudes in both hemispheres when natural cycles are accounted for. This significance results largely from the large sample size of the multi-member ensemble. Significant trends cannot be identified by 2017 at the highest latitudes, due to the large interannual variability in the data, nor in the tropics, due to the small trend magnitude, although it is projected that significant trends may be identified in these regions soon thereafter. While significant positive trends in total column ozone could be identified at all latitudes by ˜ 2030, column ozone values which are lower than the 1980 annual mean can occur in the mid-latitudes until ˜ 2050, and in the tropics and high latitudes deep into the second half of the 21st century.

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products.

PubMed

Jiang, Chongya; Ryu, Youngryel; Fang, Hongliang; Myneni, Ranga; Claverie, Martin; Zhu, Zaichun

2017-10-01

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R 2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products. © 2017 John Wiley & Sons Ltd.

Hantavirus reservoir Oligoryzomys longicaudatus spatial distribution sensitivity to climate change scenarios in Argentine Patagonia

PubMed Central

Carbajo, Aníbal E; Vera, Carolina; González, Paula LM

2009-01-01

Background Oligoryzomys longicaudatus (colilargo) is the rodent responsible for hantavirus pulmonary syndrome (HPS) in Argentine Patagonia. In past decades (1967–1998), trends of precipitation reduction and surface air temperature increase have been observed in western Patagonia. We explore how the potential distribution of the hantavirus reservoir would change under different climate change scenarios based on the observed trends. Methods Four scenarios of potential climate change were constructed using temperature and precipitation changes observed in Argentine Patagonia between 1967 and 1998: Scenario 1 assumed no change in precipitation but a temperature trend as observed; scenario 2 assumed no changes in temperature but a precipitation trend as observed; Scenario 3 included changes in both temperature and precipitation trends as observed; Scenario 4 assumed changes in both temperature and precipitation trends as observed but doubled. We used a validated spatial distribution model of O. longicaudatus as a function of temperature and precipitation. From the model probability of the rodent presence was calculated for each scenario. Results If changes in precipitation follow previous trends, the probability of the colilargo presence would fall in the HPS transmission zone of northern Patagonia. If temperature and precipitation trends remain at current levels for 60 years or double in the future 30 years, the probability of the rodent presence and the associated total area of potential distribution would diminish throughout Patagonia; the areas of potential distribution for colilargos would shift eastwards. These results suggest that future changes in Patagonia climate may lower transmission risk through a reduction in the potential distribution of the rodent reservoir. Conclusion According to our model the rates of temperature and precipitation changes observed between 1967 and 1998 may produce significant changes in the rodent distribution in an equivalent period of time only in certain areas. Given that changes maintain for 60 years or double in 30 years, the hantavirus reservoir Oligoryzomys longicaudatus may contract its distribution in Argentine Patagonia extensively. PMID:19607707

Estimating prevalence trends in adult gonorrhoea and syphilis in low- and middle-income countries with the Spectrum-STI model: results for Zimbabwe and Morocco from 1995 to 2016

PubMed Central

Korenromp, Eline L; Mahiané, Guy; Rowley, Jane; Nagelkerke, Nico; Abu-Raddad, Laith; Ndowa, Francis; El-Kettani, Amina; El-Rhilani, Houssine; Mayaud, Philippe; Chico, R Matthew; Pretorius, Carel; Hecht, Kendall; Wi, Teodora

2017-01-01

Objective To develop a tool for estimating national trends in adult prevalence of sexually transmitted infections by low- and middle-income countries, using standardised, routinely collected programme indicator data. Methods The Spectrum-STI model fits time trends in the prevalence of active syphilis through logistic regression on prevalence data from antenatal clinic-based surveys, routine antenatal screening and general population surveys where available, weighting data by their national coverage and representativeness. Gonorrhoea prevalence was fitted as a moving average on population surveys (from the country, neighbouring countries and historic regional estimates), with trends informed additionally by urethral discharge case reports, where these were considered to have reasonably stable completeness. Prevalence data were adjusted for diagnostic test performance, high-risk populations not sampled, urban/rural and male/female prevalence ratios, using WHO's assumptions from latest global and regional-level estimations. Uncertainty intervals were obtained by bootstrap resampling. Results Estimated syphilis prevalence (in men and women) declined from 1.9% (95% CI 1.1% to 3.4%) in 2000 to 1.5% (1.3% to 1.8%) in 2016 in Zimbabwe, and from 1.5% (0.76% to 1.9%) to 0.55% (0.30% to 0.93%) in Morocco. At these time points, gonorrhoea estimates for women aged 15–49 years were 2.5% (95% CI 1.1% to 4.6%) and 3.8% (1.8% to 6.7%) in Zimbabwe; and 0.6% (0.3% to 1.1%) and 0.36% (0.1% to 1.0%) in Morocco, with male gonorrhoea prevalences 14% lower than female prevalence. Conclusions This epidemiological framework facilitates data review, validation and strategic analysis, prioritisation of data collection needs and surveillance strengthening by national experts. We estimated ongoing syphilis declines in both Zimbabwe and Morocco. For gonorrhoea, time trends were less certain, lacking recent population-based surveys. PMID:28325771

The Diesel Exhaust in Miners Study: I. Overview of the Exposure Assessment Process

PubMed Central

Stewart, Patricia A.; Coble, Joseph B.; Vermeulen, Roel; Schleiff, Patricia; Blair, Aaron; Lubin, Jay; Attfield, Michael; Silverman, Debra T.

2010-01-01

This report provides an overview of the exposure assessment process for an epidemiologic study that investigated mortality, with a special focus on lung cancer, associated with diesel exhaust (DE) exposure among miners. Details of several components are provided in four other reports. A major challenge for this study was the development of quantitative estimates of historical exposures to DE. There is no single standard method for assessing the totality of DE, so respirable elemental carbon (REC), a component of DE, was selected as the primary surrogate in this study. Air monitoring surveys at seven of the eight study mining facilities were conducted between 1998 and 2001 and provided reference personal REC exposure levels and measurements for other agents and DE components in the mining environment. (The eighth facility had closed permanently prior to the surveys.) Exposure estimates were developed for mining facility/department/job/year combinations. A hierarchical grouping strategy was developed for assigning exposure levels to underground jobs [based on job titles, on the amount of time spent in various areas of the underground mine, and on similar carbon monoxide (CO, another DE component) concentrations] and to surface jobs (based on the use of, or proximity to, diesel-powered equipment). Time trends in air concentrations for underground jobs were estimated from mining facility-specific prediction models using diesel equipment horsepower, total air flow rates exhausted from the underground mines, and, because there were no historical REC measurements, historical measurements of CO. Exposures to potentially confounding agents, i.e. respirable dust, silica, radon, asbestos, and non-diesel sources of polycyclic aromatic hydrocarbons, also were assessed. Accuracy and reliability of the estimated REC exposures levels were evaluated by comparison with several smaller datasets and by development of alternative time trend models. During 1998–2001, the average measured REC exposure level by facility ranged from 40 to 384 μg m−3 for the underground workers and from 2 to 6 μg m−3 for the surface workers. For one prevalent underground job, ‘miner operator’, the maximum annual REC exposure estimate by facility ranged up to 685% greater than the corresponding 1998–2001 value. A comparison of the historical CO estimates from the time trend models with 1976–1977 CO measurements not used in the modeling found an overall median relative difference of 29%. Other comparisons showed similar levels of agreement. The assessment process indicated large differences in REC exposure levels over time and across the underground operations. Method evaluations indicated that the final estimates were consistent with those from alternative time trend models and demonstrated moderate to high agreement with external data. PMID:20876233

The diesel exhaust in miners study: I. Overview of the exposure assessment process.

PubMed

Stewart, Patricia A; Coble, Joseph B; Vermeulen, Roel; Schleiff, Patricia; Blair, Aaron; Lubin, Jay; Attfield, Michael; Silverman, Debra T

2010-10-01

This report provides an overview of the exposure assessment process for an epidemiologic study that investigated mortality, with a special focus on lung cancer, associated with diesel exhaust (DE) exposure among miners. Details of several components are provided in four other reports. A major challenge for this study was the development of quantitative estimates of historical exposures to DE. There is no single standard method for assessing the totality of DE, so respirable elemental carbon (REC), a component of DE, was selected as the primary surrogate in this study. Air monitoring surveys at seven of the eight study mining facilities were conducted between 1998 and 2001 and provided reference personal REC exposure levels and measurements for other agents and DE components in the mining environment. (The eighth facility had closed permanently prior to the surveys.) Exposure estimates were developed for mining facility/department/job/year combinations. A hierarchical grouping strategy was developed for assigning exposure levels to underground jobs [based on job titles, on the amount of time spent in various areas of the underground mine, and on similar carbon monoxide (CO, another DE component) concentrations] and to surface jobs (based on the use of, or proximity to, diesel-powered equipment). Time trends in air concentrations for underground jobs were estimated from mining facility-specific prediction models using diesel equipment horsepower, total air flow rates exhausted from the underground mines, and, because there were no historical REC measurements, historical measurements of CO. Exposures to potentially confounding agents, i.e. respirable dust, silica, radon, asbestos, and non-diesel sources of polycyclic aromatic hydrocarbons, also were assessed. Accuracy and reliability of the estimated REC exposures levels were evaluated by comparison with several smaller datasets and by development of alternative time trend models. During 1998-2001, the average measured REC exposure level by facility ranged from 40 to 384 μg m⁻³ for the underground workers and from 2 to 6 μg m⁻³ for the surface workers. For one prevalent underground job, 'miner operator', the maximum annual REC exposure estimate by facility ranged up to 685% greater than the corresponding 1998-2001 value. A comparison of the historical CO estimates from the time trend models with 1976-1977 CO measurements not used in the modeling found an overall median relative difference of 29%. Other comparisons showed similar levels of agreement. The assessment process indicated large differences in REC exposure levels over time and across the underground operations. Method evaluations indicated that the final estimates were consistent with those from alternative time trend models and demonstrated moderate to high agreement with external data.

Climate is changing, everything is flowing, stationarity is immortal

NASA Astrophysics Data System (ADS)

Koutsoyiannis, Demetris; Montanari, Alberto

2015-04-01

There is no doubt that climate is changing -- and ever has been. The environment is also changing and in the last decades, as a result of demographic change and technological advancement, environmental change has been accelerating. These affect also the hydrological processes, whose changes in connection with rapidly changing human systems have been the focus of the new scientific decade 2013-2022 of the International Association of Hydrological Sciences, entitled "Panta Rhei - Everything Flows". In view of the changing systems, it has recently suggested that, when dealing with water management and hydrological extremes, stationarity is no longer a proper assumption. Hence, it was proposed that hydrological processes should be treated as nonstationary. Two main reasons contributed to this perception. First, the climate models project a future hydroclimate that will be different from the current one. Second, as streamflow record become longer, they indicate the presence of upward or downward trends. However, till now hydroclimatic projections made in the recent past have not been verified. At the same time, evidence from quite longer records, instrumental or proxy, suggest that local trends are omnipresent but not monotonic; rather at some time upward trends turn to downward ones and vice versa. These observations suggest that improvident dismiss of stationarity and adoption of nonstationary descriptions based either on climate model outputs or observed trends may entail risks. The risks stem from the facts that the future can be different from what was deterministically projected, that deterministic projections are associated with an illusion of decreased uncertainty, as well as that nonstationary models fitted on observed data may have lower predictive capacity than simpler stationary ones. In most of the cases, what is actually needed is to revisit the concept of stationarity and try to apply it carefully, making it consistent with the presence of local trends, possibly incorporating information from deterministic predictions, whenever these prove to be reliable, and estimating the total predictive uncertainty.

Mapping and spatial-temporal modeling of Bromus tectorum invasion in central Utah

NASA Astrophysics Data System (ADS)

Jin, Zhenyu

Cheatgrass, or Downy Brome, is an exotic winter annual weed native to the Mediterranean region. Since its introduction to the U.S., it has become a significant weed and aggressive invader of sagebrush, pinion-juniper, and other shrub communities, where it can completely out-compete native grasses and shrubs. In this research, remotely sensed data combined with field collected data are used to investigate the distribution of the cheatgrass in Central Utah, to characterize the trend of the NDVI time-series of cheatgrass, and to construct a spatially explicit population-based model to simulate the spatial-temporal dynamics of the cheatgrass. This research proposes a method for mapping the canopy closure of invasive species using remotely sensed data acquired at different dates. Different invasive species have their own distinguished phenologies and the satellite images in different dates could be used to capture the phenology. The results of cheatgrass abundance prediction have a good fit with the field data for both linear regression and regression tree models, although the regression tree model has better performance than the linear regression model. To characterize the trend of NDVI time-series of cheatgrass, a novel smoothing algorithm named RMMEH is presented in this research to overcome some drawbacks of many other algorithms. By comparing the performance of RMMEH in smoothing a 16-day composite of the MODIS NDVI time-series with that of two other methods, which are the 4253EH, twice and the MVI, we have found that RMMEH not only keeps the original valid NDVI points, but also effectively removes the spurious spikes. The reconstructed NDVI time-series of different land covers are of higher quality and have smoother temporal trend. To simulate the spatial-temporal dynamics of cheatgrass, a spatially explicit population-based model is built applying remotely sensed data. The comparison between the model output and the ground truth of cheatgrass closure demonstrates that the model could successfully simulate the spatial-temporal dynamics of cheatgrass in a simple cheatgrass-dominant environment. The simulation of the functional response of different prescribed fire rates also shows that this model is helpful to answer management questions like, "What are the effects of prescribed fire to invasive species?" It demonstrates that a medium fire rate of 10% can successfully prevent cheatgrass invasion.

The role of life cycle processes on phytoplankton spring bloom composition: a modelling study applied to the Gulf of Finland

NASA Astrophysics Data System (ADS)

Lee, Soonmi; Hofmeister, Richard; Hense, Inga

2018-02-01

Diatoms are typical representatives of the spring bloom worldwide. In several parts of the Baltic Sea, however, cold-water dinoflagellates such as Biecheleria baltica have become dominant during the past decades. We have investigated the mechanisms behind this trend by using an ecosystem model which includes the life cycles of three main phytoplankton groups (diatoms, dinoflagellates and cyanobacteria). Coupled to a water column model we have applied the model system for the period 1981-2010 to the Gulf of Finland. In agreement with observations, the model results show an increasing trend in the proportion of dinoflagellates in the Gulf of Finland. Temperature and life cycle-related processes explain the relative increase of dinoflagellates and corresponding decrease of diatoms. Warming over the 30 years has enabled a head start of dinoflagellates by reducing the time lag between germination and growth of vegetative cells. Although diatoms have a much higher growth rate, they cannot compete with the high dinoflagellate concentrations that result from the inoculum. Diatoms will only dominate in years when the inoculum concentrations of dinoflagellates or the temperatures are low. Overall, the model results suggest that consideration of life cycle dynamics of competing phytoplankton groups may be crucial to understand trends and shifts in community composition.

Changes in time-trends of nutrient intake from fortified and non-fortified food in German children and adolescents--15 year results of the DONALD study. Dortmund Nutritional and Anthropometric Longitudinally Designed Study.

PubMed

Sichert-Hellert, W; Kersting, M; Manz, F

2001-04-01

Although fortified products have played an increasing role in food marketing since the 1980s in Germany, data as to the consumption of fortified food is sparse. To assess long-term data on changes in fortified food supply or consumption patterns, nutrient intake, and time trends in the DONALD Study (Dortmund Nutritional and Anthropometric Longitudinally Designed Study). Between 1985 and 2000 consumption of nutrient intake (total and from fortified foods) was evaluated and time trends in energy and nutrient intake were assessed on the basis of 3-day weighed dietary records (n = 4193) of 2-14 year-old males (n = 383) and females (n = 404) enrolled in the DONALD Study. Nutrient intake was expressed as percentage of the current German recommendations. Food products were defined as fortified if enriched with at least one of the following nutrients: Vitamin A or provitamin A carotenoids (summarised as Vitamin A), Vitamins E, B1, B2, B6, C, niacin, folate, calcium or iron. Nutrient supplements and medicine were excluded from this evaluation. Time trends were analysed using linear and non-linear regression models (PROC MIXED, SAS 6.12). In percent of German references [3], non-fortified food contributed to folate intake by 20-30%, to Vitamin E by about 40%, to Vitamin B1 by 50-65%, to Vitamin A, C, B2, calcium, iron by about 65-95%, and to Vitamin B6 and niacin intake by 100% and more. Fortified food alone provided no more than 5% of calcium intake, about 10-20% of iron, Vitamin A and folate intake, up to 40-50% of Vitamin C, B1, B2, E, niacin and up to 80% of Vitamin B6 intake. During the 15 year period of the DONALD Study with total food, we only found a significant linear time trend for Vitamin C, whereas significant non-linear time trends were found for calcium, Vitamin E, B1, B2, B6, niacin and folate. In the latter there was a uniform increase until 1994 and a decrease thereafter. For iron and Vitamin A no significant time trend could be identified. Only iron and Vitamin A intake from fortified food showed a significant linear time trend. All other nutrients studied here gave significant non-linear time trends. Nutrient intake with fortified food reached maximum values between 1994 and 1996 followed by a decrease thereafter. Signs of changing food consumption patterns were found, pointing to an almost uniform decrease of nutrient intake since 1994/96 in our population of German children and adolescents. This could be an alarming indicator of a slight but unpreferable tendency to eat energydense, nutrient-poor foods.

The dynamics of Alzheimer's disease biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort.

PubMed

Caroli, A; Frisoni, G B

2010-08-01

The aim of this study was to investigate the dynamics of four of the most validated biomarkers for Alzheimer's disease (AD), cerebro-spinal fluid (CSF) Abeta 1-42, tau, hippocampal volume, and FDG-PET, in patients at different stage of AD. Two hundred twenty-nine cognitively healthy subjects, 154 mild cognitive impairment (MCI) patients converted to AD, and 193 (95 early and 98 late) AD patients were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. For each biomarker, individual values were Z-transformed and plotted against ADAS-cog scores, and sigmoid and linear fits were compared. For most biomarkers the sigmoid model fitted data significantly better than the linear model. Abeta 1-42 time course followed a steep curve, stabilizing early in the disease course. CSF tau and hippocampal volume changed later showing similar monotonous trends, reflecting disease progression. Hippocampal loss trend was steeper and occurred earlier in time in APOE epsilon4 carriers than in non-carriers. FDG-PET started changing early in time and likely followed a linear decline. In conclusion, this study provides the first evidence in favor of the dynamic biomarker model which has recently been proposed. 2010 Elsevier Inc. All rights reserved.

On the use of Empirical Data to Downscale Non-scientific Scepticism About Results From Complex Physical Based Models

NASA Astrophysics Data System (ADS)

Germer, S.; Bens, O.; Hüttl, R. F.

2008-12-01

The scepticism of non-scientific local stakeholders about results from complex physical based models is a major problem concerning the development and implementation of local climate change adaptation measures. This scepticism originates from the high complexity of such models. Local stakeholders perceive complex models as black-box models, as it is impossible to gasp all underlying assumptions and mathematically formulated processes at a glance. The use of physical based models is, however, indispensible to study complex underlying processes and to predict future environmental changes. The increase of climate change adaptation efforts following the release of the latest IPCC report indicates that the communication of facts about what has already changed is an appropriate tool to trigger climate change adaptation. Therefore we suggest increasing the practice of empirical data analysis in addition to modelling efforts. The analysis of time series can generate results that are easier to comprehend for non-scientific stakeholders. Temporal trends and seasonal patterns of selected hydrological parameters (precipitation, evapotranspiration, groundwater levels and river discharge) can be identified and the dependence of trends and seasonal patters to land use, topography and soil type can be highlighted. A discussion about lag times between the hydrological parameters can increase the awareness of local stakeholders for delayed environment responses.

Thirty years of prospective nationwide incidence of childhood type 1 diabetes: the accelerating increase by time tends to level off in Sweden.

PubMed

Berhan, Yonas; Waernbaum, Ingeborg; Lind, Torbjörn; Möllsten, Anna; Dahlquist, Gisela

2011-02-01

During the past few decades, a rapidly increasing incidence of childhood type 1 diabetes (T1D) has been reported from many parts of the world. The change over time has been partly explained by changes in lifestyle causing rapid early growth and weight development. The current study models and analyzes the time trend by age, sex, and birth cohort in an exceptionally large study group. The present analysis involved 14,721 incident cases of T1D with an onset of 0-14.9 years that were recorded in the nationwide Swedish Childhood Diabetes Registry from 1978 to 2007. Data were analyzed using generalized additive models. Age- and sex-specific incidence rates varied from 21.6 (95% CI 19.4-23.9) during 1978-1980 to 43.9 (95% CI 40.7-47.3) during 2005-2007. Cumulative incidence by birth cohort shifted to a younger age at onset during the first 22 years, but from the birth year 2000 a statistically significant reversed trend (P < 0.01) was seen. Childhood T1D increased dramatically and shifted to a younger age at onset the first 22 years of the study period. We report a reversed trend, starting in 2000, indicating a change in nongenetic risk factors affecting specifically young children.

Glaciological constraints on current ice mass changes from modelling the ice sheets over the glacial cycles

NASA Astrophysics Data System (ADS)

Huybrechts, P.

2003-04-01

The evolution of continental ice sheets introduces a long time scale in the climate system. Large ice sheets have a memory of millenia, hence the present-day ice sheets of Greenland and Antarctica are still adjusting to climatic variations extending back to the last glacial period. This trend is separate from the direct response to mass-balance changes on decadal time scales and needs to be correctly accounted for when assessing current and future contributions to sea level. One way to obtain estimates of current ice mass changes is to model the past history of the ice sheets and their underlying beds over the glacial cycles. Such calculations assist to distinguish between the longer-term ice-dynamic evolution and short-term mass-balance changes when interpreting altimetry data, and are helpful to isolate the effects of postglacial rebound from gravity and altimetry trends. The presentation will discuss results obtained from 3-D thermomechanical ice-sheet/lithosphere/bedrock models applied to the Antarctic and Greenland ice sheets. The simulations are forced by time-dependent boundary conditions derived from sediment and ice core records and are constrained by geomorphological and glacial-geological data of past ice sheet and sea-level stands. Current simulations suggest that the Greenland ice sheet is close to balance, while the Antarctic ice sheet is still losing mass, mainly due to incomplete grounding-line retreat of the West Antarctic ice sheet since the LGM. The results indicate that altimetry trends are likely dominated by ice thickness changes but that the gravitational signal mainly reflects postglacial rebound.

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

Comparison of Decadal Water Storage Trends from Global Hydrological Models and GRACE Satellite Data

NASA Astrophysics Data System (ADS)

Scanlon, B. R.; Zhang, Z. Z.; Save, H.; Sun, A. Y.; Mueller Schmied, H.; Van Beek, L. P.; Wiese, D. N.; Wada, Y.; Long, D.; Reedy, R. C.; Doll, P. M.; Longuevergne, L.

2017-12-01

Global hydrology is increasingly being evaluated using models; however, the reliability of these global models is not well known. In this study we compared decadal trends (2002-2014) in land water storage from 7 global models (WGHM, PCR-GLOBWB, and GLDAS: NOAH, MOSAIC, VIC, CLM, and CLSM) to storage trends from new GRACE satellite mascon solutions (CSR-M and JPL-M). The analysis was conducted over 186 river basins, representing about 60% of the global land area. Modeled total water storage trends agree with those from GRACE-derived trends that are within ±0.5 km3/yr but greatly underestimate large declining and rising trends outside this range. Large declining trends are found mostly in intensively irrigated basins and in some basins in northern latitudes. Rising trends are found in basins with little or no irrigation and are generally related to increasing trends in precipitation. The largest decline is found in the Ganges (-12 km3/yr) and the largest rise in the Amazon (43 km3/yr). Differences between models and GRACE are greatest in large basins (>0.5x106 km2) mostly in humid regions. There is very little agreement in storage trends between models and GRACE and among the models with values of r2 mostly <0.1. Various factors can contribute to discrepancies in water storage trends between models and GRACE, including uncertainties in precipitation, model calibration, storage capacity, and water use in models and uncertainties in GRACE data related to processing, glacier leakage, and glacial isostatic adjustment. The GRACE data indicate that land has a large capacity to store water over decadal timescales that is underrepresented by the models. The storage capacity in the modeled soil and groundwater compartments may be insufficient to accommodate the range in water storage variations shown by GRACE data. The inability of the models to capture the large storage trends indicates that model projections of climate and human-induced changes in water storage may be mostly underestimated. Future GRACE and model studies should try to reduce the various sources of uncertainty in water storage trends and should consider expanding the modeled storage capacity of the soil profiles and their interaction with groundwater.

Methicillin-resistant Staphylococcus aureus (MRSA) is increasing in Norway: a time series analysis of reported MRSA and methicillin-sensitive S. aureus cases, 1997-2010.

PubMed

Moxnes, John F; de Blasio, Birgitte Freiesleben; Leegaard, Truls Michael; Moen, Aina E Fossum

2013-01-01

Accurate estimates of the incidence and prevalence of methicillin-resistant Staphylococcus aureus (MRSA) infections are needed to inform public health policies. In Norway, where both MRSA infection and carriage are notifiable conditions, the reported incidence of MRSA is slowly increasing. However, the proportion of MRSA in relation to all S. aureus isolates is unknown, making it difficult to determine if the rising incidence is real or an artifact of an increasing number of tests performed. To characterize recent trends in MRSA infections and obtain a more complete understanding of the MRSA level in Norway. All reported cases of MRSA and methicillin-sensitive S. aureus (MSSA) from Oslo County (1997-2010) and Health Region East (2008-2008), representing approximately 11% and 36% of the Norwegian population, respectively, were analyzed using a stochastic time series analysis to characterize trends. In Oslo County, the proportion of methicillin-resistant cases increased from 0.73% to 3.78% during the study period and was well modeled by an exponential growth with a doubling constant of 5.7 years (95% CI 4.5-7.4 years). In Health Region East, the proportion of MRSA cases increased from 0.4% to 2.1% from 2002 to 2008, with a best-fitting linear increase of 0.26% (95% CI 0.21-0.30%) per year. In both cases, the choice of a linear or exponential model for the time trend produced only marginally different model fits. We found no significant changes due to revised national MRSA guidelines published in June 2009. Significant variations in the increasing time trend were observed in the five hospitals within the region. The yearly reported incidence of MSSA was relatively stable in both study areas although we found seasonal patterns with peaks in August. The level of MRSA is increasing in Norway, and the proportion of methicillin resistance in all S. aureus isolates are higher than the reported proportion of MRSA in invasive infections.

Methicillin-Resistant Staphylococcus aureus (MRSA) Is Increasing in Norway: A Time Series Analysis of Reported MRSA and Methicillin-Sensitive S. aureus Cases, 1997–2010

PubMed Central

Moxnes, John F.; de Blasio, Birgitte Freiesleben; Leegaard, Truls Michael; Moen, Aina E. Fossum

2013-01-01

Background Accurate estimates of the incidence and prevalence of methicillin-resistant Staphylococcus aureus (MRSA) infections are needed to inform public health policies. In Norway, where both MRSA infection and carriage are notifiable conditions, the reported incidence of MRSA is slowly increasing. However, the proportion of MRSA in relation to all S. aureus isolates is unknown, making it difficult to determine if the rising incidence is real or an artifact of an increasing number of tests performed. Aim To characterize recent trends in MRSA infections and obtain a more complete understanding of the MRSA level in Norway. Methods All reported cases of MRSA and methicillin-sensitive S. aureus (MSSA) from Oslo County (1997–2010) and Health Region East (2008–2008), representing approximately 11% and 36% of the Norwegian population, respectively, were analyzed using a stochastic time series analysis to characterize trends. Results In Oslo County, the proportion of methicillin-resistant cases increased from 0.73% to 3.78% during the study period and was well modeled by an exponential growth with a doubling constant of 5.7 years (95% CI 4.5–7.4 years). In Health Region East, the proportion of MRSA cases increased from 0.4% to 2.1% from 2002 to 2008, with a best-fitting linear increase of 0.26% (95% CI 0.21–0.30%) per year. In both cases, the choice of a linear or exponential model for the time trend produced only marginally different model fits. We found no significant changes due to revised national MRSA guidelines published in June 2009. Significant variations in the increasing time trend were observed in the five hospitals within the region. The yearly reported incidence of MSSA was relatively stable in both study areas although we found seasonal patterns with peaks in August. Conclusion The level of MRSA is increasing in Norway, and the proportion of methicillin resistance in all S. aureus isolates are higher than the reported proportion of MRSA in invasive infections. PMID:23936442

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang; Yan, Sui-Hang; Lam, Joseph C

2004-09-01

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.

High School Grade Inflation from 2004 to 2011. ACT Research Report Series, 2013 (3)

ERIC Educational Resources Information Center

Zhang, Qian; Sanchez, Edgar I.

2013-01-01

This study explores inflation in high school grade point average (HSGPA), defined as trend over time in the conditional average of HSGPA, given ACT® Composite score. The time period considered is 2004 to 2011. Using hierarchical linear modeling, the study updates a previous analysis of Woodruff and Ziomek (2004). The study also investigates…

Perceived Control and Hedonic Tone Dynamics during Performance in Elite Shooters

ERIC Educational Resources Information Center

Robazza, Claudio; Bertollo, Maurizio; Filho, Edson; Hanin, Yuri; Bortoli, Laura

2016-01-01

Purpose: The purpose of the study was to investigate the individuals' dynamics of perceived control and hedonic tone over time, with respect to the 4 performance states as conceptualized within the multiaction plan (MAP) model. We expected to find idiosyncratic and differentiated trends over time in the scores of perceived control and hedonic…

Educational Modelling Language: Modelling Reusable, Interoperable, Rich and Personalised Units of Learning

ERIC Educational Resources Information Center

Koper, Rob; Manderveld, Jocelyn

2004-01-01

Nowadays there is a huge demand for flexible, independent learning without the constraints of time and place. Various trends in the field of education and training are the bases for the development of new technologies for education. This article describes the development of a learning technology specification, which supports these new demands for…

Using propensity scores in difference-in-differences models to estimate the effects of a policy change

PubMed Central

Stuart, Elizabeth A.; Huskamp, Haiden A.; Duckworth, Kenneth; Simmons, Jeffrey; Song, Zirui; Chernew, Michael; Barry, Colleen L.

2014-01-01

Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the “difference-in-differences” to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the “Alternative Quality Contract” (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees. PMID:25530705

Evidence for an earlier greenhouse cooling effect in the stratosphere before the 1980s over the Northern Hemisphere

NASA Astrophysics Data System (ADS)

Zerefos, C. S.; Tourpali, K.; Zanis, P.; Eleftheratos, K.; Repapis, C.; Goodman, A.; Wuebbles, D.; Isaksen, I. S. A.; Luterbacher, J.

2014-01-01

This study provides a new look at the observed and calculated long-term temperature changes since 1958 for the region extending from the lower troposphere up to the lower stratosphere of the Northern Hemisphere. The analysis is mainly based on monthly layer mean temperatures derived from geopotential height thicknesses between specific pressure levels. Layer mean temperatures from thickness improve homogeneity in both space and time and reduce uncertainties in the trend analysis. Datasets used include the NCEP/NCAR I reanalysis, the Free University of Berlin (FU-Berlin) and the RICH radiosonde datasets as well as historical simulations with the CESM1-WACCM global model participating in CMIP5. After removing the natural variability with an autoregressive multiple regression model our analysis shows that the time interval of our study 1958-2011 can be divided in two distinct sub-periods of long term temperature variability and trends; before and after 1980s. By calculating trends for the summer time to reduce interannual variability, the two periods are as follows. From 1958 until 1979, non-significant trends or slight cooling trends prevail in the lower troposphere (0.06 ± 0.06 °C decade-1 for NCEP and -0.12 ± 0.06 °C decade-1 for RICH). The second period from 1980 to the end of the records shows significant warming trends (0.25 ± 0.05 °C decade-1 for both NCEP and RICH). Above the tropopause a persistent cooling trend is clearly seen in the lower stratosphere both in the pre-1980s period (-0.58 ± 0.17 °C decade-1 for NCEP, -0.30 ± 0.16 °C decade-1 for RICH and -0.48 ± 0.20 °C decade-1 for FU-Berlin) and the post-1980s period (-0.79 ± 0.18 °C decade-1 for NCEP, -0.66 ± 0.16 °C decade-1 for RICH and -0.82 ± 0.19 °C decade-1 for FU-Berlin). The cooling in the lower stratosphere is a persistent feature from the tropics up to 60 north for all months. At polar latitudes competing dynamical and radiative processes are reducing the statistical significance of these trends. Model results are in line with re-analysis and the observations, indicating a persistent cooling in the lower stratosphere during summer before and after the 1980s by -0.33 °C decade-1; a feature that is also seen throughout the year. However, the lower stratosphere modelled trends are generally lower than re-analysis and the observations. The contrasting effects of ozone depletion at polar latitudes in winter/spring and the anticipated strengthening of the Brewer Dobson circulation from man-made global warming at polar latitudes are discussed. Our results provide additional evidence for an early greenhouse cooling signal in the lower stratosphere before the 1980s, which it appears well in advance relative to the tropospheric greenhouse warming signal. Hence it may be postulated that the stratosphere could have provided an early warning of man-made climate change. The suitability for early warning signals in the stratosphere relative to the troposphere is supported by the fact that the stratosphere is less sensitive to changes due to cloudiness, humidity and man-made aerosols. Our analysis also indicates that the relative contribution of the lower stratosphere vs. the upper troposphere low frequency variability is important for understanding the added value of the long term tropopause variability related to human induced global warming.

Secular changes of the M2 tide in the Gulf of Maine

NASA Technical Reports Server (NTRS)

Ray, Richard D.

2005-01-01

Analyses of long time series of hourly tide-gauge data at four stations in the Gulf of Maine reveal that the amplitude of the M2 tide underwent a nearly linear secular increase throughout most of the twentieth century. In the early 1980s, however, the amplitude of M2 abruptly dropped. Sea level changes alone appear inadequate to explain either the long-term trend or the recent trend discontinuity. Tidal models that account for Holocene sea level rise do predict an amplification of M2, but much smaller than the currently observed trends. Nor do recent annual mean sea levels correlate with the recent trend discontinuity. Some unknown fraction of the open Atlantic may be similarly affected, since the M2 discontinuity, but not the long-term secular increase in the tide, is evident also at Halifax.

An operational definition of a statistically meaningful trend.

PubMed

Bryhn, Andreas C; Dimberg, Peter H

2011-04-28

Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r(2) and p values are calculated from regressions concerning time and interval mean values. If r(2) ≥ 0.65 at p ≤ 0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.

Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling

PubMed Central

Li, Xia; Rao, Shaoqi; Jiang, Wei; Li, Chuanxing; Xiao, Yun; Guo, Zheng; Zhang, Qingpu; Wang, Lihong; Du, Lei; Li, Jing; Li, Li; Zhang, Tianwen; Wang, Qing K

2006-01-01

Background It is one of the ultimate goals for modern biological research to fully elucidate the intricate interplays and the regulations of the molecular determinants that propel and characterize the progression of versatile life phenomena, to name a few, cell cycling, developmental biology, aging, and the progressive and recurrent pathogenesis of complex diseases. The vast amount of large-scale and genome-wide time-resolved data is becoming increasing available, which provides the golden opportunity to unravel the challenging reverse-engineering problem of time-delayed gene regulatory networks. Results In particular, this methodological paper aims to reconstruct regulatory networks from temporal gene expression data by using delayed correlations between genes, i.e., pairwise overlaps of expression levels shifted in time relative each other. We have thus developed a novel model-free computational toolbox termed TdGRN (Time-delayed Gene Regulatory Network) to address the underlying regulations of genes that can span any unit(s) of time intervals. This bioinformatics toolbox has provided a unified approach to uncovering time trends of gene regulations through decision analysis of the newly designed time-delayed gene expression matrix. We have applied the proposed method to yeast cell cycling and human HeLa cell cycling and have discovered most of the underlying time-delayed regulations that are supported by multiple lines of experimental evidence and that are remarkably consistent with the current knowledge on phase characteristics for the cell cyclings. Conclusion We established a usable and powerful model-free approach to dissecting high-order dynamic trends of gene-gene interactions. We have carefully validated the proposed algorithm by applying it to two publicly available cell cycling datasets. In addition to uncovering the time trends of gene regulations for cell cycling, this unified approach can also be used to study the complex gene regulations related to the development, aging and progressive pathogenesis of a complex disease where potential dependences between different experiment units might occurs. PMID:16420705

Automated parameter tuning applied to sea ice in a global climate model

NASA Astrophysics Data System (ADS)

Roach, Lettie A.; Tett, Simon F. B.; Mineter, Michael J.; Yamazaki, Kuniko; Rae, Cameron D.

2018-01-01

This study investigates the hypothesis that a significant portion of spread in climate model projections of sea ice is due to poorly-constrained model parameters. New automated methods for optimization are applied to historical sea ice in a global coupled climate model (HadCM3) in order to calculate the combination of parameters required to reduce the difference between simulation and observations to within the range of model noise. The optimized parameters result in a simulated sea-ice time series which is more consistent with Arctic observations throughout the satellite record (1980-present), particularly in the September minimum, than the standard configuration of HadCM3. Divergence from observed Antarctic trends and mean regional sea ice distribution reflects broader structural uncertainty in the climate model. We also find that the optimized parameters do not cause adverse effects on the model climatology. This simple approach provides evidence for the contribution of parameter uncertainty to spread in sea ice extent trends and could be customized to investigate uncertainties in other climate variables.

Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Verdoolaege, G., E-mail: geert.verdoolaege@ugent.be; Laboratory for Plasma Physics, Royal Military Academy, B-1000 Brussels; Shabbir, A.

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standardmore » least squares.« less

The intra-day dynamics of affect, self-esteem, tiredness, and suicidality in Major Depression.

PubMed

Crowe, Eimear; Daly, Michael; Delaney, Liam; Carroll, Susan; Malone, Kevin M

2018-02-21

Despite growing interest in the temporal dynamics of Major Depressive Disorder (MDD), we know little about the intra-day fluctuations of key symptom constructs. In a study of momentary experience, the Experience Sampling Method captured the within-day dynamics of negative affect, positive affect, self-esteem, passive suicidality, and tiredness across clinical MDD (N= 31) and healthy control groups (N= 33). Ten symptom measures were taken per day over 6 days (N= 2231 observations). Daily dynamics were modeled via intra-day time-trends, variability, and instability in symptoms. MDD participants showed significantly increased variability and instability in negative affect, positive affect, self-esteem, and suicidality. Significantly different time-trends were found in positive affect (increased diurnal variation and an inverted U-shaped pattern in MDD, compared to a positive linear trend in controls) and tiredness (decreased diurnal variation in MDD). In the MDD group only, passive suicidality displayed a negative linear trend and self-esteem displayed a quadratic inverted U trend. MDD and control participants thus showed distinct dynamic profiles in all symptoms measured. As well as the overall severity of symptoms, intra-day dynamics appear to define the experience of MDD symptoms. Copyright © 2018 Elsevier B.V. All rights reserved.

Interpreting Space-Based Trends in Carbon Monoxide with Multiple Models

NASA Technical Reports Server (NTRS)

Strode, Sarah A.; Worden, Helen M.; Damon, Megan; Douglass, Anne R.; Duncan, Bryan N.; Emmons, Louisa K.; Lamarque, Jean-Francois; Manyin, Michael; Oman, Luke D.; Rodriguez, Jose M.;

Comparison of mixed effects models of antimicrobial resistance metrics of livestock and poultry Salmonella isolates from a national monitoring system.

PubMed

Bjork, K E; Kopral, C A; Wagner, B A; Dargatz, D A

2015-12-01

Antimicrobial use in agriculture is considered a pathway for the selection and dissemination of resistance determinants among animal and human populations. From 1997 through 2003 the U.S. National Antimicrobial Resistance Monitoring System (NARMS) tested clinical Salmonella isolates from multiple animal and environmental sources throughout the United States for resistance to panels of 16-19 antimicrobials. In this study we applied two mixed effects models, the generalized linear mixed model (GLMM) and accelerated failure time frailty (AFT-frailty) model, to susceptible/resistant and interval-censored minimum inhibitory concentration (MIC) metrics, respectively, from Salmonella enterica subspecies enterica serovar Typhimurium isolates from livestock and poultry. Objectives were to compare characteristics of the two models and to examine the effects of time, species, and multidrug resistance (MDR) on the resistance of isolates to individual antimicrobials, as revealed by the models. Fixed effects were year of sample collection, isolate source species and MDR indicators; laboratory study site was included as a random effect. MDR indicators were significant for every antimicrobial and were dominant effects in multivariable models. Temporal trends and source species influences varied by antimicrobial. In GLMMs, the intra-class correlation coefficient ranged up to 0.8, indicating that the proportion of variance accounted for by laboratory study site could be high. AFT models tended to be more sensitive, detecting more curvilinear temporal trends and species differences; however, high levels of left- or right-censoring made some models unstable and results uninterpretable. Results from GLMMs may be biased by cutoff criteria used to collapse MIC data into binary categories, and may miss signaling important trends or shifts if the series of antibiotic dilutions tested does not span a resistance threshold. Our findings demonstrate the challenges of measuring the AMR ecosystem and the complexity of interacting factors, and have implications for future monitoring. We include suggestions for future data collection and analyses, including alternative modeling approaches. Published by Elsevier B.V.

Trends in Mortality of Tuberculosis Patients in the United States: The Long-term Perspective

PubMed Central

Barnes, Richard F.W.; Moore, Maria Luisa; Garfein, Richard S.; Brodine, Stephanie; Strathdee, Steffanie A.; Rodwell, Timothy C.

2011-01-01

PURPOSE To describe long-term trends in TB mortality and to compare trends estimated from two different sources of public health surveillance data. METHODS Trends and changes in trend were estimated by joinpoint regression. Comparisons between datasets were made by fitting a Poisson regression model. RESULTS Since 1900, TB mortality rates estimated from death certificates have declined steeply, except for a period of no change in the 1980s. This decade had long-term consequences resulting in more TB deaths in later years than would have occurred had there been no flattening of the trend. Recent trends in TB mortality estimated from National Tuberculosis Surveillance System (NTSS) data, which record all-cause mortality, differed from trends based on death certificates. In particular, NTSS data showed TB mortality rates flattening since 2002. CONCLUSIONS Estimates of trends in TB mortality vary by data source, and therefore interpretation of the success of control efforts will depend upon the surveillance dataset used. The datasets may be subject to different biases that vary with time. One dataset showed a sustained improvement in the control of TB since the early 1990s while the other indicated that the rate of TB mortality was no longer declining. PMID:21820320

Temporal variability of total cloud cover at a Mediterranean megacity in the 20th century: Evidence from visual observations and climate models

NASA Astrophysics Data System (ADS)

Founda, Dimitra; Giannakopoulos, Christos; Pierros, Fragiskos

2013-04-01

Cloud cover is one of the major factors that determine the radiation budget and the climate system of the Earth. Moreover, the response of clouds has always been an important source of uncertainty in global climate models. Visual surface observations of clouds have been conducted at the National Observatory of Athens (NOA) since the mid 19th century. The historical archive of cloud reports at NOA since 1860 has been digitized and updated, spanning now a period of one and a half century. Mean monthly values of total cloud cover were derived by averaging subdaily observations of cloud cover (3 observations/day). Changes in observational practice (e.g. from 1/10 to 1/8 units) were considered, however, subjective measures of cloud cover from trained observers introduces some kind of uncertainty in the time series. Data before 1884 were considered unreliable, so the analysis was restricted to the series from 1884 to 2012. The time series of total cloud cover at NOA is validated and correlated with historical time series of other (physically related) variables such as the total sunshine duration as well as DTR (Diurnal Temperature Range) which are independently measured. Trend analysis was performed on the mean annual and seasonal series of total cloud cover from 1884-2012. The mean annual values show a marked temporal variability with sub periods of decreasing and increasing tendencies, however, the overall linear trend is positive and statistically significant (p <0.001) amounting to +2% per decade and implying a total increase of almost 25% for the whole analysed period. These results are in agreement qualitatively with the trends reported in other studies worldwide, especially concerning the period before the mid 20th century. On a seasonal basis, spring and summer series present outstanding positive long term trends, while in winter and autumn total cloud cover reveals also positive but less pronounced long term trends Additionally, an evaluation of cloud cover and/or sunshine duration/diurnal temperature range as depicted by regional climate models over Athens will be performed. Regional climate models are valuable tools for projections of future climate change but their performance is typically assessed only in terms of temperature and precipitation. The representation of non-standard parameters such as cloud cover and/or sunshine duration/diurnal temperature range has so far seen little or no evaluation in the models and can therefore be prone to large uncertainties. Regional climate models developed in the framework of recent EU projects, such as the ENSEMBLES (www.ensembles-eu.org) and the CIRCE (www.circeproject.eu) projects, will be used and an initial validation of these parameters against the historical archive of NOA will be performed.

FPGA implementation of predictive degradation model for engine oil lifetime

NASA Astrophysics Data System (ADS)

Idros, M. F. M.; Razak, A. H. A.; Junid, S. A. M. Al; Suliman, S. I.; Halim, A. K.

2018-03-01

This paper presents the implementation of linear regression model for degradation prediction on Register Transfer Logic (RTL) using QuartusII. A stationary model had been identified in the degradation trend for the engine oil in a vehicle in time series method. As for RTL implementation, the degradation model is written in Verilog HDL and the data input are taken at a certain time. Clock divider had been designed to support the timing sequence of input data. At every five data, a regression analysis is adapted for slope variation determination and prediction calculation. Here, only the negative value are taken as the consideration for the prediction purposes for less number of logic gate. Least Square Method is adapted to get the best linear model based on the mean values of time series data. The coded algorithm has been implemented on FPGA for validation purposes. The result shows the prediction time to change the engine oil.

Secular trend in age at menarche in indigenous and nonindigenous women in Chile.

PubMed

Ossa, X M; Munoz, S; Amigo, H; Bangdiwala, S I

2010-01-01

To estimate the secular trend in age at menarche, comparing indigenous and nonindigenous women, and its relationship with socio-demographic, family and nutritional factors. A study (historical cohorts) of 688 indigenous and nonindigenous women, divided into four birth cohorts (1960-69, 1970-79, 1980-89, and 1990-96) in an area in central southern Chile was carried out. Data and measurements were collected by health professionals using a previously validated questionnaire. Age at menarche was self-reported (recall). Adjusted differences among cohorts were estimated using a multivariate regression model. A secular trend (P < 0.001) in age at menarche was found in both ethnic groups, with no significant differences between them (P > 0.05). In an adjusted model, a reduction in age at menarche was estimated at 3.7 months per decade between 1960 and 1990. This trend was moderated by higher socio-economic level, smaller number of siblings, and cohabitation with a single parent during infancy. The trend has occurred in a steady progression over time in indigenous women, whereas in nonindigenous women, it was slow initially but has accelerated in recent years. Nonindigenous women have maintained a slightly lower age of menarche than their indigenous counterparts. (c) 2010 Wiley-Liss, Inc.

Trends in incidence of occupational asthma, contact dermatitis, noise-induced hearing loss, carpal tunnel syndrome and upper limb musculoskeletal disorders in European countries from 2000 to 2012.

PubMed

Stocks, S Jill; McNamee, Roseanne; van der Molen, Henk F; Paris, Christophe; Urban, Pavel; Campo, Giuseppe; Sauni, Riitta; Martínez Jarreta, Begoña; Valenty, Madeleine; Godderis, Lode; Miedinger, David; Jacquetin, Pascal; Gravseth, Hans M; Bonneterre, Vincent; Telle-Lamberton, Maylis; Bensefa-Colas, Lynda; Faye, Serge; Mylle, Godewina; Wannag, Axel; Samant, Yogindra; Pal, Teake; Scholz-Odermatt, Stefan; Papale, Adriano; Schouteden, Martijn; Colosio, Claudio; Mattioli, Stefano; Agius, Raymond

2015-04-01

The European Union (EU) strategy for health and safety at work underlines the need to reduce the incidence of occupational diseases (OD), but European statistics to evaluate this common goal are scarce. We aim to estimate and compare changes in incidence over time for occupational asthma, contact dermatitis, noise-induced hearing loss (NIHL), carpal tunnel syndrome (CTS) and upper limb musculoskeletal disorders across 10 European countries. OD surveillance systems that potentially reflected nationally representative trends in incidence within Belgium, the Czech Republic, Finland, France, Italy, the Netherlands, Norway, Spain, Switzerland and the UK provided data. Case counts were analysed using a negative binomial regression model with year as the main covariate. Many systems collected data from networks of 'centres', requiring the use of a multilevel negative binomial model. Some models made allowance for changes in compensation or reporting rules. Reports of contact dermatitis and asthma, conditions with shorter time between exposure to causal substances and OD, were consistently declining with only a few exceptions. For OD with physical causal exposures there was more variation between countries. Reported NIHL was increasing in Belgium, Spain, Switzerland and the Netherlands and decreasing elsewhere. Trends in CTS and upper limb musculoskeletal disorders varied widely within and between countries. This is the first direct comparison of trends in OD within Europe and is consistent with a positive impact of European initiatives addressing exposures relevant to asthma and contact dermatitis. Taking a more flexible approach allowed comparisons of surveillance data between and within countries without harmonisation of data collection methods. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The effect of ageing on the mechanical properties of the silk of the bridge spider Larinioides cornutus (Clerck, 1757)

NASA Astrophysics Data System (ADS)

Lepore, Emiliano; Isaia, Marco; Mammola, Stefano; Pugno, Nicola

2016-05-01

Spider silk is regarded as one of the best natural polymer fibers especially in terms of low density, high tensile strength and high elongation until breaking. Since only a few bio-engineering studies have been focused on spider silk ageing, we conducted nano-tensile tests on the vertical naturally spun silk fibers of the bridge spider Larinioides cornutus (Clerck, 1757) (Arachnida, Araneae) to evaluate changes in the mechanical properties of the silk (ultimate stress and strain, Young’s modulus, toughness) over time. We studied the natural process of silk ageing at different time intervals from spinning (20 seconds up to one month), comparing silk fibers spun from adult spiders collected in the field. Data were analyzed using Linear Mixed Models. We detected a positive trend versus time for the Young’s modulus, indicating that aged silks are stiffer and possibly less effective in catching prey. Moreover, we observed a negative trend for the ultimate strain versus time, attesting a general decrement of the resistance force. These trends are interpreted as being due to the drying of the silk protein chains and the reorientation among the fibers.

Changes in the food environment over time: examining 40 years of data in the Framingham Heart Study.

PubMed

James, Peter; Seward, Michael W; James O'Malley, A; Subramanian, S V; Block, Jason P

2017-06-24

Research has explored associations between diet, body weight, and the food environment; however, few studies have examined historical trends in food environments. In the Framingham Heart Study Offspring (N = 3321) and Omni (N = 447) cohorts, we created food environment metrics in four Massachusetts towns utilizing geocoded residential, workplace, and food establishment addresses from 1971 to 2008. We created multilevel models adjusted for age, sex, education, and census tract poverty to examine trends in home, workplace, and commuting food environments. Proximity to and density of supermarkets, fast-food, full service restaurants, convenience stores, and bakeries increased over time for residential, workplace, and commuting environments; exposure to grocery stores decreased. The greatest increase in access was for supermarkets, with residential distance to the closest supermarket 1406 m closer (95% CI 1303 m, 1508 m) by 2005-2008 than in 1971-1975. Although poorer census tracts had higher access to fast-food restaurants consistently across follow-up, this disparity dissipated over time, due to larger increases in proximity to fast-food in wealthier neighborhoods. Access to most food establishment types increased over time, with similar trends across home, workplace, and commuter environments.

Ultimate strength performance of tankers associated with industry corrosion addition practices

NASA Astrophysics Data System (ADS)

Kim, Do Kyun; Kim, Han Byul; Zhang, Xiaoming; Li, Chen Guang; Paik, Jeom Kee

2014-09-01

In the ship and offshore structure design, age-related problems such as corrosion damage, local denting, and fatigue damage are important factors to be considered in building a reliable structure as they have a significant influence on the residual structural capacity. In shipping, corrosion addition methods are widely adopted in structural design to prevent structural capacity degradation. The present study focuses on the historical trend of corrosion addition rules for ship structural design and investigates their effects on the ultimate strength performance such as hull girder and stiffened panel of double hull oil tankers. Three types of rules based on corrosion addition models, namely historic corrosion rules (pre-CSR), Common Structural Rules (CSR), and harmonised Common Structural Rules (CSRH) are considered and compared with two other corrosion models namely UGS model, suggested by the Union of Greek Shipowners (UGS), and Time-Dependent Corrosion Wastage Model (TDCWM). To identify the general trend in the effects of corrosion damage on the ultimate longitudinal strength performance, the corrosion addition rules are applied to four representative sizes of double hull oil tankers namely Panamax, Aframax, Suezmax, and VLCC. The results are helpful in understanding the trend of corrosion additions for tanker structures

Reduction of smoking in Dutch adolescents over the past decade and its health gains: a repeated cross-sectional study.

PubMed

Gielkens-Sijstermans, Cindy M; Mommers, Monique A; Hoogenveen, Rudolf T; Feenstra, Talitha L; de Vreede, Jacqueline; Bovens, Fons M; van Schayck, Onno C

2010-04-01

Smoking is the main preventable lifestyle-related risk factor threatening human health. In this study, time trends in smoking behaviour between 1996 and 2005 among adolescents enrolled in secondary school were assessed. In 1996, 2001 and 2005, a survey was conducted in the south-eastern region of the Netherlands. All students in second and fourth year of secondary education (1996: n = 20 000; 2001: n = 27 500; 2005: n = 24 000) were asked to complete a questionnaire about their smoking behaviour. A simulation model was used to estimate lifetime health gains related to the observed trends. In 1996, 2001 and 2005, the number of questionnaires analysed were 13 554 (68%), 20 767 (76%) and 17 896 (75%), respectively. The results show a decrease in 'ever smoking' as well as 'current smoking' between 1996 and 2005. Among second year high school students, current smoking prevalence decreased from 22.2% in 1996 to 8.0% in 2005 (P(trend) < 0.001). Among fourth year students, current smoking declined from 37.5% in 1996 to 22.0% in 2005 (P(trend) < 0.001). Time trends were not influenced by gender or educational level. Model projections show that if these students not take up smoking later in life, 11 500 new cases of COPD, 3400 new cases of lung cancer and 1800 new cases of myocardial infarction could be prevented for the Dutch 13-year-olds. This study found that, in the past decade, smoking prevalence among adolescents has declined by almost 50%, potentially resulting in a considerable reduction in new cases of COPD or lung cancer.

Longitudinal data analysis in support of functional stability concepts for leachate management at closed municipal landfills

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gibbons, Robert D., E-mail: rdg@uchicago.edu; Morris, Jeremy W.F., E-mail: jmorris@geosyntec.com; Prucha, Christopher P., E-mail: cprucha@wm.com

2014-09-15

Highlights: • Longitudinal data analysis using a mixed-effects regression model. • Dataset consisted of a total of 1402 samples from 101 closed municipal landfills. • Target analytes and classes generally showed predictable degradation trends. • Validates historical studies focused on macro organic indicators such as BOD. • BOD can serve as “gateway” indicator for planning leachate management. - Abstract: Landfill functional stability provides a target that supports no environmental threat at the relevant point of exposure in the absence of active control systems. With respect to leachate management, this study investigates “gateway” indicators for functional stability in terms of themore » predictability of leachate characteristics, and thus potential threat to water quality posed by leachate emissions. Historical studies conducted on changes in municipal solid waste (MSW) leachate concentrations over time (longitudinal analysis) have concentrated on indicator compounds, primarily chemical oxygen demand (COD) and biochemical oxygen demand (BOD). However, validation of these studies using an expanded database and larger constituent sets has not been performed. This study evaluated leachate data using a mixed-effects regression model to determine the extent to which leachate constituent degradation can be predicted based on waste age or operational practices. The final dataset analyzed consisted of a total of 1402 samples from 101 MSW landfills. Results from the study indicated that all leachate constituents exhibit a decreasing trend with time in the post-closure period, with 16 of the 25 target analytes and aggregate classes exhibiting a statistically significant trend consistent with well-studied indicators such as BOD. Decreasing trends in BOD concentration after landfill closure can thus be considered representative of trends for many leachate constituents of concern.« less

Instructional Time Trends. Education Trends

ERIC Educational Resources Information Center

Woods, Julie Rowland

2015-01-01

For more than 30 years, Education Commission of the States has tracked instructional time and frequently receives requests for information about policies and trends. In this Education Trends report, Education Commission of the States addresses some of the more frequent questions, including the impact of instructional time on achievement, variation…

Assessing mental stress from the photoplethysmogram: a numerical study

PubMed Central

Charlton, Peter H; Celka, Patrick; Farukh, Bushra; Chowienczyk, Phil; Alastruey, Jordi

2018-01-01

Abstract Objective: Mental stress is detrimental to cardiovascular health, being a risk factor for coronary heart disease and a trigger for cardiac events. However, it is not currently routinely assessed. The aim of this study was to identify features of the photoplethysmogram (PPG) pulse wave which are indicative of mental stress. Approach: A numerical model of pulse wave propagation was used to simulate blood pressure signals, from which simulated PPG pulse waves were estimated using a transfer function. Pulse waves were simulated at six levels of stress by changing the model input parameters both simultaneously and individually, in accordance with haemodynamic changes associated with stress. Thirty-two feature measurements were extracted from pulse waves at three measurement sites: the brachial, radial and temporal arteries. Features which changed significantly with stress were identified using the Mann–Kendall monotonic trend test. Main results: Seventeen features exhibited significant trends with stress in measurements from at least one site. Three features showed significant trends at all three sites: the time from pulse onset to peak, the time from the dicrotic notch to pulse end, and the pulse rate. More features showed significant trends at the radial artery (15) than the brachial (8) or temporal (7) arteries. Most features were influenced by multiple input parameters. Significance: The features identified in this study could be used to monitor stress in healthcare and consumer devices. Measurements at the radial artery may provide superior performance than the brachial or temporal arteries. In vivo studies are required to confirm these observations. PMID:29658894

Modelled vs. reconstructed past fire dynamics - how can we compare?

NASA Astrophysics Data System (ADS)

Brücher, Tim; Brovkin, Victor; Kloster, Silvia; Marlon, Jennifer R.; Power, Mitch J.

2015-04-01

Fire is an important process that affects climate through changes in CO2 emissions, albedo, and aerosols (Ward et al. 2012). Fire-history reconstructions from charcoal accumulations in sediment indicate that biomass burning has increased since the Last Glacial Maximum (Power et al. 2008; Marlon et al. 2013). Recent comparisons with transient climate model output suggest that this increase in global ?re activity is linked primarily to variations in temperature and secondarily to variations in precipitation (Daniau et al. 2012). In this study, we discuss the best way to compare global ?re model output with charcoal records. Fire models generate quantitative output for burned area and fire-related emissions of CO2, whereas charcoal data indicate relative changes in biomass burning for specific regions and time periods only. However, models can be used to relate trends in charcoal data to trends in quantitative changes in burned area or fire carbon emissions. Charcoal records are often reported as Z-scores (Power et al. 2008). Since Z-scores are non-linear power transformations of charcoal influxes, we must evaluate if, for example, a two-fold increase in the standardized charcoal reconstruction corresponds to a 2- or 200-fold increase in the area burned. In our study we apply the Z-score metric to the model output. This allows us to test how well the model can quantitatively reproduce the charcoal-based reconstructions and how Z-score metrics affect the statistics of model output. The Global Charcoal Database (GCD version 2.5; www.gpwg.org/gpwgdb.html) is used to determine regional and global paleofire trends from 218 sedimentary charcoal records covering part or all of the last 8 ka BP. To retrieve regional and global composites of changes in fire activity over the Holocene the time series of Z-scores are linearly averaged to achieve regional composites. A coupled climate-carbon cycle model, CLIMBA (Brücher et al. 2014), is used for this study. It consists of the CLIMBER-2 Earth system model of intermediate complexity and the JSBACH land component of the Max Planck Institute Earth System Model. The fire algorithm in JSBACH assumes a constant annual lightning cycle as the sole fire ignition mechanism (Arora and Boer 2005). To eliminate data processing differences as a source for potential discrepancies, the processing of both reconstructed and modeled data, including e.g. normalisation with respect to a given base period and aggregation of time series was done in exactly the same way. Here, we compare the aggregated time series on a hemispheric and regional scale.

The method of trend analysis of parameters time series of gas-turbine engine state

NASA Astrophysics Data System (ADS)

Hvozdeva, I.; Myrhorod, V.; Derenh, Y.

2017-10-01

This research substantiates an approach to interval estimation of time series trend component. The well-known methods of spectral and trend analysis are used for multidimensional data arrays. The interval estimation of trend component is proposed for the time series whose autocorrelation matrix possesses a prevailing eigenvalue. The properties of time series autocorrelation matrix are identified.

The accuracy of matrix population model projections for coniferous trees in the Sierra Nevada, California

USGS Publications Warehouse

van Mantgem, P.J.; Stephenson, N.L.

2005-01-01

1 We assess the use of simple, size-based matrix population models for projecting population trends for six coniferous tree species in the Sierra Nevada, California. We used demographic data from 16 673 trees in 15 permanent plots to create 17 separate time-invariant, density-independent population projection models, and determined differences between trends projected from initial surveys with a 5-year interval and observed data during two subsequent 5-year time steps. 2 We detected departures from the assumptions of the matrix modelling approach in terms of strong growth autocorrelations. We also found evidence of observation errors for measurements of tree growth and, to a more limited degree, recruitment. Loglinear analysis provided evidence of significant temporal variation in demographic rates for only two of the 17 populations. 3 Total population sizes were strongly predicted by model projections, although population dynamics were dominated by carryover from the previous 5-year time step (i.e. there were few cases of recruitment or death). Fractional changes to overall population sizes were less well predicted. Compared with a null model and a simple demographic model lacking size structure, matrix model projections were better able to predict total population sizes, although the differences were not statistically significant. Matrix model projections were also able to predict short-term rates of survival, growth and recruitment. Mortality frequencies were not well predicted. 4 Our results suggest that simple size-structured models can accurately project future short-term changes for some tree populations. However, not all populations were well predicted and these simple models would probably become more inaccurate over longer projection intervals. The predictive ability of these models would also be limited by disturbance or other events that destabilize demographic rates. ?? 2005 British Ecological Society.

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006

USGS Publications Warehouse

White, M.A.; de Beurs, K. M.; Didan, K.; Inouye, D.W.; Richardson, A.D.; Jensen, O.P.; O'Keefe, J.; Zhang, G.; Nemani, R.R.; van, Leeuwen; Brown, Jesslyn F.; de Wit, A.; Schaepman, M.; Lin, X.; Dettinger, M.; Bailey, A.S.; Kimball, J.; Schwartz, M.D.; Baldocchi, D.D.; Lee, J.T.; Lauenroth, W.K.

2009-01-01

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ±60 days and in standard deviation by ±20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring.

Trends in non-help-seeking for mental disorders in Germany between 1997-1999 and 2009-2012: a repeated cross-sectional study.

PubMed

Brandstetter, Susanne; Dodoo-Schittko, Frank; Speerforck, Sven; Apfelbacher, Christian; Grabe, Hans-Jörgen; Jacobi, Frank; Hapke, Ulfert; Schomerus, Georg; Baumeister, Sebastian E

2017-08-01

This study sought to examine trends in non-help-seeking for mental disorders among persons with a prevalent mental disorder (12-month prevalence) in Germany between 1997-1999 and 2009-2012. We examined data from 1909 persons aged 18-65 years who participated in two independent, repeated cross-sectional surveys (German National Interview and Examination Study 1997-1999, German Health Interview and Examination Survey for Adults 2009-2012) conducted 12 years apart. Prevalent mental disorders (12-month prevalence) were determined using the Composite International Diagnostic Interview, which included information on lifetime help-seeking for mental health problems. Correlates of self-reported help-seeking were analyzed according to Andersen's Behavioral Model. Multivariable Poisson regression models were used to assess time trends in the directly standardized and model-adjusted prevalence of non-help-seeking across strata of socio-economic and clinical variables. The proportion of people with a prevalent mental disorder who have never sought help in their lifetime decreased significantly from 62% (95% CI 58.7-64.7) to 57% (95% CI 52.2-60.9) between 1997-1999 and 2009-2012 in adults aged 18-65 years in Germany. Downward trends in non-help-seeking occurred in all investigated strata and reached statistical significance in women, in people who were living alone, people with medium educational level, people living in middle-sized communities, people with non-statutory health insurance, smokers, and people with co-existing somatic conditions. Despite a downward trend over the course of 12 years, a large proportion of people suffering from mental disorders are still not seeking treatment in Germany. Further efforts to increase uptake of help-seeking for mental disorders in hard-to-reach groups are warranted to continue this trend.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scalemore » patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.« less